@article{Torres2021, abstract = {This research was conducted to help the traffic policy makers and general public in preventing road incidents using the collected traffic accident dataset between the years 2016 and 2019. Data mining using classification algorithm was utilized to develop a predictive model for predicting occurrences of traffic accidents. Classification algorithms such as decision tree, k-nn, naïve bayes and neural network have been compared in identifying better classification capability in classifying stage of felony. Neural network shows a very promising result in classifying road accident with a total accuracy result of 87.63%. Nonetheless, k-nn and naïve bayes both acquired a higher than 80% accuracy which shows that this classification algorithms were also good in predicting road accidents. Moreover, public vehicle is more prone in accident rather than private vehicle in both stage of felony and accident may occur between or on 3:00pm and 6:00pm.}, author = {Kristelle Ann R. Torres and Jonardo R. Asor}, doi = {10.11591/ijeecs.v24.i2.pp993-1000}, issn = {25024760}, issue = {2}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Classification algorithm,Decision tree,Machine learning,Naïve bayes,Neural network,Road traffic accidents}, pages = {993-1000}, title = {Machine learning approach on road accidents analysis in Calabarzon, Philippines: An input to road safety management}, volume = {24}, year = {2021}, } @article{IbrahemAlhayali2020, abstract = {The most dangerous type of cancer suffered by women above 35 years of age is breast cancer. Breast Cancer datasets are normally characterized by missing data, high dimensionality, non-normal distribution, class imbalance, noisy, and inconsistency. Classification is a machine learning (ML) process which has a significant role in the prediction of outcomes, and one of the outstanding supervised classification methods in data mining is Naives Bayess Classification (NBC). Naïve Bayes Classifications is good at predicting outcomes and often outperforms other classifications techniques. Ones of the reasons behind this strong performance of NBC is the assumptions of conditional Independences among the initial parameters and the predictors. However, this assumption is not always true and can cause loss of accuracy. Hoeffding trees assume the suitability of using a small sample to select the optimal splitting attribute. This study proposes a new method for improving accuracy of classification of breast cancer datasets. The method proposes the use of Hoeffding trees for normal classification and naïve Bayes for reducing data dimensionality.}, author = {Royida A. Ibrahem Alhayali and Munef Abdullah Ahmed and Yasmin Makki Mohialden and Ahmed H. Ali}, doi = {10.11591/ijeecs.v18.i2.pp1074-1080}, issn = {25024760}, issue = {2}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Breast cancer,Classification,Hoeffding tree,Machine Learning,Naïve Bayes}, pages = {1074-1080}, title = {Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes}, volume = {18}, year = {2020}, } @article{Hannus2021, abstract = {Voluntary approaches to improving sustainability in agriculture can contribute significantly to reduce the sector’s negative environmental impacts and provide a foundation for sustainable land use and farmers’ incomes. We investigate what motivates farmers to implement comprehensive sustainability management on their farms. For this purpose, we use a structural equation model (SEM) to evaluate the individual factors influencing the decision-making process within the technology acceptance model (TAM). Our empirical data from 363 farmers fit the theoretical model very well. The model confirms a positive influence of expected economic rewards and subjective norms on the perceived usefulness of such an innovation. However, ease of use is most important, as it is related directly to the stated intention to use a standard. In addition, the data indicate a high, significant, and direct effect of prior knowledge of on-farm sustainability management on stated intent to use a standard. These findings can serve as a starting point to improve not only existing sustainability management systems, but also emerging farm management information systems (FMISs), or agri-environmental schemes with the aim to make their use more attractive. However, further research is needed to verify the results by means of practical applications.}, author = {Veronika Hannus and Johannes Sauer}, doi = {10.3390/su131910788}, issn = {20711050}, issue = {19}, journal = {Sustainability (Switzerland)}, keywords = {Ease of use,Farm sustainability standard,Innovation adoption,SEM,Standard design,Structural equation model,Sustainability management,Sustainable agriculture,TAM,Technology acceptance model}, title = {Understanding farmers’ intention to use a sustainability standard: The role of economic rewards, knowledge, and ease of use}, volume = {13}, year = {2021}, } @article{David2023, abstract = {Four moderate-complexity automated nucleic acid amplification tests for the diagnosis of tuberculosis are reported as having laboratory analytical and clinical performance similar to that of the Cepheid Xpert MTB/RIF assay. These assays are the Abbott RealTime MTB and RealTime MTB RIF/INH Resistance, Becton Dickinson MAX MDR-TB, the Hain Lifescience/Bruker FluoroType MTBDR, and the Roche cobas MTB and MTB RIF/INH assays. The study compared feasibility, ease of use, and operational characteristics of these assays/platforms. Manufacturer input was obtained for technical characteristics. Laboratory operators were requested to complete a questionnaire on the assays’ ease of use. A time-in-motion analysis was also undertaken for each platform. For ease-of-use and operational requirements, the BD MAX MDR-TB assay achieved the highest scores (86% and 90%) based on information provided by the user and manufacturer, respectively, followed by the cobas MTB and MTB-RIF/INH assay (68% and 86%), the FluoroType MTBDR assay (67% and 80%), and the Abbott RT-MTB and RT MTB RIF/INH assays (64% and 76%). The time-in-motion analysis revealed that for 94 specimens, the RealTime MTB assay required the longest processing time, followed by the cobas MTB assay and the FluoroType MTBDR assay. The BD MAX MDR-TB assay required 4.6 hours for 22 specimens. These diagnostic assays exhibited different strengths and weaknesses that should be taken into account, in addition to affordability, when considering placement of a new platform.}, author = {Anura David and Margaretha de Vos and Lesley Scott and Pedro da Silva and Andre Trollip and Morten Ruhwald and Samuel Schumacher and Wendy Stevens}, doi = {10.1016/j.jmoldx.2022.10.001}, issn = {19437811}, issue = {1}, journal = {Journal of Molecular Diagnostics}, pages = {46-56}, pmid = {36243289}, publisher = {Association for Molecular Pathology and American Society for Investigative Pathology}, title = {Feasibility, Ease-of-Use, and Operational Characteristics of World Health Organization–Recommended Moderate-Complexity Automated Nucleic Acid Amplification Tests for the Detection of Tuberculosis and Resistance to Rifampicin and Isoniazid}, volume = {25}, url = {https://doi.org/10.1016/j.jmoldx.2022.10.001}, year = {2023}, } @article{Tapia2020, abstract = {Currently, organizations face the need to create scalable applications in an agile way that impacts new forms of production and business organization. The traditional monolithic architecture no longer meets the needs of scalability and rapid development. The efficiency and optimization of human and technological resources prevail; this is why companies must adopt new technologies and business strategies. However, the implementation of microservices still encounters several challenges, such as the consumption of time and computational resources, scalability, orchestration, organization problems, and several further technical complications. Although there are procedures that facilitate the migration from a monolithic architecture to micro-services, none of them accurately quantifies performance differences. The current study aims primarily to analyze some related work that evaluated both architectures. Furthermore, we assess the performance and relationship between different variables of an application that runs in a monolithic structure compared to one of the micro-services. With this, the state-of-the-art review was initially conducted, which confirms the interest of the industry. Subsequently, two different scenarios were evaluated: the first one comprises a web application based on a monolithic architecture that operates on a virtual server with KVM, and the second one demonstrates the same web application based on a microservice architecture, but it runs in containers. Both situations were exposed to stress tests of similar characteristics and with the same hardware resources. For their validation, we applied the non-parametric regression mathematical model to explain the dependency relationship between the performance variables. The results provided a quantitative technical interpretation with precision and reliability, which can be applied to similar issues.}, author = {Freddy Tapia and Miguel ángel Mora and Walter Fuertes and Hernán Aules and Edwin Flores and Theofilos Toulkeridis}, doi = {10.3390/app10175797}, issn = {20763417}, issue = {17}, journal = {Applied Sciences (Switzerland)}, keywords = {Architecture,Cloud,Containers,Mathematical model,Metrics,Microservices,Monolithic,Performance}, title = {From monolithic systems to microservices: A comparative study of performance}, volume = {10}, year = {2020}, } @article{Li2020, abstract = {Scientifically judging and comparing different mobile e-commerce retailing applications (apps) are essential to increase online shopping efficiency and enhance design for system improvement. In this research, the use of mobile apps in e-commerce retailing is viewed as an information operation process, and distance of information-state transition (DIT) theory is introduced to measure the “convenience” of mobile apps to obtain service information. Thus, a novel DIT-based evaluation method for the ease of use of mobile apps in e-commerce retailing from the perspective of consumer online shopping behaviour patterns is proposed. Three representative Chinese enterprises, namely, Tianmao Mall, Jingdong Mall and Suning Easy-to-buy, are chosen as study objects. Moreover, the corresponding ease-of-use indicators of three mobile apps under typical online shopping behaviour patterns are evaluated quantitatively. Results show that this research has important implications not only for online consumers but also for designers of online shopping systems.}, author = {Xiong Li and Xiaodong Zhao and Wangtu (Ato) Xu and Wei Pu}, doi = {10.1016/j.jretconser.2020.102093}, issn = {09696989}, issue = {January}, journal = {Journal of Retailing and Consumer Services}, keywords = {Consumer online shopping behaviour,E-commerce retailing,Ease of use,Information distance,Mobile apps}, pages = {102093}, publisher = {Elsevier Ltd}, title = {Measuring ease of use of mobile applications in e-commerce retailing from the perspective of consumer online shopping behaviour patterns}, volume = {55}, url = {https://doi.org/10.1016/j.jretconser.2020.102093}, year = {2020}, } @article{Javale2022, abstract = {In healthcare machine learning is used mainly for disease diagnosis or acute condition detection based on patient data analysis. In the proposed work diabetic patient dataset analysis is done for hypoglycemia detection which means the lowering of blood glucose level (BGL). Often in healthcare it is observed that the dataset is imbalanced. Therefore, an Ensemble Approach using imbalanced dataset techniques synthetic minority over-sampling technique and adaptive synthetic oversampling methods with different evaluation methods like train-test, K-fold, stratified K-Fold and repeat train-test were used. This ensemble approach was implemented on diabetic dataset using K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB) and logistic regression classifiers with average Stacking-C method thereafter to conclude. Comparative analysis was done using three different considerations. The results showed that KNN and random forest gives more stable metric values both on balanced and imbalanced dataset. The confusion matrix consideration concluded that KNN and random forest were found to be better with least false negative and maximum true positive count. But if average train and test time is taken into consideration then Naïve Bayes and random forest had least average train-test time. Thus, the three different considerations concluded that the proposed ensemble approach gives better clarity for different classifier implementation using machine learning.}, author = {Deepali Pankaj Javale and Sharmishta Suhas Desai}, doi = {10.11591/ijeecs.v28.i2.pp926-933}, issn = {25024760}, issue = {2}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Adaptive synthetic,Ensemble learning,Healthcare,Stacking-C,Synthetic minority,oversampling technique}, pages = {926-933}, title = {Machine learning ensemble approach for healthcare data analytics}, volume = {28}, year = {2022}, } @article{Arman2022, abstract = {The effective use of information mining in profoundly unmistakable fields like e-business, promoting and retail has prompted its application in different enterprises. There is an absence of powerful investigation devices to find concealed connections and patterns in information. This examination paper expects to give a review of ebb and flow systems of learning revelation in databases utilizing information mining strategies that are being used in today's therapeutic research especially in medicine prediction. Correlation, Chi-square and Euclidean distance feature selections are used to select features and showing the comparison of the result between K-Nearest neighbors, Naïve Bayes, decision tree, artificial neural network. The result uncovers that decision tree beats and sometime Bayesian grouping is having comparative precision as of choice tree. The analysis of performance can be done in such as doctor's degrees may vary the diseases medicine.}, author = {Md Shohel Arman and Kaushik Sarker and Asif Khan Shakir and Shah Fahad Hossain and Afia Hasan}, doi = {10.11591/ijeecs.v26.i2.pp1125-1134}, issn = {25024760}, issue = {2}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Data mining,Feature selection,Machine learning,Medicine prediction,Neural network}, pages = {1125-1134}, title = {Medicine prediction based on doctor's degree: a data mining approach}, volume = {26}, year = {2022}, } @article{Kim2023, abstract = {Federated authentication, such as Google ID, enables users to conveniently access multiple websites using a single login credential. Despite this convenience, securing federated authentication services requires addressing a single point of failure, which can result from using a centralized authentication server. In addition, because the same login credentials are used, anonymity and protection against user impersonation attacks must be ensured. Recently, researchers introduced distributed authentication schemes based on blockchains and smart contracts (SCs) for systems that require high availability and reliability. Data on a blockchain are immutable, and deployed SCs cannot be changed or tampered with. Nonetheless, updates may be necessary to fix programming bugs or modify business logic. Recently, methods for updating SCs to address these issues have been investigated. Therefore, this study proposes a distributed and federated authentication scheme that uses SCs to overcome a single point of failure. Additionally, an updatable SC is designed to fix programming bugs, add to the function of an SC, or modify business logic. ProVerif, which is a widely known cryptographic protocol verification tool, confirms that the proposed scheme can provide protection against various security threats, such as single point of failure, user impersonation attacks, and user anonymity, which is vital in federated authentication services. In addition, the proposed scheme exhibits a performance improvement of 71% compared with other related schemes.}, author = {Keunok Kim and Jihyeon Ryu and Hakjun Lee and Youngsook Lee and Dongho Won}, doi = {10.3390/electronics12051217}, issn = {20799292}, issue = {5}, journal = {Electronics (Switzerland)}, keywords = {federated authentication,smart contracts,updatable smart contracts}, title = {Distributed and Federated Authentication Schemes Based on Updatable Smart Contracts}, volume = {12}, year = {2023}, } @article{Al-Subaihin2021, abstract = {In this paper, we study the app store as a phenomenon from the developers' perspective to investigate the extent to which app stores affect software engineering tasks. Through developer interviews and questionnaires, we uncover findings that highlight and quantify the effects of three high-level app store themes: bridging the gap between developers and users, increasing market transparency and affecting mobile release management. Our findings have implications for testing, requirements engineering and mining software repositories research fields. These findings can help guide future research in supporting mobile app developers through a deeper understanding of the app store-developer interaction.}, author = {Afnan A. Al-Subaihin and Federica Sarro and Sue Black and Licia Capra and Mark Harman}, doi = {10.1109/TSE.2019.2891715}, issn = {19393520}, issue = {2}, journal = {IEEE Transactions on Software Engineering}, keywords = {Empirical software engineering,app store analysis,mobile app development}, pages = {300-319}, publisher = {IEEE}, title = {App Store Effects on Software Engineering Practices}, volume = {47}, year = {2021}, } @article{Dai2021, abstract = {With the continuous development of the power system and the rapid growth of the scale of the power grid, the information that needs to be monitored, inquired, and maintained in the power grid dispatching system is increasing. The traditional way of understanding the realtime operation status of the power grid through dispatching workstations is increasingly unable to meet the requirements of modern power grids for monitoring, operation and maintenance in real-time, timeliness, and anywhere. In the face of such a huge business demand market, how to develop mobile applications that meet customer needs with minimal investment in resources and efficient development efficiency has become our research direction. This paper discusses a release management technology of the mobile application for electricity dispatching that meets the above requirements. This technology adopts the design mode of "shared application + personalized page", without too much intervention by developers, Users can edit and compress their own personalized pages, then upload to the version server and update the main page on the mobile devices themselves; this technology improves the efficiency of opening graphics file and the response time of operation through caching the graphic files and only refreshing the operation area mechanisms; through the publish/subscribe message bus mechanism, the hot update effect of the main page is realized; the service is restored through one-click, which provides users with more operating space.}, author = {Jinxia Dai and Donghai Li and Baoshan Gao and Ling Zhou and Wenjing Chen}, doi = {10.1088/1742-6596/1971/1/012055}, issn = {17426596}, issue = {1}, journal = {Journal of Physics: Conference Series}, pages = {1-7}, title = {Research on the release management technology of the mobile application for electricity dispatching}, volume = {1971}, year = {2021}, } @article{KhaleelFaieq2022, abstract = {The heart, like a pump, is an organ about the size of a fist, mainly composed of muscle and connective tissue that functions to distribute blood to tissues. The heart is located under the rib cage, above the diaphragm between the lungs, slightly closer to the left. Sometimes a small, unexpected problem with the veins or the valves that supply the heart affects a person's life and can lead to death. Early diagnosis is essential to predict diseases that affect the human heart and lead people to live another period of life. In this context, the authors introduce two methods for early diagnosis of heart disease, the support vector machine and artificial neural network (ANN). The medical data is taken from the University of California Irvine (UCI) Machine Learning Repository database, and it contains reports of 170 people. The investigation results confirm that the optimal execution is the support vector machine technique. It gives high-accuracy prediction results. As for the performance of the forward propagation artificial neural networks technique is acceptable.}, author = {Alaa Khaleel Faieq and Maad M. Mijwil}, doi = {10.11591/ijeecs.v26.i1.pp374-380}, issn = {25024760}, issue = {1}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Artificial neural network,Diagnosis,Heart,Prediction,Support vector machine}, pages = {374-380}, title = {Prediction of heart diseases utilising support vector machine and artificial neural network}, volume = {26}, year = {2022}, } @article{Alani2020, abstract = {Sign language (SL) is a visual language means of communication for people with deafness or hearing impairments. In Arabic-speaking countries, there are many arabic sign languages (ArSL) and these use the same alphabets. This study proposes ArSLCNN, a deep learning model that is based on a convolutional neural network (CNN) for translating Arabic SL (ArSL). Experiments were performed using a large ArSL dataset (ArSL2018) that contains 54,049 images of 32 sign language gestures, collected from forty participants. The results of the first experiments with the ArSL-CNN model returned a train and test accuracy of 98.80% and 96.59%, respectively. The results also revealed the impact of imbalanced data on model accuracy. For the second set of experiments, various re-sampling methods were applied to the dataset. Results revealed that applying the synthetic minority oversampling technique (SMOTE) improved the overall test accuracy from 96.59% to 97.29%, yielding a statistically significant improvement in test accuracy (p=0.016, 0:05). The proposed ArSL-CNN model can be trained on a variety of Arabic sign languages and reduce the communication barriers encountered by deaf communities in Arabic-speaking countries.}, author = {Ali A. Alani and Georgina Cosma}, doi = {10.11591/ijeecs.v22.i2.pp488-499}, issn = {25024760}, issue = {2}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Arabic sign language,CNNs,Convolutional neural networks,Deep learning,SMOTE}, pages = {488-499}, title = {ArSL-CNN: A convolutional neural network for arabic sign language gesture recognition}, volume = {22}, year = {2020}, } @article{Annubaha2022, abstract = {The student attendance system is what is needed in the process of recording attendance in learning and the development of student achievement. Currently several modern educational institutions have implemented a student attendance system using QR codes or fingerprints, but many still use the traditional system by calculating the number of students attending class. Based on these problems, the solution that can be given is to implement a student attendance system through face matching in the Android mobile application with Eigenface algorithm and support vector machine (SVM) algorithm. Eigenface using the principal component analysis (PCA) method can be used to reduce the dimensions of facial images so that they produce fewer variables and are easier to handle. The results obtained are then entered into a pattern classifier to determine the identity of the owner of the face. This study used 100 facial data as test data and training data. The system test results show that the use of Eigenface with SVM as a classifier can provide a fairly high level of accuracy. For facial images that were included in the training, 91% of the identification was correct.}, author = {Chakim Annubaha and Aris Puji Widodo and Kusworo Adi}, doi = {10.11591/ijeecs.v26.i3.pp1624-1633}, issn = {25024760}, issue = {3}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Attendance,Eigenface,Face recognition,Principal component analysis,Support vector machine}, pages = {1624-1633}, title = {Implementation of eigenface method and support vector machine for face recognition absence information system}, volume = {26}, year = {2022}, } @article{Muneer2022, abstract = {In this era, machines can understand human activities and their meanings. We can utilize this ability of machines in various fields or applications. One specific field of interest is a prediction of churning customers in any industry. Prediction of churning customers is the state of art approach which predicts which customer is near to leave the services of the specific bank. We can use this approach in any big organization that is very conscious about their customers. However, this study aims to develop a model that offers a meaningful churn prediction for the banking industry. For this purpose, we develop a customer churn prediction approach with the three intelligent models random forest (RF), AdaBoost, and support vector machine (SVM). This approach achieves the best result when the synthetic minority oversampling technique (SMOTE) is applied to overcome the unbalanced dataset and the combination of undersampling and oversampling. The method on SMOTED data has produced excellent results with a 91.90 F1 score and overall accuracy of 88.7% using RF. Furthermore, the experimental results show that RF yielded good results for the full feature-selected datasets.}, author = {Amgad Muneer and Rao Faizan Ali and Amal Alghamdi and Shakirah Mohd Taib and Ahmed Almaghthawi and Ebrahim Abdulwasea Abdullah Ghaleb}, doi = {10.11591/ijeecs.v26.i1.pp539-549}, issn = {25024760}, issue = {1}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {AdaBoost,Banking industry,Churning,Random forest,SMOTE,Support vector machine}, pages = {539-549}, title = {Predicting customers churning in banking industry: A machine learning approach}, volume = {26}, year = {2022}, } @article{Santoso2019, abstract = {In the data mining, a class imbalance is a problematic issue to look for the solutions. It probably because machine learning is constructed by using algorithms with assuming the number of instances in each balanced class, so when using a class imbalance, it is possible that the prediction results are not appropriate. They are solutions offered to solve class imbalance issues, including oversampling, undersampling, and synthetic minority oversampling technique (SMOTE). Both oversampling and undersampling have its disadvantages, so SMOTE is an alternative to overcome it. By integrating SMOTE in the data mining classification method such as Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) is expected to improve the performance of accuracy. In this research, it was found that the data of SMOTE gave better accuracy than the original data. In addition to the three classification methods used, RF gives the highest average AUC, F-measure, and G-means score.}, author = {Noviyanti Santoso and Wahyu Wibowo and Hilda Himawati}, doi = {10.11591/ijeecs.v13.i1.pp102-108}, issn = {25024760}, issue = {1}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Accuracy,Data mining,Imbalanced class,SMOTE}, pages = {102-108}, title = {Integration of synthetic minority oversampling technique for imbalanced class}, volume = {13}, year = {2019}, } @article{Jopri2020, abstract = {The diagnostic analytic type of harmonic source is a vital research due to diagnose and identify type of harmonic source that exist in the power system. This paper presents a comparison of machine learning (ML) algorithm namely as the Naïve Bayes (NB) and linear discriminate analysis (LDA) in identifying and diagnosing the harmonic sources. The MLs inputs are the voltage and current feature sets that estimated from the time-frequency representation (TFR) of S-transform analysis. Four specific cases of harmonic source location are considered in this research, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. The sufficiency of the proposed methodology is tested and verified on the IEEE 4-bust test feeder, and to prevent overfitting, the K-fold cross-validation technique is implemented for performance evaluation. To identify the best ML, the performance measurement consist of the accuracy, precision, geometric mean, F-measure, sensitivity, and specificity are conducted.}, author = {M. H. Jopri and M. R. Ab Ghani and A. R. Abdullah and T. Sutikno and M. Manap and J. Too}, doi = {10.11591/ijeecs.v20.i3.pp1626-1633}, issn = {25024760}, issue = {3}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Harmonic current source,Harmonic source diagnosis,Harmonic voltage source,K-nearest neighbor,Linear discriminate analysis,S-transform}, pages = {1626-1633}, title = {Naïve bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification}, volume = {20}, year = {2020}, } @article{Somantri2019, abstract = {Conducting an assessment of consumer sentiments taken from social media in assessing a culinary food gives useful information for everyone who wants to get this information especially for migrants and tourists, in th other hand that information is very valuable for food stall and restaurant owners as information in improvinf food quality. Overcoming this problem, a sentiment analysis classification model using naïve bayes algorithm (NB) was applied to get this information. This problem occurs is the level of accuracy of classification of consumer ratings of culinary food is still not optimal because the weight of values in the data preprocessing process are not optimal. In this paper proposed a hybrid feature selection models to overcome the problems in the process of selecting the feature attributes that have not been optimal by using a combination of information gain (IG) and genetic algorithm (GA) algorithms. The result of this research showed that after the experiment and compared to using others algorithms produce the best of the level occuracy is 93%.}, author = {Oman Somantri and Dyah Apriliani}, doi = {10.11591/ijeecs.v15.i1.pp468-475}, issn = {25024760}, issue = {1}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Culinary food,Customer satisfaction,Hybrid feature selection,Naïve bayes,Opinion mining}, pages = {468-475}, title = {Opinion mining on culinary food customer satisfaction using naïve bayes based-on hybrid feature selection}, volume = {15}, year = {2019}, } @article{Padirayon2021, abstract = {A massive number of documents on crime has been handled by police departments worldwide and today's criminals are becoming technologically elegant. One obstacle faced by law enforcement is the complexity of processing voluminous crime data. Approximately 439 crimes have been registered in sanchez mira municipality in the past seven years. Police officers have no clear view as to the pattern crimes in the municipality, peak hours, months of the commission and the location where the crimes are concentrated. The naïve Bayes model is a classification algorithm using the Rapid miner auto model which is used and analyze the crime data set. This approach helps to recognize crime trends and of which, most of the crimes committed were a violation of special penal laws. The month of May has the highest for index and non-index crimes and Tuesday as for the day of crimes. Hotspots were barangay centro 1 for non-index crimes and barangay centro 2 for index crimes. Most non-index crimes committed were violations of special law and for index crime rape recorded the highest crime and usually occurs at 2 o'clock in the afternoon. The crime outcome takes various decisions to maximize the efficacy of crime solutions.}, author = {Lourdes M. Padirayon and Melvin S. Atayan and Jose Sherief Panelo and Carlito R. Fagela}, doi = {10.11591/ijeecs.v23.i2.pp1084-1092}, issn = {25024760}, issue = {2}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Classification algorithm,Crime data,Mining,Naïve Bayes classifier}, pages = {1084-1092}, title = {Mining the crime data using naïve Bayes model}, volume = {23}, year = {2021}, } @article{Hamoud2022, abstract = {The educational sector faced many types of research in predicting student performance based on supervised and unsupervised machine learning algorithms. Most students' performance data are imbalanced, where the final classes are not equally represented. Besides the size of the dataset, this problem affects the model's prediction accuracy. In this paper, the Synthetic Minority Oversampling TEchnique (SMOTE) filter is applied to the dataset to find its effect on the model's accuracy. Four feature selection approaches are applied to find the most correlated attributes that affect the students' performance. The SMOTE filter is examined before and after applying feature selection approaches to measure the model's accuracy with supervised and unsupervised algorithms. Three supervised/unsupervised algorithms are examined based on feature selection approaches to predict the students' performance. The findings show that supervised algorithms (logistic model trees (LMT), simple logistic, and random forest) got high accuracy after applying SMOTE without feature selection. The prediction accuracies of unsupervised algorithms (Canopy, expectations maximization (EM), and farthest first) are enhanced after applying feature selection approaches and SMOTE filter.}, author = {Alaa Khalaf Hamoud and Mohammed Baqr Mohammed Kamel and Alaa Sahl Gaafar and Ali Salah Alasady and Aqeel Majeed Humadi and Wid Akeel Awadh and Jasim Mohammed Dahr}, doi = {10.11591/ijeecs.v28.i2.pp1105-1116}, issn = {25024760}, issue = {2}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Educational data mining,Feature selection,SMOTE filter,Students’ performance,Supervised Algorithms,Unsupervised Algorithms}, pages = {1105-1116}, title = {A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset}, volume = {28}, year = {2022}, } @article{Samsudin2019, abstract = {YouTube has become a popular social media among the users. Due to YouTube popularity, it became a platform for spammer to distribute spam through the comments on YouTube. This has become a concern because spam can lead to phishing attack which the target can be any user that click any malicious link. Spam has its own features that can be analyzed and detected by classification. Hence, enhancement features are proposed to detect YouTube spam. In order to conduct the experiments, a YouTube Spam detection framework that consists of five (5) phases such as data collection, pre-processing, features selection and extraction, classification and detection were developed. This paper, proposed the YouTube detection framework, examined and validate each of the phases by using two types of data mining tool. The features are constructed from analysis by using data collected from YouTube Spam dataset by using Naïve Bayes and Logistic Regression and tested in two different data mining tools which is Weka and Rapid Miner. From the analysis, thirteen (13) features that had been tested on Weka and RapidMiner shows high accuracy, hence is being used throughout the experiment in this research. Result of Naïve Bayes and Logistic Regression run in Weka is slightly higher than RapidMiner. In addition, result of Naïve Bayes is higher than Logistic Regression with 87.21% and 85.29% respectively in Weka. While in RapidMiner there is slightly different of accuracy between Naïve Bayes and Logistic Regression 80.41% and 80.88%. But, precision of Naïve Bayes is higher than Logistic Regression.}, author = {Nur’Ain Maulat Samsudin and Cik Feresa Binti Mohd Foozy and Nabilah Alias and Palaniappan Shamala and Nur Fadzilah Othman and Wan Isni Sofiah Wan Din}, doi = {10.11591/ijeecs.v14.i3.pp1508-1517}, issn = {25024760}, issue = {3}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Classification,Detection,Machine learning,Spam}, pages = {1508-1517}, title = {Youtube spam detection framework using naïve bayes and logistic regression}, volume = {14}, year = {2019}, } @article{Sayeedunnisa2020, abstract = {Social media is a copious source of opinionated data. With the increasing number of people using social media website to vocalize their opinions on various subject, it has become viable to automate these opinions on brand, product, news, story via sentiment analysis aka opinion mining. Opinion mining is gaining insights from these user reviews to know the public view as positive or negative about product, service or brand. This helps business organization and individuals to be proactive in decision making. It finds profound application by helping organization to identify potential product advocates or social media influencers. The current manuscript deals with mining of opinions from the Social media site Twitter on "#Me too" movement. Using tweets generates huge data, which need to be processed and then are to be classified as positive, negative or neutral. The main aim of the manuscript is to use diverse features including emoticons and slang other than conventional Bag of Word features and perform effective Sentiment Classification using these features. We apply a feature selection method to find optimal features and these optimal features are classified. I t is evident from this work that integrating emoticons and slang with conventional Bag of Word model improves the accuracy of classification. The manuscript uses Accuracy, Precision and Recall as the performance metric to analyze the opinion of Twitter users.}, author = {S Fouzia Sayeedunnisa}, doi = {10.17577/IJERTCONV8IS15016}, issn = {2278-0181}, issue = {15}, journal = {International Journal of Engineering Research & Technology (IJERT)}, keywords = {- social network,bag of,emoticons,slang,twitter}, month = {9}, pages = {69-72}, publisher = {IJERT-International Journal of Engineering Research & Technology}, title = {Using Slang and Emoticon for Sentiment Analysis of Social Media Data}, volume = {8}, url = {www.ijert.org}, year = {2020}, } @article{Ressan2022, abstract = {This paper proposes a system to analyze the sentiments of tweeters. It is to build an accurate model to detect different emotions in a tweet. The analysis takes place through several stages (i.e., pre-processing, feature extraction, and training more than one machine learning (ML)). Naïve Bayes, Multinomial Naïve Bayes and Bernoulli Naïve Bayes were selected as supervised machine learning for sentiment analysis using a dataset of 3,057 tweets with users ranging from fear to happiness, anger, and sadness because this method is suitable for solving a problem of this type. This system was also applied to another dataset of 10,000 Tweets (5,000 positive and 5,000 negatives). This approach, consisting of three Naïve Bayes classification models, was applied to two datasets to analyze the sentiment used in them and classify each category separately. The Multinomial Naïve Bayes model outperformed the other models Where it achieved an accuracy of (91.6%) when applied to the first dataset and accuracy (87.6%) when applied to the second dataset. The researchers aim to continue this research with larger data by using other methods of sentiment analysis to predict users' thoughts about COVID-19 or any other problem and to obtain higher accuracy for the models used.}, author = {Murtadha B. Ressan and Rehab F. Hassan}, doi = {10.11591/ijeecs.v28.i1.pp375-383}, issn = {25024760}, issue = {1}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Bernoulli Naïve Bayes,COVID-19,Multinomial Naïve Bayes,Naïve-Bayes,Sentiment analysis Twitter}, pages = {375-383}, title = {Naïve-Bayes family for sentiment analysis during COVID-19 pandemic and classification tweets}, volume = {28}, year = {2022}, } @article{Chamorro-Atalaya2022, abstract = {Satisfaction with teaching performance is an important measurement process in higher education institutions, for this reason, applying sentiment analysis to the opinions of university students through the support vector machine (SVM) Fine Gaussian supervised learning algorithm represents an important contribution to the academic literature. This article identifies the best classification algorithm according to performance parameters for predicting student satisfaction with teaching performance through sentiment analysis; the subsequent implementation of the research has the purpose of strengthening teaching practices, in addition to allowing continuous training of teaching for the benefit of student learning. This article has provided a compact predictive model, with literature review based on SVM and sentiment analysis techniques. Through the machine learning classification learner technique, it is identified that the SVM algorithm: Fine Gaussian SVM is the one with the best accuracy equal to 98.3%. Likewise, the performance metrics for the four classes of the model were identified, which have a sensitivity equal to 88.89%, a specificity of 98.04%, a precision of 99.21% and an accuracy of 98.85%.}, author = {Omar Chamorro-Atalaya and Dora Arce-Santillan and José Antonio Arévalo-Tuesta and Lilia Rodas-Camacho and Ronald Fernando Dávila-Laguna and Rufino Alejos-Ipanaque and Lilly Rocío Moreno-Chinchay}, doi = {10.11591/ijeecs.v28.i1.pp516-524}, issn = {25024760}, issue = {1}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {Satisfaction,Sentiment analysis,Supervised learning,Suppor vector machine,Teacher performance}, pages = {516-524}, title = {Supervised learning using support vector machine applied to sentiment analysis of teacher performance satisfaction}, volume = {28}, year = {2022}, } @article{Ramadhan2022, abstract = {Since the Coronavirus disease 2019 (COVID-19) pandemic hit the world, it had a significant negative impact on individuals, governments, and the global economy. One way to reduce the negative impact of COVID-19 is to vaccinate. Briefly, vaccination aims to enable the formed immune system to remember the characteristics of the targeted viral pathogen and be able to initiate an immune response that is rapid and strong enough to defeat future live viral pathogens. However, there are still many people in the world who are anti-vaccine. This certainly greatly hampers the process of accelerating the formation of the body's immune system widely in the community. Anti-vaccine people can be found on various social media platforms. Twitter was chosen as the data source because twitter is a common source of text for sentiment analysis. This study aims to analyze public sentiment on the COVID-19 vaccine through twitter in the form of tweets and retweets. This study uses the Gaussian Naïve Bayes method to see the results of the classification of sentiment analysis. The results obtained based on experiments prove that the Gaussian Naïve Bayes method can produce an average accuracy of 97.48% for each vaccine dataset used.}, author = {Nur Ghaniaviyanto Ramadhan and Faisal Dharma Adhinata}, doi = {10.11591/ijeecs.v26.i3.pp1765-1772}, issn = {25024760}, issue = {3}, journal = {Indonesian Journal of Electrical Engineering and Computer Science}, keywords = {COVID-19,Gaussian Naïve Bayes,Sentiment analysis,Vaccine,Word count}, pages = {1765-1772}, title = {Sentiment analysis on vaccine COVID-19 using word count and Gaussian Naïve Bayes}, volume = {26}, year = {2022}, } @article{WuriHandayani2018, abstract = {Objectives: We determined and structurally analyzed the reported effect of hydroxyapatite (HA) bone substitute on alveolar bone regeneration. To the best of our knowledge, no systematic reviews have previously reported the bone regenerative effect of the HA bone substitute. Materials and methods: A literature search was performed for articles published up to August 2015 using MEDLINE with the search terms "hydroxyapatite," "bone regeneration," and "alveolar bone" as well as their known synonyms. The inclusion criteria were set up for human trials with at least five patients. The literature search, eligible article selection, and data extraction were independently performed by two readers, and their agreement was reported by k value. Results: Of the 504 studies found using the MEDLINE literature search, 241 were included for further steps (inter-reader agreement, k ¼ 0.968). Abstract screening yielded 74 studies (k ¼ 0.910), with 42 completely fulfilling the inclusion criteria (k ¼ 0.864). In a final step, 42 studies were further analyzed, with 17 and 25 studies with and without statistical analysis, respectively. The 17 studies Cite as: Anne Handrini Dewi, Ika Dewi Ana. The use of hydroxyapatite bone substitute grafting for alveolar ridge preservation, sinus augmentation, and periodontal bone defect: A systematic review. (http://creativecommons.org/licenses/by-nc-nd/4.0/). reporting similar outcome measures were compared using the calculated 95% confidence intervals. The effect of HA on ridge preservation could not be evaluated. Conclusions: The use of the HA bone substitute interfered with the normal healing process, with significant differences found for sinus augmentation but not for periodontal bone defects. Thus, a bone substitute with optimal bone regenerative properties for alveolar ridge or socket preservation, sinus augmentation, and periodontal bony defect should be developed.}, author = {Putu Wuri Handayani and Dira Ayu Meigasari and Ave Adriana Pinem and Achmad Nizar Hidayanto and Dumilah Ayuningtyas}, doi = {10.1016/j.heliyon.2018.e00981}, issn = {24058440}, issue = {11}, journal = {Heliyon}, keywords = {Dentistry,Materials science}, month = {11}, pages = {e00981}, title = {Critical success factors for mobile health implementation in Indonesia}, volume = {4}, url = {https://doi.org/10.1016/j.heliyon.2018.e00884 https://linkinghub.elsevier.com/retrieve/pii/S2405844018324915}, year = {2018}, } @inproceedings{Lak2014, abstract = {A typical trade-off in decision making is between the cost of acquiring information and the decline in decision quality caused by insufficient information. Consumers regularly face this trade-off in purchase decisions. Online product/service reviews serve as sources of product/service related information. Meanwhile, modern technology has led to an abundance of such content, which makes it prohibitively costly (if possible at all) to exhaust all available information. Consumers need to decide what subset of available information to use. Star ratings are excellent cues for this decision as they provide a quick indication of the tone of a review. However there are cases where such ratings are not available or detailed enough. Sentiment analysis-text analytic techniques that automatically detect the polarity of text-can help in these situations with more refined analysis. In this study, we compare sentiment analysis results with star ratings in three different domains to explore the promise of this technique. © 2014 IEEE.}, author = {Parisa Lak and Ozgur Turetken}, doi = {10.1109/HICSS.2014.106}, isbn = {9781479925049}, issn = {15301605}, journal = {Proceedings of the Annual Hawaii International Conference on System Sciences}, pages = {796-805}, publisher = {IEEE Computer Society}, title = {Star ratings versus sentiment analysis - A comparison of explicit and implicit measures of opinions}, year = {2014}, } @inproceedings{Pratama2021, abstract = {Smartphone technology in health is critical, especially in improving health services quality and quality. The use of smartphone technology in health services is called Mobile Health (M-Health). The application of M-health is one solution to improve the quality of public health services. The application of M-Health will make it easier for users to obtain health information and services. JKN Mobile is an M-Health application that provides health insurance services managed by the Indonesian government. The JKN Mobile application has several features such as documentation of medical records, viewing the number of bills to be paid, updating participant data, and conducting online doctor consultations. This study aims to analyze the service quality factors that affect satisfaction and continuous use of the JKN Mobile Application using the Service Quality of M-Health Model. This study uses quantitative methods by analyzing data using Structural Equation Modeling (SEM) involving 100 respondents using the JKN Mobile Application. The results showed that the variables of interaction quality and information quality as dimensions of service quality significantly influenced user satisfaction. Information quality has a significant influence on sustainable use. Monetary cost does not significantly affect user satisfaction and continuous use of the JKN Mobile Application.}, author = {Arista Pratama and Doddy Ridwandono and Tri Lathif Mardi Suryanto and Eristya Maya Safitri and Asmaul Khusna}, doi = {10.1109/ITIS53497.2021.9791639}, isbn = {9781665408073}, journal = {Proceedings - 2021 IEEE 7th Information Technology International Seminar, ITIS 2021}, keywords = {JKN Mobile,M-Health,SEM,Service Quality Model}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, title = {Service Quality Analysis of M-Health Application Satisfaction And Continual Usage}, year = {2021}, } @statute{BPJSKesehatan2017, author = {BPJS Kesehatan}, journal = {BPJS Kesehatan}, title = {BPJS Kesehatan Board of Directors Regulation Number 30 of 2017 Concerning the Implementation of the JKN Mobile Application}, year = {2017}, } @report{BPJSKesehatan2023, author = {BPJS Kesehatan}, institution = {BPJS Kesehatan}, journal = {BPJS Kesehatan}, title = {Report on JKN Mobile Application Rating Trends}, year = {2023}, } @report{BPJSKesehatan2023a, author = {BPJS Kesehatan}, journal = {BPJS Kesehatan}, title = {Report on the Target and Achievement of Registered Users of the Mobile JKN Application from 2020 to 2022}, year = {2023}, } @statute{PresidentoftheRepublicofIndonesia2022, author = {President of the Republic of Indonesia}, journal = {The Secretariat of the Cabinet of the Republic of Indonesia}, title = {Indonesian Presidential Instruction Number 1 of 2022 on Optimizing the National Health Insurance Program Implementation}, year = {2022}, } @article{Haryani2019, abstract = {Customer Satisfaction is the most important factor to determine long term success of a company, and that includes Online Auction company. Customer Satisfaction is important for an Online Auction company to enhance its customer loyalty in the middle of today's competition. This research is done to analyze the significant Service Quality of Online Auction, using sentiment analysis in Twitter, by targeting the customers that used online auction E-Bay; one of the largest online auction platform in the world. The data is retrieved from tweets of E-Bay's customer to @Ebay and @askEbay twitter account, before being processed using Lexicon Classification method to generate sentiment analysis for each significant service quality factors. The Results shows that Information Reliability, Interface Design and Security, and Reliability are the factors that gained the most feedback and sentiment from customers.}, author = {Calandra Alencia Haryani and Achmad Nizar Hidayanto and Nur Fitriah Ayuning Budi and Herkules}, doi = {10.1109/CITSM.2018.8674365}, isbn = {9781538654330}, issue = {Citsm}, journal = {2018 6th International Conference on Cyber and IT Service Management, CITSM 2018}, keywords = {E-Bay,customer satisfaction,lexicon classification,online auction,sentiment,service quality}, pages = {1-5}, publisher = {IEEE}, title = {Sentiment Analysis of Online Auction Service Quality on Twitter Data: A case of E-Bay}, year = {2019}, } @article{Yang2004, abstract = {This exploratory research intends to extend our understanding of service quality and customer satisfaction within the setting of online securities brokerage services. Based upon conceptual frameworks from the areas of services marketing and information systems management, the authors uncovered 52 items across 16 major service quality dimensions by content analysis of 740 customer reviews. The results indicate that primary service quality dimensions leading to online customer satisfaction, with the exception of ease of use, are closely related to traditional services while key factors leading to dissatisfaction are tied to information systems quality. In addition, major drivers of satisfaction and dissatisfaction are identified at the sub-dimensional level. Theoretical contribution and managerial implications of the findings are further discussed.}, author = {Zhilin Yang and Xiang Fang}, doi = {10.1108/09564230410540953}, issn = {09564233}, issue = {3}, journal = {International Journal of Service Industry Management}, keywords = {Customer satisfaction,Electronic commence,Internet,Securities markets,Servicing}, pages = {302-326}, title = {Online service quality dimensions and their relationships with satisfaction: A content analysis of customer reviews of securities brokerage services}, volume = {15}, year = {2004}, } @statute{PresidentoftheRepublicofIndonesia2011, abstract = {Vinaigrette dressing sudah di gunakan lebih dari 2000 tahun yang lalu oleh bangsa mesir untuk menikmati salad mereka ini di kutip dari laman sejarah di associates dressing and sauce. Dan pada era ini vinaigrette dressing sangat terekenal di dunia bagian barat dan sering digunakan untuk salad. Salah satu bahan utama pembuatan Vinaigrette dressing adalah minyak, dan minyak yang banyak di gunakanan adalah olive oil atau minyak zaitun. Minyak zaitun berasal dari buah zaitun yang merupakan salah satu tanaman tertua di dunia}, author = {President of the Republic of Indonesia}, journal = {The Secretariat of the Cabinet of the Republic of Indonesia}, title = {The Republic of Indonesia Law Number 24 of 2011 Concerning Social Security Administrators}, year = {2011}, } @inproceedings{Hermanto2020, abstract = {As the online Ojek services, people often talk about them by giving their opinions and opinions through various media, one of which is Google Play opinion given by the public to the services ofonline Ojek also diverse. Users provide review reviews or comments about the application, of courseusers will choose an app that has a good review. But monitoring the reviews of the general public is not easy, because the amount is very much to be processed so that researchers want to know the extent of the user review analysis of Gojek and Grab applications based on the review of user comments using the classification technique is using the NB algorithm and SVM based technique Smote. The results of the test with the highest accuracy result 81.09% and AUC value = 0.922 is the applicationGojek while for application test results grab accuracy value of 73.20% and AUC value = 0.848. To that end, the implementation of the Support Vector Machine based Smote technique in this study has higher accuracy so that it can be used to provide solution to the sentiment analysis problems in the review user comments online Ojek application}, author = {Hermanto and Antonius Yadi Kuntoro and Taufik Asra and Eri Bayu Pratama and Lasman Effendi and Ridatu Ocanitra}, doi = {10.1088/1742-6596/1641/1/012102}, issn = {17426596}, issue = {1}, journal = {Journal of Physics: Conference Series}, month = {11}, publisher = {IOP Publishing Ltd}, title = {Gojek and Grab User Sentiment Analysis on Google Play Using Naive Bayes Algorithm and Support Vector Machine Based Smote Technique}, volume = {1641}, year = {2020}, } @article{Xu2022, abstract = {In the present information age, a wide and significant variety of social media platforms have been developed and become an important part of modern life. Massive amounts of user-generated data sourced from various social networking platforms also provide new insights for businesses and governments. However, it has become difficult to extract useful information from the vast amount of information effectively. Sentiment analysis provides an automated method of analyzing sentiment, emotion and opinion in written language to address this issue. In the existing literature, a large number of scholars have worked on improving the performance of various sentiment classifiers or applying them to various domains using data from social networking platforms. This paper explores the challenges that scholars have encountered and other potential problems in studying sentiment analysis in social media. It gives insights into the goals of the sentiment analysis task, the implementation process, and the ways in which it is utilized in various application domains. It also provides a comparison of different studies and highlights several challenges related to the datasets, text languages, analysis methods and evaluation metrics. The paper contributes to the research on sentiment analysis and can help practitioners select a suitable methodology for their applications.}, author = {Qianwen Ariel Xu and Victor Chang and Chrisina Jayne}, doi = {10.1016/j.dajour.2022.100073}, issn = {27726622}, journal = {Decision Analytics Journal}, month = {6}, pages = {100073}, publisher = {Elsevier BV}, title = {A systematic review of social media-based sentiment analysis: Emerging trends and challenges}, volume = {3}, year = {2022}, } @statute{GovernmentoftheRepublicofIndonesia1945, author = {Government of the Republic of Indonesia}, journal = {The Secretariat of the Cabinet of the Republic of Indonesia}, title = {The Constitution of the Republic of Indonesia of 1945}, } @inproceedings{Firmansyah2021, abstract = {COVID-19 statistics in Indonesia show more than 4.2 million active confirmed cases with more than 140 thousand deaths. The Indonesian government has made several policies to reduce the number of COVID-19 cases, one of them is by implementing the PeduliLindungi application. The government has socialized and recommended this application as an effort to fulfill the tracking, tracing, and fencing program. Various kinds of responses appear in the community to this application, therefore sentiment analysis is needed to find out public trends so that the government can evaluate the policies that have been made. This study aims to determine the best model from the comparison of the Naïve Bayes algorithm and the Support Vector Machine, besides that this study will also see whether a simpler model such as Naive Bayes is still good in handling binary sentiment for PeduliLindungi data reviews. The data was obtained by web scraping from the PeduliLindungi application review on the Google Play Store. The Naïve Bayes accuracy value is 81%, smaller than the Support Vector Machine which has an accuracy of 84%, although the Support Vector Machine is the best model we have, Naive Bayes itself can still be used to handle binary sentiment data because the difference in accuracy values is not too far.}, author = {Isal Firmansyah and Mohammad Hamid Asnawi and Syifa Auliyah Hasanah and Rafly Novian and Anindya Apriliyanti Pravitasari}, doi = {10.1109/ICAIBDA53487.2021.9689771}, isbn = {9781665408905}, journal = {2021 International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021}, keywords = {COVID-19,Naïve bayes,PeduliLindungi,Sentiment Analysis,Support Vector Machine}, pages = {140-145}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, title = {A Comparison of Support Vector Machine and Naïve Bayes Classifier in Binary Sentiment Reviews for PeduliLindungi Application}, year = {2021}, } @inproceedings{Mussalimun2021, abstract = {Fintech Lending/Peer-to-Peer Lending in its application has developed very rapidly. As an agricultural country, Indonesia is undoubtedly a market asset for the banking world to improve its services, especially in the capital. IGrow, as part of a crowdfunding company registered with the Indonesian Financial Services Authority (OJK), has taken an essential role in the field of agricultural capital. I Grow has a Fintech platform on the Google Play Store that has mixed reviews and opinions among users. Various opinions and reviews from users will undoubtedly have a positive and negative impact on the development of the business. The purpose of this study is to provide sentiment analysis of the IGROW platform on the Google Play Store. The method used in this study uses the K-Nearest Neighbor (K-NN) Algorithm and the Naive Bayes classification algorithm. The study results show that the accuracy, precision, and recall values of K-NN and Naive Bayes classification are (73.85%, 76.60%, 85.71 %) and (75.38%, 80.95%, 80.95%). The Naive Bayes classification produces slightly better predictions than K-NN by getting slightly better accuracy and precision values, but K-NN gets better values on recall.}, author = {Mussalimun and Elvien Hastatomo Khasby and Gempita Icky Dzikrillah and Muljono}, doi = {10.1109/ICITACEE53184.2021.9617217}, isbn = {9781665439985}, journal = {2021 8th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2021}, keywords = {Comparison,Google Play Store,K-NN,K-Nearest Neighbor,Naive Bayes}, pages = {180-184}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, title = {Comparison of K-N earest Neighbor (K-NN) and Naïve Bayes Algorithm for Sentiment Analysis on Google Play Store Textual Reviews}, year = {2021}, } @inproceedings{Amrie2022, abstract = {Nowadays there are so many mobile phone-based investment applications, ranging from mutual funds, stocks, and P2P lending. While these investment applications are gaining huge attraction among the general masses, sometimes selecting the right platform still becomes a hot issue. This research aimed to analyze the sentiment on P2P lending applications and to determine the user's response due to the increase in the number of funds distribution during the COVID-19 pandemic. By doing so, this research could give some insight into the new and existing user. Data was obtained through assessment reviews on the Play store platform for the P2P A, P2P B, and P2P C applications. Assessment reviews were classified by using a data mining approach, TF-IDF feature extraction, and Naïve-Bayes classification method. This research showed that P2P A got 77% positive sentiment and 23% negative sentiment, P2P B got 36% positive sentiment and 64% negative sentiment, and P2P C got 68% positive sentiment and 32% negative sentiment. From the results of the study, it was found that P2P A got better results than both P2P B and P2P C. those were 77% positive sentiment with 23% negative sentiment in finance topic, 56% positive sentiment with 44 % negative sentiment in account verification topic, 79% positive sentiment with 21% negative sentiment in apps review, and 99% positive sentiment with 1% negative sentiment in referral topic.}, author = {Syahrul Amrie and Sandy Kurniawan and Jauzak Hussaini Windiatmaja and Yova Ruldeviyani}, doi = {10.1109/DELCON54057.2022.9753108}, isbn = {9781665458832}, journal = {2022 IEEE Delhi Section Conference, DELCON 2022}, keywords = {Naive Bayes Classifier,P2P lending,Sentiment Analysis,TF-IDF}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, title = {Analysis of Google Play Store's Sentiment Review on Indonesia's P2P Fintech Platform}, year = {2022}, } @inproceedings{Pribadi2022, abstract = {At the end of 2019, the world was hit by the COVID-19 virus, which caused a pandemic. Indonesia has become one of the countries that are affected by this pandemic. To control the COVID-19 pandemic, the government has made various efforts, one of which is the use of the PeduliLindunig app. To access the PeduliLindungi app, the public can download it from Google Play. Google Play enables its users to write reviews on the apps that have been downloaded. This study aims to determine the sentiment analysis on the PeduliLindungi application on Google Play using the Random Forest Algorithm with SMOTE. Based on this study, public sentiment towards the PeduliLindungi app on Google Play tends to be negative. The Random Forest and SMOTE algorithms are used to classify sentiment in this study. The implementation of Random Forest and SMOTE resulted in 71% accuracy, 70% recall, and 70% precision.}, author = {Muhammad Rizky Pribadi and Danny Manongga and Hindriyanto Dwi Purnomo and Iwan Setyawan and Hendry}, doi = {10.1109/ISITIA56226.2022.9855372}, isbn = {9781665460811}, journal = {2022 International Seminar on Intelligent Technology and Its Applications: Advanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding}, keywords = {Google Play,PeduliLindungi,Random Forest,SMOTE,sentiment analysis}, month = {8}, pages = {115-119}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, title = {Sentiment Analysis of the PeduliLindungi on Google Play using the Random Forest Algorithm with SMOTE}, year = {2022}, }