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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) CESS (Journal of Computer Engineering, System and Science) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri INTEK: Informatika dan Teknologi Informasi Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Indonesian Journal of Multidisciplinary on Social and Technology SATIN - Sains dan Teknologi Informasi Journal of Engineering and Science Application INOVTEK Polbeng - Seri Informatika
Lola Oktavia
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Journal : bulletin of computer science research

 1 
Klasifikasi Sentimen Masyarakat Terhadap Revisi Undang-Undang Tentara Nasional Indonesia Menggunakan Naïve Bayes Classifier Abdul Haris Kurnia Sandi Harahap; Elin Haerani; Lola Oktavia; Okfalisa Okfalisa; Fitra Kurnia
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.615

Abstract

The revision of the Indonesian National Armed Forces Bill (RUU TNI) has become a hot topic in Indonesian public policy and has sparked controversy among the public due to its sudden emergence and lack of open planning process. This has raised concerns about the potential for military domination and the return of the dual function of the ABRI (Indonesian Armed Forces). The classification of public sentiment towards the RUU TNI is the focus of this study. Comments are categorized into two types of sentiment classes, namely positive and negative. The research stages include data collection, sentiment labeling, data cleaning, text normalization to lowercase letters, sentence or document segmentation into smaller parts, text data normalization, negation handling, stopword removal, and stemming, weighting using the TF-IDF technique, model classification development, and evaluation of the model's performance. The Naïve Bayes Classifier method classified 1,547 comment data points collected from two Instagram social media accounts. The Naïve Bayes Classifier model achieved an accuracy of 83.74%, precision of 81.17%, recall of 87.86%, and an F1-score of 84.38%. This study has limitations, including the limited amount of data collected. These include an imbalance in the amount of data between sentiment categories, data from only one social media platform, and the suboptimal identification of positive and negative sentiments. It is recommended that future research compare this method with other classification methods, expand the dataset, broaden the scope of data collection by involving various social media platforms over a wider time span, thereby providing a more comprehensive picture of public opinion, and test a wider range of algorithm combinations. This study can serve as an initial indicator for rapid policy evaluation, where positive or negative comments from the public on social media can provide important input in assessing the effectiveness of a policy.
Penerapan Support Vector Machine Dengan Smote Untuk Klasifikasi Sentimen Pada Data Ulasan Aplikasi Trading View Muhammad Badri; Elin Haerani; Fadhilah Syafria; Okfalisa Okfalisa; Lola Oktavia
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.793

Abstract

In the digital era, user feedback on mobile applications serves as highly valuable information for developers to evaluate app performance. One popular application in the field of finance and investment is TradingView, widely used for technical analysis by traders. User feedback on this application reflects various user sentiments, including positive, negative, and neutral. However, the large volume of reviews and the unstructured nature of text data make manual analysis inefficient and prone to high subjective bias. Therefore, the use of automatic classification methods capable of processing text data with reasonable accuracy is required. This study aims to implement the “Support Vector Machine (SVM)” technique to classify user feedback on the TradingView application. To address the issue of imbalanced sentiment class distribution, the study also employs the “Synthetic Minority Over-sampling Technique (SMOTE)”. The study utilizes 10,000 reviews obtained via web scraping from the Google Play Store. The study workflow consists of text preprocessing, feature extraction using “Term Frequency-Inverse Document Frequency (TF-IDF)”, data balancing, SVM model training, and model evaluation. The evaluation results show that the application of SVM with SMOTE achieves an accuracy of approximately ±85.56% across data splits (70:30, 80:20, 90:10). In each scenario, the highest F1-score was achieved for the positive sentiment class, while the performance of minority classes (negative and neutral) improved after data balancing with SMOTE, with an average F1-score increase of 1.67% for the negative class and 10.67% for the neutral class. Without SMOTE, the average negative F1-score was ±57%, and the neutral class was undetected (0.00%). Furthermore, validation using K-Fold Cross Validation yielded an average accuracy of 89.20%, which increased to 95.10% after applying SMOTE. This improvement was consistent across all data proportions (70:30, 80:20, 90:10), with an average increase of 5.44%. These findings confirm that integrating SVM with SMOTE not only enhances classification performance on imbalanced data but also maintains model stability. Therefore, this study contributes to the advancement of automated sentiment classification systems, particularly for financial mobile app reviews, and can serve as a reference for future research in user review analysis on similar applications.
Penerapan Metode Support Vector Machine Untuk Analisis Sentimen Pada Komentar Bitcoin Di Aplikasi X Yaskur Bearly Fernandes; Elin Haerani; Fadhilah Syafria; Muhammad Fikry; Lola Oktavia
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.928

Abstract

Social media has become a primary medium for users to express opinions, including those related to Bitcoin, whose fluctuating value often triggers diverse public responses. The large volume of unstructured comments makes manual sentiment analysis inefficient, thereby necessitating an automated approach based on machine learning. This study aims to classify positive and negative sentiments in Bitcoin-related comments on the X platform using the Support Vector Machine (SVM) algorithm with Term Frequency–Inverse Document Frequency (TF-IDF) feature weighting. The dataset consists of 1,750 Indonesian-language comments labeled by three annotators. The data were processed through several preprocessing stages, including case folding, text cleaning, tokenization, stopword removal, and stemming. Model evaluation was conducted using four data split ratios, namely 90:10, 80:20, 70:30, and 60:40. The experimental results indicate that the 90:10 ratio achieved the best performance, with an accuracy of 72.57%, precision of 0.75, recall of 0.73, and an F1-score of 0.67. The SVM model demonstrates strong performance in identifying positive sentiments; however, it is less effective in detecting negative sentiments due to class imbalance in the dataset. As an additional experiment, testing was performed using a balanced dataset obtained through an undersampling process and several SVM kernel types for comparison. The results show that using a balanced dataset leads to more evenly distributed classification performance across sentiment classes, while the linear kernel provides the most stable performance compared to other kernels. Overall, SVM with TF-IDF weighting proves to be an effective approach for sentiment analysis of Bitcoin-related comments on social media.
Co-Authors Abdillah, Rahmad Abdul Haris Kurnia Sandi Harahap Alwis Nazir Alwis Nazir Alwis Nazir Eka Suryani Indra Septiawati Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Emir Ramon Fadhilah Syafria Fahrul Al Hidayat Febby Kurniawan Firman Zahri Fitra Kurnia Fitra Kurnia Fitri Insani Heri Susanto Ihda Syurfi Irma Sanela Iwan Iskandar Jasril Jasril Lili Rahmawati Marliana Safitri Muhammad Affandes Muhammad Badri Muhammad Fikry Muhammad Fikry Muhammad Hanif Abdurrohman Muhammad Irsyad Muhammad Yusril Haffandi Nining Nur Habibah Novendri Wahyudy Novi Yanti Novriyanto Novriyanto Nurainun Nurainun Okfalisa Okfalisa Okfalisa Okfalisa Okfalisa Okfalisa Pizaini Pizaini Raja Sultan Firsky Reski Mai Candra Rizuan Rizuan Said Nanda Saputra said rizki zikrillah aulia Saktioto Saktioto Sofiah Sola Huddin Suwanto Sanjaya Tiara Dwi Arista Tiara Ramayanti Yaskur Bearly Fernandes Yusra Yusra Yusra Yusra, Yusra