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All Journal ComEngApp : Computer Engineering and Applications Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Annual Research Seminar Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURTEKSI Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Dinda Lestarini
Fakultas Ilmu Komputer Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Analisis Sentimen Publik Terhadap Program KIP-Kuliah Menggunakan Algoritma Random Forest pada Media Sosial X Rosdiyanah, Rosdiyanah; Lestarini, Dinda
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7366

Abstract

KIP-Kuliah program or The Indonesia Smart College Card (KIP-K) is a funding assistance from the government for students with economic difficulties who want to experience educational opportunities in higher education. This program can’t be separated from public discussion, especially regarding the issue of misuse of funds by recipients, inconsistency in fund disbursement and falsification of registration files. These problems make the public view that the KIP-K program is often still misdirected. The research aims to examine public sentiment or perception towards the KIP-K program using Random Forest algorithm combined with Word2Vec as a word weighting technique and Random Oversampling (ROS) as a balancing technique to overcome data imbalance. The dataset obtained comes via platform X or Twitter) a total of 4423 tweets with the keywords “kip-k” or “kipk” and with a vulnerable time during 2024. The model’s performance demonstrated a high accuracy of 96,57%, precision, recall, and f1-score at the same value of 97%. The results indicate that the model is effective in analyzing sentiment accurately and maintaining a balanced performance between the two sentiment classes. Based on research in this study, the Random Forest algorithm combined with Word2Vec and Random Oversampling (ROS) can produce high accuracy and can overcome data imbalance.
Analisis Klasterisasi Kualitas Internet Seluler Menggunakan Metode K-Means dan Gaussian Mixture Model Irwansyah, Muhammad Aziiz; Meiriza, Allsela; Lestarini, Dinda
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8615

Abstract

This study utilizes internet network data from Ookla Open Data (Speedtest Global Performance), comprising three main variables: download speed, upload speed, and latency. The aim is to analyze the condition and performance of mobile internet networks across 17 regencies/cities in South Sumatera Province in 2025 and to provide data-driven recommendations for the Department of Communication and Informatics to promote equitable and improved digital infrastructure through a Knowledge Discovery in Databases (KDD) approach. The applied methods include RobustScaler for data normalization, Principal Component Analysis (PCA) for dimensionality reduction, and K-Means and Gaussian Mixture Model (GMM) algorithms for clustering regions based on network characteristics. The analysis shows that both algorithms form three clusters (K=3) with distinct patterns. GMM demonstrates higher stability than K-Means, achieving a Silhouette score of 0.426 and Davies–Bouldin Index of 0.284, compared to K-Means with 0.351 and 0.688, while the lower Calinski–Harabasz score of GMM (9.960) indicates a trade-off between cluster compactness and stability, highlighting its adaptive behavior to data variation. Urban areas such as Palembang and Prabumulih belong to the high-performance cluster, whereas Ogan Komering Ulu Selatan lies in the low-performance cluster (18.87 Mbps; 33 ms), revealing a digital gap of approximately 18 Mbps across regions. These findings emphasize the need for equitable digital infrastructure strategies through fiber-optic expansion, BTS capacity enhancement, and multi-stakeholder collaboration toward Indonesia’s Digital Vision 2045.
Perbandingan Kinerja Naive Bayes, Support Vector Machine dan Random Forest Untuk Analisis Sentimen Aplikasi Brimo Darwin, Amelia; Lestarini, Dinda; Seprina, Iin
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8697

Abstract

The development of financial technology has driven the increasing use of mobile banking, including BRImo, owned by Bank Rakyat Indonesia (BRI). However, user reviews on the Google Play Store show various complaints such as login difficulties, system errors, and failed transactions. This study aims to analyze BRImo user sentiment using three machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Random Forest. Data were obtained from 4,996 reviews through web scraping and labeled based on ratings with categories 1-3 negative and 4-5 positive. The labeling process obtained 4,123 positive reviews and 873 negative reviews, which were then balanced using the Synthetic Minority Oversampling Technique (SMOTE). Feature extraction was performed using TF-IDF. Test results showed that Random Forest provided the best performance with an accuracy of 0.87, a recall of 0.70, and an F1-score of 0.65 in the negative class, and an F1-score of 0.92 in the positive class. The macro F1-score reached 0.79, higher than SVM (0.69) and Naive Bayes (0.70). This finding indicates that Random Forest is more effective in classifying BRImo user sentiment, especially after data balancing, and can serve as a reference for developers in improving the quality of application services.
Co-Authors Adelia Azahra Adelia Rizki Putri Ahmad Rifai Alfarizi, Muhammad Ali Bardadi Allsela Meiriza, Allsela Andesta Angelina Darmawahyuni, Annisa Darwin, Amelia Desty Rodiah Dinna Yunika Hardiyanti Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Fransiska Prihatini Sihotang, Fransiska Prihatini Gumay, Naretha Kawadha Pasemah Hardini Novianti Iriyani, Silfi Irwansyah, Muhammad Aziiz Kurniawan, Anggy Tias Kurniawan, Anggy Tyas Marjusalinah, Anna Dwi Muhammad Azmi Zaky Muhammad Naufal Rachmatullah Muhammad Naufal, Muhammad Muliana, Ahmad Syakir Nabila Rizki Oktadini Oktadini, Nabila Rizky Pacu Putra Pibriana, Desi Putri, Dila Okta Dwi Rahmadiana, Reva Nur Richa Pratiwi Rizka Dhini Kurnia Rosdiyanah, Rosdiyanah Rusdy, Taufiqurrahman Sarifah Putri Raflesia Sarifah Putri Raflesia Sarifah Putri Raflesia Sarifah Putri Raflesia, Sarifah Putri Seprina, Iin Silfi Iriyani Siti Nurmaini Taufiqurrahman Rusdy Taufiqurrahman Taufiqurrahman Via Mariska, Inneke