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All Journal Annual Research Seminar Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Komputer Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat INTECOMS: Journal of Information Technology and Computer Science Jurnal Teknologi Sistem Informasi dan Aplikasi JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Elektronik Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Applied Science, Engineering, Technology, and Education Jurnal Generic KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Ekonomi Jurnal Algoritma Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia ABDIKOM The Indonesian Journal of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Teknik Informatika dan Desain Komunikasi Visual
Dedy Kurniawan
Universitas Sriwijaya
Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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Journal : building of informatics technology and science

 1 
Deteksi Komentar dan Analisis Sentimen Promosi Judi Online pada Youtube Menggunakan IndoBERT dan XGBoost Putri, Naila Raihana; Kurniawan, Dedy; Tania, Ken Ditha
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.v7i3.8421

Abstract

YouTube, as a highly interactive platform, has become a medium for online gambling promotions, raising legal issues under the Electronic Information and Transactions (ITE) Law and social risks, particularly for adolescents. This study aims to analyse public responses to gambling-related comments and to develop an automatic detection system using Natural Language Processing (NLP). The research follows the Knowledge Discovery in Databases (KDD) stages, including web scraping, preprocessing, text transformation, model training, and evaluation. Sentiment analysis was performed on 999 comments labelled positive, negative, and neutral. Detection of promotional content was tested using IndoBERT and TF-IDF-based XGBoost, with 587 training samples and 885 external testing samples at an 80:20 ratio. The results show that the majority of comments (52.65%) are positive with a fairly high average confidence score (0.914), indicating public support for the eradication of online gambling. Meanwhile, negative comments (24.72%) with a confidence score of 0.888 generally contained criticism of the rampant practice of gambling promotion or YouTube's weak moderation system. For automatic detection, IndoBERT achieved superior performance with 0.94 accuracy and F1-score and only 10 misclassifications, significantly outperforming XGBoost, which reached 0.73 accuracy with 47 errors. This study highlights the effectiveness of transformer-based models in detecting gambling promotions while also indicating strong public support for eradication efforts. These findings provide an empirical foundation for advancing research on adaptive automated moderation systems capable of identifying concealed patterns of illicit content in digital platforms, particularly in the detection of online gambling promotional comments within the YouTube ecosystem.
Comparison of XGBoost and LSTM in Knowledge Discovery for GrokAI Mobile Application Sentiment Analysis Risyahputri, Aliyananda; Kurniawan, Dedy; Tania, Ken Ditha
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.v7i3.8651

Abstract

Generative AI has provided real benefits in key sectors of the public sector. However, the rapid expansion of AI assistant services also raises concerns about whether newly released products can consistently meet user expectations, especially as negative experiences are increasingly expressed through public reviews. Its positive impacts encourage competitive rivalry among AI assistant product developers, including xAI, which also participates by formulating the Grok AI application. As a relatively new product with over 50 million downloads, GrokAI needs to perform an evaluation to maintain its competitiveness. This condition leads to the research goal of analyzing user sentiment toward GrokAI application through reviews on Google Play Store and comparing the performance of Machine Learning and Deep Learning classification models within the framework of Knowledge Discovery in Databases (KDD). This study uses 11,108 review data classified using the VADER Lexicon method, resulting in 7,633 positive reviews and 3,475 negative reviews. The data is then tested on XGBoost (Extreme Gradient Boosting) and LSTM (Long-Short Term Memory) models. The results show that the XGBoost model performs slightly better with an accuracy of 87.22%, compared to LSTM, which reaches 86.58%. However, both models exhibit significant performance disparities in classifying negative classes due to the extreme difference in data quantity. The knowledge discovery process reveals that the majority of positive sentiment appreciates the free access and general functions of the application. Meanwhile, negative sentiment focuses on complaints related to response time, output quality, and specific features such as image and voice. The main recommendation is to maintain the advantage of free access also improve features and processing logic to sustain loyalty and service quality. Future research is suggested to test models with more balanced data and optimize dataset cleaning to improve accuracy in minority classes.
Komparasi Model Ensemble dan Algoritma Machine Learning Untuk Memprediksi Penyakit Jantung Muhammad Syarief Albani; Dedy Kurniawan; Ken Ditha Tania
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8346

Abstract

This study compared the performance of nine machine learning algorithms in predicting heart disease using a dataset dating back to 1988 and consisting of four databases: Cleveland, Hungary, Switzerland, and Long Beach totaling 1025 data. The dataset used includes medical features that reflect physiological states, clinical examination results, and cardiovascular risk factors, namely age, gender, type of chest pain, resting blood pressure, serum cholesterol levels, fasting blood sugar levels, resting electrocardiography results, maximum heart rate, chest pain during physical activity, ST segment depression, ST segment slope, number of major blood vessels visible by fluoroscopy, and thalassemia status. The stages of this study include data cleaning, data transformation, and evaluation carried out using the data splitting method for training and testing as well as K-fold cross-validation with metrics of accuracy, precision, recall, F1 score, and AUC-ROC. The algorithms used in this study are Decision Tree, Random Forest, Support Vector Machine, MLP Classifier, Bagging Classifier, Gradient Boosting, CatBoost, XGBoost, and LightGBM with ensemble-based models, such as CatBoost, Random Forest, XGBoost, and LightGBM, showing consistent performance on various evaluation metrics when compared to non-ensemble models. Among all models tested, CatBoost showed the best performance, with an accuracy reaching 98%, an F1-Score of 0.980, and a Recall of 0.9875 then followed by other ensemble algorithms such as Random Forest, XGBoost and LightGBM. The results of this study indicate that ensemble models are proven to be more effective in predicting heart disease. This study aims to present an in-depth comparative study of the performance of ensemble algorithms and modern machine learning in predicting heart disease, as well as enriching the literature related to the application of Knowledge Discovery in the health sector and providing a basis for selecting more reliable prediction algorithms to support clinical decision making and the development of machine learning-based heart disease diagnosis support systems.
Co-Authors A. Salwa Aurelya Putri Ahmad Rifai Ahmad Rifai Ahmad Rifai Ali Ibrahim Alif, Rahmat Allsela Meiriza Allsela Meiriza, Allsela Amalsyah, Muhammad Rizky Anik Rahmawati, Anik Annisa Khoiriah Annisa Khoiriah Apriansyah Putra Apriansyah Putra Arba'i, Sultan Ardina Ariani Ari Wedhasmara Ariansyah Saputra Ariansyah Saputra Ariansyah Saputra Athiyyah Nuha Rotifa Bayu Wijaya Putra Billan, Angel Caroline Cherliana Dwi Rosa Indah Endang Lestari Ruskan Endang Lestari Ruskan Enjelina Tampubolon Fathoni - Fatimah Azzahrah Fidela Tertia Alfino Fika Febrika Hanifah, Izzati Millah Hikmah, Lailatul Huda Ubaya Huda Ubaya Huda Ubaya Jaidan Jauhari Junia Kurniati Karisa Anjani Fakhri Kemahyanto Exaudi Ken Ditha Tania Ken Ditha Tania Lulu Monica Sari M Rudi Sanjaya M Rudi Sanjaya Sanjaya M. Rudi Sanjaya M. Rudi Sanjaya M. Rudi Sanjaya Sanjaya Michael Darwin Muhammad Mayda Ary Pratama Muhammad Qurhanul Rizqie Muhammad Syarief Albani Nadia Anggraini Nilda Syah, Naziah Oktadini, Nabila Rizky Pacu Putra Perdani, Tharisa Antya Purwita Sari Purwita Sari Purwita Sari, Purwita Puti Chalisa Wardhana Putri Eka Sevtiyuni Putri Eka Sevtiyuni Putri, Naila Raihana Rahma Ardhia Cahyani Rahmad Fadli Isnanto Rahmat Alif Akbari Rahmat Fadli Isnanto Rahmat Fadli Isnanto Risyahputri, Aliyananda Rizka Dhini Kurnia Rizki Kurniati Rossi Passarella Rudi Sanjaya Rudi Sanjaya Rusdi Effendi Sanjaya, M Rudi Sevtiyuni, Putri Eka Sultan Arba'i Sutarno Sutarno Sutarno Sutarno Syamsuryadi Syamsuryadi Yadi Utama Yesinta Florensia