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All Journal Jurnal Pendidikan Teknologi dan Kejuruan Voteteknika (Vocational Teknik Elektronika dan Informatika) Proceedings of KNASTIK Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Pekommas Jurnal Edukasi dan Penelitian Informatika (JEPIN) Infotech Journal Sistemasi: Jurnal Sistem Informasi Jurnal Ilmiah Matrik BAREKENG: Jurnal Ilmu Matematika dan Terapan Matrix : Jurnal Manajemen Teknologi dan Informatika JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) INTECOMS: Journal of Information Technology and Computer Science KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Teknologi Terpadu Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Sosial dan Teknologi Jurnal Locus Penelitian dan Pengabdian Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) J-Icon : Jurnal Komputer dan Informatika IJESPG (International Journal of Engineering, Economic, Social Politic and Government) journal Enrichment: Journal of Multidisciplinary Research and Development RESLAJ: Religion Education Social Laa Roiba Journal In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Yulison Herry Chrisnanto
Program Studi Informatika, Fakultas Sains Dan Informatika Universitas Jenderal Achmad Yani
Religion Aerospace Engineering Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : JURIKOM (Jurnal Riset Komputer)

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Identifikasi Berita Palsu di Portal Media Online Menggunakan Model IndoBERT dan LSTM Kamal, Angga Mochamad; Chrisnanto, Yulison Herry; Yuniarti, Rezki
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8660

Abstract

The rapid spread of political fake news on Indonesian online media portals poses serious threats to public trust and democratic stability. The main research problem is the limitation of existing models in handling the complexity of Indonesian political narratives containing local idioms and long text structures. The proposed solution employs a hybrid IndoBERT-LSTM model with ensemble stacking approach using logistic regression meta-learner to optimize fake news detection. IndoBERT is selected to capture Indonesian language nuances, while LSTM handles sequential dependencies in long articles. The research objective is to develop an accurate detection system for political fake news by leveraging the complementary strengths of both models. The dataset comprises 32,218 political articles from credible portals (Kompas, CNN Indonesia, Tempo, Detiknews, Viva) and Turnbackhoax.id validation from September 2021 to December 2024. Research results demonstrate that ensemble stacking achieves superior performance with F1-score 0.9544, accuracy 95.41%, and AUC-ROC 0.9936, outperforming standalone IndoBERT (F1: 0.9542) and LSTM (F1: 0.9417). Error analysis identifies 4.59% error rate with 134 false positives and 88 false negatives, particularly in long articles (average 2,739 characters). This model has potential for integration into fact-checking platforms for real-time detection of Indonesian political fake news.
Prediksi Risiko Kesehatan Mental Mahasiswa Menggunakan Klasifikasi Naive Bayes Sumantri, Fithra Aditya; Chrisnanto, Yulison Herry; Melina, Melina
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8648

Abstract

The mental health of university students is a growing concern as academic, emotional, and social pressures contribute to increased psychological risks. This study aims to classify mental health risk levels Low, Medium, and High among students using the Naïve Bayes classification algorithm. A dataset consisting of 1,000 entries and 11 key variables was utilized, covering academic, psychological, and behavioral factors. The preprocessing stage included data cleaning, label encoding, normalization, and rule-based labeling to determine the target classes. Model training and testing were conducted using stratified data splitting to preserve class distribution. The initial model achieved a classification accuracy of 88,67%, with macro average F1-score of 0.87 and weighted average F1-score of 0.88. Grid Search optimization with k-fold cross-validation was applied but showed no significant improvement, indicating the model was already in optimal configuration. Furthermore, probabilistic analysis revealed that Sleep Quality and Study Stress Level were the most influential features in predicting mental health risks. The findings suggest that Naïve Bayes is effective for multi-class classification with interpretable results. This research contributes to early detection efforts and offers a foundation for targeted interventions in university mental health management.
Analisis Sentimen Komunitas Counter-Strike 2 (CS2) Menggunakan Support Vector Machine (SVM) Riyadi, Saiful Faris; Chrisnanto, Yulison Herry; Abdillah, Gunawan
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8620

Abstract

Counter-Strike 2 (CS2) is a game that has received a lot of enthusiasm from the gaming community since its release. User reviews on the Steam platform are the main source for understanding community sentiment towards this game. This study aims to analyze sentiment towards CS2 reviews using the Support Vector Machine (SVM) method. Data was collected through the Apify platform, then cleaned through processes such as tokenization, stopword removal, and lemmatization. Text features were converted into numerical values using Term Frequency-Inverse Document Frequency (TF-IDF) to be used in the SVM model. The SVM model was used to classify review sentiment into three categories: positive, neutral, and negative. Evaluation was conducted by measuring accuracy, confusion matrix, and classification reports. In the evaluation results, the SVM model using the One-vs-Rest (OVR) approach showed that the model without SMOTE produced an accuracy of 81.95%. After applying the Synthetic Minority Over-sampling (SMOTE) technique to the training data to balance the distribution between classes, the model accuracy increased slightly to 82.18%. This study provides valuable insights for game developers in understanding players' opinions about CS2. Additionally, this study demonstrates the potential of SVM in text-based sentiment analysis on user review platforms.
Analisis Sentimen Komunitas Counter-Strike 2 (CS2) Menggunakan Support Vector Machine (SVM) Riyadi, Saiful Faris; Chrisnanto, Yulison Herry; Abdillah, Gunawan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8620

Abstract

Counter-Strike 2 (CS2) is a game that has received a lot of enthusiasm from the gaming community since its release. User reviews on the Steam platform are the main source for understanding community sentiment towards this game. This study aims to analyze sentiment towards CS2 reviews using the Support Vector Machine (SVM) method. Data was collected through the Apify platform, then cleaned through processes such as tokenization, stopword removal, and lemmatization. Text features were converted into numerical values using Term Frequency-Inverse Document Frequency (TF-IDF) to be used in the SVM model. The SVM model was used to classify review sentiment into three categories: positive, neutral, and negative. Evaluation was conducted by measuring accuracy, confusion matrix, and classification reports. In the evaluation results, the SVM model using the One-vs-Rest (OVR) approach showed that the model without SMOTE produced an accuracy of 81.95%. After applying the Synthetic Minority Over-sampling (SMOTE) technique to the training data to balance the distribution between classes, the model accuracy increased slightly to 82.18%. This study provides valuable insights for game developers in understanding players' opinions about CS2. Additionally, this study demonstrates the potential of SVM in text-based sentiment analysis on user review platforms.
Prediksi Risiko Kesehatan Mental Mahasiswa Menggunakan Klasifikasi Naive Bayes Sumantri, Fithra Aditya; Chrisnanto, Yulison Herry; Melina, Melina
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8648

Abstract

The mental health of university students is a growing concern as academic, emotional, and social pressures contribute to increased psychological risks. This study aims to classify mental health risk levels Low, Medium, and High among students using the Naïve Bayes classification algorithm. A dataset consisting of 1,000 entries and 11 key variables was utilized, covering academic, psychological, and behavioral factors. The preprocessing stage included data cleaning, label encoding, normalization, and rule-based labeling to determine the target classes. Model training and testing were conducted using stratified data splitting to preserve class distribution. The initial model achieved a classification accuracy of 88,67%, with macro average F1-score of 0.87 and weighted average F1-score of 0.88. Grid Search optimization with k-fold cross-validation was applied but showed no significant improvement, indicating the model was already in optimal configuration. Furthermore, probabilistic analysis revealed that Sleep Quality and Study Stress Level were the most influential features in predicting mental health risks. The findings suggest that Naïve Bayes is effective for multi-class classification with interpretable results. This research contributes to early detection efforts and offers a foundation for targeted interventions in university mental health management.
Identifikasi Berita Palsu di Portal Media Online Menggunakan Model IndoBERT dan LSTM Kamal, Angga Mochamad; Chrisnanto, Yulison Herry; Yuniarti, Rezki
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8660

Abstract

The rapid spread of political fake news on Indonesian online media portals poses serious threats to public trust and democratic stability. The main research problem is the limitation of existing models in handling the complexity of Indonesian political narratives containing local idioms and long text structures. The proposed solution employs a hybrid IndoBERT-LSTM model with ensemble stacking approach using logistic regression meta-learner to optimize fake news detection. IndoBERT is selected to capture Indonesian language nuances, while LSTM handles sequential dependencies in long articles. The research objective is to develop an accurate detection system for political fake news by leveraging the complementary strengths of both models. The dataset comprises 32,218 political articles from credible portals (Kompas, CNN Indonesia, Tempo, Detiknews, Viva) and Turnbackhoax.id validation from September 2021 to December 2024. Research results demonstrate that ensemble stacking achieves superior performance with F1-score 0.9544, accuracy 95.41%, and AUC-ROC 0.9936, outperforming standalone IndoBERT (F1: 0.9542) and LSTM (F1: 0.9417). Error analysis identifies 4.59% error rate with 134 false positives and 88 false negatives, particularly in long articles (average 2,739 characters). This model has potential for integration into fact-checking platforms for real-time detection of Indonesian political fake news.
Co-Authors Adam, Marcellino Ade Kania Ningsih Ade Kania Ningsih Ade Kania Ningsih, Ade Kania Aditya Prakasa Adryansyah Adryansyah Agung Wahana Agus Komarudin Andhika Karulyana Febrian Asep Id Hadianna Asep Saepul Ridwan Ashaury, Herdi Asri Maspupah Azzahra, Cynthia Nur Bania Amburika Benedictus Benny Sihotang Cahyaningrum, Amellia Fahezha Cecep M Zakariya Darmawan, Raja Dewi, Liony Puspita Didik Garbian Nugroho Drl, Indra Raja Eina, Muhammad Fikri Eka Rahmawati Emia Rosta Br. Sebayang Fadilah, Rifal Fahmy Akhmad Firdaus Faiza Renaldi, Faiza Fajar Tresnawiguna Fajri Rakhmat Umbara Fajri Rakhmat Umbara Farhan Naufal Febry Ramadhan Fitaloka, Intan Fuji Astari, Dhea Gerliandeva, Alfin Gita Mahesa Gunawan Abdillah Gunawan Abdillah Gunawan Abdillah Gunawan Abdillah, Gunawan Gunawan Abdullah Gunawan Gunawan Hadiana, Asep Id Haikal, Ahmed Hanafi, Willy Hanief Kuswanto, Muhammad Rafi Hendro Pudjiantoro, Tacbir Herdi Ashaury Herlina Napitupulu Herlinda Padillah Ibadirachman, Rifqi Karunia Id Hadiana , Asep Irma Santikarama Joko Irawan Julian Evan Chrisnanto Kamal, Angga Mochamad Kania Ningsih, Ade Kasyidi, Fatan Kharisma Jevi Shafira Sepyanto Kholidah Syaidah Kukuh Yulion Setia Prakoso Kusumaningtyas, Valentina Adimurti Luthfia Oktasari Melina Melina Melina Melina Melina, Melina Mubarak, Muhammad Munzir Rizkya Muhamad Afnan, Zikri Muhammad Rendy Raihan Mukti Kinani Mulianti, Adhani Musa Asyari Hidayat Jati Nabilla, Ulya Naufal, Farhan Nida Ulhasanah Norizan Mohamed Permana, Hary Permatasari, Nissa Aulia Prawira, Angga Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina, Puspita Nurul Puspo Dewi Dirgantari Putri Alifianti Wiyono, Tiara Putri Eka Prakasawati Raflialdy Raksanagara Rahandanu Rachmat Raja Darmawan Razaki, Adam Rd Muhammad Alfajri Reza Noviandi Rezki Yuniarti Ridwan Ilyas RIDWAN INDRANSYAH Riyadi, Saiful Faris Rizal Dwiwahyu Pribadi Santikarama, Irma Santoso, Enrico Budi Sepyanto, Kharisma Jevi Shafira Siska Vadilah Sukono . Sumantri, Fithra Aditya Taufiq Akbar Herawan Teguh Munawar Ahmad Tiara Rahmawati Umbara, Fajri Rakhmat Valentina Adimurti Kusumaningtyas Wahyu Pratama, Raka Wawan Setiawan Widinastia, Audila Gumanty Widiyantoro, Widiyantoro Wildah Fatma Lestari Wina Witanti Wisnu Uriawan, Wisnu Yosia Oktavian Pailan Zizilia, Regitha