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All Journal YUME : Journal of Management JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal Abdidas Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Economics and Digital Business Review Informatics and Digital Expert (INDEX) Jurnal Algoritma Data Sciences Indonesia (DSI) Journal of Artificial Intelligence and Digital Business Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Jurnal Pengabdian Nasional (JPN) Indonesia Jurnal Ilmu Komputer dan Informatika
Nugraha, Muhammad Rizki
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

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Journal : Jurnal Algoritma

 1 
Deteksi Komentar Spam Judi Online Berbahasa Indonesia Menggunakan XGBoost dan TF-IDF Arrayyan, Dzakwan Rafi; Guntara, Rangga Gelar; Nugraha, Muhammad Rizki
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3012

Abstract

The phenomenon of online gambling continues to show growth with increasingly worrying trends. One of the challenges faced is the proliferation of gambling promotional comments on the YouTube platform due to the suboptimal performance of spam detection systems in recognizing manipulative language patterns. To address this issue, this study proposes a model for detecting spam comments in Indonesian using a combination of Term Frequency–Inverse Document Frequency (TF-IDF) and Extreme Gradient Boosting (XGBoost). The dataset contains 10,220 YouTube comments that have been manually labeled and processed through preprocessing stages, including unicode normalization and cleaning of irrelevant characters. The model was evaluated using 20% of the test data and produced an accuracy of 91%, precision of 92%, recall of 91%, and an F1-score of 91%. These results show that the combination of TF-IDF and XGBoost is effective for classifying short texts in YouTube comments. Thus, this study contributes to the development of Indonesian-language spam comment detection models, which are still rarely researched, and can also be used as a reference for media platforms in improving the effectiveness of stopping the spread of illegal content through social media comment sections.
Peningkatan Akurasi Rekomendasi Film Menggunakan Neural Collaborative Filtering dengan Arsitektur RecommenderNet Sukmana, Dimas; Guntara, Rangga Gelar; Nugraha, Muhammad Rizki
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3013

Abstract

The rapid growth of the film industry and streaming platform users has given rise to the challenge of information overload, where users find it difficult to find films that suit their preferences amid the abundance of content choices. This study aims to develop a Neural Collaborative Filtering (NCF)-based movie recommendation system model with a RecommenderNet architecture to improve prediction accuracy and personal recommendation relevance. The model was evaluated using the Root Mean Square Error (RMSE) metric to assess rating prediction accuracy and Normalized Discounted Cumulative Gain (NDCG@100) to measure recommendation quality and order. The results show that the model achieves an RMSE of 0.1946 and an NDCG@100 of 0.8136, indicating the model's ability to learn user preferences and generate relevant and well-ordered recommendations. This research contributes to the development of more effective and personalized recommendation systems in the digital streaming domain and offers an efficient approach to reducing the impact of information overload and improving the user experience.
Co-Authors Adam Hermawan Adi Prehanto Ahmad Hamidi Al-Qorin, Fadillah Alkatili, Ali Akbar Annamayra, Ajeng Ar-Ridlo, Muhammad Dzikri Arhesa, Sandra Arrayyan, Dzakwan Rafi Bakhri, Rafdlal Saeful Chaeroni, Ahmad Destariana, Yolanda Bunga Dwi Gita Febria Friskawati Guntara, Rangga Gelar Hilwana, Atthaya Layla Zhafira Khoerunnisa, Garda Kusdinar, Yopi Maesaroh, Syti Sarah Malik, Arief Abdul Mashudan, Muhammad Vajri Muchamad Arif Al Ardha Muryan Awaludin Nilan, Fariha Nisa, Farhatun Nur, Lutfi Pratama, Nugraha Adhi Purwaamijaya, Btari Mariska Purwati Purwati Sianturi, Sara Stephany Sukmana, Dimas Teten Hidayat, Teten