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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.
PENERAPAN TECHNOLOGY ACCEPTANCE MODEL PADA MARKER BASED TRACKING UNTUK PEMBELAJARAN SISTEM TATA SURYA TERHADAP ANAK - ANAK Awaludin, Muryan; Nugraha, Muhammad Rizki
JSI (Jurnal sistem Informasi) Universitas Suryadarma Vol 8 No 1 (2021): JSI (Jurnal sistem Informasi) Universitas Suryadarma
Publisher : Universitas Dirgantara Marsekal Suryadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35968/jsi.v8i1.613

Abstract

AbstractTechnology Acceptance Model (TAM) is a method used as a measurement tool in this study, to measure the feasibility of an Augmented Reality application by collecting questionnaire data on 150 respondents using 3 variables as a reference namely Perceived Easy Of Use as X1, Perceived Usefulness as X2, and Actual Use as Y. The data processing method uses SPSS 20 supporting applications with the data validation test process determination of the value of r table by finding df (degree of freedom) df = 150-2 with a significant 5% (0.1603) must < r count on each variable then the data is considered valid , the result of this data validation is 0.6210 which means the data is valid. And the reliability test with the provisions that the value at which the cronbach's alpha value must be > 0.6 for each variable, the data can be considered reliable, the results of the reliability test for the total of these three variables is 0.906 which means the data is reliable. Keywords: Augmented Reality, Technology Acceptance Model, Information and Communication Technology, Interactive Learning Media.
Strategi Pertahanan Keamanan Siber Berbiaya Rendah untuk UMKM: Tinjauan Literatur Pratama, Nugraha Adhi; Nugraha, Muhammad Rizki
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Digital transformation is driving Micro, Small, and Medium Enterprises (MSMEs) to adopt information technology in their business activities. However, reliance on digital systems increases cybersecurity risks, while limited resources are a major obstacle for MSMEs in implementing comprehensive security systems. This study reviewed 18 scientific articles related to low-cost cybersecurity defense strategies applicable to MSMEs. The review results indicate that MSMEs are vulnerable to cyber threats due to budget constraints, low security literacy, and weak information governance. A realistic strategy emphasizes three key aspects: people, processes, and simple technology, through education and training, basic security policies, consistent operational procedures, and the use of affordable technology. This study is expected to serve as a reference for MSMEs and researchers in designing effective, efficient, and sustainable cybersecurity strategies.