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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Applied Technology and Computing Science Journal Jurnal Teknologi Informasi dan Multimedia bit-Tech Jurnal Informasi dan Teknologi Jurnal Informatika dan Rekayasa Perangkat Lunak G-Tech : Jurnal Teknologi Terapan Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) IICS KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Pengabdian Kepada Masyarakat Abdi Putra International Journal Engineering and Applied Technology (IJEAT) Jurnal Informatika Polinema (JIP) Aksi Nyata : Jurnal Pengabdian Sosial dan Kemanusiaan Prosiding SENTIMETER : Seminar Nasional Teknologi Informasi, Mekatronika, dan Ilmu Komputer
IVANA LUCIA KHARISMA
Nusa Putra University
Agriculture, Biological Sciences & Forestry Humanities 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 Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences

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Genre-Based Anime Recommendation System Using KNN with Fanbase Bias Detection Muhamad Rizky Fauzi; Imam Sanjaya; Ivana Lucia Kharisma
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2917

Abstract

The rapid growth of the anime industry presents a challenge for users, especially newcomers, in finding content that matches their personal preferences. To address this issue, this study proposes a genre-based anime recommendation system using a Content-Based Filtering approach, incorporating Term Frequency-Inverse Document Frequency (TF-IDF), the K-Nearest Neighbor (KNN) algorithm, and fanbase bias detection. This system transforms genre information into numerical vectors using TF-IDF, allowing for precise similarity calculations between anime titles based on genre relevance. KNN is used with cosine similarity to identify the top five most similar anime to a given input. A key novelty of this study is the implementation of a fanbase bias detection mechanism that filters out anime with high ratings but very low member counts, which often distort overall ratings due to a small but passionate fanbase. This filtering process ensures that the recommendation output better reflects general audience preferences. The dataset, sourced from MyAnimeList via Kaggle, includes 12,294 entries and underwent extensive preprocessing, including missing value removal, duplicate elimination, and statistical thresholding for bias detection. Evaluation of the system was performed using accuracy, precision, recall, and F1-score, with results showing strong performance (F1-score of 91.94%). Additionally, 5-fold cross-validation confirmed the consistency of the model. Designed for general anime viewers, the system is implemented using the Streamlit framework to provide an accessible and interactive web-based interface. This study demonstrates that the combination of content-based techniques and fanbase bias filtering significantly enhances recommendation quality, offering a novel and practical solution for anime discovery
Utilization Of Content-Based Filtering Method In Game Recommendation With Support Vector Machine Algorithm Indra Yustiana; Ivana Lucia Kharisma; Ade Arian
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2966

Abstract

The explosive growth of the gaming industry has led to a "paradox of choice," where an overwhelming number of titles on platforms like Steam makes it difficult for players to discover games aligned with their personal preferences. This study addresses the critical challenge of game discovery by developing a novel recommendation system that integrates a Content-Based Filtering (CBF) approach with the Support Vector Machine (SVM) algorithm, a combination not extensively explored in the gaming domain. The system provides accurate, attribute-driven recommendations to enhance user experience. Utilizing a dataset of over 55.691 Steam games, we processed textual data such as genre, tags, and categories using TF-IDF before applying the SVM classifier. To validate its effectiveness, the model was benchmarked against K-Nearest Neighbor (KNN) across various training-to-testing ratios. The results demonstrate SVM's consistent superiority, achieving up to 98% accuracy. Notably, the high F1-score of 97.94% in genre-based recommendations signifies a well-balanced model that excels at both minimizing irrelevant suggestions and identifying relevant titles, directly translating to higher user satisfaction. The successfully deployed system, built on the Streamlit framework, was validated through black-box testing, confirming its functionality. This research confirms that the CBF-SVM model offers a highly effective solution to the game discovery problem, with future potential to incorporate hybrid filtering techniques for even greater personalization.
Co-Authors adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Ade Arian Adhitia Erfina Adisti Ridha Ramadhan Algifari, M. Alwan Alida Fany Tariza Putri Alun Sujjada Alyanissa Putri Iskandar Andi Agusti Andi Nopiandi Andi Nopiandi Armelia Isabela Taek Asep Rizki Firdaus Atikah Mugiyanti Azkal Khalif Dede Serlina Dendi Nasrulloh Dewi Puspitasari Dhea Ayu Septiani Dhea Ayu Septiani Dila Aura Futri Dwi Sartika Simatupang E. Tesly Navida Fakhriyal Riyandi Yasin falentino sembiring Falya Amrina Zahra Fransiskus Octavianus Mado Hurint Galih Rakasiwi Galuh Ratna Putri Gina Purnama Insany Gina Purnama Insany Gina Purnama Insany Hermanto Ika Imam Sanjaya Indra Yustiana Ira Rohimah Junjun Junaedi Kamdan Lufita Alvira Maximillian Huang Mayang Selpiyana Meutia Riany Meylinda Nuryani Mirna Kamilah Moh. Abd. Aziz Hidayat Muhamad Galih Sundayana Muhamad Rizky Fauzi Muhammad Dafik Kholik Firdaus Muhammad Ikhsan Thohir Muhammad Ikhsan Thohir Muhammad Raihan Asshafwat Muslih, Muhamad Naufal Nuryanto Neng Syahla Nida Khofifah Nieka Julyana Nur Hidayah K. Fadhilah Paikun Pascal Aditia Muclis Purnama Insany, Gina Putri Anugrah S Putri Iskandar, Alyanissa Resma Nuraeni Rismi Nurlaely Rizki Haddi Prayoga ROSNIA YURISTA Saila Julia Sally Agustin Elisya Salman Alhidamkara Sany Noor Fauzianty Setiana Andika Putra Setiawati Siti Sarah Sobariah Lestari Somantri Somantri Somantri Somantri Suhendar Suhendar Teguh Gumelar Teguh Gumelar Tofik Hidayat Tri Hadianto Widy Karisma Wigi Januar Rahman Wilda Widyana Yusup Solehudin