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All Journal Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) JAMAIKA: Jurnal Abdi Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Journal of Artificial Intelligence and Digital Business OKTAL : Jurnal Ilmu Komputer dan Sains AMMA : Jurnal Pengabdian Masyarakat Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Rengga Herdiansyah
Universitas Pamulang
Humanities Astronomy Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Social Sciences Other

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Penerapan Data Mining Menggunakan Algoritma C4.5 Dalam Mengukur Tingkat Kepuasan Para Wali Siswa Pada SMK Letris Indonesia 2 Rengga Herdiansyah; Muhammad Ramdani
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 4 No 08 (2025): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Education is a process aimed at creating a conducive learning environment so that students can reach their full potential. The C4.5 algorithm is a popular decision tree method because it produces classification models that are easy to understand and effective at comparing data with different types of attributes. Education is the effort to prepare students thru orientation activities, teaching and learning activities, assignments, and other educational activities that can influence attitudes and behavior to improve student learning quality. Education involves complex activities, has comprehensive aspects, and is influenced by many variables. The quality of a nation's education is determined by the quality of its educators. Educators must possess qualifications and a level of proficiency that meet national education standards. The school owned is a vocational high school, also known as SMK Letris Indonesia, which initially had a small building on Raya Jombang and gained public trust thanks to the students attending SMK Letris Indonesia. In the first phase, SMK Letris is committed to building a four-story school building with a total of sixteen classrooms and administrative spaces, so that SMK Letris Indonesia 2 has 1434 students with the following core programs: multimedia, network and network technology, accounting, online business and marketing, office automation and governance, and banking. The C4.5 algorithm is a popular decision tree method that helps determine the satisfaction threshold of student guardians based on relevant indicators.
Implementasi Algoritma Neural SVD(Singular Value Decomposition) untuk Rekomendasi Film Berdasarkan Rating Muhammad Nur Rahman; Rengga Herdiansyah
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 6 No. 2 (2026): Juli : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v6i2.12333

Abstract

Digital movie platforms expose users to thousands of titles, creating information overload and making personalized discovery increasingly important. This study aims to implement and evaluate a web-based movie recommender using a Neural Singular Value Decomposition (Neural SVD) model on the MovieLens Latest Small dataset. The research applies a quantitative computational experiment. After cleaning, 100,823 ratings from 610 users and 9,716 movies were encoded and divided into training and testing sets while ensuring that every movie appeared in the training data. Neural SVD was operationalized as embedding-based matrix factorization with user and item biases and a dot-product interaction. The model was evaluated using RMSE, MAE, R², a constant baseline, statistical significance testing, black-box testing, and a user questionnaire. The model achieved RMSE 0.8768, MAE 0.6733, and R² 0.2712, outperforming the baseline RMSE 1.0276 and MAE 0.8176. The difference was statistically significant (p = 0.0015), all eight functional scenarios were valid, and user acceptance reached 87.62%. These results indicate that Neural SVD can provide accurate personalized movie recommendations and can be integrated effectively into a Flask–React web architecture
Co-Authors Cholis Hanifurohman Deanna Durbin Hutagalung Debby Rahadian Baskhara Dian Megasari Ghali Syarafi Hedy Aditya Baskhara Kayla Fadalwa Lailan Cipta Hermawan Muhammad Nur Rahman Muhammad Ramdani Nela Sandiana Liga Nela Saprudin Saprudin