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All Journal JURIKOM (Jurnal Riset Komputer) Scientica: Jurnal Ilmiah Sains dan Teknologi
Ginting, Muhammad Daffa
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Social Sciences Other

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IMPLEMENTASI FINITE STATE AUTOMATA (FSA) PADA APLIKASI SIMULASI VENDING MACHINE MINUMAN BOBA Ginting, Muhammad Daffa
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 3 No. 2 (2024): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

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Abstract

Teori bahasa dan automata merupakan teori yang berhubungan dengan mesin abstrak. Dalam teori ini terdapat kajian mengenai finite state automata (FSA) yang dapat diimplementasikan dalam perancangan vending machine. Vending machine merupakan mesin penjual minuman dan makanan ringan yang dapat beroperasi secara mandiri untuk melayani transaksi pembelian. Di Indonesia, vending machine banyak menjual produk seperti minuman kaleng, minuman kemasan plastik, kopi, makanan ringan, dan tiket. Penerapan finite state automata pada simulasi mesin penjual minuman Boba diharapkan dapat memudahkan pengguna dalam memahami konsep dan pengoperasian mesin penjual minuman. Dengan penerapan FSA, diharapkan mesin penjual otomatis dapat dirancang sesuai kebutuhan dan beroperasi lebih efisien.
Analisis dan Implementasi Singular Value Decomposition (SVD) untuk Sistem Rekomendasi Anime Berbasis Collaborative Filtering Ginting, Muhammad Daffa; Muhammad Ikhsan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9462

Abstract

The rapid growth of digital platforms has significantly increased the number of available anime titles, making it difficult for users to choose content that matches their preferences. In addition, the diversity of genres and content characteristics accessible to different age groups highlights the need for a system that can provide recommendations more accurately and efficiently. This study aims to develop an anime recommendation system based on collaborative filtering by applying the truncated Singular Value Decomposition (SVD) method to address the data sparsity problem in the user–item matrix. The dataset was collected from the AniList platform and consists of 30 users, 100 anime titles, and 1,230 explicit rating records. The evaluation was conducted using a hold-out scheme with an 80% training set (992 ratings) and a 20% testing set (248 ratings). Prediction performance was measured using an RMSE of 1.6464 and an MAE of 1.2370, while the quality of Top-N recommendations was assessed using Precision@10. The experimental results indicate that the best configuration was achieved at k = 4, which produced the lowest prediction error on the test set and generated relevant recommendations. The average Precision@10 obtained was 0.1897, meaning that approximately 18.97% of the Top-10 recommendations provided by the system were considered relevant.
Co-Authors Muhammad Ikhsan