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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Scientific Journal of Informatics IJoICT (International Journal on Information and Communication Technology) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Sistem Komputer dan Informatika (JSON) Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Abdimas Maduma
Dian Palupi Rini
Sriwijaya University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Other

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Journal : Journal of Information Systems and Informatics

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Collaborative Filtering Recommendation System Using A Combination of Clustering and Association Rule Mining Annisa, Siti; Rini, Dian Palupi; Abdiansah, Abdiansah
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.802

Abstract

A recommendation system helps collect and analyze user data to generate personalized recommendations for users. A recommendation system for movies has been implemented, considering the vast number of available films and the difficulty users face in finding movies that match their interests. One popular recommendation method is Collaborative Filtering (CF). Although widely applied, CF still has issues. Basic CF uses overlapping user data in evaluating items to calculate user similarity. This study aims to build a collaborative filtering recommendation system using clustering techniques to group users with similar interests into the same clusters. The next step in CF application is to gather recommendation candidate items by finding users with a high level of similarity to the target user. Subsequently, user pattern analysis is carried out by applying association rule mining to predict hidden correlations based on frequently watched items and the ratings given to those movies. This study uses rating data and movie data from the Movielens website. The evaluation of the recommendation results is measured using precision, recall, and f-measure. The evaluation results show that the proposed recommendation system achieves a hit rate of 95.08%, a precision of 81.49%, a recall of 98.06%, and an f-measure of 87.66%.
Optimization of Backpropagation (BP) Weight Values Using Particle Swarm Optimization (PSO) to Predict KIP Scholarship Recipients Nanda, Dika Kurnia; Rini, Dian Palupi
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1042

Abstract

The Indonesia Smart Card (KIP) Lecture program aims to improve the quality of human resources by providing educational assistance to students from underprivileged families. However, the distribution of KIP Lecture in Palembang still faces problems, such as inaccurate targeting and lack of public understanding of this program. The selection process for scholarship recipients is not optimal, causing students who should be prioritized to be overlooked. In addition, decision-making takes a long time due to the many variables that must be considered and the lack of transparency in data processing. This research discusses the Backpropagation (BP) method for predicting KIP College scholarship recipients, which has previously been applied to the classification of educational aid recipients with high accuracies results. However, BP has disadvantages such as minimum local risk and long training time. To overcome this, the Particle Swarm Optimization (PSO) algorithm is used to optimize the weights of the BP artificial neural network. PSO is a simple but effective optimization algorithm to find optimal weights more quickly and accurately. The results of previous studies show that the combination of BP with PSO can improve prediction accuracy compared to using BP alone. Therefore, this research aims to develop a more efficient and targeted prediction model for KIP College scholarship recipients through BP optimization using PSO, so that the selection process can be carried out more quickly and accurately.
Co-Authors Abdiansah Abdiansah, Abdiansah Afandi, Defrian Alvi Syahrini Utami Arnelawati Arrizal, Affandi Arum, Akhiar Wista Astuti, Dwi Lydia Zuharah Cahyadi, Gabriel Ekoputra Hartono Cindo, Mona Darmawahyuni, Annisa Desty Rodiah Dzaky, Dewa Sheva Ermatita - Fiftinova Firdaus - Firmansyah Fitriati, Sri Hidayatullah, Altundri Wahyu Idawati Idawati Kanda Januar Miraswan Kurnia Sari, Winda M. Nejatullah Sidqi Mastura Diana Marieska Meylani Utari Miftahul Falah Muhammad Naufal Rachmatullah Muhammad Taufik Roseno, Muhammad Taufik Nanda, Dika Kurnia Novi Yusliani Osvari Arsalan Passa, Rahma Satila Primanita, Anggina Putri Mirani Rosemari, Pita Samsuryadi Samsuryadi Samsuryadi Sapitri, Ade Iriani Saputra, Tommy Sari, Dwi Mei Rita Sari, Tri Kurnia Siti Annisa, Siti Siti Nurmaini Sukemi Syamsidi, Iman Carrazzi Syaputra, Hadi Utari, Aspirani Wahyu Nugraha Willy, Willy Winda Kurnia Sari YUNITA Zikri, Anharul