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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) EMITTER International Journal of Engineering Technology JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science Wahana : Tridarma Perguruan Tinggi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Antivirus : Jurnal Ilmiah Teknik Informatika CAHAYA tech Journal of Applied Data Sciences Insyst : Journal of Intelligent System and Computation
F.X. Ferdinandus
Institut Sains Dan Teknologi Terpadu Surabaya (ISTTS)
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Nursing

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Recommender System Based on Social Network Analysis of Student Workshop and Event Activities Compared to GPA and Department Setiawan, Esther; Santoso, Joan; Cahyadi, Billy Kelvianto; Afandi, Acxel Derian; Saputra, Daniel Gamaliel; Ferdinandus, FX; Fujisawa, Kimiya; Purnomo, Mauridhi Hery
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2943

Abstract

This research uses social network connections and academic data to create a recommender system that helps students choose seminars and events that suit their interests. The aim is to address the issue of students' hesitation in selecting activities. This project investigates the use of social network analysis (SNA) to provide individualized suggestions by analyzing student involvement in workshops and events, as well as their grade point average (GPA). The materials contain student data gathered between 2018 and 2023 from Institut Sains dan Teknologi Terpadu Surabaya (ISTTS), emphasizing the student's social media interactions and event participation. Metrics like centrality are employed to identify prominent nodes inside the network, and the approach combines graph-based SNA and cosine similarity for event recommendation. The network of student involvement in events was represented by a dataset comprising 2,293 edges and 602 nodes. The results show that the relevance of recommendations is improved when social network data is integrated with GPA, rather than GPA-based systems alone. The system identified key nodes, such as specific lectures, that significantly impacted student involvement and were rated highly in terms of centrality. Future research implications recommend expanding the dataset to encompass a broader range of events and refining the algorithm by including content-based filtering. The system's application is not limited to educational environments; it may also be tailored for career counselling or professional development.
Disjoint Community Detection pada Network Kegiatan Kemahasiswaan di ISTTS Menggunakan Fast Greedy dan Walktrap Setiawan, Mikhael; Gunawan; F.X.Ferdinandus
Intelligent System and Computation Vol 3 No 1 (2021): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v3i1.175

Abstract

Disjoint community detection bertujuan untuk menemukan sebuah komunitas pada network dengan melakukan pemisahan. Pada penelitian ini, disjoint akan dilakukan pada network kegiatan kemahasiswaan di ISTTS. Metode disjoint community detection yang digunakan adalah fast greedy dan walktrap algorithm.  Data kegiatan kemahasiswaan berisi mengenai mahasiswa bersama-sama dengan mahasiswa lainnya mengikuti kegiatan kemahasiswaan apa saja. Setelah disjoint berhasil dilakukan, maka akan dihitung nilai closeness centrality dari setiap mahasiswa, dimana pada akhirnya akan dihitung correlation coefficient dengan IPK mahasiswa tersebut untuk mencari hubungan antara centrality mahasiswa dengan IPK mereka. Hasil closeness centrality ini selanjutnya di rata-rata untuk semua hasil algoritma untuk melihat bagaimana korelasi closeness centrality dengan ipk mahasiswa tersebut. Uji coba dilakukan dengan membentuk gml dari kombinasi filter, yang menghasilkan sekitar 2527 gml dengan nilai akhir korelasi adalah 62 - 63% weak positif dengan diikuti 16-18% moderate positif, dan 14-16% tidak berkorelasi sama sekali. Akhirnya dapat disimpulkan bahwa closeness centrality dalam sebuah komunitasnya, hanya berpengaruh secara weak positif dengan ipk mahasiswa tersebut.
Co-Authors Afandi, Acxel Derian Aji Amijaya Cahyadi, Billy Kelvianto Eko Mulyanto Yuniarno Endang Setyati Esther Irawati Setiawan Fujisawa, Kimiya Gunawan Gunawan Gunawan Gunawan Hapantenda, Andrey Kartika Widhy Irvan Sulistiya Putra Joan Santoso Leonel Hernandez Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mikhael Setiawan Muh Burhanudin Muhaji Bayu Muhammad Alfin Fikri Pramana, Meirza Reddy Alexandro Harianto Saputra, Daniel Gamaliel Setiawan, Esther Tjendika, Patrick