Article Per Year (5 Year)
p-Index From 2021 - 2026
2.459
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Khatulistiwa Informatika Paradigma Tekinfo | Scientific Journal of Industrial and Information Engineering JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Manajemen Informatika JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Teknik Informatika (JUTIF) International Journal of Social and Management Studies (IJOSMAS) Journal of Students‘ Research in Computer Science (JSRCS) Indonesian Journal of Networking and Security - IJNS SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Inkofar Prawara Jurnal Pengabdian Masyarakat
Anus Wuryanto
Universitas Bina Sarana Informatika
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Transportation

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Prediksi Keterlambatan Pembayaran Mahasiswa untuk Mitigasi Risiko Cuti Menggunakan SVM Optimasi PSO Hafis Nurdin; Imam Nawawi; Anus Wuryanto; Dewi Yuliandari; Hari Sugiarto
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 1 (2025): Juni 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i1.15483

Abstract

Delayed tuition payments present challenges for higher education institutions, impacting both financial stability and students’ academic progress. This study proposes a predictive model using Support Vector Machine (SVM) optimized by Particle Swarm Optimization (PSO) to identify students at risk of payment delays. The dataset includes academic and social attributes. A dot kernel SVM was evaluated using 10-fold cross-validation. Results show that PSO optimization significantly improved model performance, particularly in recall, which increased from 36.10% to 65.51%, indicating better identification of delayed payment cases. The analysis also reveals that social factors, such as employment and academic status, strongly influence prediction outcomes. These findings highlight the potential of the SVM-PSO model as a decision-support tool for early intervention, enabling institutions to mitigate dropout risks and enhance financial planning. By leveraging this approach, universities can better support students while maintaining administrative efficiency and institutional sustainability.
Co-Authors - UBSI, Mia Rosmiati - Universitas Bina Sarana Informatika, Ariful Ghozali - Universitas Nusa Mandiri, Alestia Fitrika Cahyaning Adi Chandra Setiawan Agung Rudianto Alestia Fitrika - STMIK Nusa Mandiri Andharsaputri, Resti Lia Anisa Putri Ardhi Sujiono - STMIK Pelita Bangsa Asep Dony Suhendra Cepi Cahyadi Dania Azzahra, Keyza Dede Mustomi Dede Suleman Dewi Yuliandari Etik Dwi Styaningrum Feri Prasetyo H Findi Ayu Sariasih Hafis Nurdin Hafis Nurdin Hari Sugiarto - UBSI Hidayanti Murtina Imam Nawawi Imam Nawawi, Imam Indhah Pujihastuti Irmawati Carolina Mia Rosmiati Mia Rosmiati - UBSI Muhammad Tabrani Nicodias Palasara Nunung Hidayatun Nurpiah SriPriatna - UBSI Nurul Afni Nurul Afni Prameswari, Vidya Putri, Rahmayana Rahayu Ningsih Ratih Dwi Asworowati Ratih Setyo Rini Ridwansyah Ridwansyah Rini Pahmawati Simangunsong Sidik Sidik Suhardjono Suhardjono Supriatin Supriatin Susafaati - STMIK Nusa Mandiri Universitas Bina Sarana Informatika, Zahra Warsino Nardiwiyono Yudhistira Yudhistira Yusnia Maysaroh