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Journal of Information Technology, Software Engineering and Computer Science
Published by Tech Cart Press
ISSN : 29620538     EISSN : 29620635     DOI : doi.org/10.58602/itsecs
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) is a peer-review journal focusing on Information Technology, Software Engineering, and Computer Science issues. Journal of Information Technology, Software Engineering and Computer Science (ITSECS) invites academics and researchers who do original research in information technology, software engineering, and computer science. Journal of Information Technology, Software Engineering and Computer Science (ITSECS) are published by Tech Cart Press in January, April, July, and October every year. Journal ITSECS: Information Technology, Software Engineering, and Computer Science accept articles in Bahasa Indonesia and English.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 62 Documents
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Segmentasi Risiko dan Alokasi Dukungan Kesejahteraan Mahasiswa menggunakan K-Means Clustering dan CRITIC–COPRAS Asyahri Hadi Nasyuha; Muafi Muafi
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) Vol. 4 No. 1 (2025): Volume 4 Number 1 January 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v4i1.394

Abstract

Beyond identifying students at risk of academic burnout, higher education institutions also need an evidence-based mechanism to segment the student population and allocate limited wellbeing-support resources proportionally. This study proposes a decision support framework combining K-Means clustering with the CRiteria Importance Through Intercriteria Correlation (CRITIC) objective weighting method and the COmplex PRoportional ASsessment (COPRAS) ranking method to segment students and prioritize wellbeing-support allocation. Using a dataset of 60 students with eight actionable psychosocial and behavioral indicators, K-Means partitioned students into three risk-based segments (High/Moderate/Low), evaluated via silhouette scores (0.133-0.163 for k = 2-5) and set at k = 3 to align with the institution's three-tier intervention scheme. CRITIC derived data-driven, correlation-based criteria weights without expert elicitation, unlike preference-based weighting. These weights fed into COPRAS to compute a relative significance score (Qi) and rank students by support-allocation priority. The High-risk cluster captured four of five dataset-labelled High-burnout students (80%) and showed the highest mean Burnout Risk Score (41.60) versus Moderate-risk (37.03) and Low-risk (30.09) clusters. The COPRAS ranking correlated significantly with independent burnout indicators (Spearman's rho = 0.706 with Burnout Risk Score, 0.749 with Support Need, and 0.529 with Intervention Urgency, all p < 0.001), with mean ranks decreasing monotonically across Burnout_Level categories (High = 7.0, Moderate = 23.3, Low = 43.1, out of 60). These findings indicate that unsupervised segmentation combined with objective multi-criteria weighting can provide a transparent, replicable, resource-efficient basis for allocating student wellbeing support, complementing supervised prediction-based approaches used in prior studies.
Pemodelan keputusan jaringan analytic network process untuk risiko, daya saing, dan keberlanjutan industri hilir sawit Wiky Sabardi; Ryan Pramanda; Novianda; Asyahri Hadi Nasyuha
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) Vol. 3 No. 4 (2025): Volume 3 Number 4 October 2025
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v3i4.399

Abstract

Ekosistem agroindustri hilir dan manufaktur ditandai oleh saling ketergantungan yang kompleks serta adanya mekanisme umpan balik yang melibatkan variabel operasional, keandalan rantai nilai, mitigasi risiko kegagalan aset proses termal, daya saing pasar global, dan kepatuhan terhadap aspek keberlanjutan (ESG/RSPO/ISPO). Pendekatan hierarkis linear tradisional tidak memadai untuk menangkap dinamika jaringan pengambilan keputusan semacam itu. Studi ini menggunakan metode komputasi Analytic Network Process (ANP) untuk menyusun model manajemen risiko terintegrasi bagi industri hilir kelapa sawit dan oleokimia. Berdasarkan tinjauan pustaka sistematis yang mencakup teori keputusan jaringan, ketahanan rantai pasok cerdas, optimalisasi rantai nilai kelapa sawit terintegrasi, degradasi aset proses termofluida, dan keunggulan komparatif internasional, model ini diuraikan menjadi 13 sub-kriteria dominan dalam tiga klaster utama Risiko (K1), Daya Saing (K2), dan Keberlanjutan (K3) serta lima alternatif strategi manajemen (a1–a5). Proses komputasi melibatkan penyusunan *Unweighted Supermatrix*, transformasi stokastik menjadi *Weighted Supermatrix*, dan pencapaian keseimbangan konvergen melalui *Limit Supermatrix* (limk→∞ Wk). Seluruh perbandingan berpasangan menunjukkan konsistensi matematis, dengan Indeks Ketidakkonsistenan kurang dari 0,10. Sintesis prioritas global mengungkapkan bahwa Strategi Penetapan Harga dan Pemasaran (a3; 24,82%) serta Manajemen Lingkungan (a5; 24,67%) menempati peringkat sebagai strategi yang sama-sama dominan, dengan selisih tipis hanya 0,15%, diikuti oleh Diversifikasi Bisnis (a4; 20,51%), Investasi Teknologi dan Digitalisasi (a2; 17,90%), dan Penguatan Rantai Pasok (a1; 12,11%). Temuan empiris ini mengonfirmasi paradigma tekanan ganda yakni menyeimbangkan fleksibilitas komersial dan kepatuhan ekologis mutlak dalam industri hilir bernilai tambah tinggi.

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