Article Per Year (5 Year)
p-Index From 2021 - 2026
2.223
P-Index
This Author published in this journals
All Journal Jurnal Sains Teknologi dan Lingkungan (JSTL) Jurnal Penelitian Pendidikan IPA (JPPIPA) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer TESLA: Jurnal Teknik Elektro Jurnal Begawe Teknologi Informasi Jurnal Dedikasi Madani : Jurnal Hasil Pengabdian Kepada Masyarakat Jurnal Rekayasa Tropis, Teknologi, dan Inovasi (RETROTEKIN) Edu Komputika Journal Research in Education, Technology, and Multiculture
Heri Wijayanto
Department Of Information Engineering, Universitas Mataram, Mataram 83115, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Physics Public Health Social Sciences

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Integration of Skyline Query with the PROMETHEE MCDM Method: A Case Study on Structural Official Selection Budiman Wijaya; Heri Wijayanto; Ida Bagus Ketut Widiartha
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.29049

Abstract

The selection of structural officials within higher education institutions is a strategic and complex process that demands objectivity, transparency, and a data-driven approach. However, the increasing number of candidates and the diversity of evaluation criteria, such as years of service, rank, education, age, and performance, pose significant challenges in ensuring fair and efficient decision-making. Addressing this gap, this study proposes a hybrid method by integrating Skyline Query with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), offering a novel contribution to multi-criteria decision-making (MCDM) in public sector human resource selection. Skyline Query is employed as a preselection mechanism to eliminate 161 dominated candidates from an initial dataset of 228, allowing only the 67 most non-dominated candidates to advance to the ranking stage. PROMETHEE is then applied to generate rankings based on leaving and entering flow values. To evaluate the consistency and validity of this combined approach, the resulting rankings are compared with those from the pure PROMETHEE method using Spearman’s Rank Correlation. The analysis yields a high correlation coefficient of ρ = 0.967, indicating a very strong agreement between the two methods and confirming that the Skyline filtering does not distort ranking quality. The findings demonstrate that the Skyline+PROMETHEE integration significantly enhances the efficiency of the selection process by reducing computational complexity while preserving decision accuracy. Moreover, this approach strengthens the transparency and accountability of structural official selection, particularly in the context of the University of Mataram, and can be generalized to other institutional decision-making scenarios.
K-Means-Based Customer Segmentation with Domain-Specific Feature Engineering for Water Payment Arrears Management Andi Hary Akbar; Heri Wijayanto; I Wayan Agus Arimbawa; Vynska Amalia Permadi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5186

Abstract

Indonesian water utilities face persistent challenges in managing payment delinquencies due to diverse customer characteristics, geographic limitations, and inadequate analytical capabilities. Addressing this issue is essential to optimizing revenue collection and supporting sustainable operations. This study aims to develop a data-driven customer segmentation framework using K-means clustering to enhance delinquency management. The framework incorporates six engineered features—Debt Efficiency, Payment Behavior Score, Category Risk Score, Geographic Risk Score, Consumption Intensity, and Financial Risk Score—designed to capture customer payment behavior, consumption patterns, and geographic risk. We applied the model to 1,500 anonymized customer records from PT Air Minum Giri Menang, focusing on those with delinquencies exceeding four months. Risk scoring was based on quintile distribution, and optimal clustering was determined through the elbow method combined with silhouette coefficient analysis. The results produced a two-cluster solution (silhouette score = 0.538), showing statistically significant differences across features (p ¡ 0.001) and medium-to-large effect sizes (Cohen’s d = 0.52–2.12). The segmentation identified medium-risk customers (86.7%) who require preventive management and high-risk customers (13.3%) who need billing intervention. Urban areas exhibited higher delinquency risk (18.4%) than rural areas (2.5%), indicating the need for geographically targeted strategies. All customer data was anonymized following Indonesian data protection protocols. In conclusion, the proposed framework transforms manual billing supervision into an adaptive, data-driven management system, contributing to segmentation research by introducing utility-specific engineered features for Indonesian water utilities.
Co-Authors Ahmad Fatoni Dwi Putra Ahmad Zafrullah Mardiansyah Andhi Widayanto Andi Hary Akbar Annisa Octavyanti Hakim Ario Yudo Husodo Ario Yudo Husodo Ariyan Zubaidi Budiman Wijaya Dio Rizkyandita Garnika, Eneng I Gde Putu Wirarama I Gde Putu Wirarama Wedashwara Wirawan I Gede Pasek Suta Wijaya I Gede Putu Wirarama Wedashwara Wirawan I Wayan Agus Arimbawa Ida Bagus Ketut Widiartha Irma Putri Rahayu Lalu Mischa Khalqin Adhiguna Mosiur Rahaman Muhamad Nizam Azmi Muhamad Nurul Hirkan Nela Ardita Pradita Dwi Rahman Pramudya Eko Niti Taruno Ramaditia Dwiyansaputra Rizki maulana Putri Royana Afwani Satria Utama Sitha Rahma Cantika Vynska Amalia Permadi Wirarama Wesdawara