Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Perangin-angin, Moch. Iswan
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
KNN Approach to Evaluating the Feasibility of Using Scientific Publications as Final Projects Abror, Dzulchan; Nasyuha, Asyahri Hadi; Chung, Meng-Yun; Perangin-angin, Moch. Iswan
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14370

Abstract

This study aims to explore the feasibility of using scientific publications as a substitute for traditional final assignments in higher education by applying the K-Nearest Neighbors (K-NN) algorithm. Traditional final assessments, such as theses, are widely used in evaluating students, but with the increasing availability of peer-reviewed scientific publications, there is potential to use them as a more dynamic and relevant assessment tool. This study uses a dataset containing scientific publications and theses, with features such as research quality, relevance, methodology, and clarity. This study applies the K-NN algorithm to classify these materials and determine whether scientific publications can serve as an effective substitute. The results show that the K-NN algorithm, using k=4, achieved 95% accuracy, successfully distinguishing between scientific publications and theses. However, some misclassifications occurred, indicating areas for improvement, such as incorporating additional features such as citation counts or peer-review scores. These findings suggest that scientific publications, if properly classified, can indeed replace traditional final assignments, encouraging critical thinking and engagement with current research. Future research should refine the feature set and explore other machine learning models to improve accuracy. The practical implications of this research are the potential to develop more innovative and relevant approaches to assessment in higher education, which are more aligned with modern educational practice.
Optimizing Insurance Customer Segmentation with C4.5 Decision Tree Algorithm Setya, Sigit Candra; Perangin-angin, Moch. Iswan; Marsono, Marsono; Nasyuha, Asyahri Hadi; Harnaningrum, Lucia Nugraheni
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7358

Abstract

Insurance companies rely on premium payments as their primary source of revenue. However, economic instability often causes delays in premium payments, impacting revenue recording. This study applies the C4.5 Decision Tree algorithm to classify insurance customers based on premium amount, age, income, and claim history, thereby improving product recommendations. The research utilizes data mining techniques to analyze customer attributes and generate decision rules for optimal insurance product selection. The findings indicate that customers with a premium of IDR 500,000 are best suited for PRUMed Cover (PMC), while those with IDR 1,000,000 are recommended PRUCritical Benefit 88 (PCB88). For customers with IDR 750,000, additional factors such as age and income level influence the recommended insurance type. The entropy and information gain calculations identify premium amount as the most significant attribute for decision-making, followed by age, income, and claim history. By implementing this method, insurance companies can enhance customer segmentation, streamline product selection, and optimize marketing strategies. The transparent and interpretable decision tree structure ensures regulatory compliance while improving customer satisfaction. Future research should explore additional variables, such as behavioral data and regional trends, and compare C4.5 with other classification algorithms like Random Forest or Support Vector Machines (SVM) to enhance accuracy and scalability.
Co-Authors Abror, Dzulchan Asyahri Hadi Nasyuha Candra Setya, Sigit Chung, Meng-Yun Lucia Nugraheni Harnaningrum Marsono Marsono