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p-Index From 2021 - 2026
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All Journal Journal of Information Systems and Informatics Proceeding of International Conference Health, Science And Technology (ICOHETECH)
Bayu Surarso
Diponegoro University
Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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Implementation of the K-Nearest Neighbor Method to determine the Classification of the Study Program Operational Budget in Higher Education Gufron; Bayu Surarso; Rahmat Gernowo
Proceeding of International Conference on Science, Health, And Technology Proceeding of the 1st International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (304.117 KB) | DOI: 10.47701/icohetech.v1i1.803

Abstract

Sultan Agung Islamic University annually designs operational costs for work programs in the study program and of course determines the amount of financial budget for the study program work program. K-Nearest Neighbor algorithm is needed to determine the required operational budget classification based on the number of active students, the number of financial admissions, the number of employees and the percentage of work program realization programs. The results of this study are to facilitate the leadership of higher education in the budget field to classify the amount of the budget required by study programs in the classification of up, or fixed. The purpose of this study is expected to facilitate the leadership of the financial budget department to classify the budget needed by the study program and as an awareness system in the work program of the study program with a classification value of 79.96% for the operational budget of the college study program.
Modeling Student Learning Profiles from LMS Behavioral Traces Using Big Data Analytics Arief Hidayat; Kusworo Adi; Bayu Surarso
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1588

Abstract

Digital learning environments and Learning Management Systems (LMSs) generate large volumes of time-stamped behavioral traces that can be used to examine how students access resources, navigate course structures, communicate, and approach assessments. Traditional learning-style models often depend on static self-report categories and may not reflect how students actually study in digital courses. This study develops a learning analytics framework for modeling student learning profiles from authentic LMS behavioral traces. The study used a quantitative, non-experimental, longitudinal design based on Canvas LMS interaction data from 15,342 undergraduate students enrolled in 150 large-enrollment courses during the 2023–2024 academic year. More than 500 million raw interaction logs were processed into 24 engineered behavioral features representing temporal engagement, resource access, navigation behavior, interaction activity, and assessment timing. After feature normalization, K-Means clustering was applied, and the optimal cluster solution was selected using the elbow method and average silhouette score. Cluster distinctiveness was examined using one-way analysis of variance, and the association between cluster membership and academic performance category was evaluated using a Chi-squared test. The analysis supported a four-cluster solution. Assessment procrastination and navigation sequentially were the strongest differentiating features.
Co-Authors Arief Hidayat Gufron Kusworo Adi Rahmat Gernowo