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All Journal Bulletin of Electrical Engineering and Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Information Systems and Informatics
Purwanto Purwanto
Diponegoro University
Computer Science & IT Electrical & Electronics Engineering

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Bibliometrics Analysis of Bankruptcy Prediction Trends in MSMEs: Global Insights from (2020–2025) Supriyono Supriyono; Purwanto Purwanto; Aris Sugiharto
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1378

Abstract

The purpose of this study is to map the development of research on bankruptcy prediction in Micro, Small, and Medium Enterprises (MSMEs) during 2020–2025 and to identify major scientific trends, influential authors, and dominant methodological approaches. Using a bibliometric method, data were collected from the Scopus database, producing 144 initial documents that were filtered into 23 final publications based on relevance and open-access availability. Performance analysis and science mapping were carried out using VOSviewer through co-authorship, co-citation, and keyword co-occurrence networks. The findings reveal four main research clusters: (1) financial-ratio-based distress models, (2) machine-learning approaches for SME risk prediction, (3) post-pandemic MSME resilience, and (4) credit scoring using non-financial indicators. Scientometrics is identified as the most influential journal, while Edward I. Altman and Alessandro Giannozzi emerge as central scholars. The United States, Italy, and the United Kingdom appear as the most collaborative and productive countries. The novelty of this research lies in its specific focus on MSME bankruptcy prediction during the post-pandemic era, the use of an open-access-filtered dataset, and the identification of emerging thematic clusters. However, this review is limited to Scopus-indexed, English-language, and open-access publications, which may exclude relevant studies from other sources.
K-MEANS-BASED TRAINING DATA PROCESSING FOR IMPROVING TOURISM RECOMMENDATION ACCURACY Candra Agustina; Purwanto Purwanto; Farikhin Farikhin; Eka Rahmawati
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7274

Abstract

This study investigates the enhancement of tourism destination recommendation systems through the use of K-Means clustering to improve training data quality and model accuracy. The rapid advancement of information technology has increased the demand for personalized and accurate recommendation systems within the tourism industry. Despite this, achieving high prediction accuracy remains a significant challenge. This study employs K-Means clustering to segment training data into homogeneous clusters, thereby improving data representation and enhancing the predictive accuracy of recommendation models. The research methodology includes a comprehensive literature review, data collection, preprocessing, clustering, and model testing using K-Nearest Neighbors (KNN), Decision Tree, and Naive Bayes algorithms. The results show that after applying K-Means clustering, KNN's accuracy increased by 2.27%, and its kappa and precision values also improved, indicating enhanced reliability and prediction accuracy. Naive Bayes exhibited substantial improvements with a 9.09% increase in accuracy, alongside significant enhancements in kappa and precision metrics. Conversely, the Decision Tree algorithm experienced a decline in performance after clustering. Therefore, clustering techniques are not suitable for application to the Decision Tree algorithm.
Data mining approach for stunting clusters in Jumput Rejo Amir Ali; Purwanto Purwanto; Mundakir Mundakir
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.8035

Abstract

The target of reducing the stunting prevalence rate by 14% in 2024 which has been set by the government needs to be of concern to be implemented by the local health office. The purpose of the research is to cluster toddler anthropometry data with data mining algorithm. Optimize K-Means (KM) algorithm with elbow method use to cluster toddler anthropometry data (sex, height, weight, age, and health care center). A set of 580 children's anthropometric measurements were analyzed and categorized based on their similarity. Cluster 1 comprises 150 members and exhibits a narrower range of age and height values compared to the other clusters. Cluster 2, with 124 members, displays a broader range of age and height values compared to both Cluster 1 and Cluster 3. Cluster 3, consisting of 150 members, demonstrates age and height values that are higher than Cluster 1 but lower than Cluster 2 and Cluster 4. Finally, Cluster 4, encompassing 156 members, exhibits age and height values that are higher than those in the other clusters that many children are stunted based on standard anthropometric table for assessing children's nutritional status. The cluster optimization yielded four distinct clusters, which will serve as the input for identifying clusters during the data grouping process using the KM algorithm.
Co-Authors Amir Ali Aris Sugiharto Candra Agustina Eka Rahmawati Farikhin Farikhin Mundakir Supriyono Supriyono