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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOIV : International Journal on Informatics Visualization JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Information System Journal (INFOS) Journal Automation Computer Information System (JACIS) Abdiformatika: Jurnal Pengabdian Masyarakat Informatika JURNAL MULTIDISIPLIN BHATARA
Sri Ngudi Wahyuni, Sri Ngudi
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing

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K-Means Algorithm Analysis for Election Cluster Prediction Wahyuni, Sri Ngudi; Khanom, Nazmun Nahar; Astuti, Yuli
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1107

Abstract

The general election is a democratic process that is carried out in every country whose system of government is presidential, including Indonesia, which conducts it every five years. In fact, some people abstain, leading to budget wasting and missing target. Thus, it is very important to identify clusters of general election districts and map the number of voters to map the budget for the upcoming election. This process needs prediction to help reduce budgeting risk as an early warning. Based on the latest election data taken from Margokaton, Yogyakarta, Indonesia, many people voted in 2021, but the number of abstainers is high. In this case, cluster prediction is important to identify the election participants in each area. The K-Means algorithm could also predict abstainer areas in election activities to facilitate early mitigation in drafting election budgeting. Therefore, this study aimed to identify the pattern of voters in the election using the K-means algorithm. The data parameters comprised the list of voters, Unused ballot papers, and the sum of abstainers. This study is important because it contributes to reducing the election budget of each area. The data obtained from the Indonesia Ministry of Internal Affairs official website in 2021 were processed using the RapidMiner tool. The results showed more than 11% of the non-voters in cluster 1, 16% in Cluster 2, and 8% in cluster 3. The evaluation of clusters value is 2.04, indicating that the clustering using K-means is suitable, as shown by the DBI value close to 0. The results indicate that testing the cluster optimization of the K-Means algorithm using DBI is highly recommended. Based on this prediction result, the government needs special attention to clusters with many abstainers to decrease the number of abstainers and prevent overbudgeting. These results indicate the need to review the election participant data in 2024. Furthermore, there is a need for continuous socialization and education about election activities to reduce the number of abstainers and prevent overbudgeting.
Systematic Review of Adaptive User Interfaces in E-Commerce for MSMEs: Gaps and User-Centric Indicators Solehatin, Solehatin; Wahyuni, Sri Ngudi; Muhammad, Alva Hendi; Hanafi, M.
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5529

Abstract

Objective – Observations of research results related to adaptive user interfaces in e-commerce have been widely conducted; however, there is a need for evaluation and assessment of indicators based on user requirements. This study aims to conduct a systematic literature review and bibliometric analysis on adaptive user interfaces for MSMEs, based on existing empirical research. Methodology – The methodology applied is a Systematic Literature Review, using the term “adaptive user interface for MSMEs” in “Article Titles, Abstracts, and Keywords” within the Sciencedirect database, resulting in 5,622 publications from 1998 to 2025. The evaluation was carried out on November 21, 2025. The collected articles were analyzed using bibliometric analysis with VOSviewer software, based on fields of study including computer science, decision science, engineering, social sciences, business, management, accounting, and materials science. Findings – Research on adaptive user interfaces for MSMEs has been extensively conducted in line with the digitalization of the e-commerce sector. The observations sought gaps and indicators in each article. Gaps were identified; however, further research is still needed to provide more specific, comprehensive, and well-founded recommendations. Indicators focus on how to provide ease and comfort for users, as well as offering recommendations to them. Research Limitations – This study used the Sciencedirect database for articles related to adaptive user interfaces for MSMEs. Future research could enhance generalizability by integrating other databases such as the Web of Science.
Decision Support Systems in Electronic Procurement for Public Sector Procurement: A Systematic Literature Review on Machine Learning Integration Cahyono, Teguh; Muhammad, Alva Hendi; Wahyuni, Sri Ngudi; Al Fatta, Hanif
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5747

Abstract

This study analyzes the evolution of Decision Support Systems (DSS) and Multi-Criteria Decision Making (MCDM) in public sector procurement between 2020 and 2025. Using bibliometric analysis of Scopus and Web of Science articles, the research focuses on themes such as e-procurement, supplier selection, public procurement, and the integration of intelligent technology. Network visualization, overlays, and density mapping were applied to explore keyword relationships, temporal trends, and research intensity. Findings reveal that in 2020, studies concentrated on transparency and digitalization in public e-procurement, with classical MCDA methods, fuzzy TOPSIS, and semantic DSS dominating the approaches. By 2022–2023, the emphasis shifted toward intelligent technologies, including artificial intelligence, neuro-fuzzy systems, and data mining algorithms. These innovations expanded DSS functions from evaluation to predictive analytics and optimization. Core themes such as supplier selection, optimization, and public procurement remained central, while emerging topics like sustainability and clinical decision support systems pointed to new research directions. A significant gap was identified in the university context. Although public sector e-procurement has been widely studied, no research has specifically addressed DSS–MCDM applications in higher education procurement systems. Consequently, future agendas should prioritize adaptive DSS tailored to universities, blockchain integration for transparency, and AI applications in clinical and humanitarian systems.
Co-Authors Achmad Fauzi Alva Hendi Muhammad Andiyoko, Cia Andiyoko, Cia Arbiansyah, Reynaldi Ariaji, Tristanto Dewi Anisa Istiqomah Eko Sediyono Erna Daniati Grafvera, Ervira Diva Guji S. U. , Fitra Jibjaya Hanafi Hanafi Hanif Al Fatta Irwan Sembiring Khanom, Nazmun Nahar Kurnia Rasyid, Muhammad Rum Andri M. Hanafi Maemunah, Mei Majid, Ammar Waly Muhammad Ikhsan Mulyatun, Sri Rosyidah Jayanti Vijaya, Rosyidah Jayanti Solehatin, Solehatin Teguh Cahyono Triwidodo, Ahmad Widowati, Anik Sri Widyawati, Rahma Windarni , Vikky Aprelia Wiratama , Bill Bilal Yuli Astuti