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All Journal Indonesian Journal of Innovation Studies Academia Open
Kristofel Santa
Program Studi Teknik Informatika, Universitas Negeri Manado
Computer Science & IT Education Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health

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Support Vector Machine Algorithm for Classifying Public Satisfaction Index: Algoritma Mesin Vektor Dukungan untuk Klasifikasi Indeks Kepuasan Publik Efraim Ronald Stefanus Moningkey; Della Deviani Harisondak; Kristofel Santa
Academia Open Vol. 10 No. 2 (2025): December
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.10.2025.12697

Abstract

General Background: Evaluating public satisfaction with government services is vital to ensuring transparency and continuous improvement in public administration. Specific Background: At the Investment and One-Stop Integrated Services Office (DPMPTSP) of Minahasa Regency, satisfaction assessment has been limited by manual data processing and a lack of integrated systems, leading to inefficiencies in monitoring and classification. Knowledge Gap: Existing approaches to measuring the Public Satisfaction Index (IKM) have not effectively utilized machine learning to automate classification and provide real-time recommendations. Aims: This study aims to implement the Support Vector Machine (SVM) algorithm to classify public satisfaction levels and support service evaluation at DPMPTSP Minahasa. Results: Using 182 testing datasets, the system successfully categorized satisfaction into four levels—very satisfied, satisfied, less satisfied, and dissatisfied—with the majority of respondents classified as satisfied. The developed web-based system also provided actionable recommendations for each satisfaction level. Novelty: This study presents an integrated and automated framework that applies SVM to the public service domain, enabling efficient, accurate, and real-time evaluation. Implications: The findings demonstrate that machine learning can enhance public service management by facilitating data-driven decision-making and promoting service quality improvements. Highlight : The SVM algorithm effectively classifies public satisfaction levels into four categories. The web-based system improves efficiency and accuracy in service evaluation. Recommendations from the system support continuous service quality improvement. Keywords : Public Satisfaction Index, Support Vector Machine, Classification, Service Quality, DPMPTSP Minahasa
Spatial Clustering Of Tourism Investment Potential In Tomohon City: Klasterisasi Spasial Potensi Investasi Pariwisata Kota Tomohon Efraim R. S. Moningkey; Nelsi Timang; Kristofel Santa
Academia Open Vol. 11 No. 1 (2026): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.11.2026.13038

Abstract

General Background: Tourism represents a strategic driver of regional economic development that requires spatially grounded analytical approaches to support investment planning. Specific Background: Tomohon City, a volcanic tourism area in North Sulawesi, contributes substantially to regional economic output but experiences uneven spatial distribution of tourism investment due to accessibility and infrastructural variation. Knowledge Gap: Tourism investment planning in Tomohon has not previously integrated spatial clustering analysis with interactive geovisualization to classify investment potential systematically. Aims: This study aims to analyze and visualize tourism investment potential in Tomohon City using K-Means spatial clustering integrated into a WebGIS platform. Results: Based on 500 tourism sites and indicators including accessibility, visitor volume, supporting facilities, and estimated return on investment, three clusters were identified: high potential areas covering 28% of the region with ROI of 25–35%, medium potential areas covering 52% with ROI of 15–25%, and low potential areas covering 20% with ROI of 5–15%. Novelty: This research introduces an integrated spatial model combining K-Means clustering and WebGIS visualization for tourism investment assessment in a volcanic urban setting. Implications: The results provide a replicable spatial decision-support framework for tourism investment planning aligned with sustainable development objectives. Highlights • Tourism sites are spatially grouped into three investment potential categories• Central urban areas dominate high-return tourism zones• Web-based mapping supports structured spatial decision-making Keywords Spatial Analysis; Tourism Investment; WebGIS; K-Means Clustering; Tomohon City
BM25 Algorithm for Improving Academic Website Search Accuracy: Algoritma BM25 untuk Meningkatkan Akurasi Pencarian di Situs Web Akademik Glenn David Paulus Maramis; Marthasya Chantika Putri Ranti; Kristofel Santa
Indonesian Journal of Innovation Studies Vol. 27 No. 1 (2026): January
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v27i1.1799

Abstract

General Background: The increasing volume of digital information in academic environments requires search systems that can deliver accurate and relevant results. Specific Background: The Faculty of Engineering UNIMA website still relies on simple keyword matching, which often produces irrelevant outputs and reduces the effectiveness of information access. Knowledge Gap: Although the BM25 algorithm is widely recognized for its superior relevance scoring in information retrieval, its application in faculty-level academic portals remains limited. Aims: This study aims to implement the BM25 algorithm using Meilisearch integrated with Laravel and Filament to improve search relevance, speed, and data management efficiency. Results: The implemented system successfully ranks the most relevant documents at the top with response times measured in milliseconds, and automatic index synchronization ensures consistent and up-to-date search results. Novelty: This research presents one of the first BM25-based search implementations within an Indonesian faculty website, integrating Laravel, Meilisearch, and Filament into a cohesive architecture. Implications: The findings show that BM25 significantly enhances the quality of search services and user experience, providing a scalable model for academic institutions seeking effective information retrieval solutions. Highlights: Enhances search relevance and speed on a faculty academic website using BM25. Integrates Laravel, Meilisearch, and Filament into a unified, easy-to-manage system. Offers a scalable model for improving information access in higher education portals. Keywords: BM25, Information Retrieval, Meilisearch, Laravel, Academic Website
Co-Authors Della Deviani Harisondak Efraim R. S. Moningkey Efraim Ronald Stefanus Moningkey Glenn David Paulus Maramis Marthasya Chantika Putri Ranti Nelsi Timang