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Edu Komputika Journal
ISSN : -     EISSN : 2599297X     DOI : https://doi.org/10.15294/edukom
Core Subject : Education,
Edu Komputika Journal uses Open Journal Systems (OJS) for online journal management in submission, review, copyediting, and publication. Submitted manuscripts are written in English and should follow the style of the Edu Komputika Journal. Manuscripts are original research results, or theoretical/literature study results that have never been published in other journals or are not considered for publication elsewhere. The author should follow all the provisions and processes. Accepted papers will be available online and will be charged a publication fee.
Articles 24 Documents
A Modified TAM-ECT Model for Evaluating User Satisfaction and Behavioral Intention in Community-Based Internet Services Khairul Imtihan; Ahmad Tantoni; Mardi
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.24142

Abstract

This study develops and validates a modified Technology Acceptance Model–Expectation Confirmation Theory (TAM-ECT) framework to evaluate user satisfaction and behavioral intention in the context of community-based internet services (RT/RW Net). Unlike prior TAM-ECT studies predominantly conducted in commercial ISP or e-service environments, this research explicitly focuses on decentralized, community-managed internet services characterized by informal governance structures, low switching barriers, and non-contractual user relationships. Addressing the lack of research on decentralized internet service models, this study integrates external factors service quality, cost-effectiveness, system quality, and customer support and moderating factors, namely digital literacy and switching cost. A quantitative survey approach was employed, collecting valid responses from 803 active users between January and March 2025. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance-Performance Map Analysis (IPMA). The results demonstrate that perceived ease of use strongly influences perceived usefulness and behavioral intention, while perceived usefulness significantly impacts both user satisfaction and behavioral intention. Notably, and contrary to the core assumption of Expectation Confirmation Theory, user satisfaction does not significantly predict behavioral intention, indicating a context-specific deviation in community-based digital services where pragmatic usability considerations outweigh affective satisfaction. External factors such as customer support and system quality significantly affect user perceptions, highlighting the importance of technical performance and user experience in decentralized service settings. Digital literacy positively moderates the relationship between perceived ease of use and behavioral intention. The IPMA findings reveal that ease of use, service usefulness, and customer support are the most critical areas for improvement. Theoretically, this study extends TAM-ECT by demonstrating that continuance intention in community-based internet services is driven more by usability and functional value than by satisfaction-driven confirmation mechanisms commonly observed in commercial platforms. This study offers practical insights for optimizing technical quality, service functionality, and user digital competencies to foster sustainable adoption in community-managed internet infrastructures.
Integration of Skyline Query with the PROMETHEE MCDM Method: A Case Study on Structural Official Selection Wijaya, Budiman; Wijayanto, Heri; Widiartha, Ida Bagus Ketut
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.29049

Abstract

The selection of structural officials within higher education institutions is a strategic and complex process that demands objectivity, transparency, and a data-driven approach. However, the increasing number of candidates and the diversity of evaluation criteria, such as years of service, rank, education, age, and performance, pose significant challenges in ensuring fair and efficient decision-making. Addressing this gap, this study proposes a hybrid method by integrating Skyline Query with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), offering a novel contribution to multi-criteria decision-making (MCDM) in public sector human resource selection. Skyline Query is employed as a preselection mechanism to eliminate 161 dominated candidates from an initial dataset of 228, allowing only the 67 most non-dominated candidates to advance to the ranking stage. PROMETHEE is then applied to generate rankings based on leaving and entering flow values. To evaluate the consistency and validity of this combined approach, the resulting rankings are compared with those from the pure PROMETHEE method using Spearman’s Rank Correlation. The analysis yields a high correlation coefficient of ρ = 0.967, indicating a very strong agreement between the two methods and confirming that the Skyline filtering does not distort ranking quality. The findings demonstrate that the Skyline+PROMETHEE integration significantly enhances the efficiency of the selection process by reducing computational complexity while preserving decision accuracy. Moreover, this approach strengthens the transparency and accountability of structural official selection, particularly in the context of the University of Mataram, and can be generalized to other institutional decision-making scenarios.
Weakly Supervised Sentiment Analysis of Indonesian Rural Tourism Reviews: A TF-IDF Baseline for Melung Tourism Village Rifa’i, Zanuar; Mukti, Bayu Priya
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.31893

Abstract

This study investigates sentiment classification of Indonesian-language tourist reviews from the rural destination of Melung Tourism Village. A total of 724 user-generated reviews from 546 unique users are preprocessed using Indonesian-specific text cleaning, stopword filtering, and stemming, then weakly labeled through a stemmed positive–negative lexicon. TF-IDF unigram–bigram features are extracted from the preprocessed texts and used to train three classical classifiers: Naive Bayes, linear Support Vector Machine (SVM), and Logistic Regression. To address class imbalance, RandomOverSampler is applied only to the training data, and model evaluation combines stratified 5-fold cross-validation with a held-out test set, using weighted F1-score as the primary metric. Logistic Regression achieves the best performance on the test set (weighted F1 = 0.8799, accuracy = 0.8828), closely followed by SVM, while Naive Bayes lags behind. The results show that, even with a modest, weakly supervised dataset, a carefully designed classical pipeline can yield reliable sentiment indicators to support data-driven management of rural tourism destinations.
Energy Supply Chain Optimization: Design of a Transportation Vendor Assessment System Using the Simple Additive Weighting Method Pratama, Rendy Bagus; Nurhawanti, Ragil
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.36054

Abstract

In the energy logistics sector, which demands high speed and efficiency, fuel transportation vendor selection is a strategic decision that significantly impacts operational smoothness. To transform the cumbersome manual selection process into digital precision, a study developed a Vendor Management Information System based on the Simple Additive Weighting (SAW) method. This system is designed to provide objective decision-making support by analyzing 2024 performance data through eight key evaluation criteria, including service quality, price, and fleet availability. After going through a normalization and weighting process in the decision matrix, the system determined Vendor A1 (PT. X) as the best provider with the highest score. The data is descriptive quantitative in nature, where the data collection process involved respondents from three departments within the company who are experts in the field of procurement, with proof of ownership of procurement certification for goods and services. A total of 23 respondents served as the basis for SAW data processing, and 5 people served as references for creating criteria for weighting in the method. This automation logic was then technically mapped through Data Flow Diagrams (DFDs) and Entity-Relationship Diagrams (ERDs) to ensure an integrated workflow. The implementation of this system marks a significant shift towards digital efficiency, which not only minimizes human error and increases transparency but also lays a strong foundation for the adoption of more sophisticated decision-making technologies in the future.

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