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All Journal Journal of Information Systems and Informatics
Saputra, Muhammad Haykal Alfariz
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Computer Science & IT

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Evaluating Medxa SIMRS Implementation Success Using HOT-Fit and Multiple Regression Saputra, Muhammad Haykal Alfariz; Ermatita, Ermatita
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.v8i1.1392

Abstract

The implementation of Hospital Management Information Systems (HMIS) is essential for improving service quality, strengthening operational efficiency, and supporting evidence-based decision-making in hospitals. Nevertheless, the success of HMIS implementation is shaped not only by technological performance, but also by human and organizational factors. This study aimed to evaluate the implementation success of Medxa SIMRS at Mohammad Hoesin Hospital, Palembang, using the Human–Organization–Technology Fit (HOT-Fit) model. A quantitative cross-sectional survey was conducted among 67 active users of Medxa SIMRS using a structured Likert-scale questionnaire. Data were analyzed using descriptive statistics and multiple linear regression in Python to examine the influence of the Human, Organization, and Technology dimensions on Net Benefit. The descriptive findings showed that all HOT-Fit dimensions were rated in the good to very good categories, indicating generally positive user perceptions of the system. Regression analysis demonstrated that, simultaneously, the Human, Organization, and Technology dimensions significantly explained variation in Net Benefit (p < 0.05). However, in the partial analysis, only the Technology dimension had a statistically significant positive effect on Net Benefit. These results indicate that system quality, information quality, and service quality are the main determinants of perceived system benefits in this setting. The findings suggest that hospitals should prioritize technological optimization while strengthening organizational support and user readiness to maximize the success of HMIS implementation.
Analyzing the Impact of Review Sentiment on Carpentry Product Sales: Evidence from Tokopedia Kharisma, Agung Chandra; Saputra, Muhammad Haykal Alfariz; Ibrahim, Ali; Afrina, Mira
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.v8i1.1412

Abstract

The rapid growth of e-commerce in Indonesia has increased the importance of consumer reviews as signals influencing purchasing decisions. This study examines the relationship between review sentiment and sales performance in the carpentry tools category on Tokopedia. Using a 2019 Kaggle dataset consisting of 1,826 reviews across approximately 60 products, we apply an NLP-based pipeline to classify review sentiment into positive, neutral, and negative categories. Sentiment labeling combines rating-based rules and a TF-IDF + Logistic Regression baseline, with additional evaluation using IndoBERT. Product-level metrics—including the proportion of positive sentiment (pos_share), average rating, and units_sold (sales proxy)—are analyzed using descriptive statistics, correlation analysis, and cross-sectional OLS regression. The findings reveal that, in this snapshot dataset, the association between positive sentiment share and log(units_sold + 1) is weak and statistically limited, suggesting that sales variation cannot be explained solely by sentiment polarity or average ratings without considering other commercial factors. These results highlight the importance of incorporating contextual variables and temporal design in future research. Practically, the study suggests that sellers should monitor not only sentiment polarity but also the informational richness of reviews to strengthen reputation management strategies.
Co-Authors Ali Ibrahim Ermatita - Kharisma, Agung Chandra Mira Afrina