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All Journal International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Journal of Computer Science and Research
Lola Astri Nadita
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science

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Analysis of Patient Satisfaction Toward the Implementation of the Bed Management Application at Langsa General Hospital: A Case Study of Bed Management System Deployment JB, Salwa Nur; Fachrurazy, Fachrurazy; Lola Astri Nadita; Sri Hidayati
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 2 (2025): April: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v3i1.66

Abstract

The digital transformation of healthcare has become a strategic imperative for improving hospital efficiency, transparency, and patient-centered service quality. This study examines the impact of the Implementation of the Bed Management Application on Patient Satisfaction at Langsa General Hospital, integrating theoretical perspectives from the Technology Acceptance Model (TAM), the DeLone and McLean Information System Success Model (ISSM), and the SERVQUAL framework. Using a quantitative explanatory–predictive approach, the research employs both statistical regression analysis (SPSS 26.0) and algorithmic predictive modeling (Python Decision Tree Classifier) to measure and predict the relationship between system implementation and patient satisfaction. Data were collected from 120 inpatients who experienced the digital bed allocation process, using validated indicators that capture ease of use, reliability, accuracy, service speed, and transparency. The results of the regression analysis reveal that the implementation of the Bed Management Application has a positive and statistically significant effect on patient satisfaction (B = 0.687, β = 0.682, p < 0.001), with a coefficient of determination (R² = 0.465), indicating that 46.5% of the variance in satisfaction can be explained by system implementation effectiveness. Complementary algorithmic analysis using the Decision Tree Classifier achieved a prediction accuracy of 50%, identifying a key threshold at X_mean = 4.1, above which patients were predominantly classified into the High Satisfaction category. The findings confirm that technological quality, perceived usefulness, and information transparency significantly influence patient satisfaction, validating the theoretical constructs of TAM and ISSM. Furthermore, the integration of inferential and predictive analyses offers both theoretical validation and operational insight, illustrating that robust digital system implementation enhances patient experience, efficiency, and service reliability. This research contributes to advancing hybrid analytical approaches in health informatics, supporting data-driven decision-making and the national Smart Hospital Initiative to optimize patient-centered digital healthcare delivery in Indonesia.
SENTIMENT ANALYSIS OF TIX ID APPLICATION REVIEWS USING NAIVE BAYES AND SUPPORT VECTOR MACHINE Salwa Nur. JB; Lola Astri Nadita; Sri Hidayati; Fachrurazy; Muhammad Amin
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 1 (2025): DECEMBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v5i1.1599

Abstract

TIX ID is an online cinema ticket purchasing application that plays on a smartphone platform. Where users can buy cinema tickets anywhere and anytime without having to wait in line. The concept of purchasing cinema tickets is integrated with a third party, namely DANA as a digital money concept that is integrated with several large applications such as Tokopedia and Shopee. This study aims to Conduct text preprocessing (NLP) on TIX ID application user review data so that the data is ready to be used in the sentiment analysis process, Extract text features using the Term Frequency–Inverse Document Frequency (TF-IDF) method to represent reviews in numeric form, Apply the Naive Bayes and Support Vector Machine (SVM) algorithms in classifying user review sentiments into positive and negative categories, Evaluate the performance of the Naive Bayes and Support Vector Machine (SVM) models using accuracy, precision, recall, and F1-score metrics, Provides an overview of user sentiment towards the TIX ID application as a consideration for developers in improving the quality and service of the application.
Co-Authors Fachrurazy Fachrurazy, Fachrurazy JB, Salwa Nur Muhammad Amin Salwa Nur. JB Sri Hidayati