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Journal of Computer Science and Research
Published by Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer Provinsi Sumatera Utara
ISSN : -     EISSN : 29862337     DOI : -
Core Subject : Economy, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science
Journal of Computer Science and Research (JoCoSiR) is aimed to publish research articles on theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. Journal of Computer Science and Research (JoCoSiR) published quarterly and is a peer reviewed journal covers the latest and most compelling research of the time. Journal of Computer Science and Research (JoCoSiR) is managed and published by APTIKOM Wilayah 1 Sumatera Utara.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2025): April: Artificial Intelligence" : 5 Documents clear
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.
Use of Data Warehouse and Data Mining for Academic Data: A Case Study at a National University Muhammad Iqbal; Muhammad Hasyim As’ary
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

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Abstract

Universities must optimize their information resources to enhance organizational performance and support strategic decision-making. However, academic data stored in multiple operational systems often remains fragmented and difficult to analyze comprehensively. This study aims to develop a data warehouse and apply data mining techniques to integrate and analyze academic data at the National University (UNAS), Jakarta. The data warehouse was designed using a star schema model, integrating academic records from various operational databases into a centralized repository. Mondrian and JPivot were utilized for multidimensional data presentation, while Classification-Based Association (CBA) and Association Rule techniques were applied to uncover hidden patterns within the data. The results show that the data warehouse significantly improves reporting efficiency, reducing processing time from one month to one day. Data mining analysis further revealed characteristic patterns among students in selecting specialization programs based on academic performance. These findings demonstrate that the integration of data warehousing and data mining supports more accurate reporting, informed decision-making, and data-driven academic planning.
Comparative Analysis of Dijkstra and A* Algorithms for Determining the Shortest Route from SMKN 9 Medan to Gramedia Gajah Mada Ardiansyah, Ardiansyah
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

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Abstract

Determining the shortest route is an important problem in navigation system development, especially in urban environments with complex road networks. This study aims to compare the performance of the Dijkstra algorithm and the A* algorithm in finding the shortest route from SMKN 9 Medan to Gramedia Gajah Mada. Distance data between nodes and heuristic values were obtained from Google Maps and represented in a graph structure for route computation. Both algorithms were applied to three predetermined route alternatives. The results show that Dijkstra and A* produced the same optimal route, namely A–B–E–G, with a total distance of 5.7 km. However, the A* algorithm demonstrated higher efficiency by exploring fewer nodes and requiring less computational time due to the use of a heuristic function. Therefore, the A* algorithm is more suitable for intelligent navigation systems requiring faster computation, while the Dijkstra algorithm is more appropriate for smaller networks without heuristic considerations.
Machine Learning-Based Customer Segmentation and Behavioral Analysis Using K-Means Clustering Ade Guna Suteja
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

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Abstract

The rapid growth of transactional data in retail and e-commerce has created opportunities to understand customer purchasing behavior through Market Basket Analysis (MBA). This study applies the Apriori algorithm to identify product association patterns within transactional databases and evaluates the effectiveness of including product category parameters to enhance product package recommendations. A quantitative approach with an applied experimental method is used to systematically process and analyze transactional data. The study involves data preprocessing, application of the Apriori algorithm to generate frequent itemsets and association rules, and visualization of the results. Findings indicate that the algorithm successfully discovers frequently co-purchased product combinations, and the inclusion of product categories improves the relevance and quality of the resulting recommendations. This research provides practical benefits for businesses, such as guiding cross-selling strategies, optimizing inventory management, and enhancing customer satisfaction. Additionally, it contributes to the theoretical development of data mining applications in retail. The results suggest that leveraging association rules with enhanced parameters can support more effective marketing strategies and evidence-based decision-making in dynamic transactional environments.  
Sentiment Analysis of Indonesian TikTok Comments Using TF‑IDF with Naive Bayes and SVM Rambe, Rezkinah; Iqbal, Muhammad
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

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Abstract

This study aims to develop an automatic sentiment classification model for Indonesian TikTok comments using Term Frequency–Inverse Document Frequency (TF‑IDF) with Naive Bayes and Support Vector Machine (SVM). Fifteen thousand comments were collected from public TikTok videos and manually labeled as positive, negative, and neutral. Data preprocessing included case folding, tokenization, stopword removal, and stemming (Nazief‑Adriani algorithm). TF‑IDF weighting transformed text into vectors, then used to train both classifiers. Performance was evaluated using accuracy, precision, recall, and F1-score trough 5-fold cross-validation. Result show SVM outperforms Naive Bayes with 92.8% accuracy versus 83%. Findings confirm that TF-IDF combined with SVM produces more relieble result for short Indonesian text classification, offering valuable insights for social media monitoring applications.

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