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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 63 Documents
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Sentiment and Customer Loyalty Analysis of Shopee Using Machine Learning Algorithms Yoga Fitriana
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 3 (2024): July: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

The exponential growth of e-commerce platforms has transformed consumer shopping behavior globally, including in Indonesia. Shopee, as one of the dominant online marketplaces, continuously attracts millions of active users through competitive pricing strategies, promotional events, and digital convenience. However, understanding user satisfaction and loyalty remains a challenge in such dynamic environments. This research aims to analyze user sentiment and customer loyalty toward Shopee by integrating computational sentiment analysis techniques with behavioral survey assessment. A total of 3,000 Shopee user reviews were collected through web scraping, then processed using text mining methods and classified into positive and negative categories using two machine learning algorithms: Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC). Additionally, a structured loyalty survey was distributed to 30 respondents to evaluate behavioral loyalty indicators such as repeat purchase, advocacy, and emotional attachment. The SVM algorithm demonstrated superior performance with an accuracy rate of 98%, surpassing the Naïve Bayes Classifier’s 85% accuracy. The loyalty survey indicated a strong positive correlation between sentiment polarity and customer retention, revealing that satisfied users exhibit consistent repurchase intentions and brand advocacy. These findings emphasize the significance of integrating computational analytics and behavioral measurement in e-commerce performance evaluation. The results also provide managerial insights for enhancing digital service quality, consumer engagement, and long-term competitiveness in Indonesia’s online retail market
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; Nadita, Lola Astri; Hidayati, Sri
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 3 (2024): July: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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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.
Comparison of Naïve Bayes, K-Nearest Neighbors, and Decision Tree Methods for Classifying Heart Disease Risk Factors Ahmad Jihad Al Fayed; Surya Darma; Zailani Sinabariba; Surya Maruli P Pardede
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 3 (2025): July: Health Science Informatic
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Heart disease is the leading cause of death and poses a major challenge to global health systems. The classification of heart disease risk factors is crucial for preventing serious indications, but the challenge is that detection of this disease is often hampered because the classification process is not yet sufficiently accurate. This study aims to develop a heart disease risk classification model using a machine learning approach on a 2025 dataset consisting of 6025 patient data with 14 features. After going through the data collection stage and determining the attributes for comparing the performance of machine learning algorithms (Naive Bayes, K-Nearest Neighbors, and Decision Tree), it was found that the Decision Tree algorithm provided the best performance with an accuracy of 86%, followed by the K -Nearest Neighbors algorithm with an accuracy of 78% and the Naive Bayes algorithm with an accuracy of 76%.
Optimization of Nutritional Meal Allocation Using the Greedy Algorithm : A Data – Driven Approach for Food Security in Indonesia Irwansyah Sitorus; Aprilia, Katharina Tyas; Muhammad Rasyid Ridha; Ricky Martin Ginting
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 4 (2024): October: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Food security and nutrition programs play a crucial role in improving public welfare, particularly in developing countries such as Indonesia. Efficient allocation of limited government resources to regions most in need remains a key challenge in reducing poverty and malnutrition. This study applies the Greedy Algorithm as a computational optimization method to determine the most effective and equitable distribution of nutritional meal program budgets cross Indonesian provinces. The algorithm prioritizes provinces with higher poverty rates and greater nutritional needs while ensuring that the total expenditure does not exceed the national budget constraint. By employing a data-driven approach and calculating the value-to-cost ratio for each province, the algorithm selects allocations that yield the maximum nutritional impact per unit of cost. The results indicate that the Greedy-based allocation model improves efficiency by approximately 18–25% compared to traditional allocation methods. This approach offers a transparent, adaptable, and computationally efficient framework that can support policymakers in enhancing food security, promoting social equity, and advancing sustainable development goals.
Comparison of Decision Tree and Random Forest Algorithm Performance for Nutrition Classification in Fast Food Lombu, Perianus; Kiki wulandari
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 3 (2025): July: Health Science Informatic
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Fast food has become an essential part of the busy modern lifestyle, fast food is more popular because it makes eating easy and convenient. Today's young people are very fond of instant food. However, excessive consumption of instant food can trigger various health problems, including obsessive eating patterns. This raises the need to develop more accurate analytical methods for classifying fast food nutritional data, the purpose of classification is to obtain a decision tree model that can be used to anticipate and pay attention to how variables in the data are related to each other. In comparing the performance of the Decision Tree and Random Forest Algorithms in processing fast food nutritional data, it was found that all variables were correlated. The implementation results found that both models have extraordinary capabilities. The performance of the Decision Tree and Random Forest Algorithms on the same dataset, Random Forest outperformed Decision Tree with an accuracy value of 66.67%, while Decision Tree only achieved 55.56%, indicating that Random Forest is able to provide more accurate predictions for the test data class. In addition, the characteristics of the Random Forest group, where several decision trees are combined, provide advantages in handling data complexity and improve model generalization. These results indicate that group learning can improve the performance and reliability of predictions in building classification models, especially in the case of complex datasets.
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; Nadita, Lola Astri; Hidayati, Sri
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 4 (2025): Research of Biotechnology
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Conceptual Design of Interactive Virtual Museum Using Artificial Intelligence and Virtual Reality Putri, Najwa Azahra
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 3 (2024): July: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Traditional museums face challenges in preserving fragile artifacts and providing immersive experiences due to physical limitations and accessibility issues. This study aims to develop a conceptual design of an interactive virtual museum that integrates virtual reality and artificial intelligence to enhance cultural preservation and public engagement. The research adopts a descriptive qualitative design with a literature-based methodology, focusing on the analysis and synthesis of previous studies related to virtual museums and immersive technologies. The conceptual design emphasizes four main components: a virtual reality module that provides an interactive 3D museum environment, an artificial intelligence module that offers adaptive guidance and recommendations, a database module that stores digital artifacts and interaction records, and a user interface that connects visitors with the system. The results present a comprehensive conceptual framework that illustrates how virtual reality and artificial intelligence can be combined to create a more engaging and accessible museum experience. The study concludes that integrating these technologies offers an innovative solution for digital cultural preservation, enabling people to explore museum collections without physical constraints while maintaining educational and historical values. This conceptual design serves as a foundation for future research and practical implementation of virtual museum systems that promote both cultural sustainability and technological advancement
Adaptive Brain-Computer Interface Based on CNN-RNN for Medical Rehabilitation and Smart Device Control Rakha Dwi Prayoga
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 4 (2025): Research of Biotechnology
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Brain-Computer Interfaces (BCIs) based on motor imagery (MI) offer a direct communication pathway for assistive technologies and neurorehabilitation. A significant challenge lies in the inherent non-stationarity and inter-subject variability of Electroencephalography (EEG) signals, which limits the performance and adaptability of conventional systems. This paper proposes a novel adaptive BCI framework that leverages a hybrid Convolutional and Recurrent Neural Network (CNN-RNN) to dynamically learn spatio-temporal features from raw, multi-channel EEG data. This study aims to develop a lightweight and stable model for accurate MI classification. The model was designed for efficiency, utilizing a streamlined architecture with merely 41,860 parameters, and was rigorously evaluated on the public BCI Competition IV 2a dataset for four-class MI classification across nine subjects. The results demonstrate a robust validation accuracy of 62.17%, significantly surpassing the chance-level baseline of 25%. Crucially, the model exhibited exceptional stability, converging rapidly and maintaining consistent performance without overfitting, while also showcasing efficient computational properties. This study confirms the viability of lightweight, adaptive deep learning models in creating more reliable and practical BCIs, establishing a foundational step towards their application in clinical rehabilitation and smart device control.
Utilization of CRISPR and AI-Based Biotechnology for Early Detection and Therapy Development of Genetic Diseases Dili, Muhammad Assya
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 4 (2025): Research of Biotechnology
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Spinal Muscular Atrophy (SMA) remains a critical genetic disease requiring early detection, yet conventional methods like PCR and genetic sequencing suffer from high costs, extended processing times, and limited accuracy in detecting minor mutations. This study addresses these challenges by developing an innovative integrated system that combines CRISPR-Cas biotechnology with artificial intelligence to revolutionize genetic disease detection. The research employs CRISPR system remodeling to optimize guide RNA design targeting SMN1 and SMN2 genes, integrated with a hybrid deep learning model combining Convolutional Neural Network and XGBoost for intelligent mutation prediction. Unlike traditional approaches, this system achieves detection accuracy exceeding 96.5% while significantly reducing processing time through automated AI-driven interpretation of CRISPR signals. The integration enables real-time analysis of complex genetic patterns, minimizes false detection rates, and generates precision-based therapy recommendations tailored to individual mutation profiles. This breakthrough offers substantial advantages over existing methods by providing faster, more accurate, and cost-effective genetic screening suitable for neonatal programs, particularly in resource-limited settings. The system demonstrates strong potential for clinical implementation, supporting early intervention strategies that can dramatically improve patient outcomes. By bridging molecular biology and computational intelligence, this research contributes a transformative framework for genetic disease detection that is scalable, efficient, and clinically applicable, paving the way for personalized medicine approaches in managing hereditary disorders.
MediaPipe-LSTM: Multi-Task Pose Recognition for Safety and Creative Quality Control Divian Nathaniel, Raymond
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 4 (2025): Research of Biotechnology
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Spinal Muscular Atrophy (SMA) remains a critical genetic disease requiring early detection, yet conventional methods like PCR and genetic sequencing suffer from high costs, extended processing times, and limited accuracy in detecting minor mutations. This study addresses these challenges by developing an innovative integrated system that combines CRISPR-Cas biotechnology with artificial intelligence to revolutionize genetic disease detection. The research employs CRISPR system remodeling to optimize guide RNA design targeting SMN1 and SMN2 genes, integrated with a hybrid deep learning model combining Convolutional Neural Network and XGBoost for intelligent mutation prediction. Unlike traditional approaches, this system achieves detection accuracy exceeding 96.5% while significantly reducing processing time through automated AI-driven interpretation of CRISPR signals. The integration enables real-time analysis of complex genetic patterns, minimizes false detection rates, and generates precision-based therapy recommendations tailored to individual mutation profiles. This breakthrough offers substantial advantages over existing methods by providing faster, more accurate, and cost-effective genetic screening suitable for neonatal programs, particularly in resource-limited settings. The system demonstrates strong potential for clinical implementation, supporting early intervention strategies that can dramatically improve patient outcomes. By bridging molecular biology and computational intelligence, this research contributes a transformative framework for genetic disease detection that is scalable, efficient, and clinically applicable, paving the way for personalized medicine approaches in managing hereditary disorders.

Page 6 of 7 | Total Record : 63

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