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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. 2 No. 3 (2024): July: Artificial Intelligence" : 5 Documents clear
Trend Analysis and Job Classification in the Field of Artificial Intelligence Using the Support Vector Machine (SVM) Method Helmy, Ahmad; Muhammad Iqbal
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 rapid advancement of Artificial Intelligence (AI) has significantly transformed the global job landscape, creating new opportunities while redefining existing roles. This study aims to analyze emerging trends and classify job roles in the AI domain using the Support Vector Machine (SVM) method. A dataset was collected from various online job marketplaces and professional platforms to identify key skills, qualifications, and job categories associated with AI-related professions. The data preprocessing involved text normalization, feature extraction using TF-IDF, and classification modeling through SVM. The experimental results demonstrate that the SVM model achieved high accuracy in categorizing AI-related occupations into predefined job clusters, such as Data Scientist, Machine Learning Engineer, AI Researcher, and AI Product Manager. Furthermore, the trend analysis revealed a growing demand for AI professionals with strong interdisciplinary skills combining data analytics, programming, and domain expertise. These findings provide insights for educational institutions, job seekers, and policymakers to align skill development strategies with the evolving needs of the AI workforce.
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.
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
Integration of Virtual Reality and Haptic Feedback for Realistic Training Simulations Andhika Bintang Pramadya, Yohanes
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

Training in critical sectors like healthcare and engineering demands high realism, which conventional methods often fail to provide due to significant cost and safety risks. Virtual Reality (VR) offers an immersive solution but suffers from a fundamental limitation: the lack of physical touch. This research addresses this problem by designing, implementing, and evaluating an integrated simulation system combining VR with high-fidelity haptic feedback. The primary objective was to create a realistic training platform and quantitatively measure its effectiveness in enhancing practical skill acquisition. The research applied a Research and Development (R&D) methodology to build a prototype simulation in Unity 3D. A key feature is the decoupled system architecture, which runs a high-frequency haptic loop (at 1000 Hz) independently from the visual loop (at 90 Hz) to ensure stability. A proxy-based force rendering algorithm based on Hooke’s Law (F=k*d) was implemented to simulate realistic material resistance. System effectiveness was validated through a pre-test/post-test control group experiment (N=30). The experimental group using the VR-Haptic system showed a significant improvement in procedural accuracy (p < .05) and a 28% reduction in task completion time compared to the control group. User questionnaires also confirmed a high degree of perceived realism and immersion. This study concludes that an integrated, high-frequency visuo-haptic architecture is an effective and necessary solution for developing next-generation realistic training simulators.

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