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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 7 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2024): April: Computer Science" : 7 Documents clear
A Decision Support System for Selecting the Best Private Universities in Yogyakarta Using MARCOS Method Nasyuha, Asyahri Hadi
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 2 (2024): April: Computer Science
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.v2i2.37

Abstract

Decision-making in higher education often involves evaluating multiple and sometimes conflicting criteria, particularly in regions such as Yogyakarta, Indonesia, which hosts more than one hundred private universities. Selecting the best institution is therefore a critical and complex task for students, parents, and policymakers. Traditional decision support system (DSS) methods such as SAW, TOPSIS, and AHP are widely applied but remain prone to sensitivity in weight assignment and rank reversal, which may compromise reliability. This study proposes the use of the MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method, a recent multi-criteria decision-making (MCDM) technique introduced in 2019, to overcome these shortcomings. MARCOS simultaneously considers both ideal and anti-ideal solutions to achieve more stable rankings. A DSS model was developed and applied to five private universities in Yogyakarta UII, UMY, UAJY, USD, and UTDI evaluated across six criteria: accreditation, doctoral lecturers, research publications, facilities, tuition fees, and graduate employability. The results revealed that Universitas Islam Indonesia (UII) obtained the highest utility score (f(Ki)=0.7404 and ranked first, followed by Universitas Muhammadiyah Yogyakarta (0.6931), Universitas Atma Jaya Yogyakarta (0.6498), Universitas Sanata Dharma (0.6126), and Universitas Teknologi Digital Indonesia (0.5831). Sensitivity analysis further demonstrated that the ranking of UII remained unchanged across weight variations, confirming the robustness of MARCOS. Comparisons with TOPSIS also showed fewer rank reversals, reinforcing the stability of MARCOS in multi-criteria decision-making. This research contributes a novel application of MARCOS in higher education and offers stakeholders a transparent, objective, and data-driven tool for selecting the best private universities in Yogyakarta.
Classification of Health Indicators for Diabetes Mellitus Prediction Using a TabTransformer Model on Clinical Tabular Data Khaidar, Al; Kurnia, Sri
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 2 (2024): April: Computer Science
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.v2i2.54

Abstract

Diabetes mellitus is a non-communicable disease with a continuously increasing global prevalence and impacts quality of life and long-term economic burden; therefore, data-driven early detection is crucial to prevent clinical complications. This study aims to develop a diabetes prediction model using the TabTransformer architecture by utilizing a clinical dataset from Kaggle containing 100,000 patient profiles with more than 35 relevant numerical and categorical attributes. The research stages include preprocessing to remove potential leakage features, target and feature separation, numerical normalization, and categorical feature embedding. The TabTransformer model is applied for binary classification (diagnosed_diabetes) by utilizing a self-attention mechanism to capture latent interactions between tabular features, and is evaluated using accuracy, precision, recall, F1-score, and ROC AUC metrics. The results show competitive performance with an accuracy of 82.55%, a diabetes class F1-score of 0.8527, and a ROC AUC value of 0.9009, indicating the model's discriminatory ability to reliably distinguish diabetic and non-diabetic patients. Based on these results, the TabTransformer architecture has been proven effective for processing large-scale clinical tabular data and is worthy of consideration in the implementation of an artificial intelligence-based medical decision support system for predicting chronic diseases, especially diabetes mellitus.
The Clustering YouTube Videos of SMK Negeri 1 Percut Sei Tuan Based on Views and Likes Using the K-Means Algorithm Asyifaa, Nathania; Iqbal , Muhammad
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 2 (2024): April: Computer Science
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.v2i2.59

Abstract

The increasing use of YouTube as a digital learning and promotional platform has encouraged educational institutions to optimize their content strategies to enhance audience engagement. This study aims to analyze and categorize YouTube videos from SMK N 1 Percut Sei Tuan based on views and likes using the K-Means clustering algorithm. A total of 50 videos were collected and preprocessed using normalization techniques to ensure consistent data scaling. The optimal number of clusters was determined using the Elbow Method, resulting in three distinct engagement groups: high, medium, and low. The clustering process was implemented using Python with the support of the pandas, numpy, scikit-learn, and matplotlib libraries. The results show that videos categorized under high engagement typically consist of school achievements and major institutional events, while low-engagement videos are related to administrative or routine activities with limited public appeal. The clustering outcomes provide valuable insights into audience preferences, allowing educational institutions to improve future content strategies by focusing on video types that generate higher engagement. This research demonstrates that the K-Means algorithm is effective in identifying content patterns and can be used as a decision-support tool for optimizing YouTube channel growth in the educational sector.
A Study on Implementation and Performance Analysis of Basic and Advanced Image Processing Techniques Using Python and OpenCV Lubis, Syaiful Rahman; Iqbal, Muhammad
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 2 (2024): April: Computer Science
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.v2i2.60

Abstract

Abstract— Digital image processing plays a crucial role in artificial intelligence and computer vision, with widespread applications in healthcare, agriculture, security, industry, and transportation. This research focuses on implementing both basic and advanced image processing methods using Python and the OpenCV library within a desktop application. The main problem addressed is the lack of an integrated, structured approach that bridges basic and advanced techniques, limiting users' comprehensive understanding of image processing workflows. The objective is to design a complete system that allows step-by-step processing, starting from grayscale conversion, binarization, arithmetic and logical operations, to convolution and morphological transformations such as Sobel edge detection and erosion. The proposed application utilizes Tkinter for the user interface, enabling users to upload images, apply various processing techniques, and analyze results interactively. The system also includes histogram visualization and equalization to enhance contrast. Findings show that the implemented methods effectively transform images in accordance with theoretical expectations, such as edge enhancement and shape simplification. The integration of these methods into a single, user-friendly platform supports both educational and applied uses. The contribution of this research lies in its practical demonstration of digital image processing techniques, providing a comprehensive and accessible reference for developers, researchers, and students. Despite its achievements, the system lacks advanced segmentation and real-time capabilities, which are suggested for future development through integration of adaptive methods and machine learning techniques.
SENTIMENT ANALYSIS OF E-COMMERCE REVIEWS WITH NATURAL LANGUAGE PROCESSING (NLP) Idhami, Rahmat; Saputra, Andri; Fadly, Taufa; Silaban, Robet
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 2 (2024): April: Computer Science
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.v2i2.61

Abstract

E-commerce in Indonesia is growing rapidly, with Shopee as a leading platform. This study uses Natural Language Processing algorithms to analyze customer satisfaction sentiment from reviews on the Google Play Store. The results identify issues related to courier services and provide recommendations for improving service quality, delivery tracking systems, and overall customer satisfaction and loyalty towards Shopee. This chapter describes the research methodology for sentiment analysis of Shopee reviews using Natural Language Processing methods. These stages include data collection, cleaning, pre-processing, labeling, data separation, classification, and negative word analysis. This study aims to identify the dominant negative sentiment in Google Play Store reviews. This study outlines data scraping, cleaning, pre-processing, labeling, and Natural Language Processing classification to identify negative words in Shopee user reviews. This method provides insights into courier service issues and recommendations for couriers frequently highlighted in reviews, with a focus on future service improvements. Based on the study, Natural Language Processing is effective in identifying positive and negative sentiment in Shopee with an accuracy of 86-87%. Negative sentiment was dominant (62.5%), particularly regarding "recommended couriers," with complaints about delays and unprofessionalism. Recommendations included improving courier service quality, delivery tracking systems, customer communication, and courier training and supervision to improve customer satisfaction.
Implementation of Grey Wolf Optimizer (GWO) Algorithm for Predicting Multidrug Resistance Patterns in Bacteria Harefa, Ade May Luky
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 2 (2024): April: Computer Science
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.v2i2.62

Abstract

The emergence of multidrug-resistant (MDR) bacterial pathogens poses a critical threat to global health, demanding intelligent and adaptive predictive systems. This study proposes the application of the Grey Wolf Optimizer (GWO) algorithm as an innovative computational approach for predicting and analyzing multidrug resistance patterns in clinical bacterial isolates. Unlike conventional statistical methods that often fail to handle complex, nonlinear biomedical data, GWO effectively balances exploration and exploitation through swarm intelligence inspired by wolf hierarchy and hunting behavior. A dataset of 10,700 clinical bacterial samples obtained from Kaggle was analyzed, encompassing antibiotic susceptibility profiles and clinical parameters such as patient comorbidities and hospitalization history. The data were normalized and optimized using GWO to identify the most influential attributes contributing to antibiotic resistance. Experimental results demonstrate that GWO achieves strong stability in convergence, efficiently identifying dominant resistance predictors such as CTX/CRO, FOX, and IPM. Compared to traditional optimization methods, GWO offers improved accuracy and robustness in feature weighting and selection. The study concludes that GWO provides a scalable and interpretable framework for multidrug resistance prediction, enabling early identification of critical resistance trends. The implementation of this approach can assist healthcare institutions in formulating more precise antimicrobial stewardship strategies and controlling the spread of resistant pathogens in clinical environments.
Analysis and Classification of IT Professions in the Marketplace Using the Support Vector Machine Method: Analysis and Classification of IT Professions in the Marketplace Using the Support Vector Machine Method Ardya, Dwika; Iqbal, Muhammad
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 2 (2024): April: Computer Science
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.v2i2.64

Abstract

The development of the digital industry in Indonesia has driven an increasing demand for professional workers in the information technology (IT) sector. Along with this, the need arises to understand and map salary levels based on job profiles to create transparency and efficiency in the recruitment process. This study aims to predict the salary categories of IT professionals using the Support Vector Machine (SVM) method in well-known marketplace companies such as Gojek, Shopee, Tokopedia, Traveloka, Tiket.Com and Bukalapak. The dataset used contains 611 data entry records with attributes of company, work location, experience and skills as well as salary. The preprocessing process consists of label encoding, numeric normalization, and multi-hot encoding for skill features. The salary categories are divided into three: low, medium, and high. The SVM model is trained with the Radial Basis Function (RBF) kernel and evaluated with accuracy, precision, recall, and f1-score metrics. The evaluation results show that the SVM model is able to classify salary categories with an accuracy of 82%. This model shows the best performance in the Medium salary category with an f1-score of 0.93. This study proves that SVM can be used as an alternative to build an effective IT Salary Category prediction system.

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