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Jurnal Computer Science and Information Technology (CoSciTech)
Published by Universitas Muhammadiyah Riau
ISSN : 2723567X     EISSN : 27235661     DOI : https://doi.org/10.37859/coscitech
Core Subject : Science,
Computer Science & IT
Jurnal CoSciTech (Computer Science and Information Technology) merupakan jurnal peer-review yang diterbitkan oleh Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Univeritas Muhammadiyah Riau (UMRI) sejak April tahun 2020. Jurnal CoSciTech terdaftar pada PDII LIPI dengan Nomor ISSN 2723-5661 (Online) dan 2723-567X (Cetak). Jurnal CoSciTech berkomitmen menjadi jurnal nasional terbaik untuk publikasi hasil penelitian yang berkualitas dan menjadi rujukan bagi para peneliti. Jurnal CoSciTech menerbitkan paper secara berkala dua kali setahun yaitu pada bulan April dan Oktober. Semua publikasi di jurnal CoSciTech bersifat terbuka yang memungkinkan artikel tersedia secara bebas online tanpa berlangganan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 374 Documents
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PENGEMBANGAN APLIKASI PERPUSTAKAAN MOBILE DI LINGKUNGAN UNIVERSITAS LANCANG KUNING Hidayat, Rahmat; Darmayunata, Yufi
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.10138

Abstract

Libraries play an essential role in supporting academic activities in higher education. However, the limitations of web-based systems have hindered students of Universitas Lancang Kuning from optimally accessing digital collections via mobile devices. This study aims to develop a mobile library application for Android integrated with SLiMS as a data management API. The research employed the prototype method, involving requirements analysis, system design using UML, interface design, prototype development, and user feedback testing. The results show that the application runs properly on the Android platform, integrates with SLiMS for data communication, and enables users to access book collections and read digital books directly. The application performance depends on internet stability, but its features support efficiency and accessibility of library services. Therefore, this mobile application is expected to be a solution for the digitalization of library services at Universitas Lancang Kuning.
PENERAPAN VICTORIAMETRICS SEBAGAI TIMESERIES DATABASE UNTUK MONITORING KLASTER KUBERNETES Roby Yasir Amri; Nungky Awang Chandra; Mohamad Yusuf
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.10869

Abstract

Infrastructure monitoring is a critical component in managing Kubernetes clusters, particularly for ensuring service availability and analyzing system performance. As the complexity and scale of infrastructure increase, monitoring systems are required to efficiently handle large volumes of metric data. This study aims to analyze the performance of VictoriaMetrics as a time-series database within Kubernetes monitoring systems and compare it with Prometheus based on resource usage. The research employs a quantitative approach with benchmark experiments conducted under three load scenarios: 500, 750, and 1000 target hosts. The analyzed parameters include CPU usage, memory consumption, and storage capacity. The results indicate significant differences in resource efficiency, where VictoriaMetrics maintains CPU usage between 2–10% across all scenarios, substantially lower than Prometheus, which reaches 12–24%. In terms of memory consumption, VictoriaMetrics requires only 21–27%, whereas Prometheus increases to 41–67%. For storage usage, VictoriaMetrics consumes 5–13 GB, while Prometheus requires 13–45 GB. These findings are expected to serve as a reference for organizations in selecting an appropriate monitoring solution that aligns with their Kubernetes infrastructure scale and requirements.
Implementasi blockchain dalam keamanan data medis: tinjauan sistematis publish or perish Rusdha, Salma Nasira; Fauzan Ali Rahman; Zelly Salmiyati Rahman Zam; Vidi Prima Mizan; Fathihanna Yusuf; Oktarina, Rahmi
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.10903

Abstract

The digitalization of healthcare services offers substantial opportunities to improve efficiency and quality of care; however, it also introduces significant challenges related to the security, privacy, and integrity of medical data. The literature indicates that conventional Electronic Health Record (EHR) systems remain vulnerable to data breaches, information manipulation, and system failures due to their centralized architecture. This review examines the development of blockchain technology as a potential solution for modern healthcare data management. The study employs a systematic literature review using the Publish or Perish approach, with Google Scholar as the data source to identify relevant scientific articles, which were subsequently screened based on predefined inclusion and exclusion criteria and analyzed qualitatively. Owing to its characteristics of decentralization, immutability, strong cryptography, and the use of smart contracts for access control, blockchain offers significant improvements in medical data security, transparency, and interoperability. Its applications have been reported in EHR systems, telemedicine, pharmaceutical supply chains, medical imaging, and clinical trial data management. Nevertheless, several limitations continue to hinder widespread adoption, including scalability issues, computational overhead, integration complexity with legacy systems, the transparency–privacy trade-off, and regulatory challenges such as compliance with data protection laws and international standards. Future research trends point toward the integration of blockchain with artificial intelligence, the Internet of Medical Things (IoMT), and federated learning, as well as the development of lightweight blockchain solutions for resource-constrained environments. Overall, blockchain demonstrates considerable potential to strengthen the security and reliability of healthcare information systems; however, its implementation requires a gradual, standardized, and regulation-compliant approach.
Tinjauan Penerapan CNN dan YOLO pada Pengolahan Citra Agraria Medis Industri Cerdas Robby Saputra; Rianto, Yan; Kusuma, Muhammad Romadhona
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.10988

Abstract

The Rapid growth of Artificial Intelligence (AI), particularly Deep Learning, is driving significant transformations in digital image processing in the argricultural, medical, and smart industrial sectors. Two approaches are most dominant in this research Convolutional Neural Network (CNN) for image classification and You Only Look Once (YOLO) for real-time object detection. The purpose of this reasearch is to systematically review the application, performance, and defense of CNN and YOLO in various domains with different data characteristics. The method used is a Systematic Literatur Review (SLR) of the latest relevant scientific publications, focusing on evaluation matrics such as accuracy, pression, recall, F1-score, and Mean Average Precision (mAP). The review results show that CNN excels in image classification tasks with a high level of accuracy, especially on data with relatively stabel visual patterns, while YOLO is more effective in applications that demand inference speed and direct object detection. However, several major limitations were found, including decreased performance in extreme lighting conditions, complex backgrounds, small objects, and visual similarity between classes. It is concluded that the choice of architecture must be adjusted to the characteristics of the data and application needs,
Penerapan Algoritma Klasifikasi dalam Data Mining pada Berbagai Studi Kasus: Literature Review Hibatullah Naufal Ramadhan; Amran, Hasanatul Fu'adah
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11101

Abstract

Data mining is one of the most widely used approaches in the field of Information Technology to extract knowledge from large data sets. One of the main techniques in data mining is the classification method, which aims to predict a particular class or category based on available attributes. Various classification algorithms such as Naïve Bayes, Decision Tree (C4.5), Random Forest, Support Vector Machine, and Artificial Neural Network have been applied in various research domains, including health, education, government, agriculture, and cybersecurity. Differences in data characteristics and methods used cause variations in performance in each study. Therefore, this study aims to conduct a literature review on the application of data mining with classification methods in various data prediction and classification cases. The research method used is a literature review by examining eleven scientific articles from accredited national journals. The results of the study show that the Naïve Bayes and Decision Tree algorithms are the most frequently used methods due to their ease of implementation and interpretation, while Random Forest and Support Vector Machine tend to provide more stable performance on data with high complexity.
Optimization of Backpropagation Algorithms for Enhancing Market Prediction Accuracy in Emerging Industries Syaharuddin, Syaharuddin
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11122

Abstract

This research aims to systematically review and analyze the application of backpropagation algorithms in data-driven business market prediction, focusing on emerging industries. Using the Systematic Literature Review (SLR) method, this study examined research articles from the Dimensions and Scopus databases published over the last 10 years. The analysis synthesizes findings related to the effectiveness, challenges, and potential advancements of Backpropagation in improving market prediction accuracy. The results reveal that backpropagation models, particularly LSTM and MLP have shown significant promise in various sectors, including financial forecasting, customer behavior analysis, and sales prediction. However, challenges such as overfitting, high computational costs, and the integration of real-world market complexities remain. The study emphasizes the need for continuous optimization of these models, as well as improvements in data quality and computational efficiency. This research contributes valuable insights for enhancing predictive models in business market forecasting and offers directions for future studies to further refine the use of backpropagation in addressing market prediction challenges.
Pengaruh preprocessing citra retina pada klasifikasi diabetic retinopathy berbasis prototypical network Wulyono, Abi Eka Putra; Muttaqin, Faisal; Mulyo, Budi Mukhamad
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11126

Abstract

Diabetic retinopathy is a diabetes complication that can lead to progressive retinal damage and permanent blindness. Early detection through automated fundus image classification is essential but challenged by varying image quality, background noise, and color dominance that reduces lesion visibility. Prototypical networks have demonstrated good performance in few-shot learning settings, yet specialized preprocessing is rarely explored. This study proposes a prototypical network enhanced with modified circle crop to remove irrelevant regions and enhanced green channel to improve microvascular lesion contrast. Experiments were conducted on the APTOS 2019 dataset consisting of 3,662 images, split into 2,929 training and 733 testing samples, using a 5-way 5-shot configuration. The proposed preprocessing increases accuracy from 64.53 percent to 71.35 percent and improves quadratic weighted kappa from 0.5712 to 0.6990. These results indicate that preprocessing enhances feature representation and classification performance under limited data conditions.
Perbandingan Kinerja Model YOLOv8n dan YOLOv8s pada Deteksi Objek Menggunakan Metrik Evaluasi dan Confidence Score Upuy, Doms
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11189

Abstract

Deep learning-based object detection has developed rapidly and is widely used in various computer vision applications. One of the most widely used methods is the You Only Look Once (YOLO) algorithm, which is capable of real-time object detection with a high degree of accuracy. This study aims to analyze the performance comparison of two variants of the YOLOv8 model, namely YOLOv8n and YOLOv8s, in detecting objects using evaluation metrics and confidence scores. The dataset used consists of 5000 images, which are divided into training data (70%), validation data (20%), and testing data (10%). Model performance evaluation is carried out using several object detection metrics, namely precision, recall, and mean Average Precision (mAP), as well as additional analysis in the form of computation time and confidence scores to assess the stability of the model's predictions. The results show that the YOLOv8n model achieved a precision value of 0.9313, while the YOLOv8s model achieved a recall value of 0.8415 and a mean Average Precision (mAP0.5) of 0.9055, which is slightly higher than YOLOv8n with a mAP0.5 value of 0.9009. In terms of computational efficiency, YOLOv8n has a faster training time of around 2670 seconds, compared to YOLOv8s which takes around 4477 seconds. In addition, the YOLOv8s model shows a higher confidence score, which indicates a better level of prediction confidence in detecting objects. Based on these results, it can be concluded that YOLOv8n is superior in computational efficiency, while YOLOv8s provides more stable and accurate detection performance. The results of this study are expected to serve as a reference in selecting the optimal object detection model for various computer vision-based applications
Pemodelan dan Prediksi Tingkat Kemiskinan Provinsi Sumatera Barat Menggunakan Support Vector Machine Putri, Melani Septina; Junaidi, Satrio; Mardiyah, Ainil
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11207

Abstract

This research is motivated by the problem of poverty distribution in West Sumatra Province, which still varies between regions. The objectives of this study are to build a prediction model using the Support Vector Machine (SVM) algorithm, evaluate the model's performance, and implement the prediction results in the form of an interactive dashboard to support local government decision-making. The study uses secondary data from the Central Statistics Agency (BPS) of West Sumatra Province for the period 2015–2024, covering 19 districts/cities. The dependent variable is the percentage of poor people (P0), while the independent variables consist of seven socio-economic indicators. The method used refers to the CRISP-DM stages. In the data preparation stage, missing values are handled using median imputation, outliers are handled using winsorizing, standardization is carried out using Z-Score, and the addition of a one-period lag variable (P0_lag1). The data is divided into training data (2015–2022) and test data (2023–2024), with parameter optimization using GridSearchCV and TimeSeriesSplit. The results showed that the Support Vector Regression (SVR) model with a radial basis function (RBF) kernel provided the best performance with parameters C=1000, epsilon=0.05, and gamma=0.001. This model produced an MAE value of 0.32, RMSE of 0.36, and R² of 0.98. The implementation of the prediction results in the Streamlit dashboard for the 2025–2030 period showed a downward trend in poverty levels in most regions. This model is considered effective as a basis for planning and evaluating data-based poverty alleviation policies.
Analisis Efisiensi Arsitektur U-Net dengan Encoder MobileNetV2 pada Segmentasi Karat Daun Kopi Adeva, Muhammad; Muttaqin, Faisal; Mulyo, Budi Mukhamad
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11221

Abstract

Coffee Leaf Rust (Hemileia vastatrix) poses a serious threat to Robusta coffee productivity. Manual identification is often slow and subjective, while standard Deep Learning segmentation methods like U-Net with VGG16 encoder bear heavy computational loads (~24.89 million parameters), hindering deployment on resource-constrained devices. This study aims to optimize computational efficiency by proposing a Lightweight U-Net architecture based on the MobileNetV2 encoder. The model's performance was comparatively evaluated against the VGG16 baseline using the PlantSeg public dataset. Experimental results show that MobileNetV2 integration successfully reduced model size massively by 96% (to ~0.95 million parameters) and accelerated inference time by ~20% (76.28 ms). Although there was a slight F1-Score decrease of 0.3% compared to the baseline, the proposed architecture offers the best trade-off between efficiency and accuracy, making it a viable solution for mobile implementation

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