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Journal of Computer Science and Information Technology
Published by Lembaga Publikasi Ilmiah Nusantara
ISSN : -     EISSN : 3090787X     DOI : https://doi.org/10.70716/jocsit
Core Subject : Science,
Computer Science & IT
Journal of Computer Science and Information Technology (JOCSIT) is a scientific journal in computers that contains research results and literature studies, managed by Lembaga Publikasi Ilmiah Nusantara. JOCSIT journal provides a platform for researchers, academics, professionals, practitioners and students to embed and share knowledge in the form of empirical and theoretical research papers, case studies, literature reviews and book reviews related to computer science and information technology research, and or related to it with a range of themes such as Biomedical Application Computer Network and Architecture, Data Mining, E-Business, E-Commerce, E-Government E-Learning, Embedded Systems, Environmental Systems, Fuzzy Logics, Genetic Algorithms, Geographic Information System, High-Performance Computing, Human-Computer Interaction, Image Processing, Internet of Things (IoT), Computer Vision, Information Security, Information Retrieval, Modeling System and Control, Mobile Technology, Neural Networks, Pattern Recognition, Remote Sensing, Robotics, Signal Processing, Smart Home, Smart Sensor Networks. This journal will process all receipts of the script in a double-anonymized review by Bestari partners.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 20 Documents
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Pengaruh Antarmuka Pengguna (UI/UX) Terhadap Efisiensi Penggunaan Sistem Rekam Medis Elektronik oleh Tenaga Kesehatan Arya Wirawan, Datu; Suhartono, Deni; Ari Wijaya, Made
Journal of Computer Science and Information Technology Vol. 1 No. 3 (2025): Journal of Computer Science and Information Technology, December 2025
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v1i3.303

Abstract

The development of information technology in the health sector has encouraged the implementation of electronic medical records (EMR) systems as a solution to improve the quality of service and the efficiency of healthcare workers. However, the success of an EMR system is not only determined by its functionality, but is also greatly influenced by the design of the user interface (UI) and user experience (UX). This study aims to analyze the influence of UI/UX on the efficiency of EMR system use by healthcare workers in healthcare facilities. The research method used is a quantitative approach with a survey design. Data collection was conducted through questionnaires to 75 healthcare workers from various service units who have used the EMR system for at least six months. Data were analyzed using multiple linear regression to determine the relationship between UI/UX variables and efficiency of use. The results showed that UI/UX aspects have a positive and significant influence on the efficiency of EMR system use, with a coefficient of determination (R²) of 0.68. This indicates that 68% of the variation in efficiency of use can be explained by the quality of UI and UX. This finding emphasizes the importance of designing an intuitive, responsive, and user-friendly interface to support healthcare workers' performance. The recommendation of this research is to improve the quality of UI/UX as the main strategy in developing health information systems.
Evaluasi Sikap Mahasiswa Ilmu Komputer terhadap Etika dan Kebijakan AI (Studi Kasus: STMIK Lombok) Firdaus, Wianata; Maulinda Safira, Siti; Islamin , Fahdilatul
Journal of Computer Science and Information Technology Vol. 1 No. 3 (2025): Journal of Computer Science and Information Technology, December 2025
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v1i3.309

Abstract

The rapid development of artificial intelligence (AI) has given rise to various ethical and policy implications that aspiring professionals in the information technology field need to understand. This study aims to evaluate the attitudes of Computer Science students towards the ethics and policies of AI use, using a case study at STMIK Lombok. The research approach used a quantitative descriptive method by distributing questionnaires to 120 active student respondents. The research instrument was developed based on indicators of understanding technology ethics, awareness of the social impacts of AI, perceptions of regulations, and professional responsibility in AI applications. Data were analyzed using descriptive statistics and correlation tests to determine the relationship between the level of ethical knowledge and attitudes towards AI policies. The results show that most students have a fairly good understanding of AI ethics, but still have a low understanding of the formal policies and regulations governing the application of AI in Indonesia. The main factors influencing positive attitudes towards AI ethics are academic experience and exposure to global technology issues. This study emphasizes the importance of integrating technology ethics and digital policy courses into the Computer Science curriculum so that students can become socially and professionally responsible AI developers.
Analisis Sentimen Media Sosial Menggunakan Algoritma BERT dan LSTM Malasari, Novita; Ramli, Muhammad
Journal of Computer Science and Information Technology Vol. 1 No. 3 (2025): Journal of Computer Science and Information Technology, December 2025
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v1i3.318

Abstract

Social media sentiment analysis is an important field in natural language processing (NLP) to understand public opinion on a topic, product, or policy. This study aims to analyze social media user sentiment by utilizing a combination of the Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) algorithms. The BERT model is used to extract contextual features from text, while the LSTM serves to capture long-term dependencies in sequence data. The dataset used comes from Indonesian-language social media posts that have been labeled into three sentiment categories: positive, negative, and neutral. The research process includes text preprocessing, tokenization, weighting, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. Test results show that the combination of BERT and LSTM produces better performance than using a single model, with an accuracy of over 90%. This study proves that the BERT-LSTM hybrid approach is effective for understanding semantic context in complex social media texts. These findings are expected to contribute to the development of data-based opinion analysis and decision-making systems in the digital era.
Rancang Bangun Sistem Informasi Manajemen Perpustakaan Berbasis Cloud Server Abdi Ariadi, Restu; Wulandari, Sry
Journal of Computer Science and Information Technology Vol. 1 No. 3 (2025): Journal of Computer Science and Information Technology, December 2025
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v1i3.323

Abstract

The development of information technology encourages educational institutions to adopt integrated systems that can improve the effectiveness of resource management, including library services. This study aims to design and build a cloud server-based Library Management Information System that can provide real-time data access, improve operational efficiency, and support user mobility. The system development method uses the Waterfall model, which includes the stages of needs analysis, system design, implementation, testing, and maintenance. The system is designed with key features such as book collection management, catalog search, digital borrowing and returning, and monitoring usage statistics. A cloud server architecture is used to ensure library data is stored centrally, securely, and can be accessed from various devices without location restrictions. Implementation results show that the system can speed up the service process by up to 40% and minimize recording errors compared to manual methods. Black box testing shows that all functions run as needed. In addition, the system makes it easier for librarians to perform data collection and improves the user experience through faster and more responsive service access. This research is expected to become a modern solution for educational institutions in optimizing cloud technology-based library services.
Aplikasi Mobile untuk Identifikasi Hama Tanaman Menggunakan Teknik Image Processing Wardani, Kania; Saputri, Wiwin
Journal of Computer Science and Information Technology Vol. 1 No. 3 (2025): Journal of Computer Science and Information Technology, December 2025
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v1i3.341

Abstract

Pest attacks on crops are one of the main factors contributing to reduced agricultural productivity in Indonesia. Manual pest identification often requires specialized expertise and is time-consuming, making the need for a fast and accurate solution essential. This study develops a mobile application for identifying crop pests using image processing techniques. The application is designed to be used by farmers and agricultural extension workers in the field simply by photographing parts of the plant suspected to be affected by pests. The identification process consists of several stages, including image preprocessing, feature extraction, and classification using a machine learning model trained with a dataset of common crop-pest images. The system is equipped with a simple interface to ensure ease of use for non-technical users. Test results show that the application is capable of identifying pests with an accuracy level sufficient for early detection needs. In addition, the application provides appropriate control recommendations, helping users make decisions to reduce the impact of pest attacks. This study demonstrates that the use of mobile devices and image processing techniques can be a practical alternative to support efforts in improving agricultural productivity. Further development can be carried out by expanding the types of pests recognized and enhancing the quality of the classification model through a more diverse dataset.
Analisis Kinerja Deep Learning dalam Deteksi Dini Penyakit Menggunakan Citra Medis Awaludin; An Nahari , Rafiq; Pehan Goran, Anthomy; M Fauzi, Rafly
Journal of Computer Science and Information Technology Vol. 2 No. 1 (2026): Journal of Computer Science and Information Technology, March 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v2i1.393

Abstract

Advances in artificial intelligence technology, particularly deep learning, have been widely utilized in medical image processing to support early disease detection. Previous studies have shown that Convolutional Neural Networks (CNN) are capable of achieving high levels of accuracy in medical image classification, but differences in architecture and training methods result in varying performance. This study aims to analyze and compare the performance of deep learning algorithms in early disease detection using medical images, using previous research as a benchmark. Transfer learning-based CNN models, namely VGG16 and ResNet50, were used and evaluated using a labeled medical image dataset. Performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics, and the results were compared with accuracy achieved in previous studies using similar approaches. The analysis showed that the ResNet50 model achieved an accuracy of up to 95.3%, comparable to or better than several previous studies. These findings confirm that the choice of CNN architecture and training strategy significantly influences the performance of medical image-based early disease detection systems.
Deteksi Dini Diabetes Mellitus Menggunakan Algoritma Random Forest pada Data Klinis Rizky Ananda, Muhammad; Kurniawan, Rizky; Lestari Putri, Dewi
Journal of Computer Science and Information Technology Vol. 2 No. 1 (2026): Journal of Computer Science and Information Technology, March 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v2i1.410

Abstract

Early detection of Diabetes Mellitus is essential to reduce complications and improve patient outcomes. Machine learning approaches, particularly the Random Forest algorithm, have demonstrated promising performance in medical prediction tasks using clinical datasets. This study aims to analyze the effectiveness of the Random Forest algorithm for early diabetes detection based on clinical variables through a literature-based analytical research design. Data were synthesized from empirical findings reported in recent peer-reviewed studies published between 2022–2025. The analysis indicates that Random Forest consistently achieves high predictive performance, with reported accuracy ranging from 79.2% to 99.64% across multiple datasets and experimental configurations. Feature selection, data balancing techniques such as SMOTE and ADASYN, and hyperparameter optimization significantly improve model robustness. Comparative evaluation shows Random Forest outperforms several conventional machine learning classifiers in handling imbalanced medical datasets and identifying key risk factors. The findings highlight the algorithm’s reliability for clinical decision support systems and early screening applications. This study contributes a comprehensive synthesis of current evidence supporting Random Forest implementation in healthcare analytics and provides recommendations for future development of intelligent diabetes prediction systems.
Penerapan Firewall dan Intrusion Detection System pada Jaringan Komputer Hadi, Ronal; Kurniyanto Abdullah, Riska; Pribadi Fitrian, Harry
Journal of Computer Science and Information Technology Vol. 2 No. 1 (2026): Journal of Computer Science and Information Technology, March 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v2i1.411

Abstract

Network security has become a critical aspect as organizations increasingly rely on network-based information systems. Firewalls, as the first line of defense, have limitations in detecting complex and dynamic cyber attacks. Therefore, integrating Intrusion Detection Systems (IDS) is widely adopted to enhance network resilience. This study aims to analyze the implementation of firewalls and IDS in improving computer network security based on literature review and implementation studies. The research method employs a descriptive-analytical approach through a systematic literature review and comparative analysis of previous studies. The results indicate that the integration of firewalls and IDS significantly improves attack detection rates, accelerates security response, and reduces the risks of data breaches and service disruptions. IDS technologies such as Snort, OSSEC, and Suricata have proven effective in detecting brute force attacks, malware, and Distributed Denial of Service (DDoS) attacks when combined with firewall systems. This study concludes that the integrated implementation of firewalls and IDS is an effective and relevant strategy for securing modern computer networks.
Sistem Rekomendasi Produk E-Commerce Menggunakan Metode Collaborative Filtering Fauzan, Ahmad; Nurhaliza, Siti; Pratama, Rizky
Journal of Computer Science and Information Technology Vol. 2 No. 1 (2026): Journal of Computer Science and Information Technology, March 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v2i1.413

Abstract

The rapid growth of e-commerce platforms demands recommendation systems capable of delivering relevant and personalized product suggestions to users. Collaborative filtering has emerged as a dominant approach due to its ability to leverage user interaction patterns without relying on explicit product content information. This study aims to examine and design an e-commerce product recommendation system using collaborative filtering by integrating empirical findings from previous studies. A quantitative approach based on recommendation system modeling was employed, utilizing user interaction data such as purchase history, ratings, and browsing behavior. The results indicate that collaborative filtering significantly improves recommendation accuracy, user engagement, and sales potential, despite challenges such as cold start and data sparsity. The integration of hybrid models, machine learning techniques, and neural networks has proven effective in addressing these limitations. This study contributes both conceptually and practically to the development of adaptive and sustainable e-commerce recommendation systems.
Perbandingan Performansi Protokol Routing pada Jaringan Mobile Ad Hoc Network Pratama, Andi; Maulana, Rizky; Lestari, Dewi
Journal of Computer Science and Information Technology Vol. 2 No. 1 (2026): Journal of Computer Science and Information Technology, March 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v2i1.414

Abstract

Mobile Ad Hoc Networks (MANETs) rely on dynamic routing mechanisms due to their infrastructureless and highly mobile nature. Selecting an appropriate routing protocol remains a critical issue because network performance is influenced by node mobility, density, and traffic conditions. This study aims to comparatively analyze the performance of major MANET routing protocols, including AODV, DSDV, OLSR, DSR, and related variants, based on findings from prior simulation and experimental studies. A systematic literature-based comparative method was employed by synthesizing performance metrics such as packet delivery ratio, throughput, end-to-end delay, routing overhead, and energy consumption from multiple NS2/NS3 and OPNET-based research works. Results show that AODV consistently performs well in dynamic and large-scale networks, offering high packet delivery and moderate delay, while OLSR excels in dense topologies but generates higher overhead. DSDV demonstrates stability in low-mobility environments, whereas DSR shows efficiency in smaller networks. The study concludes that no single protocol is universally optimal; protocol selection should align with network scale, mobility, and application requirements. This research provides a consolidated reference to support adaptive MANET routing protocol selection.

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