Journal of Computer Science and Information Technology
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.
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<![CDATA[Pengaruh Antarmuka Pengguna (UI/UX) Terhadap Efisiensi Penggunaan Sistem Rekam Medis Elektronik oleh Tenaga Kesehatan ]]>
<![CDATA[Arya Wirawan, Datu]]>;
<![CDATA[Suhartono, Deni]]>;
<![CDATA[Ari Wijaya, Made]]>
<![CDATA[ Journal of Computer Science and Information Technology Vol. 1 No. 3 (2025): Journal of Computer Science and Information Technology, December 2025 ]]>
Publisher : <![CDATA[Lembaga Publikasi Ilmiah Nusantara ]]>
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DOI: 10.70716/jocsit.v1i3.303
<![CDATA[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.]]>
<![CDATA[Evaluasi Sikap Mahasiswa Ilmu Komputer terhadap Etika dan Kebijakan AI (Studi Kasus: STMIK Lombok) ]]>
<![CDATA[Firdaus, Wianata]]>;
<![CDATA[Maulinda Safira, Siti]]>;
<![CDATA[Islamin , Fahdilatul]]>
<![CDATA[ Journal of Computer Science and Information Technology Vol. 1 No. 3 (2025): Journal of Computer Science and Information Technology, December 2025 ]]>
Publisher : <![CDATA[Lembaga Publikasi Ilmiah Nusantara ]]>
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DOI: 10.70716/jocsit.v1i3.309
<![CDATA[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.]]>
<![CDATA[Analisis Sentimen Media Sosial Menggunakan Algoritma BERT dan LSTM ]]>
<![CDATA[Malasari, Novita]]>;
<![CDATA[Ramli, Muhammad]]>
<![CDATA[ Journal of Computer Science and Information Technology Vol. 1 No. 3 (2025): Journal of Computer Science and Information Technology, December 2025 ]]>
Publisher : <![CDATA[Lembaga Publikasi Ilmiah Nusantara ]]>
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DOI: 10.70716/jocsit.v1i3.318
<![CDATA[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.]]>
<![CDATA[Rancang Bangun Sistem Informasi Manajemen Perpustakaan Berbasis Cloud Server ]]>
<![CDATA[Abdi Ariadi, Restu]]>;
<![CDATA[Wulandari, Sry]]>
<![CDATA[ Journal of Computer Science and Information Technology Vol. 1 No. 3 (2025): Journal of Computer Science and Information Technology, December 2025 ]]>
Publisher : <![CDATA[Lembaga Publikasi Ilmiah Nusantara ]]>
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DOI: 10.70716/jocsit.v1i3.323
<![CDATA[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.]]>
<![CDATA[Aplikasi Mobile untuk Identifikasi Hama Tanaman Menggunakan Teknik Image Processing ]]>
<![CDATA[Wardani, Kania]]>;
<![CDATA[Saputri, Wiwin]]>
<![CDATA[ Journal of Computer Science and Information Technology Vol. 1 No. 3 (2025): Journal of Computer Science and Information Technology, December 2025 ]]>
Publisher : <![CDATA[Lembaga Publikasi Ilmiah Nusantara ]]>
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DOI: 10.70716/jocsit.v1i3.341
<![CDATA[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.]]>
