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Journal of Computer Science Artificial Intelligence and Communications
Published by CV. Raskha Media Group
ISSN : 31093981     EISSN : 31089828     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Earth & Planetary Sciences Engineering
Journal of Computer Science Artificial Intelligence and Communications is a multidisciplinary, peer-reviewed journal dedicated to advancing research in computer science, artificial intelligence (AI), and communication technologies. The journal publishes high-quality original articles, reviews, and case studies that explore the latest innovations, theories, algorithms, and applications shaping the digital world. Focused on the intersection of computational systems, intelligent automation, and seamless communication networks, JOCSAIC aims to foster collaboration and knowledge exchange among researchers, practitioners, and academics working across diverse sectors such as data science, machine learning, telecommunications, and intelligent systems. The journal is a key resource for cutting-edge developments and trends in these transformative fields.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 2 No 2 (2025): November 2025" : 5 Documents clear
Identification of Book Cover Titles Using the Natural Language Processing (NLP) Method Gultom, Rianti Afifah; Setiani Asih, Munjiat; Zulkarnain Hasibuan, Ade
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 2 (2025): November 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i2.54

Abstract

In the digital era, the identification of book titles on covers has become a crucial requirement in digital library management, archiving systems, and book e-commerce platforms. The main challenges lie in the limitations of manual methods and traditional pattern-matching techniques, which are inefficient, as well as in the complexity of processing the Indonesian language, which exhibits diverse morphological variations and syntactic structures. To address these issues, this study proposes the integration of Optical Character Recognition (OCR) with the Natural Language Processing (NLP) method. OCR is utilized to extract textual information from book cover images, while NLP is applied to recognize and classify the extracted text to identify the main book title. The implementation results demonstrate that this approach significantly improves title identification accuracy compared to traditional methods, particularly through the application of Named Entity Recognition (NER) techniques and modern NLP models such as BERT and LSTM. The developed system proves effective in accelerating the book digitalization process, enhancing information management efficiency, and contributing to the advancement of Indonesian language processing technology.
Classification of Customer Credit Risk Levels Using the Random Forest Method: A Case Study on Microfinance Institutions Damayanti, Fera; Budiman, Arief; Sundari, Siti; Nainggolan, Theodora MV
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 2 (2025): November 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i2.59

Abstract

Credit risk classification plays a crucial role in supporting financial institutions, especially microfinance institutions, in assessing the ability of customers to repay loans. This study aims to develop a credit risk classification model using the Random Forest method, which is known for its accuracy and robustness in handling classification problems. The research uses a dataset obtained from a microfinance institution consisting of various customer attributes such as income, age, loan amount, repayment history, and employment status. The dataset is preprocessed and divided into training and testing sets to evaluate model performance. The Random Forest algorithm is then applied to build a classification model that categorizes customers into three credit risk levels: low, medium, and high. The results show that the Random Forest model achieves a high level of accuracy, with a classification precision of 89%, recall of 87%, and F1-score of 88%. These findings indicate that Random Forest is an effective technique for credit risk classification and can be implemented by microfinance institutions to support better decision-making in credit approval processes. This research also highlights the potential of machine learning techniques in enhancing credit risk management and minimizing non-performing loans.
Analysis of the Effectiveness of Implementing a Queue Algorithm-Based Leadership Scheduling Information System in Government Agencies Mardiah; Nuranisah; Nainggolan, Theodora MV
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 2 (2025): November 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i2.61

Abstract

This study analyzes the effectiveness of implementing a leadership scheduling information system that utilizes queue algorithms in government agencies. The main objective is to evaluate how the integration of algorithm-based scheduling systems improves efficiency, accuracy, and transparency in managing executive-level appointments and meetings. The research adopts a mixed-method approach, combining quantitative analysis through system performance metrics with qualitative feedback from end-users, including administrative staff and decision-makers. Findings indicate a significant improvement in scheduling efficiency, with reduced conflicts, optimized time slots, and better coordination between departments. Furthermore, the system minimizes manual intervention, thus decreasing administrative errors and enhancing data integrity. The queue algorithm enables a first-come-first-served mechanism that ensures fairness while allowing for priority-based modifications in urgent cases. The implementation also receives positive responses in terms of user satisfaction and perceived usefulness. However, challenges such as user adaptation and technical limitations were identified, suggesting a need for continuous training and system updates. Overall, the integration of a queue algorithm-based scheduling system proves to be an effective solution for improving leadership-level administrative processes in government institutions.
Evaluation of the Usability of the Academic Information System Using the System Usability Scale (SUS) Method Rahardian, Rifky Lana; Khodijah, Siti; Rizki, Cindy Atika
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 2 (2025): November 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i2.62

Abstract

The increasing reliance on digital platforms in higher education necessitates the evaluation of system usability to ensure effective user interaction and satisfaction. This study aims to evaluate the usability of the Academic Information System (AIS) at [University Name] using the System Usability Scale (SUS) method. SUS is a reliable, standardized tool for measuring the usability of interactive systems, providing a quick and quantitative assessment. Data were collected from a sample of 100 students and academic staff who frequently use the AIS for various academic activities, including course registration, grade checking, and academic planning. The results of the SUS analysis yielded an average score of 72.5, indicating that the system falls within the “Good” usability category. However, several usability issues were identified, such as navigation complexity and visual layout inconsistencies, which slightly reduced user satisfaction. These findings highlight the importance of continuous usability testing and user-centered design in the development of academic systems. The study recommends specific design improvements to enhance user experience and system performance. Overall, the SUS method proved effective in identifying usability strengths and weaknesses, offering valuable insights for future system optimization.
Development of an Employee Performance Monitoring Information System Using a Web-Based Interactive Dashboard Prayoga, Abil Alwi; Hasanuddin, Muhammad; Khodijah, Siti; Rizki, Cindy Atika; PA, Dahrim
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 2 (2025): November 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i2.63

Abstract

This research aims to develop an employee performance monitoring information system that utilizes a web-based interactive dashboard to enhance decision-making and managerial oversight. In many organizations, traditional performance evaluation methods are often time-consuming, static, and lack real-time insight, resulting in inefficiencies in performance tracking. To address these challenges, the proposed system is designed to provide dynamic visualization of key performance indicators (KPIs), attendance records, task completion rates, and other critical metrics through an interactive and user-friendly dashboard interface. The development process follows the Waterfall methodology, encompassing stages of requirements analysis, system design, implementation, testing, and deployment. The system was built using PHP and JavaScript for front-end interactivity, with a MySQL database to manage data storage. The dashboard includes various visual tools such as graphs, charts, and progress bars to facilitate real-time monitoring and performance analysis. Testing results indicate that the system performs effectively, offering accurate and timely information that supports employee evaluation and organizational planning. User feedback also reveals a high level of satisfaction due to the dashboard's ease of use and responsiveness. Overall, the implementation of this web-based performance monitoring system is expected to improve transparency, accountability, and productivity within the organization.

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