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JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
Published by Infoteks
ISSN : 26552183     EISSN : 26557290     DOI : 10.33173
Core Subject : Science,
Computer Science & IT
data analysis, natural language processing, artificial intelligence, neural networks, pattern recognition, image processing, genetic algorithm, bioinformatics/biomedical applications, biometrical application, content-based multimedia retrievals, augmented reality, virtual reality, information system, game mobile, dan IT bussiness incubation
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 159 Documents
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Cataract Maturity Classification Using the VGG16 Deep Learning Model Anggara Putra, I Wayan Kintara; F, Ahmad Rifqi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 2 (2025): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.267

Abstract

Cataract continues to be a major contributor to vision impairment worldwide, caused by gradual lens clouding that reduces clarity of sight. Accurately identifying the maturity level of cataracts is crucial in determining appropriate treatment planning and surgical intervention timing. However, the conventional diagnosis process still depends heavily on subjective visual assessment by ophthalmologists, which can lead to variability in classification results. To address this, the present study introduces an automated cataract maturity classification system using the VGG16 deep learning architecture through a transfer learning approach. The model distinguishes between immature and mature cataracts using clinical eye images that have undergone standardized preprocessing, including resizing, normalization, and augmentation, to improve learning robustness and avoid overfitting. Experimental evaluation shows that the model achieves 88 percent accuracy, with average precision, recall, and F1-score values of 0.88, demonstrating balanced classification performance for both classes. These outcomes indicate that VGG16 is capable of capturing relevant opacity progression characteristics associated with different cataract maturity levels. Future research may focus on broadening the dataset to include additional maturity categories, integrating explainability methods, and exploring advanced deep learning architectures to further enhance diagnostic performance and support clinical adoption.
Cataract Classification in Eye Images Using MobileNetV2 Batubulan, Kadek Suarjuna; Pratama, I Putu Adi; Naswin, Ahmad
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 2 (2025): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.268

Abstract

Cataract remains one of the primary causes of visual impairment globally, with early detection being essential to prevent permanent blindness and improve patient quality of life. However, conventional diagnosis depends on ophthalmologists and clinical-grade imaging devices, which are often limited in remote or under-resourced areas. This condition highlights the need for an efficient, accessible, and automated screening solution. To address this challenge, this study utilizes the MobileNetV2 deep learning architecture to classify cataract conditions based on eye images. MobileNetV2 is selected because of its lightweight model structure and strong feature representation capabilities, making it suitable for deployment in portable or embedded medical systems. The dataset used consists of two cataract stages, namely immature and mature cataracts, with images undergoing preprocessing prior to model training. The proposed system demonstrates excellent performance, achieving an accuracy, precision, recall, and F1-score of 100% in distinguishing cataract stages. These results confirm that MobileNetV2 can effectively support cataract screening with high reliability while maintaining efficiency. Future work will involve extending the dataset to include additional cataract severity levels and non-cataract eye images, as well as integrating explainable artificial intelligence methods to provide visual diagnostic interpretations and enhance clinical trust in real-world applications.
Classification of Tuberculosis and Pneumonia Lung Diseases in X-Ray Images Using the CNN Method with VGG-19 Architecture Sri Murdhani, I Dewa Ayu; Ismanto, Heru; Suprihanto, Didit
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 2 (2025): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.269

Abstract

Tuberculosis (TB) and Pneumonia continue to be among the world’s leading causes of morbidity and mortality, particularly in low- and middle-income countries where access to advanced diagnostic tools remains limited. Conventional radiological interpretation, while effective, heavily depends on the experience and precision of radiologists, resulting in potential subjectivity and diagnostic variability. This study proposes a fully automated classification framework for lung disease detection using a Convolutional Neural Network (CNN) based on the VGG-19 architecture. The model aims to enhance diagnostic accuracy and reliability by leveraging deep learning techniques capable of capturing subtle radiographic patterns that may not be readily identifiable by human observers. A dataset of 3,623 chest X-ray images—divided into Normal, Pneumonia, and Tuberculosis classes—was compiled from Kaggle and Mendeley Data repositories. Preprocessing techniques including Contrast Limited Adaptive Histogram Equalization (CLAHE), cropping, resizing, and normalization were employed to enhance contrast and minimize noise. The model was trained and tested under four data-split configurations (80:20, 70:30, 60:40, and 50:50) to assess generalization capability. The 70:30 configuration achieved optimal performance, recording 96% accuracy, 97% precision, 95% recall, and a 96% F1-score. These findings demonstrate that the VGG-19 model can accurately distinguish between TB, Pneumonia, and Normal cases, providing a reliable foundation for AI-driven medical diagnosis. Future research will focus on dataset expansion, interpretability enhancement using Explainable AI (XAI), and the integration of this model into clinical decision-support systems.
Lightweight MobileNet-Based Deep Learning Framework for Automated Lung Infection Detection from Chest X-Ray Images Santiyuda, Kadek Gemilang
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 2 (2025): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.270

Abstract

Lung infections, especially viral pneumonia, continue to pose a significant global health challenge due to their high rates of illness and death. Traditional diagnostic approaches, such as radiologists' interpretation of chest X-ray (CXR) images, are frequently slow and subject to personal bias. The swift advancement in deep learning offers great potential for automating the detection of lung infections; however, many existing convolutional neural network (CNN) models demand substantial computational resources, which restricts their use in real-time or low-resource clinical settings. This study seeks to overcome these issues by creating a lightweight and effective diagnostic system using the MobileNet architecture for automatic lung infection identification from CXR images. The core drive for this research is to deliver an accessible and precise AI tool that aids radiologists in timely disease detection, particularly in under-resourced healthcare environments. The proposed MobileNet-based model, trained through transfer learning and fine-tuning on a binary dataset of normal and viral pneumonia images, strikes an excellent balance between performance and computational efficiency. Experimental results yielded 98% accuracy, 0.98 precision, 0.98 recall, and 0.98 F1-score, validating the model's reliability and appropriateness for embedded or mobile health uses. Moving forward, efforts will concentrate on broadening the dataset to encompass various lung disease types, incorporating explainable AI methods to boost clarity, and implementing the model in live clinical or mobile diagnostic platforms to enable widespread and effective healthcare services.
Accrual-Based Accounting Information System with Break Even Point (BEP) Approach Erawati, Kadek Nonik
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 3 (2026): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.279

Abstract

The digital transformation of financial management is essential for modern business sustainability. However, traditional cash-based systems fail to represent true financial positions, while existing accounting software often lacks integrated managerial analytics tools. This creates a functional disconnect where managers must perform manual, error-prone calculations to determine profitability thresholds. To address this, this research develops a web-based Accrual Accounting Information System integrated with a dynamic Break Even Point (BEP) approach. The system automates the double-entry recording process and real-time classification of fixed costs from the general ledger to visualize safety margins instantly. The primary contribution of this study is the unification of professional accounting standards (PSAK) with strategic decision-support algorithms in a single platform. Evaluation using Black Box testing confirms the system achieves 100 percent accuracy in generating financial statements and BEP metrics, while usability analysis demonstrates that the responsive architecture significantly enhances workflow efficiency across devices. The results indicate that the system effectively transforms passive financial data into actionable insights, empowering proactive decision-making. Future work aims to incorporate machine learning for dynamic semi-variable cost analysis to further refine predictive capabilities.
Design of Accrual-Based Accounting Information System Using Full Costing Method Angga Indrya, Dewa Ayu Giovany
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 3 (2026): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.280

Abstract

Accounting Information Systems (AIS) play an important role in supporting organizational decision-making by providing accurate and timely financial information. The adoption of accrual-based accounting is particularly essential for organizations engaged in production activities, as it enables more reliable recognition of revenues and expenses and supports transparent financial reporting. However, many organizations still rely on manual or cash-based accounting practices, which often lead to inaccurate cost calculations, incomplete financial information, and limited support for managerial decision-making. This research is motivated by the need to integrate accrual accounting principles with a comprehensive cost calculation approach to overcome these limitations. This study proposes the design and implementation of an accrual-based Accounting Information System that applies the Full Costing method to allocate both direct and indirect production costs, including direct materials, direct labor, and manufacturing overhead. The main contribution of this research lies in the integration of accrual-based transaction processing and Full Costing calculations within a unified system that supports accurate cost determination and financial transparency. The proposed system was evaluated through functional testing and user validation to assess its effectiveness in transaction recording, cost calculation, and financial reporting. The evaluation results indicate that the system improves data consistency, reduces manual processing errors, and generates more comprehensive cost and financial reports compared to conventional accounting practices. Despite these positive results, the current system implementation relies on static cost allocation rules. Future work will focus on enhancing the system by incorporating dynamic cost drivers, advanced analytical features, and integration with other enterprise systems to improve scalability and decision-support capabilities.
Development of Accrual-Based Accounting Information System for Financial Planning B, Muslimin; Racmadhani, Budi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 3 (2026): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.281

Abstract

The increasing complexity of financial management in small and medium-sized enterprises (SMEs) requires the implementation of robust accounting systems. While accrual accounting provides more accurate financial insights by recognizing revenues and expenses when incurred, many SMEs still rely on cash-based accounting, hindering their financial decision-making. This research aims to develop an accrual-based accounting information system tailored for SMEs, integrating essential features such as cost control and forecasting. The proposed system automates key processes, from transaction entry to report generation, offering a comprehensive solution to enhance financial transparency and decision-making. The system is evaluated through real-world data simulations to assess its effectiveness in improving reporting accuracy and forecasting capabilities. The results demonstrate that the system improves financial planning and resource allocation, providing valuable insights for SMEs. Future work will focus on scaling the system for larger enterprises and incorporating machine learning techniques to improve financial forecasting and anomaly detection.
Crop Yield Prediction Using Random Forest Based on Soil, Climate, and Agronomic Factors Sugiartawan, Putu; Kotama, I Nyoman Darma; Pradhana, Anak Agung Surya
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 3 (2026): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.282

Abstract

Agricultural yield prediction plays a critical role in ensuring food security and optimizing farming practices. Traditional methods of crop yield estimation often rely on expert knowledge and historical data, which can be limited and inaccurate. Machine learning algorithms, particularly Random Forest, have shown promise in improving the accuracy of crop yield predictions by considering complex interactions between soil, climate, and agronomic factors. This study aims to develop a Random Forest-based model to predict crop yield using a diverse set of agricultural datasets. The model was trained and validated using data from multiple regions, focusing on soil properties, climatic conditions, and farming practices. The results demonstrated that the Random Forest model provided reliable predictions, with performance evaluated using metrics such as MAE, RMSE, and R². However, some discrepancies between actual and predicted values were observed, indicating room for improvement. Future work will focus on integrating real-time data, such as soil moisture and pest infestation, to enhance the model's accuracy. Additionally, exploring advanced machine learning techniques like deep learning could provide better handling of complex patterns in agricultural data. This research contributes to the growing field of agricultural data science and aims to provide a scalable solution for crop yield prediction across various regions.
Forecasting the Jakarta Composite Index (IHSG) Using the Moving Average Method Ismanto, Heru
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 3 (2026): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.283

Abstract

Financial market indices play a crucial role in reflecting economic conditions and supporting investment decision-making. In Indonesia, the Jakarta Composite Index (IHSG) serves as a key benchmark for evaluating overall stock market performance. Due to its dynamic and volatile nature, accurate forecasting of IHSG movements remains a challenging task in financial time series analysis. Many recent studies employ complex machine learning and deep learning models, which often require substantial computational resources and lack interpretability, limiting their practical adoption. Motivated by the need for transparent and easily implementable forecasting approaches, this study investigates the use of the Simple Moving Average (SMA) method as a baseline model for forecasting the IHSG. The main contribution of this research lies in providing a systematic evaluation of the moving average method using different window sizes and standard error metrics. Historical IHSG data are preprocessed, analyzed descriptively, and divided into training and testing datasets. Short-term forecasts are generated by applying the SMA model with varying window configurations. The performance of the proposed approach is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results demonstrate that the moving average method is capable of capturing the general trend of the IHSG, with forecasting accuracy strongly influenced by the choice of window size. Future work may focus on integrating additional forecasting techniques, incorporating exogenous variables, and developing hybrid or adaptive models to further enhance prediction accuracy and robustness.

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