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All Journal JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
I Nyoman Darma Kotama
Okayama University
Computer Science & IT

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Accrual-Based Accounting Information System For Financial Compliance Monitoring I Nyoman Darma Kotama; Putu Sugiartawan; I Dewa Ayu Sri Murdhani
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 4 (2026): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Small and medium-sized enterprises (SMEs) often face challenges in implementing efficient and accurate financial reporting systems, primarily due to the limitations of manual accounting processes. These challenges lead to errors, delays, and compliance issues, which hinder timely decision-making and financial transparency. This research proposes the development and evaluation of an accrual-based Accounting Information System (AIS) designed to address these issues by automating financial reporting and compliance monitoring. The motivation behind this study is to improve the financial management practices of SMEs by providing a reliable system that ensures accurate financial reporting and real-time compliance monitoring. The main contribution of this research is the design of an AIS that integrates key financial functions, such as transaction processing, accrual calculations, and compliance checks, to streamline financial operations. Evaluation results from case studies indicate that the system significantly reduced reporting errors by 50%, enhanced compliance accuracy by 25%, and decreased report generation time by 40%. Despite these successes, challenges remain in system integration with legacy accounting software and handling complex financial transactions. Future work will focus on enhancing the scalability of the system, integrating advanced machine learning techniques for predictive financial analysis, and improving the integration process to allow for broader implementation in diverse business contexts. Additionally, the development of a mobile application to improve accessibility to financial reports and compliance alerts will be explored.
Applying K-Nearest Neighbors Algorithm for Wine Prediction and Classification Anak Agung Surya Pradhana; Kadek Suarjuna Batubulan; I Nyoman Darma Kotama
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 3 (2024): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.231

Abstract

This study evaluates the performance of a machine learning classification model using a confusion matrix to analyze predictions across three distinct classes. The results show the model achieving a high accuracy of 94.44%, indicating reliable classification performance. The confusion matrix highlights that most instances were classified correctly, with minimal misclassifications observed, particularly in Class 1, where some overlap with other classes was evident. The findings suggest that the model effectively distinguishes between well-separated classes while facing minor challenges with overlapping data distributions. To address these issues, potential improvements such as feature engineering, class balancing, and advanced optimization techniques are recommended. The study underscores the importance of confusion matrix analysis as a diagnostic tool for understanding classification errors and guiding model refinement. Additionally, this research emphasizes the role of high-quality datasets, proper model selection, and hyperparameter tuning in achieving optimal classification accuracy. The outcomes provide a basis for further enhancement of machine learning models in applications requiring multi-class classification. By reducing errors and improving model robustness, this approach can contribute to more accurate and reliable decision-making processes across various domains, including healthcare, finance, and natural language processing.
Predicting Wine Quality Based on Features Using Naive Bayes Classifier Anak Agung Surya Pradhana; Kadek Suarjuna Batubulan; I Nyoman Darma Kotama
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 1 (2024): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.244

Abstract

This study explores the application of the Naive Bayes classifier in predicting wine quality based on physicochemical attributes. Leveraging a dataset containing features such as acidity, pH, alcohol content, and sulfur dioxide concentrations, the research aims to address the limitations of traditional sensory evaluation methods, which are often subjective and inconsistent. Data preprocessing, including normalization and feature selection, is performed to ensure the dataset is suitable for machine learning. The Naive Bayes classifier is implemented using Python's scikit-learn library, with hyperparameter tuning conducted to optimize its performance. The model is evaluated on metrics such as accuracy, precision, recall, and F1-score, achieving competitive results compared to other machine learning techniques such as Decision Trees and Support Vector Machines. The findings demonstrate the Naive Bayes classifier’s efficiency in handling high-dimensional data, its computational simplicity, and its potential for real-time quality assessment in the wine industry. This research highlights the role of machine learning in automating and enhancing quality control processes, contributing to the broader integration of data-driven approaches in the agri-food sector. The study underscores the feasibility of using physicochemical features as objective indicators of wine quality, offering a scalable and cost-effective alternative to traditional methods.
Crop Yield Prediction Using Random Forest Based on Soil, Climate, and Agronomic Factors Putu Sugiartawan; I Nyoman Darma Kotama; Anak Agung Surya Pradhana
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.
Co-Authors Anak Agung Surya Pradhana I Dewa Ayu Sri Murdhani Kadek Suarjuna Batubulan Putu Sugiartawan Putu Sugiartawan