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All Journal JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
I Wayan Kintara Anggara Putra
National Taiwan University of Science and Technology
Computer Science & IT

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Development of an Accrual-Based Accounting Information System Using a Performance Measurement System Anak Agung Surya Pradhana; I Wayan Kintara Anggara Putra
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 | DOI: 10.33173/jsikti.312

Abstract

The integration of accounting and performance measurement systems is a crucial advancement for small and medium-sized enterprises (SMEs), which often face challenges in managing financial processes efficiently. Traditional accounting systems, while reliable, lack real-time financial insights and performance metrics necessary for informed decision-making. This study addresses the problem by proposing an accrual-based Accounting Information System (AIS) integrated with a Performance Measurement System (PMS). The motivation behind this work is to enhance financial decision-making in SMEs by automating key accounting functions and providing real-time performance feedback. The proposed system automates revenue recognition, expense matching, and cost allocation, while simultaneously tracking key performance indicators (KPIs) such as profitability and return on assets. The system was evaluated in a case study involving several SMEs, where it demonstrated improved accuracy in financial reporting, reduced manual errors by 30%, and enhanced decision-making by providing real-time performance insights. Usability testing revealed high satisfaction from users, although additional training was recommended to fully leverage the system’s capabilities. The system's scalability for larger organizations remains an area for future exploration. Future work will focus on refining predictive analytics to further improve financial forecasting and expanding the system’s scalability to accommodate larger enterprises. The results highlight the potential of integrating accrual accounting with performance measurement to enhance financial management in SMEs, offering a practical solution for improved financial decision-making.
Developing Trading Strategies for Doge Coin with Reinforcement Learning I Wayan Kintara Anggara Putra
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.232

Abstract

Cryptocurrency trading, particularly with highly volatile assets like Dogecoin, presents significant challenges due to rapid price fluctuations and external factors such as social media sentiment and speculative trading behaviors. This study proposes reinforcement learning (RL)-based trading strategies to address these complexities. RL, an advanced machine learning approach, enables dynamic adaptation to market conditions by optimizing sequential decisions for maximum cumulative rewards. Using historical market data and technical indicators, RL agents were trained and evaluated in simulated trading environments. Performance metrics, including profitability, risk-adjusted returns, and robustness under varying market conditions, demonstrate that RL-based strategies outperform traditional methods by capturing non-linear dependencies and responding effectively to delayed rewards. The results highlight the ability of RL to adapt to market volatility and optimize trading outcomes. However, the study acknowledges limitations, including the exclusion of external sentiment data and restricted testing across diverse market scenarios. Future research should integrate external data sources, such as sentiment and macroeconomic indicators, conduct real-time market testing, and explore applications to multi-asset portfolios to improve generalizability and robustness. This research contributes to the intersection of machine learning and financial markets, showcasing RL’s potential to address cryptocurrency trading challenges and offering pathways for more adaptive and robust trading strategies.
A Hybrid Approach to Chili Price Classification Using Ensemble Methods I Wayan Kintara Anggara Putra
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.241

Abstract

This study proposes a hybrid machine learning approach for predicting chili prices, integrating ensemble methods such as Random Forest, Gradient Boosting, and XGBoost to enhance forecasting accuracy. By analyzing historical price data, the model identifies key features, including day and value, as significant predictors. The hybrid model demonstrates superior performance in capturing non-linear patterns and seasonal variations compared to individual machine learning techniques. Evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) validate the model’s effectiveness in handling market volatility. The findings highlight the potential of advanced machine learning techniques in agricultural price forecasting, offering reliable and actionable insights for farmers, traders, and policymakers. This approach not only addresses challenges in market prediction but also provides a scalable framework for future enhancements, such as incorporating additional variables like weather and supply chain factors. By bridging the gap between data-driven analysis and practical application, this research contributes to stabilizing agricultural markets and supporting informed decision-making processes.
Cataract Maturity Classification Using the VGG16 Deep Learning Model I Wayan Kintara Anggara Putra; Ahmad Rifqi F
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.
Co-Authors Ahmad Rifqi F Anak Agung Surya Pradhana