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All Journal JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
Anggara Putra, I Wayan Kintara
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Computer Science & IT

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Neural Network for Predicting Dining Experiences at Restaurant X Anggara Putra, I Wayan Kintara; Santiyuda, Kadek Gemilang
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 4 (2025): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.217

Abstract

This study explores the application of neural networks to predict dining experiences at Restaurant X, utilizing a combination of customer feedback, operational data, and sales transactions. The goal is to enhance restaurant management through accurate predictions of customer satisfaction and operational performance. Customer reviews, sentiment analysis, and operational data were processed using natural language processing (NLP) and time-series analysis to prepare the data for neural network training. The model's performance was evaluated using metrics such as accuracy, precision, recall, and F1-score, and it was compared with traditional machine learning techniques like logistic regression and decision trees. The results demonstrate that neural networks outperform traditional algorithms in predicting customer sentiment and dining experiences. This study highlights the potential of deep learning to provide valuable insights into customer behavior, enabling restaurants to improve service personalization, marketing strategies, and operational efficiency. Future research can focus on expanding the dataset and exploring more advanced deep learning models to further enhance prediction accuracy and applicability in the hospitality industry.
Developing Trading Strategies for Doge Coin with Reinforcement Learning Anggara Putra, I Wayan Kintara
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 Anggara Putra, I Wayan Kintara
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.
Co-Authors Santiyuda, Kadek Gemilang