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INDONESIA
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 5 Documents
 1 
Search results for , issue "Vol 6 No 3 (2024): March" : 5 Documents clear
Applying K-Nearest Neighbors Algorithm for Wine Prediction and Classification Pradhana, Anak Agung Surya; Batubulan, Kadek Suarjuna; Kotama, I Nyoman Darma
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.
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.
Enhancing Price Classification of Chili Using Gradient Boosting Machines Sugiartawan, Putu; Wardani, Ni Wayan
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.233

Abstract

This study explores the application of Gradient Boosting Machines (GBM) to enhance the classification and prediction of chili prices. The research uses a comprehensive dataset collected from various sources, including local markets, online platforms, and agricultural databases, covering multiple attributes such as chili type, region, harvest season, weather conditions, and demand-supply dynamics. The GBM model outperforms traditional machine learning algorithms, achieving an accuracy of 87%, with a high area under the ROC curve (AUC) of 0.91. Feature importance analysis indicates that harvest season and region are the most significant factors influencing price variations. The findings suggest that the GBM model provides reliable price predictions and insights into price-driving factors, offering valuable tools for stakeholders in the agricultural market. The study emphasizes the need for broader data sources and advanced techniques, such as time-series forecasting and XGBoost, to further improve chili price prediction models. These insights can help optimize supply chain management, price forecasting, and decision-making for producers, traders, and policymakers.
Evaluating Service Quality Metrics with AdaBoost Classifier at Restaurant X Batubulan, Kadek Suarjuna; Pratama, I Putu Adi; Naswin, Ahmad
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.234

Abstract

This paper explores the use of the AdaBoost classifier to evaluate service quality metrics in the restaurant industry, specifically at Restaurant X. The study focuses on how machine learning, particularly ensemble learning algorithms, can improve the understanding of customer satisfaction by analyzing various service attributes, such as food quality, staff behavior, wait times, and ambiance. By applying AdaBoost, the model combines multiple weak classifiers to create a stronger, more accurate prediction model that identifies key factors influencing customer experience. The research highlights the importance of real-time data and customer feedback in refining service quality metrics and suggests that incorporating sentiment analysis and other dynamic data sources can provide a more comprehensive view of customer satisfaction. The findings suggest that using machine learning algorithms, like AdaBoost, can enhance operational decision-making, improve customer service, and contribute to overall business success. Additionally, the study proposes the continuous updating of the model to reflect changing customer preferences and trends in the competitive food service industry. This approach can lead to better service, customer retention, and a strategic advantage for restaurants seeking to meet the evolving demands of the market.
Predicting USD to IDR Exchange Rates with Decision Trees B, Muslimin; Karim, Syafei; Nurhuda, Asep
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.235

Abstract

Predicting currency exchange rates is a complex challenge due to the numerous factors influencing market fluctuations. This study explores the application of decision trees to predict the USD to IDR exchange rate, leveraging historical data and key economic indicators. Decision trees, known for their ability to model non-linear relationships, offer an interpretable approach to understanding the factors driving exchange rate movements. The study demonstrates that decision trees can successfully capture the patterns in the data, providing a foundation for accurate predictions. However, the volatility and unpredictability of exchange rates, driven by geopolitical events, market sentiment, and macroeconomic shifts, highlight the limitations of the model. While decision trees provide a valuable starting point, the research suggests that combining them with advanced methods, such as ensemble techniques (random forests or gradient boosting) or time-series models (ARIMA or LSTM), could improve forecasting accuracy. Incorporating a wider range of features, including macroeconomic indicators and market sentiment analysis, further enhances the model's robustness. The findings underscore the need for hybrid approaches that combine the strengths of multiple models to better capture the dynamic and complex nature of financial markets. This research contributes to the broader understanding of exchange rate prediction and offers practical insights for businesses and financial institutions seeking to make informed decisions.

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