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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 149 Documents
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Forecasting USD to IDR Exchange Rates Using Prophet Time-Series Model B, Muslimin; Afak, Richa Rachmawati; Racmadhani, Budi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 4 (2024): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.237

Abstract

This study evaluates the effectiveness of the Prophet time-series model in forecasting USD to IDR exchange rates using a historical dataset of 2812 daily records, including opening and closing prices, highs, lows, and percentage changes. Data preprocessing steps, such as handling missing values and standardizing numeric fields, were performed to ensure data quality. Prophet, developed by Facebook, was chosen for its capability to model seasonality, irregular patterns, and external regressors, outperforming traditional models like ARIMA. The model's performance was validated using error metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), demonstrating its predictive accuracy. Comparative analysis with ARIMA confirmed Prophet’s superior ability in capturing complex patterns in volatile financial data. The inclusion of external factors such as inflation rates and global economic indicators further improved the forecast accuracy. The results provide valuable insights for policymakers, investors, and financial analysts, supporting more informed decision-making and risk management strategies. This research highlights the importance of proper data preprocessing and advanced forecasting techniques for improving currency prediction accuracy, especially in emerging markets like Indonesia. Future research could explore hybrid models combining Prophet with machine learning techniques for enhanced forecasting capabilities.
K-Nearest Neighbors Algorithm for Analyzing Doge Coin Market Behavior Batubulan, Kadek Suarjuna; Pradhana, Anak Agung Surya; Kotama, I Nyoman Darma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 4 (2024): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.238

Abstract

This study investigates the application of the K-Nearest Neighbors (KNN) algorithm to analyze Dogecoin's market behavior using historical trading data, including daily metrics such as Open, High, Low, Close, and Volume, spanning from November 2017. As a proximity-based machine learning algorithm, KNN effectively captures short-term market patterns, achieving a low Mean Absolute Error (MAE) of 0.0017, demonstrating its capability in identifying general trends during stable periods. However, the model faces challenges in predicting sudden price shifts caused by external factors like social media sentiment and regulatory news, highlighting its limitations in highly volatile cryptocurrency markets. Preprocessing steps, including normalization and outlier handling, improved the algorithm’s performance, yet its scalability and sensitivity to hyperparameters remain issues. Future research directions include integrating external data sources, such as social media sentiment and macroeconomic indicators, and adopting advanced models like Gradient Boosting Machines (GBMs) or Long Short-Term Memory (LSTM) networks to enhance predictive accuracy and adaptability. These improvements aim to provide more robust insights into Dogecoin's market dynamics, aiding traders and financial analysts in navigating the complexities of cryptocurrency markets.
Predicting Wine Quality from Chemical Properties Using XGBoost Application Wardani, Ni Wayan; Sugiartawan, Putu
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 4 (2024): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.239

Abstract

This research applies XGBoost, a gradient boosting machine learning algorithm, to predict wine quality based on physicochemical properties such as acidity, alcohol content, and sulfur dioxide levels. Traditional sensory evaluations of wine, while critical, are subjective, time-consuming, and prone to variability. By utilizing XGBoost, this study aims to offer a scalable, data-driven approach to automate wine quality assessments, addressing the limitations of traditional methods. The model was fine-tuned through hyperparameter optimization, achieving high prediction accuracy and interpretability. Feature importance analysis provided actionable insights for winemakers, highlighting the key chemical attributes influencing quality. Comparative analysis against Random Forest and Support Vector Machines demonstrated XGBoost's superior efficiency and robustness, particularly in handling non-linear relationships and imbalanced datasets. This research not only enhances the automation of wine quality assessment but also provides valuable knowledge to optimize production processes. The findings underscore the transformative potential of machine learning in the food and beverage industry, enabling consistent quality control and informed decision-making for stakeholders.
Random Forest for Precise Predictions of Customer Experience at Restaurant X Santiyuda, Kadek Gemilang
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 4 (2024): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.240

Abstract

This study investigates the application of the Random Forest algorithm to predict customer satisfaction at Restaurant X, leveraging a dataset of 524 entries that include attributes such as service quality, cleanliness, food quality, and overall satisfaction levels. The research methodology comprises data preprocessing, exploratory data analysis, Random Forest model development, and evaluation using performance metrics such as accuracy, precision, recall, and F1-score. The Random Forest model demonstrated an overall accuracy of 72%, with its highest performance observed in the highly satisfied customer category, achieving an F1-score of 0.81. Analysis identified food quality as the most influential factor driving satisfaction, followed by service quality and cleanliness. However, the model encountered challenges in predicting dissatisfied customer categories due to class imbalance within the dataset. To address these issues, techniques such as Synthetic Minority Oversampling Technique (SMOTE) and additional data collection are recommended to improve model performance. This research underscores the potential of machine learning in providing actionable insights for the restaurant industry. Restaurant X can refine its operational strategies, address the root causes of dissatisfaction, and strengthen customer loyalty. This study demonstrates the capability of Random Forest to uncover critical satisfaction factors, enabling restaurants to optimize their service quality and customer experience.
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.
ARIMA Model for Time Series Forecasting of Doge Coin Prices Pratama, I Putu Adi
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.242

Abstract

The volatility and speculative nature of cryptocurrencies present significant challenges for accurate price forecasting. This study evaluates the performance of the AutoRegressive Integrated Moving Average (ARIMA) model in predicting Dogecoin (DOGE) prices based on historical data obtained from reputable cryptocurrency platforms such as Binance, Coinbase, and CoinGecko. The ARIMA(5,1,0) model demonstrated strong performance under stable market conditions, achieving a Mean Squared Error (MSE) of 0.0006656 and a Root Mean Squared Error (RMSE) of 0.0258, effectively capturing linear price trends. However, the model’s limitations in handling high volatility and non-linear dependencies—common characteristics of cryptocurrency markets—were also identified. To address these challenges, the study explores hybrid ARIMA–neural network models that integrate statistical and machine learning approaches, improving predictive accuracy during periods of market instability. The results suggest that while ARIMA provides a solid baseline for time series forecasting, hybrid and sentiment-aware models incorporating social media and blockchain metrics offer more robust and adaptive solutions for dynamic cryptocurrency markets.
Machine Learning Forecasting Techniques for Analyzing Tourist Arrivals in Bali Sugiartawan, Putu; Wardani, Ni Wayan
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.243

Abstract

This study investigates the application of machine learning (ML) techniques for forecasting tourist arrivals in Bali, leveraging a dataset spanning from 1982 to 2024. The Random Forest model, along with Linear Regression and Decision Tree, was evaluated for its ability to handle the complexities of tourism data, characterized by seasonality and nonlinear patterns. Among the models tested, Random Forest achieved the best performance, with the lowest Mean Squared Error (MSE) and Mean Absolute Error (MAE), demonstrating its robustness in capturing both short-term fluctuations and long-term trends. The findings highlight the potential of ML techniques to improve forecasting accuracy compared to traditional methods, especially in managing seasonal variations and external disruptions like the COVID-19 pandemic. However, limitations in predicting unprecedented events underscore the need for integrating external variables, such as economic indicators and travel restrictions. Future research should focus on hybrid models, scenario-based forecasting, and real-time data integration to enhance adaptability and predictive accuracy. These advancements aim to support policymakers and stakeholders in optimizing resource allocation, designing marketing strategies, and fostering sustainable tourism development in Bali.
Predicting Wine Quality Based on Features Using Naive Bayes Classifier Pradhana, Anak Agung Surya; Batubulan, Kadek Suarjuna; Kotama, I Nyoman Darma
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.
Decision Tree Model for Classifying University Students Eligible for UKT Waivers Agastyar Priatdana, Gde Yoga
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.245

Abstract

This paper develops a Decision Tree-based classification model to determine student eligibility for UKT (Single Tuition Fee) waivers using socio-economic factors such as parental income, household type, parental occupation, number of dependents, and vehicle ownership. The goal is to automate the identification of students qualifying for financial aid, improving efficiency and fairness in resource allocation. The model was trained on a dataset containing both categorical and numerical features, with the target variable being binary: "Eligible" (1) or "Not Eligible" (0). The model achieved an overall accuracy of 93.33%, with strong performance for the "Eligible" class, reflected by excellent precision, recall, and F1-score. However, the model performed poorly on the "Not Eligible" class, with low recall and F1-score, highlighting the issue of class imbalance. To address this, techniques like resampling and class weighting are recommended to improve classification of the minority class. Exploring alternative models like Random Forest or XGBoost could also provide more balanced results. This underscores the importance of addressing class imbalance and using evaluation metrics beyond accuracy when developing classification models for imbalanced datasets.
GDSS for Selecting Culinary Tourism in Bali Using Profile Matching and Borda Sugiartawan, Putu; Wardani, Ni Wayan
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 4 (2023): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.246

Abstract

This research proposes a method for determining the best culinary tourism destinations in Bali. Currently, being able to determine the best and potential destinations is a problem for people who want to travel tourism in Bali, both domestic and foreign people. Factors that have a dominant influence in determining potential destinations that still cannot be determined with certainty. This will greatly affect the results of decisions that will be taken by the community in determining the destination to be chosen. For this reason, it is very important to create a model to determine the best destination that can be chosen by the community as a decision support system in making decisions. In this journal Profile Matching has been used to determine the best destination as a category that can be obtained from the rating shown on the selected destination.

Page 13 of 15 | Total Record : 149

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