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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 5 Documents
 1 
Search results for , issue "Vol 6 No 4 (2024): June" : 5 Documents clear
LightGBM-Based Classification of Customer Feedback in Restaurant X Sri Murdhani, I Dewa Ayu; B, Muslimin
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.236

Abstract

This research aims to classify customer feedback from Restaurant X using the LightGBM model to enhance service quality and customer satisfaction amidst growing industry competition. Customer feedback, collected through surveys and online platforms, is analyzed to uncover patterns and trends related to various aspects of the dining experience. The methodology encompasses data collection, preprocessing, model training, and evaluation. LightGBM, renowned for its efficiency and accuracy with large datasets, serves as the primary tool for building a robust classification model. Analysis reveals that key features such as food quality, service, and cleanliness significantly influence customer satisfaction. The model demonstrates high classification accuracy, providing actionable insights for Restaurant X management. These insights enable targeted strategies for improving specific areas of service, fostering better customer experiences and driving loyalty. The research underscores the importance of leveraging advanced machine learning models like LightGBM for data-driven decision-making in the restaurant industry.
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.

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