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All Journal JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
Sri Murdhani, I Dewa Ayu
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Computer Science & IT

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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.
Decision Tree Model for Predicting Ethereum Price Movements Based on Trends Sri Murdhani, I Dewa Ayu
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 3 (2023): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.252

Abstract

This research investigates the application of a Decision Tree model for predicting Ethereum price movements using historical trend data. The dataset includes key attributes such as open, high, low, close prices, and trading volume, offering insights into market dynamics. The research emphasizes preprocessing and feature engineering techniques, including normalization and the introduction of derived metrics like moving averages and Relative Strength Index (RSI). Despite the model's simplicity and interpretability, it achieved an accuracy of 49.10%, indicating limited effectiveness in capturing non-linear relationships in volatile cryptocurrency markets. Analysis reveals challenges in distinguishing price trends and handling data imbalances, leading to suboptimal performance. These findings highlight the complexities of financial prediction and underscore the need for advanced machine learning methods. Future work should explore ensemble models, richer datasets incorporating sentiment analysis, and resampling techniques to improve robustness and predictive accuracy. This research contributes to the growing literature on machine learning applications in cryptocurrency analytics.
Hypertension Classification Using HistGradientBoostingClassifier, HealthD, And Model Optimization Sri Murdhani, I Dewa Ayu
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 2 (2023): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.257

Abstract

High blood pressure ranks among the world's most common heart-related conditions, carrying serious dangers like strokes and heart attacks. Even with progress in medical testing, spotting it early is tough because of the intricate mix of daily habits and inherited traits. This study seeks to solve the issue of precise hypertension forecasting using machine learning methods tailored for varied health information. Driven by the rising demand for evidence-based health prevention, the research employs the HistGradientBoostingClassifier on a collection of 1,985 patient profiles with eleven lifestyle and bodily indicators, such as age, body mass index, sleep hours, sodium consumption, and tension levels. The key innovation here is the histogram-based boosting approach, which adeptly manages diverse attributes and curbs excessive fitting via timely halting and adjustment techniques. Assessment findings show the model reaches 97% accuracy, maintaining even performance in precision, recall, and F1-score for both hypertensive and non-hypertensive groups. These findings underscore the model's reliability and suitability for inclusion in prompt alert tools for hypertension danger assessment. Upcoming efforts will investigate model clarity through SHAP analysis and pit boosting classifiers against neural network methods to boost understanding and adaptability in practical medical settings.
Hypertension Risk Prediction Using GRU-Based Deep Learning Optimized with Stochastic Gradient Descent Sri Murdhani, I Dewa Ayu; Randhika Kerlania, I Gusti Ayu Agung
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 1 (2023): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.264

Abstract

Hypertension stands out as a highly common heart disease across the globe, where spotting risks early is vital to curb its prolonged effects. Still, standard check-up approaches usually hinge on unchanging health stats that overlook habit-based risk trends entirely. This gap complicates building precise alert systems for folks with different routines and body profiles. Fueled by the push for a more flexible and trend-focused strategy, the study delves into applying a Gated Recurrent Unit (GRU)-driven neural network to predict hypertension threats using lifestyle and past health data. The model blends sequential trend analysis with two GRU layers, dropout for stability, and L2 limits, tuned via Stochastic Gradient Descent (SGD) with momentum and Nesterov boosts. It lets the network uncover intricate links between factors such as age, salt consumption, stress, BMI, sleep time, family background, and treatment history. Trials on 1,985 patient records reveal solid prediction skills, with top classification rates and well-defined categories in the confusion matrix. The training and validation plots also prove smooth learning without major overfit. Next steps cover enlarging the data with continuous health metrics, incorporating attention tools for clearer insights, and pitting it against cutting-edge optimizers like AdamW and Ranger to enhance broader applicability.
Co-Authors Muslimin B, Muslimin Randhika Kerlania, I Gusti Ayu Agung