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K-Nearest Neighbors Approach to Classify Diabetes Risk Categories Santiyuda, Kadek Gemilang
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 2 (2024): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.197

The prevalence of diabetes as a chronic disease poses significant challenges worldwide, necessitating accurate and early detection of risk categories to improve management and prevention strategies. This research evaluates the application of the K-Nearest Neighbors (KNN) algorithm to classify diabetes risk categories using the Pima Indian Diabetes dataset. The study implements rigorous preprocessing steps, including handling missing values, normalization, and feature engineering, to optimize the dataset for KNN’s distance-based calculations. Hyperparameter tuning and the exploration of various distance metrics, such as Euclidean and Manhattan, are conducted to enhance model accuracy. The KNN model achieves a moderate accuracy of 66%, with a precision of 0.52 and a recall of 0.58 for the diabetic class, highlighting its effectiveness in general pattern recognition but limited ability to handle imbalanced datasets. The research identifies glucose levels and BMI as key predictors and emphasizes the importance of balanced datasets and advanced feature selection techniques. Future recommendations include integrating additional clinical features and hybrid models to improve diagnostic accuracy and applicability in clinical settings. This study underscores KNN's potential as a foundational tool in machine learning for medical diagnostics, contributing to the broader effort to enhance healthcare outcomes through data-driven decision-making.

Adaptive Operator and Scaling Factor Selection in Differential Evolution using Parametrized Reinforcement Learning Santiyuda, Kadek Gemilang; Sugiartawan, Putu; Santiago, Gede Agus; Ardriani, Ni Nengah Dita; Kafiyanna, Moch Ilham Nur
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 3 (2025): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.206

Mutation strategy selection along with parameter settings are well known challenges in enhancing the performance of differential evolution (DE). In this paper, we propose to solve these problems as a parametrized action Markov decision process. A multi-pass deep Q-network (MP-DQN) is used as the reinforcement learning method in the parametrized action space. The architecture of MP-DQN comprises an actor network and a Q-network, both trained offline. The networks’ weights are trained based on the samples of states, actions and rewards collected on every DE iterations. We use 99 features to describe a state of DE and experiment on 4 reward definitions. A benchmark study is carried out with functions from CEC2005 to compare the performance of the proposed method to baseline DE methods without any parameter control, with random scaling factor, and to other DEs with adaptive operator selection methods, as well as to the two winners of CEC2005. The results show that DE with MP-DQN parameter control performs better than the baseline DE methods and obtains competitive results compared to the other methods.

Comparison of ResNet CNN and Optimized Vision Transformer Model for Classification of Dried Moringa Leaf Quality Santiyuda, Kadek Gemilang; Febyanti, Putu Ayu; Wibawa, Gusti Putu Sutrisna; Welson, Samuel; Sutrisna, I Made Adi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 1 (2025): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.213

The quality classification of dried Moringa leaves is an essential task in the agricultural and food processing industries due to its direct impact on product value and consumer acceptance. This study aims to compare the performance of a Convolutional Neural Network (CNN) based on ResNet architecture with an optimized Vision Transformer (ViT) model for automated classification of dried Moringa leaf quality. The methodology involved preprocessing and normalization of image data, followed by training and evaluation of both models under identical experimental settings. The ResNet CNN achieved an overall accuracy of 68%, showing strong performance in certain classes such as “A” (precision 0.78, recall 0.90) and “F” (precision 0.80, recall 1.00), but poor recognition of class “D.” Conversely, the optimized Vision Transformer model attained an accuracy of 60%, demonstrating robust classification for classes “C” (f1-score 0.77) and “D” (f1-score 0.79), though it struggled with class “E.” The findings indicate that while ResNet CNN yields higher overall accuracy, the Vision Transformer shows potential in handling complex visual variations with optimization. This study contributes to the development of AI-based agricultural quality assessment systems by providing comparative insights into deep learning architectures for image-based classification.

Comparison of ResNet CNN and Optimized Vision Transformer Model for Classification of Dried Moringa Leaf Quality Santiyuda, Kadek Gemilang; Febyanti, Putu Ayu; Wibawa, Gusti Putu Sutrisna; Welson, Samuel; Sutrisna, I Made Adi
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.215

The quality classification of dried Moringa leaves is an essential task in the agricultural and food processing industries due to its direct impact on product value and consumer acceptance. This study aims to compare the performance of a Convolutional Neural Network (CNN) based on ResNet architecture with an optimized Vision Transformer (ViT) model for automated classification of dried Moringa leaf quality. The methodology involved preprocessing and normalization of image data, followed by training and evaluation of both models under identical experimental settings. The ResNet CNN achieved an overall accuracy of 68%, showing strong performance in certain classes such as “A” (precision 0.78, recall 0.90) and “F” (precision 0.80, recall 1.00), but poor recognition of class “D.” Conversely, the optimized Vision Transformer model attained an accuracy of 60%, demonstrating robust classification for classes “C” (f1-score 0.77) and “D” (f1-score 0.79), though it struggled with class “E.” The findings indicate that while ResNet CNN yields higher overall accuracy, the Vision Transformer shows potential in handling complex visual variations with optimization. This study contributes to the development of AI-based agricultural quality assessment systems by providing comparative insights into deep learning architectures for image-based classification.

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

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.

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

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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