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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 7 No 4 (2025): June" : 5 Documents clear
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

Abstract

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.
Time Series Analysis of Tourist Arrivals to Bali Using Data Kusuma, Aniek Suryanti; Batubulan, Kadek Suarjuna
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.216

Abstract

This research performs a time series analysis on the number of tourist arrivals to Bali, using historical data to identify patterns, trends, and potential forecasting models. The tourism sector is crucial to Bali's economy, and understanding visitor trends can assist in planning and resource allocation. Data from 2010 to 2023 is analyzed, focusing on monthly arrival statistics sourced from government tourism departments. Several time series methods are employed, including seasonal decomposition, autocorrelation, and ARIMA (AutoRegressive Integrated Moving Average) modeling. The analysis reveals distinct seasonal patterns, with peaks during holiday periods and off-peak lulls. A significant impact of global events, such as the COVID-19 pandemic, is observed, causing sharp declines in tourist arrivals. By fitting ARIMA models, we forecast future trends in tourist numbers, providing insights into the potential recovery trajectory of Bali's tourism industry post-pandemic. The research concludes with recommendations for stakeholders, including government agencies and businesses, on how to prepare for future fluctuations in tourist arrivals and capitalize on seasonal trends. Understanding these patterns is essential for fostering sustainable growth and minimizing economic disruptions within the tourism sector.
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

Abstract

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.
Binary Classification of Exchange Rate Trends Using Logistic Regression Wardani, Ni Wayan; Pradhana, Anak Agung Surya; Kotama, I Nyoman Darma
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.218

Abstract

This study explores the use of logistic regression for binary classification of exchange rate trends, focusing on predicting upward and downward currency movements. Logistic regression, valued for its simplicity and interpretability, models the relationship between historical exchange rate data and macroeconomic indicators like interest rates, inflation, GDP growth, and trade balances. The methodology involves data collection, preprocessing, feature engineering, and model evaluation. Historical data is processed to address missing values, outliers, and noise, ensuring a robust dataset. Feature selection techniques, including mutual information scores and principal component analysis (PCA), identify key predictors, while L1 and L2 regularization enhance generalization. The model, implemented using Python's scikit-learn library, is optimized through grid search for hyperparameter tuning. Performance metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, indicate strong predictive capability, achieving 99% accuracy in forecasting upward and downward trends. Logistic regression's interpretability aids decision-making, making it a valuable tool for financial forecasting. However, the study notes limitations, such as challenges posed by market volatility and geopolitical factors. Future research suggests incorporating sentiment analysis from financial news and social media, and exploring hybrid models combining logistic regression with ensemble methods or deep learning to improve performance under real-world conditions.
Classification of Moringa Leaf Quality Using Vision Transformer (ViT) Sugiartawan, Putu; Murdhani, I Dewa Ayu Sri; Febyanti, Putu Ayu; Wibawa, Gusti Putu Sutrisna
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.219

Abstract

Moringa (Moringa oleifera) leaves are widely recognized for their nutritional and medicinal value, making quality assessment crucial in ensuring their market and processing standards. Traditional manual classification of leaf quality is subjective, time-consuming, and prone to inconsistency. This study aims to develop an automated classification system for Moringa leaf quality using a Vision Transformer (ViT) model, a deep learning architecture that leverages self-attention mechanisms for image understanding. The dataset consists of six leaf quality categories (A–F), representing various conditions of color, texture, and defect severity. The ViT model was trained and evaluated using labeled image datasets with standard preprocessing and augmentation techniques to improve robustness. Experimental results show an overall accuracy of 56%, with class-specific performance indicating that the model achieved the highest recall for class D (1.00) and the highest precision for class F (0.74). Despite moderate performance, the results demonstrate the potential of ViT for complex agricultural image classification tasks, highlighting its capability to capture visual patterns in small. Future improvements may include larger datasets, fine-tuning with domain-specific pretraining, and hybrid transformer–CNN architectures to enhance model generalization and accuracy.

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