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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 8 No 1 (2025): September" : 5 Documents clear
Deep Learning Approach for USD to IDR Forecasting with LSTM Ardriani, Ni Nengah Dita; Sugiartawan, Putu; Santiago, Gede Agus; Darma Wandika, I Made Pranadata; Wiwahana Prasetya, I Made Irfan
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.210

Abstract

This Research explores the use of Long Short-Term Memory (LSTM) networks for forecasting the USD to IDR exchange rate, with the goal of improving prediction accuracy in the volatile foreign exchange market. By leveraging historical data, including daily exchange rates and trading volume, the LSTM model captures long-term dependencies and patterns within the time series data. The results show that the LSTM model effectively predicts general trends and medium-term fluctuations, demonstrating its capacity to follow market dynamics. However, the model struggles with extreme volatility and sudden market shifts, particularly during unforeseen geopolitical or economic events. This limitation highlights the need for further enhancement through the incorporation of additional features, such as macroeconomic indicators, sentiment analysis, and real-time news data. Furthermore, the study suggests the potential benefits of combining LSTM with other machine learning techniques to create hybrid models that can better handle short-term fluctuations and extreme events. In conclusion, while LSTM shows promise for exchange rate forecasting, its performance can be improved by refining model parameters, incorporating diverse data sources, and exploring hybrid approaches. This research provides valuable insights for traders, investors, and policymakers seeking to make more informed decisions in the foreign exchange market.
LSTM Neural Network for Predicting Tourist Arrivals to Bali Erawati, Kadek Nonik; Sugiartawan, Putu; Ardriani, Ni Nengah Dita; Hartama, I Dewa Agung Bayu Mega; Frasetya, I Gusti Ngurah Hendra; Mahendra, I Gede Orka
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.211

Abstract

Tourism is a key pillar of Bali’s economy, contributing significantly to employment, cultural preservation, and income generation. Accurate forecasting of tourist arrivals is crucial for sustainable growth and resource optimization. This study applies Long Short-Term Memory (LSTM) neural networks to predict tourist arrivals in Bali, leveraging historical data and external factors such as global economic indicators, flight frequencies, cultural events, and environmental conditions. LSTM’s ability to model complex temporal dependencies and non-linear relationships offers significant advantages over traditional methods like ARIMA, especially in handling seasonal patterns and irregularities. The model was trained on a robust dataset, preprocessed to address missing values, outliers, and variability. Performance evaluation metrics, including RMSE, demonstrate high predictive accuracy during stable periods but highlight limitations in handling anomalies such as the COVID-19 pandemic. To address these challenges, recommendations include integrating additional external variables, employing hybrid models, and conducting scenario-based sensitivity analyses to enhance adaptability and robustness. The results highlight the practical utility of AI-driven forecasting tools in tourism management, providing actionable insights for policymakers and stakeholders to optimize planning, mitigate risks, and support sustainable development. This research contributes to the growing field of AI applications in tourism, promoting resilience and competitiveness in an increasingly dynamic global market.
Using Neural Networks for USD to IDR Exchange Rate Prediction Santiago, Gede Agus; Sugiartawan, Putu; Erawati, Kadek Nonik; Mahendra, I Gede Orka; Kumara, I Dewa Made Putra; Frasetya, I Gusti Ngurah Hendra
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.212

Abstract

Predicting the USD to IDR exchange rate is critical for financial markets, international trade, and economic policy. This research employs neural networks to model the complex and non-linear patterns inherent in time-series data. The methodology involves collecting historical daily exchange rate data, preprocessing to handle missing values, normalizing features, and transforming the data into a format suitable for modeling. The neural network architectures utilized include Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Model evaluation metrics, such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), indicate the neural networks’ effectiveness in capturing general trends with high accuracy, despite challenges during periods of high market volatility. Comparative analysis with traditional methods, such as ARIMA, highlights the superior ability of neural networks to manage non-linear relationships and long-term dependencies. This study provides valuable insights into developing advanced tools for exchange rate prediction, leveraging the power of machine learning. The results demonstrate the potential of neural networks in financial forecasting, with opportunities for improvement through integrating additional external factors and optimizing model architectures.
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

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.
Optimizing Chili Price Prediction Using Machine Learning Classification Antara, I Gede Made Yudi; Sugiartawan, Putu; Ardriani, Ni Nengah Dita; Dewa, Hari Putra Maha; Widya Dharma, I Gusti Ngurah Adi; Satya, I Putu Adnya
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.214

Abstract

Optimizing chili price prediction is critical for agricultural stakeholders, enabling better decision-making in supply chain management, market strategies, and farming practices. This research focuses on leveraging machine learning classification models to improve the accuracy and reliability of chili price predictions. The research addresses the challenges of class imbalance, which often occurs due to the uneven representation of price fluctuations in datasets. Resampling techniques, including oversampling the minority class with Synthetic Minority Oversampling Technique (SMOTE) and undersampling the majority class, were employed to balance the dataset and enhance the model's sensitivity to less frequent price drops. Key predictive features such as weather conditions, market demand, transportation costs, and economic indicators were integrated into the models. Advanced classification algorithms like Random Forests and Gradient Boosted Trees were utilized, demonstrating their effectiveness in handling non-linear relationships and class imbalance. Regularization techniques and k-fold cross-validation were applied to prevent overfitting and ensure robust model performance across different data subsets.The results show significant improvements in precision, recall, and overall model accuracy, making the approach suitable for real-world applications. By optimizing machine learning models, this research provides actionable insights for stakeholders to manage price volatility effectively, supporting sustainable agricultural practices and market stability.

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