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All Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Penelitian dan Pengembangan Sains dan Humaniora (JPPSH) JURNAL MANAJEMEN AGRIBISNIS Sinkron : Jurnal dan Penelitian Teknik Informatika SINTECH (Science and Information Technology) Journal Jurnal Ilmiah Pendidikan dan Pembelajaran JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
PUTU SUGIARTAWAN
Institut Bisnis Dan Teknologi Indonesia (INSTIKI)
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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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.
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.
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.
Development and Validation of a Virtual Reality Circumcision Training Simulator: Simulator Sickness, User Experience, and Clinical Performance in Bali, Indonesia Sindu, I Gede Partha; Kertiasih, Ni Ketut; Dinata, I Gede Surya; Sugiartawan, Putu
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111664

Abstract

Virtual Reality (VR) is increasingly integrated into medical education, yet its application in Indonesia remains limited. This study developed and validated a VR-based circumcision simulator to evaluate simulator sickness, user experience, and clinical performance. A mixed-methods, repeated-measures design was conducted with 74 participants (25 Novices, 24 Intermediates, 25 Experts). Participants engaged in three simulation modes (Autonomous, Guided, Haptic). Instruments included SSQ, FMS, VRNQ, UEQ-S, Checklist, and OSATS. Analyses employed repeated-measures ANOVA, nonparametric tests, and Spearman correlations. Simulator sickness was highest in Autonomous Mode. User experience scores improved with expertise, showing positive correlations with performance and negative correlations with sickness. Experts consistently outperformed other groups, and skill improvements were retained for up to one month. The VR circumcision simulator demonstrated strong construct validity and educational impact. Instructional modes effectively reduced sickness, while haptic integration enhanced spatial orientation. Future studies should incorporate physiological measures and assess real-world skill transfer.
Enhancing Price Classification of Chili Using Gradient Boosting Machines Sugiartawan, Putu; Wardani, Ni Wayan
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 3 (2024): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.233

Abstract

This study explores the application of Gradient Boosting Machines (GBM) to enhance the classification and prediction of chili prices. The research uses a comprehensive dataset collected from various sources, including local markets, online platforms, and agricultural databases, covering multiple attributes such as chili type, region, harvest season, weather conditions, and demand-supply dynamics. The GBM model outperforms traditional machine learning algorithms, achieving an accuracy of 87%, with a high area under the ROC curve (AUC) of 0.91. Feature importance analysis indicates that harvest season and region are the most significant factors influencing price variations. The findings suggest that the GBM model provides reliable price predictions and insights into price-driving factors, offering valuable tools for stakeholders in the agricultural market. The study emphasizes the need for broader data sources and advanced techniques, such as time-series forecasting and XGBoost, to further improve chili price prediction models. These insights can help optimize supply chain management, price forecasting, and decision-making for producers, traders, and policymakers.
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.
Machine Learning Forecasting Techniques for Analyzing Tourist Arrivals in Bali Sugiartawan, Putu; Wardani, Ni Wayan
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 1 (2024): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.243

Abstract

This study investigates the application of machine learning (ML) techniques for forecasting tourist arrivals in Bali, leveraging a dataset spanning from 1982 to 2024. The Random Forest model, along with Linear Regression and Decision Tree, was evaluated for its ability to handle the complexities of tourism data, characterized by seasonality and nonlinear patterns. Among the models tested, Random Forest achieved the best performance, with the lowest Mean Squared Error (MSE) and Mean Absolute Error (MAE), demonstrating its robustness in capturing both short-term fluctuations and long-term trends. The findings highlight the potential of ML techniques to improve forecasting accuracy compared to traditional methods, especially in managing seasonal variations and external disruptions like the COVID-19 pandemic. However, limitations in predicting unprecedented events underscore the need for integrating external variables, such as economic indicators and travel restrictions. Future research should focus on hybrid models, scenario-based forecasting, and real-time data integration to enhance adaptability and predictive accuracy. These advancements aim to support policymakers and stakeholders in optimizing resource allocation, designing marketing strategies, and fostering sustainable tourism development in Bali.
GDSS for Selecting Culinary Tourism in Bali Using Profile Matching and Borda Sugiartawan, Putu; Wardani, Ni Wayan
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 4 (2023): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.246

Abstract

This research proposes a method for determining the best culinary tourism destinations in Bali. Currently, being able to determine the best and potential destinations is a problem for people who want to travel tourism in Bali, both domestic and foreign people. Factors that have a dominant influence in determining potential destinations that still cannot be determined with certainty. This will greatly affect the results of decisions that will be taken by the community in determining the destination to be chosen. For this reason, it is very important to create a model to determine the best destination that can be chosen by the community as a decision support system in making decisions. In this journal Profile Matching has been used to determine the best destination as a category that can be obtained from the rating shown on the selected destination.
Co-Authors Adam Setiawan, Adam Adriani, Ni Nengah Dita Agung Mahadewa, I Putu Agus Aan Jiwa Permana Aniek Suryanti Kusuma, Aniek Suryanti annafi franz, annafi Ardriani, Ni Nengah Dita ariani, alda Arimawarni, Rafika Aryawan, I Made Gitra Batubulan, Kadek Suarjuna Cintya Dewi, Ni Putu Senantri Darma Wandika, I Made Pranadata Desak Made Dwi Utami Putra Dewa, Hari Putra Maha Dewi, Ni Made Gusnia Didit Suprihanto, Didit Dinata, I Gede Surya Dirgayusari, Ayu Manik Erawati, Kadek Nonik Febyanti, Putu Ayu Frasetya, I Gusti Ngurah Hendra Gebo, Alexander Hartama, I Dewa Agung Bayu Mega I Dewa Made Krishna Muku I Gede Adnyana I Gede Andika I Gede Iwan Sudipa I Gede Made Yudi Antara I Gede Totok Suryawan I Gusti Made Ngurah Desnanjaya I Komang Arya Ganda Wiguna I MADE DEDY SETIAWAN . I Made Yudiana I Nyoman Agus Suarya Putra I Nyoman Buda Hartawan I Wayan Dharma Suryawan I Wayan Ramantha I WAYAN SUDIARSA Indawan, I Gusti Agung Indra Pratistha Jumariana, I Putu Candra Junaidi, Muh. Lutfi Kafiyanna, Moch Ilham Nur KETUT BUDI SUSRUSA Kotama, I Nyoman Darma Kumara, I Dewa Made Putra Kusuma, I Made Wijaya Maharianingsih, Ni Made Mahendra, I Gede Orka Mauko, Arfan Murdhani, I Dewa Ayu Sri Murpratiwi, Santi Ika Muslimin B, Muslimin Muslimin Muslimin Negara, I Gede Sunia Ni Ketut Kertiasih Novitadewi, Ni Made Ary Ntihung, Maria Ephifania Ntihung Nurul Hidayat Paholo Iman Prakoso Palus, Petrus Pande, Ni Kadek Nita Noviani Pradhana, Anak Agung Surya Prakoso, Paholo Iman Prastika, I Kadek Aris Putra, I Wayan Kintara Anggara Putra, Putu Gede Weda Pramana Rachmat Wahid Saleh Insani radho, alpolinaris edius Riska, Putu Rizky, Muhammad Alfa Rowa, Heruzulkifli Rustina, I Dewa Ketut Rai Ryan Pratama Putra Ryan Pratama Putra, Ryan Pratama Ryanta, Made Santiago, Gede Agus Santiyuda, Kadek Gemilang Saputra, Komang Yodi Andira Sariayu, Vilomena Satya, I Putu Adnya Sindu, I Gede Partha Suardana, I Made Eka Supandi, Endang Trisnayanti, Ni Made Ratih Wadi, Faska Aris Y K Wahyuni Eka Sari Wardani, Ni Wayan Wibawa, Gusti Putu Sutrisna Widya Dharma, I Gusti Ngurah Adi Willdahlia, Ayu Gede willdalia, ayu Wiratama, Ichsan wirayanti, Ni Putu Eka Wisnawa, Komang Surya Wiwahana Prasetya, I Made Irfan Yudara, I Gede Yudiana, I Made Yuri Prima Fittryani, Yuri Prima