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All Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) IJOEM: Indonesian Journal of Elearning and Multimedia Jurnal Krisnadana
Putu Sugiartawan
Institut Bisnis dan Teknologi Indonesia
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Machine Learning Forecasting Techniques for Analyzing Tourist Arrivals in Bali Putu Sugiartawan; Ni Wayan Wardani
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.
DenseNet121 and Transfer Learning for Lung Disease Classification from Chest X-Ray Images Putu Sugiartawan; Ni Wayan Wardani
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 2 (2025): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.266

Abstract

Lung-related disorders, including pneumonia, are still among the primary causes of death and illness worldwide, particularly in areas where medical imaging facilities and trained radiologists are scarce. The manual assessment of chest X-ray (CXR) images demands significant time and is prone to subjective interpretation, limiting its scalability for mass screening and early disease identification. To overcome these challenges, this study introduces an automated classification approach utilizing the DenseNet121 convolutional neural network through transfer learning for the detection of lung diseases from CXR scans. The pretrained ImageNet weights were adopted to capture hierarchical visual features efficiently, while overfitting was mitigated using dropout and batch normalization layers. The dataset employed consisted of 1,880 training images and 235 testing images, equally distributed between Normal and Viral Pneumonia categories. Experimental evaluation revealed an overall classification accuracy of 97%, alongside precision, recall, and F1-score metrics of 0.97 each, indicating reliable and balanced model performance. These outcomes suggest that DenseNet121 offers a highly effective foundation for computer-aided diagnostic systems capable of differentiating between healthy and infected lungs with high precision. The proposed framework provides a scalable diagnostic tool suitable for healthcare environments with limited radiological expertise. Future improvements will include expanding toward multi-class disease classification, incorporating explainable artificial intelligence (XAI) techniques to enhance interpretability, and validating the system on larger, more diverse clinical datasets.
Optimizing RAG-Based Academic Chatbot Performance Using Prompt Engineering Adi Surya Artayasa; Aniek Suryanti Kusuma; Putu Sugiartawan
IJOEM: Indonesian Journal of E-learning and Multimedia Vol. 5 No. 3 (2026): Forthcoming Issue
Publisher : CV. Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijoem.v5i3.710

Abstract

Background: The increasing use of artificial intelligence in higher education has encouraged institutions to develop academic chatbots that provide faster access to official information. However, Retrieval-Augmented Generation (RAG)-based chatbots still require optimization to ensure accurate, relevant, and context-grounded responses.Aims: This study aims to optimize the performance of a RAG-based academic chatbot by applying zero-shot and few-shot prompt engineering strategies.Methods: A comparative experiment was conducted using 45 in-context academic questions for RAGAS-based quantitative evaluation, while 5 out-of-context questions were used as a qualitative robustness check. The system was developed using Python, LangChain, FAISS, OpenAI, and Streamlit, and evaluated using the RAGAS metrics: faithfulness, answer relevancy, context precision, and context recall.Results: The baseline system achieved an average RAGAS score of 0.8421. After prompt engineering was applied, zero-shot prompting achieved 0.8697, while few-shot prompting achieved 0.8565.Conclusion: Zero-shot prompting produced the best overall performance and showed the strongest improvement in faithfulness and context recall. These findings indicate that clear prompt instructions can improve the reliability of RAG-based academic chatbot responses for academic information services.
Co-Authors Adi Surya Artayasa Agus Qomaruddin Munir Aniek Suryanti Kusuma Christina Purnama Yanti I Dewa Ayu Sri Murdhani I Gede Iwan Sudipa I Gusti Made Ngurah Desnanjaya I Kadek Adiana Putra I Kadek Agus Dwipayana I Made Agus Sudiartha I Nyoman Darma Kotama I Wayan Sudiarsa Ida Bagus Ketut Sukanegara Made Marthana Yusa Ni Made Maharianingsih Ni Wayan Wardani Putu Wirayudi Aditama Rizkita Ayu Mutiarani Samuel Welson Yusril Eka Saputra