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All Journal JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
Mahendra, I Gede Orka
Unknown Affiliation
Computer Science & IT

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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.
Digitalization of Bale Beleq in Pejanggik Village Based on a 360-Degree Virtual Reality Tour Website Sandani, Rezi; Mahendra, I Gede Orka; Widya Dharma, I Gusti Ngurah Adi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 1 (2023): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.263

Abstract

Cultural heritage preservation plays a vital role in maintaining local identity and historical continuity. Bale Beleq, located in Pejanggik Village, is a significant cultural landmark representing the legacy of the Sasak community in Lombok. However, the lack of digital documentation and limited accessibility hinder public engagement and threaten the sustainability of this cultural heritage. Motivated by the need to preserve and promote local traditions through technology, this research develops a digital platform integrating a website and 360° Virtual Reality (VR) tour. The system aims to provide immersive access to cultural information, enabling users to virtually explore Bale Beleq through panoramic visualization, interactive hotspots, and multimedia narration. The system was developed using the Multimedia Development Life Cycle (MDLC) method, encompassing conceptualization, design, material collection, development, testing, and distribution. Functionality testing using the Black Box method confirmed that all features—such as the virtual tour, gallery, historical descriptions, and audio guides—performed effectively according to design specifications. The evaluation showed that over 90% of users rated the system as highly engaging and informative, proving its potential as an effective medium for cultural promotion and education. Future work will focus on expanding multilingual capabilities, optimizing mobile interfaces, and integrating AI-based virtual guides to enhance interactivity and personalized learning experiences.
Co-Authors Ardriani, Ni Nengah Dita Erawati, Kadek Nonik Frasetya, I Gusti Ngurah Hendra Hartama, I Dewa Agung Bayu Mega Kumara, I Dewa Made Putra PUTU SUGIARTAWAN Sandani, Rezi Santiago, Gede Agus Widya Dharma, I Gusti Ngurah Adi