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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Arsitektur Pendapa Jurnal Info Sains : Informatika dan Sains JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer
Kotama, I Nyoman Darma
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering

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Decision Support System for Tourist Destination Selection in Buleleng Using the Analytical Hierarchy Process (AHP) Pradhana, Anak Agung Surya; Kotama, I Nyoman Darma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 2 (2023): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.256

Abstract

Tourism is a key driver of regional economic development and cultural sustainability, particularly in destinations with diverse natural and cultural assets such as Buleleng, Bali. Yet, selecting the most suitable tourist location is often difficult because it involves various decision factors, including accessibility, attractiveness, available facilities, safety, cost, cleanliness, popularity, and visitor density, which can lead to decisions based on personal bias rather than objective evaluation. To address this challenge, this study develops a Decision Support System (DSS) using the Analytical Hierarchy Process (AHP) to systematically assess and rank tourist destinations in Buleleng based on eight priority criteria. The proposed approach provides a structured weighting mechanism and ensures logical consistency in comparisons, indicated by a Consistency Ratio (CR) of 0.000. The analysis results reveal that Pura Ulun Danu Beratan is the most recommended destination, followed by Lovina Beach and Sekumpul Waterfall, supported by their strong appeal and adequate supporting infrastructure. Future development of this system may involve incorporating real-time visitor data, sentiment analysis from online travel reviews, and GIS-based visualization, as well as deployment in web or mobile platforms to increase usability for travelers and local tourism planners.
Crop Yield Prediction Using Random Forest Based on Soil, Climate, and Agronomic Factors Sugiartawan, Putu; Kotama, I Nyoman Darma; Pradhana, Anak Agung Surya
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 3 (2026): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.282

Abstract

Agricultural yield prediction plays a critical role in ensuring food security and optimizing farming practices. Traditional methods of crop yield estimation often rely on expert knowledge and historical data, which can be limited and inaccurate. Machine learning algorithms, particularly Random Forest, have shown promise in improving the accuracy of crop yield predictions by considering complex interactions between soil, climate, and agronomic factors. This study aims to develop a Random Forest-based model to predict crop yield using a diverse set of agricultural datasets. The model was trained and validated using data from multiple regions, focusing on soil properties, climatic conditions, and farming practices. The results demonstrated that the Random Forest model provided reliable predictions, with performance evaluated using metrics such as MAE, RMSE, and R². However, some discrepancies between actual and predicted values were observed, indicating room for improvement. Future work will focus on integrating real-time data, such as soil moisture and pest infestation, to enhance the model's accuracy. Additionally, exploring advanced machine learning techniques like deep learning could provide better handling of complex patterns in agricultural data. This research contributes to the growing field of agricultural data science and aims to provide a scalable solution for crop yield prediction across various regions.
Co-Authors Aditya, Bayu R Adnyana, Anak Agung Gede Oka Kessawa Aldilla Iradianty Batubulan, Kadek Suarjuna Linggasani, Made Anggita Wahyudi Mahardika, I Gede Indra Prabawa, Made Suryanatha Pradhana, Anak Agung Surya Putra, Ida Bagus Gede Parama PUTU SUGIARTAWAN Wardani, Ni Wayan Wicaksana, Gde Bagus Andhika