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All Journal IJoICT (International Journal on Information and Communication Technology) Jurnal Riset Informatika dan Teknologi Informasi (JRITI)
Almajid, Nafis
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Computer Science & IT

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Regional Mapping Based on Tourism Destinations in West Java: K-Medoid Clustering Analysis Almajid, Nafis; Dina Atika, Prima; Fadhilla Ramdhania, Khairunnisa
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1011

Abstract

The growth of the tourism sector in West Java demands an optimal development strategy. This study aims to cluster regions in West Java based on the characteristics of their tourist destinations using the K-Medoid algorithm. This algorithm was chosen because of its superiority in producing optimal clusters and robustness to outliers. Data on tourist destination characteristics were analyzed using the K-Medoid algorithm and the Elbow method to determine the optimal number of clusters. As a result, three clusters with different characteristics were formed. The first cluster, "Medium potential and achievement", consists of 1 region with unoptimized potential for campsite tourism. The second cluster, "High potential and moderate achievement", consists of 25 regions with a diversity of attractions and a high number of visits. The third cluster, "Medium potential and high achievement", consists of 1 region with popular historical and cultural attractions and high visitation. The model evaluation showed a DBI score of 0.08, indicating good clustering quality. This research is expected to provide insights for the government and related stakeholders to formulate targeted tourism development policies in West Java. The K-Medoid algorithm helps identify certain patterns, providing deeper insights into regional differences in terms of tourism.
Penerapan Decision Tree Regression dalam Memprediksi Harga Rumah di Provinsi Jawa Barat Almajid, Nafis; Ginting, Yanuar; Ramadhan, Alif Izzudin
Jurnal Riset Informatika dan Teknologi Informasi Vol 1 No 3 (2024): April - Juli 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v1i3.64

Abstract

A house plays an important role as a basic necessity, not only as a shelter and place of rest. Additionally, the selling price of a house is greatly influenced by environmental factors such as proximity to shopping centers, office buildings, land size, and others. To gain a more accurate understanding and predict the selling price of houses, this research proposes the application of machine learning methods. This study involves the use of three machine learning algorithms: multiple linear regression, decision tree regression, and linear support vector regression to predict house prices in West Java Province. By utilizing historical data and various relevant features, such as the number of rooms, bathrooms, garages, land area, and width, the machine learning algorithms will conduct complex analyses and provide more accurate price estimations. The expected outcome of this research is to provide valuable insights for stakeholders in the property industry in West Java Province. The adoption of machine learning approaches is anticipated to enhance the ability to predict house prices and provide useful information for buyers, sellers, and other relevant parties in making better decisions in property transactions.
Co-Authors Ginting, Yanuar Khairunnisa Fadhilla Ramdhania Prima Dina Atika Ramadhan, Alif Izzudin