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All Journal Jurnal Aplikasi dan Inovasi Iptek (JASINTEK) TIERS Information Technology Journal Journal of Economics and Public Health
I Gede Karang Komala Putra
Universitas Bali Internasional
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Public Health Other

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Journal : TIERS Information Technology Journal

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Application of The K-Means Clustering Method To Search For Potential Tourists of Bendesa Hotel I Gede Karang Komala Putra; I Gede Wahyu Surya Dharma
TIERS Information Technology Journal Vol. 4 No. 1 (2023)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v4i1.4297

Abstract

Hotels play a significant role in the growth of global tourism. With intense competition in the hotel industry, hotels are shifting their focus from solely providing superior services to identifying potential tourists. In a previous study, the J48 algorithm was employed to extract hotel transaction patterns, achieving an accuracy level of 71.6418% by considering gender and age characteristics[1]. In a separate study, foreign guest ratings by province were classified into three clusters. The study concluded that nearly 90% of provinces in Indonesia exhibit low levels of tourism, supported by the analysis of the number of tourists staying, as reported by the statistical center[2]. To identify potential tourists who can bring benefits to the hotel, hotel managers can utilize the k-means algorithm. In this study, a data mining process was conducted using data collected from tourists who stayed at the Bendesa Hotel. The process began with tourist segmentation using the K-means algorithm divided into clusters. Subsequently, the accuracy of the obtained data was calculated. This research employed room class as a reference value to discover tourist characteristics at the Bendesa Hotel. The results of applying the K-means model with 4 clusters indicated that the accuracy level for identifying potential tourists reached 84.4%.
Java and Bali Shoreline Change Detection Based on Structural Similarity Index Measurement of Multispectral Images I Gede Wahyu Surya Dharma; I Gede Karang Komala Putra
TIERS Information Technology Journal Vol. 4 No. 2 (2023)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v4i2.4468

Abstract

The abstract effectively delineates the pertinent issues addressed in the research, presenting a clear exposition of the challenges associated with coastline monitoring in Indonesia. The methodology is well-defined, incorporating the utilization of Landsat images, Structural Similarity Index Measurement (SSIM), and the application of Hidden Markov Random Field for segmentation. Moreover, the influence of Indonesia's equatorial positioning on cloud cover and the subsequent application of morphological operations are appropriately highlighted. However, it is crucial to provide explicit details regarding the research findings. Specifically, the abstract should elucidate the specific outcomes or results obtained from the conducted experiments or analyses. This addition would enhance the clarity and scientific robustness of the abstract, ensuring that it accurately reflects the research objectives and their corresponding achievements. Inclusion of quantitative data or statistical analyses would be particularly valuable in this regard. This would not only bolster the abstract but also furnish a more comprehensive overview of the study's empirical contributions.
Co-Authors A A Ngr Bagus Aristayudha I Gede Wahyu Surya Dharma Khadijah Febriana Rukhmanti Udhayana Ni Kadek Winda Patriani Ni Luh Putu Surya Astitiani Reganata, Gde Palguna Richadinata, Kadek Riyan Putra Sugianta, Kadek Arya Vitalia Fina Carla Rettobjaan Widnyani, Ni Made