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Journal : TIERS Information Technology Journal

Consequences of Misclassification in Data Categorization for Tourism Attraction Recommendation DSS Using ARAS Gede Surya Mahendra; I Gede Hendrayana
TIERS Information Technology Journal Vol. 5 No. 1 (2024)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v5i1.5416

Abstract

This research focuses on optimizing tourism attraction management in Bali using DSS and the ARAS method, emphasizing the importance of accurate data categorization. Bali’s tourism industry, faced significant challenges during the COVID-19 pandemic, highlighting the need for effective management strategies. This study addresses these challenges by utilizing the ARAS method to analyze and rank tourist attractions. The research methodology follows the CRISP-DM model. The study demonstrates that improper use of conversion scales for quantitative data can lead to inaccurate rankings, as seen when comparing converted and non-converted data rankings. Alter01, Alter03, and Alter02 occupy the top three ranks in the non-converted data, while Alter09, Alter06, and Alter15 rank highest in the converted data. These findings highlight the need to use precise numerical values for criteria whenever possible and to reserve conversion scales for qualitative data, to ensure accurate and reliable recommendations. ARAS has a simple and easy-to-understand computational procedure. However, the results from ARAS heavily depend on the weights assigned to the criteria. Inaccurate determination of these weights can lead to outcomes that do not reflect actual preferences. The research concludes that implementing more refined data categorization techniques can enhance tourism management, promoting sustainable growth and more informed decision-making.
YOLOv8-Based Quality Detection of Bali MSMEs Staple Food Dewi, Ni Putu Dita Ariani Sukma; Aryasa, Jiyestha Aji Dharma; Hendrayana, I Gede; Prayoga, I Made Ade; Putri, Sulin Monica
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7144

Abstract

Ensuring the quality of staple foods such as rice, cooking oil, milk, and meat is crucial for consumer safety and health. In Indonesian Micro, Small and Medium Enterprises (MSMEs), quality assessment often depends on subjective and time-consuming visual inspection. This study develops an automatic quality detection system using YOLOv8, applied to food MSMEs in Bali, to detect 14 quality categories across the four commodities based on image data. The methodology includes dataset collection from MSMEs, image annotation, preprocessing, training YOLOv8s and YOLOv8m models, and evaluating performance using mAP50, accuracy, precision, recall, and F1-score. Results show that YOLOv8m achieved a mAP50 of 96.5%, indicating high detection accuracy. The system, implemented as a web-based application, has strong potential to improve efficiency, ensure consistent product quality, and support Sustainable Development Goals (SDGs) 2, 3, 8, and 9.