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Penerapan Metode Saw dan Topsis pada Pemilihan Lokasi Kuliner di Kota Denpasar Ulfatun Farika Novitasari; Eka N. Kencana; I GN Lanang Wijayakusuma
Konstanta : Jurnal Matematika dan Ilmu Pengetahuan Alam Vol. 2 No. 4 (2024): Desember : Jurnal Matematika dan Ilmu Pengetahuan Alam
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/konstanta.v2i4.4193

Abstract

Bali is a renowned tourist destination that attracts visitors from around the world, particularly for its natural beauty, rich culture, and delicious cuisine. The increasing number of tourists in Bali has driven rapid growth in the culinary industry. In Denpasar City, selecting the right location is a key factor for the success of culinary businesses, as each location has different characteristics and potentials. This study employs the Multiple Attribute Decision Making (MADM) model, combining the Simple Additive Weighting (SAW) and Technique for Orders Preference by Similarity to Ideal Solution (TOPSIS) methods, to determine the optimal location for culinary businesses in Denpasar City. Data were collected through surveys of 154 culinary business owners, considering eight criteria: Accessibility, Visibility, Traffic, Facilities, Expansion, Environment, Competition, and Regulations. The study's findings indicate that both SAW and TOPSIS methods identify high population density areas as the best choice. The SAW and TOPSIS method provides the highest preference value of 0,8815 and 0.7082 respectively, making it the more effective method for recommending optimal culinary locations in Denpasar City.
ANALYSIS OF CONSUMER PREFERENCES IN CONSUMING PROCESSED COFFEE PRODUCTS AT CAFE NECTAR BALI Isabel Divya Georgiana Walewangko; I Komang Gde Sukarsa; I Gusti Ngurah Lanang Wijayakusuma; I Putu Eka Nila Kencana; I Gusti Ayu Made Srinadi; Ratna Sari Widiastuti
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 4 No. 3 (2023)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v4i3.2104

Abstract

Coffee beverages have become a highly sought-after product, particularly in tourist areas that are favoured by both local and foreign tourists. For this reason, there are many business owners who want to expand their business with coffee as the main menu. Cafe Nectar Bali, not far from the tourist attraction Garuda Wisnu Kencana (GWK), is one of the places frequented by both locals and foreign tourists. The purpose of this study is to identify the characteristics consumers often consider when consuming processed coffee products at Cafe Nectar Bali and to understand the preferences of local residents and foreign tourists regarding processed coffee products offered. The research method used is the analysis of local and foreign tourist preferences using conjoint analysis techniques. The findings show that consumers are prioritizing the type of coffee and how it is served. Both locals and foreign tourists value the diversity feature more than the presentation method feature. Local consumers choose the stimulus of latte variant and hot serving methods. On the other hand, foreign tourists chose the stimulus of latte variant and the cold serving method. Coffee; Conjoint Analysis; Consumer Preferences
Comparison of Online Gambling Promotion Detection Performance Using DistilBERT and DeBERTa Models Pratama, Halim Meliana; Wijayakusuma, IGN Lanang; Widiastuti, Ratna Sari
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11293

Abstract

Online gambling promotions on social media have become a serious concern in Indonesia, where perpetrators use ambiguous and disguised language to evade detection. This study compares two transformer-based models, DistilBERT and DeBERTa, in detecting such content within Indonesian YouTube comments. Using a balanced dataset of 6,350 comments, both models were fine-tuned with optimized hyperparameters (learning rate 1e-5, batch size 32, 5 epochs) and evaluated through five-fold cross-validation. Results show that DeBERTa achieves superior performance with 99.84% accuracy and perfect recall, while DistilBERT achieves 99.29% accuracy. Error and linguistic analyses indicate that DeBERTa’s disentangled attention and Byte-Pair Encoding provide better understanding of non-standard and ambiguous language. Despite requiring higher computational cost, DeBERTa is ideal for high-accuracy applications, whereas DistilBERT remains suitable for real-time and resource-limited environments.