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Clustering Analysis of Tourism Regions in Bali Province Using Similarity Matrix and Log-Log Regression Methods Candhrika Padma; Nila Armelia Windasari
Jurnal Impresi Indonesia Vol. 5 No. 8 (2026): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v5i8.7990

Abstract

The tourism accommodation sector in Bali Province has grown rapidly; however, its distribution across regencies/cities remains highly uneven, motivating the need for a rigorous clustering method to map accommodation-capacity similarities among regions. This study aims to group the nine regencies/cities of Bali Province based on the similarity of ten types of accommodation and tourism-support facilities (star hotels, non-star hotels, villas, apartments, homestays, camping grounds, restaurants, bars, spas, and water tourism facilities), and to examine their relationship with Gross Regional Domestic Product (GRDP). The method combines log-log regression to test the elasticity of star hotel capacity on GRDP, squared Euclidean distance and maximum distance (dmax) to compute a similarity index between every pair of regencies/cities, and the Kruskal-Wallis test to validate differences in GRDP across zones. The regression results show that the star hotel variable has a very strong and statistically significant effect on GRDP (R² = 84.89%; p = 0.00042). The similarity matrix reveals extreme inequality: Badung Regency shows a similarity value that is essentially zero relative to every other regency/city, whereas Jembrana and Tabanan demonstrate a very high similarity value (0.9547). Based on tertile scores, the regions are classified into three zones: Zone I, representing high capacity (Badung and Denpasar); Zone II, representing medium capacity (Tabanan, Gianyar, and Buleleng); and Zone III, representing low capacity with the highest homogeneity (Jembrana, Klungkung, Bangli, and Karangasem). The Kruskal-Wallis test confirms that the GRDP differences across zones are statistically significant (H = 7.00; df = 2; p = 0.0302). These findings indicate that tourism-equalization policies in Bali should be designed differently for each zone rather than applied uniformly across all regions.
Customer Centric Pricing Strategy for Private Brand Products Using Van Westendorp Price Sensitivity Meter Safira Aulia Putri; Nila Armelia Windasari
Wawasan : Jurnal Ilmu Manajemen, Ekonomi dan Kewirausahaan Vol. 4 No. 4 (2026): Oktober: Wawasan : Jurnal Ilmu Manajemen, Ekonomi dan Kewirausahan
Publisher : Fakultas Teknik Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/wawasan.v4i4.4826

Abstract

This study examines how price sensitivity influences consumer purchase decisions toward private brand food products at Supermarket ABC and how customer-based pricing can improve its competitive position. A quantitative approach was used by collecting data through a structured survey of 377 respondents. The data were processed using the Van Westendorp Price Sensitivity Meter to identify consumer-acceptable price ranges for 21 high-turnover food SKUs through PMC, OPP, IPP, and PME. The findings show that Supermarket ABC private brands products face a strong pricing issue, as 63.9% of consumers have switched to competitors and 56.8% perceive Supermarket ABC private brands prices as higher than competing private brand from competitors. The PSM results further reveal that 12 out of 21 SKUs are priced above the consumer’s maximum acceptable price, indicating that overpricing is a major driver of switching behaviour. This study highlights the importance of consumer-based price sensitivity in shaping effective pricing strategies for private brand products. These findings are framed by Price Sensitivity Theory, Perceived Value Theory, Customer Switching Behaviour Theory, and Reference Group Theory and translates them into customer-centric pricing strategy to identify and correct price misalignment. The strategy classifies SKUs into three actions: base-price correction for severely overpriced SKUs, High-Low promotional pricing for moderately overpriced SKUs, and Everyday Low-Price maintenance for SKUs already within the acceptable consumer price range.