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All Journal Jurnal Sewaka Bhakti INSERT: Information System and Emerging Technology Journal
Ni Putu Noviyanti Kusuma
Primakara University
Computer Science & IT Education Engineering Environmental Science Languange, Linguistic, Communication & Media Other

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PENERAPAN BUSINESS MODEL CANVAS DALAM STRATEGI PENGEMBANGAN USAHA WISATA KEBUN BINATANG TASTA ZOO TABANAN Ni Wayan Listia Dewi; Ni Putu Noviyanti Kusuma; Jauzaa Maylia Suhendro
JURNAL SEWAKA BHAKTI Vol 12 No 1 (2026): Sewaka Bhakti
Publisher : UNHI Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32795/8xyf0g27

Abstract

Strategic Development of Tasta Zoo Tabanan through the Application of the Business Model Canvas (BMC). This activity was initiated by the problem of suboptimal business development and digital marketing strategies at Tasta Zoo Tabanan, which potentially hindered its competitiveness in Bali's tourism. The method applied was a descriptive qualitative approach with triangulation techniques (observation, interviews, questionnaires), focused on implementing and validating the Business Model Canvas (BMC) framework to map and redesign the destination's business model. The activity resulted in a holistic BMC Version 2, with a core value proposition centered on conservation-based edutainment (education and entertainment) experiences. The partial implementation of BMC-based strategies, particularly in digital marketing and interactive education programs, proved to increase audience engagement on social media (e.g., a 408% increase in TikTok followers), visitor satisfaction, and operational revenue. The activity concludes that the BMC is an effective strategic tool for creating sustainable competitive advantage for educational tourism destinations, with main recommendations focusing on strengthening human resources, deepening partnerships, and technology integration.
Harnessing Machine Learning for Financial Inclusion: SVM Vs. Logistic Regression in Microfinance Credit Eligibility Classification Ni Putu Noviyanti Kusuma; Ni Wayan Nanik Suaryani Taro Putri
INSERT : Information System and Emerging Technology Journal Vol. 7 No. 1 (2026)
Publisher : Information System Study Program, Faculty of Engineering and Vocational, Undiksha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/insert.v7i1.113782

Abstract

Village Credit Institutions, known locally as Lembaga Perkreditan Desa (LPD), play a fundamental role in Bali's financial inclusion ecosystem by integrating formal credit evaluations with the socio-cultural considerations of indigenous communities. As the volume of credit applications escalates, relying on subjective manual assessments becomes susceptible to inconsistency, potentially triggering non-performing loan risks while simultaneously hindering capital access for potential borrowers. This comparative study empirically evaluates the performance of Support Vector Machine (SVM) and Logistic Regression (LR) algorithms in classifying customer creditworthiness at LPD Sibetan, Karangasem Regency. Utilizing 4,000 historical credit application records from January 2020 to December 2024, both models were extensively optimized using a Grid Search approach with 5-fold cross-validation. The results demonstrate that the SVM model with a linear kernel consistently outperforms Logistic Regression across all evaluation metrics. SVM achieved a classification accuracy of 90.00%, precision of 90.91%, recall (sensitivity) of 96.77%, and an Area Under the Curve (ROC-AUC) score of 98.92%. Conversely, the Logistic Regression model with L1 regularization recorded an accuracy of 86.12% and an ROC-AUC of 97.52%. An anatomy of the confusion matrix reveals that SVM is vastly superior in suppressing both False Positives (financial risk) and False Negatives (opportunity cost). By comparing the robustness of SVM and Logistic Regression, this study contributes to financial inclusion by providing a more accurate credit scoring model for microfinance institutions, ensuring that communities previously deemed high-risk can be reassessed more fairly and gain access to capital.
Co-Authors Jauzaa Maylia Suhendro Ni Wayan Listia Dewi Ni Wayan Nanik Suaryani Taro Putri