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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika TECHNOVATE
Astari, Gusti Ayu Shinta Dwi
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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Master Stockist Customer Segmentation Using RFM Model and Self-Organizing Maps Algorithm Nirwana, Ni Kadek Ayu; Dewi, Ni Putu Wahyuni; Asana, I Made Dwi Putra; Dewi, Ni Wayan Jeri Kusuma; Astari, Gusti Ayu Shinta Dwi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14112

Abstract

Master Stockist PT SNS 21 Bali struggles to identify member performance based on purchasing behavior because the applicable system only records transactions and stock of goods without providing insight into customers. Customer segmentation can be carried out to identify and understand differences in customer purchasing behavior. Therefore, this study aims to determine customer segmentation using the RFM (Recency, Frequency, Monetary) model and the Self-Organizing Maps (SOM) algorithm. Segmentation development uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) approach. The RFM model numerically represents customer behavior through three variables, while the Self-Organizing Maps algorithm groups customers into segments with similar characteristics. In this research, the best SOM parameters are 750 iterations, learning rate 0.5, radius 0.5, and grid size 1x3, resulting in 3 clusters with a Silhouette Score of 0.647608 and a Davies-Bouldin Index of 0.536503. Cluster 1 consists of 226 new customers with low RFM values who need encouragement to be more active. Cluster 2, comprising seven members, has low recency, high frequency, and high monetary values, representing loyal customers who need to be retained. Cluster 3 consists of 239 inactive customers with high recency, low frequency, and low monetary values, requiring a reactivation strategy.
Evaluating Student Investment Interest Amidst Financial Technology Ease Astari, Gusti Ayu Shinta Dwi; Wijaya, Bagus Kusuma
TECHNOVATE: Journal of Information Technology and Strategic Innovation Management Vol. 2 No. 4 (2025): October 2025
Publisher : PT.KARYA GEMAH RIPAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52432/technovate.2.4.2025.163-167

Abstract

The emergence of investment features within Fintech ecosystems has democratized access to capital markets for Generation Z. However, empirical evidence suggests a disconnect between accessibility and actual investment participation. This study aims to analyze the gap between investment interest (intention) and actual investment behavior (action) among university students. Employing a descriptive quantitative method with 30 respondents, the research investigates the determinants of low investment uptake despite high digital fluency. The findings reveal a significant "Intention-Behavior Gap": while 85% of respondents expressed a strong desire to invest, only 23% actively utilize investment features. The primary barriers identified are not capital constraints, but rather low "Risk Literacy" and "Herding Behavior" where students rely on influencers rather than fundamental analysis. The study concludes that technological ease without fundamental financial education triggers "Fear of Missing Out" (FOMO) rather than rational investment decision-makin.
Co-Authors Asana, I Made Dwi Putra Dewi, Ni Putu Wahyuni Dewi, Ni Wayan Jeri Kusuma Nirwana, Ni Kadek Ayu Wijaya, Bagus Kusuma