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All Journal Jurnal Krisnadana
I Made Dwi Putra Asana
Program Studi Informatika, Institut Bisnis Dan Teknologi Indonesia, Denpasar, Bali, Indonesia
Computer Science & IT Control & Systems Engineering

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Customer Segmentation Using RFM Model and Fuzzy C-Means at PT SNS 21 Bali I Made Dwi Putra Asana; Made Leo Radhitya; Dewa Nyoman Yogantara; I Gede Sudiantara; I Putu Noven Hartawan
Jurnal Krisnadana Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i1.929

Abstract

This study aims to develop an effective customer segmentation model for PT Sukses Nusantara Sakti 21 Bali, a large-scale multi-level marketing distributor with over 60,000 members. The proposed approach integrates the Recency, Frequency, Monetary (RFM) model with the Fuzzy C-Means (FCM) algorithm to analyze one year of sales transaction data. The CRISP-DM framework was adopted to ensure a structured process, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Customer transaction records were preprocessed to compute normalized RFM scores, which were then clustered using FCM to capture overlapping membership patterns and better reflect behavioral diversity. The segmentation results were validated using the Silhouette Coefficient and Davies–Bouldin Index, achieving scores of 0.6005 and 0.5093, respectively, indicating high-quality cluster compactness and separation. Three distinct customer segments were identified, each providing actionable insights for targeted marketing strategies, including retention, engagement, and reactivation programs. The findings confirm that integrating RFM and FCM offers a robust and flexible approach for customer segmentation in large-scale MLM contexts. Future work may involve real-time segmentation and integration with predictive analytics to further enhance marketing decision-making.
Hyperparameter Analysis of an LSTM Model for Product Sales Forecasting I Putu Noven Hartawan; I Gede Sudiantara; I Dewa Putu Gede Wiyata Putra; Ayu Gede Willdahlia; I Made Dwi Putra Asana
Jurnal Krisnadana Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i1.952

Abstract

We study monthly retail demand forecasting with an LSTM using a large real-world transaction history. The model is trained on historical monthly aggregates and evaluated on a two-month held-out horizon (January–February 2022) across 450 items. To avoid conflating optimization with forecasting performance, we report test-set accuracy using RMSE and MAPE, while training/validation losses are used only for model selection via early stopping. We run a controlled sweep over learning rate, batch size, and hidden units and find that the learning rate primarily drives convergence and accuracy, whereas the other two hyperparameters are secondary under our configuration. We also document practical data-preparation choices (outlier handling and chronological splitting) common in retail deployments. The study is framed as a single-customer case to ensure a consistent assortment and complete history; we therefore discuss external validity and reproducibility guidance. These findings help practitioners prioritize hyperparameter-tuning effort and set realistic expectations for short-horizon monthly demand forecasting in operational settings
A Binary Particle Swarm Optimization–Based Association Rule Mining Model for Retail Transactions Ni Kadek Bumi Krismentari; I Made Dwi Putra Asana; I Kayan Herdiana; Ida Bagus Gede Anandita; Nabila Fitri Syarifah
Jurnal Krisnadana Vol 5 No 2 (2026): Jurnal Krisnadana- January 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i2.1064

Abstract

This study aims to optimize the association rule mining process on retail transaction data by applying the Binary Particle Swarm Optimization (BPSO) algorithm. Classical methods such as Apriori and FP-Growth often face efficiency limitations, particularly when dealing with large-scale datasets, due to repeated candidate generation processes and high memory requirements. BPSO is employed as a metaheuristic approach capable of adaptively exploring the search space through binary itemset representation and a fitness function based on support, confidence, and lift values. This research follows the CRISP-DM framework, encompassing the stages of data understanding, data preparation, modeling, and evaluation. Based on retail transaction data from KLM, the BPSO process produced the best particle containing 16 potential itemsets. The calculation of support and confidence resulted in seven item combinations that met the minimum threshold and generated eighteen association rules. Evaluation using the lift ratio showed that all rules have lift values greater than one, indicating strong and meaningful relationships among products. These findings demonstrate that BPSO is effective in discovering relevant association patterns and can support retail decision-making, such as product arrangement, cross-selling strategies, and the development of recommendation systems.
Real-time Data Visualization of Inventory Lending Services using Metabase: A Case Study Ida Bagus Gede Anandita; Ni Putu Anggi Karolina; I Made Dwi Putra Asana
Jurnal Krisnadana Vol 5 No 2 (2026): Jurnal Krisnadana- January 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i2.1079

Abstract

Effective inventory management is critical in higher education but is often constrained by rigid reporting mechanisms. At the Institut Bisnis dan Teknologi Indonesia (INSTIKI), substantial asset volumes are currently presented in static tabular formats, resulting in suboptimal information availability. This impedes the Bureau of General Affairs from extracting rapid strategic insights regarding asset conditions and maintenance. This study addresses these inefficiencies by developing a real-time data visualization system utilizing Metabase as an open-source Business Intelligence (BI) tool. The system integrates directly with operational databases to transform raw data into interactive visual insights without complex ETL processes. The implementation yielded 14 comprehensive dashboard menus, covering metrics such as asset distribution, depreciation analysis, lending trends, and infrastructure quality control. User Acceptance Testing (UAT) indicated a 96% approval rate within the "Strongly Agree" category. These results demonstrate that the system successfully simplifies inventory data complexity, accelerates managerial decision-making, and provides an accurate foundation for budget planning and facility maintenance.
Co-Authors Ayu Gede Willdahlia Dewa Nyoman Yogantara I Dewa Putu Gede Wiyata Putra I Gede Sudiantara I Kayan Herdiana I Putu Noven Hartawan Ida Bagus Gede Anandita Made Leo Radhitya Nabila Fitri Syarifah Ni Kadek Bumi Krismentari Ni Putu Anggi Karolina