Pengembangan Dashboard Rekapitulasi Sales Order dan Klasifikasi Mitra Rumah Pangan Kita Berbasis RFM, Two-Stage Hybrid, dan SAW pada Perum BULOG

Perum BULOG Bukittinggi Branch Office still monitors Sales Order (SO) transactions, records daily stock allocations, and identifies Rumah Pangan Kita (RPK) partners at risk of churn manually through separate Excel files, which makes recapitulation slow and allocation priorities subjective. This study aims to build a web-based dashboard that integrates SO quantity recapitulation, allocation tracking, and two partner classification models. The dashboard was developed using Python and Streamlit through seven stages: observation, data collection, requirement analysis, design, implementation, testing, and documentation. Model 1 classifies partner activity using Recency and Frequency (RFM), while Model 2 determines allocation priority using a Two-Stage Hybrid approach (RFM and Decision Tree) that is compared with Simple Additive Weighting (SAW). Of 288 partners, 152 (52.8%) were active, 6 (2.1%) were at risk, and 130 (45.1%) were passive or archived. SAW distinguished priority levels more finely than the Two-Stage Hybrid, whereas the Two-Stage Hybrid was simpler and easier to trace. Black box testing of 19 scenarios initially showed 16 valid scenarios (84.2%); the three failures, related to data type validation and partner identification, were fixed by validating input types and using buyer ID as the unique key, and were valid on retesting. This dashboard has the potential to support data-driven decision-making for stock distribution; however, its benefits have not yet been quantified through time-based metrics or user evaluations comparing processes before and after implementation.

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Publication Details

Journal
Saturnus
Published
2026-10-26
DOI
https://doi.org/10.61132/saturnus.v4i4.1867
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

Pengembangan Dashboard Rekapitulasi Sales Order dan Klasifikasi Mitra Rumah Pangan Kita Berbasis RFM, Two-Stage Hybrid, dan SAW pada Perum BULOG

Budy Satria, Kaela Assyura Syadira
Saturnus
Data Mining and Machine Learning Applications
article

Pengembangan Dashboard Rekapitulasi Sales Order dan Klasifikasi Mitra Rumah Pangan Kita Berbasis RFM, Two-Stage Hybrid, dan SAW pada Perum BULOG

Budy Satria, Kaela Assyura Syadira
article en

Abstract

Perum BULOG Bukittinggi Branch Office still monitors Sales Order (SO) transactions, records daily stock allocations, and identifies Rumah Pangan Kita (RPK) partners at risk of churn manually through separate Excel files, which makes recapitulation slow and allocation priorities subjective. This study aims to build a web-based dashboard that integrates SO quantity recapitulation, allocation tracking, and two partner classification models. The dashboard was developed using Python and Streamlit through seven stages: observation, data collection, requirement analysis, design, implementation, testing, and documentation. Model 1 classifies partner activity using Recency and Frequency (RFM), while Model 2 determines allocation priority using a Two-Stage Hybrid approach (RFM and Decision Tree) that is compared with Simple Additive Weighting (SAW). Of 288 partners, 152 (52.8%) were active, 6 (2.1%) were at risk, and 130 (45.1%) were passive or archived. SAW distinguished priority levels more finely than the Two-Stage Hybrid, whereas the Two-Stage Hybrid was simpler and easier to trace. Black box testing of 19 scenarios initially showed 16 valid scenarios (84.2%); the three failures, related to data type validation and partner identification, were fixed by validating input types and using buyer ID as the unique key, and were valid on retesting. This dashboard has the potential to support data-driven decision-making for stock distribution; however, its benefits have not yet been quantified through time-based metrics or user evaluations comparing processes before and after implementation.

SaturnusVol. 4(4)
Andalas University (ID)
Openalex Percentile: Top 6%
Data Mining and Machine Learning Applications
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