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Henny Yulianti
Prodi Informatika, Universitas Siber Asia
Computer Science & IT

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Segmentasi Anggota Koperasi Berdasarkan Extend-RFM (Recency, Frequency, Monetary) Menggunakan Algoritma K-Means Henny Yulianti
MULTINETICS Vol. 12 No. 1 (2026): MULTINETICS Mei (2026)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v12i1.8101

Abstract

Cooperatives are economic institutions that play a vital role in Indonesia’s economy, with their number continuing to grow in line with the increasing public interest. However, cooperatives face challenges in maintaining member loyalty due to limited monitoring of transaction behaviors and the rising competition among cooperatives. This study aims to cluster cooperative members based on transaction patterns using the extended-RFM (Recency, Frequency, Monetary) model with additional variables of Number of Items and Total Profit, applying the K-Means algorithm. The optimal number of clusters was determined using the Elbow method and Silhouette Score, while cluster quality was evaluated through the Davies-Bouldin Index (DBI). The results show that the best clustering consists of four clusters, with a Silhouette Score of 0.395 and a DBI of 0.885. The Analytical Hierarchy Process (AHP) indicates that Total Profit has the highest weight, whereas Number of Items has the lowest. The clustering categorizes members into Platinum, Gold, Silver, and Bronze segments, each with different contribution levels. These findings are expected to serve as a reference for cooperatives in formulating more effective marketing strategies and enhancing member loyalty.
Prediksi Kebutuhan Beras Dengan Metode Neural Network di Pulau Sumatera Indonesia Henny Yulianti
MULTINETICS Vol. 11 No. 02 (2025): MULTINETICS Nopember (2025)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v11i02.7477

Abstract

Rice is a strategic food commodity that provides more than 21% of global human caloric needs and up to 76% in Southeast Asia, including Indonesia. This study aims to analyze the dynamics of rice production and consumption on the island of Sumatra, predict annual rice demand, identify the dominant factors affecting production, and determine the leading rice-producing provinces in Sumatra. The method employed is a Neural Network integrated with the CRISP-DM research methodology to predict annual rice demand, identify key production factors, and determine the top rice-producing provinces. This study uses a dataset from the Central Bureau of Statistics (BPS) covering the years 1993–2020, consisting of six variables: province, year, production, harvested area, rainfall, humidity, and average temperature. The results show that harvested area is the most dominant factor influencing rice production across all provinces in Sumatra. The provinces with the highest rice production are Lampung, South Sumatra, and West Sumatra. The Neural Network model used has an architecture comprising six input nodes, five hidden layers, and one output layer. Model evaluation using Root Mean Square Error (RMSE) yielded a value of approximately ± 636.267 grams (0.636267 tons), indicating the predicted annual change in rice production per province. These findings are expected to assist the government and stakeholders in formulating strategies to stabilize rice production and distribution in Sumatra, thereby reducing price fluctuations and addressing supply imbalances that impact national food security
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