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Journal : The Indonesian Journal of Computer Science

Implementasi Association Rule Untuk Rekomendasi Strategi Up-Selling dan Cross-Selling Produk Menggunakan FP-Growth Nabiilah, Nabiilah; M. Afdal; Novita, Rice; Mustakim
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4025

Abstract

BC 4 HNI Pekanbaru is a subsidiary of PT. HNI-HPAI Indonesia offers a diverse range of items for sale. Insufficiently effective promotions, despite high transaction volumes, can result in certain items being less recognized and thus impractical. The purpose of employing the FP-Growth algorithm in data mining is to uncover product association patterns and produce rules for sales tactics using the CRM approach. Implementing CRM strategies that incorporate cross-selling and up-selling techniques can enhance sales. Cross-selling involves offering additional products or services connected to the items purchased, while up-selling involves encouraging customers to buy higher-value goods than initially intended, boosting sales of more expensive items. Among the 20 results obtained from analyzing transaction data from July 2023 to December 2023 using FP-Growth, only the rules with a minimum support value of 5% and a minimum confidence of 70% are considered for cross-selling strategies. Additionally, the rules with a minimum support value of 5% and a minimum confidence of 10% are considered for up-selling.
Implementation of Density-Based Spatial Clustering of Applications with Noise and Fuzzy C – Means for Clustering Car Sales Auliani, Sephia Nazwa; Mustakim; Novita, Rice; Afdal, M
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4135

Abstract

This study compares the performance of two clustering algorithms, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM), in clustering car sales data at PT. XYZ. The dataset, comprising sales transactions from 2020 to 2023, includes information about vehicles, customers, and transactions. Preprocessing methods such as data transformation and normalization were applied to prepare the data. The results indicate that DBSCAN produces clusters with better validity, measured using the Silhouette Score, compared to FCM. Specifically, DBSCAN achieves the highest Silhouette Score of 0.7874 in cluster 2, while FCM reaches a maximum score of 0.3666 in cluster 3. Thus, DBSCAN proves to be more optimal for clustering car sales data at PT. XYZ, highlighting its superior performance in terms of cluster validity.