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All Journal Systematics Jurnal Komtekinfo
Sarjo Defit
Universitas Putra Indonesia YPTK Padang
Computer Science & IT Library & Information Science

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Mining Data In Identification Of Consumer Patterns Of Agricultural Machine Sales Using Fp-Growth Algorithm Eka Sofianti; Sarjo Defit; Yuhandri
SYSTEMATICS Vol 2 No 3 (2020): December 2020
Publisher : Universitas Singaperbangsa Karawang

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Abstract

The sales transaction data for agricultural machinery at the Mandiri Jaya Teknik Solok store is a large data set making it difficult to identify consumer purchasing patterns. Large data sets can be processed into useful information. Sales transaction data available at the Mandiri Jaya Teknik Solok store can be processed into useful information to increase sales. This study aims to identify consumer purchasing patterns in order to know which items are often sold and to find out which items need to be stocked more and to increase sales. The data that is processed in this study uses the sales transaction data obtained from the sales invoice of Toko Mandiri Jaya Teknik Solok. Data is in the form of sales data for 13 weeks of 20 items with a minimum support value of 15% and a confidence value of 60%. The method uses one of the data mining techniques associated with the FP-Growth algorithm, where the Fp-Growth algorithm uses the concept of tree development in searching for the types of items that are often purchased (frequency item sets). The tools used are Rapidminer 9.8 so that the purchase patterns of goods are obtained which are used as information to predict the level of frequently sold items. The result of the sales data processing process is the association rule. Association Rule is obtained in the form of a relationship between goods sold together with other goods in a transaction. From this pattern, it can be recommended to the shop owner as information for preparing stock of goods to increase sales results. This research is very suitable to be applied to determine the patterns of consumer spending such as the relationship of each item purchased by consumers, so this research is appropriate for use by stores.
Analysis of Clean Water Consumption Segmentation And Classification Using K-Means Clustering And Random Forest Algorithms Ika Melinia Sapitri Fitriyanti; Sarjo Defit; Rini Sovia
Jurnal KomtekInfo Vol. 13 No. 1 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

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The administrative grouping of PERUMDA Air Minum Kota Padang customers is not yet able to accurately represent actual customer water consumption patterns. This condition makes it difficult for the company to formulate service policies, customer management, and make appropriate data-based decisions. This study aims to analyze and map customer water consumption patterns to produce more representative customer segmentation as a basis for decision making. The research method used is a data mining approach with the application of Principal Component Analysis (PCA) for dimension reduction, K-Means Clustering for customer segmentation, and Random Forest for customer classification, using primary data from the Padang City Water Company's Customer Meter Reading Report with an initial amount of 371 data. The results of the study show that the clustering process successfully formed three customer segments, namely premium customers with high consumption bills, regular customers with moderate and stable consumption, and new customers with low consumption rates. The evaluation of the Random Forest model's performance resulted in an accuracy rate of 68.85% on the training data and 67.69% on the testing data, with an average precision value above 0.84 and an average F1-score value of around 0.68. The consistency of performance between the training data and the testing data shows that the model has fairly good generalization capabilities and does not experience overfitting.
Co-Authors Eka Sofianti Ika Melinia Sapitri Fitriyanti Rini Sovia Yuhandri Yuhandri