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All Journal Jurnal Mantik Vertex
Luky Fabrianto
Universitas Nusa Mandiri, Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Languange, Linguistic, Communication & Media

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Inventory management for essential oil UMKM: enhancing business performance with data mining Luky Fabrianto; Johan Hendri Prasetyo; Novianti Madhona Faizah; Susi Solichatun
Jurnal Mantik Vol. 7 No. 2 (2023): Agustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i2.3909

Abstract

Micro, small, and medium enterprises (UMKM) play a critical role in driving economic growth and employment opportunities in Indonesia. PT Sukacita Kokoh Bersama (SKB), a company specializing in essential oil sales, exemplifies the dynamic nature of UMKM by embracing technological advancements to thrive in the competitive market. To address the challenge of optimizing inventory management, SKB implemented data mining techniques, specifically the Apriori algorithm, to uncover hidden relationships among different oil types and gain insights into consumer preferences and purchasing behavior. The study aimed to identify relevant sales patterns and predict demand for each oil type. With a threshold of 30%, several rules were generated, including "If customers purchase Lavender Oil and Lemon Oil together, there is a 53% confidence that they will also purchase Peppermint Oil". This research showcases the importance of data mining in enhancing inventory management and decision-making processes for UMKM like SKB.
Strategic insights from clustering analysis of essential oil sales in UMKM: A comprehensive study on product types, sizes, couriers, and distribution across Indonesian Provinces Novianti Madhona Faizah; Desyi Erawati; Shirlyani Shirlyani; Luky Fabrianto; Tiwuk Wahyuli Prihandayani
Jurnal Mantik Vol. 7 No. 4 (2024): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i4.4694

Abstract

This study explores the sales transactions of a Micro, Small, and Medium Enterprise (UMKM) that sells over 40 types of essential oils, totaling 2305 items sold in 2023. The products, packaged in small bottles (10-50 ml), were distributed to almost every province in Indonesia. The main objective is to cluster the data based on variables such as oil type, bottle size, courier company, and destination province. The elbow method determined an optimal number of clusters (k=4), and the Silhouette Coefficient validated the effectiveness of the clustering (0.7614). To simplify the complex clustering results, Principal Component Analysis (PCA) was used for visualization, providing a clear representation of 5 variables and 4 clusters. This study offers valuable insights for informed decision-making in UMKM's service enhancement and development
Analyzing Consumer Purchasing Behavior in Electrical Supply Stores Using Association Rules Yunas Akbar; Tiwuk Wahyuli Prihandayani; Novianti Madhona Faizah; Luky Fabrianto; Ryan Rakryan
Vertex Vol. 15 No. 2 (2026): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/8ed7jg14

Abstract

This study aims to improve inventory management and to analyze consumer purchasing behavior through data exploration and the application of association rule mining. The dataset used in this research consists of sales transaction records of electrical products collected over a one-year period. Due to the wide variety of items sold, product categorization is conducted to support more effective analysis and interpretation of purchasing patterns. The method applied in this study is association rule mining using the Apriori algorithm. This method is employed to discover relationships and co-occurrence patterns among items in transaction data. The minimum thresholds used in this study are support ≥ 10% and confidence ≥ 30%, ensuring that only significant and reliable association rules are generated. The results of the analysis reveal several important patterns, with the strongest rule identified as: “Lakban, Switch, and Socket → Cable,” which has a confidence value of 46%. This indicates that customers who purchase Lakban, switches, and sockets have a 46% likelihood of also purchasing cables. The findings provide insights into customer purchasing behavior that can be utilized to optimize inventory control, improve product arrangement, and develop effective cross-selling strategies. Furthermore, this study demonstrates that the application of association rule mining can support data-driven decision-making, enhance operational efficiency, and contribute to increased sales performance and customer satisfaction
Co-Authors Desyi Erawati Johan Hendri Prasetyo Novianti Madhona Faizah Novianti Madhona Faizah Novianti Madhona Faizah Ryan Rakryan Shirlyani Shirlyani Susi Solichatun Tiwuk Wahyuli Prihandayani Tiwuk Wahyuli Prihandayani Yunas Akbar