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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer DECODE: Jurnal Pendidikan Teknologi Informasi Jurnal INFOTEL Scientific Journal of Informatics
Fitriah
Universitas Ahmad Dahlan, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Optimizing Inventory with Frequent Pattern Growth Algorithm for Small and Medium Enterprises Imam Riadi; Herman Herman; Fitriah Fitriah; Suprihatin Suprihatin
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3363

Abstract

The success of a business heavily relies on its ability to compete and adapt to the ever-changing market dynamics, especially in the fiercely competitive retail sector. Amidst intensifying competition, retail business owners must strategically manage product placement and inventory to enhance customer service and meet consumer demand, considering the challenges of finding items. Poor inventory management often results in stock shortages or excess. To address this, adopting suitable inventory management techniques is crucial, including techniques from data mining, such as association rule mining. This research employed the FP-Growth algorithm to identify patterns in product placement and purchases, utilizing a dataset from clothing store sales. Analyzing 140 transactions revealed 24 association rules, comprising rules with 2-itemsets and frequently appearing 3-itemset rules. The highest support value in the final association rules with 2-itemsets was 10% with a confidence level of 56%, and the highest support value in the 3-itemsets was 67% with the same confidence level. Additionally, three rules had a confidence level of 100%. Thus, the association rules generated by the FP-Growth frequent itemset algorithm can serve as valuable decision support for sales of goods in small and medium-sized retail businesses.
Implementation of association rule using apriori algorithm and frequent pattern growth for inventory control Imam Riadi; Herman Herman; Fitriah Fitriah; Suprihatin Suprihatin; Alwas Muis; Muhajir Yunus
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.980

Abstract

Business success is a business that is able to compete and grow keep abreast of developments in the business world. Especially in the retail sector, where competition is getting tighter. Business owners need to pay attention to the layout of goods and stock management to improve service and meet consumer needs because consumers often have difficulty in finding goods. On the other hand, shortages and excess stock often occur due to lack of goods management. Based on these problems, appropriate techniques are needed for the management of goods supply, one of which is to apply techniques found in the branch of science. Data mining is a technique of association rules. This study aims to find patterns of placement and purchase of goods in generating Association Rule using FP-Growth algorithm. The dataset in this study used data on sales of goods in clothing stores. The results of the study of 140 transactions there are 24 association rules consisting of 7 association rules with 2-itemsets and 17 association rules with 3-itemsets that most often appear in transactions. Based on the order of the highest support value, namely CKJ→STX^LK with a support value of 67%, while the highest confidence value, there are 3 association rules that get the same value, namely STX^CKJ→LK, STX^CAK→LK, STX^RI→LK with a value of 100%. Thus, the rules of association produced by the frequent itemset algorithm, FP-growth, can serve as decision support for the sales of goods in small and medium-sized retail businesses
Analisis Data Mining Sistem Inventory Menggunakan Algoritma Apriori: Analysis Data Mining of Inventory System Using Apriori Algorithm Fitriah; Imam Riadi; Herman
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 3 No. 1: MARET 2023
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v3i1.132

Abstract

Dalam manajemen rantai persedian barang (supply chain management) diperlukan kebijakan persediaan barang yang maksimal agar ketersedian barang tetap tersedia dan tidak terlambat dalam restock barang. Hal ini dibutuhkan manajemen persedian barang untuk menentukan cara yang tepat dan mempermudah dalam pengendalian persedian barang tersebut. Salah satu cara yang dilakukan adalah dengan menerapkan teknik yang terdapat pada cabang ilmu Data Mining yaitu teknik aturan asosiasi (Association Rule). Tujuan penelitian ini adalah menganalisis informasi transaksi penjualan barang untuk menghasilkan association rules dari pola kombinasi itemsets yang sesuai agar membantu pemilik dalam melakukan peletakan dan persedian barang. Langkah terpenting aturan asosiasi adalah mengetahui seberapa sering kombinasi item yang disebut frequent pattern, muncul dalam database. Objek penelitian ini adalah data transaksi penjualan barang pakaian. Berdasarkan hasil pengujian menggunakan Ms. Excel dan RapidMiner diperoleh hasil dari association rules dengan minimum support 0,2% dan confidence sesuai dengan kriteria pengujian yang telah ditentukan bahwa hasil yang memenuhi nilai support minimum dan confidence 0,8% ditemukan 7 aturan asosiasi. Dari pengurutan nilai support tertinggi yaitu STX dan LK dengan nilai support 10% dengan nilai confidence 88% dan nilai Association rules Final 8,8%. Hal ini menunjukkan bahwa produk STX dan LK merupakan produk yang paling sering dibeli secara bersamaan.
Human Digital Twin Modeling for Cardiovascular System Herman, Herman; Annafii, Moch. Nasheh; Kunta Biddinika, Muhammad; Fitriah, Fitriah
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.16012

Abstract

Purpose: The cardiovascular system is a vital system responsible for the distribution of oxygen and nutrients throughout the body. The complexity of interactions between the heart and blood vessels often presents challenges in monitoring and analyzing health conditions. The research proposes the development of a Human Digital Twin (HDT) for the cardiovascular system through application of two different modelling approaches geometric modeling and physic-based modeling. Through this model physical conditions can be represented and real time data integrated to offer insights into the dynamics of the cardiovascular system. Methods: This model development is based on two major components: a geometric modeling and a physic-based modeling. The geometric model is done in 3D to show the structure of the heart in detail, while the physical-based model is tabulated with different measurable physical parameters in the cardiovascular system, such as blood pressure and flow rate. This information is integrated into the Five Dimension Digital Twin model, including physical, virtual, data, connection, and service dimensions for the accurate simulation of cardiovascular conditions. Result: Results confirm that the Five-Dimensional Digital Twin (DT) could give further development to how the dynamics of the cardiovascular system behave, possibly in real-time updates on conditions and a supply of data that is far more detailed in view of analyzing risk and further representation of specific cardiovascular disorders while providing personalized medical support. Novelty: The Five-Dimensional Human Digital Twin Model (HDTM) developed in this research introduces novel innovations in the monitoring and simulation of the cardiovascular system through the application of geometric and physic-based modeling techniques. This approach offers a higher level of detail, compared to previous models, and added value for the advancement of health technology by integrating real time data into the simulations. This model serves not only as an advanced analytical tool but also as a reference for further research on DT technology in the medical field.
Co-Authors Alwas Muis Annafii, Moch. Nasheh Herman Herman Herman Herman Herman Imam Riadi Imam Riadi Imam Riadi Muhajir Yunus Muhammad Kunta Biddinika Suprihatin Suprihatin Suprihatin Suprihatin Wiwik Handayani