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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Proceeding of the Electrical Engineering Computer Science and Informatics JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknoinfo JUMANJI (Jurnal Masyarakat Informatika Unjani) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal ICT : Information Communication & Technology Jurnal Informatika dan Rekayasa Elektronik Informatics and Digital Expert (INDEX) Journal of Informatics and Communication Technology (JICT) Industrial Research Workshop and National Seminar SisInfo : Jurnal Sistem Informasi dan Informatika IJESPG (International Journal of Engineering, Economic, Social Politic and Government) journal Riwayat: Educational Journal of History and Humanities
Kasyidi, Fatan
Program Studi Teknik Informatika, Universitas Jenderal Achmad Yani, Cimahi
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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Journal : Riwayat: Educational Journal of History and Humanities

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Application of the Modified Apriori Algorithm to Determine Sales Patterns of Capacitor Products Erna Sesarliana*; Fajri Rakhmat Umbara; Fatan Kasyidi
Riwayat: Educational Journal of History and Humanities Vol 6, No 3 (2023): Social, Political, and Economic History
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v6i3.33718

Abstract

The sales pattern becomes a benchmark for increasing product sales in a company. Sales patterns are one way that can be used to determine sales strategies by looking at how often an item is purchased simultaneously. The large number of daily transactions makes it difficult for companies to determine sales strategies. Data mining analyses extensive data to find relationships between data and can produce valuable information. Patterns of sales or consumer transactions look for relationships between one product and another in one transaction using the Association Rule method. The algorithm used is the Modified Apriori Algorithm. The data used is transaction data on capacitor products. The data used is 15513 transactions with the variables LotNo and Material Code. Processed with the Python programming language and Flask as the user interface, the minimum support used is 0.01, and the minimum confidence is 0.5, resulting in rules with the lowest reliability of 50% and the highest reliability of 100%. Based on the results of a comparison of the performance time of the Modified Apriori Algorithm and the Classic Apriori Algorithm in processing 15513 transaction data with the given conditions, namely minimum support = 0.02 and also minimum confidence = 0.5 with the time obtained by the Modified Apriori Algorithm for 5 minutes 5 seconds and the Classic Apriori Algorithm for 6 minutes 6 seconds
Application of the Modified Apriori Algorithm to Determine Sales Patterns of Capacitor Products Erna Sesarliana*; Fajri Rakhmat Umbara; Fatan Kasyidi
Riwayat: Educational Journal of History and Humanities Vol 6, No 3 (2023): Social, Political, and Economic History
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v6i3.33718

Abstract

The sales pattern becomes a benchmark for increasing product sales in a company. Sales patterns are one way that can be used to determine sales strategies by looking at how often an item is purchased simultaneously. The large number of daily transactions makes it difficult for companies to determine sales strategies. Data mining analyses extensive data to find relationships between data and can produce valuable information. Patterns of sales or consumer transactions look for relationships between one product and another in one transaction using the Association Rule method. The algorithm used is the Modified Apriori Algorithm. The data used is transaction data on capacitor products. The data used is 15513 transactions with the variables LotNo and Material Code. Processed with the Python programming language and Flask as the user interface, the minimum support used is 0.01, and the minimum confidence is 0.5, resulting in rules with the lowest reliability of 50% and the highest reliability of 100%. Based on the results of a comparison of the performance time of the Modified Apriori Algorithm and the Classic Apriori Algorithm in processing 15513 transaction data with the given conditions, namely minimum support = 0.02 and also minimum confidence = 0.5 with the time obtained by the Modified Apriori Algorithm for 5 minutes 5 seconds and the Classic Apriori Algorithm for 6 minutes 6 seconds
Co-Authors Ade Kania Ningsih Ade Kania Ningsih AGIEL FADILLAH HERMAWAN Agung Besti Agus Komarudin Agya Java Maulidin Aldi Sidik Permana Anggoro, Sigit Armandika, Fladio Besti, Agung Djamal, Esmeralda Contesa Djuniardi Suhardinata Eddie Khrisna Putra Erna Sesarliana* Esmeralda Contessa Djamal Faiza Renaldi Fajri Rakhmat Umbara Fajri Rakhmat Umbara FAJRI RAKHMAT UMBARA FAJRI RAKHMAT UMBARA fery bayu aji Fikri Nugraha FIQRI FAKHRUL GUNAWAN Fladio Armandika Hadiana, Asep Id Irma Santikarama Janjan Nurjaman Melina Melina Muhamad Rizal Firmansyah NIDA MUTHI ANNISA Nugraha, Fikri Permana, Aldi Sidik Puspita Nurul Sabrina Rafli Firdaus Reza Dwi Putra Rezky Yuniarti Ridwan Ilyas RINDU TEGAR SENJAWATI Rizky Ryan Dwi Yudistira Seffiera Fauziah Wildan Pratama Yoga Esa Mahendra Yustiana Fauziyah