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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Teknologi Komputer dan Sistem Informasi JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Supriyanto Supriyanto
Institut Teknologi Bisnis dan Bahasa Dian Cipta Cendikia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Implementation Of Data Mining Sales Of Household Furniture At Smart Kitchen Shop Using The Method K-Means Sandi Kurnia Wati; Supriyanto Supriyanto; Rima Mawarni; Herman Afandi; I Putu Brama Arya; Yogas Habib Nurfaizi
JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi) Vol 6, No 1 (2023): JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Publisher : STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jtksi.v6i1.1334

Abstract

Household furniture is an item that is very much in demand by many people, especially mothers, the higher the number, the more demand. To get the desired information so that a store can sort out the inventory that must be met. So it takes a prediction for the sale of furniture products that are most requested by consumers which aims to facilitate the provision of stock goods. The purpose of this research is to apply data mining to determine what products or goods are most in demand, moderately desirable, less desirable. From the various data that the authors observe at the Smart Kitchen Store, namely the Smart Kitchen Store, it is still difficult to predict product inventory in the future. With this problem, we need to group the data based on the characteristics of product sales. In the grouping process, a grouping method will be used using the K-Means Algorithm as a manual calculation method and in its implementation a data mining software using RapidMiner Studio version 7.1 will be used.The results of the study consisted of 3 clusters, namely, 42 Most Interested Products (Double Stan Hangers, Napkins, and Stainless Dish Racks), 46 Moderately Interested Products (Super Mop Floor Mops, Surpets, and Electric Grater) and 32 Less Interested Products (Shower Hood 4 stacking, Ring Light, and stainless hood). at the Smart Kitchen Store, so that the data is used as a reference for the Smart Kitchen Store to manage the stock of goods so that the store does not disappoint customers because the goods or products you want to buy are not available.
Implementation Of Data Mining Sales Of Household Furniture At Smart Kitchen Shop Using The Method K-Means Sandi Kurnia Wati; Supriyanto Supriyanto; Rima Mawarni; Herman Afandi; I Putu Brama Arya; Yogas Habib Nurfaizi
JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi) Vol 6, No 1 (2023): JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Publisher : Institut Bakti Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jtksi.v6i1.1334

Abstract

Household furniture is an item that is very much in demand by many people, especially mothers, the higher the number, the more demand. To get the desired information so that a store can sort out the inventory that must be met. So it takes a prediction for the sale of furniture products that are most requested by consumers which aims to facilitate the provision of stock goods. The purpose of this research is to apply data mining to determine what products or goods are most in demand, moderately desirable, less desirable. From the various data that the authors observe at the Smart Kitchen Store, namely the Smart Kitchen Store, it is still difficult to predict product inventory in the future. With this problem, we need to group the data based on the characteristics of product sales. In the grouping process, a grouping method will be used using the K-Means Algorithm as a manual calculation method and in its implementation a data mining software using RapidMiner Studio version 7.1 will be used.The results of the study consisted of 3 clusters, namely, 42 Most Interested Products (Double Stan Hangers, Napkins, and Stainless Dish Racks), 46 Moderately Interested Products (Super Mop Floor Mops, Surpets, and Electric Grater) and 32 Less Interested Products (Shower Hood 4 stacking, Ring Light, and stainless hood). at the Smart Kitchen Store, so that the data is used as a reference for the Smart Kitchen Store to manage the stock of goods so that the store does not disappoint customers because the goods or products you want to buy are not available.
Predicting Student Graduation Grades Using the C4.5 Algorithm: An Implementation Study Bela amlia Amalia Wiranti; Supriyanto Supriyanto; Nurmayanti Nurmayanti
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9639

Abstract

SMP KEMALA BHAYANGKARI as one of the private schools, students follow the learning process and are carried out mid-semester exams, semester final exams in completing junior high school level education. test scores as one of the requirements for grade promotion or for graduation mark the end of junior high school level, and the value of knowledge is one of the keys to a person's ability to complete education. predicting student graduation with the C4.5 algorithm method In the application of the C4.5 algorithm using Rapidminer tools, after manual calculation, the results will be tested using Rapidminer tools. the results of manual calculation of the C4.5 algorithm and the results of calculations using rapidminer tools will produce an Accuracy Level resulting from this calculation of 92.22% for graduation grades at SMP Kemala Bhayangkari Kotabumi.
Co-Authors Bela amlia Amalia Wiranti Herman Afandi I Putu Brama Arya Mawarni, Rima Nurmayanti Nurmayanti Sandi Kurnia Wati Yogas Habib Nurfaizi