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All Journal Jurnal Ilmiah Teknologi dan Komputer (JITTER)
Khrisnawati, Erlin Ayu
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Computer Science & IT

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IMPLEMENTASI ALGORITMA K-MEANS UNTUK PENGELOMPOKAN DATA KEMISKINAN PROVINSI ACEH Renita, Dayu; Khrisnawati, Erlin Ayu
JITTER : Jurnal Ilmiah Teknologi dan Komputer Vol 5 No 1 (2024): JITTER, Vol.5, No.1, April 2024
Publisher : Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana

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Abstract

During the period September 2021-March 2022, percentage the poor population in Aceh Province fell from 15.53% to 14.64%. Even though it tends to decrease, this number must be managed so that the poverty rate in Aceh province decreases. The poverty rate in each district or city in Aceh province is influenced by different indicators. In this study using the K-Means algorithm method to determine classifying poverty levels in Aceh Province. Data was taken from 23 districts/cities with 3 variables, namely average school time (years), average per capita expenditure (thousands of rupiah) and number of poor people (thousands of people). The data was processed using RapidMiner Studio and obtained 4 clusters with each cluster namely cluster 0 consisting of 13 items, cluster 1 consisting of 4 items, cluster 2 consisting of 1 item, cluster 3 consisting of 5 items.
Analisis Pengambilan Keputusan Untuk Korelasi Pembelian Produk Menggunakan Metode Association Rules Khrisnawati, Erlin Ayu; Renita, Dayu
JITTER : Jurnal Ilmiah Teknologi dan Komputer Vol 4 No 3 (2023): JITTER, Vol.4, No.3 December 2023
Publisher : Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JTRTI.2023.v04.i03.p02

Abstract

Business people are required to be able to keep up with the increasingly dynamic times. Business people must have the right strategy to be able to compete with competitors so that the business they run continues to generate profits. From the existing sales transaction data, it can be used for decision making for business people in developing a good strategy in developing their business. By using data mining with the Association Rules method can be used to analyze existing sales patterns. In this test, the author uses the Apriori Algorithm to analyze the market basket analysis to find out the correlation between product purchases and products that are often purchased by consumers. From the analysis of the a priori algorithm, it produces frequent items that meet the minimum values ??of support and confidence. The result of this test is to provide product recommendations to consumers based on interrelated items and as a reference in product promotion to support decisions in developing good strategies in their business.
Co-Authors Renita, Dayu