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All Journal KREDO : Jurnal Ilmiah Bahasa dan Sastra KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal ICT : Information Communication & Technology JALL (Journal of Applied Linguistics and Literacy) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Edukasi (Ekonomi, Pendidikan dan Akuntansi) JATI (Jurnal Mahasiswa Teknik Informatika) SOSIOLOGI: Jurnal Ilmiah Kajian Ilmu Sosial dan Budaya Instal : Jurnal Komputer BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat
Narasati, Riri Narasati
STMIK IKMI Cirebon
Arts Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Social Sciences Other

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Journal : Jurnal Informatika dan Rekayasa Perangkat Lunak

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Penerapan Data Mining dengan Metode Clustering untuk menentukan Strategi Peningkatan Penjualan Berdasarkan Data Transaksi Muhamad Sulaiman; Riandy Yudistira; Riri Narasati; Ruli Herdiana
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10329

Abstract

Improving marketing strategies in mini markets by applying the clustering method as the basis of the approach. By using the K-Means cluster algorithm on data on the number of transactions and total sales, this research aims to identify groups of customers who have similar purchasing patterns. This clustering is the basis for formulating a more targeted and efficient marketing strategy. The K-Means approach is used to group customers into segments that have similarities in transaction behavior. The results of this clustering are then used to develop more personalized marketing strategies, understand the unique needs of each customer group, and increase the effectiveness of marketing efforts. This research involves collecting data on the number of transactions and total sales from mini markets during a certain time period. The data is then analyzed using the K-Means algorithm to produce customer segments that have similar characteristics. The results of this analysis resulted in 4 clusters being formed, consisting of cluster 0, cluster 1, cluster 2, cluster 3 consisting of 7303 data that had gone through the preprocessing stage, divided into cluster 0 including low clusters and cluster 1 including high clusters and clusters 2 and 3 including Meanwhile, from these results, strategies can be concluded that can be implemented to improve minimarket performance by identifying these results.
Co-Authors Ade Rizki Rinaldi Aditia Adniana, Royana Ahmad Faqih Amalia, Dita Rizki Anawar, Saeful Antika, Rindi Aulia Putri, Adinda Ayu Febriaan Lesmana, Alfira Chaidir Ali Dienwati Nuris, Nisa Dikananda, Fatihanursari Edi Tohidi Eka Permana, Sandy Fajar, Achmad Fazrian, Vivi Febiyanto, Anggi Gifthera Dwilestari Haekal Susanto, Ahmad Hajaroh, Hajaroh Hamonangan, Ryan Herdiana, Ruli Herdiyana, Ruli Hery Widijanto Imam Arifin Indriya Efendi, Dendy Lestari, Ratih Liawati, Ayu Lukman Rohmat, Cep Muhamad Sulaiman Mulyawan Nining Rahaningsih Nur alamsyah, Deri Nuraini, Asyifa Nurwahidah, Dalilah Nuryadi Raditya Danar Dana Riandy Yudistira Rifa’I, Ahmad Rizky Maulana, Rizky Rudi Kurniawan Ruli Herdiana Saeful Anwar Saeful Anwar, Saeful Sandy Eka Permana Solehudin, Dodi Solihudin, Dodi Sri Nurmala, Ai Syarif Maulana Yaasin Tati Suprapti Yati Ningrum, Riska