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All Journal Biopropal Industri Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Digital Zone: Jurnal Teknologi Informasi dan Komunikasi IJECS: Indonesian Journal of Empowerment and Community Services Insearch: Information System Research Journal Muhasabatuna: Jurnal Akuntansi Syariah Jurnal Pengabdian dan Pemberdayaan Masyarakat Kepulauan Riau (JPPM KEPRI) COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Indonesia (JPKMI) Journal of Social Outreach Innovative: Journal Of Social Science Research Abdimas Mandalika Jurnal Multidisiplin Indonesia Jurnal PGSD Indonesia Relevan : Jurnal Pendidikan Matematika Jurnal Info Kesehatan Mizan: Journal of Islamic Law
Novia Lestari
Politeknik Negeri Lhokseumawe, Prodi Teknologi Kimia Industri, Lhokseumawe, NAD
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Journal : Insearch: Information System Research Journal

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IMPLEMENTASI DATA MINING UNTUK MENENTUKAN POLA PENJUALAN DENGAN MARKET BASKET ANALYSIS Lestari, Novia; Gunawan, Refika Fitria
Insearch: Information System Research Journal Vol 1, No 02 (2021): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1022.343 KB) | DOI: 10.15548/isrj.v1i02.2992

Abstract

The increasingly fierce competition in the business world lately has forced business actors to have the right strategy to increase their product sales, one of which is to determine sales patterns. However, along with the amount of sales transaction data that is carried out every day, it makes it difficult for business actors to analyze existing sales patterns. So we need a method that can help determine sales patterns from even large transaction data, one of which uses the Market Basket Analysis method which uses customer data that has been stored in the database to find new information in it. By utilizing the sales transaction data of consumers supported by the Market Basket Analysis method, it is possible to determine the right marketing tactics for business actors so as to increase sales
Penerapan Algoritma Klasifikasi untuk Menentukan Segmentasi Pelanggan Berbasis Machine Learning Lestari, Novia; Julianti, Julianti; Fathan, M. Rafly Syahrul
Insearch: Information System Research Journal Vol 4, No 02 (2024): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v4i02.9927

Abstract

Customer segmentation is a very important marketing concept in the context of relationship marketing that can improve understanding of customer needs to create a more effective and personalized marketing strategy. Therefore, an appropriate analysis is needed to determine customer segmentation according to the existing characteristics. In this research, three classification algorithms are used to identify hidden patterns and automatically segment customers: Random Forest, Support Vector Machine (SVM), and Gradient Boosting. The results show that customer segmentation successfully divides consumers into four different groups: A, B, C, and D, each with unique characteristics. The Gradient Boosting model has the best performance in cross-validation with 54% accuracy, although all models show a decrease in accuracy on test data. The customer segmentation results in this study can provide valuable insights for automotive companies to design more targeted and effective marketing strategies to increase profitability.Abstrak (Indonesia):Segmentasi pelanggan merupakan konsep pemasaran yang sangat penting dalam konteks relationship marketing yang dapat meningkatkan pemahaman tentang kebutuhan pelanggan yang lebih baik untuk menciptakan strategi pemasaran yang lebih efektif dan personal. Oleh karena itu dibutuhkan analisis yang tepat sesuai karakteristik yang ada untuk menentukan segmentasi pelanggan tersebut. Pada penelitian ini, untuk mengidentifikasi pola tersembunyi dan secara otomatis mengelompokkan pelanggan digunakan tiga algoritma klasifikasi yaitu Random Forest, Support Vector Machine (SVM), dan Gradient Boosting. Hasilnya menunjukkan bahwa segmentasi pelanggan berhasil membagi konsumen ke dalam empat kelompok yang berbeda: A, B, C, dan D, masing-masing dengan karakteristik yang unik. Model Gradient Boosting memiliki kinerja terbaik dalam validasi silang dengan akurasi 54%, meskipun semua model menunjukkan penurunan akurasi pada data uji. Hasil segmentasi pelanggan pada penelitian ini dapat memberikan wawasan yang berharga bagi perusahaan otomotif untuk merancang strategi pemasaran yang lebih tepat sasaran dan efektif untuk meningkatkan profitabilitas.
Co-Authors 'Ul Haq, Ahmad Dhiya Afri, Lisa Dwi Alfian Putra Arham, Aulia Bagus Pribadi, Bagus Bambang Suwerda, Bambang Darvi Mailisa Putri Elwira Handayani Enawati, Desma Fathan, M. Rafly Syahrul Fauziah Nasution Febdarina, Sri Gunawan, Refika Fitria Hartono Hartono Hasibuan, Lilis Harianti Heru Subaris Kasjono, Heru Subaris Hesti Meilina Julianti Julianti, Julianti Khomsatun, Siti Latif, Syariah Lucky Lhaura Van FC, Lucky Lhaura Miranti Miranti Muhammad Naufan Rizqullah Novizal Wendry, Novizal Prayudhy Yushananta Putra, Rahman Pranovri Putra, Razaq Aulia Rahma Putri Andita Riza, Ozzy Secio Saputra, Suryo Sefrona Syaiful, Sefrona Sepri, Domi Shafira Prajawati A, Shafira Subhan Ajrin Sudirman Suhari Suhari Syahrani, Sendy Syarifah Gustiawati Mukri Yuharnida, Yuharnida