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All Journal JURNAL INSTEK (Informatika Sains dan Teknologi) Agents: Journal of Artificial Intelligence and Data Science
Aldi Perdana Asri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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ANALISIS CLUSTERING UNTUK SEGMENTASI PENGGUNA KARTU KREDIT DENGAN MENGGUNAKAN ALGORITMA K-MEANS DAN PRINCIPAL COMPONENT ANALYSIS Muhammad Nur Akbar; Azizah Salsabila; Aldi Perdana Asri; Muhammad Syawir
AGENTS: Journal of Artificial Intelligence and Data Science Vol 3 No 1 (2023): September - Februari
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (832.897 KB) | DOI: 10.24252/jagti.v3i1.56

Abstract

Customer segmentation is a process used by companies to group customers based on common characteristics. The goal is to understand customer needs and preferences better so that companies can provide products and services that match customer needs. One way to segment customers is to use clustering algorithms, such as k-means. This algorithm groups data into adjacent clusters with randomly selected centroids. In the case of credit card customer segmentation, the k-means algorithm can be used to group customers based on characteristics such as number of transactions, amount of payments, and credit history. Thus, companies can better understand the needs and preferences of credit card customers and determine more effective marketing strategies. The advantages of the k-means algorithm and the clustering method are that the developed models can help companies determine more effective marketing strategies, easy-to-use algorithms with fast computation time and accurate results, and the PCA algorithm is also used to reduce dimensions and makes data visualization easier. Based on the test results and analysis of credit card customer data, the performance of the k-means algorithm is considered relatively good for segmentation with the number of clusters = 3 and the Davies Bouldin value = -0.778.
RANCANG BANGUN APLIKASI BLENDED LEARNING SEBAGAI MEDIA PENGEMBANGAN MINAT DAN BAKAT ILMIAH MAHASISWA MENGGUNAKAN METODE LEAN UX Sri Wahyuni; Erfina; Andi Putra Aditya Pratama; Aldi Perdana Asri
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 9 No 2 (2024): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v9i2.52227

Abstract

Fenomena ketidaksesuaian antara bidang studi dan pekerjaan lulusan di Indonesia menimbulkan tantangan bagi institusi pendidikan untuk meningkatkan relevansi kurikulum dengan kebutuhan industri. Penelitian ini bertujuan merancang aplikasi blended learning untuk mendukung pengembangan minat dan bakat ilmiah mahasiswa menggunakan metode Lean UX. Metode penelitian ini melibatkan observasi, studi literatur, dan pengembangan aplikasi berbasis Minimum Viable Product (MVP), diikuti oleh pengujian Blackbox dan System Usability Scale (SUS) untuk mengukur fungsionalitas dan pengalaman pengguna. Hasil pengujian menunjukkan semua fitur aplikasi berjalan sesuai dengan harapan dan aplikasi memiliki tingkat kegunaan yang baik, dengan skor SUS rata-rata sebesar 74.1 (acceptable). Pendekatan Lean UX dalam pengembangan aplikasi blended learning ini efektif dalam menciptakan pengalaman pengguna yang positif, namun perlu disempurnakan untuk memastikan aksesibilitas dan kemudahan penggunaan yang lebih merata di kalangan pengguna.
Co-Authors Andi Putra Aditya Pratama Azizah Salsabila Erfina Muhammad Nur Akbar Muhammad Syawir Sri Wahyuni