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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal PIKOM (Penelitian Komunikasi dan Pembangunan) JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) SEIKO : Journal of Management & Business Jiko (Jurnal Informatika dan komputer) Journal of Secretary and Business Administration MIND (Multimedia Artificial Intelligent Networking Database) Journal Progresif: Jurnal Ilmiah Komputer Syntax Idea Jurnal Ilmiah Edunomika (JIE) E-Link: Jurnal Teknik Elektro dan Informatika JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Darma Agung JOINT (Journal of Information Technology Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Masyarakat Indonesia JURNAL ILMIAH GLOBAL EDUCATION JUSTIN (Jurnal Sistem dan Teknologi Informasi) Brilliance: Research of Artificial Intelligence Prosiding Konferensi Nasional PKM-CSR Jurnal Algoritma Malcom: Indonesian Journal of Machine Learning and Computer Science International Journal of Engineering Continuity
Sulianta, Feri
Teknik Informatika, Universitas Widyatama
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Journal : Jurnal Algoritma

 1 
Analisis Klasterisasi Data Peserta Asuransi PT Xyz Menggunakan Metode Denisty-Based Spatial Clustering of Applications with Noise (DBSCAN) Fathur Rizki, Muhamad; Sulianta, Feri
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2417

Abstract

Penelitian ini bertujuan untuk menganalisis klasterisasi data peserta asuransi di PT XYZ menggunakan algoritma Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Atribut utama yang digunakan dalam proses klasterisasi adalah usia, pekerjaan, dan gaji. Aplikasi berbasis web dikembangkan menggunakan Laravel untuk frontend/backend dan Python (Flask) untuk pemrosesan data dan implementasi DBSCAN. Data yang dikumpulkan dari file Excel yang di unggah diproses melalui REST API, dan hasil klasterisasi dievaluasi menggunakan Silhouette Coefficient untuk menilai validitas klaster. Analisis berhasil mengidentifikasi 16 klaster utama dan 1 kategori noise, dengan klaster dominan berisi lebih dari 4.800 peserta. Skor Silhouette Coefficient sebesar 0,74 menunjukkan struktur klasterisasi yang kuat dan valid, menyoroti efektivitas DBSCAN dalam mengidentifikasi pengelompokan yang padat dan mendeteksi outlier. Hasil ini dapat digunakan untuk lebih memahami profil peserta dan mendukung pengambilan keputusan di masa mendatang dalam perencanaan program asuransi dan strategi pemasaran.
Penerapan Association Rule Mining untuk Rekomendasi Promo Bundling dalam Sistem CRM Berbasis Online Herdian, Gentala Virgiawan; Sulianta, Fery
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2398

Abstract

The online ordering system is an important strategy in improving service efficiency and customer loyalty, especially in micro businesses such as Coffee Kane. This study applies the Association Rule Mining (Apriori) algorithm within the CRISP-DM framework to identify customer purchasing patterns and design bundling promotions based on Customer Relationship Management (CRM). The data used is transaction history from the last two months. The analysis results produced a number of significant association rules, such as product combinations with the highest lift value of 31.50. These rules were implemented into the Laravel-based ordering system and automatically displayed to customers. This study shows that this data-driven approach not only improves the effectiveness of promotions but also strengthens customer engagement through an adaptive and personally relevant system.
Sistem Rekrutmen Online Berbasis TOPSIS untuk Seleksi Kandidat Faturrohman, Adit; Sulianta, Feri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2430

Abstract

An efficient and accurate recruitment process is crucial for companies to ensure the quality of human resources that support growth and innovation. For Coffee Kane, acquiring the best talent is key to maintaining competitive advantage. However, traditional recruitment methods often face challenges, such as time inefficiency, high operational costs, and limitations in the search for quality candidates. This study proposes a web-based online recruitment system that integrates the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. This system is designed to accelerate the selection process, reduce operational costs, and expand the search for candidates without geographical limitations. The implementation of this system has successfully automated the selection process, increased efficiency, and resulted in more accurate, objective, and transparent decision-making in selecting the best candidates.
Co-Authors Ahmad Arif Ahmad Zaeni, Ahmad Ai Rosita Amalia, Endang Ari Purno Wahyu Wibowo Aswari Aswari Daud Rifki Gunawan Deden Farhan Dzikriyana Zuhdia Aziz Esa Fauzi Fadllur Rohman, Muhammad Naufal Fathur Rizki, Muhamad Faturrohman, Adit Fitrah Rumaisa Galang Raka Siwi Herdian, Gentala Virgiawan Kautsar Baihaqi Supriyadi Mendonca, Fernando Muharam, Rahman Mutaqin, Safira Mardiyah Philothra Clarissa Raina Pranata, Aryoga Prayogo, Eriko Putri, Vianty Julia Suryana Rachmat, Alam Maulana Rah Utami Nugrahani Rahman Muharam Raina, Philothra Clarissa Reza Syafrian Setiadi Rizky Muhammad Fauzi Rosita, Ai Saputra, Rinaldy Buana Sriyani Violina Suryaresmana, Rizka Putera SY Yuliani Ulfah, Khaerani yakub, sy yuliani yuliani Yan Puspitarani Yenie Syukriyah