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p-Index From 2021 - 2026
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All Journal bit-Tech Jurnal Pengabdian Kepada Masyarakat Abdi Putra
Kemal Fiqri Handoko
Universitas Nusa Putra
Humanities Computer Science & IT Education Engineering Public Health

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Sosialisasi Pencegahan Stunting di Desa Ridogalih Dihak Maulana Nur A; Dita Nursafitri; Kemal Fiqri Handoko; Luthfiyya Junia Sumantri; Marinus Halawa
Jurnal Pengabdian Kepada Masyarakat Abdi Putra Vol 4 No 3 (2024): September 2024
Publisher : Universitas Nusa Putra & Persatuan Insinyur Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/abdiputra.v4i3.238

Abstract

Stunting poses a serious health challenge affecting child growth. This study evaluates the effectiveness of stunting prevention socialization in Ridogalih Village, emphasizing the promotion of a healthy lifestyle. Through educational activities and distributing informative materials, the community gains an understanding of balanced nutrition, sanitation, and proper childcare. Pre- and post-socialization surveys measure changes in knowledge and community actions related to a healthy lifestyle as a stunting prevention strategy. Results show improved understanding and implementation of a healthy lifestyle. However, sustained efforts are needed to reinforce behavioral changes toward a lifestyle supportive of optimal child growth. This study contributes to enhancing awareness and community participation in stunting prevention by focusing on a healthy lifestyle, thereby supporting the well-being of children and future generations in Ridogalih Village.
Customer Segmentation-Based Promotion Recommendation Using RFM and K-Means Clustering on a Web Platform Kemal Fiqri Handoko; Nugraha Nugraha; Alun Sujjada
bit-Tech Vol. 8 No. 3 (2026): bit-Tech - IN PROGRESS
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3677

Abstract

Traditional micro-scale retail businesses, such as neighborhood stores, frequently apply promotions uniformly due to limited analytical capacity, leading to inefficient resource use and weak customer retention. Existing promotion recommendation studies predominantly focus on large-scale or online retail settings, leaving a methodological and translational gap in data-driven promotional decision support for micro-scale traditional retail contexts. This study aims to address this gap by developing and evaluating a web-based promotion recommendation system tailored to operational constraints of small neighborhood stores. Customer purchasing behavior is modeled using the Recency, Frequency, and Monetary (RFM) framework, and customer segmentation is performed with the K-Means clustering algorithm. The study utilizes transaction records from 1,043 registered customers comprising 1,500 transactions collected between February and April 2025. Five customer segments are identified, namely VIP customers, frequent buyers, occasional shoppers, at-risk customers, and new customers. Clustering quality is assessed using the Silhouette Score, achieving a value of 0.4464, which indicates moderate cluster cohesion and separation. Promotional performance is evaluated through a pre–post implementation comparison, where the observed 157.09% sales increase reflects an associative improvement rather than a causal estimate of system impact. Analytically, the study contributes a validated customer segmentation pipeline suitable for sparse micro-retail data, while at the system level it delivers an operational web-based decision support tool that translates segmentation results into actionable promotional recommendations. Although practically useful, the evaluation covers one store and a short period, limiting generalizability and causal inference.
Co-Authors Alun Sujjada Dihak Maulana Nur A Dita Nursafitri Luthfiyya Junia Sumantri Marinus Halawa Nugraha