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All Journal Journal of Electrical Engineering and Computer (JEECOM) Madani: Multidisciplinary Scientific Journal
Saputra, Muhammad Arya
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Analisis Marketing Mix Dalam Menimgkatkan Volume Penjualan Pada Ayam Geprek Kawasan Kampus Bukit Palembang Saputra, Muhammad Arya; Saputra, Tedy Setiawan; Meilina, Echie; Ladyva, Jolinka Velichea
Madani: Jurnal Ilmiah Multidisiplin Vol 3, No 1 (2025): February
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

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Abstract

This study aims to analyze the impact of marketing mix strategies on increasing sales volume for Ayam Geprek SMEs located in the Bukit Palembang campus area. Using a qualitative method with structured interviews with five SME owners and a literature review, the study found that elements of the marketing mix, including product, price, place, and promotion, significantly influence consumer attraction and sales volume growth. Product innovation, competitive pricing, strategic location, and social media promotion proved effective in attracting university students as the target market. The findings indicate that the implementation of an appropriate marketing mix strategy can provide a competitive advantage and boost the growth of Ayam Geprek in the area.
Real-Time Packaging Quality Inspection and Sorting Using YOLOv8 and ESP32-Based IoT Integration Saputra, Muhammad Arya; Aziz, As'ad Shidqy
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 3 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i3.17208

Abstract

Manual product inspection and sorting in small and medium-sized enterprises (SMEs) are often slow and error-prone, whereas conventional computer vision systems require expensive infrastructure. This study proposes a low-cost, real-time packaging inspection and sorting system utilizing YOLOv8 integrated with an ESP32 microcontroller. The proposed closed-loop architecture combines object detection, servo-based actuation, and dual data logging via LCD, Blynk IoT, and Google Sheets, alongside end-to-end latency characterization. The dataset used consists of 1,510 packaging images annotated into two classes: Good Product and Damaged Product. Five dataset-splitting scenarios were evaluated using the early stopping method, with performance assessed based on precision, recall, mAP50, and mAP50-95 metrics. The optimal configuration achieved 99.32% accuracy and an mAP50 of 97.6%, with zero false negatives across 292 test samples. The measured response time was approximately 0.51 seconds, providing a real-time margin of 1.40 seconds at a conveyor speed of 30 RPM. Physical sorting achieved success rates of 86% for Damaged Products and 96% for Good Products, while data logging achieved 100% transmission success. These findings demonstrate that the YOLOv8–ESP32–IoT integration is a viable solution for low-cost, real-time packaging inspection and sorting, although mechanical factors remain crucial and further validation at an industrial scale is required.
Co-Authors Aziz, As’ad Shidqy Ladyva, Jolinka Velichea Meilina, Echie Saputra, Tedy Setiawan