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All Journal Jurnal Ilmiah Ekonomi Manajemen : Jurnal Ilmiah Multi Science Jurnal INFOTEL Riau Jurnal Teknik Informatika
Yoeyong Rahsel
Institut Bakti Nusantara
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences Other

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Implementation of MobileNetV2 Transfer Learning for Chicken Egg Quality Classification Using Jetson Nano Dita Novita Sari; Panji Andhika Pratomo; Yoeyong Rahsel; Akhmad Jayadi; Dwi Ely Kurniawan
JURNAL INFOTEL Vol 18 No 1 (2026): February
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i1.1499

Abstract

Eggs are an important source of animal protein and are widely consumed by the public. However, quality issues such as cracked or broken eggs are still frequently encountered during distribution and storage. Egg quality sorting has been largely done manually, making it prone to human error, time-consuming, and inconsistent. This study aims to develop a deep learning-based egg quality classification system with a transfer learning approach using the MobileNetV2 architecture that is efficient for devices with limited computing capacity. The research method involves acquiring egg image datasets (good and broken), preprocessing data with normalization and augmentation, designing a MobileNetV2 model, conducting two-stage training (feature extraction and fine-tuning), and evaluating model performance. Implementation was carried out both in the development environment and on a Jetson Nano edge computing device to test real-time application. The results showed that training with fine-tuning increased classification accuracy to 92% with an average precision, recall, and F1-score of 0.95. Confusion matrix evaluation demonstrated the model's ability to distinguish egg classes well, although there were still small errors in the classification of "good" eggs. Implementation on the Jetson Nano demonstrated relatively fast inference times (50–70 ms) with low resource consumption, demonstrating the system's applicability at both farm and small-to-medium scale distribution. This research successfully presented an accurate, lightweight, and practically implementable egg classification model as a first step towards automating the egg sorting process in the livestock industry. Implementasi pada Jetson Nano menunjukkan waktu inferensi yang relatif cepat (50–70 ms) dengan konsumsi sumber daya yang rendah, menunjukkan penerapan sistem pada pertanian dan distribusi skala kecil hingga menengah.
PENGARUH LINGKUNGAN KERJA TERHADAP KEPUASAN KERJA KARYAWAN INDO LAMPUNG Yoeyong Rahsel; Kasmi Kasmi; Septiana Mar’atus Sholikhah
Jurnal Ilmiah Ekonomi Manajemen Jurnal Ilmiah Multi Science Vol. 15 No. 01 (2024): Jurnal Ilmiah Ekonomi Manajemen
Publisher : Universitas Muhammadiyah Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52657/jiem.v15i01.2385

Abstract

Pada dasarnya kepuasan kerja merupakan hal yang bersifat individual. Setiap pegawai akan mempunyai tingkat kepuasan yang berbeda-beda sesuai dengan sistem dan nilai-nilai yang berlaku pada pegawai tersebut. Berdasarkan hasil pengujian diketahui tidak terdapat pengaruh positif signifikan secara parsial antara lingkungan kerja terhadap kepuasan kerja pada PT Indo Lampung Perkasa, hal ini berdasarkan jawaban responden mengenai cahaya pantulan sinar matahari membuat responden tidak puas. dalam melakukan pekerjaan dan kebisingan yang terdapat di lingkungan kerja membuat responden tidak fokus dalam melakukan pekerjaan di PT Indo Lampung Perkasa.
Smart Farming: Optimalisasi Produksi Telur Ayam Petelur menggunakan Sistem Cerdas Monitoring Suhu dan Kelembaban Kandang Berbasis IoT Panji Pratomo; Kurniawan Saputra; Dita Novita Sari; Yoeyong Rahsel; Ricco Herdiyan Saputra; Bambang Suprapto; Henry Simanjuntak
Riau Jurnal Teknik Informatika Vol. 4 No. 1 (2025): Maret 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i1.3264

Abstract

Egg production of laying hens is influenced by various factors, including temperature, humidity, and the quality of the cage environment. The main problem in this study is the fluctuation of production due to changes in environmental conditions that are not optimal. This study aims to develop and implement a smart farming system based on the internet of things (IoT) that is able to optimize egg production of laying hens through automatic monitoring of cage temperature and humidity. The methods used include needs analysis, design, implementation and testing. The results showed that the accuracy of the system reached 80% which could maintain the cage environmental conditions within the optimal range, so that egg production increased from an average of 383.67 eggs per month to 390.33 eggs per month.
Co-Authors Akhmad Jayadi Bambang Suprapto Dita Novita Sari Dita Novita Sari, Dita Dwi Ely Kurniawan Henry Simanjuntak Kasmi Kurniawan Saputra Panji Andhika Pratomo Panji Pratomo Ricco Herdiyan Saputra Septiana Mar’atus Sholikhah