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All Journal KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Jurnal Teknik Kimia USU JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Computer Based Information System Journal SAINTEK: Jurnal ilmiah Sains dan Teknologi Industri Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Insyst : Journal of Intelligent System and Computation Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Kimia USU STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer PROCEEDING INTERNATIONAL BUSINESS AND ECONOMICS CONFERENCE (IBEC) CITACONOMIA : Economic and Business Studies
Vincent
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Detection of Chicken Egg Quality with Digital Image using EfficientNet-B7 Vincent; Pasaribu, Hendra Handoko Syahputra; Audrey, Wilbert; Jefanya Alexander Meidi Bangun; Deryck Ethan Hong
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.15233

Abstract

Chicken eggs are one of the staple food ingredients in Indonesia, playing a vital role in fulfilling the nutritional needs of the community. Therefore, an efficient, accurate, and reliable method for assessing egg quality is essential, especially to support the distribution process in the food industry. This study aims to develop a digital image-based classification system for assessing the quality of chicken eggs using deep learning methods with the EfficientNet-B7 architecture. EfficientNet-B7 was selected for its proven high accuracy in image classification tasks through the application of compound scaling, which simultaneously optimizes depth, width, and resolution. The dataset used in this study combines images collected from public sources and primary documentation, representing various conditions commonly found in chicken eggs. The preprocessing stage involved trimming techniques to focus on the egg object, followed by data augmentation using ImageDataGenerator, including rotation, shifting, zooming, and flipping to enhance dataset diversity. Model training was carried out with the early stopping technique to prevent overfitting. The experimental results showed that the model achieved an accuracy of 98.08% in classifying egg quality based on shell condition and other visual indicators. These findings demonstrate that the implementation of the EfficientNet-B7 model has great potential to support the automation of chicken egg quality assessment processes in a faster and more consistent manner. Thus, this research is expected to contribute to improving the efficiency of the food industry, particularly in the distribution process of chicken eggs in Indonesia.
KEGIATAN SOSIALISASI PENGENALAN MARKETING SECARA DIGITAL BERSAMA TOKO KERUPUK DAN KEMPLANG 770 Wijaya, Andrian; Hartanti, Ery; Feriyanto; Wijaya, Laurentius Ricardo; Vincent
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 7 No 3 (2024): APTEKMAS Volume 7 Nomor 2 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36257/apts.v7i2.8761

Abstract

This community service activity aimed to introduce digital marketing to the employees of Toko Kerupuk dan Kemplang 770. The approach involved delivering educational materials on digital marketing and training on creating poster designs for marketing purposes. Conducted on April 21, 2024, the activity successfully helped employees understand and apply the concepts of digital marketing. The main advantage was the enhancement of employees' knowledge and skills in digital marketing, although the attendance was not optimal, affecting the overall effectiveness of the training. This initiative is expected to assist the store in attracting more customers through improved marketing strategies.
Penerapan Hidden Markov Model untuk Prediksi Pergerakan Harga Bitcoin Vincent; Putri Pratiwi, Mariska
Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Vol 7 No 2 (2025): Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya
Publisher : STMIK Bina Nusantara Jaya Lubuk Linggau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52303/jb.v7i2.164

Abstract

Pergerakan harga Bitcoin yang sangat fluktuatif dan volatil telah menjadi tantangan bagi para investor dan peneliti dalam melakukan prediksi harga secara akurat. Penelitian ini bertujuan untuk mengimplementasikan metode Hidden Markov Model (HMM) dalam menganalisis dan memprediksi pergerakan harga Bitcoin dengan pendekatan berbasis machine learning. Tujuan utama dari penelitian ini adalah untuk mengembangkan model prediksi yang mampu mengidentifikasi pola tersembunyi dalam data historis harga Bitcoin dan memberikan insight mengenai kondisi pasar, apakah sedang berada dalam tren naik (bullish), tren turun (bearish), atau stabil (sideways). Metode yang digunakan adalah unsupervised learning dengan pendekatan HMM berbasis Gaussian, menggunakan data harga penutupan (close), moving average (MA200), dan volume perdagangan Bitcoin dari tahun 2020 hingga 2025. Proses penelitian mencakup praproses data, ekstraksi fitur, pelatihan model HMM, dan visualisasi hasil berupa klasifikasi status pasar dan analisis transisi antar status. Hasil penelitian menunjukkan bahwa model HMM berhasil mengelompokkan data ke dalam tiga status tersembunyi dengan interpretasi tren yang konsisten terhadap kondisi pasar aktual. Status sideways mendominasi sepanjang periode, diikuti oleh status bearish dan bullish. Durasi rata-rata masing-masing status menunjukkan bahwa bearish berlangsung lebih lama dibanding bullish, yang hanya muncul secara singkat. Analisis transisi antar status memperkuat pemahaman terhadap pergerakan pasar kripto. Kesimpulannya, metode HMM terbukti efektif untuk mengidentifikasi pola pergerakan harga Bitcoin dan dapat dijadikan dasar dalam pengembangan sistem prediksi dan peringatan dini di pasar aset digital.
Co-Authors Andrian Wijaya Audrey, Wilbert Budhijanto Dasa Putri, Anggia Deryck Ethan Hong Dewi, Grace Levina Dianaris, Audrey Ayu Felix Saputra Feriyanto Hartanti, Ery Jefanya Alexander Meidi Bangun Lisa Elianti Nasution Mikhael Setiawan Pancasakti, Bima Prasetya Pasaribu, Hendra Handoko Syahputra Putri Pratiwi, Mariska Setiono, Kevin Shendy Pratama Solly Aryza Theresia Sunarni Wijaya, Laurentius Ricardo Willy Christy Yuliana Melita Pranoto yulisa geni, bias