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Andri Putra Kesmawan
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Perumahan Sidorejo, Jl. Sidorejo Gg. Sadewa No.D3, Sonopakis Kidul, Ngestiharjo, Kapanewon, Kasihan, Kabupaten Bantul, Daerah Istimewa Yogyakarta 55184
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INDONESIA
Indonesian Journal of Applied Technology
Published by Indonesian Journal Publisher
ISSN : -     EISSN : 30320224     DOI : https://doi.org/10.47134/ijat
Core Subject : Engineering,
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Industrial & Manufacturing Engineering Mechanical Engineering
Indonesian Journal of Applied Technology is officially registered in the National Research and Innovation Agency, Directorate of Multimedia Repository and Scientific Publishing. This journal is published four times a year (January, April, July and October) by Indonesian Journal Publisher. IJAT a scientific journal, double-blind peer-reviewed and open-access journal. IJAT is an academic journal organized which focus and scope: Information and Communication Technology (ICT), Engineering and Manufacturing Technology, Biotechnology and Bioengineering, Energy and Environmental Technology, Health Technology and Biomedical Engineering, Agricultural and Food Technology, Renewable and Clean Energy, Educational Technology.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 2 Documents
 1 
Search results for , issue "Vol. 2 No. 4 (2025): October" : 2 Documents clear
Teknik Fermentasi Buah Berbasis Fruit Classic Enzyme Untuk Mengatasi Masa Simpan Buah Melalui Minuman Organik Ulfah, Maria; Ihtiar, Ade; Fillah, Alifia; Sari, Gita; Faizsyahrani, Lilla
Indonesian Journal of Applied Technology Vol. 2 No. 4 (2025): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ijat.v2i4.5066

Abstract

The short shelf life of fruits often leads to quick spoilage, making them unsuitable for further use. Fruit Classic Enzyme offers an alternative to extend fruit shelf life through the principles of functional food biotechnology. This study aims to identify the techniques of Fruit Classic Enzyme in prolonging fruit freshness through organic fermented beverages and to explore its potential as an entrepreneurial product. The research employed a qualitative descriptive method, describing the quality and characteristics of the Fruit Classic Enzyme product. The results revealed the production process, laboratory analysis, and various derivative products, including Fruit Classic Enzyme Pure, Ready to Drink, Anti-Inflammatory, and Jelly variants. In conclusion, Fruit Classic Enzyme serves as an effective innovation to extend fruit shelf life using functional food biotechnology fermentation, resulting in diverse organic beverage products.
Penerapan Metode Transfer Learning untuk Klasifikasi Penyakit Tanaman Kembang Kol dengan Arsitektur Inception V3 Marsevin, Randy; Aviani, Tri Hasanah Bimastari; Martadinata, A. Taqwa; Santoso, Budi
Indonesian Journal of Applied Technology Vol. 2 No. 4 (2025): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ijat.v2i4.5167

Abstract

Indonesia sebagai negara agraris menghadapi tantangan dalam produksi pertanian, khususnya akibat serangan hama dan penyakit yang menurunkan kualitas hasil panen kembang kol (Brassica oleracea var. botrytis L). Penelitian ini mengembangkan model klasifikasi penyakit tanaman kembang kol menggunakan metode transfer learning berbasis arsitektur Inception V3. Dataset terdiri dari empat kelas: Bacterial Spot Rot, Black Rot, Downy Mildew, dan No Disease, diperoleh dari Kaggle. Proses pengembangan mengikuti tahapan CRISP-DM, mulai dari pra-pemrosesan data, pelatihan model, hingga evaluasi. Model dilatih dengan memanfaatkan bobot awal dari ImageNet, diikuti dengan penyesuaian beberapa lapisan klasifikasi dan penggunaan teknik fine-tuning serta augmentasi data. Evaluasi performa dilakukan dengan metrik akurasi, precision, recall, dan F1-score. Hasil akhir menunjukkan akurasi validasi sebesar 93,75% dan akurasi pengujian mencapai 99%, dengan nilai precision dan recall yang seimbang (93,75%). Model terbukti efektif dalam mengklasifikasi penyakit tanaman dan memiliki potensi untuk diterapkan pada sistem deteksi otomatis berbasis citra guna mendukung pertanian presisi secara real-time.

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