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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)
Al-Riza, Dimas Firmanda
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Agriculture, Biological Sciences & Forestry Computer Science & IT Engineering Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering

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Pendugaan Rendemen Tebu Menggunakan Sifat Biolistrik dan ANN untuk Pengembangan Alat Ukur Cepat Rendemen Tebu Sucipto, Sucipto; Utomo, Rhamdani Widyo; Al-Riza, Dimas Firmanda; Yuliatun, Simping; Supriyanto, Supriyanto; Somantri, Agus Supriatna
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 3: Juni 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (140.8 KB) | DOI: 10.25126/jtiik.201853635

Abstract

Tujuan penelitian ini yaitu untuk mengetahui hubungan sifat biolistrik pada berbagai ruas tebu dan waktu tunda giling serta hubungan sifat biolistrik dengan rendemen tebu menggunakan metode jaringan syaraf tiruan (ANN). Sifat biolistrik yang digunakan meliputi frekuensi, kapasitansi (C), impedansi (Z), dan konstanta dielektrik (k). Pada penelitian ini digunakan faktor ruas tebu (atas, tengah, dan bawah) dan waktu tunda giling (hari ke-0, 1, dan 2). Hasil riset menunjukkan bahwa ruas tebu bagian bawah memiliki nilai rendemen lebih besar dari bagian tebu lain. Rendemen semakin berkurang seiring waktu penundaan. Nilai kapasitansi dan konstanta dielektrik menurun seiring lama waktu penundaan. Topologi ANN terpilih adalah 4-40-30-1, dengan 4 node (frekuensi, kapasitansi, konstanta dielektrik, dan frekuensi) sebagai input layer, 40 node pada hidden layer ke 1 dan 30 node pada hidden layer ke-2, serta 1 node yakni rendemen sebagai output layer. Topologi jaringan terpilih memiliki akurasi 99,13% saat training dan 97,29% saat pengujian. Sifat biolistrik dan ANN dapat dikembangkan sebagai alat ukur cepat rendemen tebu.
Enzyme dosage detection to degrade feathers in edible bird’s nests: A comparative convolutional neural networks study Liana, Verianti; Arifiandika, Rizal; Rohmatulloh, Bagas; Nafi’ah, Riris Waladatun; Hidayat, Arif; Hendrawan, Yusuf; Al-Riza, Dimas Firmanda; Mahatmanto, Tunjung; Nugroho, Hermawan
Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Vol 6, No 4 (2023)
Publisher : Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.afssaae.2023.006.04.6

Abstract

Edible Bird’s Nest (EBN), a costly food product made from swiftlet’s saliva, has encountered a longstanding problem of plucking the swiftlet’s feather from the nests. The destructive and inefficient manual process of plucking the feathers can be substituted with a serine protease enzyme alternative. Accurate detection of enzyme dosage is crucial for ensuring efficient feather degradation with cost-effective enzyme usage. This research employed the transfer learning method using pretrained Convolutional Neural Networks (Pt-CNN) to detect enzyme dosage based on EBN’s images. This study aimed to compare the image classification mechanisms, architectures, and performance of three Pt-CNN: Resnet50, InceptionResnetV2, and EfficientNetV2S. InceptionResnetV2, using parallel convolutions and residual networks, significantly contributes to learning rich informative features. Consequently, the InceptionResnetV2 model achieved the highest accuracy of 96.18%, while Resnet50 and EfficientNetV2S attained only 30.44% and 17.82%, respectively. The differences in architecture complexity, parameter count, dataset characteristics, and image resolution also play a role in the performance disparities among the models. The study’s findings aid future researchers in streamlining model selection when facing limited datasets by understanding the reasons for the model’s performance and contributing to a non-destructive and quick solution for EBN’s cleaning process.  
Co-Authors Agus Supriatna Somantri Arif Hidayat Arifiandika, Rizal Liana, Verianti Nafi’ah, Riris Waladatun Nugroho, Hermawan Rohmatulloh, Bagas Simping Yuliatun, Simping Sucipto Sucipto Supriyanto Supriyanto Tunjung Mahatmanto Utomo, Rhamdani Widyo Yusuf Hendrawan