Judul: Image Classification Model of Foot and Mouth Disease  in Cow Using Convolutional Neural Network
Abstrak: Penyakit Mulut dan Kuku (PMK) pada sapi merupakan ancaman serius bagi industri peternakan di Indonesia yang dapat mempengaruhi kesehatan dan produktivitas ternak. Penelitian ini bertujuan mengembangkan aplikasi sederhana berbasis website untuk klasifikasi citra sapi sehat dan terinfeksi PMK menggunakan CNN dengan arsitektur VGG-16 dan MobileNet-V2. Dataset yang digunakan terdiri dari 197 citra, yaitu citra kelas sehat dan pmk. Proses penelitian meliputi pengumpulan dataset, pra-proses data, pembuatan model CNN, hyperparameter tuning, evaluasi model, dan implementasi sistem berbasis website. Model terbaik untuk arsitektur VGG-16 menggunakan optimizer Adam, learning rate 0,01, dan epoch 50, mencapai akurasi training 0,941, akurasi validasi 0,937, dan akurasi testing 0,905 dengan precision 0,846, recall 1,000, dan F1-score 0,917. Untuk MobileNet-V2, konfigurasi terbaik menggunakan optimizer Adam, learning rate 0,001, dan epoch 100, memberikan akurasi model pada training 0,988, akurasi model pada validasi 0,947, dan akurasi model pada testing 0,952 dengan precision 1,000, recall 0,910, dan F1-score 0,952. Penelitian ini telah berhasil membangun aplikasi sederhana berbasis web untuk klasifikasi citra sapi sehat dan PMK menggunakan framework Flask. Aplikasi ini bekerja berdasarkan sistem voting dari dua model, yaitu VGG-16 dan MobileNet-V2 yang memberikan hasil identifikasi yang sama jika kedua model menghasilkan output yang sama., Foot and Mouth Disease (FMD) in cow is a serious threat to the livestock industry in Indonesia that can affect the health and productivity of livestock. This research aims to develop a simple web-based application for image classification of healthy and FMD-infected cow using CNN with VGG-16 architecture and MobileNet-V2. The dataset used consists of 197 images, namely healthy and FMD class images. The research process includes dataset collection, data pre-processing, CNN model building, hyperparameter tuning, model evaluation, and website-based system implementation. The best model for VGG-16 architecture using Adam optimizer, learning rate 0.01, and epoch 50, achieved training accuracy 0.941, validation accuracy 0.937, and testing accuracy 0.905 with precision 0.846, recall 1.000, and F1-score 0.917. For MobileNet-V2, the best configuration uses Adam optimizer, learning rate 0.001, and epoch 100, giving model accuracy on training 0.988, model accuracy on validation 0.947, and model accuracy on testing 0.952 with precision 1.000, recall 0.910, and F1-score 0.952. This research has successfully built a simple web-based application for the classification of healthy and FMD cow images using the Flask framework. This application works based on a voting system of two models, namely VGG-16 and MobileNet-V2 which gives the same identification result if both models produce the same output.
Keyword: klasifikasi citra, Convolutional Neural Network, MobileNet-V2, Penyakit Mulut dan Kuku, VGG-16

Judul: Detection of Foot-and-mouth disease virus (FMDV) Genome Segment Using Reverse Transcription Polymerase Chain Reaction Technique
Abstrak: Penyakit mulut dan kuku (PMK) merupakan penyakit penting pada ternak yang disebabkan oleh virus RNA untai tunggal positif yang disebut Foot-and-mouth disease virus (FMDV) anggota famili Picornaviridae. Wabah PMK kembali terjadi di Indonesia pada April 2022 dan pertama kali dilaporkan di Provinsi Jawa Timur, setelah sebelumnya menyandang status bebas PMK tanpa vaksinasi sejak tahun 1990 dari World Organisation of Animal Health (WOAH). Gejala klinis PMK mirip dengan penyakit lain, sehingga dibutuhkan diagnosis konfirmasi antara lain menggunakan teknik reverse transcription polymerase chain reaction (RT-PCR) dengan prinsip amplifikasi segmen genom. Penelitian ini bertujuan menerapkan RT-PCR konvensional untuk melacak segmen genom FMDV pada sampel swab mulut dan kuku dari 18 ekor sapi suspek PMK di Kabupaten Bogor. Amplifikasi menggunakan dua pasang primer 1F/1R dan FM8/FM9 tidak menunjukkan pita amplikon sepanjang 328 base pair (bp) dan 644 bp, secara berurutan. Beberapa kemungkinan dapat memengaruhi hasil negatif tersebut. Pertama, virus sudah tidak ada pada lesi di mukosa mulut maupun bekas luka pada kuku atau jumlah virus berada di bawah ambang deteksi dari teknik ini jika sapi tersebut pernah terinfeksi FMDV. Kemungkinan lainnya adalah lesi tersebut bukan disebabkan oleh infeksi FMDV., Foot and mouth disease (FMD) is an important disease of livestock caused by positive sense single-stranded RNA virus known as Foot-and-mouth disease virus (FMDV) belonging to Picornaviridae family. Outbreaks of FMD reemerged in Indonesia in April 2022 and first reported in East Java Province, after previously having “FMD-free without vaccination” status since 1990 from the World Organization of Animal Health (WOAH). Clinical signs of FMD are similar to other vesicular diseases, confirmatory diagnosis such as reverse transcription polymerase chain reaction (RT-PCR) by amplifying FMDV genome region is necessary. This study aimed to apply conventional RT-PCR to detect the genomic region of FMDV in mouth and hoof swabs collected from 18 FMD suspected cattle in Bogor area. Amplification using two pairs of primers 1F/1R and FM8/FM9 did not produced an amplicon band of 328 base pair (bp) and 644 bp, respectively. Several things can influence the negative result. First, the virus is no longer present in the oral or hoof lesions or the amount of virus is below the detection threshold of this technique if the cow wasinfected with FMDV. Another possibility is the lesions were not caused by FMDV.
Keyword: FMDV, primer, RT-PCR, genome, vesicular

Judul: Pembuatan Sel Surya Berbasis Film Ferroelektrik Barium Strontium Titanate (Ba0,5Sr0,5TiO3)
Abstrak: Growth of films Ba0,5Sr0,5TiO3 (BST) 1 M has been done with variation of annealing in hold time 8 hours, 15 hours, 22 hours, and 29 hours at constant temperature 8500C, above Si (100) substrate p-type, by using the sol-gel process method which followed with spin coating process at 3000 rpm for 30 seconds. Optics spectroscopy shows that BST film solar cells with annealing hold time 8 hours, 15 hours, and 22 hours, works effectively in wavelength ≥ 700 nm absorption range. Yet for sample with annealing hold time about 29 hours effectively absorb in ≤ 700 nm wavelength range. It obtained BST film refractive index at range 2.0 to 7.0 to wavelength range ±370-870 nm. Band gap BST film value based on annealing hold time 8 hours, 15 hours, 22 hours, and 29 hours respectively 2.60 eV; 3.12 eV: 3.24 eV, and 2.66 eV. Electrical conductivity value which obtained, indicate that the BST film is classified as semiconductor material with conductivity order 10-5-10-4 S / cm. The result of I-V photovoltaic characterization shows the BST film potentially to be a solar cells device with efficiency according to the annealing hold time 8 hours, 15 hours, 22 hours, and 29 hours in a row are 2.68x10-5%, 3.51x10-5%, 4.53x10-5% and 3.22x10-5%
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