Judul: Detection of Alternaria Leaf Spot and Black Rot Diseases in Cauliflower (Brassica oleracea var. botrytis) Based on Deep Learning
Abstrak: Produksi kembang kol pada tahun 2022 menurun sebesar 5,54% dari produksi tahun sebelumnya. Penurunan produksi dapat disebabkan oleh berbagai faktor, salah satunya adalah serangan hama dan penyakit. Deteksi terhadap penyakit tertentu serta tindakan pengendalian yang tepat oleh petani sangat diperlukan untuk mencegah kerugian yang ditimbulkan. Tujuan dari penelitian ini adalah membuat model deep learning untuk deteksi penyakit bercak daun alternaria dan busuk hitam pada kembang kol dan mengintegrasikannya dengan website sebagai sistem pakar. Tahapan penelitian terdiri dari (1) pengumpulan dataset, (2) augmentasi dataset, (3) pelabelan dataset, (4) training model, (5) evaluasi model, dan (6) implementasi model. Dataset terdiri dari 3 kelas yaitu bercak daun alternaria, busuk hitam, dan sehat. Hasil evaluasi model pada 354 gambar uji menunjukkan bahwa YOLOv8 memiliki accuracy 99%, precision 98,89%, recall 98,60%, dan F1-score 98,74%. SSD MobileNetV2 memiliki accuracy 96,24%, precision 94,11%, recall 91,73%, dan F1-score 92,91%. Dengan demikian, YOLOv8 lebih baik dalam mendeteksi penyakit kembang kol dan telah mampu mendeteksi penyakit kembang kol dengan akurasi yang tinggi. Model dapat digunakan untuk mendeteksi penyakit kembang kol melalui website yang dikembangkan menggunakan HTML, CSS, dan Flask yang dapat diakses secara publik., The production of cauliflower in 2022 decreased by 5.54% compared to the previous year. This decline in production can be attributed to various factors, one of which is pest and disease infestation. Detection of specific diseases and appropriate control measures by farmers are crucial to prevent resulting losses. The objective of this research is to develop a deep learning model for detecting alternaria leaf spot and black rot diseases in cauliflower and integrate it into a website as an expert system. The research phases consist of (1) dataset collection, (2) dataset augmentation, (3) dataset labeling, (4) model training, (5) model evaluation, and (6) model implementation. The dataset comprises three classes: alternaria leaf spot, black rot, and healthy. The evaluation results of the model on 354 test images show that YOLOv8 achieves an accuracy of 99%, precision of 98,89%, recall of 98,60%, and F1-score of 98,74%. SSD MobileNetV2 achieves an accuracy of 96,24%, precision of 94,11%, recall of 91,73%, and F1-score of 92,91%. Therefore, YOLOv8 is superior in detecting cauliflower diseases and has demonstrated high accuracy in detecting cauliflower diseases. The model can be used to detect cauliflower diseases through a website developed using HTML, CSS, and Flask, which can be executed on a local The model can be used to detect cauliflower diseases through a website developed using HTML, CSS, and Flask, which is publicly accessible.
Keyword: cauliflower disease, deep learning, SSD MobileNetV2, YOLOv8

Judul: Model Deep Learning Untuk Deteksi Penyakit Pada Tanaman Paprika (Capsicum Annuum L.), Tomat (Solanum Lycopersicum) Dan Strawberry (Fragaria Sp.).
Abstrak: Penyakit yang menyerang tanaman dapat memberikan dampak yang serius terhadap produktivitas tanaman jika tidak dikendalikan dengan tepat. Cara efektif mengendalikan penyakit serta meningkatkan hasil panen adalah melakukan identifikasi dini penyakit tanaman secara akurat. Melihat perkembangan tersebut, metode deep learning sangat berpotensi untuk dikembangkan di Indonesia. Penelitian ini bertujuan mengembangkan model deep learning untuk mendeteksi penyakit pada tanaman paprika (Capsicum Annuum L.), tomat (Solanum Lycopersicum) dan strawberry (Fragaria Sp.). Penelitian ini menggunakan deep learning dengan algoritma YOLOv7 yang terdiri dari tahapan: (1) pengumpulan dataset, (2) pengolahan dataset, (3) pelabelan dataset, (4) training dataset, (5) uji model hasil training, dan (6) implementasi model. Training deep learning terhadap 9813 dataset dengan 4 kelas parameter penyakit yaitu bercak daun, hangus daun, hawar daun, dan virus kuning keriting dilakukan dengan menggunakan algoritma YOLOv7 dan YOLOv7-X. Hasil pengujian model deep learning untuk deteksi penyakit dengan menggunakan algoritma YOLOv7 dan YOLOv7-X menunjukkan tingkat akurasi sebesar 98,90% dan 98,98%, presisi 97,83% dan 98,12%, dan nilai recall 98,07% dan 98,63%. Berdasarkan penelitian ini dapat disimpulkan bahwa model cukup stabil dalam mendeteksi penyakit dengan tepat sehingga model deep learning untuk deteksi penyakit pada tanaman paprika, tomat, dan strawberry dapat digunakan., Diseases that affect plants can have a serious impact on crop productivity if not properly controlled. An effective way to control disease and increase crop yields is by conducting early identification of plant diseases accurately. Based on these problems, the deep learning method has the potential to be developed in Indonesia. The aim of this study is to develop a deep learning model for detecting diseases of pepper plants (Capsicum Annuum L.), tomatoes (Solanum Lycopersicum), and strawberries (Fragaria Sp.). This research consists of several activities such as (1) datasets collections, (2) datasets pre-processing, (3) datasets labeling, (4) training deep learning models, and (5) testing and validating model. The model was developed using a deep learning model with YOLOv7 and YOLOv7-X algorithms using 9813 datasets with 4 classes of disease parameters, namely bacterial spot, leaf scorch, leaf blight, and yellow leaf curl virus. The results of the deep learning model test for plant diseases detection using YOLOv7 and YOLOv7-X algorithms showed accuracy rates of 98.90% and 98.98%, precision of 97.83% and 98.12%, and recall values of 98.07% and 98.63%, respectively. These results indicate that the deep learning model was capable to detect bell pepper, tomato, and strawberry plant with high accuracy and precision.
Keyword: confusion matrix, deep learning, disease detection, YOLOv7

Judul: Hamiltonian formulation for linear waves motion in two layers fluid
Abstrak: The surface wave could be considered as a wave that separates two fluids, namely water and air. Based on this assumption, it is introduced the interfacial wave, a wave between two layers of fluid with different density. The formulation of interfacial waves motion begins with deriving the base equation of irrotational ideal fluid. Furthermore, according to irrotational fluid assumption, the base equation can be stated in velocity potential. In this derivation, the fluids domain is assumed to be restricted by rigid lid boundary conditions, both at the upper and lower limit. Therefore, the interfacial waves motion can be explained in a hamiltonian formulation. In the hamiltonian formulation, total energy is defined as the sum of kinetic and potential energy. The hamiltonian system is obtained from reduction of kinetic energy by using the Dirichlet Neumann Operator. The resulted kinetic energy equation is nonlinear. Therefore, this form is linearized by first part of the Taylor expansion. This linearization gives a dispersion relation of linear wave. Based on this dispersion relation, the phase speed of the linear wave depends on the density ratio of the two layers fluid.
Keyword: