Judul: Design of Cartesian Robot Model for Controlling Leaf Rust and Spots on Arabica Coffee Seedlings Based on Deep Learning
Abstrak: Bibit kopi arabika merupakan fase yang sangat rentan terhadap serangan penyakit karat dan bercak daun. Umumnya pengendalian penyakit dilakukan dengan penyemprotan fungisida secara menyeluruh. Teknik ini dinilai berbahaya karena dapat meracuni tanaman yang sehat dan mendorong timbulnya penyakit baru. Penelitian ini bertujuan merancang bangun model cartesian robot untuk mengendalikan penyakit karat dan bercak daun pada bibit kopi arabika yang dipandu computer vision berbasis deep learning. Metode pengembangan algoritma deep learning diawali dengan pengumpulan dan pelabelan dataset. Pelabelan dataset ditempuh melalui dua cara yaitu pelabelan secara langsung dan pelabelan dengan proses segmentasi. Hasil training dari pelabelan melalui proses segmentasi memiliki nilai mAP (Mean Average Precision) 93,33% dengan nilai akurasi 72,59%, presisi 77,04%, recall 92,63%, dan F1 score 84,12% dan hasil training dengan pelabelan secara langsung memiliki nilai mAP 72,11% dengan nilai akurasi 49,29%, presisi 57,49%, recall 77,57%, dan F1 score 66,03%. Unit aplikator fungisida dibuat dengan prinsip two-axis cartesian robot. Berdasarkan uji peforma model, dihasilkan rata-rata error pergerakan aktuator untuk menggerakkan endeffector pada sumbu X dan Y sebesar 0,28 mm dan error jarak antara pergerakan end effector terhadap jarak aktual penyakit diperoleh rata rata error sebesar 3,5 mm.
Keyword: arabica coffee seedlings, cartesian robot, deep learning, leaf rust, leaf spot

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: Peran Surfaktan Setiltrimetilamonium Bromida dan Natrium Dodesil Sulfat pada Sintesis ZnO dengan Metode Hidrotermal
Abstrak: Zno crystal is the main preference material in various applications recently, such as in semiconductors, ultraviolet lasers, and catalysts. This study aims to synthesize ZnO with addition of surfactant. ZnO crystals were synthesized by reacting ZnSO4, KOH, and surfactants using hydrothermal method. The first step was to determine the best ratio of ZnSO4: KOH (1:2; 1:3; 1:4, and 1:5) based on X-ray diffraction. The second step was to evaluate the role of surfactan cetyltrimethylammonium bromide (CTAB) and sodium dodecyl sulfate (SDS) on the synthesis of ZnO crystals. The result of this study showed the best ratio of ZnSO4:KOH was 1:3, based on the least of impurities and the highest degree of crystallinity (85.64%). The addition of surfactant CTAB and SDS in the synthesis of ZnO increased the degree of crystallinity, which were 98.28% and 92.17%, respectively. Based on mathematical calculation using Scherrer equation, the crystal size without surfactant was 32 nm, whereas with CTAB and SDS the crystallited size were 28 nm and 29 nm, respectively. The degree of crystallinity and crystal size indicated that CTAB surfactant produce ZnO crystals with the best characteristics as compared to SDS surfactant or without surfactant.
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