Judul: Detection of Mechanical Damage  in Cherry Tomatoes Based on Fluorescent Imagery with Convolutional Neural  Network
Abstrak: Tomat ceri merupakan komoditas buah dengan nilai ekonomis tinggi bagi Indonesia. Namun, hingga saat ini, kerusakan mekanis sering terjadi dalam berbagai tahap, mulai dari panen hingga penyimpanan. Deteksi kerusakan mekanis pada tomat ceri umumnya masih dilakukan secara manual, yakni dengan menggunakan mata secara langsung. Proses tersebut rentan terhadap subjektivitas, kelelahan, dan kesalahan pandang. Tujuan penelitian ini mengembangkan suatu metode deteksi kerusakan mekanis buah tomat ceri seperti memar dan sayat dengan citra fluoresen menggunakan metode convolutional neural network (CNN). Hasil penelitian diharapkan mampu memberikan kemudahan dalam deteksi kerusakan mekanis pada buah tomat ceri secara lebih akurat yang nantinya dapat diimplementasikan untuk proses sortasi. Penelitian ini menggunakan lampu ultraviolet tipe-c, longpass filter, circular polarizer filter. Penggunaan lampu ultraviolet dan filter-filter kamera tersebut untuk menghasilkan citra fluoresen sesuai dengan karakteristik buah tomat ceri. Penelitian ini menggunakan algoritma YOLOv8 pada Google Colaboratory. Penelitian ini terdiri dari beberapa tahapan: (1) pengumpulan dataset, (2) identifikasi dan pelabelan data, (3) training dataset, (4) pengujian model deep learning, (5) analisis hasil uji fungsional model deep learning. Nilai akurasi, presisi, recall, dan F1-score berturut-turut sebesar 95,33%; 100,00%; 87,78%, 93,49%. Hasil tersebut menunjukkan bahwa model yang dibangun mampu mendeteksi kerusakan mekanis buah tomat ceri dengan akurat dan presisi., Cherry tomatoes are an economically important crop for Indonesia. However, until now, mechanical damage has often accured at various stages, from harvest to storage. Damage detection is usually done manually through visual inspection. This process is susceptible to subjective, fatigue, and errors of view. The research aims to develop a damage detection method for cherry tomatoes using fluorescence imaging and a convolutional neural network (CNN) approach. The research results are expected to easier detect mechanical damage to cherry tomatoes more accurately, which can later be implemented in the sorting process. This research uses a type-c ultraviolet lamp, a longpass filter, a circular polarizer filter. The use of ultraviolet lights and camera filters to produce fluorescent images according to the characteristics of cherry tomatoes. This research uses the YOLOv8 algorithm on Google Colaboratory. This research consists of several stages: (1) dataset collection, (2) data identification and labeling, (3) training dataset, (4) deep learning model testing, and (5) analysis of functional test results of the deep learning model. The accuracy, precision, recall, and F1-score values were 95.33%; 100.00%; 87.78%, 93.49%. These results show that the model built can detect mechanical damage to cherry tomatoes with accuracy and precision.
Keyword: cherry tomatoes, CNN, damage detection, fluorescent, YOLO

Judul: Ripeness Detector of Cherry Tomato Based on Color Using TCS230 Sensor with Moving Conditions on Belt Conveyor
Abstrak: Proses pemilihan tomat ceri secara manual bersifat subjektif dan tidak efektif. Oleh karena itu, dibutuhkan sebuah mesin yang dapat melakukannya secara otomatis dan akurat. Penelitian ini bertujuan merancang prototipe mesin pendeteksi kematangan tomat ceri berdasarkan warna menggunakan sensor TCS230. Kemudian menguji kinerjanya dengan beberapa variasi perlakuan kecepatan dan variasi jarak antar tomat ceri. Sebanyak 10 tomat ceri merah, 10 tomat ceri oranye, dan 10 tomat ceri hijau diuji dalam penelitian ini. Sampel diuji mulai dari kecepatan 0 cm/s, kemudian dijalankan mulai dari kecepatan minimal konveyor. Selanjutnya dipercepat secara perlahan sampai dari salah satu kategori tomat menunjukkan hasil yang buruk. Kemudian pengujian jarak antar tomat sebesar 3 cm, 5 cm, dan 7 cm dengan kelajuan dari kecepatan ideal yang didapat. Hasil penelitian menunjukkan persentase keberhasilan pendeteksian tomat merah sebesar 89%, tomat oranye sebesar 98% dan untuk tomat hijau sebesar 100% pada kecepatan konveyor 30 cm/s dengan jarak antar tomat 7 cm. Kapasitas teoritisnya dapat mencapai 4 buah/detik atau 14.400 buah/jam, dengan menggunakan satu lorong sensor., The process of manually selecting cherry tomatoes is very subjective and ineffective. Therefore, needed a machine that can do it automatically and accurately. This research aims to design a prototype ripeness detector of cherry tomato-based on color using the TCS230 sensor. Then, tested its performance with some variation of speed and variation of distance between cherry tomatoes. A total of 10 red cherry tomatoes, 10 orange cherry tomatoes, and 10 green cherry tomatoes were tested in this research. The samples were tested starting at a speed of 0 cm/s, then running starting from the minimal speed of the conveyor. Further accelerated slowly up, until one of the categories of tomatoes showed bad results. Then, the next testing is to test the distance between tomatoes by 3 cm, 5 cm, and 7 cm with moving by its ideal speed. The results showed that the success percentage of red tomatoes by 89%, orange tomatoes by 98%, and green tomatoes by 100% at a conveyor speed of 30 cm/s with the distance between tomatoes by 7 cm. Its theoretical capacity can reach 4 pieces/second or 14,400 pieces/hour, just by using one aisle of the sensor.
Keyword: cherry tomatoes, color, detector, distance, speed

Judul: Asymptotic Distribution of Estimator for the Intensity Function of a Doubly Periodic Poisson Process
Abstrak: In this manuscript, estimation of the intensity function of a doubly periodic Poisson process is discussed. It is assumed that only a single realization of the Poisson process is observed in a bounded window [0,n]. It is also assumed that the period of the intensity function is known. The rate of consistency and asymptotic normality of the estimator are formulated. Finally, a confidence interval for the local intensity function is also formulated.
Keyword: