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: Classification of Cherry Tomato Grade Based on Color Using Image Processing
Abstrak: Tomat cherry biasanya dijual dalam kemasan retail dengan ukuran dan warna yang hampir seragam, oleh karena itu perlu digolongkan ke dalam kelas mutu tertentu sebelum dikemas. Salah satu parameter mutu yang sering digunakan karena mudah adalah tingkat kematangan buah yang diwakili oleh warna kulit buah, namun pekerjaan ini dalam waktu lama cukup melelahkan pekerja sehingga dapat menurunkan konsistensi pemutuan. Metode pengolahan citra dan machine learning dapat digunakan untuk melakukan klasifikasi mutu tomat cherry untuk menggantikan pekerjaan manual. Tujuan dari penelitian ini adalah mempelajari mengolah informasi warna pada tomat cherry menggunakan pengolahan citra serta membangun, menguji, dan menentukan akurasi model klasifikasi mutu tomat cherry menggunakan machine learning. Citra tomat diambil dari berbagai sisi yaitu sisi atas, bawah, kanan dan kiri. Fitur yang diekstrak dan digunakan dalam pembuatan model adalah color moment dan algoritma machine learning yang digunakan adalah Support Vector Machine (SVM). Model dengan akurasi tertinggi didapatkan bila menggunakan data citra buah tomat dari sisi bawah dan kanan yaitu sebesar 98,81%. Sedangkan, model dengan akurasi terendah didapatkan menggunakan data citra buah tomat dari sisi atas yaitu sebesar 96,43%., Cherry tomatoes are usually sold in retail packaging with almost uniform sizes and colors, therefore they need to be classified into a certain quality class before being packaged. One of the quality parameters that is often used is fruit ripeness level indicated by the color of the fruit skin, but working for a long time is quite tiring for workers so that it can reduce consistency in grading. Image processing methods and machine learning can be used to classify the quality of cherry tomatoes to replace manual work. The purpose of this research is to study the color information on cherry tomatoes using image processing and to build, test, and determine the accuracy of model for the quality classification of cherry tomatoes using machine learning. Tomato images are taken from various sides, namely the top, bottom, right and left sides. The features extracted and used in the model development are color moments and the machine learning algorithm used is Support Vector Machine (SVM). The model with the highest accuracy is obtained when using tomato image data form the bottom and right sides, which was 98.81%. Meanwhile, the model with the lowest accuracy was obtained using tomato image data from the top side, which was 96.43%.
Keyword: Cherry tomatoes, color, image processing, quality, SVM

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: