Judul: Detection of Over Roasting Coffee Beans and Lack of Nutrition After Roasting Process Using YOLOv8.
Abstrak: Coffee is one of the most consumed commodities worldwide, including Indonesia. With a lot of consumption, Indonesia is also one of the countries that produce the 4th most coffee in the world. In producing good coffee beans there are good and correct processing processes. One of the processes is sorting coffee beans that have been roasted and removing coffee beans with defects such as roasting too long and lack of nutrients in the coffee beans. The process of sorting coffee beans can be very laborious and time-consuming. The process of sorting coffee beans can be combined with machine learning. Because each defect has a different color, machine learning can be applied to coffee bean images to determine coffee defects. There are 225 images used with each image having defects and non-defects. While the model used is YOLOv8 as object detection which applies a convolutional neural network to extract features in images. The epoch used is 300 and the batch is 16. As a result, the model can identify defective coffee beans and separate them from non-defective coffee beans. Evaluation is applied to validation dataset and produces 54,6% precision, 84,2% recall and 73,6% Mean Average Precision (mAP), Kopi merupakan salah satu komoditas yang paling banyak di konsumsi di seluruh dunia, termasuk indonesia. Dengan konsumsi yang banyak, indonesia merupakan salah satu negara yang paling banyak memproduksi kopi ke-4 seDunia. Dalam menghasilkan biji kopi yang baik terdapat proses-proses pengolahan yang baik dan benar. Salah satu prosesnya adalah menyortir biji kopi yang sudah dipanggang dan membuang biji kopi yang cacat seperti terlalu lama dipanggang dan kurangnya nutrisi pada biji kopi. Proses sortir biji kopi bisa sangat melelahkan dan menghabiskan waktu yang lama. Proses sortir biji kopi dapat digabungkan dengan machine learning. Karena setiap defect memiliki warna yang berbeda, maka machine learning dapat diterapkan pada gambar biji kopi untuk menentukan cacat pada kopi. Gambar yang dipakai berjumlah 225 dengan setiap gambar memiliki defect dan non-defect. Sedangkan model yang digunakan adalah YOLOv8 sebagai deteksi objek yang menerapkan convolutional neural network untuk mengekstrak fitur dalam gambar. Epoch yang digunakan 300 dan batch bernilai 16. Hasilnya model dapat mengenali biji kopi cacat dan memisahkannya dari biji kopi non-defect. Evaluasi diterapkan pada dataset validasi dan menghasilkan 54,6% precision, 84,2% recall dan 73,6% Mean Average Precision (mAP)
Keyword: computer vision, deep learning, neural network, YOLOv8

Judul: A Deep Learning-Based Computer Vision Model for Ubiquitous Quality Assessment on Green Coffee Bean
Abstrak: The green coffee quality assessment process, significantly defective beans inspection, is one of the most crucial aspects that significantly impact the taste and flavor of coffee beverages. The current green coffee defect bean quality assessment was done by a skilled human and expert who knows coffee quality. The process itself consumes much time and is unsuitable during trading negotiation or other instances that need a fast result. Continuous and repetitive assessing green coffee beans can also lower accuracy and quality due to fatigue, bias, and decreasing concentration. Computer vision is an alternative to develop assisting tool for green coffee quality assessment. This work presents a deep learning-based computer vision model for green coffee defective bean identification for quality assessment and an ANN model for green coffee quality grade prediction. The computer vision model is developed to be deployed in the mobile environment to create a ubiquitous quality assessment system. The requirement analysis for the quality assessment system was done by observation and interview with quality assessor along with the green coffee trade. The computer vision model was developed in compliance with the requirement analysis and achieved 76.5% accuracy. The ANN model for quality grade prediction accuracy reached 90.58%. It is imperative to add other defect types for different green coffee standards and train the model to various green coffee processing methods to broaden its capabilities. This work can help lessen the workload of coffee quality assessors in defective bean inspection and speed up the work process., Proses penilaian mutu biji kopi hijau, terutama inspeksi terhahap biji buruk adalah salah satu apsek paling penting yang berdampak signifikan terharap rasa dan aroma dari minuman kopi. Saat ini, penilaian mutu tersebut dilakukan oleh pakar yang merupakan ahli mengenai kualitas kopi. Proses penilaian tersebut membutuhkan banyak waktu dan tidak cocok untuk dilakukan pada saat negosiasi perdagangan atau kondisi tertentu yang membutuhkan hasil cepat. Kegiatan penilaian kualitas biji kopi yang dilakukan terus menerus dan berulang-ulang juga dapat menurunkan keakuratan dan kualitas penilaian karena kelelahan, bias, dan menurunnya konsentrasi penilai. Computer vision adalah salah satu alternatif untuk membuat sebuah alat bantu untuk penilaian kualitas biji kopi. Penelitian ini menggunakan model computer vision berbasis deep learning untuk melakukan deteksi dan identifikasi biji buruk dan model ANN untuk menentukan kelas kualitas biji kopi hijau. Model computer vision dibuat untuk tujuan penggunaan pada lingkungan seluler agar dapat membuat sebuah sistem penilaian kualitas yang dapat digunakan dimana-mana. Analisis kebutuhan untuk sistem penilaian kualitas dilakukan melalui observasi dan interview dengan pakar kopi pada perdagangan kopi. Model computer vision dikembangkan sesuai dengan analisis kebutuhan dan berhasil mencapai akurasi sebesar 76,5%. sedangkan Model ANN untuk penentuan kelas mutu mencapai akurasi 90,58%. Untuk meningkatkan efikasi dari penelitian ini, perlu ditambahkan jenis dan tipe biji buruk untuk kopi hijau berdasarkan standar lain. Pengembangan model lebih lanjut untuk meningkatkan kemampuan model dapat dilakukan dengan menggunakan tipe biji kopi hijau yang berbeda. Peneitian ini dapat mengurangi beban kerja pakar kopi dalam melakukan inspeksi biji buruk dan mempercepat proses kerjanya.
Keyword: computer vision, quality assessment, green coffee bean

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: