Judul: Evaluasi Mutu Green Beans Kopi Arabika dengan Model Deep Learning YOLO
Abstrak: Proses penilaian mutu biji kopi untuk memperoleh biji kopi berkualitas baik masih dilakukan secara manual. Proses tersebut cukup menyita waktu, sehingga memungkinkan terjadinya kesalahan akibat kelelahan mata pekerja. Tujuan penelitian ini membuat model deep learning untuk mengevaluasi mutu green beans kopi arabika menggunakan algoritma You Only Look Once (YOLO). Penelitian ini terdiri dari beberapa tahapan: (1) pengumpulan dataset, (2) pelabelan dataset, (3) training dataset, (4) uji fungsional model deep learning, dan (5) analisis hasil uji fungsional model untuk mengetahui nilai akurasi, presisi, recall, dan F1-Score. Training deep learning terhadap 11.591 dataset asli dilakukan menggunakan algoritma YOLOv5m dan YOLOv8m. Jumlah epoch yang digunakan pada algoritma YOLOv5 sebanyak 330, sedangkan algoritma YOLOv8 sebanyak 121 epoch. Nilai akurasi, presisi, dan recall pada algoritma YOLOv5 masing-masing sebesar 90,49%; 58,24%; dan 53,61%. Nilai akurasi, presisi, dan recall pada algoritma YOLOv8 sebesar 91,56%; 62,09%; dan 63,10%. Ditinjau dari nilai F1-Score algoritma YOLOv8 lebih baik daripada YOLOv5, yaitu sebesar 0,56%. YOLOv8 menggunakan nilai epoch yang sedikit dapat memberikan nilai akurasi, presisi, recall dan F1-Score yang lebih tinggi daripada YOLOv5. Hal tersebut dikarenakan adanya pembaharuan fitur dataloader mosaic dan anchor-free detection system pada algoritma YOLOv8 sehingga dapat meningkatkan kinerja model. Namun, kedua algoritma YOLO tersebut masih kurang baik dalam mendeteksi objek-objek kecil., The process of assessing coffee beans quality to obtain good coffee beans is still carried out manually. The process is quite time-consuming, making it possible for errors due to workers' eyestrain. The purpose of this study was to create a deep learning model to evaluate the quality of arabica coffee green beans using the You Only Look Once (YOLO) algorithm. This research consists of several stages: (1) dataset collection, (2) dataset labeling, (3) dataset training, (4) deep learning model functional test, and (5) analysis of model functional test results to determine the value of accuracy, precision, recall, and F1-Score. Deep learning training on 11,591 original datasets was conducted using YOLOv5m and YOLOv8m algorithms. The number of epochs used in the YOLOv5 algorithm is 330, while the YOLOv8 algorithm is 121 epochs. The accuracy, precision, and recall values of the YOLOv5 algorithm were 90.49%; 58.24%; and 53.61%, respectively. The accuracy, precision, and recall values of the YOLOv8 algorithm were 91.56%; 62.09%; and 63.10%. Judging from the F1-Score value, the YOLOv8 algorithm is better than YOLOv5, which is 0.56%. YOLOv8 using a small epoch value can provide higher accuracy, precision, recall, and F1-Score than YOLOv5. This is due to the update of the mosaic data loader feature and anchor-free detection system on the YOLOv8 algorithm so that it can improve model performance. However, both YOLO algorithms are still not good at detecting small objects.
Keyword: arabica coffee, deep learning, green bean, quality evaluation, YOLO

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: Inducible and constitutive synthesis of penicillinase by staphylococcus aureus
Abstrak: When tested quantitatively for the production of penicillinase, eighteen of one hundred and twenty-two naturally occurring penicillin-resistant staphylococci were classified as constitutive or microconstitutive. Fourteen of the constitutive strains were not typable, the other four were lysed by group III phage. The inducible and constitutive strains of Staphylococcus aureus were resistant to concentrations of mercuric chloride that inhibited strains sensitive to penicillin. Transfer of the penicillinase determinant by transduction from a microconstitutive strain to a sensitive strain resulted in recombinants that produced inducible penicillinase.
Keyword: Staphylococcus aureus, Staphylococcal penicillinase, Bacteriophage