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: 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: 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