Judul: Development of grading system based on image processing and artificial neural network for green coffee sorting equipment with belt konveyor type
Abstrak: The objective of this research was to develop a grading system which consisted of a computer program of image processing and artificial neural network to identify quality of green coffee namely A, B, C, and RJ (reject). Two images of the green coffee from top and bottom captured by two cameras were analyzed to get six quality parameters which matched the green coffee quality criteria namely length, area, perimeter, defect area, index of red color, and index of green color. Those six quality parameters were used as training data inputs (75% - 768 kernel) of the developed artificial neural network (ANN). Eighteen variations of ANN were developed for ANN training purposes. The weight of the selected ANN architecture were used to identify the quality class of testing data (25%), and then integrated with image processing program so the program could identify green coffee quality class automatically. The total accuracy of ANN was 67% from top camera with A 59%, B 53%, C 70%, RJ 84%, and the total accuracy was 71% from bottom camera with A 75%, B 45%, C 73%, and RJ 92% based from 256 testing data. The total accuracy of ANN from combination of both cameras was 68%. New training was developed in order to increase prediction accuracy by modified ANN inputs. Prediction accuracy produced by the new ANN weight were A 78%, B 53%, C 70%, RJ 98%, and the total accuracy was 75%
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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: Koksidiosis pada Sapi Disebabkan Eimeria Zuernii (Rivolta,1887)
Abstrak: Koksidia merupakan parasit intraseluler yang menyerang traktus digestiyus terutama bagian USUS. Penyebab penyakit ini adalah dari genus Eimeria. Koksidia yang paling patogen terhadap ternak sapi adalah Eimeria Ziuernii (Rivolta, 1887 dalam Todd dan Ernst, 1977). Siklus hidup Eimeria zuernii terdiri dari fase eksogen yang disebut juga dengan tahap sporogoni dan fase endogen yang terdiri dari tahap skizogoni dan tahap gametogoni (Soulsby, 1982).
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