Judul: Design and Development of Document Control for Quality Management System using Neural Network.
Abstrak: At the end of 2008 the number of organizations (products and services) in Indonesia that are implementing ISO 9001:2008 Quality Management System (QMS) is as much as 5713 organizations. In the implementation of QMS, the organization is required to deploy six documented procedures and two of them are document control procedures and records control procedures. This research aims to to design a prototype of document control applications by applying Back Propagation Artificial Neural Networks (ANN) algorithms to recognize the document number. The document were in hardcopy or recorded form. To get the best pattern recognition, training process and testing were carried out using a number of data. The amount of data used in this research as many as 1,400 data, 75% is used as training data and 30% used as testing data. The best ANN model was obtained using 80 input layer, 30 hidden layer, and 11 output layer. The model is able to produce a validation accuracy of 99.49% and testing accuration of 97.62%. The prototype of document control application is done by creating a six module interface of document control process, namely the process of user data input, data input unit, form and image data input forms, distribution forms, input data and image recording, viewing and deleting expired recording, and tracking recording. At the end of the research was conducted simulation and testing of applications that are built. The results of the tests provide an accuracy of 97% success.
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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: Formulasi Hamiltonian untuk Menggambarkan Gerak Gelombang Soliter Dimensi Tiga di Permukaan Laut
Abstrak: Gelombang permukaan adalah gelombang yang terjadi perbedaan rapat massa antara air dan udara. Salah satu gelombang permukaan yang ditinjau adalah gelombang soliter. Formulasi gelombang soliter dilakukan dengan menggunakan formulasi Hamiltonian.
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