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Abstrak :
[ABSTRAK
Perkembangan teknologi yang semakin cepat menjadikan teknologi penting di berbagai sektor kehidupan, khususnya di bidang industri. Perkembangan zaman membuat tingkat permintaan akan suatu produk menjadi berubah sehingga industri harus meningkatkan kinerja produksinya. Teknologi yang digunakan merupakan teknologi automasi di mana di dalamnya terdapat pengendali. Pengendali yang digunakan oleh kebanyakan industri merupakan pengendali konvensional karena pengendali konvensional relatif murah dan efektif. Akan tetapi pengendali konvensional ini tidak dapat digunakan untuk sistem yang kompleks dan non linear. Pengendali konvensional, misalnya pengendali PID, tidak dapat mengatasi terjadinya perubahan karakteristik dari sistem secara otomatis. Untuk itu diperlukan sistem pengendali yang mampu mengatasi perubahan karakteristik secara otomatis dan dapat beradaptasi dengan dinamika perubahan sistem yang diakibatkan adanya perubahan kondisi lingkungan kerja. Sistem pengendali yang dianggap mampu untuk beradaptasi dengan perubahan karakteristik dari sistem secara otomatis adalah pengendali berbasis Neural Network. Dalam percobaan ini parameter yang digunakan untuk menentukan pengendali yang baik adalah adaptivity serta kecepatan respon pengendali. Pada hasil simulasi ini didapatkan bahwa pengendali berbasis Neural Network dengan metode Radial Basis Function Neural Network (RBFNN) lebih baik dan lebih cepat dalam menanggapi perubahan karakteristik sistem dibandingkan dengan pengendali Neural Network berbasis backpropagation. ABSTRACT
Development of technology has been rapidly increasing that make technology as an important aspect in many sectors of life, especially in industrial sector. The times have changed the demand of a product so that industry has to enhance its production capacity. Technology used in industry is automation technology which has controller inside. Controller used in industry mostly is conventional controller because it has low price and good effectivity. However, conventional controller can?t be used for complex and non-linear system. For example, PID controller, it can?t handle the changes of system?s characteristic automatically. PID controller has to be reset to handle the new system?s characteristic. Because of that, industry need a controller that has ability to handle the changes of the system?s characteristic automatically and adapt with the dynamics of system?s changes caused by external factor. Controller system that has been considered for the ability of handling the changes of system?s characteristic automatically is Neural Network based controller. In this experiment, the parameters used to determine good controller is adaptivity of the system also the speed of controller response. The result of the experiment shows that Neural Network with Radial Basis Function Neural Network (RBFNN) based controller has better response to the changes of the system?s characteristic than Backpropagation based Neural Network controller.;Development of technology has been rapidly increasing that make technology as an important aspect in many sectors of life, especially in industrial sector. The times have changed the demand of a product so that industry has to enhance its production capacity. Technology used in industry is automation technology which has controller inside. Controller used in industry mostly is conventional controller because it has low price and good effectivity. However, conventional controller can?t be used for complex and non-linear system. For example, PID controller, it can?t handle the changes of system?s characteristic automatically. PID controller has to be reset to handle the new system?s characteristic. Because of that, industry need a controller that has ability to handle the changes of the system?s characteristic automatically and adapt with the dynamics of system?s changes caused by external factor. Controller system that has been considered for the ability of handling the changes of system?s characteristic automatically is Neural Network based controller. In this experiment, the parameters used to determine good controller is adaptivity of the system also the speed of controller response. The result of the experiment shows that Neural Network with Radial Basis Function Neural Network (RBFNN) based controller has better response to the changes of the system?s characteristic than Backpropagation based Neural Network controller., Development of technology has been rapidly increasing that make technology as an important aspect in many sectors of life, especially in industrial sector. The times have changed the demand of a product so that industry has to enhance its production capacity. Technology used in industry is automation technology which has controller inside. Controller used in industry mostly is conventional controller because it has low price and good effectivity. However, conventional controller can’t be used for complex and non-linear system. For example, PID controller, it can’t handle the changes of system’s characteristic automatically. PID controller has to be reset to handle the new system’s characteristic. Because of that, industry need a controller that has ability to handle the changes of the system’s characteristic automatically and adapt with the dynamics of system’s changes caused by external factor. Controller system that has been considered for the ability of handling the changes of system’s characteristic automatically is Neural Network based controller. In this experiment, the parameters used to determine good controller is adaptivity of the system also the speed of controller response. The result of the experiment shows that Neural Network with Radial Basis Function Neural Network (RBFNN) based controller has better response to the changes of the system’s characteristic than Backpropagation based Neural Network controller.]

Abstrak :
Diabetes mellitus merupakan salah satu penyakit tidak menular dengan angka kematian tertinggi di dunia. Hal ini terjadi karena tingginya resiko komplikasi yang disebabkan pernyakit tersebut. Salah satu cara pencegahan yang dapat dilakukan adalah dengan melakukan pendeteksian lebih awal, salah satunya dengan menggunakan metode iridologi. Metode ini dapat mendeteksi kerusakan organ tubuh melalui tanda-tanda yang muncul pada iris. Dengan menggunakan metode tersebut penelitian ini dilakukan untuk mengklasifikasi penyakit diabetes menggunakan Convolutional Neural Network. Sistem ini mengevaluasi sebanyak 35 subjek normal dan 14 subjek diabetes. Adapun beberapa tahapan yang dilakukan untuk mengelola citra, di antaranya filtering, grayscaling, normalisasi, segmentasi, dan klasifikasi. Selain itu, sistem ini juga melakukan berbagai variasi untuk memperoleh konfigurasi terbaik, seperti variasi citra segmentasi dan tanpa segmentasi, variasi lebar iris, variasi bagian-bagian pankreas, variasi jumlah k-fold, dan variasi algoritma pengoptimalan menggunakan SGDM, Adam dan RMSProp. Sistem ini memperoleh akurasi sebesar 96,43% dengan variasi citra tanpa segmentasi berukuran  piksel menggunakan algoritma Adam dengan learning rate 0,001.

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Diabetes mellitus is one of the uncontagious diseases with the highest mortality rate in the world. This happens because of the high risk of complications caused by this disease. One of the preventative ways is to do early detection, one of which is by using the iridology method. This method detects damage to the body's organs through the signs that appear on the iris. Using that method, this study was conducted to classify diabetes using Convolutional Neural Network. This system evaluates 35 normal subjects and 14 diabetes subjects. Several steps are taken to process the image, such as filtering, grayscaling, normalization, segmentation, and classification. Other than that, this system also performs various variations to obtain the best configuration, such as variations in image segmentation and without segmentation, variations in iris width, variations in parts of the pancreas, variations in the number of k-fold, and variations in optimization algorithms using SGDM, Adam and RMSProp. This system obtained an accuracy of 96.43% with variations image without segmentation size pixel using Adam's algorithm with a learning rate of 0.001.