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Abstrak :
ABSTRACT
Skripsi ini membahas mengenai reduksi suatu kumpulan data menggunakan metode penggabungan data. Kumpulan data yang digunakan dalam penelitian ini adalah data bunga iris dengan 3 macam kelas dan data aroma dengan 18 macam kelas. Hasil penggabungan kumpulan data tersebut akan menjadi data masukan dalam pembelajaran algoritma jaringan saraf tiruan propagasi balik dan jaringan saraf probabilistik yang dipergunakan dalam penelitian ini. Hasil pembelajaran menggunakan data hasil penggabungan tersebut akan dibandingkan dengan hasil pembelajaran menggunakan data tanpa penggabungan. Hasil penelitian ini menyatakan bahwa penggunaan data hasil penggabungan akan mempercepat pembelajaran dan meningkatkan kestabilan keluaran sistem, namun mengurangi akurasi tingkat pengenalan

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ABSTRACT
This thesis discusses about reduction of a data set using data merging method. The data set used in this study are iris set data with 3 kinds of classes and odor set data with 18 kinds of classes. The result of merging the data set become the input data in the learning algorithm backpropagation neural network and probabilistic neural network on learning part. Learning output using data with merging method will be compared with the results of the learning using data without merging. The results of this study suggest that the use of data resulting from this combination will accelerate learning and improve stability of output system, but reduces the level of recognition accuracy.

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 :
Seiring dengan perkembangan teknologi komputer yang cepat maka para ilmuwan tertantang untuk menggunakan komputer agar dapat mengerjakan tugas yang dianggap sebagai tugas yang mudah oleh manusia. Dengan belajar dan pengalaman maka kita dapat membedakan antara huruf A dan bukan A, atau lain sebagainya. Pengembangan jaringan syaraf tiruan dimulai kurang lebih 65 tahun yang lalu , dipicu oleh keinginan untuk mengerti cara kerja otak manusia. Neural Network yang dapat bekerja seperti otak manusia, dengan proses pembelajaran dari himpunan data pembelajaran dan himpunan data pengujian. Neural Network digunakan untuk mengelompokkan pola huruf tertentu dengan metoda backpropagation.