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"Pada masa sekarang ini perkembangan teknologi cenderung memiliki kemampuan untuk berpikir dan mengambil keputusan layaknya manusia. Salah satu dari banyak metode untuk mengembangkan teknologi yang cerdas adalah dengan menggunakan Jaringan Saraf Tiruan Radial Basis Function. Penelitian ini membandingkan antara Jaringan Saraf Tiruan Radial Basis Function dengan Fungsi Error Kuadratis dan Cross-Entropy dalam mengenal empat set data dari ?UCI Repository of Machine Learning Database? dan satu set data uranium dari BATAN. Selain itu, kedua jaringan tersebut dibandingkan dengan Jaringan Saraf Tiruan Backpropagation. Berdasarkan hasil percobaan dapat dilihat bahwa algoritma Radial Basis Function lebih sederhana dan memiliki waktu komputasi yang lebih cepat dibandingkan dengan algoritma Backpropagation.

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At the present, technological developments tend to have the ability to think and making decisions like human beings. One of the many methods to develop intelligent technologies is to use Radial Basis Function Neural Networks. This study compares the Radial Basis Functions Neural Networks with Mean Square Error Function and Cross-Entropy Error Function in identifying four sets of data from the "UCI Repository of Machine Learning Databases" and a set of data uranium from BATAN. In addition, both networks are compared with Backpropagation Neural Networks. Based on the results of the study, it is shown that Radial Basis Functions algorithm has simpler and faster computational capability compared to Backpropagation algorithm."

"Jaringan Saraf Tiruan (JST) merupakan suatu model matematis yang dewasa ini banyak digunakan dan algoritma pembelajarannya merupakan hal yang menarik untuk dipelajari. Pada skripsi ini, akan dibahas mengenai JST berbasis Radial Basis Function (RBF) dan perbandingannya dengan JST berbasis backpropagation yang dewasa ini banyak digunakan. Optimasi JST ensemble dengan algoritma Negative Correlation Learning (NCL) berbasis RBF juga akan dilakukan pada skripsi ini. Set data yang akan digunakan selama percobaan adalah data UC Irvine Machine Learning Repository (UCI) dan citra manusia cahaya tampak dan infra merah.

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Artificial neural network is a mathematical model that nowadays has been applied widely and its learning algorithm has become an interesting object to learn. This thesis is going to discuss artificial neural network based on Radial Basis Function (RBF) and its comparison with backpropagation that has been widely purposed. Afterward, optimation is conducted in term of ensemble artificial neural network with Negative Correlation Learning (NCL) algorithm based on RBF. The databases to use are UC Irvine Machine Learning Repository (UCI) and pictures of human face that are achieved from infra-red and visible-light cameras. "

"Dalam beberapa tahun ini, telah banyak penelitian yang berhubungan dengan pengenalan pola dilakukan untuk mengindentifikasi berbagai macam bentuk pola. Tesis ini membahas pengembangan jaringan saraf tiruan fungsi basis radial fuzzy. Dalam penelitian ini dilakukan dua percobaan, yaitu jaringan saraf fungsi basis radial fuzzy menggunakan SOM dan jaringan saraf fungsi basis radial fuzzy tanpa SOM.Hasil yang dicapai dari recognition rate menunjukkan jaringan saraf fungsi basis radial fuzzy menggunakan SOM memberikan performa yang baik. Jaringan saraf ini diharapkan dapat dikembangkan oleh peneliti-peneliti yang lain untuk kemajuan keilmuan dalam segala bidang.

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In recent years, has been much research related to pattern recognition performed to identify various forms of patterns. This thesis discusses the development of artificial neural networks fuzzy radial basis functions. In this study conducted two experiments, namely radial basis function neural network fuzzy neural network using the SOM and fuzzy radial basis function without SOM.The result of recognition rate shows the radial basis function neural networks using a fuzzy SOM gives a good performance. Neural network is expected to be developed by other researchers for the advancement of knowledge in all fields."

"[Kondisi chaotic merupakan kondisi dimana sistem selalu berkembang. Prediksi dari sistem yang memiliki kondisi chaotic sangat penting untuk menentukan keputusan yang akan diambil. Dalam skripsi ini dibahas mengenai prediksi dari Lorenz’s Chaotic System, dimana prediksi yang dilakukan menggunakan jaringan saraf tiruan Radial Basis Function Extreme Learning Machine. Jaringan saraf tiruan dipilih karena kemampuannya untuk dapat beradaptasi dengan sistem sehingga diharapkan dapat melakukan prediksi dengan baik. Hasil penelitian ini menunjukkan bahwa jaringan saraf tiruan Radial Basis Function Extreme Learning Machine dapat melakukan prediksi dengan baik yaitu dengan waktu pembelajaran yang sangat cepat dan tingkat akurasi yang tinggi., Chaotic condition is a condition where the system is always evolving. Prediction of the system that has chaotic condition is very important for determining the decisions to be taken. In this thesis discussed about the prediction of Lorenz's Chaotic System, where the predictions are made using Radial Basis Function Extreme Learning Machine neural network. Artificial neural network is chosen for its ability to be able to adapt to a system that is expected to do well prediction. The results of this study indicate that the Radial Basis Function Extreme Learning Machine neural network can perform good prediction with very fast learning time and high accuracy.]"

"Pada masa sekarang ini, teknologi semakin berkembang dan terus berkembang dengan cepat. Terutama kebutuhan adanya teknologi automasi yang memerlukan pengembangan lebih dalam lagi sehingga dapat menghasilkan teknologi cerdas yang dapat merespon tanggapan dengan cepat. Penelitian ini melakukan percobaan penerapan jaringan saraf tiruan radial basis function menggunakan metode backward dan metode Orthogonal Least Square (OLS). Berdasarkan hasil percobaan dapat dilihat bahwa penerapan jaringan saraf tiruan radial basis function metode OLS memiliki proses pelatihan yang lebih cepat dibandingkan penerapan jaringan saraf tiruan radial basis function metode backward. Selain itu, tingkat keakuratan yang dimiliki jaringan saraf tiruan radial basis function metode OLS juga tinggi.

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In recent years, technology get better and better. The need of automatic technology that need to be developed more serious so it can result smart technology that can response the stimulation quickly. This research do experimentation on radial basis function neural network using backward methode and Orthogonal Least Square (OLS) methode and then compared with backpropagation neural network.

Based on result of experimentation we can conclude that radial basis function neural network using Orthogonal Least Square (OLS) method has training processing time faster than radial basis function neural network using backward method. Beside of that, radial basis function neural network using Orthogonal Least Square (OLS) method has high accuracy too."

"Model deep learning adalah model dengan banyak lapisan jaringan saraf tiruan. Model Bidirectional Gated Recurrent Unit (BiGRU) adalah salah satu jenis model deep learning yang memproses urutan data dalam dua arah, yaitu arah maju dan arah mundur. Hal tersebut memungkinkan model BiGRU untuk mengakses informasi masa depan dan masa lalu dari setiap titik dalam urutan data untuk pemahaman konteks yang lebih baik. Model BiGRU dapat digunakan untuk analisis sentimen, yaitu proses mengategorikan sentimen opini dalam teks menjadi negatif, netral, atau positif. Representasi teks yang digunakan pada penelitian ini adalah Bidirectional Encoder Representations from Transformers (BERT) karena kemampuannya memahami kata secara kontekstual sehingga meningkatkan akurasi. Salah satu masalah umum pada analisis sentimen adalah ketidakseimbangan data Penggunaan data tidak seimbang mempengaruhi kinerja model dalam melakukan klasifikasi sentimen karena bias terhadap kelas mayoritas. Oleh karena itu, penggunaan Synthetic Minority Oversampling Technique (SMOTE) dalam mengatasi ketidakseimbangan kelas pada data dilakukan pada penelitian ini. SMOTE digunakan untuk melakukan oversampling pada data kelas minoritas dan dipasangkan dengan model BiGRU yang menggunakan fungsi kerugian categorical cross entropy menghasilkan kinerja dengan nilai akurasi sebesar 85,52% yang merupakan akurasi tertinggi dibandingkan dengan daripadamodel BiGRU dengan fungsi kerugian categorical cross entropy tanpa penanganan SMOTE (model standar dalam penelitian ini) dan model BiGRU dengan fungsi kerugian weighted cross entropy yang dibangun untuk memperkuat bukti bahwa model yang diajukan adalah model terbaik.

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Deep learning models are models with multiple layers of artificial neural networks. The Bidirectional Gated Recurrent Unit (BiGRU) model is one type of deep learning model that processes data sequences in two directions, the forward direction and the backward direction. This allows the BiGRU model to access future and past information from each point in the data sequence for better context understanding. The BiGRU model can be used for sentiment analysis, which is the process of categorizing the sentiment of opinions in text into negative, neutral, or positive. The text representation used in this research is Bidirectional Encoder Representations from Transformers (BERT) because of its ability to understand words contextually to increase accuracy. One of the common problems in sentiment analysis is data imbalance. The use of unbalanced data affects the performance of the model in performing sentiment classification due to bias towards the majority class. Therefore, the use of Synthetic Minority Oversampling Technique (SMOTE) in overcoming class imbalance in the data is done in this study. SMOTE is used to perform oversampling on minority class data and paired with the BiGRU model using the categorical cross entropy loss function results in performance with an accuracy value of 85.52% which is the highest accuracy compared to the BiGRU model with the categorical cross entropy loss function without SMOTE handling (the standard model in this study) and the BiGRU model with the weighted cross entropy loss function built to strengthen the evidence that the proposed model is the best model."

"Jaringan Saraf Tiruan adalah salah satu metode baru yang dikembangkan untuk pemecahan berbagai masalah kompleks yang tidak dapat diselesaikan secara analitik. Salah satu pengembangannya adalah metode jaringan saraf pembelajaran Radial Basis Function, dengan metode inisialisasi bobot Nguyen-Widrow dan Orthogonal Least Square (OLS). Akurasi dan kecepatan pembelajaran yang dimiliki oleh Radial Basis Function (RBF) sangat menarik untuk diaplikasikan pada sistem kendali. Pemodelan Forward dan Invers sistem dilakukan dengan metode RBF dengan mengambil data sistem SISO Pressure Process Rig. Setelah dilakukan pemodelan, jaringan saraf tiruan akan diuji dengan Direct Inverse Test. Hasil identifikasi sistem dan identifikasi invers pada sistem Pressure Process Rig memiliki hasil yang baik. Begitu pula saat diuji coba dengan Direct Inverse Test, sistem kendali mempunyai performa cukup baik, namun tidak menutup kemungkinan adanya skema model lain yang dapat digunakan dalam pemodelan sistem.

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Artificial Neural Network is a newer field of study that could solve any complex problem that could not be done by analytical solution. Radial Basis Function (RBF) is one of the newer method of Artificial Neural Network with two distinct weight initialization method ; Nguyen-Widrow and Orthogonal Least Square (OLS) methods. RBF?s high recognition rate and very fast learning speed are interesting enough to be used in control system. RBF is used in forward and inverse identification in modelling Pressure Process Rig system. Direct Inverse Test is also done in order to make sure Radial Basis Function perform well in identifying a particular system. Radial Basis Function had a great perfomance in both forward and inverse system identification and also in Direct Inverse Test, but it is possible to have another learning scheme in system modelling."

"Mata kering merupakan penyakit yang beredar pada masyarakat umum. Mata kering menyebabkan rasa tidak nyaman dan mengganggu aktivitas sehari-hari. Faktanya, lebih dari 85% penderita penyakit mata kering disebabkan kerusakan kelenjar meibom (meibomian gland dysfunction, MGD). Akibatnya mata yang memilki MGD menjadi kering karena intensitas evaporasi air mata meningkat. Untuk mendeteksi tingkat MGD dilakukanmeibography. Dari hasil meibography, klinisi (dokter spesialis mata) menilai tingkat MGD yang disebut meiboscore. Namun realitanya, penilaian meiboscore masih sangat subjektif antar para klinisi. Alat yang digunakan juga mahal dan tidak seluruh klinik mata memiliki alat tersebut. Oleh karena itu pada tugas akhir ini dilakukan deteksi tingkat kerusakan kelenjar meibom dengan pendekatan faktor-faktor potensi MGD dan machine learning. Metode machine learning yang digunakan dalam tugas akhir ini ini adalah radial basis function neural network (RBFNN). Metode machine learning dalam studi ini dilakukan Teknik SMOTE terelebih dahulu untuk menyeimbangkan jumlah data antar kelas, lalu data dibagi menjadi data training dan data testing dengan rasio sebesar 90%: 10%, 80%: 20%, 70%: 30%, dan 60%: 40% . Selain itu dilakukan pengurangan fitur-fitur yang kurang relevan menggunakan seleksi fitur Chi square. Hasil evaluasi metode RBFNN memperoleh nilai rata-rata akurasi, presisi, recall dan f1-score terbaik dicapai menggunakan data testing 20% dengan masing-masing mencapai nilai 96%, 95%, 100%, dan 95% secara berurut

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Dry eye is a common disease happened among the public. Dry eye causes discomfort and distracts daily activities. More than 85% dry eye suffers are caused by meibomian gland dysfunction (MGD). As a result, eyes with MGD becomes dry due to high tear evaporation intensity. Detecting MGD can be done by meibography. The MGD level is scored by clinicians which is called meiboscore. However, scoring the meiboscore is still very subjective among the clinicians. The tool that is used are expensive and not all eye clinics have this tool. Therefore, this study aims to detect the MGD level with the approach of MGD potential factors and machine learning. In this study radial basis function neural network (RBFNN) is used. The machine learning method performs SMOTE technique to balance the amount of data in each class, then all data is divided into training data and testing data by90%: 10%, 80%: 20%, 70%: 30%, and 60%: 40% respectively. Moreover, irrelevant features are reduced to optimize using feature selection, Chi Square. To reduce the features that are less relevant, Chi square feature selection is performed. RBFNN method obtained the best average accuracy 96%, average precision 95%, average recall 100%, and average f1-score 95% using the 20% data testing."

"[ABSTRAKPerkembangan 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.ABSTRACTDevelopment 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.]"

"Cylindrical uranium dioxide pellets, which are the main components for nuclear fuel elements in light water reactors, should have a high density profile, a uniform shape, and a minimum standard quality for their safe use as a reactor fuel component. The quality of green pellets is conventionally monitored by laboratory measurement of the physical pellet characteristics; however, this conventional classification method shows some drawbacks, such as difficult usage, low accuracy, and high time consumption. In addition, the method does not address the non-linearity and complexity of the relationship between pellet quality variables and pellet quality. This paper presents the development and application of a modified Radial Basis Function neural network (RBF NN) as an automatic classification system for green pellet quality. The weight initialization of the neural networks in this modified RBF NN is calculated through an orthogonal least squared method, and in conjunction with the use of a sigmoid activation function on its output neurons. Experimental data confirm that the developed modified RBF NN shows higher recognition capability when compared with that of the conventional RBF NNs. Further experimental results show that optimizing the quality classification problem space through eigen decomposition method provides a higher recognition rate with up to 98% accuracy."