Perancangan Aplikasi Untuk Mendeteksi Sabuk Pengaman Mobil Menggunakan Algoritma Backpropagation Neural Network (Bpnn)

- To enforce traffic regulations on the highway, police officers face obstacles in monitoring the use of seat belts on cars because cars on the highway are always on the move and almost all cars use window film to avoid the heat of the sun entering the car. Based on the above problems, the authors build a software that can monitor or detect drivers wearing seat belts or not with Microsoft Visual C # 2010. To monitor the use of seat belts, digital cameras are used to conduct image acquisition to be processed by computers. . Furthermore, the image is studied by a system using the Back Propagation Neural Network (BPNN) Artificial Neural Network method as the image of the driver using a seat belt. To make a detection, the test image is input that is the same size as the training image. The test results obtained the level of accuracy for image acquisition with a distance of 1 meter with a similarity using a seat belt with a maximum value of 9% and a degree of similarity without a seat belt of less than 4%. Keywords - Image, Neural Network, Backpropagation Algorith

Utilizing Soft Computing for Determining Protein Deficiency

Abstract— In recent years, the occurrence of protein shortage of children under 5 years old in many poor area has dramatically increased. Since this situation can cause serious problem to children like a delay in their growth, delay in their development and also disfigurement, disability, dependency, the early diagnose of protein shortage is vital. Many applications have been developed in performing disease detection such as an expert system for diagnosing diabetics and artificial neural network (ANN) applications for diagnosing breast cancer, acidosis diseases, and lung cancer. This paper is mainly focusing on the development of protein shortage disease diagnosing application using Backpropagation Neural Network (BPNN) technique. It covers two classes of protein shortage that are Heavy Protein Deficiency. On top of this, a BPNN model is constructed based on result analysis of the training and testing from the developed application. The model has been successfully tested using new data set. It shows that the BPNN is able to early diagnose heavy protein deficiency accurately. Keywords— Artificial Neural Network, Backpropagation Neural Network, Protein Deficiency

Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The results showed that this model can predict CPO production in 2019

Classification of Malaysian vowels using formant based features

Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software, especially using English as the language of choice. Despite of all these advances, machines cannot match the performance of their human counterparts in terms of accuracy and speed, especially in case of speaker independent speech recognition. In this paper, a new feature based on formant is presented and evaluated on Malaysian spoken vowels. These features were classified and used to identify vowels recorded from 80 Malaysian speakers. A back propagation neural network (BPNN) model was developed to classify the vowels. Six formant features were evaluated, which were the first three formant frequencies and the distances between each of them. Results, showed that overall vowel classification rate of these three formant combinations are comparatively the same but differs in terms of individual vowel classification

DIAGNOSISFAKTOR RISIKO STROKEMENGGUNAKAN METODE ROUGH SETDAN BACKPROPAGATION NEURAL NETWORK

Diagnosis faktor risiko Stroke merupakan suatu upaya yang dilakukan dalam rangka pencegahan adanya kemungkinan penyakit Stroke di dalam diri pasien. Pada penderita Stroke, kebanyakan yang terkena penyakit Stroke tidak mengetahui faktor risiko Stroke yang meliputi gejala, pola makan dan pola hidup. Beberapa penderita Stroke bahkan menganggap faktor risiko Stroke adalah hal yang sudah biasa terjadi di kehidupan sehari-hari. Dalam penelitian ini, menggunakan metode Rough Set dan Backpropagation Neural Network (BPNN) sebagai metode klasifikasi. Metode Rough Set digunakan untuk seleksi fitur, dan metode BPNN digunakan untuk klasifikasi stoke berdasarkan faktor risiko Stroke setelah selesai di seleksi fitur. Pada penelitian penulis mengimplementasikan Analisa rough set dengan bahasa pemrograman python dan Backpropagation Neural Network (BPNN) diimplentasikan dengan pemrograman Matlab. Data yang digunakan adalah data kuisioner yang telah dikonsultasikan dengan dokter. Data yang digunakan berjumlah 86 data pasien penyakit Stroke. Berdasarkan analisa rough set didapatkan faktor risiko yang mempengaruhi yakni Riwayat Stroke, Riwayat Hipertensi, Riwayat Jantung, Riwayat Stroke Keluarga, Merokok dan Perubahan. Selanjutnya dilakukan implementasi algoritma BPNN untuk penentuan jenis penyakit Stroke. Berdasarkan pengujian menggunakan learning rate 0,1 Hidden Layer 1, jumlah Epoch 1000 dan fungsi aktivasi Sigmoid Biner didapat akurasi tertinggi pada pembagian data 80% data latih dan 20% data uji yaitu mencapai 94.117%. Kata Kunci: Kesehatan, Stroke, Rough Set, Backpropagation Neural Network (BPNN), Focus Group Discussion (FGD) dan Jaringan Saraf Tiruan (JST

Optimisasi Backpropagation Neural Network dalam Memprediksi IHSG

Covid-19 has become a global epidemic and has spread to many countries in the world, including Indonesia. The COVID-19 pandemic is one source of uncertainty that causes financial data to fluctuate and cause data to be volatile. This outbreak had an impact on financial data, not only on the Rupiah exchange rate but also on the Jakarta Composite Index (JCI). The uncertainty of the JCI makes it difficult for investors, data managers, and business people to predict data for the future. JCI is one indicator of the capital market (stock exchange). The uncertainty of the JCI data causes the need for predictions, so that investors, data managers, and business people can make the right decisions so that they can reduce risk and optimize profits when investing. One of the factors causing the JCI's decline was sentiment caused by investor panic over the rapid spread of COVID-19 in various cities in Indonesia. This research uses Backpropagation Neural Network (BPNN) in making predictions and continues with optimization of BPNN using ensemble techniques. Historical data from the JCI used were obtained from yahoo.finance. The ensemble technique used consists of two approaches, namely combining different architectures and initial weights with the same data and combining different architectures and weights. The results of network performance using ensemble technique optimization show good performance and can outperform the individual network performance of BPNN. Keywords: prediction, JCI, Optimization, BPNN, volatil

Prediksi Kedatangan Turis Asing Ke Indonesia Menggunakan Backpropagation Neural Networks

In this paper, a backpropagation neural network (BPNN) method with time series data have been explored. The BPNN method to predict the foreign tourist's arrival to Indonesia datasets have been implemented. The foreign tourist's arrival datasets were taken from the center agency on statistics (BPS) Indonesia. The experimental results showed that the BPNN method with two hidden layers were able to forecast foreign tourist's arrival to Indonesia. Where, the mean square error (MSE) as forecasting accuracy has been indicated. In this study, the BPNN method is able and recommended to be alternative methods for predicting time series datasets. Also, the BPNN method showed that effective and easy to use. In other words, BPNN method is capable to producing good value of forecasting.Keywords - BPNN; foreign tourists; BPS; MSEPemanfaatan backpropagation neural network (BPNN) dengan data deret waktu telah digunakan dalam paper ini. Metode BPNN telah digunakan untuk memprediksi data kedatangan turis asing ke Indonesia, dimana data turis tersebut diambil dari badan pusat statistik Indonesia (BPS). Hasil pengujian menunjukkan bahwa metode BPNN dengan dua lapisan tersembunyi mampu memodelkan dan meramalkan data kedatangan turis asing ke Indonesia yang diindikasikan dengan nilai mean square error (MSE). Penelitian ini merekomendasikan bahwa metode BPNN mampu menjadi alternative metode dalam memprediksi data yang berjenis deret waktu karena metode BPNN efektif dan lebih mudah digunakan serta mampu menghasilkan akurasi nilai peramalan yang baik

PENERAPAN ALGORITMA INISIALISASI BOBOT NGUYEN WIDROW UNTUK MENDIAGNOSA PENYAKIT DIABETES MELLITUS MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK

Diabetes Mellitus (DM) adalah penyakit kronis yang disebabkan oleh ketidakmampuan tubuh untuk memproduksi hormon insulin, hal ini ditandai dengan tingginya kadar gula dalam darah. Pada penelitian ini menerapkan algoritma inisialisasi bobot nguyen widrow untuk mendiagnosa penyakit diabetes mellitus menggunakan metode Backpropagation Neural Network(BPNN). Tujuan dari penelitian ini adalah untuk mengetahui tingkat akurasi yang dihasilkan menggunakan inisialisasi bobot nguyen widrow. Dalam hal initotal data yang digunakan adalah 150 data hasil laboratorium, dengan 3 kelompok penyakit DM sebagai keluaran yang digunakan sebagai target yaitu DMtipe I, DMtipe II dandiabetes neuropati. Parameter yang digunakan yaitu learning rate(α) =0.01-0.09, dengan epoch= 5-30, dengan arsitektur layer yang digunakan input, hidden, dan output masing-masing adalah [19; 19; 2], [19; 25; 30], [19; 30; 2], dengan pembagian data = 90:10%, 80:20%, 70:30%. Berdasarkanhasilpengujianyang telah dilakukan dengan pembagian data = 90:10%, α= 0.03, epoch=15 danhidden layer=30,menghasilkan akurasi terbaik yaitu 93.33%. Sedangkan dengan bobot random dilakukan pengujian dengan parameter yang sama didapat tingkat akurasi terbaiknya yaitu 66.67%. Dengan demikian algoritma inisialisasi bobot nguyen widrow dalam metode BPNN dapat diterapkan untuk mendiagnosa penyakit DM. Kata Kunci :Nguyen Widrow, Jaringan Saraf Tiruan, BPNN, DiagnosaPenyakitD