Identifikasi Warna Kerabang Telur Ayam Ras Menggunakan Jaringan Syaraf Tiruan

2018 ◽  
Vol 6 (2) ◽  
pp. 189-200
Author(s):  
Iman Budi Darmawan ◽  
Maimunah Maimunah ◽  
Retno Nugroho Whidiasih
Keyword(s):  
Food Source ◽  
Training Data ◽  
Nutritional Content ◽  
The People ◽  
Testing Data ◽  
Color Identification ◽  
Hidden Layer ◽  
Chicken Eggs ◽  
Backpropagation Artificial Neural Network ◽  
Better Than

Abstract   Eggs are a food source of animal protein that is cheap and easy to get by the people of Indonesia. Eggs have a complete nutritional content ranging from protein, fat, vitamins and minerals. This study aims to identify the color of broiler eggshell to dark brown, brown, and light brown, it shows that dark brown chicken eggs are better than brown and light brown. The estimator variable used is RGB (red, green, blue), extraction from the egg image taken using a 20 megapixel DSLR camera. The data used is 90 egg images. Training data amounted to 72 data and testing data totaling 18 data. Color identification of eggshells using backpropagation artificial neural network with the penduga red, green, blue parameters of the egg image, the most optimal weight obtained at 59th epoch at 03 seconds with neurons 2 in the hidden layer, with an MSE value of 0.160 and a success rate of 72%.   Keywords: backpropagation, identification, race chicken eggs, RGB, shell color.   Abstrak   Telur merupakan makanan sumber protein hewani yang murah dan mudah untuk didapatkan oleh masyarakat Indonesia. Telur memiliki kandungan gizi yang lengkap mulai dari protein, lemak, vitamin, dan mineral. Penelitian ini bertujuan untuk mengidentifikasi warna kerabang telur ayam ras menjadi warna coklat tua, coklat, dan coklat muda, hal tersebut menunjukkan bahwa telur ayam dengan warna coklat tua lebih baik dibandingkan dengan warna coklat dan coklat muda. Variabel penduga yang digunakan adalah rgb (red, green, blue), ekstraksi dari citra telur yang diambil menggunakan kamera DSLR 20 megapiksel. Data yang digunakan berjumlah 90 citra telur. Data training berjumlah 72 data dan data testing berjumlah 18 data. Identifikasi warna kerabang telur menggunakan jaringan syaraf tiruan backpropagation dengan parameter penduga red, green, blue dari citra telur, bobot yang paling optimal didapatkan pada epoch ke 59 di detik ke 03 dengan neuron 2 pada lapisan tersembunyi, dengan nilai MSE sebesar  0.160 dan tingkat keberhasilan sebesar 72%.   Kata kunci: backpropagation, identifikasi, RGB, telur ayam ras, warna kerabang.

scholarly journals Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System

2018 ◽  
Vol 13 (3) ◽  
pp. 408-428 ◽  
Author(s):  
Phu Vo Ngoc
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Sentiment Classification ◽  
Training Data ◽  
The Novel ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Novel Model ◽  
Better Than

We have already survey many significant approaches for many years because there are many crucial contributions of the sentiment classification which can be applied in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a novel model using a Latent Semantic Analysis (LSA) and a Dennis Coefficient (DNC) for big data sentiment classification in English. Many LSA vectors (LSAV) have successfully been reformed by using the DNC. We use the DNC and the LSAVs to classify 11,000,000 documents of our testing data set to 5,000,000 documents of our training data set in English. This novel model uses many sentiment lexicons of our basis English sentiment dictionary (bESD). We have tested the proposed model in both a sequential environment and a distributed network system. The results of the sequential system are not as good as that of the parallel environment. We have achieved 88.76% accuracy of the testing data set, and this is better than the accuracies of many previous models of the semantic analysis. Besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. Many different fields can widely use the results of the novel model in many commercial applications and surveys of the sentiment classification.

scholarly journals KLASIFIKASI CITRA DIGITAL BUMBU DAN REMPAH DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)

2020 ◽  
Vol 9 (3) ◽  
pp. 273-282
Author(s):  
Isna Wulandari ◽  
Hasbi Yasin ◽  
Tatik Widiharih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activation Function ◽  
Training Data ◽  
Young Generation ◽  
Testing Data ◽  
Filter Size ◽  
Hidden Layer ◽  
Herbs And Spices

The recognition of herbs and spices among young generation is still low. Based on research in SMK 9 Bandung, showed that there are 47% of students that did not recognize herbs and spices. The method that can be used to overcome this problem is automatic digital sorting of herbs and spices using Convolutional Neural Network (CNN) algorithm. In this study, there are 300 images of herbs and spices that will be classified into 3 categories. It’s ginseng, ginger and galangal. Data in each category is divided into two, training data and testing data with a ratio of 80%: 20%. CNN model used in classification of digital images of herbs and spices is a model with 2 convolutional layers, where the first convolutional layer has 10 filters and the second convolutional layer has 20 filters. Each filter has a kernel matrix with a size of 3x3. The filter size at the pooling layer is 3x3 and the number of neurons in the hidden layer is 10. The activation function at the convolutional layer and hidden layer is tanh, and the activation function at the output layer is softmax. In this model, the accuracy of training data is 0.9875 and the loss value is 0.0769. The accuracy of testing data is 0.85 and the loss value is 0.4773. Meanwhile, testing new data with 3 images for each category produces an accuracy of 88.89%. Keywords: image classification, herbs and spices, CNN. 

scholarly journals IMPLEMENTASI JARINGAN SYARAF TIRUAN BACKPROPAGATION DENGAN ALGORITMA CONJUGATE GRADIENT UNTUK KLASIFIKASI KONDISI RUMAH (Studi Kasus di Kabupaten Cilacap Tahun 2018)

2020 ◽  
Vol 9 (1) ◽  
pp. 41-49
Author(s):  
Johanes Roisa Prabowo ◽  
Rukun Santoso ◽  
Hasbi Yasin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Conjugate Gradient ◽  
Training Data ◽  
Gradient Algorithm ◽  
Output Layer ◽  
Average Accuracy ◽  
Testing Data ◽  
Artificial Neural ◽  
Hidden Layer

House is one aspect of the welfare of society that must be met, because house is the main need for human life besides clothing and food. The condition of the house as a good shelter can be known from the structure and facilities of buildings. This research aims to analyze the classification of house conditions is livable or not livable. The method used is artificial neural networks (ANN). ANN is a system information processing that has characteristics similar to biological neural networks. In this research the optimization method used is the conjugate gradient algorithm. The data used are data of Survei Sosial Ekonomi Nasional (Susenas) March 2018 Kor Keterangan Perumahan for Cilacap Regency. The data is divided into training data and testing data with the proportion that gives the highest average accuracy is 90% for training data and 10% for testing data. The best architecture obtained a model consisting of 8 neurons in input layer, 10 neurons in hidden layer and 1 neuron in output layer. The activation function used are bipolar sigmoid in the hidden layer and binary sigmoid in the output layer. The results of the analysis showed that ANN works very well for classification on house conditions in Cilacap Regency with an average accuracy of 98.96% at the training stage and 97.58% at the testing stage.Keywords: House, Classification, Artificial Neural Networks, Conjugate Gradient

scholarly journals Sistem Pendukung Keputusan Peramalan Jumlah Kunjungan Pasien Menggunakan Metode Extreme Learning Machine (Studi Kasus : Poli Gigi Rsu Dr. Wahidin Sudiro Husodo Mojokerto)

2015 ◽  
Vol 1 (1) ◽  
pp. 33
Author(s):  
Delia Putri Fardani ◽  
Eto Wuryanto ◽  
Indah Werdiningsih
Keyword(s):  
Decision Support ◽  
Extreme Learning Machine ◽  
Support System ◽  
Training Data ◽  
Number Of Patients ◽  
Testing Data ◽  
Decision Supporting ◽  
Decision Supporting System ◽  
Learning Machine ◽  
Hidden Layer

Abstrak— Penelitian ini bertujuan merancang dan membangun sistem pendukung keputusan untuk meramalkan jumlah kunjungan pasien RSU Dr. Wahidin Sudiro Husodo Kota Mojokerto dengan menggunakan metode Extreme Learning Machine (ELM). Dengan adanya  sistem pendukung keputusan ini direktur Rumah Sakit dapat meramalkan jumlah kunjungan pasien dan membantu dalam pembuatan kebijakan rumah sakit, mengatur sumber daya manusia dan keuangan, serta mendistribusikan sumber daya material dengan benar khususnya pada poli gigi. Dalam rancang bangun sistem pendukung keputusan ini dilakukan dalam beberapa tahap. Tahap yang pertama, pengumpulan data untuk mengidentifikasi inputan yang dibutuhkan dalam penghitungan metode ELM. Tahap kedua, pengolahan data, data dibagi menjadi data training dan data testing dengan komposisi data training sebanyak 80% (463 data) dari total 579 data dan 20% (116 data) sisanya sebagai data testing yang kemudian di normalisasi. Tahap ketiga, peramalan jumlah kunjungan pasien menggunakan metode ELM. Tahap terakhir, perancangan sistem menggunakan sysflow dan pembangunan sistem berbasis desktop serta evaluasi sistem. Hasil penelitian berupa aplikasi sistem pendukung keputusan untuk meramalkan jumlah kunjungan pasien. Dan melalui uji coba menggunakan 116 data testing berdasarkan fungsi aktivasi sigmoid biner dengan jumlah hidden layer sebanyak 7 unit dan Epoch 500 diperoleh hasil optimal MSE sebesar 0.027 Kata Kunci— Sistem Pendukung Keputusan, Peramalan, Jaringan Syaraf Tiruan, Extreme Learning MachineAbstract— In this research, a decision support system to predict the number of patients visit RSU Dr. Wahidin Sudiro Husodo Kota Mojokerto was designed and developed using Extreme Learning Machine (ELM) method which aims to assist director in making decision for the hospital, managing human and financial resource, as well as distributing material resource properly especially in the Department of Dentistry. The design of this decision support system to predict the number of patients visit with ELM method is divided into several stages. The first stage is to identify the input data collection needed in the calculation method of ELM. The next stage is processing the data; the data is divided into training data and testing data and then normalized, in which training data is 80% (452 data) and testing 579 data 20% (116 data). The third stage is problem solving using ELM. The last stage is the design and development of systems using sysflow and desktop-based system that includes the implementation and evaluation of the system. The result of this research is an application of decision supporting system to predict number of patients. By using 116 testing data based on the binary sigmoid activation function using 7 units of hidden layer and 500 Epoch then Optimal MSE value that was obtained is 0.027. Keywords— Decision Supporting System, Prediction, Artificial Neural Network, Extreme Learning Machine

scholarly journals NGHIÊN CỨU ỨNG DỤNG MẠNG TRẠNG THÁI PHẢN HỒI ĐỂ DỰ BÁO CHẤT LƯỢNG KHÔNG KHÍ

2016 ◽  
Vol 54 (1) ◽  
pp. 54 ◽  
Author(s):  
Mac Duy Hung ◽  
Nghiem Trung Dung
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Air Pollutant ◽  
Training Data ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Multilayer Perception ◽  
Testing Data ◽  
Better Than

A study on the application of Echo State Network (ESN) for the forecast of air quality in Hanoi for a period of seven days, which is based on the nonlinear relationships between the concentrations of an air pollutant to be forecasted and meteorological parameters, was conducted. Three air pollutants being SO2, NO2 and PM10 were selected for this study. Training data and testing data were extracted from the database of Lang air quality monitoring station, Hanoi, from 2003 to 2009. Values forecasted by ESN are compared with those by MLP (Multilayer Perception). Results shown that, in almost experiments, the performance of ESN is better than that of MLP in terms of the values and the correlation of concentration trends. The averages of RMSE of ESN and MLP for SO2 are 5.9 ppb and 6.9 ppb, respectively. For PM10, the accuracy of ESN is 83.8% with MAE of 53.5 μg/m3, while the accuracy of MLP is only 77.6% with MAE of 68.2 μg/m3. For NO2, the performance of ESN and MLP is similar; the accuracy of both models is in the range of 60% to 72.7%. These suggest that, ESN is a novel and feasible approach to build the air forecasting model. Keywords: Forecast, air quality, ESN, MLP, ANN, Hanoi, Vietnam.

scholarly journals River Water Salinity Prediction Using Hybrid Machine Learning Models

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2951 ◽  
Author(s):  
Assefa M. Melesse ◽  
Khabat Khosravi ◽  
John P. Tiefenbacher ◽  
Salim Heddam ◽  
Sungwon Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Resource Management ◽  
Model Performance ◽  
Hybrid Algorithms ◽  
Training Data ◽  
Northern Iran ◽  
Water Quality Variables ◽  
Testing Data ◽  
Better Than

Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual—M5 Prime (M5P) and random forest (RF)—and eight novel hybrid algorithms—bagging-M5P, bagging-RF, random subspace (RS)-M5P, RS-RF, random committee (RC)-M5P, RC-RF, additive regression (AR)-M5P, and AR-RF—to predict EC. Thirty-six years of observations collected by the Mazandaran Regional Water Authority were randomly divided into two sets: 70% from the period 1980 to 2008 was used as model-training data and 30% from 2009 to 2016 was used as testing data to validate the models. Several water quality variables—pH, HCO3−, Cl−, SO42−, Na+, Mg2+, Ca2+, river discharge (Q), and total dissolved solids (TDS)—were modeling inputs. Using EC and the correlation coefficients (CC) of the water quality variables, a set of nine input combinations were established. TDS, the most effective input variable, had the highest EC-CC (r = 0.91), and it was also determined to be the most important input variable among the input combinations. All models were trained and each model’s prediction power was evaluated with the testing data. Several quantitative criteria and visual comparisons were used to evaluate modeling capabilities. Results indicate that, in most cases, hybrid algorithms enhance individual algorithms’ predictive powers. The AR algorithm enhanced both M5P and RF predictions better than bagging, RS, and RC. M5P performed better than RF. Further, AR-M5P outperformed all other algorithms (R2 = 0.995, RMSE = 8.90 μs/cm, MAE = 6.20 μs/cm, NSE = 0.994 and PBIAS = −0.042). The hybridization of machine learning methods has significantly improved model performance to capture maximum salinity values, which is essential in water resource management.

scholarly journals Tomato Maturity Classification using Naive Bayes Algorithm and Histogram Feature Extraction

2018 ◽  
Vol 3 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Arya Kusuma ◽  
De Rosal Ignatius Moses Setiadi ◽  
M. Dalvin Marno Putra
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Classifier ◽  
Nutritional Content ◽  
Classification Technique ◽  
Computational Speed ◽  
Testing Data ◽  
Bayes Algorithm ◽  
Histogram Feature

Tomatoes have nutritional content that is very beneficial for human health and is one source of vitamins and minerals. Tomato classification plays an important role in many ways related to the distribution and sales of tomatoes. Classification can be done on images by extracting features and then classifying them with certain methods. This research proposes a classification technique using feature histogram extraction and Naïve Bayes Classifier. Histogram feature extractions are widely used and play a role in the classification results. Naïve Bayes is proposed because it has high accuracy and high computational speed when applied to a large number of databases, is robust to isolated noise points, and only requires small training data to estimate the parameters needed for classification. The proposed classification is divided into three classes, namely raw, mature and rotten. Based on the results of the experiment using 75 training data and 25 testing data obtained 76% accuracy

scholarly journals Logistic Broken Adaptive Ridge Procedure for Colon Cancer Data Analysis

2019 ◽  
pp. 1-5
Author(s):  
Hong Yin ◽  
Hong Yin ◽  
Liangzhen Lei ◽  
Suyun Zhao
Keyword(s):  
Colon Cancer ◽  
Cancer Detection ◽  
Training Data ◽  
Cancer Data ◽  
The People ◽  
The World ◽  
Testing Data ◽  
Causes And Risk Factors ◽  
Or Genes ◽  
Colon Cancer Data

Background: Colon cancer is the leading cause of cancer-related deaths in the world in both man and women. Knowing the causes and risk factors for colon cancer can help you understanding the importance of routine screening for colon cancer, as well as learn if you are one of the people who should begin screening at the earlier age. Due to the limitation of clinical diagnose, management and treatment outcomes, it is of great necessity to develop effective methods for colon cancer detection and prediction especially cDNA Microarrays and high- density oligonucleotide chips are increasingly used in cancer research. Methods: Here we propose a novel logistic broken adaptive ridge procedure to address the problem of colon cancer results prediction through selecting effective few variables or genes from 2000 candidate genes. Results: In total 62 cases with 40 colon cancer patients and 22 healthy patients were included in our analysis. Each case consists of 2000 genes which challenged all the competitive method. From the results, we are so surprised that our proposed method outperforms the classical variable selection approaches in error rate of training data and extra testing data. Conclusions: Logistic adaptive ridge procedure is very effective for colon cancer predictions, either in terms of prognosis or diagnose. It may benefit patients by guiding therapeutic options. We hope it will contribute to the wider biology and related communities.

Pengenalan Berbagai Olahan Susu Sebagai Alternatif Usaha yang Menjanjikan di Masyakarat Desa Cisempur Jatinangor Sumedang Jawa Barat

2020 ◽  
Vol 1 (4) ◽  
pp. 299-305
Author(s):  
Primiani Edianingsih ◽  
Raden Febrianto Christi
Keyword(s):  
Human Body ◽  
Dairy Products ◽  
Public Awareness ◽  
Active Participation ◽  
Nutritional Content ◽  
Milk Products ◽  
Pasteurized Milk ◽  
The People ◽  
Post Test

Abstrak: Susu merupakan produk hasil ternak berupa cairan putih dengan kandungan gizi yang lengkap serta memberikan manfaat bagi tubuh manusia. Sebagai upaya dalam meningkatkan kesadaran masyarakat dalam pemahaman berbagai produk olahan susu maka diadakan penyuluhan. Pengabdian ini telah dilaksanakan kepada masyarakat Desa Cisempur Kecamatan Jatinangor dengan diikuti sebanyak 22 peserta yang terdiri atas kalangan ibu rumah tangga. Metode pelaksanaan dengan cara partisipasi aktif dari peserta dengan pengenalan berbagai produk olahan susu. Tahapan dimulai dengan sebaran kuisioner pre test  sebelum kegiatan dilakukan dengan 20 pertanyaan yang diajukan, lalu pemaparan materi berbagai olahan susu mulai dari pendahuluan terkait susu sampai produk olahan susu, Penyebaran kuisioner Post test kepada peserta setelah acara selesai dengan pertanyaan yang sama seperti pre test. Kemudian membuat salah satu produk susu kepada peserta berupa susu pasteurisasi. Hasil menunjukkan bahwa terjadi peningkatan pengenalan produk olahan susu pada masyarakat Desa Cisempur Kecamatan Jatinangor yang hadir setelah melakukan pre test dan post test.Abstract: Milk is a livestock product in the form of a white liquid with complete nutritional content and provides benefits to the human body. As an effort to increase public awareness in understanding various dairy products, counseling was held. This service has been carried out for the community of Cisempur Village, Jatinangor District, followed by 22 participants consisting of housewives. The method of implementation is by means of active participation of the participants with the introduction of various dairy products. The stages began with the distribution of pre-test questionnaires before the activity was carried out with 20 questions, then the presentation of various dairy products, from the introduction to milk to dairy products, the distribution of post test questionnaires to participants after the event was over with the same questions as the pre test. Then make one of the milk products for the participants in the form of pasteurized milk. The results showed that there was an increase in the introduction of dairy products in the people of Cisempur Village, Jatinangor District who attended after doing the pre test and post test.

An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based fractal analysis and Multiple Kernel SVM

2020 ◽  
Vol 13 ◽  
Author(s):  
Jianfeng Jiang
Keyword(s):  
Fault Diagnosis ◽  
Fractal Analysis ◽  
Analog Circuit ◽  
Training Data ◽  
Support Vector ◽  
Pass Filter ◽  
Multiple Kernel ◽  
Testing Data ◽  
Circuit Fault Diagnosis ◽  
Diagnosis Approach

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

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