Penerapan K-nearest Neighbor Berbasis Algoritma Genetika Untuk Klasifikasi Mutu Padi Organik

Penentuan kualitas mutu padi memegang peranan yang sangat penting terhadap terjaminnya keterediaan padi, sebab di Indonesia beras merupakan bahan makanan pokok dan merupakan sumber kalori bagi sebagian besar penduduk, sehingga stabilitas pertumbuhan tanaman padi juga akan berpengaruh terhadap ketahanan pangan, untuk mengetahui jenis mutu padi yang berkualitas harus dilihat dri beberapa aspek yang ditelusuri dari karakteristiknya seperti, mutu, warna, variatas, panjang, bentuk, rasa, teknik, musim, ama, dan PH. Untuk itu dalam makalah ini akan menjelaskan bagaimana menghasilkan mutu padi yang akurat dan maka dilakukan sebuah penelitian dalam mencari pola dari mutu padi dengan menggunakan metode k-nearest neighbor dan algoritma genetika dengan proses k-fold cross validation dan confusion matrik bertujuan agar pengkalsifikasian tidak lagi sekedar hanya menggunakan perkiraan semata tapi menggunakan data dari pengalaman expert yang sudah di eksrak dan menjadi acuan utama dalam menentukan klasifikasi mutu dari sebuah padi. Proses yang dilakukan menghitung dari data testig dan training dengan k-fold validation dan menghasilkan akurasi yang sangat signifikan, yang semula dari proses pengolahan data dengan K-NN menghasilkan 81.60% kemudian lebih meningkat ketika pengolahan data kombinasi K-NN dan Algoritma Genetika yaitu 96,40% untuk dataset 4952..<satu

Wavelet based approach for facial expression recognition

Facial expression recognition is one of the most active fields of research. Many facial expression recognition methods have been developed and implemented. Neural networks (NNs) have capability to undertake such pattern recognition tasks. The key factor of the use of NN is based on its characteristics. It is capable in conducting learning and generalizing, non-linear mapping, and parallel computation. Backpropagation neural networks (BPNNs) are the approach methods that mostly used. In this study, BPNNs were used as classifier to categorize facial expression images into seven-class of expressions which are anger, disgust, fear, happiness, sadness, neutral and surprise. For the purpose of feature extraction tasks, three discrete wavelet transforms were used to decompose images, namely Haar wavelet, Daubechies (4) wavelet and Coiflet (1) wavelet. To analyze the proposed method, a facial expression recognition system was built. The proposed method was tested on static images from JAFFE database

Prediction of daily COVID-19 cases in European countries using automatic ARIMA model

The recent pandemic (COVID-19) emerged in Wuhan city of China and after causing a lot of destruction there recently changed its epicenter to Europe. There are countless people affected and reported cases are increasing day by day. Predictive models need to consider previous reported cases and forecast the upcoming number of cases. Automatic ARIMA, one of the predictive models used for forecasting contagions, was used in this study to predict the number of confirmed cases for next 10 days in four top European countries through R package “forecast”. The study finds that Auto ARIMA applied on the sample satisfactorily forecasts the confirmed cases of coronavirus for next ten days. The confirmed cases for the four countries show an increasing trend for the next ten days with Spain with a highest number of expected new confirmed cases, followed by Germany and France. Italy is expected to have lowest number of new confirmed cases among the four countries

Quality and productivity in aquaculture: Prediction of oreochromis mossambicus growth using a transfer function ARIMA model

[EN] The cultivation of aquatic species in aquaculture aims at improving fish production, making the activity more dependent on human capacity, especially with what concerns to the application of efficient methods for its management. The weight and standard length are the biometric parameters mostly used to control the growth of tilapia in the cultivation process. In this work there is presented a statistical method based on transfer function ARIMA model to adjust and predict the weight of tilapia with the support of the values of the standard length. The model was adjusted to the data recorded in 21 weeks in the tilapia growing tank of the company Aquapesca de Mocambique, and the results showed that there was a good adjustment (with an explained variability of 97.8%) with which the model can be used to predict the future values of tilapia weight and make the activity more controllable and profitable.I would like to thank the Universidade Pedagógica de Moçambique, Delegação de Quelimane for funding this research and the the company Aquapesca de Moçambique for guaranteeing the experimental phase of this work.Mulema, SA.; Carrión García, A. (2018). Quality and productivity in aquaculture: Prediction of oreochromis mossambicus growth using a transfer function ARIMA model. International Journal for Quality Research. 12(4):823-834. https://doi.org/10.18421/IJOR12.04-03S82383412

Applying Time Series Modeling to Assess the Dynamics and Forecast Monthly Reports of Abuse, Neglect and/or Exploitation Involving a Vulnerable Adult

Background Application of time series modeling to predict reports related to maltreatment of vulnerable adults can be helpful for efficient early planning and resource allocation to handle a high volume of investigations. The goal of this study is to apply: (1) autoregressive integrated moving average (ARIMA) time series modeling to fit and forecast monthly maltreatment reports accepted for assessment reported to adult protective services (APS), and (2) interrupted time series analysis to test whether the implementation of intake hubs have a significant impact in the number of maltreatment reports after the implementation period. Methods A time series analysis on monthly APS intake reports was conducted using administrative data from SC Child and Adult Protective Services between January 2014 and June 2018. Monthly APS data were subjected to ARIMA modeling adjusting for the time period when intake hubs were implemented. The coefficient of determination, normalized SBC, AIC, MSE, and Ljung-Box Q-test were used to evaluate the goodness-of-fit of constructed models. The most parsimonious model was selected to predict the monthly APS intakes from July to December 2018. Poisson regression was fit to examine the association of the implementation of the hubs and the number of intake reports received to APS, adjusting for confounders. Results Over 24,000 APS intakes accepted for investigation were identified over a period of four calendar years with an increase in the monthly average of APS intakes between 2014 and 2017. An increase in the number of monthly APS intakes was found after the intake hubs were implemented in 2015 (Phase-1) and 2017 (Phase-2). Of all the models tested, an ARIMA (12), 1, 1 model was found to work best after evaluating all fit measures for both models. For Phase-1, the optimum model predicted an average of 488 APS intake reports between July and December 2018, representing a 9% increase from January–June 2018 (median = 445). For Phase-2, the percent increase was 32%. Conclusions The implementation of the intake hubs has a significant impact in the number of reports received after the implementation period. ARIMA time series is a valuable tool to predict future reports of maltreatment of vulnerable adults, which could be used to allow appropriate planning and resource allocation to handle a high volume of monthly intake reports

Analisis dan Model Peramalan Data Ekspor-Impor dengan Metode Gabungan ARIMA-Neural Networks

An accurate forecasting model for a time series data is still difficult to obtain if the data is complex. This study aims to analyze and make the model of import export data forecasting with the combined method ARIMA - Neural Networks. This method is expected to improve NN's ability to complex problems and improve forecasting accuracy. The forecasting model obtained is used to predict the value of import-export in the next period. From the available data, ARIMA forecasting model for export value is ARIMA (1,1,12) with error 0,968 and forecasting model of NN with sigmoid bipolar gives error 0,180732 while NN model with semilinier gives error 0,081521 . For import value, obtained ARIMA (0, 1, 0) model with error 0,971 and forecasting model of NN with sigmoid bipolar gives error 1,437723 while model of NN with semilinier gives error 0,957831. Based on these results, a combined forecasting model of ARIMA and Neural Network with a semilinier activation function will be performed because it has a smaller error value compared to the sigmoid bipolar activation function. The ARIMA- NNforecasting model with the semilinier activation function yield error 0.046010 for the export value data and 1.081964 for the import value data

A PSO-GRNN model for railway freight volume prediction: empirical study from China

Purpose: The purpose of this paper is to propose a mathematical model for the prediction of railway freight volume, and therefore provide railway freight resource allocation with an accurate direction. With an accurate railway freight volume prediction, railway freight enterprises can integrate the limited resources and organize transport more reasonably. Design/methodology/approach: In this paper, a PSO-GRNN model is proposed to predict the railway freight volume. In this model, GRNN is applied to carry out the nonlinear regression analysis and output the prediction value, PSO algorithm is applied to optimize the GRNN model by searching the best smoothing parameter. In order to improve the performance of PSO algorithm, time linear decreasing inertia weight algorithm and time varying acceleration coefficient algorithm are applied in the paper. Originality/value: A railway freight volume prediction index system containing seventeen indexes from five aspects is established in this paper. And PSO-GRNN model constructed in this paper are applied to predict the railway freight volume from 2007 to 2011. Finally, an empirical study is given to verify the feasibility and accuracy of the PSO-GRNN model by comparing with RBFNN model and BPNN model. The result shows that PSO-GRNN model has a good performance in reducing the prediction error, and can be applied in actual production easilyPeer Reviewe