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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Tatang Rohana
Universitas Buana Perjuangan Karawang, Karawang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Kajian Model Backpropagation dan Hybrid ANFIS Dalam Memprediksi Pertumbuhan Penduduk di Kabupaten Karawang Tatang Rohana; Jamaludin Indra; Gugy Guztaman Munzi
Journal of Information System Research (JOSH) Vol 4 No 2 (2023): January 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i2.2547

Abstract

Population growth rate prediction is a process of estimating the population in the future. Predictions are made so that the government can prepare strategic steps in anticipating the negative impact of an uncontrolled population increase. The research data is the population of Karawang Regency from 2011 to 2020. Backpropagation and Hybrid ANFIS are the models used in this study. The purpose of this study was to determine the RMSE value and scatter data formed from the results of the ANFIS Backpropagation and Hybrid training models in predicting population growth rates in Karawang Regency. In addition, this study is intended to determine the level of accuracy of the two models. The research step begins with research data validation, preprocessing, training and testing, as well as accuracy testing. Accuracy testing uses the Mean Absolute Percentage Error (MAPE) method. Backpropagation and Hybrid models in predicting the rate of population growth have worked well. This can be seen from the training results of the two models. Backpropagation model has the best RMSE of 0.0328 and Hybrid has the best RMSE of 0.021884. The results of the analysis of the accuracy of predicting population growth rates for 2019 and 2020 that have been carried out, both models have a good level of accuracy. Backpropagation has an average accuracy rate of 84.76%, while the Hybrid model has an average accuracy rate of 93.71%. Based on the results of accuracy testing, the Hybrid model has a better level of accuracy than the Backpropagation model.
Klasifikasi Jenis Mangga Menggunakan Algoritma Convolutional Neural Network Risma Yati; Tatang Rohana; Adi Rizky Pratama
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6445

Abstract

The name of the mango is Mangnifera IndicaL. It originated in India and spread to Indonesia. There are various types of mango variations with different shapes and colors according to the type. To distinguish each mango is seen by its shape and color. However, if in the harvest process mango farmers have to choose manually it takes a long time and potentially mistaken in determining the type. So it needs technology that can make it easier to differentiate the type of mango based on its shape. The study aims to create models with the best accuracy on the process of classifying 5 types of mango based on its shape. The data used in the research this time there are 5 types of mango that will be classified, namely Mangga Apel, Arumanis mango, Mangga Gedong Gincu, Golek mango and Mangga Manalagi. Used 375 images of mango as data sets. The data set before entering the previous training process is undergoing a pre-processing phase that includes the augmentation and resize process. The number of images increased to 2250. The data set is divided into three parts: 70% training data, 20% validation data, and 10% test data. Next is the process of segmentation, the segmentation used in this research is otsu segmentation. The classification process uses the Convolutional Neural Network (CNN) architecture with 3 layers of convolution 16,32 and 64, also using the Adam optimizer. 4 experimental scenarios were performed to find the best accuracy value by distinguishing between learning rate and batch size. From the confusion matrix test results, the best accuracy values were obtained from the input hyperparameter size100x100, epoch 100, learning rate 0,001 and batch size 15 with accurate values of 99.56%, precision 100%, recall 100%, and f1-score 100%.
Co-Authors Adi Rizky Pratama Gugy Guztaman Munzi Indra, Jamaludin Risma Yati