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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) KLIK: Kajian Ilmiah Informatika dan Komputer
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%.
Deteksi Nominal Mata Uang Rupiah Menggunakan Metode Convolutional Neural Network dan Feedforward Neural Network Dede Aprillia; Tatang Rohana; Tohirin Al Mudzakir; Deden Wahiddin
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1711

Abstract

This research aims to develop a nominal detection system for the Rupiah currency for the 2022 emission year using the Convolutional Neural Network (CNN) and Feedforward Neural Network (FNN) methods, especially in the context of applications for vending machines. This research explores the potential of computer vision technology to facilitate the introduction of Rupiah banknotes and contribute to the development of vending machines. The dataset used includes variations in lighting conditions, orientation, and position of banknotes, thus involving various augmentation and preprocessing processes. The model evaluation results include nominal detection accuracy in various conditions, considering the success of the system to support the performance of the vending machine. This research is expected to contribute to the development of more comprehensive technology and expand the application of CNN and FNN in the context of currency detection. In this research, the CNN method produced the best accuracy of 100% for testing in bright conditions, then in sufficient light conditions it produced an accuracy of 96.43%. Meanwhile, testing in dark conditions got quite low results, only 78.56%. Then the FNN method produces the same accuracy of 53.57% in bright light, sufficient light and low light conditions.
Penerapan Metode Naive Bayes Dalam Klasifikasi Spam SMS Menggunakan Fitur Teks Untuk Mengatasi Ancaman Pada Pengguna Fathimah Noer Azzahra; Tatang Rohana; Rahmat Rahmat; Ayu Ratna Juwita
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5070

Abstract

One of the negative impacts of current digital advances is the increasing number of SMS spam. Spam SMS poses a security risk to users because they can contain malicious links or requests for personal information that are used for malware, smishing, or fraud attacks. However, with the various protection measures available, not all spam SMS can be classified and prevented effectively. However, this problem can be minimized by creating an anti-spam SMS model which aims to classify SMS types. So this research aims to classify types of SMS that contain spam and spam by applying the Naïve Bayes algorithm. In this study, the dataset consisted of 5572 records consisting of 2 categories, namely spam and ham. This algorithm is able to show satisfactory performance in differentiating spam and spam messages because, according to the diversity of literature, the Naïve Bayes algorithm is suitable for use in English language datasets. The evaluation model displays good results with accuracy reaching 93.2%, precision 93.7%, recall 93.2%, and F1-score 91.6%. In addition, analysis in the research using the Receiver Operating Characteristic (ROC) curve shows an accuracy rate of 97.3%, indicating that the model has very good performance in classifying spam in SMS messages. However, there is still room for improvement through the use of new methods and larger and more diverse data sets. This research has an important involvement in working on communication security and user experience in using short message services.
Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Terhadap Ulasan Pengguna Aplikasi Mypertamina Menggunakan Confusion Matrix Ade Syahril; Yana Cahyana; Dwi Sulistya Kusumaningrum; Tatang Rohana
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5639

Abstract

The large number of vehicles in Indonesia makes fuel oil (BBM) very important, especially for cars and motorbikes. The Indonesian government works closely with PT Pertamina Persero and requires transactions using the MyPertamina application to ensure that fuel subsidies are properly targeted. However, the MyPertamina app has received mixed feedback and criticism from users, such as complaints about frequent bugs, instability of the app during use and difficulties in the registration or login process. User feedback on the app has been both positive and negative. Users also provided their ratings and reviews on the Google Play Store. The purpose of this research is to analyse the opinions of MyPertamina application user comments and compare the accuracy of the Decision Tree and K-Nearest Neighbor algorithms. This research includes scraping, text preprocessing, weighting, algorithm implementation and evaluation. The data used was obtained from Google Play Store as much as 10,000 data based on the latest reviews, after data cleaning such as removing duplicate data and missing values obtained 8,072 reviews. The data is then grouped into positive classes (2,506 reviews) and negative classes (5,566 reviews), with more negative data. The classification results using the Decision Tree and K-NN methods, it is known that the Decision Tree method has a higher accuracy of 83%, while K-NN method is 58%. This finding indicates that the Decision Tree method is more effective in analysing user reviews of the MyPertamina application compared to the K-NN method.
Co-Authors Ade Syahril Adi Rizky Pratama Ayu Ratna Juwita Dede Aprillia Deden Wahiddin Dwi Sulistya Kusumaningrum Fathimah Noer Azzahra Gugy Guztaman Munzi Indra, Jamaludin Rahmat Rahmat Risma Yati Tohirin Al Mudzakir Yana Cahyana