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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi ESTIMASI: Journal of Statistics and Its Application
Awalin, Qonita Ilmi
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Mathematics

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Penentuan Arsitektur Terbaik Model NAR-NN untuk Peramalan Kasus Covid-19 Awalin, Qonita Ilmi; Anggraeni, Dian; Hadi, Alfian Futuhul
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 1, Januari, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i1.21365

Abstract

The NAR-NN model will be applied in time series forecasting, namely data on confirmed cases of Covid- 19 in East Kalimantan Province. The use of time series data as the basis for forecasting so that it can recognize patterns that occur which can then be used as a reference to predict the number of cases that will occur. This research data is 300 daily data for the time period from October 23, 2020 to August 18, 2021, which follows a nonlinear pattern and experiences an upward trend. In this study, the best architecture was determined for the NAR-NN model using the sigmoid activation function and the Levenberg-Marquadt Backpropagation training algorithm. The NAR-NN architecture consists of three layers, namely the input layer, the hidden layer, and the output layer. The evaluation model used is the Mean Absolute Percentage Error (MAPE). The results of this study by experimenting with the number of hidden neurons showed that the model with the best architecture at the time of delay was 4 and the number of hidden neurons was 8 with the MAPE value forecast with actual data of 7.5083%.
Optimizing Data Classification in Support Vector Machines Using Metaheuristic Algorithms Awalin, Qonita Ilmi; Agustin, Ika Hesti; Hadi, Alfian Futuhul; Dafik, Dafik; Sunder, R.
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 9, No 2 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v9i2.29320

Abstract

To categorize patient diagnosis data related to Chronic Kidney Disease (CKD), this study compares the classification performance of Support Vector Machines (SVM) enhanced by Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). CKD is a severe illness in which the kidneys fail to adequately filter blood and perform their normal functions. This study utilized secondary data consisting of patient conditions and health information. Based on references from CKD-related journals, 15 independent variables and one dependent variable were selected from an initial set of 54 variables. To address the issue of unbalanced data, an oversampling technique was applied, and the data was subsequently split into 80% for training and 20% for testing. During the training phase, SVM-PSO and SVM-GA models were developed, and the gamma value was optimized using the RBF kernel function of SVM. The results indicated that in classifying CKD patient diagnosis data, the SVM-PSO model (97.54% accuracy) outperformed the SVM-GA model (97.37% accuracy). This finding suggests that PSO-based hyperparameter optimization yields a superior model for data classification
Co-Authors Alfian Futuhul Hadi D. Dafik Dian Anggraeni Ika Hesti Agustin, Ika Hesti Sunder, R.