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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Zeta - Math Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Nitasari, Alfi Nur
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Optimizing Brain Tumor MRI Classification with Transfer Learning: A Performance Comparison of Pre-Trained CNN Models Mardianto, M. Fariz Fadillah; Pusporani, Elly; Salsabila, Fatiha Nadia; Nitasari, Alfi Nur; Lu’lu’a, Na’imatul
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.87377

Abstract

This study aims to classify brain MRI images into several types of brain tumors using the Convolutional Neural Network (CNN) approach with transfer learning. This method has the advantage of processing complex images in a shorter time than conventional CNN approaches. In this study, the data used was a public database from Kaggle, which consisted of four categories: glioma, meningioma, no tumor, and pituitary. Before entering the transfer learning process, data augmentation is carried out on the training data. Four pre-trained CNN models were used: VGG19, ResNet50, InceptionV3, and DenseNet121. The four models compared their ability to classify MRI images with several evaluation metrics: accuracy, precision, recall, and F1 score. The results of the performance comparison of the four pre-trained models show that the ResNet50 is the best model, with an accuracy of 98%. Meanwhile, VGG19, DenseNet121, and InceptionV3 produce 97%, 96%, and 95% accuracy, respectively. The ResNet50 architecture demonstrated superior performance in brain tumor classification, achieving 98% accuracy. It can be attributed to its residual learning structure, which efficiently manages complex MRI features.  Further research should concentrate on larger, more diverse datasets and advanced preprocessing techniques to enhance model generalizability.
MODELING LONGITUDINAL FLOOD DATA IN WEST SUMATRA USING THE GENERALIZED ESTIMATING EQUATION (GEE) APPROACH Nitasari, Alfi Nur; Sa'idah, Andini; Faizun, Nurin; Darmawan, Kezia Eunike; Fitri, Marfa Audilla; Chamidah, Nur
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2181-2190

Abstract

Flooding is one of the many natural disasters that often hit Indonesia. In July 2023, three areas in West Sumatra experienced floods and landslides which caused damages and even 2 missing victims. Since November 16th, 2023, 8 hamlets in Meranti Village, Landak District, West Sumatra have been inundated by floods which affected families and many public facilities. This research uses data from West Sumatra Province Central Statistics Agency. The data used is 2014, 2018 and 2021. The response variable used is the number of villages/sub-districts experiencing natural disasters according to district/city ( ). The predictor variables used are regional topography , the number of water channels such as rivers, reservoirs, etc. , the number of fields cleared through burning , the number of villages/sub-districts in C excavation area , and the number of dumpsters . This research uses Negative Binomial Regression with the Generalized Estimating Equation (GEE) approach. In the Poisson regression test, the QIC value based on Independent Working Correlation Structure (WCS) is with deviance value of , degree of freedom of , and dispersion score of 4,6144. Because the dispersion value is greater than 1, it can be concluded that there is overdispersion. Because there is more than one overdispersion, it is overcome by using negative binomial. The results of parameter estimation using negative binomial regression based on Independent WCS showed that only one variable was significant, which is the number of fields cleared through burning with deviance value of , degrees of freedom of and a QIC of . Negative Binomial regression model that was formed is ). From the two regression models used, namely Poisson and negative binomial, it was found that the negative binomial regression model was the best model because it had the lowest QIC value of .
Reduksi Faktor-Faktor yang Mempengaruhi Kualitas Air Hujan dengan Metode Analisis Komponen Utama Nitasari, Alfi Nur; Salsabila, Fatiha Nadia; Ramadhanty, Devira Thania; Anggriawan, Muhammad Rizal; Amelia, Dita; Mardianto, M. Fariz Fadillah; Ana, Elly
Zeta - Math Journal Vol 8 No 1 (2023): Mei
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2023.8.1.7-15

Abstract

The increase in air pollution that occurs due to industrial and economic activities will affect the rainwater content. The vital function of one of the sources of clean, namely rainwater, makes the urgency of research on indications of a decrease in rainwater quality by analyzing the substances contained. The parameters used for the determination of rainwater quality are the degree of acidity, conductivity, calcium, magnesium, sodium, potassium, ammonium, sulfate ions, nitrate ions, chloride ions, total hardness, and acidity. The data source used is data from the Central Statistics Agency (BPS) entitled "Indonesian Environmental Statistics 2022". By using the main component analysis method to reduce the variables of rainwater quality influence factors in 35 city/station samples in Indonesia, results were obtained, namely rainwater quality influence factors that can be formed into three main components with each containing 10 variables consisting of acidity, conductivity, magnesium, sodium, potassium, chloride ions, sulfate ions, nitrate ions, total hardness, and acidity capable of explaining 73.695% of the total variance.
UNILEVER STOCK PRICES FORECASTING WITH ENSEMBLE AVERAGING APPROACH ARIMA-GARCH AND SUPPORT VECTOR REGRESSION Pusporani, Elly; Nitasari, Alfi Nur; Salsabila, Fatiha Nadia; Indrasta, Irma Ayu; Mardianto, M. Fariz Fadillah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0137-0154

Abstract

Investment, mainly in stock prices, plays a significant role in the Indonesian economy. Accurate stock price forecasting can help investors make informed decisions. Unilever Indonesia Tbk (UNVR) exhibits high volatility in its closing stock prices, making it crucial to develop a reliable forecasting model. This study applies an ensemble averaging method that integrates the ARIMA-GARCH model and Support Vector Regression (SVR) to predict UNVR's closing stock prices from January 6, 2019, to November 5, 2023. The results indicate that the data can be modeled using ARIMA (0,2,1). However, the squared residuals of the model show heteroscedasticity, necessitating variance modeling using the ARCH-GARCH approach. The best combination of mean and variance modeling is achieved with ARIMA (0,2,1) – GARCH (1,1), yielding a Mean Absolute Percentage Error (MAPE) of 2.865%. Additionally, a nonparametric SVR model with parameters C = 4 and ε = 0 is applied, resulting in a MAPE of 2.94%. An ensemble averaging approach is implemented to optimize forecasting accuracy further, combining ARIMA-GARCH and SVR models. This ensemble approach improves predictive performance, achieving a final MAPE of 1.682%. These findings demonstrate that ensemble averaging effectively enhances stock price forecasting accuracy by leveraging linear and nonlinear modeling techniques.
Co-Authors Anggriawan, Muhammad Rizal Darmawan, Kezia Eunike Dita Amelia, Dita Elly Ana Elly Pusporani Faizun, Nurin Fitri, Marfa Audilla Indrasta, Irma Ayu Lu’lu’a, Na’imatul M. Fariz Fadillah Mardianto Nur Chamidah Ramadhanty, Devira Thania Sa'idah, Andini Salsabila, Fatiha Nadia