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All Journal Media Statistika Jurnal Gaussian Journal of Applied Data Sciences
Arief Rachman Hakim
Department Of Statistics, Faculty Of Sciences And Mathematics, Diponegoro University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Other

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Lightweight Brain Tumor Classification with Histogram Oriented Gradients (HOG) Features and Class-Weighted Support Vector Machine (SVM) Budi Warsito; Husni Fadhilah; Puspita Kartikasari; Arief Rachman Hakim
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1018

Abstract

Early detection of brain tumors via MRI is crucial for improving patient outcomes. This study investigates a lightweight machine learning approach for multiclass brain tumor classification (glioma, meningioma, pituitary tumor, or no tumor) using Histogram of Oriented Gradients (HOG) for feature extraction and a Support Vector Machine (SVM) classifier. This study utilizes the public Brain Tumor Classification MRI Kaggle dataset, consisting of 2870 training and 394 testing MRI images across four classes. After converting the MRIs to grayscale and resizing them to 16×16 pixels, this study extracts HOG features and applies Principal Component Analysis (PCA) to retain 98% of the variance. An SVM is then trained with a GridSearchCV-optimized kernel and hyperparameters, and a custom class-weighted variant is compared. The best model, a polynomial-kernel SVM with custom class weights, achieved 91.8% test accuracy (95% CI (confidence interval): 90.9-92.7) with an F1-score of 0.919 ± 0.01, outperforming the best unweighted SVM (accuracy 86.0% ± 0.02, F1≈0.847). These results demonstrate that HOG+SVM, with proper weighting for class imbalance, can effectively classify brain tumors on small datasets at low computational cost. The novelty of this work lies in demonstrating that an optimized, class-weighted SVM leveraging compact HOG-PCA features can deliver over 91.8% accuracy with strong generalization on small-scale MRI data, providing a viable and interpretable alternative to complex Convolutional Neural Network (CNN) models. Future work can explore CNN and hybrid feature fusion to improve accuracy and generalization further.
PERAMALAN HARGA BERAS DI INDONESIA MENGGUNAKAN METODE HOLT-WINTERS ADDITIVE EXPONENTIAL SMOOTHING DENGAN OPTIMASI GOLDEN SECTION Yuni Nurul Faiza; Suparti Suparti; Arief Rachman Hakim
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.257-268

Abstract

Rice is one of the staples that must be fulfilled to support human survival. As a result, if the price of rice is instable, it can cause a decrease in people's purchasing power. Therefore, a system is needed that can forecast rice prices to help maintain food security. This study uses the Holt-Winters Additive method because it can be used to predict time series data that has trend and seasonal patterns. The optimum parameter is found using the Golden Section optimization method that minimize the MAPE value. The data used is the average monthly data of rice prices at the level of large trade (wholesale) Indonesia. The results showed that the data contained elements of trend and seasonality additives and obtained the best model with α = 0.999702, β = 0.059114, γ = 0.145618. The results of measuring the forecasting ability of the formed model show that the forecast results are close to the actual data and are evidenced by the MAPE out sample value of 7.006% which is include MAPE criteria < 10% so that the forecasting ability is very high. The forecast results for 2023 show that rice prices have fluctuated but the changes are not too significant.
PEMODELAN REGRESI GAMMA MENGGUNAKAN METODE OPTIMASI BROYDEN-FLETCHER-GOLDFARB-SHANNO (BFGS) (Studi Kasus : Pencemaran Sungai di Kota Semarang) Efifah Nur Safitri; Arief Rachman Hakim; Suparti Suparti
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.247-256

Abstract

Semarang City is one of the major industrial areas in Central Java and is located not far from residential areas. These large industries utilize a variety of chemicals to meet their needs, producing various wastes that are the main cause of high concentrations of Chemical Oxygen Demand (COD) in waters. Measurements on the COD value obtained are continuous data with Gamma distribution. In this study, Gamma regression is used to model the relationship between one or more predictor variables and a positive continuous response variable following a Gamma distribution. Parameter estimation in the Gamma regression model uses the Maximum Likelihood Estimation (MLE) method because it does not produce an analytical solution, an optimization method will be carried out with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. This study aims to determine the Gamma regression model in the case of river pollution in Semarang City, determine the form of parameter estimation in the Gamma regression model and get what factors affect river pollution in Semarang City. Based on the  value of 0.4431498 where the ability of the predictor variables to explain the response variable is 44.32%, the remaining 55.68% of the response variable is explained by other factors not contained in the model.
Co-Authors Ade Irma Prianti Agus Rusgiyono Alan Prahutama Anestasya Nur Azizah Arnisa Melani Kahar Budi Warsito Budi Warsito Budi Warsito Chalimatus Sa&#039;diah Efifah Nur Safitri Elva Nadia Nugroho Hasbi Yasin Husni Fadhilah Iut Triutami Lutfia Nuzula Lutfiah Maharani Siniwi Maghfiroh Hadadiah Mukrom Mifta Fara Sany Mustafid Mustafid Puspita Kartikasari Rahmasari Nur Azizah Rezky Dwi Hanifa Riza Rizqi Robbi Arisandi Rizki Faizatun Nisa Rukun Santoso Sisca Indah Pratiwi Sofiana Sofiana Stevanus Sandy Prasetyo Sugito Sugito Suparti Suparti Tatik Widiharih Tri Ernayanti Yuni Nurul Faiza