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All Journal Jurnal Riset Informatika Journal Medical Informatics Technology
Kartika Puspita
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Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health

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Improved Brain Tumor Detection MRI Using Advanced Processing Techniques: Enhancement and Convolution Case Studies Kartika Puspita
Journal Medical Informatics Technology Volume 2 No. 3, September 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i3.43

Abstract

Brain tumors present a significant challenge in medical imaging due to their complexity, requiring early detection and precise analysis for effective treatment. This study develops and evaluates advanced image processing workflows aimed at enhancing brain tumor image analysis. The proposed method involves four main steps: enlargement, pre-processing with min-max filters, enhancement, and convolution. The dataset used is from Kaggle, comprising 3,364 images categorized into Glioma (100 images), Meningioma (115 images), No Tumor (105 images), and Pituitary Tumor (74 images). For this study, images from the Glioma, Meningioma, and Pituitary Tumor categories were used, with one image selected from each category for technique evaluation. The results showed significant improvements in image clarity and detail, with high correlation values of 0.9851 for Meningioma and 0.9886 for Pituitary. These findings highlight the effectiveness of the proposed techniques in enhancing image quality and diagnostic accuracy.
IMPLEMENTATION OF SUPPORT VECTOR MACHINE, PARTICLE SWARM OPTIMIZATION, AND NAÏVE BAYES ALGORITHMS IN SENTIMENT ANALYSIS OF PRODUCT REVIEWS: A CASE STUDY OF E-COMMERCE LAZADA Mery Oktaviyanti Puspitaningtyas; Kartika Puspita; Yuris Alkhalifi; Yulita Ayu Wardani
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.362

Abstract

Sentiment analysis is pivotal in deciphering customer opinions and attitudes towards products on e-commerce platforms such as Lazada. Machine learning algorithms like Support Vector Machine (SVM), SVM with Particle Swarm Optimization (PSO), and Naïve Bayes (NB) are leveraged to automate this process, aiding decision-making in business settings. This study specifically aims to assess the performance of SVM, SVM + PSO, and NB in analyzing sentiment from Lazada product reviews, focusing on key metrics like accuracy and Area Under the Curve (AUC). Using a dataset of Lazada reviews, each algorithm is rigorously trained and evaluated. SVM achieves 72.74% accuracy and an AUC of 0.893, while integrating PSO boosts accuracy significantly to 84.84% with an AUC of 0.898. In contrast, NB achieves 75.34% accuracy and an AUC of 0.663. These results highlight SVM + PSO's superior performance in sentiment classification compared to SVM and NB. The findings suggest that SVM + PSO presents a robust solution for sentiment analysis in e-commerce, surpassing traditional SVM and NB methods in accuracy and AUC metrics. This underscores the potential of optimization techniques like PSO to enhance machine learning algorithms for effective sentiment analysis in practical e-commerce applications.
Co-Authors Mery Oktaviyanti Puspitaningtyas Yulita Ayu Wardani Yuris Alkhalifi