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All Journal Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Rahmawan, Rizki Dwi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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SENTIMENT ANALYSIS CLASSIFICATION IN WOMEN'S E-COMMERCE REVIEWS WITH MACHINE LEARNING APPROACH Afan Firdaus, Alfiki Diastama; Rahmawan, Rizki Dwi; Mahendra, Yuzzar Rizky; Cahyono, Hasan Dwi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2392

Abstract

User reviews on e-commerce are one of the important elements in e-commerce. User reviews can help potential buyers make decisions based on the experiences and opinions of other people, for example women's e-commerce reviews. In providing positive, neutral or negative sentiment reviews, understanding customer perceptions is challenging. Classifying sentiment reviews will solve this problem, several classification techniques have been carried out, but there is still room for development in the use of simple machine learning techniques and sampling to overcome data class imbalance. Classification techniques used in this paper include Naive Bayes, SVM, and KNN. These algorithms will be compared to determine the most accurate model. Several preprocessing techniques are also carried out such to balance the dataset using ROS and SMOTE. It was obtained that the SVM method with ROS had the highest accuracy of around 0.94 for accuracy value, 0.93 for precision value, 0.94 for recall, and 0.92 for F1-score value. This research shows that the use of sampling techniques such as ROS and SMOTE can be effective in balancing imbalanced datasets, thereby improving model classification performance. These findings can be a reference for developing more efficient and accurate sentiment classification models, especially in the case of imbalanced data.
Hybrid features to classify lung tumor using machine learning Rahmawan, Rizki Dwi; Salamah, Umi; Yudha, Ery Permana
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.101

Abstract

A lung tumor is an abnormal mass of cells inside a body. As a benign tumor is unproblematic, but a malignant tumor is cancerous because it can travel across the body and interfere with its surrounding tissue. Detecting these cancerous cells in the lung is important because delayed detection may hamper effective treatment options, leading to a lower survival rate. However, classifying tumor malignancy is highly dependent on the knowledge and experience of the radiologist. This study combines texture-based features extracted from lung Computed Tomography Scan (CT Scan) images such as Gray Level Co-occurrence Matrix (GLCM), Gray Level Run-length Matrix (GLRLM), Gray Level Size-zone Matrix (GLSZM), and Haralick Features aims to create a lung tumor classification system. This research contributes by creating an efficient and reliable system through Relief-F feature selection that uses features with the highest weight in rank that are able to differentiate classes of tumor malignancy and help medical professionals diagnose tumors more early in the treatment.  As a comparison, several conventional machine learning classifiers, including SVM RBF, KNN, RF, DT, and XGBoost, were utilized to evaluate classifier performance. The result showed that the accuracy of the proposed hybrid features with a random forest classifier was the most performing approach with an evaluation score of accuracy of 99.55%, precision of 99.55%, recall of 99.55%, and F1-Score of 99.54%. Furthermore, accuracy among other classifiers was also higher than 90%. Proofing the selected features retain essential class information, demonstrating the study’s applicability in developing automated lung tumor classification systems from CT scans.
Co-Authors Afan Firdaus, Alfiki Diastama Hasan Dwi Cahyono Mahendra, Yuzzar Rizky Umi Salamah Yudha, Ery Permana