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All Journal Jurnal Teknologi Informasi dan Terapan (J-TIT)
Sriani Sriani
Universitas Islam Negeri Sumatera Utara, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Sentiment Analysis of the Use of Makeup Products Using the Support Vector Machine Method Khairunnisa Khairunnisa; Sriani Sriani
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.439

Abstract

Many beauty products have emerged from various brands by providing attractive offers for women who are their main targets. Product reviews can help consumers regarding the quality of using the product. However, the problem is, on the femaledaily.com website there is no distinction between negative, neutral, and positive reviews so that consumers must first read the review and it takes a lot of time and this problem really requires a classification process on the review into negative, neutral, and positive classes. This process cannot be done automatically, therefore sentiment analysis is needed. To find out the classification of positive, negative, and neutral sentiment on the product, the Support Vector Machine (SVM) method is used, the advantage of SVM in this case lies in its ability to handle high-dimensional datasets and still produce effective classification and SVM is also a good choice for sentiment analysis in the context of cosmetic product reviews. The classification results using the SVM method produce data into 3 classes, namely 510 positive reviews, 98 neutral, and 29 negative with an accuracy value of 77.97%, precision 78%, recall 100%, fi-score 88%
Classification of Chicken Meat Freshness Using Support Vector Machine and Hue Saturation Intensity Cintana Aisyah Rilia; Sriani Sriani
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.440

Abstract

Chicken meat is a popular source of animal protein in Indonesia due to its high nutritional value, affordable price, and easy processing. The identification of chicken meat freshness is currently still done manually through visual or tactile inspection, but this method has limitations, especially if consumers are less skilled in distinguishing the quality of chicken meat freshness. Therefore, an automated system is needed to classify the freshness level of chicken meat based on images. This research aims to develop an image processing system in classifying the freshness level of chicken meat by utilizing the Support Vector Machine (SVM) method with Hue Saturation Intensity (HSI) based color feature extraction. This process is done by converting the RGB image into HSI, then extracting the Hue, Saturation, and Intensity values and classifying using a polynomial kernel. This study used 450 chicken meat images, with 360 training data and 90 test data. The developed system successfully achieved an accuracy of 65.56%. The test results show that the system is reliable in classifying the freshness level of chicken meat. This system has the potential to support the identification of meat freshness efficiently and objectively, while at the same time improving food safety.
Co-Authors Cintana Aisyah Rilia Khairunnisa Khairunnisa