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All Journal Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro
Akbar, A. Syamsu Irfan
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Computer Science & IT Electrical & Electronics Engineering Energy

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Prediksi Diabetes Melitus Tipe-2 Menggunakan Sequential Forward Selection (SFS) Dengan Algoritma Support Vector Machine (SVM) Saputra; Akbar, A. Syamsu Irfan; Cipta Ramadhani
DIELEKTRIKA Vol 11 No 1 (2024): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/dielektrika.v11i1.381

Abstract

Diabetes is a chronic disease and can cause long-term complications if not handled properly. To prevent this, a machine learning model is needed to predict diabetes with high accuracy. This study aims to see the effect of reducing feature dimensions on model performance and to see the effect of data cleaning on model performance. This study used the Pima Indian Dataset, two models were created with different preprocessing stages. The first model was created without performing data cleansing, and the second model was created by performing data cleansing. After the next preprocessing stage, the number of features that produce the best performance is sought using Sequential Forward Selection and the model is drilled using the Support Vector Machine algorithm. After going through the training stage, the two models will be tested and their performance will be compared. The results showed that reducing the number of features made the model have better performance. And of the two types of models, the model that uses the data cleaning stage shows better performance.
Marketplace Data Analysis Using Web Scraping Akbar, A. Syamsu Irfan; Ramadhani, Cipta
DIELEKTRIKA Vol 11 No 2 (2024): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/dielektrika.v11i2.397

Abstract

The rapid development of the internet in fast communication has provided significant benefits for business people, allowing them to offer and sell product information boldly. One of the main advantages is the ability to search and find a variety of products easily through the internet. This convenience has led to the proliferation of many online stores in Indonesia, so that the need for applications that can help users search and combine product data from the internet arises. This study aims to develop a web application for product data analysis by utilizing web scraping techniques on the Tokopedia marketplace website, using the BeautifulSoup4 (Bs4) method. The web application is designed using the Streamlit module from Python. The results of this study are web applications that process data analysis taken from the Tokopedia website, which are classified based on certain criteria such as price range, highest and lowest ratings, highest and lowest sales, highest and lowest prices, percentage of location distribution, percentage of seller stores, and product recommendations.
Co-Authors Cipta Ramadhani Saputra