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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

Penerapan Learning Vector Quantization Dalam Pengolahan Citra Digital Untuk Deteksi Penyakit Kulit Rizki Akbar Pratama; Barry Ceasar Octariadi; Syarifah Putri Agustini Alkadri
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.9270

Abstract

Skin, as the largest human organ, covers more than two square meters and accounts for about 15% of body mass. Consisting of three main layers of epidermis, dermis, and subcutaneous tissue, the skin serves as a physical shield and barrier against infection, injury, and UV radiation. Skin diseases such as chickenpox, monkey pox, measles and herpes are medical challenges that require quick and accurate diagnosis. This study used 520 digital images (130 per category) from Mendeley Data and online sources. The Learning Vector Quantization (LVQ) algorithm was applied for image classification based on the extracted features. Results showed an overall accuracy of 90.74%, with respective accuracies: 97% (chickenpox), 98% (monkey pox), 91% (measles), and 100% (herpes). Evaluation using confusion matrix resulted in accuracy, precision, recall, and F1-score values of 0.91, indicating strong model performance. These findings demonstrate the potential of LVQ as a digital image-based skin disease diagnosis tool.
Prediksi Harga Mobil Bekas Menggunakan Algoritma Support Vector Regression Herlangga, Herlangga; Pangestika, Menur Wahyu; Alkadri, Syarifah Putri Agustini
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10545

Abstract

The growth of the automotive industry in Indonesia has contributed to high demand for used cars as a more economical alternative to new cars. However, determining the price of used cars is often a challenge for showrooms and prospective buyers because it involves many factors and is subjective. This study aims to develop a used car price prediction model using the Support Vector Regression (SVR) algorithm with a Radial Basis Function (RBF) kernel approach. A total of 1,000 entries were obtained through web scraping from the cintamobil.com website. The research methodology refers to the CRISP-DM framework, starting from business understanding to model deployment through a web application using Streamlit. The preprocessing process involves handling missing values, outliers, data duplication, and numerical and categorical feature transformations. The SVR model was evaluated using RMSE, MAPE, and MAE metrics to assess prediction accuracy. The results show that SVR is capable of providing fairly accurate price predictions, with parameters C=1, gamma=0.1, and epsilon=0.1 producing the best performance, namely an MAE value of IDR 6,472,572, an RMSE of IDR 8,958,555, and a MAPE of 3.41%. Referring to the prediction accuracy category based on the MAPE value, where a MAPE value ≤ 10% is categorized as high accuracy, it can be concluded that this model has high prediction accuracy. This shows that the SVR model used is capable of estimating used car prices with a low error rate and good accuracy.