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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of Computer and Information System (IJCIS)
Wiradinata, Marcell Jeremy
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Used Car Price Prediction Model: A Machine Learning Approach Budiono, Daniel Aprillio; Utomo, Kevin Sander; Wibowo, Kenny Jinhiro; Wiradinata, Marcell Jeremy
International Journal of Computer and Information System (IJCIS) Vol 5, No 1 (2024): IJCIS : Vol 5 - Issue 1 - 2024
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v5i1.147

Abstract

The impact of the Covid-19 pandemic over the past two years has slowed down the economy, including the market of used cars. However, the recent decline in the number of cases infected with Covid-19 has reignited interest in the used car market. One of many persisting issues found in the used car market is that sellers want the highest price possible; but, buyers and used car dealers bid the lowest price due to economic stability uncertainty. To accelerate the recovery of the used car industry, various innovations are required. This study proposes the use of the K-Nearest Neighbors (KNN) regression model to predict used car prices to address this issue. The proposed KNN model is a machine learning algorithm which is capable of handling multi-dimensional data and its robustness to noisy data, making it suitable for predicting used car prices based on multiple factors. By analyzing collected data on used car prices, a machine learning-based regression model can be developed to predict used car prices based on factors commonly used in the used car industry, such as year of production, car type, car condition, and others. This study makes use of 504 used car data collected through web scraping as a secondary data collection method. With a relatively small error rate of 8.3% and an R2 value of 98.8%, the results of this analysis can provide insight for used car buyers and sellers, to better gauge the price of used cars in the market.
The Effects of Preprocessing Techniques on Nasnetmobile's Performance for Classifying Knee Osteoarthritis Based on the Kellgren-Lawrence System Wiradinata, Marcell Jeremy; Wonohadidjojo, Daniel Martomanggolo
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8713

Abstract

Knee osteoarthritis (KOA) is a degenerative joint disorder characterized by the progressive deterioration of protective cartilage at the ends of bones, leading to pain and limited mobility. Deep learning provides an effective approach to classify whether X-ray images indicate the presence of KOA; however, dataset preprocessing techniques can enhance the efficacy of deep learning models. This study highlights the importance of preprocessing techniques in improving image contrast, particularly in utilizing the NASNetMobile model to assess the severity of KOA through X-ray images. KOA classification based on the Kellgren-Lawrence system consists of five severity levels; however, simplifying it into two categories can improve the performance of deep learning models. By fine-tuning parts of the NASNetMobile model and using the Nadam optimizer, the model initially achieved only 59% validation accuracy. However, by applying various preprocessing techniques, the model's validation accuracy improved to 80%.
Co-Authors Budiono, Daniel Aprillio Daniel Martomanggolo Wonohadidjojo Utomo, Kevin Sander Wibowo, Kenny Jinhiro