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All Journal Media Jurnal Informatika Journal of Social Sciences and Technology for Community Service
Wartariyus
Universitas Lampung
Humanities Computer Science & IT Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Machine Learning Regression Model: Exploring Regression Algorithms for Mercedes-Benz Price Prediction Ridho Sholehurrohman; Muhaqiqin; Igit Sabda Ilman; Agung Pambudi; Wartariyus; Joko Triloka; Handoyo Widi Nugroho
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6476

Abstract

Predicting luxury car prices, such as Mercedes-Benz, remains challenging due to multiple interacting variables, including model, ratings, and market conditions. This study compares six regression algorithms, Linear Regression, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and AdaBoost, to identify the most effective model for Mercedes-Benz price prediction. A Kaggle dataset of 10,432 records was preprocessed through cleaning, removal of missing values (resulting in 10,307 records), One-Hot Encoding for categorical variables, and standardization of numerical features using StandardScaler, then split into 80% training and 20% testing data. Model performance was evaluated using MSE, RMSE, and R². Random Forest achieved the best performance (R² = 0.97; RMSE: $3,917), followed closely by Gradient Boosting (R² = 0.96; RMSE: $4,359) and XGBoost (R² = 0.96; RMSE: $4,305). Linear Regression achieved a similar R² (0.96) but higher errors (RMSE: $4,767), while AdaBoost (R² = 0.95; RMSE: $4,897) and KNN (R² = 0.90; RMSE: $5,657) showed lower performance. These findings confirm that ensemble methods, particularly Random Forest, significantly outperform traditional and distance-based approaches for luxury car price prediction. This study provides a comprehensive comparative framework for automotive pricing analytics, with future research directions including additional features, hyperparameter tuning, and integration of external market factors to further enhance prediction accuracy.
Pelatihan Pemanfaatan Gemini AI dalam Pembuatan Aplikasi Mobile untuk Guru TIK Lampung Selatan Muhaqiqin; Mohamad Zainudin; Ridho Sholehurrohman; Igit Sabda Ilman; Wartariyus; Riska Amalia Praptiwi; Istiana Ruswita
Journal Social Science And Technology For Community Service Vol. 6 No. 2 (2025): Volume 6, Nomor 2, September 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jsstcs.v6i2.860

Abstract

This community service activity aimed to enhance the digital literacy and skills of ICT teachers in South Lampung in developing educational mobile applications using Gemini AI, given that most teachers had not mastered application programming or the use of AI in teaching. The offline training, held on July 27, 2025, at SMA Negeri 1 Kalianda, involved 25 ICT teachers and integrated practical-based training, the diffusion of Gemini AI science and technology, and intensive technical assistance. The material covered basic AI concepts, Gemini AI implementation with Android Studio Cloud, and practical prototype development for applications like practice questions and ICT dictionaries. As a result, 92% of participants found the training very helpful, and teachers successfully created AI-based application prototypes. This activity successfully improved teachers' literacy and skills, demonstrating their significant potential in developing learning technology. Further training with gradual difficulty levels, increased practice time, and program expansion are recommended to strengthen the digital learning ecosystem.
Co-Authors Agung Pambudi Handoyo Widi Nugroho Igit Sabda Ilman Istiana Ruswita Joko Triloka Mohamad Zainudin Muhaqiqin Ridho Sholehurrohman Riska Amalia Praptiwi