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All Journal Jurnal Sistem Cerdas Nuansa Informatika
Ferdinan, Dani
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Predicting Basic Shipping Tariff Using Machine Learning: Prediksi Tarif Dasar Pengiriman Menggunakan Machine Learning Harani, Nisa Hanum; Setyawan, M. Yusril Helmi; Ferdinan, Dani
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.388

Abstract

This study explores the application of machine learning algorithms in predicting the Basic Shipping Tariff for logistics, focusing on variables such as Item Price, Shipment Weight, and Distance (KM). Random Forest Regressor and Linear Regression models were used as comparison methods. Experimental results show that the Random Forest Regressor outperforms Linear Regression, achieving an R² value of 0.915 and RMSE of 0.154, while Linear Regression reached an R² value of 0.706 and RMSE of 0.113. Additionally, the Random Forest model achieved lower error values with MSE of 0.000 and MAE of 0.003, compared to Linear Regression with MSE of 0.001 and MAE of 0.007. These error metrics further highlight the superiority of the Random Forest model. In-depth analysis reveals significant relationships between these variables and the Basic Shipping Tariff, showcasing the model's potential application in dynamic pricing strategies within the Indonesian logistics industry. This study aims to contribute to operational efficiency and improve pricing accuracy in the logistics business in Indonesia.
Feature Selection and Reduction in Happiness Index Analysis: A Systematic Literature Review Ferdinan, Dani; Harani, Nisa Hanum
Jurnal Sistem Cerdas Vol. 8 No. 2 (2025): August
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i2.540

Abstract

This study investigates the role and effectiveness of feature selection and feature reduction techniques in improving the accuracy, validity, and efficiency of predictive models for survey-based happiness indices. A Systematic Literature Review (SLR) was conducted following the PRISMA 2020 protocol, evaluating 40 peer-reviewed articles published between 2020 and 2025. The results demonstrate that feature selection methods namely wrapper, filter, and embedded approaches can significantly enhance model performance, yielding higher coefficients of determination (R²) and lower prediction errors. Furthermore, the identification of relevant features has been shown to improve construct validity and the reliability of happiness indicators. The integration of feature selection and feature reduction techniques also contributes to more efficient and stable models, particularly in high-dimensional data contexts. However, the limited number of studies directly addressing happiness and the methodological heterogeneity across works pose challenges to the generalizability of the findings. This review provides valuable insights for establishing evidence-based practices and guiding strategic developments in future happiness index analytics
Co-Authors Harani, Nisa Hanum Setyawan, M. Yusril Helmi