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All Journal JTERA (Jurnal Teknologi Rekayasa) Jurnal Sistem Cerdas
Ferdinan, Dani
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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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)
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
Model Prediktif Indeks Kebahagiaan Berbasis Gradient Boosting Regressor dengan Optimalisasi Seleksi Fitur dan Implementasi Web Ferdinan, Dani; Harani, Nisa Hanum; Pane, Syafrial Fachri
JTERA (Jurnal Teknologi Rekayasa) Vol 10, No 2: Desember 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i2.2025.59-68

Abstract

Penelitian ini menghadapi tantangan dalam memodelkan Indeks Kebahagiaan 2021 dari Badan Pusat Statistik (BPS) yang memiliki dimensi fitur sangat tinggi dan potensi redundansi, yang dapat menurunkan akurasi dan interpretabilitas model. Tujuan utama penelitian ini adalah untuk mengidentifikasi fitur-fitur paling berpengaruh dalam data tersebut untuk meningkatkan akurasi, efisiensi komputasi, dan transparansi model prediksi berbasis pohon keputusan. Metodologi mencakup pra-pemrosesan data dengan imputasi modus, transformasi Yeo-Johnson, dan Robust Scaler. Tiga algoritma regresi diuji: Decision Tree, Random Forest, dan Gradient Boosting Regressor, yang dioptimalkan menggunakan Particle Swarm Optimization (PSO). Model terbaik dievaluasi menggunakan metrik R², MSE, RMSE, dan MAE serta dianalisis lebih lanjut menggunakan SHAP untuk interpretasi. Hasil menunjukkan bahwa Gradient Boosting Regressor adalah model paling unggul dengan nilai R² sebesar 0,696 saat menggunakan 20 fitur terseleksi. Selain itu, sebagai bentuk implementasi praktis, model diimplementasikan ke dalam sebuah aplikasi web interaktif berbasis Flask yang memungkinkan pengguna memasukkan data melalui antarmuka kuisioner dan menerima prediksi indeks kebahagiaan secara real-time. Integrasi ini menjembatani hasil riset dengan pemanfaatan nyata oleh pengguna akhir.
Co-Authors Harani, Nisa Hanum Syafrial Fachri Pane, Syafrial Fachri