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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Pendidikan dan Teknologi Indonesia
Angga Laksana, Eka
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Penerapan Kombinasi Random Forest dan Optuna Hyperparameter Tuning Untuk Meningkatkan Akurasi Prediksi Harga Rumah Muhammad Fadzryan, Reza; Angga Laksana, Eka
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 6 (2025): JPTI - Juni 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.846

Abstract

Prediksi harga rumah merupakan aspek penting dalam pengambilan keputusan investasi di sektor properti. Penelitian ini bertujuan untuk membandingkan beberapa algoritma Machine Learning dan Deep Learning dalam memprediksi harga rumah, serta mengoptimalkan Random Forest Regressor menggunakan Optuna dengan implementasi Tree-structured Parzen Estimators (TPE). Dataset yang digunakan adalah House Price 2023 Dataset dari Kaggle, yang mencakup 168.000 entri data properti di Pakistan. Metodologi penelitian ini meliputi tahap preprocessing data, rekayasa fitur, serta penerapan beberapa algoritma prediksi, yaitu Artificial Neural Networks (ANN) dengan model Feedforward Neural Network, KNeighborsRegressor, Linear Regression, dan Random Forest Regressor. Model-model ini dievaluasi menggunakan metrik MAE, MSE, RMSE, R-squared, dan Akurasi. Random Forest Regressor memberikan hasil terbaik dengan R-squared 0.91 dan Akurasi 91.33%. Untuk meningkatkan performa model, diterapkan optimasi hyperparameter menggunakan Optuna dengan pendekatan TPE yang berbasis Bayesian Optimization. Hasil model yang dioptimalkan mencapai peningkatan performa dengan R-squared 0.92 dan akurasi 91.75%. Hasil ini menunjukkan bahwa optimasi hyperparameter menggunakan Optuna berbasis Bayesian Optimization dapat meningkatkan akurasi prediksi harga rumah yang dapat diaplikasikan dalam analisis investasi properti.
Optimasi LDA untuk Analisis Keluhan Nasabah Perbankan dengan Grid Search: Grid Search Parameter Tuning Afriyani, Rika; Angga Laksana, Eka
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 2 (2025): Agustus 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i2.2025.98-106

Abstract

This study aims to analyze topics in banking customer complaint data using the Latent Dirichlet Allocation (LDA) method, enhanced with parameter tuning via Grid Search. The dataset is sourced from ConsumerFinance.gov, containing a total of 6.3 million complaint entries from 2011 to 2024, with 50% of the data used to maintain representation and simplify analysis. In this analysis, the LDA method is employed to identify hidden topics, while Grid Search enhances model coherence. The results indicate that customer complaints can be categorized into 10 main topics, including complaint report issues (25.67%), payment errors (18.10%), data authorization (12.20%), and credit policy (10.77%). Parameter optimization successfully improved the model's coherence score from 0.49 to 0.56, reflecting an enhancement in topic clustering quality. A comparison between standard LDA and LDA with Grid Search reveals that the optimization method yields a higher average coherence score (0.52 vs. 0.42). This study provides insights into common complaints received by banks and key terms such as "report," "authorization," and "investigation," which can assist banks in better understanding and addressing customer complaints more effectively.
Co-Authors Afriyani, Rika Muhammad Fadzryan, Reza