Implementation of Random Forest Method with Information Gain Selection and Hyperparameter Tuning for Alzheimer’s Disease Classification
Abstract
Alzheimer's disease is one of the leading causes of decreased quality of life in the elderly aged 65 years and above. One of the problems facing Alzheimer's cases is the difficulty of making an early diagnosis to prevent disease progression, as early symptoms are often mistaken for senile dementia. Using the Random Forest method with information gain feature selection and hyperparameter tuning optimization, this study aims to determine the results of optimization with feature selection and hyperparameter tuning using Random Search and Grid Search to classify Alzheimer's medical record data consisting of 32 variables, including lifestyle factors, clinical measurements, cognitive and functional assessments, as well as symptoms that indicate Alzheimer's. The results showed that applying Information Gain and parameter optimization with the Grid Search method achieved the highest accuracy among all tested experiments. Random Forest with Information Gain and Grid Search gave an accuracy of 95.57%, sensitivity of 92.93%, and specificity of 96.99%, which showed better performance than the Random Search method. This indicates that parameter optimization has a vital role in improving model performance. This research contributes to assisting paramedics in determining whether a patient has Alzheimer's disease based on the characteristics derived from the data.
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References
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[25] W. Agwil, H. Fransiska, and N. Hidayati, “Analisis Ketepatan Waktu Lulus Mahasiswa Dengan Menggunakan Bagging Cart,” FIBONACCI : Jurnal Pendidikan Matematika dan Matematika, vol. 6, no. 2, p. 155, 2020, doi: 10.24853/fbc.6.2.155-166.
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[27] R. Tuntun, K. Kusrini, and K. Kusnawi, “Analisis Perbandingan Kinerja Algoritma Klasifikasi dengan Menggunakan Metode K-Fold Cross Validation,” Jurnal Media Informatika Budidarma, vol. 6, no. 4, p. 2111, 2022, doi: 10.30865/mib.v6i4.4681.
[28] G. A. Pradipta and Putu Desiana Wulaning Ayu, “Kombinasi Inisial Filtering Oversampling dengan Metode Ensemble Classifier pada Klasifikasi Data Imbalanced,” Jurnal Sistem dan Informatika, vol. 17, no. 2, pp. 137–145, 2023, doi: 10.30864/jsi.v17i2.591.
[29] T. A. E. Putri, T. Widiharih, and R. Santoso, “Penerapan Tuning Hyperparameter Randomsearchcv Pada Adaptive Boosting Untuk Prediksi Kelangsungan Hidup Pasien Gagal Jantung,” Jurnal Gaussian, vol. 11, no. 3, pp. 397–406, 2023, doi: 10.14710/j.gauss.11.3.397-406.
[30] M. Fajri and A. Primajaya, “Komparasi Teknik Hyperparameter Optimization pada SVM untuk Permasalahan Klasifikasi dengan Menggunakan Grid Search dan Random Search,” Journal of Applied Informatics Computing, vol. 7, no. 1, pp. 14–19, 2023, doi: 10.30871/jaic.v7i1.5004.
[31] Hajiar Yuliana, “Hyperparameter Optimization of Random Forest for 5G Coverage Prediction,” Buletin Pos dan Telekomunikasi, vol. 22, no. 1, pp. 75–90, 2024, doi: 10.17933/bpostel.v22i1.390.
[32] I. Afdhal, R. Kurniawan, I. Iskandar, R. Salambue, E. Budianita, and F. Syafria, “Penerapan Algoritma Random Forest Untuk Analisis Sentimen Komentar Di YouTube Tentang Islamofobia,” Jurnal Nasional Komputasi dan Teknologi Informasi, vol. 5, no. 1, pp. 122–130, 2022, doi: https://doi.org/10.32672/jnkti.v5i1.4004.
[33] C. J. S. Leo Breiman, Jerome Friedman, R.A. Olshen, Classification and Regression Trees. 2022. doi: https://doi.org/10.1201/9781315139470.
[34] I. Wardhana, Musi Ariawijaya, Vandri Ahmad Isnaini, and Rahmi Putri Wirman, “Gradient Boosting Machine, Random Forest dan Light GBM untuk Klasifikasi Kacang Kering,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 1, pp. 92–99, 2022, doi: 10.29207/resti.v6i1.3682.
[35] S. Lestari, A. Akmaludin, and M. Badrul, “Implementasi Klasifikasi Naive Bayes Untuk Prediksi Kelayakan Pemberian Pinjaman Pada Koperasi Anugerah Bintang Cemerlang,” PROSISKO Jurnal Pengembangan Riset dan Observasi Sistem Komputer, vol. 7, no. 1, pp. 8–16, 2020, doi: 10.30656/prosisko.v7i1.2129.
[36] Y. Farida, N. Ulinnuha, S. K. Sari, and L. N. Desinaini, “Comparing Support Vector Machine and Naïve Bayes Methods with A Selection of Fast Correlation Based Filter Features in Detecting Parkinson’s Disease,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 14, no. 2, p. 80, 2023, doi: 10.24843/lkjiti.2023.v14.i02.p02.
[37] P. R. Undersampling, “Effect of Random Under sampling , Oversampling , and SMOTE on the Performance of Cardiovascular Disease Prediction Models terhadap Kinerja Model Prediksi Penyakit Kardiovaskular,” Jurnal Matematika Statistika dan Komputasi, vol. 21, no. 1, pp. 88–102, 2024, doi: 10.20956/j.v21i1.35552.
Published
2025-06-04
How to Cite
FADHILA, Riska Nuril; ULINNUHA, Nurissaidah; YULIATI, Dian.
Implementation of Random Forest Method with Information Gain Selection and Hyperparameter Tuning for Alzheimer’s Disease Classification.
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 16, n. 1, p. 1-13, june 2025.
ISSN 2541-5832.
Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/122136>. Date accessed: 03 nov. 2025.
doi: https://doi.org/10.24843/LKJITI.2025.v16.i01.p01.
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