PENERAPAN ALGORITMA RANDOM FOREST DALAM BERBAGAI BIDANG KEILMUAN : SYSTIMATIC LITERATUR REVIEW
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P. : Randi et al., ALGORITMA PEMBELAJARAN MESIN (Dasar, Teknik, dan Aplikasi). 2024. [Online]. Available:
www.buku.sonpedia.com
A. Satria, R. M. Badri, and I. Safitri, “Prediksi Hasil Panen Tanaman Pangan Sumatera dengan Metode Machine
Learning,” Digital Transformation Technology, vol. 3, no. 2, pp. 389–398, Sep. 2023, doi: 10.47709/digitech.v3i2.2852.
D. B. Dinova and B. Prasetiyo, “Implementasi Random Forest dalam Klasifikasi Kanker Paru-Paru,” JOINTERJOURNAL OF INFORMATICS ENGINEERING, vol. 05, no. 01, pp. 27–31, 2024.
F. Orte, J. Mira, M. J. Sánchez, and P. Solana, “A random forest-based model for crypto asset forecasts in futures
markets with out-of-sample prediction,” Res Int Bus Finance, vol. 64, Jan. 2023, doi: 10.1016/j.ribaf.2022.101829.
S. M. Natzir, “Perbandingan Kinerja Model Pembelajaran Mesin dalam Prediksi Banjir menggunakan KNN, Naive
Bayes, dan Random Forest,” JURNAL TEKNOLOGI INFORMASI, vol. 14, no. 2, pp. 59–64, 2023, doi:
52972/hoaq.vol14no1.p59-64.
M. C. M. Oo and T. Thein, “An efficient predictive analytics system for high dimensional big data,” Journal of King
Saud University - Computer and Information Sciences, vol. 34, no. 1, pp. 1521–1532, Jan. 2022, doi:
1016/j.jksuci.2019.09.001.
A. Riza Febiunca, P. Yulianti, U. Airlangga, K. Surabaya, B. Pusat Statistik, and K. Kepulauan Talaud,
“WHISTLEBLOWING INTENTION : SEBUAH TINJAUAN LITERATUR SISTEMATIS,” Jurnal Ilmiah MEA
(Manajemen, Ekonomi, dan Akuntansi), vol. 8, no. 1, 2024.
A. Cahaya Puspitaningrum, L. Dica Fitrani, and E. Septa Sintiya, “SISTEMASI: Jurnal Sistem Informasi Systematic
Literature Review: Implementasi COBIT sebagai Best Practice Tata Kelola Sistem Pemerintahan berbasis Elektronik
(SPBE) Systematic Literature Review: Implementation COBIT as a Best Practice of Electronic based Government
System Governance,” 2024. [Online]. Available: http://sistemasi.ftik.unisi.ac.id
M. L. Wallace et al., “Use and misuse of random forest variable importance metrics in medicine: demonstrations
through incident stroke prediction,” BMC Med Res Methodol, vol. 23, no. 1, Dec. 2023, doi: 10.1186/s12874-023-
-x.
M. Bakro et al., “Building a Cloud-IDS by Hybrid Bio-Inspired Feature Selection Algorithms Along With Random
Forest Model,” IEEE Access, vol. 12, pp. 8846–8874, 2024, doi: 10.1109/ACCESS.2024.3353055.
L. P. Cavalheiro, S. Bernard, J. P. Barddal, and L. Heutte, “Random Forest Kernel for High-Dimension Low Sample
Size Classification,” Oct. 2023, doi: 10.1007/s11222-023-10309-0.
H. Talebi, L. J. M. Peeters, A. Otto, and R. Tolosana-Delgado, “A Truly Spatial Random Forests Algorithm for Geoscience Data Analysis and Modelling,” Math Geosci, vol. 54, no. 1, Jan. 2022, doi: 10.1007/s11004-021-09946-w.
M. G. El-Shafiey, A. Hagag, E. S. A. El-Dahshan, and M. A. Ismail, “A hybrid GA and PSO optimized approach for
heart-disease prediction based on random forest,” Multimed Tools Appl, vol. 81, no. 13, pp. 18155–18179, May
, doi: 10.1007/s11042-022-12425-x.
C. N. Noviyanti and A. Alamsyah, “Early Detection of Diabetes Using Random Forest Algorithm,” Journal of Information System Exploration and Research, vol. 2, no. 1, Jan. 2024, doi: 10.52465/joiser.v2i1.245.
C. Zhan, Y. Zheng, H. Zhang, and Q. Wen, “Random-Forest-Bagging Broad Learning System with Applications for
COVID-19 Pandemic,” IEEE Internet Things J, vol. 8, no. 21, pp. 15906–15918, Nov. 2021, doi:
1109/JIOT.2021.3066575.
L. Wan, K. Gong, G. Zhang, X. Yuan, C. Li, and X. Deng, “An efficient rolling bearing fault diagnosis method
based on spark and improved random forest algorithm,” IEEE Access, vol. 9, pp. 37866–37882, 2021, doi:
1109/ACCESS.2021.3063929.
X. Zhang et al., “Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery,” Ecol Indic, vol. 159, Feb. 2024, doi: 10.1016/j.ecolind.2024.111752.
L. Torre-Tojal, A. Bastarrika, A. Boyano, J. M. Lopez-Guede, and M. Graña, “Above-ground biomass estimation
from LiDAR data using random forest algorithms,” J Comput Sci, vol. 58, Feb. 2022, doi:
1016/j.jocs.2021.101517.
T. Yan, R. Xu, S. H. Sun, Z. K. Hou, and J. Y. Feng, “A real-time intelligent lithology identification method based
on a dynamic felling strategy weighted random forest algorithm,” Pet Sci, vol. 21, no. 2, pp. 1135–1148, Apr. 2024,
doi: 10.1016/j.petsci.2023.09.011.
L. Xue, Y. Liu, Y. Xiong, Y. Liu, X. Cui, and G. Lei, “A data-driven shale gas production forecasting method based
on the multi-objective random forest regression,” J Pet Sci Eng, vol. 196, Jan. 2021, doi: 10.1016/j.petrol.2020.107801.
S. Asadi, S. E. Roshan, and M. W. Kattan, “Random forest swarm optimization-based for heart diseases diagnosis,”
J Biomed Inform, vol. 115, Mar. 2021, doi: 10.1016/j.jbi.2021.103690.
M. Minnoor and V. Baths, “Diagnosis of Breast Cancer Using Random Forests,” in Procedia Computer Science,
Elsevier B.V., 2022, pp. 429–437. doi: 10.1016/j.procs.2023.01.025.
B. K. Meher, M. Singh, R. Birau, and A. Anand, “Forecasting stock prices of fintech companies of India using random forest with high-frequency data,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 10,
no. 1, Mar. 2024, doi: 10.1016/j.joitmc.2023.100180.
D. Rey-Blanco, J. L. Zofío, and J. González-Arias, “Improving hedonic housing price models by integrating optimal
accessibility indices into regression and random forest analyses,” Expert Syst Appl, vol. 235, Jan. 2024, doi:
1016/j.eswa.2023.121059.
A. B. Adetunji, O. N. Akande, F. A. Ajala, O. Oyewo, Y. F. Akande, and G. Oluwadara, “House Price Prediction
using Random Forest Machine Learning Technique,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 806–
doi: 10.1016/j.procs.2022.01.100.
M. R. Ali, S. M. A. Nipu, and S. A. Khan, “A decision support system for classifying supplier selection criteria using
machine learning and random forest approach,” Decision Analytics Journal, vol. 7, Jun. 2023, doi: 10.1016/j.dajour.2023.100238.
R. A. Prasojo et al., “Precise transformer fault diagnosis via random forest model enhanced by synthetic minority
over-sampling technique,” Electric Power Systems Research, vol. 220, Jul. 2023, doi: 10.1016/j.epsr.2023.109361.
I. Lillo-Bravo, J. Vera-Medina, C. Fernandez-Peruchena, E. Perez-Aparicio, J. A. Lopez-Alvarez, and J. M. Delgado-Sanchez, “Random Forest model to predict solar water heating system performance,” Renew Energy, vol. 216,
Nov. 2023, doi: 10.1016/j.renene.2023.119086.
N. Jalal, A. Mehmood, G. S. Choi, and I. Ashraf, “A novel improved random forest for text classification using feature ranking and optimal number of trees,” Journal of King Saud University - Computer and Information Sciences,
vol. 34, no. 6, pp. 2733–2742, Jun. 2022, doi: 10.1016/j.jksuci.2022.03.012.
Y. Priantama, T. Azhima, and Y. Siswa, “OPTIMASI CORRELATION-BASED FEATURE SELECTION UNTUK
PERBAIKAN AKURASI RANDOM FOREST CLASSIFIER DALAM PREDIKSI PERFORMA AKADEMIK
MAHASISWA,” Jurnal Informatika dan Komputer), vol. 6, no. 2, pp. 251–260, 2022.
M. Iqbal Baihaqi, A. Syaripudin, and F. Agung Nugroho, “Implementasi Algoritma Random Forest Pada Prediksi
Harga Saham Berdasarkan Data Historis,” 2023.
M. M. Mutoffar et al., “KLASIFIKASI KUALITAS AIR SUMUR MENGGUNAKAN ALGORITMA RANDOM
FOREST,” vol. 04, 2022.
E. Rosta Br Sebayang, Y. Herry Chrisnanto, U. Jenderal Achmad Yani Cimahi, J. Terusan Jend Sudirman, C. Selatan, and J. Barat, “Klasifikasi Data Kesehatan Mental di Industri Teknologi Menggunakan Algoritma Random Forest,” 2023. [Online]. Available: http://ijespgjournal.org
H. Hairani, A. Anggrawan, and D. Priyanto, “INTERNATIONAL JOURNAL ON INFORMATICS
VISUALIZATION journal homepage : www.joiv.org/index.php/joiv INTERNATIONAL JOURNAL ON
INFORMATICS VISUALIZATION Improvement Performance of the Random Forest Method on Unbalanced Diabetes Data Classification Using Smote-Tomek Link,” 2023. [Online]. Available: www.joiv.org/index.php/joiv
M. Putri, “Prediksi Penyakit Stroke Menggunakan Machine Learning Dengan Algoritma Random Forest,” Jurnal
Infomedia: Teknik Informatika, vol. 9, no. 2, pp. 16–21, 2024.
H. Tantyoko, D. Kartika Sari, and A. R. Wijaya, “PREDIKSI POTENSIAL GEMPA BUMI INDONESIA
MENGGUNAKAN METODE RANDOM FOREST DAN FEATURE SELECTION,” 2023. [Online]. Available:
http://jom.fti.budiluhur.ac.id/index.php/IDEALIS/indexHenriTantyoko|http://jom.fti.budiluhur.ac.id/index.php/IDEALIS/index|
Yoga Religia, Agung Nugroho, and Wahyu Hadikristanto, “Klasifikasi Analisis Perbandingan Algoritma Optimasi
pada Random Forest untuk Klasifikasi Data Bank Marketing,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 1, pp. 187–192, Feb. 2021, doi: 10.29207/resti.v5i1.2813.
Z. A. Dwiyanti and C. Prianto, “Prediksi Cuaca Kota Jakarta Menggunakan Metode Random Forest,” Jurnal Tekno
Insentif, vol. 17, no. 2, pp. 127–137, Oct. 2023, doi: 10.36787/jti.v17i2.1136.
D. A. Fife and J. D’Onofrio, “Common, uncommon, and novel applications of random forest in psychological research,” Behav Res Methods, vol. 55, no. 5, pp. 2447–2466, Aug. 2023, doi: 10.3758/s13428-022-01901-9.
S. Nurohanisah, R. Astuti, and F. M. Basysyar, “DETEKSI BERITA PALSU MENGGUNAKAN ALGORITMA
RANDOM FOREST,” 2024.
F. O. Aghware et al., “Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for
Credit-Card Fraud Detection,” Journal of Computing Theories and Applications, vol. 1, no. 4, pp. 407–420, Mar.
, doi: 10.62411/jcta.10323.
B. Fransiskus Sitanggang and P. Sitompul, “Deteksi Awal Kelangsungan Hidup Pasien Gagal Jantung,
Menggunakan Machine Learning Metode Random Forest,” INNOVATIVE: Journal Of Social Science Research, vol.
, pp. 3347–3357, 2024.
A. Y. Perdana, R. Latuconsina, and A. Dinimaharawati, “PREDIKSI STUNTING PADA BALITA DENGAN
ALGORITMA RANDOM FOREST,” 2021.
W. Muhtar, A. Mas, and R. N. Handayani, “Klasifikasi Kesehatan Ibu Hamil Menggunakan Metode Random Forest
Dengan Optimasi Algoritma Genetika,” vol. 5, no. 1, 2024.
E. P. Febtiawan, L. A. Syamsul, I. Akbar, and A. S. Rachman, “Forecasting Produksi Energi Photovoltaic
Menggunakan Algoritma Random Forest Classification,” Journal of Information System Research (JOSH), vol. 5,
no. 4, pp. 1053–1062, 2024, doi: 10.47065/josh.v5i4.5514.
M. Chrisna Basila Rahman and U. Hayati, “ANALISIS TINGKAT KECENDERUNGAN FEAR OF MISSING
OUT MENGGUNAKAN ALGORITMA RANDOM FOREST PADA MEDIA SOSIAL,” 2024.
M. Taqiyuddin and T. Bayu Sasongko, “Prediksi Cuaca Kabupaten Sleman Menggunakan Algoritma Random Forest,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 8, no. 3, p. 1683, Jul. 2024, doi:
30865/mib.v8i3.7897.
O. Pahlevi and Y. Handrianto, “Implementasi Algoritma Klasifikasi Random Forest Untuk Penilaian Kelayakan
Kredit,” 2023. [Online]. Available: http://ejournal.bsi.ac.id/ejurnal/index.php/infortech
A. Suleymanov et al., “Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas,” Agriculture (Switzerland), vol. 13, no. 5, May 2023, doi: 10.3390/agriculture13050976.
A. Arisusanto, N. Suarna, and G. Dwilestari, “Analisa Klasifikasi Data Harga Handphone Menggunakan Algoritma
Random Forest Dengan Optimize Parameter Grid,” Jurnal Teknologi Ilmu Komputer, vol. 1, no. 2, pp. 43–47, 2023,
doi: 10.56854/jtik.v1i2.51.
U. Sunarya and T. Haryanti, “Perbandingan Kinerja Algoritma Optimasi pada Metode Random Forest untuk Deteksi
Kegagalan Jantung,” Jurnal Rekayasa Elektrika, vol. 18, no. 4, Dec. 2022, doi: 10.17529/jre.v18i4.26981.
R. F. N. Iskandar, D. H. Gutama, D. P. Wijaya, and D. Danianti, “Klasifikasi Menggunakan Metode Random Forest
untuk Awal Deteksi Diabetes Melitus Tipe 2,” Jurnal Teknik Industri Terintegrasi, vol. 7, no. 3, pp. 1620–1626, Jul.
, doi: 10.31004/jutin.v7i3.26916.
J. Hu and S. Szymczak, “A review on longitudinal data analysis with random forest,” Mar. 01, 2023, Oxford University Press. doi: 10.1093/bib/bbad002.
M. Pal and S. Parija, “Prediction of Heart Diseases using Random Forest,” in Journal of Physics: Conference Series,
IOP Publishing Ltd, Mar. 2021. doi: 10.1088/1742-6596/1817/1/012009.
A. Triyono, ; Rahmawan, B. Trianto, D. Malita, and P. Arum, “EARLY DETECTION OF DIABETES MELLITUS
USING RANDOM FOREST ALGORITHM,” 2021. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Earl
F. Wang, Y. Wang, X. Ji, and Z. Wang, “Effective Macrosomia Prediction Using Random Forest Algorithm,” Int J
Environ Res Public Health, vol. 19, no. 6, Mar. 2022, doi: 10.3390/ijerph19063245.
H. A. Zeini, D. Al-Jeznawi, H. Imran, L. F. A. Bernardo, Z. Al-Khafaji, and K. A. Ostrowski, “Random Forest Algorithm for the Strength Prediction of Geopolymer Stabilized Clayey Soil,” Sustainability (Switzerland), vol. 15, no. 2,
Jan. 2023, doi: 10.3390/su15021408.
Q. Xu and J. Yin, “Application of Random Forest Algorithm in Physical Education,” Sci Program, vol. 2021, 2021,
doi: 10.1155/2021/1996904.
J. Magidi, L. Nhamo, S. Mpandeli, and T. Mabhaudhi, “Application of the random forest classifier to map irrigated
areas using google earth engine,” Remote Sens (Basel), vol. 13, no. 5, pp. 1–15, Mar. 2021, doi:
3390/rs13050876.
F. Özen, “Random forest regression for prediction of Covid-19 daily cases and deaths in Turkey,” Heliyon, vol. 10,
no. 4, Feb. 2024, doi: 10.1016/j.heliyon.2024.e25746.
Z. Ji, M. Zhou, Q. Wang, and J. Huang, “Predicting the International Roughness Index of JPCP and CRCP Rigid
Pavement: A Random Forest (RF) Model Hybridized withModified Beetle Antennae Search (MBAS) for Higher
Accuracy,” CMES - Computer Modeling in Engineering and Sciences, vol. 139, no. 2, pp. 1557–1582, 2024, doi:
32604/cmes.2023.046025.
DOI: https://doi.org/10.29100/jipi.v11i1.7316
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