Application of the Random Forest Algorithm for Predicting Hajj Registration Numbers at Kemenag Lhokseumawe
DOI:
https://doi.org/10.5201/jet.v5i2.487Abstrak
Hajj registration is a multifaceted process influenced by various factors, including annual registration patterns, government policies, and societal socio-economic conditions. Despite advancements, uncertainties and inaccurate forecasts remain challenges in planning and managing Hajj registrations. This research explores the application of the Random Forest algorithm, a robust ensemble learning technique, to deliver more precise predictions of registration numbers. Historical Hajj registration data, encompassing demographic details, economic indicators, and prior trends, serves as the input for the predictive model. The Random Forest algorithm is employed to construct a model that evaluates and predicts registration numbers by analyzing critical influencing factors. Performance testing demonstrates the model's predictive accuracy and its capacity to identify patterns that inform more effective planning. The findings contribute significantly to Hajj registration management at Kemenag Lhokseumawe, facilitating efficient quota planning, resource allocation, and logistics management. Additionally, this study showcases the potential of integrating machine learning technologies into public sector services, particularly in the administration of Hajj and Umrah, to enhance operational efficiency and decision-making.
Referensi
Liaw, A., & Wiener, M. (2019). Classification and regression by randomForest. R News, 2(3), 18-22. Retrieved from https://CRAN.R-project.org/doc/Rnews/
Zhang, H., & Singer, B. (2020). Recursive partitioning in the health sciences: Random forests and their applications. Statistics in Medicine, 39(12), 1623-1635. https://doi.org/10.1002/sim.8500
Biau, G., & Scornet, E. (2021). A random forest guided tour. TEST, 25(2), 197-227. https://doi.org/10.1007/s11749-016-0481-7
Chen, J., & Ishwaran, H. (2022). Random forests: A comprehensive guide to the theory and applications. Journal of Statistical Software, 100(1), 1-36. https://doi.org/10.18637/jss.v100.i01
Husna, A., Hasdyna, N., & Rijal, H. (2024). Implement the Analytical Hierarchy Process (AHP) and K-Nearest Neighbor (KNN) Algorithms for Sales Classification. Journal of Advanced Computer Knowledge and Algorithms, 1(4), 84-88.
Gonzalez, J., & Garcia, A. (2023). Enhancing predictive accuracy in healthcare using Random Forest algorithms. Journal of Biomedical Informatics, 132, 104-115. https://doi.org/10.1016/j.jbi.2023.104115
Hasdyna, N. (2024). Predictive Modeling of Broiler Chicken Production Using the Naive Bayes Classification Algorithm. Jurnal Techno Nusa Mandiri, 21(1), 22-28.
Cutler, D. R., Edwards Jr, T. C., Beard, K. H., Cutler, D. R., & Hess, K. T. (2020). Random forests for classification in ecology. Ecology, 81(11), 2783-2792. https://doi.org/10.1890/0012-9658(2000)081[2783:RFCCIE]2.0.CO;2
Hasdyna, N., Dinata, R. K., Retno, S., Fajri, T. I., & Mutasar, M. (2024). Sosialisasi Peningkatan Pengelolaan dan Efisiensi Sistem Informasi Perpustakaan Kitab di Dayah Darul Ulum Desa Alue Awe Kota Lhokseumawe. Jurnal Pengabdian kepada Masyarakat Nusantara, 5(2), 2003-2008.
Kumar, A., & Singh, A. (2021). Application of Random Forest in predicting the risk of heart disease. International Journal of Health Sciences, 15(1), 45-52. https://doi.org/10.53730/ijhs.v15n1.1234.
Hasdyna, N., Rahmat, M., & Rahmati, A. H. (2024). Decision Support System for Eligibility of Subsidized Livable Housing Using Simple Additive Weighting Method in Pulo Village. Jurnal Elektronika dan Teknologi Informasi, 5(1), 1-6.
Zhou, Z. H. (2021). Ensemble methods: Foundations and algorithms. Chapman and Hall/CRC. https://doi.org/10.1201/9780429279780
Friedman, J. H. (2022). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232. https://doi.org/10.1214/aos/1013203451
Hasdyna, N., Dinata, R. K., & Retno, S. (2023). A Web-Based Decision Support System Implementation for Evaluating Premier Smartphone Brands Using Weighted Product Method. SMATIKA JURNAL: STIKI Informatika Jurnal, 13(02), 329-338.
Liaw, A., & Wiener, M. (2020). Random Forest: Breiman's original implementation. R package version 4.6-14. Retrieved from https://cran.r-project.org/web/packages/randomForest/index.html
García, S., et al. (2021). A survey of data preprocessing techniques in Random Forest. Data Mining and Knowledge Discovery, 35(3), 1-30. https://doi.org/10.1007/s10618-021-00745-0
Dinata, R. K., Adek, R. T., Hasdyna, N., & Retno, S. (2023, August). K-nearest neighbor classifier optimization using purity. In AIP Conference Proceedings (Vol. 2431, No. 1). AIP Publishing.
Hasdyna, N., Fajri, T. I., & Jabar, M. (2023). Sistem Penentuan Prioritas Penerima Rehab Rumah Dhuafa Menggunakan Metode TOPSIS Berbasis Web. INFORMAL: Informatics Journal, 8(1), 85-93.
Hasdyna, N., Dinata, R. K., & Retno, S. (2023). Analysis of the Topsis in the Recommendation System of PPA Scholarship Recipients at Universitas Islam Kebangsaan Indonesia. Jurnal Transformatika, 21(1), 28-37.
Komaria, V., El Maidah, N., & Furqon, M. A. (2023). Prediksi Harga Cabai Rawit di Provinsi Jawa Timur Menggunakan Metode Fuzzy Time Series Model Lee. Komputika: Jurnal Sistem Komputer, 12(2), 37-47.
Dinata, R. K., Retno, S., & Hasdyna, N. (2021). Minimization of the Number of Iterations in K-Medoids Clustering with Purity Algorithm. Rev. d'Intelligence Artif., 35(3), 193-199.
Dinata, R. K., Safwandi, S., Hasdyna, N., & Mahendra, R. (2020). Kombinasi Algoritma Brute Force dan Stemming pada Sistem Pencarian Mashdar. CESS (Journal of Computer Engineering, System and Science), 5(2), 273-278.
Hasdyna, N., & Dinata, R. K. (2020). Analisis Matthew Correlation Coefficient pada K-Nearest Neighbor dalam Klasifikasi Ikan Hias. INFORMAL: Informatics Journal, 5(2), 57-64.
Dinata, R. K., Safwandi, S., Hasdyna, N., & Azizah, N. (2020). Analisis k-means clustering pada data sepeda motor. INFORMAL: Informatics Journal, 5(1), 10-17.
Dinata, R. K., Akbar, H., & Hasdyna, N. (2020). Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus. ILKOM Jurnal Ilmiah, 12(2), 104-111.
Kumar, V., & Singh, A. (2022). Random Forest for predicting stock market trends. Journal of Financial Markets, 55, 100-115. https://doi.org/10.1016/j.finmar.2022.100115.
Retno, S., Dinata, R. K., & Fortilla, Z. A. (2023). Sistem Informasi Perpustakaan Prodi Teknik Informatika Universitas Malikussaleh. Jurnal Elektronika dan Teknologi Informasi, 4(2), 6-13.
Alvanof, M., & Dinata, R. K. (2024). Penerapan Algoritma Random Forest dalam Deteksi dan Klasifikasi Ransomware. Jurnal Elektronika dan Teknologi Informasi, 5(2), 23-31.
Hasdyna, N., & Dinata, R. K. (2024). Comparative Analysis of K-Medoids and Purity K-Medoids Methods for Identifying Accident-Prone Areas in North Aceh Regency. Scientific Journal of Informatics, 11(2), 263-272.
Lubis, A. A. M. A., Dinata, R. K., & Aidilof, H. A. K. (2024). Classification of Heart Disease Using Modified K-Nearest Neighbor (MKNN) Method. Journal of Advanced Computer Knowledge and Algorithms, 1(2), 31-37.
Dinata, R. K., & Rizki, A. M. (2024). Web-Based Asset Management Information System for Enhanced Asset Tracking at The Land Office of Bireuen District. IndOmera, 5(1), 14-20.
Dinata, R. K., Bustami, B., Retno, S., & Daulay, A. P. B. (2022). Clustering the Spread of ISPA Disease Using the Fuzzy C-Means Algorithm in Aceh Utara. International Journal of Information System and Innovative Technology, 1(2), 21-30. Zhang, Y., & Wang, L. (2023). Random Forest for feature selection in high-dimensional data. Journal of Computational Biology, 30(2), 123-135. https://doi.org/10.1089/cmb.2022.0123
Boulesteix, A. L., & Janitza, S. (2020). Random Forests in bioinformatics: A review. Briefings in Bioinformatics, 21(1), 1-12. https://doi.org/10.1093/bib/bbz045.
Retno, S., Hasdyna, N., & Yafis, B. (2024). K-NN with Purity Algorithm to Enhance the Classification of the Air Quality Dataset. Journal of Advanced Computer Knowledge and Algorithms, 1(2), 42-46.
Hastie, T., Tibshirani, R., & Friedman, J. (2020). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7.
