Spatial Analysis of Random Forest Classification Model for Availability Mapping of Sports Facilities in Jakarta

Hansen Candra, Andrianingsih Andrianingsih

Sari


This research analyzes the distribution of sports facilities in DKI Jakarta Province using spatial modeling and Machine Learning Random Forest algorithm in order to support Indonesia Emas 2045. The goal is to classify areas based on the level of availability of sports facilities into low, sufficient, and high categories, and evaluate the accuracy of the Random Forest algorithm in the classification. CRISP-DM methodology is used in this research, including Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The data analyzed includes spatial sub-district areas and attributes of sports facilities in DKI Jakarta. Random Forest was chosen because of its ability to classify complex data and identify feature importance. The results show that the distribution of sports facilities is uneven, with low categories more in Central Jakarta and North Jakarta, while high categories are scattered in other areas. Random Forest accuracy reached 89%, with high precision and recall in the high category.

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A. Supriyanto, A. Nasrulloh, Y. Prasetyo, D. E. W. Saputra, S. S. Nugroho, S. N. Utomo, and M. H. Afiludin, “Analisis Standarisasi Fasilitas Olahraga Di Kompleks Gor Stadion Wilis Kota Madiun,†MAJORA Maj. Ilm. Olahraga, vol. 29, no. 2, pp. 36–45, 2023.

I. Meidodga, A. Syahrin, R. T. Putra, F. Warfandu, and A. N. Bimasena, “Pemanfaatan Data Geospasial dalam Mewujudkan Sistem Informasi Pertanahan Multiguna Bagi Multipihak,†Widya Bhumi, vol. 3, no. 1, pp. 62–80, 2023.

Nur Suci, Arya T Candra, and Lutfi Irawan Rahmat, “Analisis Perbedaan Tingkat Partisipasi Olahraga Masyarakat di RTH Wilayah Perkotaan dan Pedesaan di Kabupaten Banyuwangi,†SPRINTER J. Ilmu Olahraga, vol. 3, no. 2, pp. 102–108, 2022.

R. Hidayat, I. R. Kusumasari, Z. A. Sophia, D. Rahma, A. Bisnis, F. Ilmu, I. Politik, U. Pembangunan, N. Veteran, and J. Timur, “Peran Teknologi AI dalam Mengoptimalkan Pengambilan Keputusan dalam Pengembangan Bisnis,†no. 4, 2024.

L. Rahmawati, W. D. Febrian, Fachruzzaki, R. Lengam, I. P. Dody, and Suarnatha, “Pengembangan Sistem Informasi Geografis (SIG) Untuk Analisis Spasial Dalam Pengambilan Keputusan,†J. Rev. Pendidik. dan Pengajaran, vol. 7, no. 2, pp. 4058–4068, 2024.

A. Khaidar, M. Arhami, and M. Abdi, “Application of the random Forest Method for UKT Classification at Politeknik Negeri Lhokseumawe,†Journal of Artificial Intelligence and Software Engineering (J-AISE), vol. 4, no. 2, p. 94, Nov. 2024, doi: 10.30811/jaise.v4i2.6131.

A. Wandani, “Sentimen Analisis Pengguna Twitter pada Event Flash Sale Menggunakan Algoritma K-NN, Random Forest, dan Naive Bayes,†J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 651–665, 2021.

Suci Amaliah, M. Nusrang, and A. Aswi, “Penerapan Metode Random Forest Untuk Klasifikasi Varian Minuman Kopi di Kedai Kopi Konijiwa Bantaeng,†VARIANSI J. Stat. Its Appl. Teach. Res., vol. 4, no. 3, pp. 121–127, 2022.

N. Rahmadani, A. S. Handayani, and I. Hadi, “Penerapan Algoritma Random Forest untuk Memprediksi Curah Hujan pada Masa Mendatang di Daerah Berpotensi Banjir,†vol. 6, no. 2, pp. 1222–1230, 2024.

I. C. Azhari and T. Haryanto, “Modeling Of Hyperparameter Tuned RNN-LSTM and Deep Learning For Garlic Price Forecasting In Indonesia,†J. Informatics Telecommun. Eng., vol. 7, no. 2, pp. 502–513, 2024.

L. Rangga, A. Tarigan, T. Informatika, R. Forest, O. Fitur, and F. Selection, “OPTIMALISASI FITUR DENGAN FORWARD SELECTION PADA ESTIMASI TINGKAT PENYAKIT PARU-PARU MENGGUNAKAN ALGORITMA,†vol. 8, no. 5, pp. 10341–10348, 2024.




DOI: http://dx.doi.org/10.30811/jaise.v5i1.6556

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