OPTIMASI ALGORITMA XGBOOST UNTUK KLASIFIKASI STATUS GIZI BALITA BB/TB MENGGUNAKAN GRIDSEARCHCV
DOI:
https://doi.org/10.35145/joisie.v9i2.5601Kata Kunci:
XGBoost, Pencarian Grid, Nutrisi Balita, KlasifikasiAbstrak
Masalah gizi tetap menjadi perhatian utama kesehatan masyarakat di banyak negara, termasuk Indonesia. Studi ini merancang dan mengevaluasi sistem klasifikasi otomatis untuk status gizi balita berdasarkan indikator berat badan terhadap tinggi badan menggunakan algoritma XGBoost. Dataset terdiri dari 1.763 anak Indonesia berusia 24–60 bulan, dengan status gizi dikategorikan menjadi enam kelas (gizi baik, risiko gizi lebih, gizi kurang, gizi buruk, gizi lebih, obesitas). Variabel prediktor meliputi jenis kelamin, usia dalam bulan, berat badan, tinggi badan, dan skor Z berat badan terhadap tinggi badan. Praproses meliputi pembersihan data, penghapusan duplikat, pengkodean label, standardisasi, dan pembagian data latih-uji 80:20. Model XGBoost dasar dibandingkan dengan model yang disetel menggunakan GridSearch dengan validasi silang 3-fold dan 10-fold. Kinerja dievaluasi menggunakan akurasi, presisi makro, recall makro, skor F1 makro, dan log loss. Model terbaik, XGBoost dengan GridSearch 10-fold, mencapai akurasi 0,8689, skor F1 0,8149, dan log loss 0,3395, lebih baik daripada log loss dasar sebesar 0,4190. Temuan ini menunjukkan bahwa penyetelan hyperparameter XGBoost dengan GridSearch menghasilkan prediksi probabilistik yang terkalibrasi dengan baik dan merupakan alat pendukung keputusan yang menjanjikan untuk deteksi dini kekurangan gizi dan obesitas pada balita di fasilitas perawatan kesehatan primer.
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Referensi
Abdurrahman, G., Oktavianto, H., & Sintawati, M. (2022). Optimasi Algoritma XGBoost Classifier Menggunakan Hyperparameter Gridesearch dan Random Search Pada Klasifikasi Penyakit Diabetes. In Informatics Journal (Vol. 7, Issue 3).
Amalia Alhamid, S., Tiara Carolin, B., Lubis, R., Studi Kebidanan, P., Ilmu Kesehatan, F., & Nasional Jakarta, U. (2021). Studi Mengenai Status Gizi Balita (Vol. 7, Issue 1).
Anggoro, D. A., & Mukti, S. S. (2021). Performance Comparison of Grid Search and Random Search Methods for Hyperparameter Tuning in Extreme Gradient Boosting Algorithm to Predict Chronic Kidney Failure. International Journal of Intelligent Engineering and Systems, 14(6), 198–207. https://doi.org/10.22266/ijies2021.1231.19
Bahar, Muh. A., Galistiani, G. F., Eliyanti, U., & Mohi, A. R. (2024). Gambaran Nilai Utilitas Kesehatan Anak dengan Malnutrisi : Studi pada Kasus Stunting, Wasting, dan Underweight di Indonesia. Jurnal Mandala Pharmacon Indonesia, 10(2), 610–617. https://doi.org/10.35311/jmpi.v10i2.656
Çetin, V., & Yıldız, O. (2022). A comprehensive review on data preprocessing techniques in data analysis. Pamukkale University Journal of Engineering Sciences, 28(2), 299–312. https://doi.org/10.5505/pajes.2021.62687
Chiang, H. Y., Liang, L. Y., Lin, C. C., Chen, Y. J., Wu, M. Y., Chen, S. H., Wu, P. H., Kuo, C. C., & Chi, C. Y. (2021). Electronic medical record-based deep data cleaning and phenotyping improve the diagnostic validity and mortality assessment of infective endocarditis: Medical big data initiative of CMUH. BioMedicine (Taiwan), 11(3), 59–67. https://doi.org/10.37796/2211-8039.1267
Erlin, E., Desnelita, Y., Nasution, N., Suryati, L., & Zoromi, F. (2022). Dampak SMOTE terhadap Kinerja Random Forest Classifier berdasarkan Data Tidak seimbang. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(3), 677–690. https://doi.org/10.30812/matrik.v21i3.1726
Fan, C., Chen, M., Wang, X., Wang, J., & Huang, B. (2021). A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data. In Frontiers in Energy Research (Vol. 9). Frontiers Media S.A. https://doi.org/10.3389/fenrg.2021.652801
Fibrinika Tuta Setiani, Heny Lestari, & Abdullah Azam Mustajab. (2024). Nutritional Status of Toddlers Aged 0-59 Months: a Descriptive Study. International Journal of Health and Medicine, 1(4), 156–164. https://doi.org/10.62951/ijhm.v1i4.101
Goswami, P., & Bhatia, D. (2023). Application of Machine Learning in FPGA EDA Tool Development. IEEE Access, 11, 109564–109580. https://doi.org/10.1109/ACCESS.2023.3322358
Handayani, S., Toresa, D., Informatika, T., Ilmu Komputer, F., & Lancang Kuning, U. (n.d.-a). Peningkatan Performa Model Gradient Boosting dalam Klasifikasi Stroke Melalui Optimasi Grid Search.
Handayani, S., Toresa, D., Informatika, T., Ilmu Komputer, F., & Lancang Kuning, U. (n.d.-b). Peningkatan Performa Model Gradient Boosting dalam Klasifikasi Stroke Melalui Optimasi Grid Search.
Heydarian, M., Doyle, T. E., & Samavi, R. (2022). MLCM: Multi-Label Confusion Matrix. IEEE Access, 10, 19083–19095. https://doi.org/10.1109/ACCESS.2022.3151048
Lidya, N., Fakultas, S., Klabat, K. U., & Mononutu, J. A. (2021). Hubungan Antara Status Sosial Ekonomi Dengan Status Gizi Balita Di Kelurahan Buha Kecamatan Mapanget Kota Manado (Vol. 3, Issue 1). http://ejournal.unklab.ac.id/index.php/kjn
Liew, X. Y., Hameed, N., & Clos, J. (2021). An investigation of XGBoost-based algorithm for breast cancer classification. Machine Learning with Applications, 6, 100154. https://doi.org/10.1016/j.mlwa.2021.100154
Masturina, M. L., Salam, A., Indriasari, R., Razak Thaha, A., Jafar, N., Studi Ilmu Gizi, P., & Kesehatan Masyarakat, F. (2023). Description of family characteristics and nutritional status in toddlers Gambaran karakteristik keluarga dan status gizi balita. Community Research of Epidemiology Journal, 3(2). https://doi.org/10.24252/corejournal.v%vi%i.37731
Muhamad Fikri. (2024). Klasifikasi Status Stunting Pada Anak Bawah Lima Tahun Menggunakan Extreme Gradient Boosting. Merkurius : Jurnal Riset Sistem Informasi Dan Teknik Informatika, 2(4), 173–184. https://doi.org/10.61132/merkurius.v2i4.159
Nugraha, W., & Sasongko, A. (n.d.-a). SISTEMASI: Jurnal Sistem Informasi Hyperparameter Tuning pada Algoritma Klasifikasi dengan Grid Search Hyperparameter Tuning on Classification Algorithm with Grid Search (Vol. 11, Issue 2). http://sistemasi.ftik.unisi.ac.id
Nugraha, W., & Sasongko, A. (n.d.-b). SISTEMASI: Jurnal Sistem Informasi Hyperparameter Tuning pada Algoritma Klasifikasi dengan Grid Search Hyperparameter Tuning on Classification Algorithm with Grid Search (Vol. 11, Issue 2). http://sistemasi.ftik.unisi.ac.id
Ogunsanya, M., Isichei, J., & Desai, S. (2023). Manufacturing Letters Grid Search Hyperparameter Tuning in Additive Manufacturing Processes-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the Scientific Committee of the NAMRI/SME. www.sciencedirect.com
Paramita, I. S., Atasasih, H., & Rahayu, D. (2024). Penilaian Status Gizi Antropometri Pada Balita Penerbit Salnesia (CV. Sarana Ilmu Indonesia).
Pathak, A. K., Chaubey, M., & Gupta, M. (n.d.). Randomized-Grid Search for Hyperparameter Tuning in Decision Tree Model to Improve Performance of Cardiovascular Disease Classification.
Puerwandono, E., & Maulana, I. (2023). Penerapan Algoritma Svm Untuk Klasifikasi Citra Daun Sirih Application Of The Svm Algorithm To Image Classification Of Betel Leaf. Journal of Information Technology and Computer Science (INTECOMS), 6(2).
Syahputra, A. A., & Saputro, R. E. (2024a). Application of the XGBoost Model with Hyperparameter Tuning for Industry Classification for Job Applicants. Sinkron, 8(3), 1920–1931. https://doi.org/10.33395/sinkron.v8i3.13840
Syahputra, A. A., & Saputro, R. E. (2024b). Application of the XGBoost Model with Hyperparameter Tuning for Industry Classification for Job Applicants. Sinkron, 8(3), 1920–1931. https://doi.org/10.33395/sinkron.v8i3.13840
Tran, N. T., Tran, T. T. G., Nguyen, T. A., & Lam, M. B. (2023). A new grid search algorithm based on XGBoost model for load forecasting. Bulletin of Electrical Engineering and Informatics, 12(4), 1857–1866. https://doi.org/10.11591/eei.v12i4.5016
Uzir, N., Raman, S., Banerjee, S., & Nishant Uzir Sunil R, R. S. (2016). Experimenting XGBoost Algorithm for Prediction and Classification of Different Datasets Experimenting XGBoost Algorithm for Prediction and Classifi cation of Different Datasets. International Journal of Control Theory and Applications, 9. https://www.researchgate.net/publication/318132203
Zhang, Y., & Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 58, 308–324. https://doi.org/10.1016/j.trc.2015.02.019
Amalia Alhamid, S., Tiara Carolin, B., Lubis, R., Studi Kebidanan, P., Ilmu Kesehatan, F., & Nasional Jakarta, U. (2021). Studi Mengenai Status Gizi Balita (Vol. 7, Issue 1).
Anggoro, D. A., & Mukti, S. S. (2021). Performance Comparison of Grid Search and Random Search Methods for Hyperparameter Tuning in Extreme Gradient Boosting Algorithm to Predict Chronic Kidney Failure. International Journal of Intelligent Engineering and Systems, 14(6), 198–207. https://doi.org/10.22266/ijies2021.1231.19
Bahar, Muh. A., Galistiani, G. F., Eliyanti, U., & Mohi, A. R. (2024). Gambaran Nilai Utilitas Kesehatan Anak dengan Malnutrisi : Studi pada Kasus Stunting, Wasting, dan Underweight di Indonesia. Jurnal Mandala Pharmacon Indonesia, 10(2), 610–617. https://doi.org/10.35311/jmpi.v10i2.656
Çetin, V., & Yıldız, O. (2022). A comprehensive review on data preprocessing techniques in data analysis. Pamukkale University Journal of Engineering Sciences, 28(2), 299–312. https://doi.org/10.5505/pajes.2021.62687
Chiang, H. Y., Liang, L. Y., Lin, C. C., Chen, Y. J., Wu, M. Y., Chen, S. H., Wu, P. H., Kuo, C. C., & Chi, C. Y. (2021). Electronic medical record-based deep data cleaning and phenotyping improve the diagnostic validity and mortality assessment of infective endocarditis: Medical big data initiative of CMUH. BioMedicine (Taiwan), 11(3), 59–67. https://doi.org/10.37796/2211-8039.1267
Erlin, E., Desnelita, Y., Nasution, N., Suryati, L., & Zoromi, F. (2022). Dampak SMOTE terhadap Kinerja Random Forest Classifier berdasarkan Data Tidak seimbang. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(3), 677–690. https://doi.org/10.30812/matrik.v21i3.1726
Fan, C., Chen, M., Wang, X., Wang, J., & Huang, B. (2021). A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data. In Frontiers in Energy Research (Vol. 9). Frontiers Media S.A. https://doi.org/10.3389/fenrg.2021.652801
Fibrinika Tuta Setiani, Heny Lestari, & Abdullah Azam Mustajab. (2024). Nutritional Status of Toddlers Aged 0-59 Months: a Descriptive Study. International Journal of Health and Medicine, 1(4), 156–164. https://doi.org/10.62951/ijhm.v1i4.101
Goswami, P., & Bhatia, D. (2023). Application of Machine Learning in FPGA EDA Tool Development. IEEE Access, 11, 109564–109580. https://doi.org/10.1109/ACCESS.2023.3322358
Handayani, S., Toresa, D., Informatika, T., Ilmu Komputer, F., & Lancang Kuning, U. (n.d.-a). Peningkatan Performa Model Gradient Boosting dalam Klasifikasi Stroke Melalui Optimasi Grid Search.
Handayani, S., Toresa, D., Informatika, T., Ilmu Komputer, F., & Lancang Kuning, U. (n.d.-b). Peningkatan Performa Model Gradient Boosting dalam Klasifikasi Stroke Melalui Optimasi Grid Search.
Heydarian, M., Doyle, T. E., & Samavi, R. (2022). MLCM: Multi-Label Confusion Matrix. IEEE Access, 10, 19083–19095. https://doi.org/10.1109/ACCESS.2022.3151048
Lidya, N., Fakultas, S., Klabat, K. U., & Mononutu, J. A. (2021). Hubungan Antara Status Sosial Ekonomi Dengan Status Gizi Balita Di Kelurahan Buha Kecamatan Mapanget Kota Manado (Vol. 3, Issue 1). http://ejournal.unklab.ac.id/index.php/kjn
Liew, X. Y., Hameed, N., & Clos, J. (2021). An investigation of XGBoost-based algorithm for breast cancer classification. Machine Learning with Applications, 6, 100154. https://doi.org/10.1016/j.mlwa.2021.100154
Masturina, M. L., Salam, A., Indriasari, R., Razak Thaha, A., Jafar, N., Studi Ilmu Gizi, P., & Kesehatan Masyarakat, F. (2023). Description of family characteristics and nutritional status in toddlers Gambaran karakteristik keluarga dan status gizi balita. Community Research of Epidemiology Journal, 3(2). https://doi.org/10.24252/corejournal.v%vi%i.37731
Muhamad Fikri. (2024). Klasifikasi Status Stunting Pada Anak Bawah Lima Tahun Menggunakan Extreme Gradient Boosting. Merkurius : Jurnal Riset Sistem Informasi Dan Teknik Informatika, 2(4), 173–184. https://doi.org/10.61132/merkurius.v2i4.159
Nugraha, W., & Sasongko, A. (n.d.-a). SISTEMASI: Jurnal Sistem Informasi Hyperparameter Tuning pada Algoritma Klasifikasi dengan Grid Search Hyperparameter Tuning on Classification Algorithm with Grid Search (Vol. 11, Issue 2). http://sistemasi.ftik.unisi.ac.id
Nugraha, W., & Sasongko, A. (n.d.-b). SISTEMASI: Jurnal Sistem Informasi Hyperparameter Tuning pada Algoritma Klasifikasi dengan Grid Search Hyperparameter Tuning on Classification Algorithm with Grid Search (Vol. 11, Issue 2). http://sistemasi.ftik.unisi.ac.id
Ogunsanya, M., Isichei, J., & Desai, S. (2023). Manufacturing Letters Grid Search Hyperparameter Tuning in Additive Manufacturing Processes-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the Scientific Committee of the NAMRI/SME. www.sciencedirect.com
Paramita, I. S., Atasasih, H., & Rahayu, D. (2024). Penilaian Status Gizi Antropometri Pada Balita Penerbit Salnesia (CV. Sarana Ilmu Indonesia).
Pathak, A. K., Chaubey, M., & Gupta, M. (n.d.). Randomized-Grid Search for Hyperparameter Tuning in Decision Tree Model to Improve Performance of Cardiovascular Disease Classification.
Puerwandono, E., & Maulana, I. (2023). Penerapan Algoritma Svm Untuk Klasifikasi Citra Daun Sirih Application Of The Svm Algorithm To Image Classification Of Betel Leaf. Journal of Information Technology and Computer Science (INTECOMS), 6(2).
Syahputra, A. A., & Saputro, R. E. (2024a). Application of the XGBoost Model with Hyperparameter Tuning for Industry Classification for Job Applicants. Sinkron, 8(3), 1920–1931. https://doi.org/10.33395/sinkron.v8i3.13840
Syahputra, A. A., & Saputro, R. E. (2024b). Application of the XGBoost Model with Hyperparameter Tuning for Industry Classification for Job Applicants. Sinkron, 8(3), 1920–1931. https://doi.org/10.33395/sinkron.v8i3.13840
Tran, N. T., Tran, T. T. G., Nguyen, T. A., & Lam, M. B. (2023). A new grid search algorithm based on XGBoost model for load forecasting. Bulletin of Electrical Engineering and Informatics, 12(4), 1857–1866. https://doi.org/10.11591/eei.v12i4.5016
Uzir, N., Raman, S., Banerjee, S., & Nishant Uzir Sunil R, R. S. (2016). Experimenting XGBoost Algorithm for Prediction and Classification of Different Datasets Experimenting XGBoost Algorithm for Prediction and Classifi cation of Different Datasets. International Journal of Control Theory and Applications, 9. https://www.researchgate.net/publication/318132203
Zhang, Y., & Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 58, 308–324. https://doi.org/10.1016/j.trc.2015.02.019
