Optimasi Algoritma Naïve Bayes Menggunakan Fitur Seleksi Backward Elimination untuk Klasifikasi Prevalensi Stunting

Muhajir Yunus; Muhammad Kunta Biddinika; Abdul Fadlil

doi:10.51454/decode.v3i2.188

Authors

Muhajir Yunus Informatika Universitas Ahmad Dahlan
Muhammad Kunta Biddinika Informatika Universitas Ahmad Dahlan
Abdul Fadlil Teknik Elektro Universitas Ahmad Dahlan

DOI:

https://doi.org/10.51454/decode.v3i2.188

Keywords:

backward elimination, data mining, machine learning, naïve bayes, stunting

Abstract

Stunting adalah masalah kekurangan gizi kronis yang ditandai dengan tinggi badan anak di bawah normal untuk usianya. Anak yang mengalami stunting memiliki risiko lebih tinggi terhadap berbagai penyakit kronis dan masalah kesehatan lainnya dan cenderung memiliki intelligence quotient yang lebih rendah dan performa yang buruk di sekolah karena sebanyak 90% jumlah sel otak tercipta sejak dalam kandungan hingga anak berumur 24 bulan. Tujuan dari penelitian ini adalah untuk mengklasifikasi prevalensi stunting pada anak usia di bawah 5 tahun dengan mengimplementasikan metode naïve bayes menggunakan fitur seleksi backward elimination berdasarkan data perhitungan z-score dengan data sampel berjumlah 224 record, yang terdiri dari 4 atribut dan 1 label yaitu jenis kelamin, usia, berat badan, tinggi badan dan status gizi. Dari hasil penelitian yang telah dilakukan diperoleh nilai akurasi tertinggi sebesar 92,54% sedangkan hasil dari pengujian model tanpa menggunakan seleksi fitur mendapatkan akurasi sebesar 53,50%. Penelitian ini menggunakan data traning dan testing dengan ratio sebesar 70%:30%.

References

Anita, A., P. Purwati, and Ulty Desmarnita. 2021. “Book of Stunting Risk Detection and Monitoring Health (DRSMK) and Stunting Prevention Behavior in Children the First 1000 Days of Life.” Jurnal Aisyah : Jurnal Ilmu Kesehatan 6(1), 279-284. 10.30604/jika.v6i1.605.

Asaad, R. R., & Abdulhakim, R. M. (2021). The Concept of Data Mining and Knowledge Extraction Techniques. Qubahan Academic Journal, 1(2), 17-20. https://doi.org/10.48161/qaj.v1n2a43

Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International journal of production economics, 229, 107776. https://doi.org/10.1016/j.ijpe.2020.107776

Bitew, F. H., Sparks, C. S., & Nyarko, S. H. (2022). Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia. Public health nutrition, 25(2), 269-280. 10.1017/S1368980021004262.

Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192, 105361. 10.1016/j.knosys.2019.105361.

Chilyabanyama, O. N., Chilengi, R., Simuyandi, M., Chisenga, C. C., Chirwa, M., Hamusonde, K., ... & Bosomprah, S. (2022). Performance of machine learning classifiers in classifying stunting among under-five children in Zambia. Children, 9(7), 1-11. https://doi.org/10.3390/children9071082

Demir, K. A., Döven, G., & Sezen, B. (2019). Industry 5.0 and human-robot co-working. Procedia computer science, 158, 688-695.

Deng, X., Li, Y., Weng, J., & Zhang, J. (2019). Feature selection for text classification: A review. Multimedia Tools and Applications, 78(5), 3797-3816. 10.1007/s11042-018-6083-5.

Fentiana, N., Achadi, E. L., Besral, B., Kamiza, A., & Sudiarti, T. (2022). A Stunting Prevention Risk Factors Pathway Model for Indonesian Districts/Cities with a Stunting Prevalence of≥ 30%. Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal), 17(3), 175-183. http://dx.doi.org/10.21109/kesmas.v17i3.5954

Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Van Erven, G. (2019). Educational data mining: Predictive analysis of academic performance of public-school students in the capital of Brazil. Journal of business research, 94, 335-343. https://doi.org/10.1016/j.jbusres.2018.02.012

Imam, A., Hassan-Hanga, F., Sallahdeen, A., & Farouk, Z. L. (2021). A cross-sectional study of prevalence and risk factors for stunting among under-fives attending acute malnutrition treatment programmes in north-western Nigeria: Should these programmes be adapted to also manage stunting?. International Health, 13(3), 262-271. https://doi.org/10.1093/inthealth/ihaa043

Islam, Md Merajul, Md Jahanur Rahman, Md Moidul Islam, Dulal Chandra Roy, N. A. M. Faisa. Ahmed, Sadiq Hussain, Md Amanullah, Md Menhazul Abedin, and Md Maniruzzaman. 2022. “Application of Machine Learning Based Algorithm for Prediction of Malnutrition among Women in Bangladesh.” International Journal of Cognitive Computing in Engineering 3:46–57. doi: 10.1016/j.ijcce.2022.02.002.

Mutiarasari, D., Miranti, M., Fitriana, Y., Pakaya, D., Sari, P., Bohari, B., ... & Hadju, V. (2021). A determinant analysis of stunting prevalence on under 5-year-old children to establish stunting management policy. Open Access Macedonian Journal of Medical Sciences, 9(B), 79-84. https://doi.org/10.3889/oamjms.2021.5622

Nagesh Singh Chauhan. 2020. “Naïve Bayes Algorithm: Everything You Need to Know.” Retrieved (https://www.kdnuggets.com/2020/06/naive-bayes-algorithm-everything.html).

Ponum, M., Khan, S., Hasan, O., Mahmood, M. T., Abbas, A., Iftikhar, M., & Arshad, R. (2020). Stunting diagnostic and awareness: Impact assessment study of sociodemographic factors of stunting among school-going children of Pakistan. Bmc Pediatrics, 20, 1-9.

Sima, V., Gheorghe, I. G., Subić, J., & Nancu, D. (2020). Influences of the industry 4.0 revolution on the human capital development and consumer behavior: A systematic review. Sustainability, 12(10), 4035. https://doi.org/10.3390/su12104035

Singh, G., Kumar, B., Gaur, L., & Tyagi, A. (2019, April). Comparison between multinomial and Bernoulli naïve Bayes for text classification. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM) (pp. 593-596).

Wahono, R. S. (2016) “Literature Review: Pengantar Dan Metode.” Retrieved (https://romisatriawahono.net/2016/05/07/literature-review-pengantar-dan-metode/).

World Health Organization (WHO). 2014. Global Nutrition Targets 2025. Vol. 122. World Health Organization.