Keywords: obesitas, klasifikasi, algoritma SVM


Abstract—The news of the death of a man in Indonesia is in the public spotlight because doctors have difficulty treating his illness because being overweight or obese causes the organs in the body to fail to function properly. Overweight causes the body to experience several health problems, including heart defects, diabetes, and several other diseases that can attack vital organs in the body. According to data on deaths caused by obesity, there are as many as 60 per 100,000 Indonesian population, and are a very feared killer. Faster handling of recognizing our body weight is important for each individual’s health. Classification can also help overweight in a person known more quickly. In this study, the classification algorithm that will be used is the Support vector machine (SVM). With 252 data, this study will use the SVM algorithm and look for the level of accuracy of the two classification classes, namely normal and overweight. This study produces an accuracy rate of 92.11% with a ROC curve value of 0.990 which means that the classification in this study is very good.


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Al Hammadi, H., & Reilly, J. J. (2020). Classification accuracy of body mass index for excessive body fatness in kuwaiti adolescent girls and young adult women. Diabetes, Metabolic Syndrome and Obesity, 13, 1043–1049. https://doi.org/10.2147/DMSO.S232545

Amanda, D., & Martini, S. (2018). HUBUNGAN KARAKTERISTIK DAN STATUS OBESITAS SENTRAL DENGAN KEJADIAN HIPERTENSI. 6(March), 51–59. https://doi.org/10.20473/jbe.v6i1.2018

Azhari, M., Situmorang, Z., & Rosnelly, R. (2021). Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes. Jurnal Media Informatika Budidarma, 5(2), 640. https://doi.org/10.30865/mib.v5i2.2937

Azizah, K. N. (2023). Penyebab Fajri Pria Obesitas 300 Kg Meninggal Dunia, Begini Penjelasan RSCM Baca artikel detikHealth, “Penyebab Fajri Pria Obesitas 300 Kg Meninggal Dunia, Begini Penjelasan RSCM” selengkapnya https://health.detik.com/berita-detikhealth/d-6788296/penyebab. https://health.detik.com/berita-detikhealth/d-6788296/penyebab-fajri-pria-obesitas-300-kg-meninggal-dunia-begini-penjelasan-rscm

Balasubramaniam, V. (2021). Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis. Journal of Artificial Intelligence and Capsule Networks, 3(1), 34–42. https://doi.org/10.36548/jaicn.2021.1.003

Çiǧşar, B., & Ünal, D. (2019). Comparison of Data Mining Classification Algorithms Determining the Default Risk. Scientific Programming, 2019. https://doi.org/10.1155/2019/8706505

Dagdevir, E., & Tokmakci, M. (2021). Optimization of preprocessing stage in EEG based BCI systems in terms of accuracy and timing cost. Biomedical Signal Processing and Control, 67(July 2020), 102548. https://doi.org/10.1016/j.bspc.2021.102548

Fitriyah, N., Warsito, B., & Maruddani, D. A. I. (2020). Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm. Jurnal Gaussian, 9(3), 376–390. https://doi.org/10.14710/j.gauss.v9i3.28932

Hidayatulloh, T., & Yusuf, L. (2023). Klasifikasi Obesitas dengan Support Vector Mechine.

Huang, W., Liu, H., Zhang, Y., Mi, R., Tong, C., Xiao, W., & Shuai, B. (2021). Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM. Applied Soft Computing, 109, 107541. https://doi.org/10.1016/j.asoc.2021.107541

Ji, M., Zhang, S., & An, R. (2018). Effectiveness of A Body Shape Index (ABSI) in predicting chronic diseases and mortality: a systematic review and meta-analysis. Obesity Reviews, 19(5), 737–759. https://doi.org/10.1111/obr.12666

Ketu, S., & Mishra, P. K. (2021). Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare. Complex and Intelligent Systems, 7(5), 2597–2615. https://doi.org/10.1007/s40747-021-00435-5

Nickerson, B. S., McLester, C. N., McLester, J. R., & Kliszczewicz, B. M. (2020). Relative accuracy of anthropometric-based body fat equations in males and females with varying BMI classifications. Clinical Nutrition ESPEN, 35(xxxx), 136–140. https://doi.org/10.1016/j.clnesp.2019.10.014

Phinandita, V. (2023). Penyebab Pria Obesitas BB 300 Kg Meninggal di RSCM: Syok Sepsis-Gagal Organ Baca artikel detikHealth, “Penyebab Pria Obesitas BB 300 Kg Meninggal di RSCM: Syok Sepsis-Gagal Organ” selengkapnya https://health.detik.com/berita-detikhealth/d-6789833/penyebab. https://health.detik.com/berita-detikhealth/d-6789833/penyebab-pria-obesitas-bb-300-kg-meninggal-di-rscm-syok-sepsis-gagal-organ

Santika, E. (2023). Belum Ada Penurunan Tren Kematian Akibat Obesitas Selama 20 Tahun Terakhir di Indonesia. Katadata Media Network. https://databoks.katadata.co.id/datapublish/2023/04/18/belum-ada-penurunan-tren-kematian-akibat-obesitas-selama-20-tahun-terakhir-di-indonesia

Sjarif, N. N. A., Yahya, Y., Chuprat, S., & Azmi, N. H. F. M. (2020). Support vector machine algorithm for SMS spam classification in the telecommunication industry. International Journal on Advanced Science, Engineering and Information Technology, 10(2), 635–639. https://doi.org/10.18517/ijaseit.10.2.10175

Tineges, R., Triayudi, A., & Sholihati, I. D. (2020). Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM). Jurnal Media Informatika Budidarma, 4(3), 650. https://doi.org/10.30865/mib.v4i3.2181

Wibowo, A., Rahayu, Y., Riyanto, A., & Hidayatulloh, T. (2018). Classification algorithm for edible mushroom identification. 2018 International Conference on Information and Communications Technology, ICOIACT 2018, 2018-Janua, 250–253. https://doi.org/10.1109/ICOIACT.2018.8350746

Wong, J. C., O’Neill, S., Beck, B. R., Forwood, M. R., & Khoo, S. K. (2021). Comparison of obesity and metabolic syndrome prevalence using fat mass index, body mass index and percentage body fat. PLoS ONE, 16(1 January), 1–11. https://doi.org/10.1371/journal.pone.0245436

How to Cite
Hidayatulloh, T., & Yusuf, L. (2023). KLASIFIKASI TIPE BERAT TUBUH MENGGUNAKAN METODE SUPPORT VECTOR MACHINE. INTI Nusa Mandiri, 18(1), 71 - 77. https://doi.org/10.33480/inti.v18i1.4254
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