Classification of Hearing Loss Degrees with Naive Bayes Algorithm

  • Okky Putra Barus Universitas Pelita Harapan
  • Romindo Universitas Pelita Harapan
  • Jefri Junifer Pangaribuan Universitas Pelita Harapan
Keywords: Classification, Hearing Loss Degrees, Naive Bayes


According to the World Health Organization (WHO), hearing loss is one of the fourth leading causes of disability. The number of people with hearing loss continues to increase yearly. This increase occurred due to delays in recognizing hearing loss, leading to delays in providing treatment. To solve this problem, one solution to deal with this is early identification to detect the degree of hearing loss. This research will use machine learning to classify the degree of hearing loss. The algorithm implemented in this study is naive Bayes. This study uses a data set from the Zenodo open access repository with 3105 raw data and 19 features. This study evaluates the performance of overall accuracy, precision, recall, and f1-score and classified four classes: mild, moderate, moderately severe, and severe. The methodology classification stages in this study include data preprocessing, data training, data testing, and evaluation. From evaluating the performance of the Naive Bayes algorithm,  the classification results obtained the highest impacts in the form of 94% overall accuracy, 100% precision, 100% recall and 97% f1-score in classifying the degree of hearing loss.



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L. Marta, M.-S. P. A, P. E. M, M. Maximiliano, B. Riccardo, and P. Alessia, “Evaluation of Machine Learning Algorithms and Explainability Techniques to Detect Hearing Loss From a Speech-in-Noise Screening Test,” Am J Audiol, vol. 31, no. 3S, pp. 961–979, Sep. 2022, doi: 10.1044/2022_AJA-21-00194.

A. C. Davis and H. J. Hoffman, “Hearing loss: rising prevalence and impact,” Bull World Health Organ, vol. v97(10), pp. 646-646A., 2019, Accessed: Dec. 13, 2022. [Online]. Available:

M. Zhang et al., “A parsimonious approach for screening moderate-to-profound hearing loss in a community-dwelling geriatric population based on a decision tree analysis,” BMC Geriatr, vol. 19, no. 1, pp. 1–11, 2019, doi: 10.1186/s12877-019-1232-x.

Y. Zhao et al., “Machine Learning Models for the Hearing Impairment Prediction in Workers Exposed to Complex Industrial Noise: A Pilot Study,” Ear Hear, vol. 40, no. 3, pp. 690–699, 2019, doi: 10.1097/AUD.0000000000000649.

O. P. Barus, J. Happy, Jusin, J. J. Pangaribuan, S. Z. H, and F. Nadjar, “Liver Disease Prediction Using Support Vector Machine and Logistic Regression Model with Combination of PCA and SMOTE,” in 2022 1st International Conference on Technology Innovation and Its Applications (ICTIIA), 2022, pp. 1–6. doi: 10.1109/ICTIIA54654.2022.9935879.

D. A. Omondiagbe, S. Veeramani, and A. S. Sidhu, “Machine Learning Classification Techniques for Breast Cancer Diagnosis,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, 2019. doi: 10.1088/1757-899X/495/1/012033.

H. Zaw, N. Maneerat, and K. Win, Brain tumor detection based on Naïve Bayes Classification. 2019. doi: 10.1109/ICEAST.2019.8802562.

K. Wabang, Oky Dwi Nurhayati, and Farikhin, “Application of The Naïve Bayes Classifier Algorithm to Classify Community Complaints,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 5, pp. 872–876, Nov. 2022, doi: 10.29207/resti.v6i5.4498.

A. Wibawa et al., “Naïve Bayes Classifier for Journal Quartile Classification,” International Journal of Recent Contributions from Engineering, Science & IT (iJES), vol. 7, p. 91, Jun. 2019, doi: 10.3991/ijes.v7i2.10659.

D. Irawan, H. Oktavianto, M. K. Anam, T. Informatika, and U. M. Jember, “Analisis Penerapan Algoritma Naive Bayes,” JASIE “Jurnal Aplikasi Sistem Informasi Dan Elektronika,” vol. 1, no. 2, pp. 127–134, 2019.

A. B. Wibisono and A. Fahrurozi, “Perbandingan Algoritma Klasifikasi Dalam Pengklasifikasian Data Penyakit Jantung Koroner,” Jurnal Ilmiah Teknologi dan Rekayasa, vol. 24, no. 3, pp. 161–170, 2019, doi: 10.35760/tr.2019.v24i3.2393.

S. G. Fitri, R. Selsi, Z. Rustam, and J. Pandelaki, “Naïve bayes classifier models for cerebral infarction classification,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Jun. 2020. doi: 10.1088/1742-6596/1490/1/012019.

N. Salmi and Z. Rustam, “Naïve Bayes Classifier Models for Predicting the Colon Cancer,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Jul. 2019. doi: 10.1088/1757-899X/546/5/052068.

O. Putra Barus and A. Tehja, “Prediksi Kesembuhan Pasien Covid-19 Di Indonesia Melalui Terapi Menggunakan Metode Naïve Bayes,” Journal Information System Development (ISD), vol. 6, no. 2, pp. 59–66, Jul. 2021, Accessed: Jan. 19, 2023. [Online]. Available:

O. Putra Barus and T. Sanjaya, “Prediksi Tingkat Keberhasilan Pengobatan Kanker Menggunakan Imunoterapi Dengan Metode Naive Bayes,” vol. 5, no. 1, Jan. 2020, Accessed: Jan. 19, 2023. [Online]. Available:

B. O. Olusanya, A. C. Davis, and H. J. Hoffman, “Hearing loss grades and the International classification of functioning, disability and health,” Bull World Health Organ, vol. 97, no. 10, pp. 725–728, Oct. 2019, doi: 10.2471/BLT.19.230367.

A. Hijra Ferdinan, A. S. Briand Osmond, and C. S. Setianingsih, “Klasifikasi Emosi Pada Lirik Lagu Menggunakan Metode K-Nearest Neighbor Emotion Classification In Song Lyrics Using K-Nearest Neighbor Method,” Dec. 2018. Accessed: Dec. 13, 2022. [Online]. Available:

R. Chandra, “Peningkatan Kinerja Algoritma Learning Vector Quantization (LVQ) Menggunakan Nguyen Widrow,” Repositori Institusi Universitas Sumatera Utara, 2021. (accessed Dec. 13, 2022).

How to Cite
Barus, O. P., Romindo, & Jefri Junifer Pangaribuan. (2023). Classification of Hearing Loss Degrees with Naive Bayes Algorithm. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(4), 751 - 757.
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