Analisis Efektivitas Model Convolutional Neural Network (CNN) dalam Mendiagnosis Maloklusi Gigi Berdasarkan Foto Senyum

Muhammad Cahya Rifqi; Intan Kumalasari

doi:10.69693/ijmst.v4i3.12881

Authors

Muhammad Cahya Rifqi Universitas Pamulang
Intan Kumalasari Universitas Pamulang

DOI:

https://doi.org/10.69693/ijmst.v4i3.12881

Keywords:

Convolutional Neural Network, MobileNetV2, Maloklusi, Foto Senyum, Deep Learning

Abstract

Maloklusi merupakan kondisi susunan gigi atau hubungan antara rahang atas dan rahang bawah yang tidak berada pada posisi sebagaimana mestinya, dan pemeriksaannya pada umumnya dilakukan secara langsung oleh dokter gigi atau ortodontis. Perkembangan teknologi kecerdasan buatan membuka peluang pemanfaatan citra digital untuk membantu proses pengenalan kondisi gigi secara otomatis. Penelitian ini menerapkan metode Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2 untuk mengklasifikasikan foto senyum menjadi dua kategori, yaitu normal dan maloklusi. Dataset terdiri atas 1.000 citra yang dibagi menjadi data training, validation, dan testing. Sebelum pelatihan, citra disesuaikan menjadi ukuran 224 x 224 piksel dan melalui tahap preprocessing serta augmentasi data. Model dikembangkan menggunakan pendekatan transfer learning dengan memanfaatkan bobot awal ImageNet, dan kinerjanya dievaluasi menggunakan accuracy, precision, recall, F1-score, serta confusion matrix. Hasil pengujian menunjukkan model mencapai accuracy sebesar 95%, dengan kelas maloklusi memperoleh precision 0,96, recall 0,94, dan F1-score 0,95, sedangkan kelas normal memperoleh precision 0,94, recall 0,96, dan F1-score 0,95. Model yang telah dilatih selanjutnya diimplementasikan pada aplikasi web dengan fitur unggah foto, prediksi, penyimpanan riwayat menggunakan SQLite, dan pembuatan laporan PDF. Pengujian Black Box menunjukkan seluruh fungsi utama aplikasi berjalan sesuai rancangan. MobileNetV2 terbukti dapat digunakan untuk klasifikasi awal kondisi normal dan maloklusi berdasarkan foto senyum, namun sistem yang dikembangkan hanya ditujukan sebagai alat bantu skrining awal dan bukan pengganti pemeriksaan klinis oleh dokter gigi atau ortodontis.

References

[1] S. Azizi, S. Hatampoor, And S. Tahamtan, “Applications Of Artificial Intelligence In Diagnosis And Treatment Planning Of Orthodontics: A Narrative Review,” Oct. 01, 2025, Springer International Publishing. Doi: 10.1007/S44445-025-00077-0.

[2] R. M. Kabalan, R. K. Tayyar, T. Z. Khattab, And M. Y. Hajeer, “Characteristics And Dynamics Of Smile In Patients With Skeletal Class Ii Malocclusion Versus Class I Malocclusion Using Still Digital Video Captures: A Three-Group, Cross-Sectional, Comparative Study,” Cureus, Oct. 2022, Doi: 10.7759/Cureus.30704.

[3] W. S. Shin Et Al., “Deep Learning Based Prediction Of Necessity For Orthognathic Surgery Of Skeletal Malocclusion Using Cephalogram In Korean Individuals,” Bmc Oral Health, Vol. 21, No. 1, Dec. 2021, Doi: 10.1186/S12903-021-01513-3.

[4] S. B. A. Kartbak, M. B. Özel, D. N. C. Kocakaya, M. Çakmak, And E. A. Sinanoğlu, “Classification Of Intraoral Photographs With Deep Learning Algorithms Trained According To Cephalometric Measurements,” Diagnostics, Vol. 15, No. 9, May 2025, Doi: 10.3390/Diagnostics15091059.

[5] R. Zhang Et Al., “Development And Evaluation Of A Deep Learning Model For Occlusion Classification In Intraoral Photographs,” Peerj, Vol. 13, P. E20140, Sep. 2025, Doi: 10.7717/Peerj.20140.

[6] D. N. C. Kocakaya, M. B. Özel, S. B. A. Kartbak, M. Çakmak, And E. A. Sinanoğlu, “Profile Photograph Classification Performance Of Deep Learning Algorithms Trained Using Cephalometric Measurements: A Preliminary Study,” Diagnostics, Vol. 14, No. 17, Sep. 2024, Doi: 10.3390/Diagnostics14171916.

[7] J. E. C. G. De Oro Et Al., “Hyperparameter Tuning And Automatic Image Augmentation For Deep Learning-Based Angle Classification On Intraoral Photographs—A Retrospective Study,” Diagnostics, Vol. 12, No. 7, Jul. 2022, Doi: 10.3390/Diagnostics12071526.

[8] M. Hung Et Al., “Applications Of Artificial Intelligence In Dental Malocclusion: A Scoping Review Of Recent Advances (2020–2025),” Ai, Vol. 7, No. 1, P. 10, Dec. 2025, Doi: 10.3390/Ai7010010.

[9] R. S. Gracea Et Al., “Artificial Intelligence For Orthodontic Diagnosis And Treatment Planning: A Scoping Review,” Jan. 01, 2025, Elsevier Ltd. Doi: 10.1016/J.Jdent.2024.105442.

[10] H. Mohammed, B. K. Daniel, And M. Farella, “Smile Analysis In Dentistry And Orthodontics–A Review,” 2025, Taylor And Francis Asia Pacific. Doi: 10.1080/03036758.2024.2316226.

[11] Y. M. Alsakar, N. Elazab, N. Nader, W. Mohamed, M. Ezzat, And M. Elmogy, “Multi-Label Dental Disorder Diagnosis Based On Mobilenetv2 And Swin Transformer Using Bagging Ensemble Classifier,” Sci. Rep., Vol. 14, No. 1, Dec. 2024, Doi: 10.1038/S41598-024-73297-9.

[12] L. Brigato, B. Barz, L. Iocchi, And J. Denzler, “Image Classification With Small Datasets: Overview And Benchmark,” 2022, Institute Of Electrical And Electronics Engineers Inc. Doi: 10.1109/Access.2022.3172939.

[13] C. Patrício, J. C. Neves, And L. F. Teixeira, “Explainable Deep Learning Methods In Medical Image Classification: A Survey,” Acm Comput. Surv., Vol. 56, No. 4, Apr. 2024, Doi: 10.1145/3625287.

[14] N. T. Singh, C. Kaur, A. Chaudhary, And S. Goyal, “Preprocessing Of Medical Images Using Deep Learning: A Comprehensive Review,” In Proceedings Of The 2023 2nd International Conference On Augmented Intelligence And Sustainable Systems, Icaiss 2023, Institute Of Electrical And Electronics Engineers Inc., 2023, Pp. 521–527. Doi: 10.1109/Icaiss58487.2023.10250462.

[15] D. B. Rathod And K. Saritha, “Enhanced Mobilenetv2 With An Attention Mechanism For Real-Time Mri-Based Brain Tumor Classification: A Deep Learning Approach,” Architecture Image Studies, Vol. 6, No. 3, Pp. 1309–1319, Sep. 2025, Doi: 10.62754/Ais.V6i3.447.

[16] S. A. Hicks Et Al., “On Evaluation Metrics For Medical Applications Of Artificial Intelligence,” Sci. Rep., Vol. 12, No. 1, Dec. 2022, Doi: 10.1038/S41598-022-09954-8.

[17] O. Rainio, J. Teuho, And R. Klén, “Evaluation Metrics And Statistical Tests For Machine Learning,” Sci. Rep., Vol. 14, No. 1, Dec. 2024, Doi: 10.1038/S41598-024-56706-X.

[18] D. J. Evan And P. O. N. Saian, “Implementasi Python Framework Flask Pada Modul Transfer Out Toko Di Pt Xyz,” Jipi (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), Vol. 8, No. 4, Pp. 1121–1131, Nov. 2023, Doi: 10.29100/Jipi.V8i4.4020.

[19] Y. Yuliani, “Rancangan Aplikasi Belajar Bahasa Inggris Berbasis Mobile Menggunakan Database Sqlite,” Jorapi : Journal Of Research And Publication Innovation, Vol. 2, No. 1, 2024, [Online]. Available: Https://Jurnal.Portalpublikasi.Id/Index.Php/Jorapi/Index

[20] I. S. Ahmad, J. Dai, Y. Xie, And X. Liang, “Deep Learning Models For Ct Image Classification: A Comprehensive Literature Review,” Jan. 02, 2025, Ame Publishing Company. Doi: 10.21037/Qims-24-1400.

[21] R. Archana And P. S. E. Jeevaraj, “Deep Learning Models For Digital Image Processing: A Review,” Artif. Intell. Rev., Vol. 57, No. 1, Jan. 2024, Doi: 10.1007/S10462-023-10631-Z.

[22] E. Bardideh, F. Lal Alizadeh, M. Amiri, And M. Ghorbani, “Designing An Artificial Intelligence System For Dental Occlusion Classification Using Intraoral Photographs: A Comparative Analysis Between Artificial Intelligence-Based And Clinical Diagnoses,” American Journal Of Orthodontics And Dentofacial Orthopedics, Vol. 166, No. 2, Pp. 125–137, Aug. 2024, Doi: 10.1016/J.Ajodo.2024.03.012.

[23] I. D. Mienye, T. G. Swart, G. Obaido, M. Jordan, And P. Ilono, “Deep Convolutional Neural Networks In Medical Image Analysis: A Review,” Mar. 01, 2025, Multidisciplinary Digital Publishing Institute (Mdpi). Doi: 10.3390/Info16030195.

[24] E. Supriyadi Et Al., “Pendampingan Guru Matematika Di Cianjur Dalam Penggunaan Bahasa Pemrograman Python Dengan Google Collaboratory,” 2023.

[25] W. Wahyuningsih, G. S. Nugraha, And R. Dwiyansaputra, “Classification Of Dental Caries Disease In Tooth Images Using A Comparison Of Efficientnet-B0, Mobilenetv2, Resnet-50, Inceptionv3 Architectures,” Jurnal Teknik Informatika (Jutif), Vol. 5, No. 4, Pp. 177–185, Jul. 2024, Doi: 10.52436/1.Jutif.2024.5.4.2187.

[26] S. Talaat Et Al., “The Validity Of An Artificial Intelligence Application For Assessment Of Orthodontic Treatment Need From Clinical Images,” Semin. Orthod., Vol. 27, No. 2, 2021, Doi: 10.1053/J.Sodo.2021.05.012.