Classification of Human Skull Bones on Gender Using Backpropagation in Forensic Anthropology
Keywords:
backpropagation, klasifikasi, skull bones, antropologi forensik, backpropagation, classification, skull bones, forensic anthropology
Abstract
Klasifikasi tulang tengkorak berdasarkan jenis kelamin merupakan langkah utama pada antropologi forensik dalam mengidentifikasi profil sisa-sisa kerangka. Klasifikasi jenis kelamin bertujuan untuk menentukan apakah kerangka tertentu adalah milik laki-laki atau perempuan. Penelitian ini berfokus pada klasifikasi tulang tengkorak berdasarkan jenis kelamin dengan menggunakan teknik pembelajaran mesin tingkat lanjut, khususnya Backpropagation Neural Network (BPNN). Tujuan dari penelitian ini adalah untuk menunjukkan kinerja BPNN. Data yang digunakan dalam penelitian ini diperoleh dari Dr. William Howells, meliputi pengukuran kraniometri dari 2524 sampel tengkorak laki-laki dan perempuan, dengan 86 variabel seperti lebar bizygomatic dan panjang glabello-oksipital. Teknik BPNN digunakan karena kemampuannya untuk memodelkan hubungan yang kompleks dan tidak linier. Kinerja model ini dievaluasi dengan menggunakan metrik standar akurasi. Pembagian data latih dan data uji menggunakan k-fold cross-validation dengan k = 10. Penelitian ini menjalankan dua skenario uji, yaitu menggunakan satu hidden layer dan dua hidden layer. Untuk masing-masing model arsitektur menggunakan learning rate sebagai parameter uji, yaitu 0,1; 0,01; dan 0,001. Hasil penelitian menunjukkan bahwa pendekatan pembelajaran mesin dapat secara efektif membedakan antara tulang tengkorak laki-laki dan perempuan, dengan akurasi rata-rata 92,32% untuk satu hidden layer dan 90,74% untuk dua hidden layer. Hasil tersebut menunjukkan, model klasifikasi tulang tengkorak manusia berbasis gender dengan menggunakan jaringan syaraf tiruan backpropagation sangat disarankan sebagai teknik yang berhasil dalam mengklasifikasikan tulang tengkorak manusia.
Downloads
Download data is not yet available.
References
[1] A. M. Christensen, N. V. Passalacqua, and E. J. Bartelink, “Introduction to forensic anthropology,” in Forensic Anthropology, Elsevier, 2019, pp. 1–31. doi: 10.1016/b978-0-12-815734-3.00001-4.
[2] Arthy, R. Goel, and S. M, “Determination of Sex by Osteometry of Third Metatarsal,” Indian Journal of Forensic Medicine & Toxicology, vol. 14, no. 3, pp. 1–5, 2020.
[3] E. Nikita and P. Nikitas, “On the use of machine learning algorithms in forensic anthropology,” Leg Med, vol. 47, Nov. 2020, doi: https://doi.org/10.1016/j.legalmed.2020.101771.
[4] Y. Harni, I. Afrianty, S. Sanjaya, R. Abdillah, F. Yanto, and F. Syafria, “Performance Analysis of LVQ 1 Using Feature Selection Gain Ratio for Sex Classification in Forensic Anthropology,” Building of Informatics, Technology and Science (BITS), vol. 5, no. 1, pp. 211–218, Jun. 2023, doi: 10.47065/bits.v5i1.3625.
[5] I. Afrianty, D. Nasien, and H. Haron, “Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology,” JURNAL TEKNIK INFORMATIKA, vol. 15, no. 1, pp. 63–72, Jun. 2022, doi: https://doi.org/10.15408/jti.v15i1.25254.
[6] D. H. Ubelaker and H. Khosrowshahi, “Estimation of age in forensic anthropology: historical perspective and recent methodological advances,” Jan. 02, 2019, Taylor and Francis Ltd. doi: https://doi.org/10.1080/20961790.2018.1549711.
[7] N. L. Hairuddin, L. M. Yusuf, and M. S. Othman, “Gender Classification On Skeletal Remains: Efficiency of Metaheuristic Algorithm Method and Optimized Back Propagation Neural Network,” Journal of Information and Communication Technology, vol. 19, no. 2, pp. 251–277, Apr. 2020, doi: 10.32890/jict2020.19.2.5.
[8] H. Haeril, S. Sulaeman, and M. A. Syafruddin, “Profil Indeks Massa Tubuh Atlet Cabang Bela Diri Komite Olahraga Nasional Indonesia Kota Makassar,” Jurnal Sport Science, vol. 12, no. 2, pp. 90–98, Aug. 2022, doi: 10.17977/um057v12i2p90-98.
[9] M. N. Bustan, I. Aprilo, and K. Anwar, “Derajat Kesehatan Jasmani dan Postur Siswa Sekolah di Makassar,” Media Kesehatan Masyarakat Indonesia, vol. 14, no. 1, pp. 93–99, Mar. 2018, doi: 10.30597/mkmi.v14i1.3781.
[10] Y. Etli, M. Asirdizer, Y. Hekimoglu, S. Keskin, and A. Yavuz, “Sex estimation from sacrum and coccyx with discriminant analyses and neural networks in an equally distributed population by age and sex,” Forensic Sci Int, vol. 303, pp. 1–9, Oct. 2019, doi: http://dx.doi.org/10.1016/j.forsciint.2019.109955.
[11] N. Techataweewan, J. T. Hefner, L. Freas, N. Surachotmongkhon, R. Benchawattananon, and N. Tayles, “Metric sexual dimorphism of the skull in Thailand,” Forensic Science International: Reports, vol. 4, pp. 1–12, Nov. 2021, doi: 10.1016/j.fsir.2021.100236.
[12] S. Braun, A. F. Ridel, E. N. L’Abbé, C. E. Theye, and A. C. Oettlé, “Repeatability of a morphoscopic sex estimation technique for the mental eminence on micro-focus X-ray computed tomography models,” Forensic Imaging, vol. 28, pp. 1–5, Mar. 2022, doi: 10.1016/j.fri.2022.200500.
[13] D. H. Toneva, S. Y. Nikolova, G. P. Agre, D. K. Zlatareva, V. G. Hadjidekov, and N. E. Lazarov, “Data mining for sex estimation based on cranial measurements,” Forensic Sci Int, vol. 315, pp. 1–14, Oct. 2020, doi: 10.1016/j.forsciint.2020.110441.
[14] J. Tetteh, A. K. Appiah, C. S. Abaidoo, and C. Adjei-Antwi, “The forensic use of percutaneous femur length in height and sex estimation among Ghanaians,” Forensic Science International: Reports, vol. 4, pp. 1–6, Nov. 2021, doi: 10.1016/j.fsir.2021.100234.
[15] M. K. Misiani, T. Amuti, S. Darbar, P. Mandela, E. Maranga, and M. Obimbo, “Sex determination from dimensions of distal tibiae in adult Kenyans: A discriminant function analysis,” Translational Research in Anatomy, vol. 20, pp. 1–5, Sep. 2020, doi: 10.1016/j.tria.2020.100075.
[16] D. Toneva, S. Nikolova, G. Agre, D. Zlatareva, V. Hadjidekov, and N. Lazarov, “Machine learning approaches for sex estimation using cranial measurements,” Int J Legal Med, May 2020, doi: 10.1007/s00414-020-02460-4.
[17] S. Sri Rahayu et al., “Klasifikasi Tulang Tengkorak Berdasarkan Jenis Kelamin Dalam Antropologi Forensik Menggunakan Metode Support Vector Machine,” Jurnal Inovtek Polbeng, vol. 9, no. 1, pp. 243–256, 2024.
[18] F. Curate, “The Estimation of Sex of Human Skeletal Remains in the Portuguese Identified Collections: History and Prospects,” Mar. 01, 2022, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/forensicsci2010021.
[19] W. Yang, M. Zhou, P. Zhang, G. Geng, X. Liu, and H. Zhang, “Skull Sex Estimation Based on Wavelet Transform and Fourier Transform,” Biomed Res Int, vol. 2020, pp. 1–10, 2020, doi: 10.1155/2020/8608209.
[20] H. K. Khartade, V. Meshram, S. P. Garg, and D. K. Mishra, “Identification of skeletal remains by orthopaedic implant: A case report and brief review of the literature,” Med Sci Law, vol. 61, no. 2, pp. 1–4, Apr. 2020, doi: 10.1177/0025802420975304.
[21] S. R. Kelley and S. D. Tallman, “Population-Inclusive Assigned-Sex-at-Birth Estimation from Skull Computed Tomography Scans,” Forensic Sciences, vol. 2, no. 2, pp. 321–348, Jun. 2022, doi: 10.3390/forensicsci2020024.
[22] C. E. Hughes, C. Juarez, and A. Di Yim, “Forensic anthropology casework performance: Assessing accuracy and trends for biological profile estimates on a comprehensive sample of identified decedent cases,” J Forensic Sci, vol. 66, no. 5, pp. 1–15, Sep. 2021, doi: 10.1111/1556-4029.14782.
[23] Y. Cao et al., “Use of deep learning in forensic sex estimation of virtual pelvic models from the Han population,” Forensic Sci Res, vol. 7, no. 3, pp. 540–549, 2022, doi: 10.1080/20961790.2021.2024369.
[24] D. Nasien, M. Hasmil Adiya, Y. Rahayu, and D. Willieam Anggara, “A MACHINE LEARNING MODEL FOR DETERMINATION OF GENDER UTILIZING HYBRID CLASSIFIERS,” Journal of Applied Engineering and Technological Science, vol. 5, no. 1, pp. 542–556, 2023.
[25] M. Robles, R. M. Carew, C. Rando, S. Nakhaeizadeh, and R. M. Morgan, “Sex estimation from virtual models: exploring the potential of stereolithic 3D crania models for morphoscopic trait scoring,” Forensic Sci Res, vol. 8, no. 2, pp. 123–132, Jun. 2023, doi: 10.1093/fsr/owad017.
[26] N. Hong et al., “State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review,” JMIR Med Inform, vol. 10, no. 3, pp. 1–15, Mar. 2022, doi: 10.2196/28781.
[27] M. Shohel Rana and J. Shuford, “AI in Healthcare: Transforming Patient Care through Predictive Analytics and Decision Support Systems,” Journal of Artificial Intelligence General Science (JAIGS), vol. 1, no. 1, 2024, [Online]. Available: https://ojs.boulibrary.com/index.php/JAIGS
[28] W. Shan et al., “Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population,” Nature Research, Dec. 2022. doi: 10.1038/s41598-022-20034-9.
[29] W. Yang, X. Liu, K. Wang, J. Hu, G. Geng, and J. Feng, “Sex Determination of Three-Dimensional Skull Based on Improved Backpropagation Neural Network,” Comput Math Methods Med, vol. 2019, pp. 1–8, 2019, doi: 10.1155/2019/9163547.
[30] A. Almomani et al., “Age and Gender Classification Using Backpropagation and Bagging Algorithms,” Computers, Materials and Continua, vol. 74, no. 2, pp. 3045–3062, 2023, doi: 10.32604/cmc.2023.030567.
[31] I. K. A. G. Wiguna, P. Sugiartawan, I. G. I. Sudipa, and I. P. Y. Pratama, “Sentiment Analysis Using Backpropagation Method to Recognize the Public Opinion,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 16, no. 4, pp. 423–434, Oct. 2022, doi: 10.22146/ijccs.78664.
[32] F. Damayanti, S. Sundari, and R. Liza, “Analisis Laju Pembelajaran pada Backpropagation dalam Memprediksi Bencana Alam Akibat Cuaca Ekstrim,” Unitek, vol. 16, no. 1, pp. 61–70, 2023.
[33] F. F. Wenando, Y. Fatma, A. Ulfa, and J. Suroya Taurin, “Aplikasi dan Kerentanan Algoritma Probabilistic Neural Network (PNN): Systematic Literature Review,” vol. 4, no. 2, pp. 491–499, 2023, doi: 10.37859/coscitech.v4i2.5676.
[2] Arthy, R. Goel, and S. M, “Determination of Sex by Osteometry of Third Metatarsal,” Indian Journal of Forensic Medicine & Toxicology, vol. 14, no. 3, pp. 1–5, 2020.
[3] E. Nikita and P. Nikitas, “On the use of machine learning algorithms in forensic anthropology,” Leg Med, vol. 47, Nov. 2020, doi: https://doi.org/10.1016/j.legalmed.2020.101771.
[4] Y. Harni, I. Afrianty, S. Sanjaya, R. Abdillah, F. Yanto, and F. Syafria, “Performance Analysis of LVQ 1 Using Feature Selection Gain Ratio for Sex Classification in Forensic Anthropology,” Building of Informatics, Technology and Science (BITS), vol. 5, no. 1, pp. 211–218, Jun. 2023, doi: 10.47065/bits.v5i1.3625.
[5] I. Afrianty, D. Nasien, and H. Haron, “Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology,” JURNAL TEKNIK INFORMATIKA, vol. 15, no. 1, pp. 63–72, Jun. 2022, doi: https://doi.org/10.15408/jti.v15i1.25254.
[6] D. H. Ubelaker and H. Khosrowshahi, “Estimation of age in forensic anthropology: historical perspective and recent methodological advances,” Jan. 02, 2019, Taylor and Francis Ltd. doi: https://doi.org/10.1080/20961790.2018.1549711.
[7] N. L. Hairuddin, L. M. Yusuf, and M. S. Othman, “Gender Classification On Skeletal Remains: Efficiency of Metaheuristic Algorithm Method and Optimized Back Propagation Neural Network,” Journal of Information and Communication Technology, vol. 19, no. 2, pp. 251–277, Apr. 2020, doi: 10.32890/jict2020.19.2.5.
[8] H. Haeril, S. Sulaeman, and M. A. Syafruddin, “Profil Indeks Massa Tubuh Atlet Cabang Bela Diri Komite Olahraga Nasional Indonesia Kota Makassar,” Jurnal Sport Science, vol. 12, no. 2, pp. 90–98, Aug. 2022, doi: 10.17977/um057v12i2p90-98.
[9] M. N. Bustan, I. Aprilo, and K. Anwar, “Derajat Kesehatan Jasmani dan Postur Siswa Sekolah di Makassar,” Media Kesehatan Masyarakat Indonesia, vol. 14, no. 1, pp. 93–99, Mar. 2018, doi: 10.30597/mkmi.v14i1.3781.
[10] Y. Etli, M. Asirdizer, Y. Hekimoglu, S. Keskin, and A. Yavuz, “Sex estimation from sacrum and coccyx with discriminant analyses and neural networks in an equally distributed population by age and sex,” Forensic Sci Int, vol. 303, pp. 1–9, Oct. 2019, doi: http://dx.doi.org/10.1016/j.forsciint.2019.109955.
[11] N. Techataweewan, J. T. Hefner, L. Freas, N. Surachotmongkhon, R. Benchawattananon, and N. Tayles, “Metric sexual dimorphism of the skull in Thailand,” Forensic Science International: Reports, vol. 4, pp. 1–12, Nov. 2021, doi: 10.1016/j.fsir.2021.100236.
[12] S. Braun, A. F. Ridel, E. N. L’Abbé, C. E. Theye, and A. C. Oettlé, “Repeatability of a morphoscopic sex estimation technique for the mental eminence on micro-focus X-ray computed tomography models,” Forensic Imaging, vol. 28, pp. 1–5, Mar. 2022, doi: 10.1016/j.fri.2022.200500.
[13] D. H. Toneva, S. Y. Nikolova, G. P. Agre, D. K. Zlatareva, V. G. Hadjidekov, and N. E. Lazarov, “Data mining for sex estimation based on cranial measurements,” Forensic Sci Int, vol. 315, pp. 1–14, Oct. 2020, doi: 10.1016/j.forsciint.2020.110441.
[14] J. Tetteh, A. K. Appiah, C. S. Abaidoo, and C. Adjei-Antwi, “The forensic use of percutaneous femur length in height and sex estimation among Ghanaians,” Forensic Science International: Reports, vol. 4, pp. 1–6, Nov. 2021, doi: 10.1016/j.fsir.2021.100234.
[15] M. K. Misiani, T. Amuti, S. Darbar, P. Mandela, E. Maranga, and M. Obimbo, “Sex determination from dimensions of distal tibiae in adult Kenyans: A discriminant function analysis,” Translational Research in Anatomy, vol. 20, pp. 1–5, Sep. 2020, doi: 10.1016/j.tria.2020.100075.
[16] D. Toneva, S. Nikolova, G. Agre, D. Zlatareva, V. Hadjidekov, and N. Lazarov, “Machine learning approaches for sex estimation using cranial measurements,” Int J Legal Med, May 2020, doi: 10.1007/s00414-020-02460-4.
[17] S. Sri Rahayu et al., “Klasifikasi Tulang Tengkorak Berdasarkan Jenis Kelamin Dalam Antropologi Forensik Menggunakan Metode Support Vector Machine,” Jurnal Inovtek Polbeng, vol. 9, no. 1, pp. 243–256, 2024.
[18] F. Curate, “The Estimation of Sex of Human Skeletal Remains in the Portuguese Identified Collections: History and Prospects,” Mar. 01, 2022, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/forensicsci2010021.
[19] W. Yang, M. Zhou, P. Zhang, G. Geng, X. Liu, and H. Zhang, “Skull Sex Estimation Based on Wavelet Transform and Fourier Transform,” Biomed Res Int, vol. 2020, pp. 1–10, 2020, doi: 10.1155/2020/8608209.
[20] H. K. Khartade, V. Meshram, S. P. Garg, and D. K. Mishra, “Identification of skeletal remains by orthopaedic implant: A case report and brief review of the literature,” Med Sci Law, vol. 61, no. 2, pp. 1–4, Apr. 2020, doi: 10.1177/0025802420975304.
[21] S. R. Kelley and S. D. Tallman, “Population-Inclusive Assigned-Sex-at-Birth Estimation from Skull Computed Tomography Scans,” Forensic Sciences, vol. 2, no. 2, pp. 321–348, Jun. 2022, doi: 10.3390/forensicsci2020024.
[22] C. E. Hughes, C. Juarez, and A. Di Yim, “Forensic anthropology casework performance: Assessing accuracy and trends for biological profile estimates on a comprehensive sample of identified decedent cases,” J Forensic Sci, vol. 66, no. 5, pp. 1–15, Sep. 2021, doi: 10.1111/1556-4029.14782.
[23] Y. Cao et al., “Use of deep learning in forensic sex estimation of virtual pelvic models from the Han population,” Forensic Sci Res, vol. 7, no. 3, pp. 540–549, 2022, doi: 10.1080/20961790.2021.2024369.
[24] D. Nasien, M. Hasmil Adiya, Y. Rahayu, and D. Willieam Anggara, “A MACHINE LEARNING MODEL FOR DETERMINATION OF GENDER UTILIZING HYBRID CLASSIFIERS,” Journal of Applied Engineering and Technological Science, vol. 5, no. 1, pp. 542–556, 2023.
[25] M. Robles, R. M. Carew, C. Rando, S. Nakhaeizadeh, and R. M. Morgan, “Sex estimation from virtual models: exploring the potential of stereolithic 3D crania models for morphoscopic trait scoring,” Forensic Sci Res, vol. 8, no. 2, pp. 123–132, Jun. 2023, doi: 10.1093/fsr/owad017.
[26] N. Hong et al., “State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review,” JMIR Med Inform, vol. 10, no. 3, pp. 1–15, Mar. 2022, doi: 10.2196/28781.
[27] M. Shohel Rana and J. Shuford, “AI in Healthcare: Transforming Patient Care through Predictive Analytics and Decision Support Systems,” Journal of Artificial Intelligence General Science (JAIGS), vol. 1, no. 1, 2024, [Online]. Available: https://ojs.boulibrary.com/index.php/JAIGS
[28] W. Shan et al., “Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population,” Nature Research, Dec. 2022. doi: 10.1038/s41598-022-20034-9.
[29] W. Yang, X. Liu, K. Wang, J. Hu, G. Geng, and J. Feng, “Sex Determination of Three-Dimensional Skull Based on Improved Backpropagation Neural Network,” Comput Math Methods Med, vol. 2019, pp. 1–8, 2019, doi: 10.1155/2019/9163547.
[30] A. Almomani et al., “Age and Gender Classification Using Backpropagation and Bagging Algorithms,” Computers, Materials and Continua, vol. 74, no. 2, pp. 3045–3062, 2023, doi: 10.32604/cmc.2023.030567.
[31] I. K. A. G. Wiguna, P. Sugiartawan, I. G. I. Sudipa, and I. P. Y. Pratama, “Sentiment Analysis Using Backpropagation Method to Recognize the Public Opinion,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 16, no. 4, pp. 423–434, Oct. 2022, doi: 10.22146/ijccs.78664.
[32] F. Damayanti, S. Sundari, and R. Liza, “Analisis Laju Pembelajaran pada Backpropagation dalam Memprediksi Bencana Alam Akibat Cuaca Ekstrim,” Unitek, vol. 16, no. 1, pp. 61–70, 2023.
[33] F. F. Wenando, Y. Fatma, A. Ulfa, and J. Suroya Taurin, “Aplikasi dan Kerentanan Algoritma Probabilistic Neural Network (PNN): Systematic Literature Review,” vol. 4, no. 2, pp. 491–499, 2023, doi: 10.37859/coscitech.v4i2.5676.
Published
2024-12-27
How to Cite
Afrianty, I., Mhd. Kadarman, Elvia Budianita, & Fadhilah Syafria. (2024). Classification of Human Skull Bones on Gender Using Backpropagation in Forensic Anthropology. Jurnal CoSciTech (Computer Science and Information Technology), 5(3), 619-625. https://doi.org/10.37859/coscitech.v5i3.8235
Section
Articles
Abstract views: 135 , 
PDF downloads: 104
