Penerapan Information Gain Untuk Seleksi Fitur Pada Klasifikasi Jenis Kelamin Tulang Tengkorak Menggunakan Backpropagation
DOI:
https://doi.org/10.47065/bulletincsr.v5i4.637Keywords:
Backpropagation Neural Network; Feature Selection; Information Gain; Sex Classification; SkullAbstract
Forensic anthropology and skull analysis play a crucial role in the biological identification of individuals, including sex determination. This study aims to improve the accuracy of gender classification based on skull structure by combining the Information Gain feature selection method with the Backpropagation algorithm. The dataset used is the craniometric data compiled by William W. Howells, consisting of 2,524 samples with 85 measurement features. The preprocessing stage includes data selection, data cleaning, and normalization. Feature selection was conducted using the Information Gain method with three threshold values: 0.01, 0.05, and 0.1, resulting in 79, 46, and 38 selected features, respectively. The model was evaluated using the K-Fold Cross Validation method with K=10 and K=20. The highest accuracy of 93.91% was achieved at the 0.01 threshold using the Backpropagation architecture [79:119:1], a learning rate of 0.01, and K=20. These results demonstrate that feature selection using Information Gain enhances the performance of the Backpropagation model by eliminating irrelevant features and minimizing the risk of overfitting.
Downloads
References
A. M. Christensen, N. V. Passalacqua, and E. J. Bartelink, “Introduction to forensic anthropology,” Forensic Anthropol., pp. 1–31, 2019, doi: 10.1016/B978-0-12-815734-3.00001-4.
Arthy, R. Goel, and M. Sreenivas, “Determination of sex by osteometry of third metatarsal,” Indian J. Forensic Med. Toxicol., vol. 14, no. 3, pp. 1–6, 2020, doi: 10.37506/ijfmt.v14i3.10315.
E. Nikita and P. Nikitas, “On the use of machine learning algorithms in forensic anthropology,” Leg. Med., vol. 47, Nov. 2020, doi: 10.1016/J.LEGALMED.2020.101771.
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,” Build. Informatics, Technol. Sci., vol. 5, no. 1, pp. 211?218-211?218, Jun. 2023, doi: 10.47065/BITS.V5I1.3625.
I. Afrianty, D. Nasien, and H. Haron, “Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology,” J. Tek. Inform., vol. 15, no. 1, pp. 63–72, Jun. 2022, doi: 10.15408/JTI.V15I1.25254.
D. H. Ubelaker and H. Khosrowshahi, “Estimation of Age in Forensic Anthropology: Historical Perspective and Recent Methodological Advances,” Forensic Sci. Res., vol. 4, no. 1, pp. 1–9, Jan. 2019, doi: 10.1080/20961790.2018.1549711.
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,” J. Inf. Commun. Technol., vol. 19, no. 2, pp. 251–277, Mar. 2020, doi: 10.32890/JICT2020.19.2.5.
Haeril, Sulaeman, and M. Akbar Syafruddin, “Profil indeks massa tubuh atlet cabang bela diri Komite Olahraga Nasional Indonesia Kota Makassar,” J. Sport Sci., vol. 12, no. 2, pp. 90–98, 2022, doi: 10.17977/um057v12i2p90-98.
N. Techataweewan, J. T. Hefner, L. Freas, N. Surachotmongkhon, R. Benchawattananon, and N. Tayles, “Metric sexual dimorphism of the skull in Thailand,” Forensic Sci. Int. Reports, vol. 4, Nov. 2021, doi: 10.1016/J.FSIR.2021.100236.
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, p. 200500, Mar. 2022, doi: 10.1016/J.FRI.2022.200500.
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, Oct. 2020, doi: 10.1016/j.forsciint.2020.110441.
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 Sci. Int. Reports, vol. 4, p. 100234, Nov. 2021, doi: 10.1016/J.FSIR.2021.100234.
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,” Transl. Res. Anat., vol. 20, p. 100075, Sep. 2020, doi: 10.1016/J.TRIA.2020.100075.
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., vol. 135, no. 3, pp. 951–966, May 2021, doi: 10.1007/S00414-020-02460-4,.
S. Sri Rahayu et al., “Klasifikasi Tulang Tengkorak Berdasarkan Jenis Kelamin dalam Antropologi Forensik Menggunakan Metode Support Vector Machine,” INOVTEK Polbeng - Seri Inform., vol. 9, no. 1, p. 2024, Jun. 2024, doi: 10.35314/ISI.V9I1.4046.
F. Curate, “The Estimation of Sex of Human Skeletal Remains in the Portuguese Identified Collections: History and Prospects,” Forensic Sci., vol. 2, no. 1, pp. 272–286, Mar. 2022, doi: 10.3390/FORENSICSCI2010021.
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, 2020, doi: 10.1155/2020/8608209,.
Darmila, “Evaluasi Perbandingan Performansi Lvq 1, Lvq 2, Dan Lvq 3 Dalam Klasifikasi Jenis Kelamin Menggunakan Tulang Tengkorak,” J. instek (informatika sains dan Teknol., vol. 7, pp. 344–353, 2022, [Online]. Available: http://journal.uin-alauddin.ac.id/index.php/instek/index
M. Kadarman, I. Afrianty, E. Budianita, and F. Syafria, “Klasifikasi Tulang Tengkorak Manusia Berdasarkan Jenis Kelamin Menggunakan Backpropagation Pada Antropologi Forensik,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 5, no. 3, pp. 619–625, Dec. 2024, doi: 10.37859/COSCITECH.V5I3.8235.
A. Herdiansah, R. I. Borman, D. Nurnaningsih, A. A. J. Sinlae, and R. R. Al Hakim, “Klasifikasi Citra Daun Herbal Dengan Menggunakan Backpropagation Neural Networks Berdasarkan Ekstraksi Ciri Bentuk,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 2, p. 388, Apr. 2022, doi: 10.30865/JURIKOM.V9I2.4066.
A. Gunawan, S. Thamrin, Y. D. Kuntjoro, and A. M. Idris, “Backpropagation Neural Network (BPNN) Algorithm for Predicting Wind Speed Patterns in East Nusa Tenggara,” Trends Renew. Energy, vol. 8, no. 2, pp. 107–118, Apr. 2022, doi: 10.17737/TRE.2022.8.2.00143.
D. S. Al-Azzawi, “Application and evaluation of the neural network in gearbox,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 18, no. 1, pp. 19–29, Feb. 2020, doi: 10.12928/TELKOMNIKA.V18I1.13760.
F. N. Fajriyan, M. Ahsan, and W. Harianto, “Komparasi Tingkat Akurasi Information Gain Dan Gain Ratio Pada Metode K-Nearest Neighbor,” JATI (Jurnal Mhs. Tek. Inform., vol. 6, no. 1, pp. 386–391, Apr. 2022, doi: 10.36040/JATI.V6I1.4694.
D. Setiawan, A. Nugraha, and A. Luthfiarta, “Komparasi Teknik Feature Selection Dalam Klasifikasi Serangan IoT Menggunakan Algoritma Decision Tree,” J. Media Inform. Budidarma, vol. 8, no. 1, p. 83, 2024, doi: 10.30865/mib.v8i1.6987.
T. Azhima Yoga Siswa and W. Joko Pranoto, “Implementasi Seleksi Fitur Information Gain Ratio Pada Algoritma Random Forest Untuk Model Data Klasifikasi Pembayaran Kuliah,” Din. Inform. J. Ilm. Teknol. Inf., vol. 15, pp. 41–49, 2023, doi: 10.35315/INFORMATIKA.V15I1.9465.
S. N. Salsabila, B. N. Sari, and R. Mayasari, “Klasifikasi Ulasan Pengguna Aplikasi Discord Menggunakan Metode Information Gain Dan Naïve Bayes Classifier,” INFOTECH J., vol. 9, no. 2, pp. 383–392, Jul. 2023, doi: 10.31949/INFOTECH.V9I2.6277.
I. K. Hasan, R. Resmawan, and J. Ibrahim, “Perbandingan K-Nearest Neighbor dan Random Forest dengan Seleksi Fitur Information Gain untuk Klasifikasi Lama Studi Mahasiswa,” Indones. J. Appl. Stat., vol. 5, no. 1, pp. 58–66, May 2022, doi: 10.13057/IJAS.V5I1.58056.
R. A. Azizah, F. A. Bachtiar, and S. Adinugroho, “Klasifikasi Kinerja Akademik Siswa Menggunakan Neighbor Weighted K-Nearest Neighbor Dengan Seleksi Fitur Information Gain,” J. Teknol. Inf. dan Ilmu Komput., vol. 9, no. 3, pp. 605–614, 2022, doi: 10.25126/jtiik.202295751.
P. a Bagaskara, N. Y. Setiawan, and A. R. . . Perdanakusuma, “Analisis dan Perbaikan Proses Bisnis dengan Menggunakan Metode Business Process Improvement (BPI) Pada PT. Andynni Chitta Sejahtera,” … Teknol. Inf. dan …, vol. 3, no. 8, pp. 7635–7641, 2019, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/5928
H. Azis, P. Purnawansyah, F. Fattah, and I. P. Putri, “Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung,” Ilk. J. Ilm., vol. 12, no. 2, pp. 81–86, 2020, doi: 10.33096/ilkom.v12i2.507.81-86.
W. Wijiyanto, A. I. Pradana, S. Sopingi, and V. Atina, “Teknik K-Fold Cross Validation untuk Mengevaluasi Kinerja Mahasiswa,” J. Algoritm., vol. 21, no. 1, pp. 239–248, 2024, doi: 10.33364/algoritma/v.21-1.1618.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Information Gain Untuk Seleksi Fitur Pada Klasifikasi Jenis Kelamin Tulang Tengkorak Menggunakan Backpropagation
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2025 Nada Tsawaabul Khair, Iis Afrianty, Fadhilah Syafria, Elvia Budianita, Siska Kurnia Gusti
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
