Sistem Deteksi Otomatis Titik Minutiae Sidik Jari Berbasis MinutiaeNet

Studi Kasus Unit Identifikasi Polda Bali

Authors

  • Ni Putu Ayu Sri Laksmi Universitas Primakara
  • Eddy Muntina Dharma Universitas Primakara
  • Ida Bagus Kresna Sudiatmika Universitas Primakara

DOI:

https://doi.org/10.55606/jcsr-politama.v4i4.6528

Keywords:

Fingerprint Analysis, Forensic Identification, Latent Fingerprint, Minutiae, Minutiaenet

Abstract

Fingerprint identification is an important biometric method used in criminal investigations. However, at the Identification Unit of the Bali Regional Police, the minutiae extraction process is still done manually. This causes limitations in accuracy, processing speed, and dependence on the skills of the officers. This study develops an automatic minutiae detection system based on MinutiaeNet. The system is equipped with a graphical user interface and an SQLite database to store extraction results, using a Research and Development (R&D) approach. The system was tested on 55 fingerprint images, including plain and latent fingerprints. The results show that the system performs well, with an average Precision of 0.83, Recall of 0.88, and F1-Score of 0.85. The system works stably on plain fingerprints with clear ridge patterns but shows lower performance on latent fingerprints due to thin, incomplete ridges and noise, which increases False Negative results. Although over- detection occurs in some cases, the system still provides consistent results and significantly reduces processing time. The maximum processing time is about 1 minute and 3 seconds per image, which is much faster than manual minutiae extraction. This study shows that the proposed system can support forensic identification by improving efficiency and consistency in fingerprint analysis.

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References

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 53. https://doi.org/10.1186/s40537-021-00444-8

Fuadi, R. S., Ira, D., & Maerani, A. (2019). Prosiding KONFERENSI ILMIAH MAHASISWA UNISSULA (KIMU) 2 Universitas Islam Sultan KEDUDUKAN SIDIK JARI DALAM PROSES PENYIDIKAN TINDAK PIDANA (Studi Kasus Di Kepolisian Resor Pati) FINGERPRINT STATUS IN CRIMINAL ACTION PROCESS (Case Study in Police Sector Pati).

Herdianto Situmorang, B., Rama Putra, G., & Hidayatullah, S. (2023). IDENTIFIKASI BIOMETRIKA MENGGUNAKAN EKSTRAKSI MINUTIAE PADA CITRA SIDIK JARI (Vol. 17, Number 1). https://ejurnal.teknokrat.ac.id/index.php/teknoinfo/index

Li, Z., Wang, Y., Yang, Z., Tian, X., Zhai, L., Wu, X., Yu, J., Gu, S., Huang, L., & Zhang, Y. (2022). A novel fingerprint recognition method based on a Siamese neural network. Journal of Intelligent Systems, 31(1), 690–705. https://doi.org/10.1515/jisys-2022-0055

Mardona, R., & Yenti, N. (2019). FUNGSI SIDIK JARI DALAM PENGUNGKAPAN TINDAK PIDANA PENCURIAN DI RESKRIM POLRESTA PADANG.

Meiliyen Dharma Sara, E., & Johar, A. (2019). IMPLEMENTASI METODE POINT MINUTIAE UNTUK MENGIDENTIFIKASI JENIS BATIK PADA BATIK BESUREK DENGAN BERBASIS TEKSTUR. In Jurnal Rekursif (Vol. 7, Number 1). http://ejournal.unib.ac.id/index.php/rekursif/59

Nguyen, D.-L., Cao, K., & Jain, A. K. (2017). Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge. http://arxiv.org/abs/1712.09401

Norbertus Tri Suswanto Saptadi, Muh. Nurtanzis Sutoyo, Hedie Kristiawan, Agung Yuliyanto Nugroho, Nina Rahayu, Suwarmiyati, Bayu Waseso, Indo Intan, Khairunnas, Imam Yunianto, Pramana Yoga Saputra, Oleh Soleh, Martono, Sutriawan, Soekarman, Kodrat Mahatma, Bambang Siswoyo, & Aliyah. (2025). Deep Learning Teori Algoritma dan Aplikasi.

Pontoh, F. J. (2024). Teknik Deteksi Biometrika untuk Pengenalan Sidik Jari Menggunakan Deteksi Minutiae. Pixel :Jurnal Ilmiah Komputer Grafis, 17(1), 193–200. https://doi.org/10.51903/pixel.v17i1.2000

Pratama, Y., & Ramadhani, D. W. (2021). THE ROLE OF THE INDONESIAN AUTOMATIC FINGER PRINT IDENTIFICATION SYSTEM (INAFIS) IN IDENTIFYNG PERPETRATORS OF MURDER CASES (A CASES STUDY IN THE REGIONAL POLICE OF WEST SUMATERA). Activa Yuris: Jurnal Hukum, 1(2).

Septian Wiradharma, K. E., Agung, A., Dewi, S. L., & Suryani, L. P. (2023). PERANAN UNIT IDENTIFIKASI UNTUK MENGUNGKAP SUATU TINDAK PIDANA DALAM PROSES PENYIDIKAN. 4(1), 2746–5047. https://doi.org/10.55637/juinhum.4.1.6753.45-49

Setyowati, I., & Ariyani, I. S. (2018). POLICE ROLE IN IDENTIFYING FINGERPRINT BUSINESS CRIME (Studies in the Central Java) Police). https://doi.org/10.26532/jph.v5i3.3746

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Published

2026-08-01

How to Cite

Ni Putu Ayu Sri Laksmi, Eddy Muntina Dharma, & Ida Bagus Kresna Sudiatmika. (2026). Sistem Deteksi Otomatis Titik Minutiae Sidik Jari Berbasis MinutiaeNet : Studi Kasus Unit Identifikasi Polda Bali. Journal of Creative Student Research, 4(4), 242–260. https://doi.org/10.55606/jcsr-politama.v4i4.6528

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