Deep Learning-Based Image Classification of B.1-KWK-Perseorangan Forms Using K-Means and Convolutional Neural Networks

Sandi Mahardika; Yuhandri; Mardison

doi:10.35134/komtekinfo.v13i3.702

Authors

Sandi Mahardika Komisi Pemilihan Umum Kota Sungai Penuh https://orcid.org/0009-0000-4127-3054
Yuhandri Universitas Putra Indonesia "YPTK" Padang
Mardison Universitas Putra Indonesia "YPTK" Padang

DOI:

https://doi.org/10.35134/komtekinfo.v13i3.702

Keywords:

Deep Learning, CNN, K-Means, Image Classification, B.1-KWK-Perseorangan Forms

Abstract

The verification process of B.1-KWK-Perseorangan forms at General Election Commission (KPU) is still conducted manually, resulting in time-consuming procedures and increasing the risk of document misclassification. The application of deep learning provides a promising solution for improving the efficiency and accuracy of administrative election document image classification. This study aims to analyse the capability of Convolutional Neural Networks (CNN) in classifying images of B.1-KWK-Perseorangan forms and to investigate the effect of K-Means as an initial image segmentation stage on improving classification accuracy and the effectiveness of document verification. The proposed method employs a deep learning approach by integrating K-Means for image segmentation and CNN for image classification. The research workflow consists of image preprocessing, segmentation, model training, and performance evaluation. The dataset comprises digital images of B.1-KWK-Perseorangan forms from the 2024 election, provided by the KPU of Sungai Penuh City, with an image resolution of 512 × 783 pixels. The experimental results demonstrate that the proposed model achieved an accuracy of 94% and a macro-average F1-score of 0.95, indicating that the integration of K-Means and CNN is effective for the classification of B.1-KWK-Perseorangan form images. Furthermore, the developed model has the potential to improve the speed, consistency, and accuracy of document verification within the KPU, while supporting the development of more effective, accurate, and efficient Deep Learning-based classification systems for election administrative documents.

References

D. Nimma and A. Uddagiri, “Advancements in Deep Learning Architectures for Image Recognition and Semantic Segmentation,” International Journal of Advanced Computer Science and Applications, vol. 15, no. 8, 2024, doi: 10.14569/IJACSA.2024.01508114.

J. Jareño, G. Bárcena-González, J. Castro-Gutiérrez, R. Cabrera-Castro, and P. L. Galindo, “Automatic labeling of fish species using deep learning across different classification strategies,” Front. Comput. Sci., vol. 6, Feb. 2024, doi: 10.3389/fcomp.2024.1326452.

X. Zhang, D. Miao, and L. Guo, “Flame Image Classification Based on Deep Learning and Three-Way Decision-Making,” Forests, vol. 16, no. 3, p. 544, Mar. 2025, doi: 10.3390/f16030544.

S. M. Saqib, O. Muhammad, T. Mazhar, M. Iqbal, S. Guizani, and H. Hamam, “Medicine image classification using deep learning: highlighting the MedNet-MoBiL hybrid model,” Discover Computing, vol. 28, no. 1, p. 103, Jun. 2025, doi: 10.1007/s10791-025-09619-w.

C. Liu, “A Review of Digital Image Processing Techniques and Future Prospects,” International Journal of Computer Science and Information Technology, vol. 4, no. 3, pp. 223–233, Dec. 2024, doi: 10.62051/ijcsit.v4n3.22.

I. Basyari and D. P. Nastitie, “Silon Bermasalah, Tahapan Verifikasi Dukungan Bakal Calon Perseorangan Diperpanjang,” Kompas. Accessed: Dec. 20, 2025. [Online]. Available: https://www.kompas.id/artikel/silon-bermasalah-tahapan-verifikasi-dukungan-bakal-calon-perseorangan-diperpanjang

P. B. Damanik, “SERTIFIKASI ELEKTRONIK DAN DIGITAL SIGNATURE: UPAYA PENGURANGAN SAMPAH PEMILU.” Accessed: Apr. 15, 2026. [Online]. Available: https://journal.kpu.go.id/index.php/TKP/article/view/2260/176

Komisi Pemilihan Umum Republik Indonesia, “Peraturan Komisi Pemilihan Umum Nomor 8 Tahun 2024 Tentang Pencalonan Gubernur Dan Wakil Gubernur, Bupati Dan Wakil Bupati, Serta Walikota Dan Wakil Walikota,” 2024.

M. A. Muhammad, F. Febrianto, R. Annisa, S. Ferbangkara, P. Wijaya, and M. R. Afif dan A M Putri, “Seminar Nasional Ilmu Teknik dan Aplikasi Industri (SINTA) Implementasi Convolutional Neural Network (CNN) untuk Identifikasi Spesifik Cassia fistula dalam Mendukung Pelestarian Hutan,” 2024.

W. P. Kusumo and C. S. K. Aditya, “Klasifikasi Citra Makanan Berdasarkan Asal Daerah Menggunakan Convolutional Neural Network,” Techno.Com, vol. 23, no. 1, pp. 87–95, Feb. 2024, doi: 10.62411/tc.v23i1.9735.

B. Suswati, “Implementasi Convolutional Neural Network pada Klasifikasi Citra Daun Tanaman Hias,” Decode: Jurnal Pendidikan Teknologi Informasi, vol. 4, no. 2, pp. 554–565, Jun. 2024, doi: 10.51454/decode.v4i2.590.

N. Yasmin, S. Chairunnissa Deliva Akbar, and A. Ramadhanu, “Penerapan K-Means Clustering untuk Klasifikasi Citra Cabai Keriting: Studi Ekstraksi Warna dan Tekstur GLCM,” Indonesian Journal Computer Science, vol. 3, no. 2, pp. 65–71, Oct. 2024, doi: 10.31294/ijcs.v3i2.5758.

M. Dicky Imansyah and A. Ramadhanu, “Implementasi Algoritma K-Means untuk Klasifikasi Citra Biota Laut: Gurita, Lobster, dan Kerang Laut,” Jurnal Teknologi Dan Sistem Informasi Bisnis-JTEKSIS, vol. 7, no. 4, pp. 507–513, 2025, doi: 10.47233/jteksis.v7i4.2271.

N. M. Aznawi, M. I. Setiadi, Z. Aina, S. Manullang, and S. N. Rahmadiyah, “Implementasi K-Means Clustering pada Citra Digital Tomat untuk Identifikasi Kondisi Segar dan Busuk,” Journal of Students‘ Research in Computer Science, vol. 6, no. 1, pp. 59–68, May 2025, doi: 10.31599/srtqmw49.

W. Wirdawati, S. Yulihartati, and A. Ramadhanu, “Implementasi Euclidean Distance dan Segmentasi K-Means Clustering Pada Identifikasi Citra Jeruk Nipis,” Indonesian Journal Computer Science, vol. 3, no. 2, pp. 72–79, Oct. 2024, doi: 10.31294/ijcs.v3i2.5600.

I. D. Mienye and T. G. Swart, “A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications,” Information, vol. 15, no. 12, p. 755, Nov. 2024, doi: 10.3390/info15120755.

A. Antoni, T. Rohana, and A. R. Pratama, “Implementasi Algoritma Convolutional Neural Network Untuk Klasifikasi Citra Kemasan Kardus Defect dan No Defect,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 4, Mar. 2023, doi: 10.47065/bits.v4i4.3270.

A. A. Handoko, M. A. Rosid, and U. Indahyanti, “Implementasi Convolutional Neural Network (CNN) Untuk Pengenalan Tulisan Tangan Aksara Bima,” SMATIKA JURNAL, vol. 14, no. 01, pp. 96–110, Jul. 2024, doi: 10.32664/smatika.v14i01.1196.

R. Saputra, A. Ramadhanu, and R. Sovia, “Sistem Identifikasi Citra Huruf Aksara Minangkabau Berbasis Convolutional Neural Network,” Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence), vol. 5, no. 2, pp. 335–348, Aug. 2023, doi: 10.55382/jurnalpustakaai.v5i2.1214.

D. D. Indriani.S, E. J. A. Sinaga, G. Oktavia, H. Syahputra, and F. Ramadhani, “Identifikasi Tanda Tangan Dengan Menggunakan Metode Convolution Neural Network (CNN),” J-INTECH, vol. 12, no. 1, pp. 138–147, Jun. 2024, doi: 10.32664/j-intech.v12i1.1273.

L. Hakim, H. R. Rahmanto, S. P. Kristanto, and D. Yusuf, “KLASIFIKASI CITRA MOTIF BATIK BANYUWANGI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK,” Jurnal Teknoinfo, vol. 17, no. 1, p. 203, Jan. 2023, doi: 10.33365/jti.v17i1.2342.

F. Lipa Payon, “Segmentasi Citra CT-Scan Pada Stroke Menggunakan Metode Algoritma K-Means Clustering dan Thresholding di Rumah Sakit Samarinda Medika Citra,” Jurnal Ilmu dan Inovasi Fisika, vol. 8, no. 2, pp. 17–23, Aug. 2024, doi: 10.24198/jiif.v8i2.54408.

K. D. Linda, K. Kusrini, and A. D. Hartanto, “Studi Literatur Mengenai Klasifikasi Citra Kucing Dengan Menggunakan Deep Learning: Convolutional Neural Network (CNN),” Journal of Electrical Engineering and Computer (JEECOM), vol. 6, no. 1, pp. 129–137, May 2024, doi: 10.33650/jeecom.v6i1.7480.

P. A. Suri, M. A. Setiono, A. Andrew, and M. Fajar, “Comparative Study of CNN-based Deep Learning Models for Animal, Digit, and Flower Image Classification,” Engineering, MAthematics and Computer Science Journal (EMACS), vol. 7, no. 3, pp. 321–326, Sep. 2025, doi: 10.21512/emacsjournal.v7i3.14317.