Comparing the Prediction of Numeric Patterns on Form C1 Using the K-Nearest Neighbors (K-NN) Method and a Combination of K-Nearest Neighbors (K-NN) with Connected Component Labeling (CCL)

Uci Suriani; Tri Basuki Kurniawan

doi:10.51519/journalisi.v5i4.592

Uci Suriani Universitas Bina Darma, Indonesia
Tri Basuki Kurniawan Universitas Bina Darma, Indonesia

Keywords: Predicting numeric patterns, K-Nearest Neighbors Method (K-NN), Connected Component Labeling (CCL), General Election Commission Form C1

Abstract

Indonesia's elections serve as a cornerstone of its democratic system, with the active participation of its citizens being of paramount importance. To bolster transparency and civic engagement during these elections, the SITUNG system (Election Result Information System) is employed for the tabulation of election results. However, the current tabulation process remains manual, potentially leading to data entry errors and a reduced accuracy of election outcomes. This research endeavor seeks to enhance the efficiency and accuracy of election result tabulation by employing the K-Nearest Neighbors (K-NN) method for recognizing numeric patterns on Form C1, both independently and in combination with Connected Component Labeling (CCL). The K-NN method demonstrates a commendable 60.0% accuracy in recognizing numeric patterns from the original Form C1 data. However, when combined with CCL, the accuracy drops to 51.2%. This research makes a significant contribution by simplifying the tabulation process and improving the accuracy of election results in Indonesia through the application of the K-NN method. The technology is anticipated to fortify democracy by promoting a more transparent and participatory electoral process for the citizens.

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References

M. Haboddin, Pemilu dan partai politik di Indonesia. Universitas Brawijaya Press, 2016.

A. S. Alam and M. I. Sultan, “Keterbukaan informasi publik melalui sistem penghitungan (situng) online hasil pilkada terhadap pengetahuan, sikap, dan perilaku masyarakat di Kota Palu,” KAREBA J. Ilmu Komun., pp. 92–103, 2016.

A. Priyono, “Eksistensi Putusan Badan Pengawas Pemilu Republik Indonesia Nomor 07/LP/PP/ADM/RI/00.00/V2019 Terhadap Pelanggaran Tata Cara dan Prosedur Dalam Input Data Sistem Informasi Penghitungan Suara (SITUNG) Pemilu 2019 yang Tidak Dilaksanakan Oleh KPU Berdasarkan Undang-Undang Nomor 7 Tahun 2017 Tentang Pemilu,” 2020.

M. Fadhilla, M. R. A. Saf, and D. S. S. Sahid, “Pengenalan kepribadian seseorang berdasarkan pola tulisan tangan menggunakan jaringan saraf tiruan,” J. Nas. Tek. Elektro Dan Teknol. Inf. JNTETI, vol. 6, no. 3, pp. 365–373, 2017.

R. S. Akbar Eko Adi, “Studi Analisis Pengenalan Pola Tulisan Tangan Angka Arabic (Indian) menggunakan Metode K- Nearest Neighbors dan Connected Component Labeling,” Din. Rekayasa, no. Vol 12, No 2 (2016): Dinamika Rekayasa-Agustus 2016, pp. 45–51, 2016.

A. Husain, A. H. A. Prastian, and A. Ramadhan, “Perancangan Sistem Absensi Online Menggunakan Android Guna Mempercepat Proses Kehadiran Karyawan Pada PT. Sintech Berkah Abadi,” Technomedia J., vol. 2, no. 1, pp. 105–116, 2017.

R. Akbar and E. A. Sarwoko, “Aplikasi Pengenalan Pola Tulisan Tangan Angka Arabic (Indian) menggunakan Metode Connected Component Labeling dan Template Matching,” 2016.

A. Budianto, R. Ariyuana, and D. Maryono, “Perbandingan K-Nearest Neighbor (KNN) Dan Support Vector Machine (SVM) Dalam Pengenalan Karakter Plat Kendaraan Bermotor,” J. Ilm. Pendidik. Tek. Dan Kejuru., vol. 11, no. 1, pp. 27–35, 2018.

A. Budianto, T. B. Adji, and R. Hartanto, “Deteksi nomor kendaraan dengan metode connected component dan SVM,” J. Teknol. Inf. Magister, vol. 1, no. 01, pp. 106–117, 2016.

H. Masrani and I. R. Ilhamsyah, “Aplikasi Pengenalan Pola pada Huruf Tulisan Tangan Menggunakan Jaringan Saraf Tiruan dengan Metode Ekstraksi Fitur Geometri,” Coding J. Komput. Dan Apl., vol. 6, no. 2, 2018.

Vinita Chandani, Romi Satria Wahono, and Purwanto Purwanto, “Komparasi Algoritma Klasifikasi Machine Learning dan Feature Selection pada Analisis Sentimen Review Film,” J. Intell. Syst., vol. 1, no. 1, pp. 56–60, 2015.

M. Lestari, “Penerapan algoritma klasifikasi Nearest Neighbor (K-NN) untuk mendeteksi penyakit jantung,” Fakt. Exacta, vol. 7, no. 4, pp. 366–371, 2015.

L. A. R. Hakim, A. A. Rizal, and D. Ratnasari, “Aplikasi Prediksi Kelulusan Mahasiswa Berbasis K-Nearest Neighbor (K-NN),” JTIM J. Teknol. Inf. Dan Multimed., vol. 1, no. 1, pp. 30–36, 2019.

R. K. Dinata, F. Fajriana, Z. Zulfa, and N. Hasdyna, “Klasifikasi Sekolah Menengah Pertama/Sederajat Wilayah Bireuen Menggunakan Algoritma K-Nearest Neighbors Berbasis Web,” CESS J. Comput. Eng. Syst. Sci., vol. 5, no. 1, pp. 33–37, 2020.

A. Muzakir, H. Syaputra, and F. Panjaitan, “A Comparative Analysis of Classification Algorithms for Cyberbullying Crime Detection: An Experimental Study of Twitter Social Media in Indonesia,” Sci. J. Informatics; Vol 9, No 2 Novemb. 2022, doi: 10.15294/sji.v9i2.35149 , Oct. 2022.

U.Ependi, A.F.Rochim, A.Wibowo, “A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods,” International Journal of Intelligent Engineering and Systems, vol. 16, no.3, pp. 345-361, 2023.