Integrasi Strategi Pre-processing Data untuk Optimalisasi Akurasi Algoritma Backpropagation

  • Widodo Saputra STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Saifullah Saifullah STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Eka Irawan STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Anjar Wanto * Mail STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
DOI: https://doi.org/10.47065/explorer.v6i2.2743
Keywords: Backpropagation; data pre-processing; artificial neural network; accuracy; prediction

Abstract

Backpropagation is one of the artificial neural network algorithms widely used in classification and prediction processes due to its ability to recognize data patterns accurately. However, the performance of this algorithm is highly influenced by the quality of the input data. Unstructured data, differences in data scales, missing values, and irrelevant features can reduce the model’s accuracy. This study aims to analyze the effect of integrating data pre-processing strategies to optimize the accuracy of the Backpropagation algorithm. The dataset used in this research was obtained from the Badan Pusat Statistik (BPS) in the form of Open Unemployment Rate data for the population aged 15 years and above in North Sumatra Province from 2019 to 2024. The applied pre-processing stages included data cleaning, normalization, missing value handling, and feature reduction. The research method was conducted by comparing the model testing results using standard pre-processing and partial pre-processing on several network architectures. The results showed that the implementation of pre-processing strategies was able to improve the performance of the Backpropagation model. The highest accuracy value was obtained in the 3-56-1 architecture with an increase from 80.00% to 85.88%. In addition to improving accuracy, the model training process became more stable and the error convergence was achieved faster. Therefore, the integration of data pre-processing strategies has proven to be effective in optimizing the accuracy of the Backpropagation algorithm for numerical data-based prediction problems

References

[1] P. Prihandoko and P. Alkhairi, “Optimasi JST Backpropagation dengan Adaptive Learning Rate Dalam Memprediksi Hasil Panen Padi,” Jurasik (Jurnal Ris. Sist. Inf. dan Tek. Inform., vol. 10, no. 1, p. 441, 2025, doi: 10.30645/jurasik.v10i1.887.
[2] E. Sofiati, “Optimasi Prediksi Pencetakan E-Ktp: Penerapan Revolusioner Jaringan Syaraf Tiruan Dengan Metode Backpropagation,” JIKO (Jurnal Inform. dan Komputer), vol. 9, no. 1, p. 68, 2025, doi: 10.26798/jiko.v9i1.1420.
[3] F. Marwati and R. Fauzi, “Prediksi Penyakit Diabetes Melitus Menggunakan Jaringan Syaraf Tiruan Dengan Metode Backpropagation,” Jitu J. Inform. Utama Hal, vol. 2, no. 1, pp. 26–34, 2024.
[4] D. Kurniasari, M. N. A. Rafdiono, and W. Warsono, “Analisis Struktur Terbaik Neural Network dengan Algoritma Backpropagation dalam Memprediksi Indeks Kandungan Sulfida (SO2) di Ibu Kota Jakarta,” J. Sist. dan Teknol. Inf., vol. 12, no. 2, p. 321, 2024, doi: 10.26418/justin.v12i2.76166.
[5] F. K. Wardhana et al., “Penerapan Backpropagation Jaringan Saraf Tiruan untuk Prediksi Diabetes Menggunakan Dataset Pima Indians,” Pros. Semin. Nas. AMIKOM Surakarta, vol. 2, no. November, pp. 331–44, 2024.
[6] Nico Nico and Pipin Farida Ariyani, “Implementasi Metode Jaringan Saraf Tiruan Backpropagation Untuk Memprediksi Harga Emas,” J. Ticom Technol. Inf. Commun., vol. 13, no. 2, pp. 85–91, 2025, doi: 10.70309/ticom.v13i2.145.
[7] M. R. Raharjo, Indra Riyana Rahadjeng, M. N. H. Siregar, and P. Alkhairi, “Optimasi Fungsi Aktivasi pada Artificial Neural Network untuk Prediksi Gagal Jantung Secara Akurat,” Explorer (Hayward)., vol. 5, no. 1, pp. 42–51, 2025, doi: 10.47065/explorer.v5i1.1840.
[8] U. Bumigora, L. Heri Aguswandi, D. Syafitri, and T. T. Sujaka, “Seminar Nasional Teknologi, Inovasi dan Ekonomi PEMODELAN PREDIKTIF MENGGUNAKAN ALGORITMA BACKPROPAGATION,” pp. 149–157.
[9] D. K. P. Wee, G. M. V. Kawung, and I. Masloman, “Analisis Faktor-Faktor yang Mempengaruhi Tingkat Pengangguran Terbuka dan Kemiskinan di Kabupaten Minahasa,” J. Berk. Ilm. Efisiensi, vol. 24, no. 2, pp. 133–145, 2024.
[10] L. Hakim, Hailuddin, and E. Agustiani, “Pengaruh Tingkat Pengangguran Terbuka Terhadap Jumlah Kemiskinan Di Provinsi Ntb Pada Tahun 2010-2021,” J. Econ. Bus., vol. 10, no. 1, pp. 21–32, 2024, doi: 10.29303/ekonobis.v10i1.158.
[11] M. Azhima, I. Afrianty, E. Budianita, and S. Kurnia Gusti, “Penerapan Metode Backpropagation Neural Network untuk Klasifikasi Penyakit Stroke,” Media Online), vol. 4, no. 6, pp. 3013–3021, 2024.
Dimensions Badge
Published
2026-07-25
Issue
Vol 6 No 2 (2026): July 2026
Section
Articles

Copyright (c) 2026 Widodo Saputra, Saifullah Saifullah, Eka Irawan, Anjar Wanto

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors retain copyright and grant the EXPLORER right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) the work for any purpose, even commercially with an acknowledgement of the work's authorship and initial publication in EXPLORER.

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 acknowledgement of its initial publication in EXPLORER.

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 (See The Effect of Open Access).