Predicting Accounts Receivable of the Social Security Administration for Employment Using LSTM Algorithm

Authors

Pages Icon

DOI:

https://doi.org/10.63158/journalisi.v7i4.1274

Keywords:

Long Short-Term Memory (LSTM), Predictive Modeling, BPJS Ketenagakerjaan, Receivables Management, Financial Risk Management

Abstract

This study explores the use of Long Short-Term Memory (LSTM) networks for predicting outstanding contributions from employers to the BPJS Ketenagakerjaan, Indonesia’s social security agency. The research aims to address the challenges BPJS faces due to delayed or unpaid contributions, which impact the institution's operational stability and financial health. The LSTM model, a deep learning technique well-suited for time-series prediction, was applied to historical data from BPJS Ketenagakerjaan to predict overdue contributions across three different training-validation splits: 70:30, 80:20, and 90:10. The results demonstrate that the 80:20 split achieved the highest validation accuracy of 84.71%, offering the optimal balance between training data and model generalization. The model's ability to predict overdue contributions with high accuracy could significantly improve BPJS's receivables management, allowing for more proactive financial planning and risk mitigation. The study also highlights the integration of an attention mechanism within the LSTM model, enhancing its predictive capabilities by focusing on the most relevant historical data. This research contributes to the field of predictive analytics in public sector financial management, showcasing the potential of machine learning in enhancing the efficiency and effectiveness of social security programs.

Downloads

Download data is not yet available.

References

S. U. Adillah and A. Purnawan, “Pelaksanaan Program BPJS Ketenagakerjaan Bagi Pekerja Informal Di Bidang Peternakan Dan Pertanian Di Kecamatan Gamping Kabupaten Sleman,” Journal of Morality and Legal Culture, vol. 1, no. 1, pp. 1–14, 2016, doi: 10.20961/JMAIL.V1I1.44757.

Irna Sri Wijayanti, Sri Rahayu, and Rachmad Rachmad, “Analisis Pengelolaan Piutang Pasien BPJS Dalam Rangka Mengoptimalkan Kinerja Keuwangan Di Direktorat Keuangan RSAB Harapan Kita Jakarta,” Jurnal Manajemen dan Administrasi Rumah Sakit Indonesia (MARSI), vol. 1, no. 1, pp. 64–81, 2017.

Ghaesani Alifa, Nur Fitriyah, and Nungki Kartikasari, “Analisis Pengelolaan Piutang Jasa Pelayanan BPJS Dalam Rangka Mengoptimalkan Kinerja Keuangan (Studi Kasus Pada Rumah Sakit Cahaya Medika Praya),” Jurnal Riset Mahasiswa Akuntansi, vol. 3, no. 3, pp. 12–25, 2023.

D. Efriadi, R. Rahmaddeni, A. Agustin, and J. Junadhi, “Prediksi Penambahan Piutang Iuran Jaminan Sosial Ketenagakerjaan menggunakan Algoritma K-Nearest Neighbor,” Edumatic: Jurnal Pendidikan Informatika, vol. 6, no. 1, pp. 49–57, Jun. 2022, doi: 10.29408/EDUMATIC.V6I1.5255.

H. Hu, L. Tang, S. Zhang, and H. Wang, “Predicting the direction of stock markets using optimized neural networks with Google Trends,” Neurocomputing, vol. 285, no. 4, pp. 188–195, 2018, doi: 10.1016/J.NEUCOM.2018.01.038.

Ferdiansyah, S. H. Othman, R. Zahilah Raja Md Radzi, D. Stiawan, Y. Sazaki, and U. Ependi, “A LSTM-Method for Bitcoin Price Prediction: A Case Study Yahoo Finance Stock Market,” 2019 International Conference on Electrical Engineering and Computer Science (ICECOS), pp. 206–210, Oct. 2019, doi: 10.1109/ICECOS47637.2019.8984499.

F. Anggraeni, “Determinant of compliance in paying contributions of independent participants in BPJS Kesehatan at RSUD Haji Kota Makassar,” Jurnal Kesehatan Masyarakat, vol. 15, pp. 25–35, 2020.

Gultom A, “Pengaruh perkembangan makroekonomi Indonesia terhadap pertumbuhan kepesertaan BPJS Ketenagakerjaan,” Jurnal Ekonomi dan Pembangunan, vol. 14, pp. 112–125, 2016.

Dennis Indah Indra Putri, Lukytawati Anggraeni, and Widyastutik, “Faktor yang Mempengaruhi Kelancaran Pembayaran Premi BPJS Ketenagakerjaan (Studi Kasus Di Kota Tangerang),” Jurnal Ekonomi dan Kebijakan Pembangunan, vol. 12, pp. 84–100, 2023.

M. A. Majeed, H. Z. M. Shafri, A. Wayayok, and Z. Zulkafli, “Prediction of dengue cases using the attention-based long short-term memory (LSTM) approach,” Geospat Health, vol. 18, no. 1, p. 176, 2023, doi: 10.4081/GH.2023.1176.

S. Qiao, F. Gao, J. Wu, and R. Zhao, “An Enhanced Vehicle Trajectory Prediction Model Leveraging LSTM and Social-Attention Mechanisms,” IEEE Access, vol. 12, pp. 1718–1726, 2024, doi: 10.1109/ACCESS.2023.3345643.

T. Yu, Y. Zhang, S. Zhao, J. Yang, W. Li, and W. Guo, “Vessel trajectory prediction based on modified LSTM with attention mechanism,” 2024 4th International Conference on Neural Networks, Information and Communication (NNICE), pp. 912–918, 2024, doi: 10.1109/NNICE61279.2024.10498270.

Y. Chang, S. Zhang, and G. You, “Research on Electric Heating Energy Consumption Prediction Based on SSA-LSTM-Attention,” Academic Journal of Engineering and Technology Science, vol. 7, no. 4, 2024, doi: 10.25236/AJETS.2024.070416.

X. Wen and W. Li, “Time Series Prediction Based on LSTM-Attention-LSTM Model,” IEEE Access, vol. 11, pp. 48322–48331, 2023, doi: 10.1109/ACCESS.2023.3276628.

S. Sang and L. Li, “A Novel Variant of LSTM Stock Prediction Method Incorporating Attention Mechanism,” Mathematics, vol. 12, no. 7, Apr. 2024, doi: 10.3390/MATH12070945.

Downloads

Published

2025-12-09

Issue

Vol. 7 No. 4 (2025): December

Section

Articles

License

Authors Declaration

  1. The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
  2. The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
  3. The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
  4. The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
  5. The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
  6. The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
  7. If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
  8. The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights

How to Cite

[1]
A. Khansa and U. Ependi, “Predicting Accounts Receivable of the Social Security Administration for Employment Using LSTM Algorithm”, journalisi, vol. 7, no. 4, pp. 3361–3378, Dec. 2025, doi: 10.63158/journalisi.v7i4.1274.

Most read articles by the same author(s)