Digitalisasi Model Altman Z-Score untuk Prediksi Financial Distress Perusahaan Negara Non-Manufaktur Berbasis Data Mining

Eka Patriya; Handayani; Rini  Arianty

doi:10.51454/decode.v5i2.1173

Authors

Eka Patriya Manajemen Universitas Gunadarma
Handayani Manajemen Universitas Gunadarma
Rini Arianty Sistem Informasi Universitas Gunadarma

DOI:

https://doi.org/10.51454/decode.v5i2.1173

Keywords:

Altman Z-Score, Financial Distress, PT Aneka Tambang Tbk, Rasio, Working Capital

Abstract

Penelitian ini bertujuan untuk mendigitalisasi model Altman Z-Score dalam memprediksi potensi financial distress pada PT Aneka Tambang Tbk sebagai perusahaan negara non-manufaktur. Studi ini menggunakan data laporan keuangan tahunan periode 2019–2023 yang diperoleh dari Bursa Efek Indonesia. Proses awal meliputi normalisasi data melalui konversi tipe string menjadi numerik, penghapusan simbol mata uang, serta penambahan kolom EBIT dan working capital. Metode Altman Z-Score non-manufaktur diterapkan dengan menghitung empat rasio utama: X1 (WCTA), X2 (RETA), X3 (EBITTA), dan X4 (BVE/TL), kemudian dikalikan dengan koefisien masing-masing untuk memperoleh nilai Z-Score. Hasil analisis menunjukkan bahwa seluruh nilai Z-Score PT Aneka Tambang Tbk selama lima tahun berada di atas 2,6, yang menempatkan perusahaan secara konsisten dalam kategori safe zone. Tren Z-Score yang meningkat tiap tahun mengindikasikan perbaikan kesehatan keuangan perusahaan. Visualisasi interaktif menggunakan matplotlib memperkuat interpretasi hasil. Penelitian ini membuktikan bahwa digitalisasi model prediksi financial distress berbasis data efektif meningkatkan efisiensi analisis dan pengambilan keputusan. Pengembangan sistem ini diharapkan dapat diintegrasikan ke dalam sistem pelaporan keuangan digital untuk pemantauan keuangan real-time yang lebih akurat dan bermanfaat.

References

Abrahamsen, N. G. B., Nylén-Forthun, E., Møller, M., de Lange, P. E., & Risstad, M. (2024). Financial Distress Prediction in the Nordics: Early Warnings from Machine Learning Models. Journal of Risk and Financial Management, 17(10). https://doi.org/10.3390/jrfm17100432

ANTAM. (2019). Company Report - ANTAM Aneka Tambang (Persero) Tbk. (Issue JANUARY).

ANTAM. (2023). Laporan Tahunan 2023: Memperkuat Fundamental Bisnis Menuju Perusahaan Global Terkemuka. Www.Antam.Com. https://www.antam.com/en/reports/annual-reports

Aswirah, A., & Imaduddin, I. (2024). Z-Score Analysis as a Tool for Predicting Financial Distress in Insurance Sector Companies Listed on the Indonesia Stock Exchange. Journal La Bisecoman, 05(01), 016–025. https://doi.org/10.37899/journallabisecoman.v5i6.1867

Fakhar, S., Khan, F. M., Tabash, M. I., Ahmad, G., Akhter, J., & Al-Absy, M. S. M. (2023). Financial distress in the banking industry: A bibliometric synthesis and exploration. Cogent Economics and Finance, 11(2), 1–24. https://doi.org/10.1080/23322039.2023.2253076

Issa, S., Bizel, G., Jagannathan, S. K., & Gollapalli, S. S. C. (2024). A Comprehensive Approach to Bankruptcy Risk Evaluation in the Financial Industry. Journal of Risk and Financial Management, 17(1). https://doi.org/10.3390/jrfm17010041

Kaikan, K. (2022). Menciptakan masa depan bersama. 1–3.

Kalogiannidis, S., Chatzitheodoridis, F., Giannarakis, G., & Mavrommati, A. (2022). Business Organizations’ Flexibility as an Innovation Tool: Factors Affecting Flexibility in Organizations. Journal of Logistics, Informatics and Service Science, 9(4), 259–312. https://doi.org/10.33168/LISS.2022.0417

Kim, K., & Sumner, A. (2021). Bringing state-owned entities back into the industrial policy debate: The case of Indonesia. Structural Change and Economic Dynamics, 59, 496–509. https://doi.org/10.1016/j.strueco.2021.10.002

Liu, W., Suzuki, Y., & Du, S. (2025). Ensemble learning algorithms based on easyensemble sampling for financial distress prediction. Annals of Operations Research, 346(3), 2141–2172. https://doi.org/10.1007/s10479-025-06494-y

Lokanan, M. E., & Ramzan, S. (2024). Predicting financial distress in TSX-listed firms using machine learning algorithms. Frontiers in Artificial Intelligence, 7(November), 1–19. https://doi.org/10.3389/frai.2024.1466321

Mind ID. (2022). Unleashing Indonesia’s Sustainable Practices. https://mind.id/temp/20221201-SRMIND21.pdf

Movsesyan, S., & Seissian, L. A. (2025). Analysis of Financial Distress Factors in the Mining Industry: Empirical Study of the Developing Market. Journal of Governance and Regulation, 14(1), 218–229. https://doi.org/10.22495/jgrv14i1art20

Nguyen, T. V., Tram Le, T. H., & Huong Le, T. X. (2023). Application of the Altman Z-Score Model in Assessing the Financial Health of Garment Manufacturing Enterprises Listed on the Vietnam Stock Market. International Journal of Multidisciplinary Research and Analysis, 06(06), 2687–2692. https://doi.org/10.47191/ijmra/v6-i6-67

Pambudi, D. S. (2022). Implementasi Etika Bisnis Islam Di Industri Pengecoran Logam Batur, Ceper, Klaten. Jurnal Ekonomi Islam, 8(2), 201–226. c.org/10.35891/ml.v8i2.597

Rachman, R. A. (2022). The Determinants of Financial Distress in Emerging Country: Empirical Evidence from Indonesia. Jurnal Keuangan Dan Perbankan, 26(4), 735–747. https://doi.org/10.26905/jkdp.v26i4.7891

Sorout, A., & Singh, N. P. (2025). Bankruptcy Prediction Using The Altman Z-Score Modification Model in India : A Case Study of Bharti Airtel Limited. 0. https://doi.org/10.20473/sabr.v3i1.63958

Srinivas, T. (2023). Using the Altman Z-Score Model to Forecast the Financial Distress of a Subset of NIFTY 50 Companies in the Indian Stock Market. Qeios, 1–10. https://doi.org/10.32388/xn0wtw

Toly, A. A., Permatasari, R., & Wiranata, E. (2020). The Effect of Financial Ratio (Altman Z-Score) on Financial Distress Prediction in Manufacturing Sector in Indonesia 2016-2018. 144(Afbe 2019), 47–53. https://doi.org/10.2991/aebmr.k.200606.008

Ummah, M. S. (2019). ANTAM Recorded the Company All-Time High of Revenue in 2024 and Increased Profit for the Year of Rp3.85 Trillion. Sustainability (Switzerland), 11(1), 1–14.

Yunisa, R., & Santi, F. (2023). Analysis Financial Distress Potential in Asean Industrial Companies Using Altman Z-Score and Springate Methods. East Asian Journal of Multidisciplinary Research, 2(12), 4993–5008. https://doi.org/10.55927/eajmr.v2i12.6902