Main Article Content
Abstract
Purpose: This study analyzes the solvency of PT Telkom Indonesia (Persero) Tbk and assesses the adequacy of documentary evidence regarding the use of predictive analytics in financial decision-making.
Research Method: This study employs a descriptive case study approach with a documentary analysis of the audited consolidated financial statements for 2025 and the restated comparative figures for 2024. The analysis covers the liability structure, profitability, free cash flow, lease-adjusted leverage, net debt, and cost of capital coverage.
Results and Discussion: Long-term liabilities increased by 4.72%, while operating income decreased by 16.42% and the TIER proxy fell from 7.96 to 6.66 times. Conversely, operating cash flow increased by 3.64%, net financial debt decreased, and the debt-to-equity ratio (DER), adjusted for leases, remained relatively stable at 49.76%. The analyzed document does not provide specifications or validation of the predictive model; therefore, the effectiveness of its implementation cannot be concluded.
Implications: Solvency assessments need to use multidimensional indicators and verifiable disclosures regarding model governance.
Originality: This study identifies the empirical boundary between descriptive financial analysis and predictive analytics in assessing the solvency of telecommunications companies.
Keywords
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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- Dewi, E. V., & Wessiani, N. A. (2021). Analisis penilaian usaha dan manajemen risiko pada keputusan kelayakan investasi dengan mempertimbangkan ketidakpastian (Studi kasus: Akuisisi jalan tol oleh PT X). Jurnal Teknik ITS, 10(2), E121–E128.
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- Kanzola, A.-M., Papaioannou, K., & Petrakis, P. E. (2024). Exploring the other side of innovative managerial decision-making: Emotions. Journal of Innovation & Knowledge, 9(4), 100588. https://doi.org/https://doi.org/10.1016/j.jik.2024.100588
- Kebede, T. N., Tesfaye, G. D., & Erana, O. T. (2024). Determinants of financial distress: evidence from insurance companies in Ethiopia. Journal of Innovation and Entrepreneurship, 13(1), 17. https://doi.org/10.1186/s13731-024-00369-5
- Kraus, A., & Litzenberger, R. H. (1973). A State-Preference Model of Optimal Financial Leverage. The Journal of Finance, 28(4), 911–922. https://doi.org/10.2307/2978343
- Lindner, T., Puck, J., & Puhr, H. (2025). Artificial intelligence in international business: IB theory under augmented decision-making. Journal of World Business, 60(6), 101676. https://doi.org/https://doi.org/10.1016/j.jwb.2025.101676
- Magfiroh, D., & Komarudin, K. (2025). Leadership Strategy For Addressing Digital Disruptions: A Case Study in Banking Companies. Leadership Insights and Innovation Journal, 1(1), 36–42. https://doi.org/10.59261/jequi.v6i2.227
- Maidiana, M., & Harahap, S. P. R. (2021). Pembuatan Keputusan Dalam Proses Manajemen Dan Aspek Manajemen. Journal Ability:: Journal of Education and Social Analysis, 2(3). https://doi.org/10.51178/jesa.v2i3.222
- Marlina, D., & Bakri, M. (2021). Penerapan Data Mining Untuk Memprediksi Transaksi Nasabah Dengan Algoritma C4.5. J. Teknol. Dan Sist. Inf, 2(1), 23–28.
- Purwanti, D. (2023). The Strategic Imperative of Treasury and Financial Risk Management in a Volatile Economic Landscape. Advances in Management & Financial Reporting, 1(3 SE-Articles), 119–128. https://doi.org/10.60079/amfr.v1i3.224
- Rismaninda Putri Dwi Prasetya, Azizah, R. N., Halwa, J. B. W., Nugroho, R. H., & Kusumasari, I. R. (2024). Implementasi Penggunaan Data Analytics untuk Mengoptimalkan Pengambilan Keputusan Bisnis di Era Digital. Jurnal Bisnis dan Komunikasi Digital, 2(2 SE-Articles), 12. https://doi.org/10.47134/jbkd.v2i2.3459
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- Sætra, H. S. (2023). The AI ESG protocol: Evaluating and disclosing the environment, social, and governance implications of artificial intelligence capabilities, assets, and activities. Sustainable Development, 31(2), 1027–1037. https://doi.org/10.1002/sd.2438
- Shang, D., Yuan, D., Wu, X., & Li, D. (2024). Can digital transformation alleviate corporate fraud? Evidence from China. Internet Research, 35(4), 1554–1583. https://doi.org/10.1108/INTR-01-2024-0031
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- Steinhardt, G. (2024). Data-driven Decision-making for Product Managers: A Primer to Data Literacy in Product Management. Springer Nature. https://doi.org/10.1007/978-3-031-74664-2
- Sun, B., Zhang, Y., Zhu, K., Mao, H., & Liang, T. (2024). Is faster really better? The impact of digital transformation speed on firm financial distress: Based on the cost-benefit perspective. Journal of Business Research, 179, 114703. https://doi.org/https://doi.org/10.1016/j.jbusres.2024.114703
- Szukits, Á. (2022). The illusion of data-driven decision making – The mediating effect of digital orientation and controllers’ added value in explaining organizational implications of advanced analytics. Journal of Management Control, 33(3), 403–446. https://doi.org/10.1007/s00187-022-00343-w
- Szukits, Á., & Móricz, P. (2024). Towards data-driven decision making: the role of analytical culture and centralization efforts. Review of Managerial Science, 18(10), 2849–2887. https://doi.org/10.1007/s11846-023-00694-1
- Turi, J. A., Khwaja, M. G., Tariq, F., & Hameed, A. (2023). The role of big data analytics and organizational agility in improving organizational performance of business processing organizations. Business Process Management Journal, 29(7), 2081–2106. https://doi.org/10.1108/BPMJ-01-2023-0058
- Yavuz, M. S., Tatlı, H. S., & Bozkurt, G. (2025). Exploring the financial impact of digital transformation: A comprehensive analysis on firms. Journal of Innovation & Knowledge, 10(5), 100795. https://doi.org/https://doi.org/10.1016/j.jik.2025.100795
- Zareie, M., Attig, N., El Ghoul, S., & Fooladi, I. (2024). Firm digital transformation and corporate performance: The moderating effect of organizational capital. Finance Research Letters, 61, 105032. https://doi.org/https://doi.org/10.1016/j.frl.2024.105032
- Zhao, J., Ouenniche, J., & De Smedt, J. (2024). Survey, classification and critical analysis of the literature on corporate bankruptcy and financial distress prediction. Machine Learning with Applications, 15, 100527. https://doi.org/https://doi.org/10.1016/j.mlwa.2024.100527
- Zhou, F., Fu, L., Li, Z., & Xu, J. (2022). The recurrence of financial distress: A survival analysis. International Journal of Forecasting, 38(3), 1100–1115. https://doi.org/https://doi.org/10.1016/j.ijforecast.2021.12.005
References
Abdelkader, N. A. M., & Wahba, H. H. (2024). A proposed multidimensional model for predicting financial distress: an empirical study on Egyptian listed firms. Future Business Journal, 10(1), 42. https://doi.org/10.1186/s43093-024-00328-2
Al-Khazaleh, S., Badwan, N., Qubbaj, I., & Almashaqbeh, M. (2024). Level of financial disclosures for listed insurance companies using ISO 31000: empirical evidence from Jordan and Palestine. Asian Review of Accounting, 33(2), 386–407. https://doi.org/10.1108/ARA-05-2024-0151
Arhinful, R., & Radmehr, M. (2023). The effect of financial leverage on financial performance: evidence from non-financial institutions listed on the Tokyo stock market. Journal of Capital Markets Studies, 7(1), 53–71. https://doi.org/10.1108/JCMS-10-2022-0038
Arifin, P. D. A., Syahdan, R., & Sinen, K. (2024). Analisis Pinjaman Daerah Dalam Pembiayaan Pembangunan Pada Daerah Kabupaten Halmahera Utara. Jurnal Ilmiah Akuntansi dan Keuangan (JIAKu), 3(3), 233–239. https://doi.org/10.24034/jiaku.v3i2.6462
Asrul, A., Windayani, W., Putra, A., Bahar, H., Baihaqi, B., Ladianto, A. J., Pebrianti, H., & Qadri, M. S. (2024). Pemanfaatan Big Data Analytics dalam Proses Manajemen Teknologi untuk Prediksi Permintaan Pasar. Jurnal Minfo Polgan, 13(2), 2433–2438. https://doi.org/10.33395/jmp.v13i2.14516
Bedford, D. S., Derichs, D., Hoozée, S., Malmi, T., Messner, M., Sinha, V. K., Van der Kolk, B., & Verbeeten, F. (2025). Digitalization of the finance function: Automation, analytics, and finance function effectiveness. Management Accounting Research, 67, 100942. https://doi.org/https://doi.org/10.1016/j.mar.2025.100942
Chen, G., Liu, P., & Jiang, A. (2024). Does digital transformation in enterprises reduce debt default risk? International Review of Economics & Finance, 96, 103529. https://doi.org/https://doi.org/10.1016/j.iref.2024.103529
Chouaibi, J., Benmansour, H., Ben Fatma, H., & Zouari-Hadiji, R. (2023). Does environmental, social, and governance performance affect financial risk disclosure? Evidence from European ESG companies. Competitiveness Review, 34(6), 1057–1076. https://doi.org/10.1108/CR-07-2023-0181
Cui, L., & Wang, Y. (2023). Can corporate digital transformation alleviate financial distress? Finance Research Letters, 55, 103983. https://doi.org/https://doi.org/10.1016/j.frl.2023.103983
Dainelli, F., Bet, G., & Fabrizi, E. (2024). The financial health of a company and the risk of its default: Back to the future. International Review of Financial Analysis, 95, 103449. https://doi.org/https://doi.org/10.1016/j.irfa.2024.103449
Dewi, E. V., & Wessiani, N. A. (2021). Analisis penilaian usaha dan manajemen risiko pada keputusan kelayakan investasi dengan mempertimbangkan ketidakpastian (Studi kasus: Akuisisi jalan tol oleh PT X). Jurnal Teknik ITS, 10(2), E121–E128.
Elgendy, N., Elragal, A., & Päivärinta, T. (2023). Evaluating collaborative rationality-based decisions: a literature review. Procedia Computer Science, 219, 647–657. https://doi.org/https://doi.org/10.1016/j.procs.2023.01.335
Erjha, M., Sari, M. M., Febriani, V., Rapenia, V., & Aqilah, A. (2023). Dampak Rasio Solvabilitas Dalam Keputusan Pendanaan Perusahaan. Jurnal Bisnis Manajemen dan Akuntansi (BISMAK), 3(2), 104–112. https://doi.org/10.47701/bismak.v3i2.2928
Hajek, P., & Munk, M. (2024). Corporate financial distress prediction using the risk-related information content of annual reports. Information Processing & Management, 61(5), 103820. https://doi.org/https://doi.org/10.1016/j.ipm.2024.103820
Hezam, Y., Luong, H., & Anthonysamy, L. (2025). Machine learning in predicting firm performance: a systematic review. China Accounting and Finance Review, 27(3), 309–339. https://doi.org/10.1108/CAFR-03-2024-0036
Hoang, D., & Wiegratz, K. (2023). Machine learning methods in finance: Recent applications and prospects. European Financial Management, 29(5), 1657–1701. https://doi.org/10.1111/eufm.12408
Hu, C., & Yang, X. (2024). A study on the impact of digital governance on disclosure quality of listed companies. Finance Research Letters, 69, 106062. https://doi.org/https://doi.org/10.1016/j.frl.2024.106062
Huang, H., Wang, C., Wang, L., & Yarovaya, L. (2023). Corporate digital transformation and idiosyncratic risk: Based on corporate governance perspective. Emerging Markets Review, 56, 101045. https://doi.org/https://doi.org/10.1016/j.ememar.2023.101045
Husein, A. M., Lubis, F. R., & Harahap, M. K. (2021). Analisis Prediktif untuk Keputusan Bisnis: Peramalan Penjualan. Data Sciences Indonesia (DSI), 1(1), 32–40. https://doi.org/10.47709/dsi.v1i1.1196
Kanzola, A.-M., Papaioannou, K., & Petrakis, P. E. (2024). Exploring the other side of innovative managerial decision-making: Emotions. Journal of Innovation & Knowledge, 9(4), 100588. https://doi.org/https://doi.org/10.1016/j.jik.2024.100588
Kebede, T. N., Tesfaye, G. D., & Erana, O. T. (2024). Determinants of financial distress: evidence from insurance companies in Ethiopia. Journal of Innovation and Entrepreneurship, 13(1), 17. https://doi.org/10.1186/s13731-024-00369-5
Kraus, A., & Litzenberger, R. H. (1973). A State-Preference Model of Optimal Financial Leverage. The Journal of Finance, 28(4), 911–922. https://doi.org/10.2307/2978343
Lindner, T., Puck, J., & Puhr, H. (2025). Artificial intelligence in international business: IB theory under augmented decision-making. Journal of World Business, 60(6), 101676. https://doi.org/https://doi.org/10.1016/j.jwb.2025.101676
Magfiroh, D., & Komarudin, K. (2025). Leadership Strategy For Addressing Digital Disruptions: A Case Study in Banking Companies. Leadership Insights and Innovation Journal, 1(1), 36–42. https://doi.org/10.59261/jequi.v6i2.227
Maidiana, M., & Harahap, S. P. R. (2021). Pembuatan Keputusan Dalam Proses Manajemen Dan Aspek Manajemen. Journal Ability:: Journal of Education and Social Analysis, 2(3). https://doi.org/10.51178/jesa.v2i3.222
Marlina, D., & Bakri, M. (2021). Penerapan Data Mining Untuk Memprediksi Transaksi Nasabah Dengan Algoritma C4.5. J. Teknol. Dan Sist. Inf, 2(1), 23–28.
Purwanti, D. (2023). The Strategic Imperative of Treasury and Financial Risk Management in a Volatile Economic Landscape. Advances in Management & Financial Reporting, 1(3 SE-Articles), 119–128. https://doi.org/10.60079/amfr.v1i3.224
Rismaninda Putri Dwi Prasetya, Azizah, R. N., Halwa, J. B. W., Nugroho, R. H., & Kusumasari, I. R. (2024). Implementasi Penggunaan Data Analytics untuk Mengoptimalkan Pengambilan Keputusan Bisnis di Era Digital. Jurnal Bisnis dan Komunikasi Digital, 2(2 SE-Articles), 12. https://doi.org/10.47134/jbkd.v2i2.3459
Rusma, R., Jumady, E., & Sohilauw, I. (2026). Pengaruh Profitabilitas, Likuiditas, dan Solvabilitas terhadap Nilai Perusahaan melalui Kebijakan Dividen. Advances: Jurnal Ekonomi & Bisnis, 4(3 SE-Articles), 521–541. https://doi.org/10.60079/ajeb.v4i3.805
Sætra, H. S. (2023). The AI ESG protocol: Evaluating and disclosing the environment, social, and governance implications of artificial intelligence capabilities, assets, and activities. Sustainable Development, 31(2), 1027–1037. https://doi.org/10.1002/sd.2438
Shang, D., Yuan, D., Wu, X., & Li, D. (2024). Can digital transformation alleviate corporate fraud? Evidence from China. Internet Research, 35(4), 1554–1583. https://doi.org/10.1108/INTR-01-2024-0031
Simon, H. A. (1977). The Organization of Complex Systems BT - Models of Discovery: And Other Topics in the Methods of Science (H. A. Simon (ed.); pp. 245–261). Springer Netherlands. https://doi.org/10.1007/978-94-010-9521-1_14
Steinhardt, G. (2024). Data-driven Decision-making for Product Managers: A Primer to Data Literacy in Product Management. Springer Nature. https://doi.org/10.1007/978-3-031-74664-2
Sun, B., Zhang, Y., Zhu, K., Mao, H., & Liang, T. (2024). Is faster really better? The impact of digital transformation speed on firm financial distress: Based on the cost-benefit perspective. Journal of Business Research, 179, 114703. https://doi.org/https://doi.org/10.1016/j.jbusres.2024.114703
Szukits, Á. (2022). The illusion of data-driven decision making – The mediating effect of digital orientation and controllers’ added value in explaining organizational implications of advanced analytics. Journal of Management Control, 33(3), 403–446. https://doi.org/10.1007/s00187-022-00343-w
Szukits, Á., & Móricz, P. (2024). Towards data-driven decision making: the role of analytical culture and centralization efforts. Review of Managerial Science, 18(10), 2849–2887. https://doi.org/10.1007/s11846-023-00694-1
Turi, J. A., Khwaja, M. G., Tariq, F., & Hameed, A. (2023). The role of big data analytics and organizational agility in improving organizational performance of business processing organizations. Business Process Management Journal, 29(7), 2081–2106. https://doi.org/10.1108/BPMJ-01-2023-0058
Yavuz, M. S., Tatlı, H. S., & Bozkurt, G. (2025). Exploring the financial impact of digital transformation: A comprehensive analysis on firms. Journal of Innovation & Knowledge, 10(5), 100795. https://doi.org/https://doi.org/10.1016/j.jik.2025.100795
Zareie, M., Attig, N., El Ghoul, S., & Fooladi, I. (2024). Firm digital transformation and corporate performance: The moderating effect of organizational capital. Finance Research Letters, 61, 105032. https://doi.org/https://doi.org/10.1016/j.frl.2024.105032
Zhao, J., Ouenniche, J., & De Smedt, J. (2024). Survey, classification and critical analysis of the literature on corporate bankruptcy and financial distress prediction. Machine Learning with Applications, 15, 100527. https://doi.org/https://doi.org/10.1016/j.mlwa.2024.100527
Zhou, F., Fu, L., Li, Z., & Xu, J. (2022). The recurrence of financial distress: A survival analysis. International Journal of Forecasting, 38(3), 1100–1115. https://doi.org/https://doi.org/10.1016/j.ijforecast.2021.12.005