ANALISIS KOMPARATIF TINGKAT AKURASI MODEL PREDIKSI FINANCIAL DISTRESS: STUDI EMPIRIS PADA BUMN DAN/ATAU ANAK USAHA TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2019—2023

Kurniati, Indah Rahmah (2025) ANALISIS KOMPARATIF TINGKAT AKURASI MODEL PREDIKSI FINANCIAL DISTRESS: STUDI EMPIRIS PADA BUMN DAN/ATAU ANAK USAHA TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2019—2023. Skripsi thesis, Politeknik Keuangan Negara STAN.

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Abstract

Abstrak Di samping mencari keuntungan, BUMN memiliki peran strategis sebagai agent of value creator dan agent of development. Adanya deteksi kondisi keuangan yang lebih dini menggunakan model prediksi financial distress yang sesuai dengan karakteristik BUMN dan/atau anak usaha diperlukan agar pemerintah dapat merumuskan kebijakan strategis dan intervensi fiskal lebih cepat dan tepat. Penelitian ini dilakukan untuk menguji apakah model Altman (1993), Springate, Zmijewski, dan Grover dapat digunakan dalam memprediksi kondisi financial distress BUMN dan/atau anak usaha yang terdaftar di BEI tahun 2019—2023 serta menentukan model prediksi yang memiliki tingkat akurasi tertinggi. Pendekatan penelitian yang digunakan yaitu kuantitatif deskriptif dengan teknik analisis statistik menggunakan uji regresi logistik sederhana. Sampel penelitian ditentukan menggunakan metode purposive sampling yang menghasilkan sebanyak 27 BUMN dan/atau anak usaha yang terdaftar di BEI tahun 2019—2023. Data penelitian merupakan data sekunder berupa laporan keuangan periode 2019—2023 dengan jumlah 135 data observasi. Variabel independen penelitian merupakan model prediksi Altman (1993), Springate, Zmijewski, dan Grover sedangkan variabel dependen penelitian merupakan kondisi financial distress. Hasil penelitian menunjukan bahwa model Altman (1993), Springate, Zmijewski, dan Grover memiliki kemampuan dalam memprediksi financial distress BUMN dan/atau anak usaha yang terdaftar di BEI. Dari keempat model yang dianalisis, model Springate merupakan model prediksi yang memiliki tingkat akurasi paling tinggi yaitu sebesar 44,1%. Kata kunci: financial distress, model prediksi financial distress, Altman z score, Springate s score, Zmijewski x score, Grover g score. Abstract In addition to generating profits, State-Owned Enterprises (SOEs) have a strategic role as agents of value creation and agents of development. Early detection of financial conditions using financial distress prediction models that align with the characteristics of SOEs and/or their subsidiaries is needed so that the government can formulate strategic policies and fiscal interventions more quickly and accurately. This study aims to examine whether the Altman (1993), Springate, Zmijewski, and Grover models can be used to predict the financial distress of SOEs and/or their subsidiaries listed on the Indonesia Stock Exchange (IDX) during the 2019–2023 period, and to determine which model has the highest level of accuracy. The research approach used is descriptive quantitative with statistical analysis techniques using simple logistic regression tests. The sample was selected using a purposive sampling method which resulted in 27 SOEs and/or subsidiaries listed on the IDX during 2019–2023. The study uses secondary data in the form of financial statements for the 2019–2023 period with a total of 135 observations data. xii The independent variables are the Altman (1993), Springate, Zmijewski, and Grover prediction models, while the dependent variable is the financial distress. The results show that all four models are capable of predicting the financial distress of SOEs and/or their subsidiaries listed on the IDX. Among the models analyzed, the Springate model is a prediction model that has the highest accuracy rate of 44.1%. Keywords: financial distress, financial distress prediction models, Altman z score, Springate s score, Zmijewski x score, Grover g score.

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