Electronic Theses and Dissertation

Penelitian ini bertujuan untuk membandingkan tingkat akurasi enam model prediksi financial distress, yaitu altman z-score, springate, ohlson, taffler, zmijewski, dan grover, serta mengidentifikasi model yang paling akurat dalam memprediksi kondisi financial distress pada perusahaan sektor consumer cyclicals yang terdaftar di bursa efek indonesia (bei) periode 2021–2023. sampel penelitian terdiri dari 34 perusahaan yang dipilih melalui metode purposive sampling dari populasi sebanyak 128 perusahaan. evaluasi akurasi masing-masing model dilakukan dengan membandingkan hasil klasifikasi model terhadap kondisi aktual perusahaan berdasarkan laporan keuangan. hasil penelitian menunjukkan bahwa terdapat perbedaan tingkat akurasi di antara model yang dianalisis, di mana model altman z-score memiliki akurasi tertinggi sebesar 77,45 persen. model lainnya mencatatkan akurasi sebagai berikut: grover (74,51 persen), taffler (72,55 persen), springate (55,88 persen), ohlson (39,22 persen), dan zmijewski (23,53 persen). temuan ini menunjukkan bahwa pemilihan model prediksi yang tepat sangat bergantung pada konteks sektoral dan karakteristik keuangan perusahaan. model altman z-score terbukti paling sesuai digunakan dalam memprediksi financial distress pada sektor consumer cyclicals di indonesia untuk periode 2021–2023.

This study aims to compare the accuracy levels of six financial distress prediction models, namely Altman Z-Score, Springate, Ohlson, Taffler, Zmijewski, and Grover, and identify the most accurate model in predicting financial distress conditions in consumer cyclicals companies listed on the Indonesia Stock Exchange (IDX) period 2021–2023. The research sample consists of 34 companies selected through purposive sampling from a population of 128 companies. The accuracy of each model was evaluated by comparing the model's classification results with the actual conditions of the companies based on their financial statements. The results of the study indicate that there are differences in the accuracy levels among the analyzed models, with the Altman Z-Score model having the highest accuracy at 77,45 percent. The other models recorded the following accuracy rates: Grover (74,51 percent), Taffler (72,55 percent), Springate (55,88 percent), Ohlson (39,22 percent), and Zmijewski (23,53 percent). These findings indicate that the selection of the appropriate prediction model is highly dependent on the sectoral context and financial characteristics of the company. The Altman Z-Score model proved to be the most suitable for predicting financial distress in the consumer cyclicals sector in Indonesia for the period 2021–2023.