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Analysis of Financial Distress Predictions Using Altman, Zavgren, Fulmer, Ohlson, Taffler, and Ca-Score Models as Early Warning Systems in Manufacturing Companies Dukalang, Erfina; Zamzam, Irfan; Abu, Zulkifli
Nominal: Barometer Riset Akuntansi dan Manajemen Vol 13, No 1 (2024): Nominal April 2024
Publisher : Yogyakarta State University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/nominal.v13i1.65081

Abstract

ABSTRACTThis research aims to analyze the application of the Altman, Zavgren, Fulmer, Ohlson, Taffler, and CA-Score models as early warning systems, as well as the differences in the ability of the accuracy level of the six early warning system models to predict financial distress and which of the six prediction models is most accurate and is most accurate in predicting financial distress. The sampling technique in this research was with purposive sampling criteria. The analysis technique is to use financial distress prediction models. The results showed that the highest and most reliable method for forecasting financial trouble in this research was the CA-Score model which had an accuracy rate of 97.14% and error type II of 2.86%. compared to Altman, Zavgren, Ohlson, Taffler, and CA-Score models. Then, followed by the Ohlson model whose accuracy rate is 94.29% and error type II of 5.71%.Keywords: Financial Distress, Altman, Zavgren, Fulmer, Ohlson, Taffler, CA-ScoreABSTRAKPenelitian ini bertujuan untuk menganalisis penerapan model Altman, Zavgren, Fulmer, Ohlson, Taffler, dan CA-Score sebagai early warning system, serta adanya perbedaan kemampuan tingkat akurasi dari keenam model early warning system dalam memprediksi financial distress dan manakah diantara enam model prediksi yang mempunyai tingkat akurasi tertinggi dan paling akurat dalam memprediksi financial distress. Teknik menentukan sampel pada penelitian ini dengan kriteria purposive sampling. Teknik analisis ialah menggunakan model-model prediksi financial distress. Hasil penelitian menunjukkan kalau model tertinggi dan sangat akurat dalam memprediksi financial distress untuk studi penelitian ini adalah model CA-Score yang tingkat akurasi sebesar 97,14% dan tipe error II sebesar 2,86%. dibandingkan model Altman, Zavgren, Ohlson, Taffler, dan CA-Score. Kemudian, diikuti model Ohlson dengan tingkat akurasi sebesar 94,29% dan tipe error II sebesar 5,71%.Kata Kunci: Financial Distress, Altman, Zavgren, Fulmer, Ohlson, Taffler, CA-Score
THE INFLUENCE OF THE MOTOR VEHICLE TAX BLEACHING PROGRAM, EXEMPTION OF MOTOR VEHICLE TITLE TRANSFER FEES, TAXATION SOCIALIZATION AND TAXPAYER AWARENESS ON MOTOR VEHICLE TAXPAYER COMPLIANCE (CASE STUDY AT THE JOINT OFFICE OF THE ONE-ROOF SINGLE ADMINISTRA Fadlun Adnan, Najwa; Dara , Amin; Abu, Zulkifli
Jurnal Ilmiah Akuntansi dan Keuangan (JIAKu) Vol 4 No 3 (2025)
Publisher : Sekolah Tinggi Ilmu Ekonomi Indonesia (STIESIA) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24034/jiaku.v4i3.6681

Abstract

The objectives of this study were: (1) to analyze whether the influence of the motor vehicle tax bleaching program on the compliance of motor vehicle taxpayers (2) to analyze whether the exemption of motor vehicle title transfer fees affects the compliance of motor vehicle taxpayers (3) to analyze whether taxation socialization affects the compliance of motor vehicle taxpayers (4) to analyze whether taxpayer awareness affects the compliance of motor vehicle taxpayers. This type of research is quantitative research. The analysis model used in this research is multiple linear analysis using SPSS 26. Based on the results of the study, it shows that (1) the motor vehicle tax bleaching program has no significant effect on taxpayer compliance (2) exemption of motor vehicle title transfer fees has a positive and significant effect on motor vehicle taxpayer compliance (3) tax socialization has no effect with a negative relationship direction is not significant (4) taxpayer awareness has a positive and significant effect on motor vehicle taxpayer compliance