ANALISIS UJI AKURASI MODEL GROVER, SPRINGATE, DAN ZMIJEWSKI DALAM MEMPREDIKSI KEBANGKRUTAN PERUSAHAAN DELISTED DI BEI - Komang Agus Rudi Indra Laksmana; Ayu Darmawati (JMM UNRAM - MASTER OF MANAGEMENT JOURNAL, 2019)
This study aimed at analyzing how the results of the Grover, Springate and Zmijewski models predict the bankruptcy of PT Citra Maharlika Nusantara Corpora Tbk for the period of June 2013 - September 2016. This study also aimed at measuring the accuracy of the bankruptcy prediction model and determined which predictive model of the three models was the most accurate. From the data analysis, it was found that Springate model was the most accurate prediction model with 100% accuracy rate to predict the bankruptcy of PT Citra Maharlika Nusantara Corpora Tbk compared to the Grover model with an accuracy rate of 71.48% and Zmijewski model with the lowest accuracy rate of 21.48%. The limitations of this study was this study only carried out in one company, thus in the future it is expected that the model will be tested in more than one company and type of business sector.Keywords: Financial Distress, Grover, Springate, Zmijewski ModelsPenelitian ini bertujuan untuk menganalisis bagaimana hasil dari model Grover, Springate dan Zmijewski dalam memprediksi kebangkrutan PT Citra Maharlika Nusantara Corpora Tbk periode Juni 2013 – September 2016 serta mengukur tingkat akurasi model prediksi kebangkrutan tersebut dan menentukan model prediksi manakah diantara ketiga model tersebut yang paling akurat. Model Springate menjadi model prediksi paling akurat dengan tingkat akurasi 100% untuk memprediksi kebangkrutan PT Citra Maharlika Nusantara Corpora Tbk dibandingakan dengan model Grover dengan tingkat akurasi 71,48% dan model Zmijewski dengan tingkat akurasi paling rendah sebesar 21,48%.Keterbatasan penelitian ini terletak pada pengujian model pada satu perusahaan di satu unit sektor usaha, kedepan bisa dilakukan pengujian pada berbagai jenis sektor usaha.Kata kunci: Financial Distress, Model Grover, Springate, Zmijewski