Jurnal Algoritma, Logika dan Komputasi

The worst thing about financial failure is bankruptcy. The bankruptcy of a company can be analyzed from financial statements. the results of financial statement analysis is very useful for corporate leaders and investors to know the true condition of the company. Financial statement analysis can be done by calculating financial ratios. This study uses five variable financial ratios to predict corporate bankruptcy with repeated neural networks that apply Elman model. The sample data used in this study are 50 companies listed on the IDX 2007-2010 period. data is divided into two groups, 80% for training data and 20% for test data. Based on the function obtained from the training data, 10 companies will be tested. The best results from testing show that 9 out of 10 got the correct data.

Adnan, M.A. , Kurniasih, E. 2000. Analisis Tingkat Kesehatan Perusahaan untuk Memprediksi Potensi Kebangkrutan dengan Pendekatan Metode Altman (Kasus pada Sepuluh Perusahaan di Indonesia). Jurnal Akuntansi dan Auditing Indonesia, Volume 4 No.2 Desember : hal 131-151.

Adnan, M.A. , Taufiq M. 2001. Analisis Ketepatan Prediksi Metode Altman Terhadap Terjadinya Likuidasi Pada Lembaga Perbankan.

D. C. Sulaiman and T. M. S. Mulyana, “Web-Based Writing Learning Application of Basic Hanacaraka Using Convolutional Neural Network Method,” Ultimatics : Jurnal Teknik Informatika, pp. 28–34, Jun. 2023, doi: 10.31937/ti.v15i1.2993.

M. Freddy and T. M. S. Mulyana, “Determining Computer Opponent’s Actions in Strategy Game Using K-Nearest Neighbour Algorithm,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 8, no. 3, Dec. 2022, doi: 10.28932/jutisi.v8i3.5137.

T.M.S. Mulyana, “SEGMENTASI CITRA MENGGUNAKAN HEBB-RULE DENGAN INPUT VARIASI RGB,” Jurnal Teknologi Informasi, vol. 11, no. 1, Juni 205, pp. 34–443, 2015.

Jurnal Ekonomi & Auditing Volume 5 No.2 Desember. Penerbit :Fakultas ekonomi Universtas Islam Indonesia. Yogyakarta.

Boden, Mikael. 2001. A guide to recurrent neural networks and backpropagation.[Online] Available http://www.itee.uq.edu.au/~mikael/papers/rn_dallas.pdf. Diakses: Agustus 2012.

Cynthia, G.Y.P. 2009. Prediksi Kebangkrutan Perusahaan Menggunakan Artificial Neural Network. Institut Teknologi Sepuluh Nopember Surabaya. http://digilib.its.ac.id/prediksi-kebangkrutan-perusahaanmenggunakanartificial-neural-network-9167.html. Diakses : Agustus 2012

Elman, J. L. 1990. Finding Structure in Time.Cognitive Science, 14:179-211

Fausset, Laurence. 1994. Fundamental of Neural Network : Architecture, Algorithm, and Application. New Jersey : Prentice-Hall

Foster, G. 1986. Financial Statement Analysis. 2nd Ed. Prentice Hall

Hadi, S., Anggraeni, A. 2008. Pemilihan Prediktor Delisting Terbaik (Perbandingan Antara The Zmijewski model, The Altman Model, Dan The Springate Model). Universitas Islam Indonesia. [Online] Available

Harahap, S.S. 2006. Analisis Kritis Atas Laporan Keuangan. Jakarta : PT. RajaGrafindo Persada

Haykin, Simon. 2001. Neural Network : A Comprehensive foundation. Second Edition Prentice Hall, Inc.

Ike N.D, Yeshy. 2011. Identifikasi Kondisi Financial Distress Perusahaan Pertambangan Di Jakarta Islamic Index Dengan Aplikasi Model Z-Score Dan X-Score. Universitas Islam Negeri Maulana Malik Ibrahim. [Online] Available http://lib.uin-malang.ac.id/?mod=th_detail&id=07510031 diakses 2 desember 2012

Kusumadewi, Sri. 2003. Artificial Intelligence (Teknik dan Aplikasinya). Yogyakarta : Andi.

Kristanto, Andri. 2004. Jaringan Saraf Tiruan (Konsep Dasar, Algoritma dan Aplikasi). Yogyakarta : Gava Media.