Prediksi Financial Distress Perusahaan Sektor Non-Finansial di Indonesia

Putri, Ayu Adearista (2023) Prediksi Financial Distress Perusahaan Sektor Non-Finansial di Indonesia. Diploma thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Perusahaan yang terdaftar di Bursa Efek Indonesia (BEI) terdiri atas perusahaan sektor finansial dan sektor non-finansial. Perusahaan sektor non-finansial memiliki keragaman data rasio keuangan atau varians yang lebih besar jika dibandingkan perusahaan sektor finansial, oleh karena itu perusahaan sektor non-finansial dipilih untuk digunakan sebagai objek pada penelitian ini. Dalam menjalankan berbagai kegiatan usahanya, setiap perusahaan pasti memiliki bagian khusus yang mengelola sistem keuangan agar tetap stabil karena hal ini sangat penting bagi kelancaran seluruh kegiatan yang ada di perusahaan. Adanya ketidakstabilan sistem keuangan dapat menyebabkan suatu perusahaan mengalami krisis yang dapat berujung dengan kebangkrutan. Tahapan perusahaan mengalami kebangkrutan diawali dengan adanya penurunan kondisi keuangan seperti kekurangan dana, kekurangan uang tunai, bahkan tidak mampu membayar hutang pada waktu yang telah ditentukan. Keadaan inilah yang dinamakan ketika perusahaan sedang mengalami financial distress. Jika kondisi financial distress terjadi secara terus-menerus, perusahaan akan berpeluang besar mengalami bangkrut. Sebagai salah satu upaya untuk mencegah kebangkrutan, perlu dilakukan proses prediksi sebagai bentuk early warning terhadap perusahaan, khususnya perusahaan yang mengalami financial distress. Penelitian ini akan membahas tentang prediksi financial distress perusahaan sektor non-finansial di Indonesia tahun 2018-2021 menggunakan teknik single classifier dengan metode Support Vector Machine (SVM) dan teknik hybrid classifier dengan metode kombinasi K-Means dan Support Vector Machine (SVM). Diperoleh hasil bahwa kedua metode yang digunakan sama-sama baik dalam mengklasifikasikan perusahaan sektor non-finansial di Indonesia. Model SVM lebih baik digunakan jika tingkat sensitivitas diutamakan. Model kombinasi K-Means dan SVM lebih baik digunakan jika akurasi dan spesifisitas diutamakan.
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Companies listed on the Indonesia Stock Exchange (IDX) consist of companies in the financial and non-financial sectors. Non-financial sector companies have a greater diversity of financial ratio data or variance when compared to financial sector companies, therefore non-financial sector companies were chosen to be used as objects in this study. In carrying out various business activities, each company must have a special section that manages the financial system to remain stable because this is very important for the smooth running of all activities in the company. The existence of financial system instability can cause a company to experience a crisis that can lead to bankruptcy. The stages of a company experiencing bankruptcy begin with a decline in financial conditions such as lack of funds, lack of cash, and even being unable to pay debts at a predetermined time. This situation is called when the company is experiencing financial distress. If financial distress conditions occur continuously, the company will have a high chance of going bankrupt. As an effort to prevent bankruptcy, it is necessary to carry out a prediction process as a form of early warning for companies, especially companies experiencing financial distress. This research will discuss the prediction of financial distress of non-financial sector companies in Indonesia in 2018-2021 using a single classifier technique using the Support Vector Machine (SVM) method and a hybrid classifier technique using a combination of K-Means and Support Vector Machine (SVM) methods. The results show that the two methods used are equally good in classifying non-financial sector companies in Indonesia. The SVM model is better used if the sensitivity level is important. The K-Means and SVM combination model is better to use if accuracy and specificity are prioritized.

Item Type:	Thesis (Diploma)
Uncontrolled Keywords:	Financial Distress, Hybrid Classifier, K-Means, Single Classifier, Support Vector Machine
Subjects:	H Social Sciences > HA Statistics
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	Ayu Adearista Putri
Date Deposited:	07 Jul 2023 04:06
Last Modified:	07 Jul 2023 04:06
URI:	http://repository.its.ac.id/id/eprint/98372

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