Analisis Perkreditan Perbankan Terhadap Kemampuan Mengelola Kredit pada Bank Menggunakan Regresi Data Panel Dinamis

Rabbani, Nadhira Adiba (2024) Analisis Perkreditan Perbankan Terhadap Kemampuan Mengelola Kredit pada Bank Menggunakan Regresi Data Panel Dinamis. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Salah satu fungsi bank sebagai financial intermediary ialah untuk menghimpun dan menyalurkan dana, maka kredit menjadi aktivitas utama sekaligus sumber pendapatan terbesar dalam dunia perbankan. Adanya aktivitas pemberian kredit yang paling mendatangkan keuntungan, membuat bank kemudian dihadapkan pada risiko yang paling menimbulkan kerugian, yaitu risiko kredit yang terjadi ketika debitur tidak melakukan pembayaran sesuai kesepakatan yang dihitung dari rasio Non-Performing Loan (NPL). Terdapat 5 indikator kinerja bank umum konvensional diantaranya Capital Adequacy Ratio (CAR), Return On Asset (ROA), Net Interest Margin (NIM), Beban Operasional terhadap Pendapatan Operasional (BOPO), dan Loan to Deposit Ratio (LDR). Indikator-indikator ini diduga mempengaruhi rasio Non-Performing Loan (NPL). Variabel tersebut merupakan variabel ekonomi yang pada dasarnya adalah variabel yang dinamis. Untuk menganalisis hal tersebut, maka dibutuhkan model data panel dinamis. Metode estimasinya dikembangkan oleh Arellano dan Bond (1991) dengan estimasi Generalized Method of Moments (GMM) untuk menghasilkan parameter yang tidak bias, konsisten dan efisien. Berdasarkan uraian tersebut maka akan dilakukan penelitian untuk memodelkan pengaruh rasio kinerja perbankan terhadap NPL pada bank umum konvensional menggunakan regresi data panel dinamis. Sumber data dari website OJK dengan sampel 15 bank pada periode triwulan 3 tahun 2021 hingga triwulan 2 tahun 2023. Hasil estimasi pemodelan NPL pada kategori Bank KBMI 3 yaitu setiap kenaikan 1% pada variabel CAR dan ROA akan berpengaruh terhadap NPL pada jangka pendek masing-masing sebesar 0,6132% dan -0,0023% .Akan tetapi pengaruh jangka panjang variabel CAR dan ROA terhadap NPL adalah sebesar 1,4520% dan -0,0055%. Hasil estimasi pemodelan NPL pada kategori Bank KBMI 4 yaitu setiap kenaikan 1% pada variabel CAR, BOPO dan LDR akan berpengaruh terhadap NPL pada jangka pendek masing-masing sebesar -0,8453%; 7,0325%; dan 10,0789%. Akan tetapi pengaruh jangka panjang variabel CAR, BOPO dan LDR terhadap NPL adalah sebesar -2,8501%; 23,7104%; dan 33,9816%.
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One of the functions of banks as financial intermediaries is to collect and distribute funds, so credit is the main activity and the largest source of income in the banking world. The existence of lending activities that generate the most profits means banks are then faced with the risk that causes the most losses, namely credit risk which occurs when debtors do not make payments according to the agreement which is calculated from the Non-Performing Loan (NPL) ratio. The high NPL value is an indicator of banking failure in managing funds distributed to the public for business, which can affect banking performance and the level of bank health. Therefore, it is important for a bank to maintain a stable NPL percentage and know what factors influence the NPL value. There are 5 performance indicators for conventional commercial banks, including Capital Adequacy Ratio (CAR), Return On Assets (ROA), Net Interest Margin (NIM), Operating Expenses to Operating Income (BOPO), and Loan to Deposit Ratio (LDR). These indicators are thought to influence the Non-Performing Loan (NPL) ratio. This variable is an economic variable which is basically a dynamic variable. To analyze this, a dynamic panel data model is needed. The estimation method was developed by Arellano and Bond (1991) with Generalized Method of Moments (GMM) estimation to produce unbiased, consistent and efficient parameters. Based on this description, research will be carried out to model the effect of banking performance ratios on NPLs in conventional commercial banks using dynamic panel data regression. Data source from the OJK website with up to 15 banks in the period from quarter 3 of 2021 to quarter 2 of 2023. The estimated results of the NPL modeling in the 3rd KBMI Bank category are that every 1% increase in the CAR and ROA variables will have an effect on NPL in the short term of 0,6132% and -0,0023% respectively. However, the long-term effect of the CAR and ROA variables to NPL is 1,4520% and -0,0055%. Meanwhile The estimated results of NPL modeling in the 4th KBMI Bank category are that every 1% increase in the CAR, BOPO and LDR variables will have an effect on NPL in the short term of -0,8453%; 7,0325%; and 10,0789%. However, the long-term effect of the CAR, BOPO and LDR variables on NPL is -2,8501%; 23,7104%; and 33,9816%.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Non-Performing Loan (NPL), Indikator Kinerja Perbankan, Regresi Data Panel Dinamis, Generalized Method of Moments (GMM), Efek Jangka Panjang, Non-Performing Loan (NPL), Banking Performance Indicators, Dynamic Panel Data Regression, Generalized Method of Moments (GMM), Long Term Effect
Subjects:	H Social Sciences > HA Statistics > HA30.3 Time-series analysis H Social Sciences > HB Economic Theory > Economic forecasting--Mathematical models. H Social Sciences > HG Finance > HG3751 Credit--Management. Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry)
Divisions:	Faculty of Mathematics and Science > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Nadhira Adiba Rabbani
Date Deposited:	08 Aug 2024 13:15
Last Modified:	08 Aug 2024 13:15
URI:	http://repository.its.ac.id/id/eprint/114738

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