Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal Journal of Islamic Monetary Economics and Finance Journal of Applied Data Sciences
Mohammad Mahbubi Ali
International Institute of Advanced Islamic Studies, Malaysia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
DETERMINANTS OF ISLAMIC FINANCIAL EXCLUSION IN INDONESIA Mohammad Mahbubi Ali; Abrista Devi; Hamzah Bustomi
Journal of Islamic Monetary Economics and Finance Vol 6 No 2 (2020)
Publisher : Bank Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21098/jimf.v6i2.1093

Abstract

The study aims to uncover the determinants of Islamic financial exclusion in Indonesia by gathering the response from financially-excluded respondents. A total of 110 respondents were surveyed, representing five provinces, namely West Java, South Sulawesi, Aceh, East Kalimantan, and North Maluku. The criteria of financially-excluded respondents are those who do not have any Islamic financial products, neither saving, financing, nor capital market account. The study employs Confirmatory Factor Analysis (CFA) to identify indicators explaining Islamic financial exclusion determinants in Indonesia. The paper found that location is the key barrier to obtain financing from and participate in saving in Islamic banks/Islamic microfinance, while lack of financial knowledge is identified as the critical barrier to deal with Islamic capital market products. Overall, most of the respondents perceive human capital, as well as product and services as the two most significant determinant of Islamic financial exclusion in Indonesia, followed by infrastructure, policies and regulation, financial literacy, social influence, and religious commitment, respectively. The originality of the paper lies in detailed insight into the perception of financially-excluded on the factors leading to Islamic financial exclusion.
Bank Soundness Level Prediction: ANFIS vs Deep Learning Satia Nur Maharani; Bambang Sugeng; Makaryanawati Makaryanawati; Mohammad Mahbubi Ali
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.116

Abstract

The systemic nature of the risk of bankruptcy of financial institutions has become an important issue in maintaining the existence and stability of domestic and global finance. The use of statistics for bankruptcy prediction so far provides optimal benefits. However, this approach has limitations, especially since the model is built based on systematic relationships, so the linearity and normality aspects are often weaknesses. This can be overcome very efficiently through linear and non-linear patterns built by artificial intelligence models. One of the most popular of these techniques is the Artificial Neural Network (ANN). Many studies show that ANN and fuzzy set theory is more accurate, adaptive, and strong in predicting compared to statistical models. One technique to integrate ANN with fuzzy logic systems is through the Adaptive-Network-Based Fuzzy Inference System (ANFIS). ANFIS is an adaptive network that is functionally equivalent to fuzzy inference and has the advantages of ANN and fuzzy logic. One of the important features of ANFIS is its acclimatization capability where the membership function parameters can adapt and change in the learning procedure. Utilizing the ANN model and fuzzy logic for bankruptcy prediction is still very limited in Indonesia. Therefore, this study aims to construct a financial institution bankruptcy prediction model that is much more accurate, operational quickly, and effective through ANFIS as a hybrid of fuzzy logic and ANN. The results showed that ANFIS can be used to predict the bankruptcy of financial institutions with the best MAPE 0.140335507.
Co-Authors Bambang Sugeng Devi, Abrista Hamzah Hamzah Maharani, Satia Nur Makaryanawati, Yana -