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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Surya Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Surya Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Aspirasi : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat Jurnal Komputer dan Teknologi Informasi
Muhammad Munsarif
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Peer to peer lending risk analysis based on embedded technique and stacking ensemble learning Muhammad Munsarif; Muhammad Sam’an; Safuan Safuan
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.3927

Abstract

Peer to peer lending is famous for easy and fast loans from complicated traditional lending institutions. Therefore, big data and machine learning are needed for credit risk analysis, especially for potential defaulters. However, data imbalance and high computation have a terrible effect on machine learning prediction performance. This paper proposes a stacking ensemble learning with features selection based on embedded techniques (gradient boosted trees (GBDT), random forest (RF), adaptive boosting (AdaBoost), extra gradient boosting (XGBoost), light gradient boosting machine (LGBM), and decision tree (DT)) to predict the credit risk of individual borrowers on peer to peer (P2P) lending. The stacking ensemble model is created from a stack of meta-learners used in feature selection. The feature selection+ stacking model produces an average of 94.54% accuracy and 69.10 s execution time. RF meta-learner+Stacking ensemble is the best classification model, and the LGBM meta-learner+stacking ensemble is the fastest execution time. Based on experimental results, this paper showed that the credit risk prediction for P2P lending could be improved using the stacking ensemble model in addition to proper feature selection.
Feature selection in P2P lending for default prediction using grey wolf optimization and machine learning Sam'an, Muhammad; Safuan, Safuan; Munsarif, Muhammad
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7651

Abstract

Online loan services like peer-to-peer (P2P) lending enable lenders to transact without bank intermediaries. Predicting which lenders are likely to default is crucial to avoid bankruptcy since lenders bear the risk of default. However, this task becomes challenging when the P2P lending dataset contains numer- ous features. The prediction performance could be improved if the dataset fea- tures are relevant. Hence, applying feature selection to remove redundant and irrelevant features is essential. This paper introduces a novel wrapper feature selection model to identify the optimal feature subset for predicting defaults in P2P lending. The proposed method includes two main phases: feature selection and classification. Initially, grey wolf optimization (GWO) is used to select the best features in P2P lending datasets. Then, the fitness function of GWO is as- sessed using ten different machine learning (ML) models. Experimental results indicate that the proposed model outperforms previous related work, achieving average accuracy, recall, precision, and F1-score of 96.77%, 80.73%, 97.52%, and 80.06%, respectively.
Convolution neural network hyperparameter optimization using modified particle swarm optimization Munsarif, Muhammad; Sam'an, Muhammad; Fahrezi, Andrian
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6112

Abstract

Based on the literature review, a convolutional neural network (CNN) is one of the deep learning techniques most often used for classification problems, especially image classification. Various approaches have been proposed to improve accuracy performance. In CNN architecture, parameter determination is very influential on accuracy performance. Particle swarm optimization (PSO) is a type of metaheuristic algorithm widely used for hyperparameter optimization. PSO convergence is faster than genetic algorithm (GA) and attracts many researchers for further studies such as genetic algorithms and ant colony. In PSO, determining the value of the weight parameter is very influential on accuracy. Therefore, this paper proposes CNN hyperparameter optimization using modified PSO with linearly decreasing randomized weight. The experiments use the modified National Institute of Standards and Technology (MNIST) dataset. The accuracy of the proposed method is superior, and the execution time is slower to random search. In epoch 1, epoch 3, and epoch 5, the proposed method is superior to baseline CNN, linearly decreasing weight PSO (LDWPSO), and RL-based optimization algorithm (ROA). Meanwhile, the accuracy performance of the proposed method is superior to previous studies, namely LeNet-1, LeNet-2, LeNet-3, PCANet-2, RANDNet-2, CAE1, CAE-2, and bee colony. Otherwise, lost to PSO-CNN, distributed PSO (DPSO), recurrent CNN, and CNN-PSO. However, the four methods have a complex architecture and wasteful execution time.
Co-Authors A. Awoseyi, Ayomikun Abdollahi, Jafar Adinullhaq, Juyus Muhammad Agatra, Denaya Ferrari Noval Ahmad Ilham Ahmad Miftakhul Akhmad Fathurrohman Akhmad Fathurrohman Alfi Widianto Alif Hardika Ardhani, Yevi Alviatul Arfido Ramadhani ARIF KURNIAWAN Ariyanto, Nova Asdani Kindarto Caesarizky, Ovien Yoga Cornella, Barisma Ami Edi Noersasongko Edi Noersasongko Eny Winaryati Fahrezi, Andrian Febrianto Febrianto Febrianto Febrianto, Febrianto Galih Ratna Hakim, Muhammad Ilham Ilham, Ahmad Irvan Nurmantoro Javier Rayhan Jian Janata Juyus Muhammad Adinullhaq Kholifatul Afwah Kindarto, Asdani Luqman Assaffat Luqman Assaffat Mardiana Mardiana Meike Amelia Hanifah Moch Arief Soeleman Mochammad Arief Soeleman Muhammad Sam'an Muhammad Saman Muhammad Sam’an Muza'in, Muhammad Nova Ariyanto Nurmantoro, Irvan Pulung Nurtantio Andono Purbandanu, Muhammad Wigig Ramadhani, Arfido Safuan Safuan Safuan Safuan Safuan Safuan Safuan Safuan Salsabila Rahma Sam'an, Muhammad Samsudi Raharjo SAMSUDI RAHARJO Sefrina Ainun Shabrina Mirza Siti Muzdalifah Suwahono Suwahono Tirta Ramadhani Wibowo, Bhanu Lintang Yanuarta, Rizky Yevi Alviatul Ardhani