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All Journal Jurnal Info Sains : Informatika dan Sains Profitabilitas
Nurul Ichsan
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Computer Science & IT Economics, Econometrics & Finance

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An implementation of machine learning on loan default prediction based on customer behavior Robi Aziz Zuama; Nurul Ichsan; Achmad Baroqah Pohan; Mohammad Syamsul Azis; Mareanus Lase
Jurnal Info Sains : Informatika dan Sains Vol. 14 No. 01 (2024): Informatika dan Sains , Edition March 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the banking sector, loans have become a key component that steers the economy, encourages company expansion, and directly impacts the growth of a nation's economy. Banks must evaluate borrowers' ability to repay loans given the inherent risks involved in order to reduce the likelihood of default. In particular, machine learning (ML) has shown promise as a revolutionary tool for loan default prediction using advanced methodologies to examine historical data relating to customer behavior, this study investigates the application of machine learning (ML) in forecasting loan outcomes. The results show that XGBoost performs better than other machine learning algorithms, with an accuracy rate of 89%. Random forest and logistic regression come in second and third, respectively, with 88% accuracy. KNN and decision trees come next, both with somewhat lower accuracy rates (87%). By incorporating consumer behavior domain variables, this study fills in the gaps in the literature and offers a more thorough understanding of loan projections. In order to improve model performance and strengthen the predictive power of machine learning algorithms in loan scenarios, further research incorporating trials to optimize algorithm parameters is necessary as financial institutions continue to experience difficulties.
Perancangan Sistem Informasi Pengendalian Persediaan Barang Produksi pada PT. Harmonics Techindo Agung Basri, Hasan; Devi Alisa Putri; Alif Rizqi Mulyawan; Salman Alfarizi; Nurul Ichsan
PROFITABILITAS Vol 5 No 1 (2025): JURNAL PROFITABILITAS
Publisher : Sistem Informasi Akuntansi Kampu Kabupaten Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/profitabilitas.v5i1.8752

Abstract

Information technology that is very important for an agency and company can be used to facilitate a job in achieving goals optimally. PT. Harmonics Techindo Agung is a company engaged in the manufacturing industry, Designing an information system for controlling production goods inventory at PT. Harmonics Techindo Agung is done because the system used is still done manually which results in data being easily lost, a long time in data processing, and frequent errors in data input. To overcome this, researchers use the Prototype method in the process of controlling production goods inventory at PT. Harmonics Techindo Agung to help software development in creating software models that provide an idea of ideas, experimenting with designs so that they can provide convenience for companies in managing data faster, more accurately and effectively.
Prediksi Cacat Software Menggunakan Class Balancer Bagging C4.5 dan Analisis Statistik SPSS dalam Konteks Akuntansi Nurul Ichsan; Haerul Fatah; Tri Wahyuni; Erni Ermawati; Indriyanti
PROFITABILITAS Vol 5 No 1 (2025): JURNAL PROFITABILITAS
Publisher : Sistem Informasi Akuntansi Kampu Kabupaten Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/profitabilitas.v5i1.9081

Abstract

Prediksi cacat software merupakan langkah krusial dalam proses pengembangan perangkat lunak guna meminimalkan risiko kerugian akibat kegagalan sistem. Namun, tantangan utama dalam prediksi ini terletak pada ketidakseimbangan data (class imbalance) yang menyebabkan performa model prediksi menjadi tidak optimal. Penelitian sebelumnya menunjukkan bahwa algoritma C4.5 memiliki performa cukup baik, namun masih belum mampu menangani permasalahan ketidakseimbangan kelas secara efektif. Untuk mengatasi masalah tersebut, penelitian ini mengusulkan pendekatan integrasi Class Balancer dan teknik Bagging pada algoritma C4.5 sebagai solusi prediksi yang lebih robust. Hasil penelitian menunjukkan bahwa model Class Balancer+Bagging+C4.5 memberikan peningkatan nilai AUC pada dataset PC4.arff hingga mencapai 0.834 dengan akurasi 83.35%, yang masuk dalam kategori Good Classification. Meskipun rata-rata akurasi menurun dibandingkan C4.5 original, rata-rata nilai AUC meningkat secara signifikan dari 0.599 menjadi 0.672, yang menunjukkan peningkatan kualitas klasifikasi dari Failure menjadi Poor Classification. Temuan ini menunjukkan bahwa integrasi teknik Class Balancer dan Bagging pada algoritma C4.5 mampu meningkatkan kemampuan model dalam mengenali cacat software, terutama dari sisi kestabilan prediksi terhadap data yang tidak seimbang.
Co-Authors Achmad Baroqah Pohan Alif Rizqi Mulyawan Devi Alisa Putri Erni Ermawati Haerul Fatah Hasan Basri Indriyanti Mareanus Lase Mohammad Syamsul Azis Robi Aziz Zuama Salman Alfarizi Tri Wahyuni