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All Journal Jurnal Mandiri IT PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi
Andriani, Wresty
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science Mathematics Mechanical Engineering

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Analisis perbandingan machine learning untuk prediksi kelayakan kredit perbankan pada Bank BRI Tegal Andriani, Wresty; Gunawan; Naja, Naella Nabila Putri Wahyuning
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 1 (2025): IT-Explore Februari 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i1.2025.pp82-92

Abstract

Predicting credit worthiness is an important step for banks to reduce the risk of bad credit. This research compares the performance of four classification algorithms, namely SVM, Naïve Bayes, Random Forest and Decision Tree using simulated datasets. The results obtained on the metrics of accuracy, precision, recall, F1 score, and AUC-ROC, show that Decision Tree has the best performance with 42.5% accuracy, 48.3% precision, 47.5% recall, 47.5% F1 score, and AUC 0.60, indicating its ability to is in differentiating credit worthiness. Random Forest achieved an accuracy of 37.5% and an AUC of 0.493, while Naïve Bayes had the lowest accuracy with an accuracy of 27.5% and an AUC of 0.425. SVM gives better results than Naïve Bayes but is still inferior to Decision Tree. This research recommends implementing a Decision Tree as the main model with optimization through hyperparameter tuning, adding relevant features, and handling data accounting. These results are expected to support banking decision making more effectively and efficiently.
PREDIKSI JUMLAH KUNJUNGAN PASIEN RAWAT JALAN MENGGUNAKAN METODE REGRESI LINIER SEDERHANA Nugroho, Bangkit Indarmawan; Rozak, Fatkhur; Andriani, Wresty; Gunawan
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 11 No. 2 (2024): Prosisko Vol. 11 No. 2 September 2024
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v11i2.8756

Abstract

Penelitian ini berfokus pada prediksi jumlah kunjungan pasien rawat jalan di Rumah Sakit Islam Pati menggunakan metode regresi linier sederhana. Topik ini dipilih karena pentingnya perencanaan manajemen rumah sakit yang lebih efektif dan efisien, mengingat fluktuasi dan ketidakpastian jumlah kunjungan pasien seringkali menjadi tantangan. Tujuan dari penelitian ini adalah mengembangkan model prediksi yang memberikan estimasi jumlah kunjungan pasien dengan lebih akurat, sehingga dapat digunakan dalam proses perencanaan. Metode penelitian yang digunakan adalah regresi linier sederhana dengan analisis data kunjungan pasien dari tahun 2021, 2022, dan 2023. Data yang digunakan telah melalui proses pra-pemrosesan untuk memastikan validitas dan kebersihannya. Hasil penelitian menunjukkan bahwa model regresi linier sederhana memberikan performa yang baik dalam memprediksi jumlah kunjungan pasien. Hasil penelitian ini menunjukkan bahwa prediksi jumlah kunjungan pasien dapat membantu rumah sakit dalam merencanakan sumber daya dan meningkatkan efisiensi operasional. Kesimpulan dari penelitian ini adalah bahwa metode regresi linier sederhana dapat menjadi solusi dalam menghadapi ketidakpastian jumlah kunjungan pasien, memungkinkan manajemen rumah sakit melakukan perencanaan yang lebih baik dan efisien.
Application of association rule for prediction of menu ordered at café minapadi Zain Hidayatullah, Fikri; Surorejo, Sarif; Andriani, Wresty; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 12 No. 4 (2024): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v12i4.279

Abstract

This research aims to develop a predictive model that helps prepare menus based on customer preferences at Café Minapadi, hoping to improve operational efficiency and customer satisfaction. Using rule-association data mining techniques, the study uncovered hidden patterns in extensive transaction data, applying a priori algorithms in datasets to explore menu ordering frequencies and trends. Data analysis includes cleansing, transforming, and selecting features to generate relevant insights. The results found that items such as coffee and chocolate cake were often purchased together, providing an opportunity for menu optimization and special promotions. Evaluation of predictive models shows the possibility of increased accuracy in stock preparation and adjustment of menu offerings, providing significant benefits in business decision-making in the culinary sector.
Co-Authors Bangkit Indarmawan Nugroho Gunawan Gunawan Gunawan Naja, Naella Nabila Putri Wahyuning Rozak, Fatkhur Sarif Surorejo Zain Hidayatullah, Fikri