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All Journal Prosiding SNATIKA Vol 01 (2011) Record and Library Journal Indonesian Journal of Tropical and Infectious Disease Journal of Information Systems Engineering and Business Intelligence MNJ (Malang Neurology Journal) Jurnal Sisfokom (Sistem Informasi dan Komputer) Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Cerdas MULTINETICS Jurnal Layanan Masyarakat (Journal of Public Service) Indonesian Journal of Global Health research Sewagati: Jurnal Pengabdian Masyarakat Indonesia English Education Journal
Endah Purwanti
Program Studi Sistem Informasi, Departemen Matematika, Fakultas Sains Dan Teknologi, Universitas Airlangga
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Other

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Journal : Jurnal Sisfokom (Sistem Informasi dan Komputer)

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Identifying Credit Card Fraud in Illegal Transactions Using Random Forest and Decision Tree Algorithms Werdiningsih, Indah; Purwanti, Endah; Wira Aditya, Gede Rangga; Hidayat, Auliya Rakhman; Athallah, R. Sulthan Rafi; Sahar, Virda Adisty; Wibisono, Tio Satrio; Nura Somba, Darren Febriand
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1730

Abstract

The use of credit cards is increasing in today's digital era. This increase has resulted in many cases of fraud which have had a negative impact on credit card owners. To overcome this, many financial institutions have developed credit card fraud detection systems that can identify suspicious transactions. This study uses a classification method, namely random forest and decision tree to identify illegal transactions using a credit card, which then compares the results and attempts to create a model that can be useful for detecting fraud using a credit card that is more accurate and effective. The result of this study is that the accuracy provided by the Decision Tree Classifier is 0.98, while the accuracy provided by the Random Forest Classification is also 0.975. The conclusion obtained that the decision tree has a higher level of accuracy compared to the Random Forest Classification Algorithm, which is 98%. On the other hand, the Random Forest classification algorithm has a slightly lower level of accuracy compared to the Decision Tree classification algorithm, with an accuracy rate of 97.5%
Identifying Credit Card Fraud in Illegal Transactions Using Random Forest and Decision Tree Algorithms Werdiningsih, Indah; Purwanti, Endah; Wira Aditya, Gede Rangga; Hidayat, Auliya Rakhman; Athallah, R. Sulthan Rafi; Sahar, Virda Adisty; Wibisono, Tio Satrio; Nura Somba, Darren Febriand
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1730

Abstract

The use of credit cards is increasing in today's digital era. This increase has resulted in many cases of fraud which have had a negative impact on credit card owners. To overcome this, many financial institutions have developed credit card fraud detection systems that can identify suspicious transactions. This study uses a classification method, namely random forest and decision tree to identify illegal transactions using a credit card, which then compares the results and attempts to create a model that can be useful for detecting fraud using a credit card that is more accurate and effective. The result of this study is that the accuracy provided by the Decision Tree Classifier is 0.98, while the accuracy provided by the Random Forest Classification is also 0.975. The conclusion obtained that the decision tree has a higher level of accuracy compared to the Random Forest Classification Algorithm, which is 98%. On the other hand, the Random Forest classification algorithm has a slightly lower level of accuracy compared to the Decision Tree classification algorithm, with an accuracy rate of 97.5%
Co-Authors Adhipratama, Javier Ihsan Adri Supardi, Adri Agustin, Dewien Nabila Akhlaqulkarimah, Fildzah Alfath, Muhammad Fauzan Alifian Sukma, Alifian Amadea Kurnia Nastiti, Amadea Kurnia Amma, Fadli Anggrek Citra Nusantara, Anggrek Citra Athallah, R. Sulthan Rafi Azzahra, Zahwa Arsy Badrus Zaman Bagus Fery Yanto Baihaqi, Joevans Mikail Chandra Satria Arisgraha, Franky Eva Hariyanti Fahira Firdausi Faried Effendy Ferrari, Rifky Mahdiyah Fikri Yoma Rosyidan Firdhausi Wardhani, Inten Fitriyatul Qulub Handayani, Arum Tiyas Hanik Badriyah Hidayati,* Mohammad Hasan Machfoed,* Kuntoro,** Soetojo,*** Budi Santoso,**** Suroto,***** Budi Utomo****** Hidayat, Auliya Rakhman Indah Safarina Indah Safarina, Indah Indah Werdiningsih Indra Kharisma Raharjana Kastur, Annita Kathrina Rustipa Keyesa, Talidah Nur Khusnul Ain Laili Nur Hidayati, Laili Lydia Wijaya Mardiyana, Iin Muid, Faruq Abdul Nania Nuzulita Novia Nurhasanah Arrasyid Nur Rahma, Osmalina Nur Septia Handayani Nura Somba, Darren Febriand Nurjanah, Endang Oktavia Intifada Husna Pramiyas, Naurah Hedy Pramudita, Alfian Pratama, Muhammad Fadhil Putra Prihartini Widiyanti Purbandini Soeparman, Purbandini Qomari, Yusuf Maulana Retrialisca, Fitri Riries Rulaningtyas Rudi Hartono Sahar, Virda Adisty Sembiring, Rinawati Soegianto Soelistiono Sugeng, Santoso Sukoco, Bekti Suryadewi, Kharristantie Sekarlangit Taufik Taufik Taufik Taufik Wibisono, Tio Satrio Wijianto, Buyung Wira Aditya, Gede Rangga Yahrani, Fakhrana Almas Syah Zaen, Maulana Biagi