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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JIKO (Jurnal Informatika dan Komputer) JURNAL TEKNOLOGI DAN OPEN SOURCE Simtek : Jurnal Sistem Informasi dan Teknik Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Bumigora Information Technology (BITe) Infotek : Jurnal Informatika dan Teknologi Jurnal Teknik Informatika (JUTIF) Information System Journal (INFOS) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Teknik Informatika Jurnal Nasional Teknologi Komputer Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Innovative: Journal Of Social Science Research Dedikasi Saintek Jurnal Pengabdian Masyarakat SmartComp CSRID DEDIKASI SAINTEK Jurnal Pengabdian Masyarakat
Irawan, Ridwan Dwi
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Application of Deep Learning Algorithm to Detect Fraud in Online Transaction Networks Ridwan Dwi Irawan; Agus Fatkhurohman
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3890

Abstract

Online transaction fraud is a severe problem that may cost businesses and people a lot of money. This paper suggests using deep learning algorithms to detect fraud as a remedy to this issue. These algorithms were chosen based on their ability to handle large amounts of intricate data and identify patterns that are difficult to identify using traditional techniques. Important components of this research include gathering and preprocessing transaction data, creating deep learning models, and assessing model performance. This investigation examines a variety of financial transaction types that may have involved fraud. The deep learning approach uses deep neural network designs, including Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), to maximize detection accuracy. The study's findings demonstrate that the deep learning models created are excellent at identifying questionable transactions and can lower the false positive rate, which raises the overall effectiveness of fraud detection systems. As a result, deep learning algorithms have demonstrated a high degree of efficacy in identifying fraudulent activity inside internet-based transaction networks, so they play a vital role in fraud prevention.
EVALUASI PENERAPAN E-PUSKESMAS MENGGUNAKAN MODEL DELONE & MCLEAN DI PUSKESMAS TRUCUK II Endarwati, Elisabet Christina; Irawan, Ridwan Dwi; Muhammad, Nibras Faiq
Simtek : jurnal sistem informasi dan teknik komputer Vol. 11 No. 1 (2026): April 2026
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v11i1.1768

Abstract

Quantitative evaluations of information system success in primary healthcare facilities are still rarely conducted, even though such assessments are important for measuring the system's benefits to users. This study aims to evaluate the implementation of E-Puskesmas at Puskesmas Trucuk II Klaten, which has been operating since January 2023, using the DeLone & McLean model. A quantitative approach was applied using a saturated sampling technique involving 34 respondents. The collected data were analyzed using descriptive statistics and three-stage multiple regression with SPSS. The results showed that all variables fell into the high category (3.61–4.10). Of the nine hypotheses proposed, four were accepted: the effect of system quality on user satisfaction (p = 0.044), the effect of service quality on user satisfaction (p < 0.001; Beta = 0.486) as the strongest influence, the effect of use on user satisfaction (p = 0.013), and the effect of use on net benefits (p = 0.013). The other five hypotheses were rejected. The main obstacles identified included disruptions in BPJS bridging, overly long forms, and manual informed consent. In conclusion, the implementation of E-Puskesmas is considered relatively successful, with service quality being the most dominant factor.
Co-Authors Abdul Harits, Muhammad Agus Fatkhurohman Akbar Pratama, Hamzah Alva Hendi Muhammad Amelia, Dinda Anjarwati, Pipin Antoni DM, Nikolas Aprilisa Arum Sari Ardiyanto, Marta Artdelia Pingkan Salsabilla Ayu Setyowati, Sintiya Brilliant Firdaus, Azkha Choirul Ananda, Naufal Eko Purwanto Eko Purwanto Ema Utami Endarwati, Elisabet Christina Fathoni, Syahrul Agung Febrian Fitiraldy Gilang Pramudito, Vincensius Ihza Yuzar Vianda, Rizqi Ika Putri Pujianti, Ika Putri Pujianti Imanuel, Christian Indra Kristiawan, Yohanes Intan Rahmawati Kresna Agung Yudhianto Maulana Akbar, Reza Maulindar, Joni Muh Adha Muh Adha Muhamad Paliya Sadana Muhammad Paliya Sadana Nailurrizqi, Adistya Nibras Faiq Muhammad Nugroh, Heri Nur Rohman, Sofyan Nurmalitasari Oktaviana Az Zahra, Erika Permatasari, Hanifah Pramono Pramono Pratama, Dika Adi Rahmaning Umiadi, Amara Riyadsyah, Deprinico Rizqy Alfiansyah, Rafif Rudi Susanto Satria Yudha, Rafiq Satrio Atmojo, Fattah Setiawan, Stevania Shalikashilla Tiaramadhan, Faiza Sidik Prasetyo, Sidik Sofa Marwati Stevania Setiawan Suryanti, Ika Syarif , Muhammad Tri Hartanti, Ninik Viery, Andrean Yoga Saputra, Muchammad Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah