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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE bit-Tech Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Ridwan Dwi Irawan
Universitas Duta Bangsa Surakarta
Religion Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Nursing Public Health Social Sciences

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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.
Comparison of BARS and SKP Methods in Evaluating Lecturer Performance in Higher Education Marta Ardiyanto; Ridwan Dwi Irawan; Kresna Agung Yudhianto
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3116

Abstract

The evaluation of lecturer performance in higher education is vital for maintaining the quality of teaching, research, and community service. In Indonesia, civil servant lecturer assessments rely heavily on the Sasaran Kinerja Pegawai (SKP), which emphasizes quantitative indicators such as teaching load, publications, and community engagement. However, this approach often overlooks qualitative aspects, including teaching innovation, professional ethics, and student interaction. To address these gaps, this study proposes integrating the Behaviorally Anchored Rating Scale (BARS) with SKP to create a more comprehensive evaluation framework. The research, conducted at Universitas Duta Bangsa Surakarta in the 2024/2025 academic year, applied the Rapid Application Development (RAD) method to design a web-based prototype. Key stages included instrument design, weighting mechanisms, and reliability testing using Cronbach’s Alpha, which yielded a strong coefficient of 0.850. Evaluation involved six faculty leaders and eight rectorate leaders (bureau heads, vice rectors, and the rector), while the foundation board acted as validators to ensure objectivity. System testing demonstrated promising results: blackbox testing confirmed 100% functional accuracy, and usability testing showed an average satisfaction score of 4.2 out of 5 (84%). Findings indicate that combining SKP and BARS enhances accountability, transparency, and professional feedback. This model contributes practically to higher education human resource management by enabling data-driven decision-making and fostering lecturer development. While further validation across diverse institutions is needed, the integrated framework offers a more holistic and adaptive approach to lecturer performance evaluation.
Pelatihan Media Pembelajaran Interaktif Berbasis AI Menggunakan Gamma.App di SMKN 1 Nglipar Ridwan Dwi Irawan; Nibras Faiq Muhammad
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 1 (2026): Januari 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i1.979

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kompetensi guru dalam mengembangkan media pembelajaran interaktif dengan memanfaatkan teknologi Artificial Intelligence (AI) melalui platform Gamma.App. Dalam era digital saat ini, kemampuan pendidik dalam memanfaatkan AI menjadi kunci untuk menciptakan pembelajaran yang menarik, efisien, dan adaptif terhadap kebutuhan siswa. Metode pelaksanaan kegiatan dilakukan melalui pelatihan dan pendampingan intensif kepada guru-guru SMK Negeri 1 Nglipar Gunungkidul. Materi pelatihan meliputi pengenalan konsep AI dalam media pembelajaran, penggunaan AI Presentation Generator, serta praktik langsung pembuatan presentasi interaktif berbasis AI. Hasil kegiatan menunjukkan peningkatan signifikan dalam kemampuan guru dalam mendesain PowerPoint otomatis, efisiensi waktu persiapan materi, serta kreativitas visual presentasi. Peserta juga mampu memanfaatkan fitur AI untuk penyesuaian gaya presentasi, penerjemahan otomatis, dan tata letak konten yang menarik. Kegiatan ini berkontribusi terhadap penguatan ekosistem akademik berbasis digital di lingkungan SMK dan mendorong transformasi pembelajaran vokasi menuju era AI-driven education. 
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.
Co-Authors Agus Fatkhurohman Kresna Agung Yudhianto Marta Ardiyanto Nibras Faiq Muhammad