Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Teknik dan Science (JTS) Journal of Deep Learning, Computer Vision and Digital Image Processing
Rifda Triani Mutmainah
Universitas Adhirajasa Reswara Sanjaya
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Pengembangan Website SpendNote sebagai Sistem Pencatatan Pengeluaran Keuangan Menggunakan Framework Laravel Rifda Triani Mutmainah; Syarif Hidayatulloh
Jurnal Teknik dan Science Vol. 5 No. 1 (2026): Februari : Jurnal Teknik dan Science
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/jts.v5i1.2492

Abstract

The rapid development of information technology has encouraged the use of web-based applications in various aspects of daily life, including personal financial management. However, many individuals still do not consistently record their financial transactions due to limitations of existing applications, such as complex interfaces, dependence on paid features, and limited accessibility. This study aims to develop the SpendNote Website as a web-based financial expense recording system that is easy to use, free of charge, and capable of presenting financial information in a clear and structured manner. The research method employed is applied research, consisting of problem identification, system requirements analysis, system design, implementation, testing, and evaluation. The SpendNote Website was developed using the Laravel framework with the Model–View–Controller (MVC) architecture. The results show that SpendNote is able to record income and expenses, display financial summaries, provide transaction history, period-based filtering, and data visualization in the form of graphs and charts. Functional testing indicates that all system features operate properly and meet user requirements. Therefore, the SpendNote Website can serve as a practical and effective solution to support systematic and sustainable personal financial management.
Comparative Analysis of a Simple CNN and Fine-Tuned ResNet50 for Deepfake Image Detection: Performance and Computational Efficiency Evaluation Rifda Triani Mutmainah; Asti Herliana
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1629

Abstract

Purpose – Deepfake-related cybercrime is an increasingly troubling cybersecurity threat, since such content can now be produced from a single facial photo using freely available face-swapping techniques, while human ability to distinguish real from fake images remains limited (48.2–59%). This study compares a simple CNN and a fine-tuned ResNet50 within an identical, controlled framework.Methods – A controlled experiment compared a simple CNN (trained from scratch) and ResNet50 (two-phase fine-tuning) on 20,000 images from the Kaggle Deepfake and Real Images dataset (80:10:10 split, seed = 42). Candidate images were deduplicated before the data split; zero cross-split duplicates were confirmed. ResNet50 used ResNet-specific preprocessing; the CNN used inputs normalized to [0,1]. Evaluation used accuracy, precision, recall, F1-score, AUC-ROC, and specificity, with a paired McNemar's test as the primary significance measure.Findings – The CNN outperformed ResNet50 on six of seven metrics (accuracy 90.90% vs. 89.80%; AUC-ROC 97.08% vs. 96.52%), while ResNet50 achieved higher recall (93.10% vs. 91.80%). The accuracy difference was not statistically significant (p = 0.200). The CNN was 69.5 times smaller and trained faster (12.2 vs. 16.5 minutes). Neither model showed clear overfitting. Research implications – The findings rest on a single subset, a single split, and a single training run per model, limited to 20 epochs; the potential of ResNet50 with longer training has not been explored.Originality – This study combines accuracy, generalization-related diagnostics, and efficiency in a single controlled comparison, applies a paired test appropriate for a shared test set, and filters duplicates before the data split.
Co-Authors Asti Herliana, Asti Syarif Hidayatulloh