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INDONESIA
Generation Journal
Published by Universitas Nusantara PGRI Kediri
ISSN : 25804952     EISSN : -     DOI : https://doi.org/10.29407
Core Subject : Science,
Computer Science & IT
Generation (Genius Research Implementation Of Information Technology) Journal diterbitkan oleh Universitas Nusantara PGRI Kediri dan dikelola oleh Prodi Teknik Infomatika Universitas Nusantara PGRI Kediri. Tujuan dari Jurnal ini adalah untuk memfasilitasi publikasi ilmiah dari hasil-hasil penelitian di Indonesia dan berpartisipasi untuk meningkatkan kualitas dan kuantitas penelitian untuk akademisi dan peneliti dalam bidang teknologi informasi. GENERATION Journal diterbitkan setiap bulan Januari dan Juli.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 143 Documents
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Epistemological and Axiological Analysis of ResNet18-Based Dysgraphia Classification Kirana, Kartika Candra; Handayani, Anik Nur; Patmanthara, Syaad; Eva, Nur
Generation Journal Vol 10 No 1 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i1.27419

Abstract

Based on an ontological perspective, there is a gap in feature representation and in binary dysgraphia classification using ResNet18, an area that has not been explored simultaneously. Thus, our contribution is an analysis of research on dysgraphia classification using ResNet18 that employs epistemological and axiological approaches. ResNet18 was chosen as the backbone of the proposed framework because it has shortcut connections that can degrade residues into useless features. As a representation of new knowledge, ResNet18 was pre-trained on ImageNet. Classification was tested on challenging word assignments, comprising 145 dysgraphia images and 188 non-dysgraphia images. Epoch trials were conducted to find the best architecture. The results showed that ResNet18 at epoch 10 achieved the best performance in binary classification, with a recall of up to 93.55%. This indicates that ResNet18 is sensitive to recognizing dysgraphia classes. Challenges outlined in this study serve as a foundation for further research.
Development of a CNN-Based Knowledge System for Rupiah Currency Authenticity Detection and Nominal Classification Romadhon, Ahmad Sahru; Patmanthara, Syaad; Handayani, Anik Nur
Generation Journal Vol 10 No 1 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i1.27464

Abstract

The circulation of counterfeit money in Indonesia inflicts substantial losses on the public and financial institutions. Manual verification of money is inefficient and error-prone, especially during high transaction volumes, because counterfeit bills exhibit physical characteristics nearly identical to genuine currency. To uncover counterfeit notes, an ultraviolet lamp exposes invisible ink. This research employs the Convolutional Neural Network (CNN) to detect authenticity and classify Indonesian rupiah banknotes. The CNN is trained using images of authentic banknotes captured with a camera and ultraviolet light across various denominations. The system stores the images and trains the model to identify authenticity and denomination features. Experimental results demonstrate that the proposed approach achieves high classification accuracy in distinguishing genuine and counterfeit Rupiah banknotes, as well as in recognising their respective denominations. The testing phase introduces real notes exposed to ultraviolet light, producing images that reveal invisible ink patterns. The authenticity detection achieved a 100% success rate, while the denomination recognition rates were 70% for Rp. 5,000 notes, 80% for Rp. 10,000 and Rp. 20,000 notes, and 90% for Rp. 50,000 and Rp. 100,000 notes. The system’s overall success rate is 82%.
Use of API in Data Warehouse Integration for One Data Stunting Presentation Tohir, Arik Sofan; Wiseno, Bambang; Zulvana, Zulvana
Generation Journal Vol 10 No 1 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i1.27602

Abstract

The Office of Population Control, Family Planning, Empowerment of Women and Child Protection (DP2KBP3A) of Kediri Regency is one of the key government agencies responsible for stunting management. To monitor the current status of stunting in the region, DP2KBP3A utilizes the 'Aksipenting' application (Stunting Monitoring Integration Application), a platform designed for stunting data entry across Kediri Regency. Beyond stunting monitoring, Aksipenting is also employed to track the health conditions of prospective brides, pregnant women, and postpartum mothers. The system processes data across three hierarchical levels: the cadre level, the district level, and the regency level. While DP2KBP3A manages comprehensive stunting data through this application, data integration across Regional Government Agencies (OPD) continues to face challenges regarding standarization and interoperability. This study aims to design and implement a Data Warehouse-based Application Programming Interface (API) to support the 'One Data Stunting' policy across various agencies. The system development follows a Research and Development (R&D) approach using the Rapid Application Development (RAD) method to produce a reliable, standarized, and accessible data integration solution as a foundation for decision-making in accelerating stunting reduction

Page 15 of 15 | Total Record : 143

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