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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 148 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
The Application of The C4.5 Algorithm Method Calculation The Number of Sales of Paving Production on CV. SR Lamongan Agus Setia Budi; Muhammad Hasan Wahyudi
Generation Journal Vol 10 No 2 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i2.27766

Abstract

Paving production is essential for ensuring proper road infrastructure for the community. The amount of paving demand in each month is uncertain, so it is challenging for company owners to maintain optimal inventory levels to maximize profits. The purpose of this research is to design a decision support system (SPK) to help company owners determine sales decisions in the future and make it easier to decide whether or not the paving sales are in demand. The method used in this study is the C4.5 Algorithm method. From the results of this study, it was found that the C4.5 Algorithm method is the most appropriate method in determining sales decisions in the next period. The sample used is data from CV. SR Lamongan has been in sales for the last 2 years. There are 25 paving production data used for the sample. From there, the recommended method in calculating sales in the following year can be obtained, namely using the C4.5 Algorithm method due to its higher accuracy rate, achieving up to 76%.
Classification of Diabetes Mellitus (DM) Using the Naïve Bayes Method with Chi-Square Variable Selection Farhan Arizal Ginanjar Ginanjar; Ambar Winarni; Nur'aini Muhassanah
Generation Journal Vol 10 No 2 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i2.27878

Abstract

Diabetes mellitus (DM) is a chronic disease that can cause serious complications, making early detection essential. Technological advances enable the use of data mining techniques, particularly the Naïve Bayes classification method, to support early diabetes detection. Although Chi-Square variable selection is known to improve Naïve Bayes accuracy, studies examining the impact of different significance levels remain limited. Therefore, this study applies the Naïve Bayes method with and without Chi-Square variable selection at three significance levels (α = 0.05, α = 0.01, and α = 0.001) to evaluate their effects on classification performance and identify the optimal significance level. The results show that Naïve Bayes without variable selection achieved an accuracy of 87.50%, precision of 93.01%, and recall of 86.21%. After applying Chi-Square selection, performance improved across all significance levels. At α = 0.05, the accuracy reached 87.88%, with precision of 93.06% and recall of 86.85%. At α = 0.01, accuracy increased to 88.46%, precision to 94.25%, and recall to 86.53%. The best performance was obtained at α = 0.001, achieving an accuracy of 88.65%, precision of 94.19%, and recall of 86.86%. These findings indicate that Chi-Square variable selection effectively enhances the performance of the Naïve Bayes algorithm for diabetes classification  
Sentiment Analysis of The Incident of The Downing of an Indian Rafale Fighter Jet by a Pakistani J-10CE Fighter Jet Using a Deep Learning Model Arifatur al Hafidz; Estu Sinduningrum
Generation Journal Vol 10 No 2 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i2.28062

Abstract

The rapid growth of digital technology and social media has significantly influenced the dissemination of information and public opinion worldwide. YouTube, as one of the largest social media platforms, is widely used by users to express opinions on international issues, including geopolitical conflicts. One event that attracted substantial public attention was the reported downing of an Indian Rafale fighter jet by a Pakistani J-10CE within the context of the India–Pakistan conflict. This study aims to analyze public sentiment expressed in YouTube comments related to this incident using a Deep Learning approach based on the Long Short-Term Memory (LSTM) algorithm. A total of 1,336 English-language YouTube comments were collected using the YouTube Data API v3. The data were automatically labeled into three sentiment categories: positive (38.32%), negative (31.21%), and neutral (30.46%). The research process includes text preprocessing, sentiment labeling using VADER, LSTM model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. Experimental results show that the proposed model achieved an accuracy of 61% with a macro-averaged F1 score of 0.61 on the test set. These findings indicate that the model provides moderate and stable performance in analyzing sentiment within conflict-driven geopolitical discussions on social media
Classification Of Cirebon Typical Batik Motifs Using The Convolutional Neural Network (CNN) Algorithm Firmansyah Maulana; Ahmad Fauzi; Yusuf Eka Wicaksana
Generation Journal Vol 10 No 2 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i2.28290

Abstract

The visual identification of traditional Cirebon batik motifs frequently relies on subjective observation, leading to inconsistent recognition results. To resolve this issue, this study implements a Convolutional Neural Network (CNN) with a four-layer convolutional architecture as an automated classification system. The dataset used in this research contains 1,492 images of Cirebon batik motifs, which are partitioned into a scheme of 80% for training and 20% for validation. Data augmentation is applied during the preprocessing phase to improve the variety and quality of the information processed by the model. The results show that the CNN model achieves an overall accuracy of 92%. Furthermore, the Area Under the Curve (AUC) values ranging from 0.98 to 1.00 confirm the model's strong capability in distinguishing between different motif classes, even though minor challenges persist in identifying motifs with high visual similarities, such as Singa Barong and Paksi Naga Liman.  
Implementation of Topic modeling for Multilingual Document Summarization based on Bag of Itemset Bambang Subeno
Generation Journal Vol 10 No 2 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i2.28393

Abstract

With the increasing number of electronic text documents, the process of searching and processing information has become increasingly complex, especially when these documents come from multiple sources and languages. Consequently, document summarization methods are needed to help users retrieve important information more quickly. However, existing multilingual summarization methods, such as ELSA, are limited by dataset size and the need to pre-determine themes. By integrating the Bag of Itemset representation and the Latent Dirichlet Allocatio Algorithm Modification (LDA-AM) approach, this study aims to improve the quality of multilingual document summarization. The proposed method first uses topic modeling to divide different multilingual documents into several topics. Then, for each topic, a sentence selection process is performed to generate topic-based summaries, which are then combined into a general summary. Using the ROUGE evaluation metric, experiments were conducted to compare the proposed method with baseline. Experimental results show that the proposed method performs better than ROUGE-1 with a value of 0.2623, ROUGE-2 with a value of 0.1802, and ROUGE-L with a value of 0.1231. The results indicate that in the process of summarizing multilingual documents, summary quality can be improved by combining the Bag of Itemset representation and LDA-AM.

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