Article Per Year (5 Year)
p-Index From 2021 - 2026
0.961
P-Index
This Author published in this journals
All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Teknik Informatika (JUTIF) Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal Informatika: Jurnal Pengembangan IT
Wijaya, Anggito Karta
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Mathematics

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
CLASSIFICATION OF RUPIAH CURRENCY IN THE FORM OF PAPER USING THE MOBILENETV3 LARGE METHOD Wijaya, Anggito Karta; Royan, Ando Zamhariro
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.2040

Abstract

Money plays an important role in everyday life as a legal tender and a symbol of a country's economic strength. The ability to accurately classify rupiah banknotes has many practical applications such as in automated payment systems, currency exchange, and cash management. However, conventional classification approaches based on digital image processing and image processing techniques are often limited in terms of accuracy and computational efficiency, especially when dealing with a variety of banknote conditions such as wrinkles, stains, or damage. This research aims to propose a new approach by utilising the MobileNetV3 Large architecture, an efficient and lightweight deep learning model, to address the challenges of paper currency classification. The main objective is to improve classification accuracy while minimising computational resources. The dataset used consists of 2873 images of paper rupiah currency of various denominations and conditions from seven classes. These images were processed and trained using the MobileNetV3 Large model that has been customised for this classification task by applying various data augmentation techniques. Experimental results show that the proposed approach is able to achieve 100% classification accuracy on a test dataset with a relatively small model size so that it can be run efficiently on mobile devices or embedded systems. This research makes an important contribution to the development of accurate and efficient rupiah banknote classification techniques for various practical applications in the future.
Advanced Sleep Disorder Classification: An ML-Based Study with Optuna for Model Optimization Gaib, Amalan Fadil; Mahayudha, I Gusti Ngurah Bagus Ferry; Wijaya, Anggito Karta; Andini, Nurul; Royan, Ando Zamhariro
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7380

Abstract

Hyperparameter optimization plays a crucial role in improving the performance of machine learning models, particularly in sleep disorder classification. However, searching for optimal hyperparameters often requires extensive computational resources and prolonged execution time. To address this issue, this study implements Optuna, a hyperparameter optimization framework based on the Tree-structured Parzen Estimator (TPE) and pruning mechanisms to enhance the efficiency of model configuration search adaptively. This study compares the performance of Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Random Forest, and Multi-Layer Perceptron (MLP) in classifying sleep disorders based on health and lifestyle variables. The data undergoes several preprocessing steps, including handling missing values, encoding, normalization (StandardScaler), and class balancing using SMOTE. The models are then developed and optimized using Optuna to determine the best hyperparameter configurations. Evaluation is conducted using Accuracy, Precision, Recall, and F1-score. Experimental results show that before optimization, the Random Forest model achieved an accuracy of 94%, XGBoost 96%, SVM 93%, and MLP 96%. After being optimized with Optuna, accuracy increased to 97% for Random Forest, 97% for XGBoost, 98% for SVM, and 97% for MLP. This improvement indicates that Optuna effectively enhances model performance, especially for SVM, which experienced the most significant accuracy boost after optimization. Thus, the use of Optuna not only accelerates hyperparameter tuning but also improves the efficiency and accuracy of machine learning models in sleep disorder classification. This approach has great potential in supporting AI-based medical diagnosis systems, enabling faster and more accurate detection of sleep disorders.
Perbandingan IndoBERT dan IndoRoBERTa Untuk Analisis Sentimen Pada Film Dokumenter Dirty Vote Apriansyah, Fadhel Muhammad; Ramadhan, Teguh Ikhlas; Hidayat, Cepi Rahmat; Wijaya, Anggito Karta
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8607

Abstract

Sentiment analysis is a technique in Natural Language Processing (NLP) used to identify and categorize opinions or emotions in text. This study compares the performance of two Transformer-based models, IndoBERT and IndoRoBERTa, in analyzing sentiment toward the documentary film Dirty Vote. The research process includes data collection, text preprocessing, lexicon-based sentiment labeling, and model evaluation using K-Fold Cross-Validation. The results show that IndoBERT achieved an average accuracy of 99%, higher than IndoRoBERTa, which achieved 94%. IndoBERT also demonstrated better alignment with lexicon-based labeling in classifying positive, negative, and neutral sentiments. In terms of architecture, IndoBERT employs static masking, while IndoRoBERTa applies dynamic masking, leading to differences in the models' sensitivity to textual meaning. IndoBERT tends to provide more definitive classifications for opinions or strong criticisms, whereas IndoRoBERTa more frequently categorizes ambiguous comments as neutral sentiment. The conclusion of this study indicates that IndoBERT outperforms IndoRoBERTa in sentiment analysis of the documentary film Dirty Vote, both in terms of accuracy and consistency with lexicon-based labeling. These findings provide insights into the effectiveness of Transformer-based models for sentiment analysis in the Indonesian language and can serve as a reference for further NLP model development.
Comparative Analysis of LSTM, GRU and Meta Prophet Stock Forecasting Methods with Var-Es Risk Evaluation Wijaya, Anggito Karta; Pandunata, Priza; Hidayat, Muhamad Arief
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7259

Abstract

This study compares the performance of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Prophet models in predicting real estate stock prices on the Indonesia Stock Exchange (2019–2024) and evaluates investment risks using Value at Risk (VaR) and Expected Shortfall (ES). Historical stock data underwent normalization and dataset splitting (ratios of 70:30, 80:20, and 90:10), with time steps of 40, 60, and 100, and three dense layers (25 and 50 neurons). Performance was evaluated using MSE, RMSE, MAE, and MAPE. Results indicate that GRU achieved the highest accuracy, especially for PWON, ASRI, and DILD stocks, with the lowest MSE values (PWON: 120.7436, ASRI: 26.3150, DILD: 28.9713). LSTM showed competitive performance, while Prophet had the lowest accuracy for short-term predictions. Risk analysis revealed Prophet had the lowest historical risk but the highest risk for 150-day forecasts. LSTM demonstrated superior long-term risk mitigation. Comparison with actual prices revealed that LSTM and GRU more accurately captured stock price fluctuations than Prophet, particularly during sharp price changes. GRU provided the closest predictions in the 150-day forecast scenario, making it the most effective model for real estate stock forecasting. This study offers valuable insights for investors and portfolio managers in understanding stock price movements and managing investment risks in the real estate sector.
An Explainable Deep Learning Approach for Brain Tumor Detection Using MobileNet and Grad-CAM Visualization Gaib, Amalan Fadil; Ardiyansa, Safrizal Ardana; Wijaya, Anggito Karta; Julianto, Eric; Mahayudha, I Gusti Ngurah Bagus Ferry; Royan, Ando Zamhariro
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.35901

Abstract

Brain tumor detection remains a significant challenge due to the complex variations in tumor appearance. Although deep learning models have demonstrated high accuracy, their limited interpretability hinders clinical adoption. To address this issue, this study integrates Gradient-weighted Class Activation Mapping (Grad-CAM) into Convolutional Neural Networks (CNNs) to enhance the visual interpretability of predictions. Grad-CAM extends Class Activation Mapping (CAM) and is applicable to a wide range of deep learning architectures. The primary contribution of this work is the demonstration that combining Grad-CAM with MobileNet architectures yields an interpretable and efficient framework for diagnosis of brain tumor, effectively balancing accuracy, computational efficiency, and clinical transparency. Using a Brain Tumor MRI dataset, MobileNetV4 achieved an accuracy of 98.29% with the shortest training time (1738.82 seconds) and an ROC accuracy of 99.96%. MobileNetV3 achieved 99.62% accuracy with an ROC accuracy of 99.92%. Grad-CAM effectively highlighted tumor regions while showing uniform attention in non-tumor cases, thereby reducing false positives. These results demonstrate that lightweight models can achieve a strong balance between predictive performance, training efficiency, and interpretability. The proposed framework thus supports the development of explainable and efficient diagnostic tools for clinical practice.
Co-Authors Andini, Nurul Apriansyah, Fadhel Muhammad Ardiyansa, Safrizal Ardana Cepi Rahmat Hidayat Gaib, Amalan Fadil Hidayat, Muhamad Arief Julianto, Eric Mahayudha, I Gusti Ngurah Bagus Ferry Priza Pandunata Ramadhan, Teguh Ikhlas Royan, Ando Zamhariro Zanestri, Tegar Fadillah