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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Journal of ICT Research and Applications Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Proceeding of the Electrical Engineering Computer Science and Informatics JADECS (Journal of Art, Design, Art Education and Culture Studies) Jurnal Teknologi dan Sistem Komputer SISFOTENIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Information System Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Journal of Fuzzy Systems and Control (JFSC) Journal of Information System and Application Development Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
De Rosal Ignatius Moses Setiadi
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Other

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Journal : Journal of Computing Theories and Applications

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Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine Gomiasti, Fita Sheila; Warto, Warto; Kartikadarma, Etika; Gondohanindijo, Jutono; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10106

Abstract

This research aims to improve the effectiveness of lung cancer classification performance using Support Vector Machines (SVM) with hyperparameter tuning. Using Radial Basis Function (RBF) kernels in SVM helps deal with non-linear problems. At the same time, hyperparameter tuning is done through Random Grid Search to find the best combination of parameters. Where the best parameter settings are C = 10, Gamma = 10, Probability = True. Test results show that the tuned SVM improves accuracy, precision, specificity, and F1 score significantly. However, there was a slight decrease in recall, namely 0.02. Even though recall is one of the most important measuring tools in disease classification, especially in imbalanced datasets, specificity also plays a vital role in avoiding misidentifying negative cases. Without hyperparameter tuning, the specificity results are so poor that considering both becomes very important. Overall, the best performance obtained by the proposed method is 0.99 for accuracy, 1.00 for precision, 0.98 for recall, 0.99 for f1-score, and 1.00 for specificity. This research confirms the potential of tuned SVMs in addressing complex data classification challenges and offers important insights for medical diagnostic applications.
Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition Rachman, Rahadian Kristiyanto; Setiadi, De Rosal Ignatius Moses; Susanto, Ajib; Nugroho, Kristiawan; Islam, Hussain Md Mehedul
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10459

Abstract

In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to transcend the limitations of CNNs by capturing image features in a comprehensive global context. This research embarks on an ambitious journey to refine and adapt the ViT Base(B) transfer learning model for the nuanced task of rice disease recognition. Through meticulous reconfiguration, layer augmentation, and hyperparameter tuning, the study tests the model's prowess across both balanced and imbalanced datasets, revealing its remarkable ability to outperform traditional CNN models, including VGG, MobileNet, and EfficientNet. The proposed ViT model not only achieved superior recall (0.9792), precision (0.9815), specificity (0.9938), f1-score (0.9791), and accuracy (0.9792) on challenging datasets but also established a new benchmark in rice disease recognition, underscoring its potential as a transformative tool in the agricultural domain. This work not only showcases the ViT model's superior performance and stability across diverse tasks and datasets but also illuminates its potential to revolutionize rice disease recognition, setting the stage for future explorations in agricultural AI applications.
Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost Ako, Rita Erhovwo; Aghware, Fidelis Obukohwo; Okpor, Margaret Dumebi; Akazue, Maureen Ifeanyi; Yoro, Rume Elizabeth; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Odiakaose, Chris Chukwufunaya; Abere, Reuben Akporube; Emordi, Frances Uche; Geteloma, Victor Ochuko; Ejeh, Patrick Ogholuwarami
Journal of Computing Theories and Applications Vol. 2 No. 1 (2024): JCTA 2(1) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10562

Abstract

Customer attrition has become the focus of many businesses today – since the online market space has continued to proffer customers, various choices and alternatives to goods, services, and products for their monies. Businesses must seek to improve value, meet customers' teething demands/needs, enhance their strategies toward customer retention, and better monetize. The study compares the effects of data resampling schemes on predicting customer churn for both Random Forest (RF) and XGBoost ensembles. Data resampling schemes used include: (a) default mode, (b) random-under-sampling RUS, (c) synthetic minority oversampling technique (SMOTE), and (d) SMOTE-edited nearest neighbor (SMOTEEN). Both tree-based ensembles were constructed and trained to assess how well they performed with the chi-square feature selection mode. The result shows that RF achieved F1 0.9898, Accuracy 0.9973, Precision 0.9457, and Recall 0.9698 for the default, RUS, SMOTE, and SMOTEEN resampling, respectively. Xgboost outperformed Random Forest with F1 0.9945, Accuracy 0.9984, Precision 0.9616, and Recall 0.9890 for the default, RUS, SMOTE, and SMOTEEN, respectively. Studies support that the use of SMOTEEN resampling outperforms other schemes; while, it attributed XGBoost enhanced performance to hyper-parameter tuning of its decision trees. Retention strategies of recency-frequency-monetization were used and have been found to curb churn and improve monetization policies that will place business managers ahead of the curve of churning by customers.
Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Warto, Warto; Gondohanindijo, Jutono; Ojugo, Arnold Adimabua
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11638

Abstract

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.
A Quantum Circuit Learning-based Investigation: A Case Study in Iris Benchmark Dataset Binary Classification Akrom, Muhamad; Herowati, Wise; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications Vol. 2 No. 3 (2025): JCTA 2(3) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11779

Abstract

This study presents a Quantum Machine Learning (QML) architecture for perfectly classifying the Iris flower dataset. The research addresses improving classification accuracy using quantum models in machine-learning tasks. The objective is to demonstrate the effectiveness of QML approaches, specifically the Variational Quantum Circuit (VQC), Quantum Neural Network (QNN), and Quantum Support Vector Machine (QSVM), in achieving high performance on the Iris dataset. The proposed methods result in perfect classification, with all models attaining accuracy, precision, recall, and an F1-score of 1.00. The main finding is that the QML architecture successfully achieves flawless classification, contributing significantly to the field. These results underscore the potential of QML in solving complex classification problems and highlight its promise for future applications across various domains. The study concludes that QML techniques can offer transformative solutions in machine learning tasks, particularly those leveraging VQC, QNN, and QSVM.
Feature Fusion with Albumentation for Enhancing Monkeypox Detection Using Deep Learning Models Pratama, Nizar Rafi; Setiadi, De Rosal Ignatius Moses; Harkespan, Imanuel; Ojugo, Arnold Adimabua
Journal of Computing Theories and Applications Vol. 2 No. 3 (2025): JCTA 2(3) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12255

Abstract

Monkeypox is a zoonotic disease caused by Orthopoxvirus, presenting clinical challenges due to its visual similarity to other dermatological conditions. Early and accurate detection is crucial to prevent further transmission, yet conventional diagnostic methods are often resource-intensive and time-consuming. This study proposes a deep learning-based classification model by integrating Xception and InceptionV3 using feature fusion to enhance performance in classifying Monkeypox skin lesions. Given the limited availability of annotated medical images, data augmentation was applied using Albumentation to improve model generalization. The proposed model was trained and evaluated on the Monkeypox Skin Lesion Dataset (MSLD), achieving 85.96% accuracy, 86.47% precision, 85.25% recall, 78.43% specificity, and an AUC score of 0.8931, outperforming existing methods. Notably, data augmentation significantly improved recall from 81.23% to 85.25%, demonstrating its effectiveness in enhancing sensitivity to positive cases. Ablation studies further validated that augmentation increased overall accuracy from 82.02% to 85.96%, emphasizing its role in improving model robustness. Comparative analysis with other models confirmed the superiority of our approach. This research enhances automated Monkeypox detection, offering a robust and efficient tool for low-resource clinical settings. The findings reinforce the potential of feature fusion and augmentation in improving deep learn-ing-based medical image classification, facilitating more reliable and accessible disease identification.
Aspect-Based Sentiment Analysis on E-commerce Reviews using BiGRU and Bi-Directional Attention Flow Setiadi, De Rosal Ignatius Moses; Warto, Warto; Muslikh, Ahmad Rofiqul; Nugroho, Kristiawan; Safriandono, Achmad Nuruddin
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12376

Abstract

Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.
Integrating Hybrid Statistical and Unsupervised LSTM-Guided Feature Extraction for Breast Cancer Detection Setiadi, De Rosal Ignatius Moses; Ojugo, Arnold Adimabua; Pribadi, Octara; Kartikadarma , Etika; Setyoko, Bimo Haryo; Widiono, Suyud; Robet, Robet; Aghaunor, Tabitha Chukwudi; Ugbotu, Eferhire Valentine
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12698

Abstract

Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To address class imbalance, the training set was balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, an LSTM encoder extracted non-linear latent features from the selected features. A fusion strategy was applied by concatenating the statistical and latent features, followed by re-selection of the top 30 features. The final classification was performed using a Support Vector Machine (SVM) with RBF kernel and evaluated using 5-fold cross-validation and a held-out test set. Experimental results showed that the proposed method achieved an average training accuracy of 98.13%, F1-score of 98.13%, and AUC-ROC of 99.55%. On the held-out test set, the model reached an accuracy of 99.30%, precision of 100%, and F1-score of 99.05%, with an AUC-ROC of 0.9973. The proposed pipeline demonstrates improved generalization and interpretability compared to existing methods such as LightGBM-PSO, DHH-GRU, and ensemble deep networks. These results highlight the effectiveness of combining statistical selection and LSTM-based latent feature encoding in a balanced classification framework.
Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction Kusuma, Muh Galuh Surya Putra; Setiadi, De Rosal Ignatius Moses; Herowati, Wise; Sutojo, T.; Adi, Prajanto Wahyu; Dutta, Pushan Kumar; Nguyen, Minh T.
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): JCTA 3(2) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14873

Abstract

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.
Co-Authors Abdul Syukur Abdussalam Abdussalam Abdussalam Abdussalam Abdussalam Abere, Reuben Akporube Abugor Okpako, Ejaita Achmad Nuruddin Safriandono Adhitya Nugraha Adigwe, Wilfred Adimabua Ojugo, Arnold Adityawan, Harish Trio Afotanwo, Anderson Afridiansyah, Rahmanda Aghaunor, Tabitha Chukwudi Aghware, Fidelis Obukohwo Agustina, Feri Ahmad Salafuddin Ajib Susanto Akazue, Maureen Ifeanyi Akbar Aji Nugroho Akbar, Ismail Akhmad Dahlan Ako, Rita Erhovwo Akrom, Muhamad Alvin Faiz Kurniawan Amir Musthofa Anak Agung Gede Sugianthara Andik Setyono Antonio Ciputra Antonius Erick Handoyo Aprilah, Thania Araaf, Mamet Adil Arya Kusuma Binitie, Amaka Patience Budi Widjajanto Budi, Setyo Cahaya Jatmoko Chaerul Umam Chaerul Umam Christian, Henry Christy Atika Sari Chukwudi Aghaunor, Tabitha Cinantya Paramita Ciputra, Antonio Danu Hartanto Daurat Sinaga Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Devi Purnamasari Dhendra Marutho Dian Kristiawan Nugroho Dumebi Okpor, Margaret Dutta, Pushan Kumar Dwi Puji Prabowo Dwi, Bernadetta Sri Endah Eboka, Andrew Okonji Edy Winarno Egia Rosi Subhiyakto Ejeh, Patrick Ogholuwarami Eko Hari Rachmanto Eko Hari Rachmawanto Eko Septyasari Elkaf Rahmawan Pramudya Eluemnor Anazia, Kizito Emordi, Frances Uche Enadona Oweimeito, Amanda Erhovwo Ako, Rita Erlin Dolphina Erna Zuni Astuti Etika Kartikadarma Farah Zakiyah Rahmanti Farooq, Omar Farroq, Omar Ferda Ernawan Firnando, Fadel Muhamad Fittria Shofrotun Ni'mah Florentina Esti Nilawati Florentina Esti Nilawati Gan, Hong-Seng Geteloma, Victor Ochuko Ghosal, Sudipta Kr Giovani Ardiansyah Gomiasti, Fita Sheila Hanny Haryanto Henry Christian Herowati, Wise Heru Agus Santoso Ibnu Gemaputra Ramadhan Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibor, Ayei Egu Ihya Ulumuddin, Dimas Irawan Imanuel Harkespan Imanulloh, Satrio Bagus Indra Gamayanto Islam, Hussain Md Mehedul Isworo Nugroho Iwan Setiawan Wibisono Jutono Gondohanindijo, Jutono Kartikadarma , Etika Kusuma, Edi Jaya Kusuma, Muh Galuh Surya Putra L. Budi Handoko Lalang Erawan M. Dalvin Marno Putra Md Kamruzzaman Sarker Md Kamruzzaman Sarker Minh T. Nguyen, Minh T. Mohammad Rizal, Mohammad Muchamad Akbar Nurul Adzan Muhamada, Keny Mulyono, Ibnu Utomo Wahyu Musfiqur Rahman Sazal Muslikh, Ahmad Rofiqul Mustofa, Fachrul Nartriani, Yulian Dwi Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Nova Rijati Nugroho, Sandy Ochuko Geteloma, Victor Octara Pribadi Odiakaose , Christopher Chukwufunaya Odiakaose, Chris Chukwufunaya Odiakaose, Christopher Chukwufunaya Ojugo, Arnold Adimabua Okpor, Margaret Dumebi Omar Farooq Omoruwou, Felix Pradana, Akbar Ganang Prajanto Wahyu Adi, Prajanto Wahyu Pratama, Nizar Rafi Purnamasari, Devi Rachman, Rahadian Kristiyanto Ramadhan, Pramudia Ricardus Anggi Pramunendar Robet Robet Ruri Suko Basuki Sahu, Aditya Kumar Santoso, Siane Sarker, Md Kamruzzaman Sasono Wibowo Setiawan, Marcell Adi Setyoko, Bimo Haryo Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Stefanus Santosa Sudibyo, Usman Sukamto, Titien S Sunarjo, Macellino Setyaji Suyud Widiono Syahputra, Zulfikar Adi Syahroni Wahyu Iriananda, Syahroni Wahyu T Sutojo T. Sutojo Tan Samuel Permana Tan Samuel Permana Titien S sukamto Trisnapradika, Gustina Alfa Ugbotu, Eferhire Valentine Valentine Ugbotu, Eferhire Warto - Wellia Shinta Sari Wellia Shinta Sari Wibowo, Mochammad Abdurrochman Ari Wijaya, Nantalira Niar Wijayanti, Ella Budi Yoro, Rume Elizabeth Yusianto Rindra Zuama, Leygian Reyhan