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All Journal Cakrawala Pendidikan IBDA` : Jurnal Kajian Islam dan Budaya Jurnal The Messenger Media Penelitian Pendidikan : Jurnal Penelitian dalam Bidang Pendidikan dan Pengajaran Indonesian Journal of History Education Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Artefak JIIP (Jurnal Ilmiah Ilmu Pendidikan) International Journal of Multi Discipline Science (IJ-MDS) J-KIP (Jurnal Keguruan dan Ilmu Pendidikan) Journal of Comprehensive Science Safari : Jurnal Pengabdian Masyarakat Indonesia International Journal of Islamic Thought and Humanities Jurnal Pengabdian Sosial Indonesia Journal of Computing Theories and Applications Danadyaksa Historica
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Journal : Journal of Computing Theories and Applications

 1 
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.
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.
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.
Co-Authors Abdul Aziz Abdur Rahim, Abdur Achmad Buchori Achmad Nuruddin Safriandono Adela Salsabila Adiwilaga, Mochamad Hisam Akhmad Arif Musadad Almuqtadir, Thariq Ananda Dyta Marlina Setiawan Anwar Nababan, Siharta Leman Arief Rahman Assidiqi, Muadz Budhijana, R Bambang De Rosal Ignatius Moses Setiadi Dian Kristiawan Nugroho Djono Djono Dody Darly Ediono, Suryo Erna Sulistyorini Etika Kartikadarma Fikri, Muhamad Mufid Al Ganda Febri Kurniawan Gomiasti, Fita Sheila Haris Firmansyah Hermanu Joebagio Ika Rahmatika Chalimi Indarti, Adrian Jamin Safi Jutono Gondohanindijo, Jutono Kaswoto, Junet Khoirudin, Achmad Didik kusrini, Desi Leo Agung S Mamdukh Budiman Muhammad Lutfi Muslikh, Ahmad Rofiqul Ngatmini Ngatmini Ojugo, Arnold Adimabua Panca Kusuma, Bangbang Putri, Supanca Ade Qomar, Akhlis Syamsal Rahayu, Ratih Sariyatun Sariyatun Sholeh, Kabib Sofyan Hadi Sudarmanto, Eko Sudarto Sudarto Sugara, Badra Supriyono Supriyono Sutimin, Leo Agung Syaeful Rohman, Pupung Syahputra, Guntur Adham Syahroni Wahyu Iriananda, Syahroni Wahyu Titis Srimuda Pitana, Titis Srimuda Triana Rejekiningsih dkk Try Adhi Bangsawan Tubagus Ilham Nugaraha Wahyono, Zubaidi Wahyu, Itama Citra Dewi Kurnia