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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Transmisi: Jurnal Ilmiah Teknik Elektro FIKI 2013 Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Pseudocode International Journal of Advances in Intelligent Informatics Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Moneter : Jurnal Keuangan dan Perbankan Community: Jurnal Pengabdian Pada Masyarakat
Ika Novita Dewi
Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Enhancing Liver Cirrhosis Staging Accuracy using Optuna-Optimized TabNet Arifin, Muhammad Farhan; Dewi, Ika Novita; Salam, Abu; Utomo, Danang Wahyu; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.11011

Abstract

Liver cirrhosis is a progressive chronic disease whose early detection poses a clinical challenge, making accurate severity staging crucial for patient management. This research proposes and evaluates a TabNet deep learning model, specifically designed for tabular data, to address this challenge. In the initial evaluation, a baseline TabNet model with its default configuration achieved a baseline accuracy of 65.11% on a public clinical dataset. To enhance performance, hyperparameter optimization using Optuna was implemented, which successfully increased the accuracy significantly to 70.37%, with precision, recall, and F1-score metrics each reaching 70%. The model's discriminative ability was also validated as reliable in multiclass classification through AUC metric evaluation. In addition to accuracy improvements, the model's interpretability was validated through the identification of key predictive features such as Prothrombin and Hepatomegaly, which align with clinical indicators. This study demonstrates that Optuna-optimized TabNet is an effective and interpretable approach, possessing significant potential for integration into clinical decision support systems to support a more precise diagnosis of liver cirrhosis.
Enhancing Interpretable Multiclass Lung Cancer Severity Classification using TabNet Norman, Maria Bernadette Chayeenee; Dewi, Ika Novita; Salam, Abu; Utomo, Danang Wahyu; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11417

Abstract

Lung cancer poses a significant global mortality challenge, with early clinical detection hindered by non-specific symptoms making accurate diagnosis dependent on extracting subtle patterns from often complex medical tabular data. Traditional machine learning approaches often fall short in capturing intricate patterns within such heterogeneous datasets, hindering effective clinical decision support. This research introduces TabNet, an interpretable deep learning architecture, for multiclass lung cancer severity prediction (low, medium, high). Utilizing the Kaggle Lung Cancer dataset, our methodology leverages TabNet's unique attention-based feature selection for end-to-end processing of tabular data, enabling adaptive identification of key predictors and crucial model interpretability. To effectively assess its predictive capabilities and ensure robust performance, the model was trained with default configurations and validated through stratified 5-fold cross-validation, achieving outstanding performance on the test set: 98.50% accuracy, a 0.98 F1-score, and a 0.9996 macro-AUC-ROC. Beyond its robustness, confirmed by stable learning curves, interpretability analysis highlighted 'Genetic Risk' and 'Shortness of Breath' as dominant factors. Our results underscore TabNet's efficacy as a reliable, robust, and inherently interpretable solution, offering significant potential to improve the precision and transparency of lung cancer severity assessment in clinical practice.
Co-Authors Abas Setiawan Abdul Syukur Abdul Syukur Abu Salam Adhitya Nugraha Adriani, Mira Riezky Agung Priyo Utomo, Rino Alzami, Farrikh Ardytha Luthfiarta Arifin, Muhammad Farhan Arry Maulana Syarif, Arry Maulana Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Ayuningsih, Dewi Putri Azhari Azhari Bramantyo, Satrio Bisma Candra Irawan Catur Supriyanto Diana Aqmala Dwi Puji Prabowo, Dwi Puji Dzaki, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erwin Yudi Hidayat Erwin Yudi Hidayat Fahri Firdausillah Fajar Agung Nugroho Hafiizhudin, Lutfi Azis Handayani, Sri Haresta, Alif Agsakli Hasan Asari Heribertus Himawan Ifan Rizqa Indrayani, Heni Irawan, Enrico Irvan Muzakkir Irvan Muzakkir Isworo, Slamet Junta Zeniarja Khafiizh Hastuti Khariroh, Shofiyatul Kurniawan, Defri Laurent, Feby Lisa Mardiana Marjuni, Aris Megantara, Rama Aria Muljono Muljono Mumtaz, Najma Amira Norman, Maria Bernadette Chayeenee Octaviani, Dhita Aulia Priyo Utomo, Rino Agung Puri Sulistiyawati Pusung, Elvanro Marthen Ramadhan Rakhmat Sani Reza, Ivan Muhammad Ricardus Anggi Pramunendar Rifamuthia, Titis Ritzkal, Ritzkal Salsabila, Shafa Saputra, Filmada Ocky Sholikun, Sholikun Sindhu Rakasiwi Sri Winarno Subowo, Moh Hadi Sulistyono, Teguh Syarifah, Ulima Muna Utomo, Danang Wahyu Wellia Shinta Sari Wibowo, Isro' Rizky Yanuaresta, Dianna Zainal Arifin Hasibuan