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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik ITS IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Bulletin of Electrical Engineering and Informatics Proceeding International Conference on Information Technology and Business Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics International conference on Information Technology and Business (ICITB) JAIS (Journal of Applied Intelligent System) Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Emerging Science Journal Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Makara Journal of Technology Sewagati Jurnal Penelitian IPTEKS
Riyanarto Sarno
Departemen Informatika Institut Teknologi Sepuluh Nopember Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Attention-enhanced wasserstein GAN for agricultural market data imputation Ulima Inas Shabrina; Riyanarto Sarno; Ratih Nur Esti Anggraini; Agus Tri Haryono
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10549

Abstract

Crop price prediction in ASEAN markets is hindered by incomplete and inconsistent data, making data imputation essential. This study introduces the Wasserstein generative adversarial imputation network with attention (WGAIN+Att) to improve data quality for forecasting. Four configurations—GAIN, GAIN+Att, WGAIN, and WGAIN+Att—were evaluated on rice, corn, and soybean datasets (1961–2023). Results show that WGAIN+Att, particularly when attention is applied across all matrices (x, m, and z), achieved the best imputation performance, minimizing mean absolute error (MAE) and preserving statistical distributions, with optimal results at a 0.1 missing rate and 0.9 hint rate. In predictive tasks, GAIN-based imputations combined with convolutional neural networks (CNN)-long short-term memory networks (LSTM)-gated recurrent units (GRU) models consistently outperformed others in forecasting accuracy, achieving lower MAE and root mean squared error (RMSE). The findings highlight the role of attention in stabilizing imputation and ensuring realistic reconstructions, while also showing that aligning imputation with forecasting objectives improves agricultural price predictions.
Tuberculosis severity classification from exhaled breath using an electronic nose system with Inception-1D and ResNet-1D Dava Aulia; Riyanarto Sarno; Muhammad Rivai; Muhammad Amin; Alfian Nur Rosyid; Kelly Rossa Sungkono
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.12212

Abstract

Exhaled breath contains volatile organic compounds (VOCs) that can be analyzed for tuberculosis (TB) detection. Electronic nose systems have demonstrated promise for this application; however, accurately distinguishing between healthy individuals and TB patients with different severity levels, namely, low and high TB, remains challenging and requires advanced deep-learning methods. Unlike previous studies that focused on binary TB detection, this study proposes an electronic nose system combined with one-dimensional deep learning models, including residual network (ResNet), visual geometry group (VGG), EfficientNet, and Inception architectures, for multiclass classification of healthy individuals and TB severity levels using exhaled-breath analysis. The results show that Inception-1D and ResNet-1D achieve the best performance for healthy and TB classification, each attaining an F1-score of 94.99%. For TB severity classification, ResNet-1D outperforms other models with an F1-score of 68.50%. Meanwhile, Inception-1D yields the highest performance on the healthy and TB severity classification dataset, with an F1-score of 74.07%. Moreover, dimensionality reduction using principal component analysis (PCA) reduces the healthy and TB dataset to ten principal components and improves the F1-score to 95.64% with Inception-1D. Overall, the proposed framework successfully captures distinctive gas sensor-response patterns associated with TB presence and severity and may support clinical decision-making in resource-limited healthcare settings.
Co-Authors A.A. Ketut Agung Cahyawan W ABDUL MUNIF ABDUL MUNIF Adhatus Solichah Ahmadiyah Adhatus Solichah Ahmadiyah, Adhatus Solichah Afrianda Cahyapratama Agung Wiratmo Agus Tri Haryono Agus Tri Haryono, Agus Tri Agus Zainal Arifin Ahmad Saikhu Ahmad Yusuf Ardiansyah Ahmadiyah, Adhatus Solichah Ainul Yaqin Alfian Ma'arif Alfian Ma’arif Alief Yoga Priyanto Andrean Hutama Koosasi Anggraini, Ratih Nur Esti Anto Satriyo Nugroho Ardy Januantoro Arifin, Mohammad Nazir Aziz Fajar Azzam Jihad Ulhaq Azzam Jihad Ulhaq Bagus Priambodo Bagus Setya Rintyarna Bambang Jokonowo Bilqis Amaliah Buliali, Joko Lianto Cahyaningtyas Sekar Wahyuni Chastine Fatichah Chastine Fatichah Chastine Fatihah Danica Virlianda Marsha Daniel Oranova Siahaan Dava Aulia Dedy Rahman Wijaya Dewi Rahmawati Dieky Adzkiya Dini Adni Navastra Dwi Sunaryo Dwi Sunaryono Dwo Sunaryono Edi Faisal Effendi, Yutika Amelia Endang Wahyu Pamungkas Faisal Rahutomo Faizal Anugrah Bhaswara Fajar, Aziz Farza Nurifan Fauzan Prasetyo Fauzan, Hermawan Feri Eko Herman Fernandes Sinaga Fika Hastarita Rachman Fony Revindasari Gabriel Sophia Gelu, Leonard Peter Gita Intani Budiawati HANA RATNAWATI Hendra Darmawan Hermawan Hermawan Hidayat, Husnul I Gusti Agung Chintya Prema Dewi Ida Ayu Putu Sri Widnyani Imam Cholissodin Imam Ghozali Imam Ghozali Imam Mukhlash Imam Riadi Ismail Eko Prayitno Rozi Isnaini Nurul Kurnia Sari Isnaini Nurul KurniaSari Jan Claes Johanes Andre Ridoean Joko Buliali Kartini Kartini Kelly Rosa Sungkono Kelly Rossa Sungkono Kholed Langsari Kholed Langsari Lailil Muflikhah Langsari, Kholed M. Jupri Margo Pudjiantara Mochammad Faris Ponighzwa Rizkanda Mohammad Fikri Mohammad Nazir Arifin Muhammad Ainul Yaqin Muhammad Amin Muhammad Nezar Mahardika Muhammad Nicko Rahmadano Muhammad Rivai Muhammad Suzuri Hitam Muhammad Taufiqulsa’di Muhammad Taufiqulsa’di Nashi Widodo Navinda Meutia Navinda Meutia Nurlaili, Afina Lina Nurlaili, Afina Lina Nurul Fajrin Ariyani Nurul Fajrin Ariyani Nurul Fajrin Ariyani Peter Gelu Pradipta Ghusti Puji Budi Setia Asih Purwono, Purwono R.V Hari Ginardi Rachmad Abdullah Rachmad Abdullah Rahmawati, Dewi Ratih Nur Esti Anggraeni Ratih Nur Esti Anggraini Ratih Nur Esti Anggraini, Ratih Nur Esti Rizky Widhanto Herlambang Rosyid, Alfian Nur Ryco Puji Setyono Salsabila, Salsabila Sarwosri Sarwosri Setiaputra G, Riswandy Shintami Chusnul Hidayati Sholiq Sinarring Azi Laga Siti Maimunah Siti Maimunah Siti Rochimah Solichul Huda Suhariyanto Suhariyanto Suhariyanto Suhariyanto Sungkono, B.J. Santosa Tohari Ahmad Tyas, Salsabila Mazya Permataning Ulima Inas Shabrina Umi Salamah Untoro, Meida Cahyo Widya Nilam Rumana Widyasari Ayu Wibowo Yutika Amelia Zahrul Zizki Dinanto Zahrul Zizki Dinanto