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All Journal International Journal of Electrical and Computer Engineering Jurnal Informatika Jurnal Teknik Elektro Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Informatika Komputer Jurnal Manajemen Teknologi Jurnal Momentum TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics Jurnal Teknobisnis Seminar Nasional Informatika (SEMNASIF) Journal of Information Systems Engineering and Business Intelligence The Asian Journal of Technology Management (AJTM) Indonesian Journal on Computing (Indo-JC) Proceeding of the Electrical Engineering Computer Science and Informatics JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Filsafat Indonesia Jurnal Teknik Informatika (JUTIF) International Journal of Social Service and Research Media Riset Akuntansi Auditing & Informasi Jurnal Komputer Bisnis JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Kridanto Surendro
Institut Teknologi Bandung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Social Sciences

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Unsupervised Anomaly Detection in Hospital Wastewater Effluent Using Convolutional Autoencoder Hibban, Daffa Maulana; Surendro, Kridanto
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Abstract

Background: Hospital wastewater treatment plants (WWTPs) play a crucial role in maintaining environmental sustainability. However, conventional monitoring has difficulty identifying minor differences in effluent quality, leading to non-compliance. While machine learning is increasingly applied in water quality analysis, the specific application of deep representation learning in hospital effluent analysis, focusing on identifying anomalies within stable and low variation factors, is not much explored. Objective: This study aims to evaluate the effectiveness of a proposed Convolutional Autoencoder (Conv-AE) for anomaly detection in the effluent of hospital WWTP. To ensure the efficacy of the algorithm, it is compared with two popular statistical algorithms: Isolation Forest (IF) and One-Class Support Vector Machine (OCSVM). Methods: Internet of Things (IoT) sensor data covering pH, temperature, Total Dissolved Solids (TDS), and ammonia gas parameters were collected from the effluent tank of a hospital WWTP. The Conv-AE model was designed to learn the latent nonlinear representations of normal effluent patterns. The model’s performance was evaluated using precision, recall, F1-score, accuracy, and inference time metrics. Results: The proposed Conv-AE model performed best in terms of detection, having the best values ​​for all three metrics, with a recall of 0.980, an F1 score of 0.960, and an accuracy of 0.980. This indicates a robust ability to identify subtle deviations that statistical baselines often miss. In terms of operational feasibility, while the Isolation Forest baseline exhibited the fastest inference time of 0.000014 seconds, the Conv-AE remained highly efficient for real-time applications with a inference time of 0.000348 seconds. Conclusion: In conclusion, the Conv-AE algorithm offers an optimal trade-off between high detection sensitivity and operational feasibility. By prioritizing the minimization of false negatives, this deep learning approach provides a more reliable solution for safety-critical hospital effluent monitoring compared to traditional statistical partitioning methods.   Keywords: Anomaly Detection, Hospital Wastewater Treatment Plant (WWTP) Effluent, Convolutional Autoencoder, Deep Learning
Co-Authors , Ronaldi Adelia Adelia Adelia Adelia, Adelia Adiska Fardani Aradea Ari Fajar Santoso Ayu Purwarianti Basuki Sugiharto Basuki Sugiharto Cicely Martini Asihwardji Dicky Prima Satya Dimitri Mahayana Dwi H Widyantoro Dwi H. Widyantoro Effendi, Mohammad Ridwan Erwin B. Setiawan Erwin B. Setiawan Erwin S. Panjaitan Erwin S. Panjaitan Eva Hariyanti Farrell Yodihartomo Febriantoro, Wicaksono Hanung Nindito Prasetyo Heru Nugroho Heru Nugroho Hibban, Daffa Maulana Indah Werdiningsih Indra Lukmana Sardi Iping S Suwardi Iping Supriana Iping Supriana Iping Supriana Iping Supriana Suwandi Irawan, Aditya Putra Irfan Walhidayah Kristian Telaumbanua Lenny Putri Yulianti Ma'mun, Nurhayati Maniah Maniah Maria Irmina Prasetiyowati Megawati . Muhammad Furqan Nazuli Muhammad Shalahuddin Neng Ayu Herawati Nur Ulfa Maulidevi Nurhajati Ma'mun Olivia Olivia Ririn Dwi Agustin Rita Rismala Ronaldi Ronaldi Roni Yunis Roni Yunis Roni Yunis Sakam, R. Djunaedy Sarifah Putri Raflesia Setiawan, Erwin B. Soni Fajar Surya G Sri Agustina Rumapea Sri Handayaningsih Stevanus Wisnu Wijaya Sugondo Hadiyoso Sukma Nirad, Dwi Welly Sunindyo, Wikan Danar Tati Latifah Erawati Rajab Wahyu Hidayat Wawan Dhewanto Widyantoro, Dwi H Yudi Setiawan Yusep Rosmansyah