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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Riset Informatika
Angel Metanosa Afinda
Telkom University
Computer Science & IT Electrical & Electronics Engineering Engineering

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A SEASONAL IMPUTATION METHOD FOR ADDRESSING MISSING DATA IN ENVIRONMENTAL IOT SENSOR TIME SERIES Ardiansyah Ramadhan; Surya Micrandi Nasution; Reza Rendian Septiawan; I Kadek Nuary Trisnawan; Angel Metanosa Afinda
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.475

Abstract

Missing and incomplete observations in Environmental IoT sensor networks reduce data reliability and disrupt analyses, especially for temperature and humidity time series exhibiting strong diurnal seasonality. This study develops and evaluates a seasonal imputation method to address missing data in IoT-based environmental monitoring, using a workflow of anomaly detection, outlier removal, time-of-day-aware imputation, and performance evaluation under varying missing-rate scenarios. Key challenges include sensor noise, connectivity issues, and intermittent hardware failures, which degrade data integrity and affect trend analysis, forecasting, and anomaly detection. To mitigate these, the method uses hourly and minute-level seasonal patterns after filtering out physically unrealistic values. Experimental results show high accuracy and robustness in reconstructing temperature and humidity data: temperature imputation achieves MAE values of approximately 0.86–0.87°C, and humidity yields MAE values of 3.92–4.01%RH, with no performance drop even at 50% data loss. The imputed series preserves natural diurnal dynamics without introducing distortions, effectively restoring continuity and structural consistency in environmental IoT time series for reliable modeling, feature extraction, and decision support.
An HHO-Optimized LSTM Framework for Predicting Adverse Effects Associated with Reproductive and Breast Disorders ANGEL METANOSA AFINDA; IGA NARENDRA PRAMAWIJAYA; FAUZAN FIRDAUS
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 14, No 3: Published July 2026
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v14i3.358

Abstract

Reproductive toxicity prediction is a major challenge in drug development as side effects are often difficult to detect early. SMILES representations provide a compact sequential format suitable for deep learning. This study proposes an HHO-optimized LSTM model to predict reproductive and breast-related side effects. Four architectural schemes were evaluated including L (LSTM only), CL (Convolution + LSTM), LD (LSTM + Dense), and CLD (Convolution + LSTM + Dense). Results show that the tuned L scheme achieved the best performance with accuracy increasing from 0.6304 to 0.6739 and F1-score from 0.6792 to 0.7097. These findings highlight the effectiveness of metaheuristic optimization in computational toxicology modeling.
Benchmarking YOLOv12 Variants for Indonesian Traditional Cuisine Detection Fauzan Firdaus; Lidya Ningsih; Aminah Indahsari Marsuki; Angel Metanosa Afinda
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12625

Abstract

YOLOv12 is one of the latest YOLO versions currently. Several studies have proven that YOLOv12 has better performance compared to previous versions. YOLOv12 itself has five model variants based on its architectural complexity, namely nano, small, medium, large and extra larges. This study tests the performance of YOLOv12 model variants (n, s, m, l, x) for traditional Indonesian culinary detection using a domain-specific object detection dataset. The dataset contains 718 images with 720 bounding-box instances annotated across 20 culinary classes, divided into 418/150/150 images for training/validation/testing. Data processing was performed in Roboflow with automatic orientation and stretching resizing to 640×640, while the training split was enriched using augmentation (horizontal and vertical flips) to increase sample diversity. All YOLOv12 variants were trained with the same configuration and environment, for 50 epochs using the Ultralytics framework with default hyperparameters on an NVIDIA A100-SXM4 80GB GPU. On the validation set, all variants achieved high detection accuracy (mAP@0.5 = 0.985–0.991), while differences emerged under a more stringent localization criterion (mAP@0.5:0.95). The best overall localization performance was achieved by YOLOv12-L (mAP@0.5:0.95 = 0.874), while YOLOv12-N provided the fastest inference (0.8 ms/image) with competitive accuracy (mAP@0.5:0.95 = 0.822). These findings provide preliminary guidance for selecting YOLOv12 variants based on the trade-off between accuracy and speed.
Co-Authors AMINAH INDAHSARI MARSUKI Ardiansyah Ramadhan FAUZAN FIRDAUS I Kadek Nuary Trisnawan Iga Narendra Pramawijaya Lidya Ningsih Reza Rendian Septiawan Surya Micrandi Nasution