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All Journal Jurnal Riset Informatika eProceedings of Engineering
I Kadek Nuary Trisnawan
Telkom University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Implementasi Mobile Sensor Untuk Deteksi Kebocoran Gas Menggunakan Mekanisme Active Sensing Localization I Kadek Nuary Trisnawan; Agung Nugroho Jati; Novera Istiqomah
eProceedings of Engineering Vol 6, No 2 (2019): Agustus 2019
Publisher : eProceedings of Engineering

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Abstract

Kebocoran gas merupakan salah satu permasalahan yang sering muncul di beberapa bidang industri, hal ini membuat beberapa kecelakaan yang terjadi karena keberadaan gas yang tidak dapat terdeteksi secara kasat mata. Perkembangan mobile sensor menjadi salah satu cara untuk menanggulangi kerugian baik dari materiil maupun non materiil. Sensor LiDAR dan sensor gas merupakan jenis sensor yang dapat digunakan untuk mendeteksi gas. Pada Tugas Akhir ini, akan membahas bagaimana mendeteksi kebocoran gas pada suatu ruangan. Dibantu dengan metode SLAM sebagai navigasi dan gabungan metode source-seeking dan active-sensing localization sebagai pengidentifikasi titik-titik kebocoran, mobile sensor mengidentifikasi titik-titik kebocoran yang diambang batas normal. Setelah dilakukan finalisasi titik kebocoran gas, maka mobile sensor akan memberikan feedback kepada user. Target capaian dalam penulisan proposal ini yaitu bagaimana Mobile Sensor dapat mengidentifikasi titik-titik yang terindikasi adanya kebocoran gas dan bisa mengidentifikas nilai kebocoran dari titik tersebut. Dengan memanfaatkan sensor LiDAR dan sensor gas, diharapkan agar nilai yang didapatkan memiliki akurasi >85%. Kata kunci: SLAM, Mobile Sensor, source-seeking, dan active-sensing localization
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.
Co-Authors Agung Nugroho Jati Angel Metanosa Afinda Ardiansyah Ramadhan Novera Istiqomah Reza Rendian Septiawan Surya Micrandi Nasution