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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Siteba
Burhan, Rifa’atul Mahmudah
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Design and Implementation of an IoT-Based Low-Emission Mobile Plastic Melting Machine for Sustainable Paving Block Production in Batam City Lawi, Ansarullah; Aranski, Alvendo Wahyu; Burhan, Rifa’atul Mahmudah; Hernando, Luki; Aritonang, Muhammad Adi Setiawan; Dermawan, Aulia Agung; Kurniawan, Dwi Ely; Leman, Abdul Mutalib
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12044

Abstract

Plastic waste accumulation poses a severe environmental burden, particularly in urban and archipelagic regions where centralized treatment infrastructure is limited. While thermal processing offers a pathway for volume reduction and material recovery, inadequate temperature control frequently leads to uncontrolled combustion and the formation of hazardous air pollutants. This study addresses this gap by developing and experimentally validating a low-emission, IoT-enabled mobile plastic melting system designed for decentralized paving block production. The proposed system integrates real-time thermal sensing using a K-type thermocouple and an ESP32-based controller with a compact three-nozzle water spray filtration unit. The control architecture maintains the melting process at approximately 270 °C, thereby preserving polymer viscosity for molding while preventing temperature excursions beyond 300 °C that may initiate combustion and toxic by-product formation. The filtration module operates as a simplified wet scrubber, capturing airborne particulates and simultaneously cooling the exhaust stream. Experimental evaluations confirm that the integrated control–filtration framework achieves stable thermal regulation and substantial suppression of visible exhaust emissions. Under these conditions, molten plastic was consistently transformed into dense paving blocks with smooth surface morphology and without evidence of polymer degradation or charring. The results demonstrate that combining IoT-based thermal governance with low-cost water-spray emission control provides an effective and scalable alternative to open burning for community-level plastic waste recycling. This mobile platform enables environmentally safer conversion of plastic waste into value-added construction materials, offering a practical pathway toward decentralized circular-economy implementation in resource-constrained regions.
Pengembangan Framework Deep Learning Berbasis ResNet50V2 untuk Klasifikasi Lumpy Skin Disease pada Sapi Menggunakan Citra Digital Farasalsabila, Fidya; Arnomo, Sasa Ani; Aranski, Alvendo Wahyu; Alhamidi; Jabnabillah, Faradiba; Burhan, Rifa’atul Mahmudah; Noviardi, Refli; Althaaf, Muhammad Nabiil
JURNAL SITEBA Vol. 4 No. 1 (2026): Jurnal Sistem Informasi ITEBA
Publisher : LPPM-ITEBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62375/shaw4509

Abstract

Penyakit Kulit Lumpy pada sapi dikenal sebagai risiko utama bagi produksi ternak dan berdampak besar terhadap kesejahteraan mata pencaharian dan ketahanan pangan khususnya bagi negara Indonesia. Saat ini pendeteksian penyakit kulit di negara kita umumnya dinilai secara manual. Namun, evaluasi manual membutuhkan banyak waktu dan membutuhkan orang-orang profesional yang berpengalaman dan terlatih, sehingga biasanya membutuhkan biayanya cukup tinggi. Pada penelitian ini dibangun model deteksi penyakit kulit Lumpy pada sapi menggunakan salah satu model Deep learning yakni Convolutional Neural Network (CNN). Kelas yang akan digunakan untuk mendeteksi dan mengklasifikasikan hewan berpenyakit kulit lumpy menjadi sehat dan sakit. Pengumpulan data tersebut dikumpulkan dari situs internet kaggle. Dari total 1364 dataset citra, 80% digunakan untuk pelatihan, 10% untuk validasi dan 10% untuk pengujian. Hasil eksperimen menunjukkan bahwa arsitektur terbaik untuk klasifikasi penyakit kulit lumpy pada sapi adalah Proposed Lumy ResNet50v2 dengan akurasi 94.85%, presisi 94%, Recall 94%, dan F1 Score 95%. Kemudian diikuti oleh arsitektur NASNetMobile dengan nilai akurasi 92.65%, Xception dan ResNet50v2 dengan akurasi 91,18% dan VGG19 dengan akurasi sebesari 90,44%.
Co-Authors Alhamidi Althaaf, Muhammad Nabiil Alvendo Wahyu Aranski Aritonang, Muhammad Adi Setiawan Arnomo, Sasa Ani Aulia Agung Dermawan Dwi Ely Kurniawan Fidya Farasalsabila Hernando, Luki Jabnabillah, Faradiba Lawi, Ansarullah Leman, Abdul Mutalib Noviardi, Refli