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All Journal Jurnal Informatika dan Teknik Elektro Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Abdi Insani Journal of Soft Computing Exploration SmartComp Intechno Journal : Information Technology Journal
Saputro, Uyock Anggoro
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Design of an IoT-Based Air Quality System with Web Integration in a Palm Oil Mill Environment Rohim, Sigid Nur; Saputro, Uyock Anggoro
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9876

Abstract

This research aims to design and implement an Internet of Things (IoT)-based air quality monitoring system with real-time data integration to web applications in the PT Gemareksa Mekarsari palm oil mill environment. This system utilizes NodeMCU ESP32 as the main microcontroller connected with MQ-2 and MQ-135 sensors to detect CO, CH₄, and NH₃ gases. Data is sent in real-time to the ThingSpeak platform and displayed through a responsive web dashboard. Testing was conducted over two days at three different location points (open area, fruit processing, and factory office) with a total of 45 measurements. Results showed that the system was able to transmit data with a delivery accuracy rate of 86.67%, with most data received without delay.. The detected gas concentrations were within safe limits, although mild fluctuations occurred, especially in the fruit processing area. The system also showed stable performance in displaying data on mobile and desktop devices. Thus, this system can be an effective solution for automatic and real-time industrial air monitoring, and support efforts to mitigate health risks due to air pollution.
Implementation of SSL-Vision Transformer (ViT) for Multi-Lung Disease Classification on X-Ray Images Baasith, Rafi Haqul; Sasongko, Theopilus Bayu; Hadinegoro, Arifiyanto; Saputro, Uyock Anggoro
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.v10i1.11844

Abstract

Chest X-ray imaging is one of the most widely used modalities for lung disease screening; however, manual interpretation remains challenging due to overlapping pathological patterns and the frequent presence of multiple coexisting abnormalities. In recent years, Vision Transformer (ViT) models have demonstrated strong potential for medical image analysis by capturing global contextual relationships. Nevertheless, their performance is highly dependent on large-scale labeled datasets, which are costly and difficult to obtain in clinical settings. To address this limitation, this study proposes a Self-Supervised Learning Vision Transformer (SSL-ViT) framework for multi-label lung disease classification using the CheXpert-v1.0-small dataset. The proposed approach leverages self-supervised pretraining to learn robust and transferable visual representations from unlabeled chest X-ray images prior to supervised fine-tuning. A total of twelve clinically relevant thoracic disease labels are retained, while non-disease labels are excluded to enhance interpretability and reduce confounding effects. Experimental results demonstrate that SSL-ViT achieves a high recall of 0.73 and a peak AUC of 0.75 on the test set, indicating strong sensitivity in detecting pathological cases. Compared to the baseline ViT model, SSL-ViT exhibits a recall-oriented performance profile that is particularly suitable for screening applications, where minimizing false negatives is critical. Furthermore, Grad-CAM visualizations confirm that the model focuses on anatomically meaningful lung regions, supporting its clinical relevance. These findings suggest that SSL-enhanced Vision Transformers provide a robust and effective solution for multi-label chest X-ray screening tasks.
Co-Authors AGIT AMRULLAH Alvares, Jannio Andhika, Riyan Ariadi, Muhammad Vicri Arifiyanto Hadinegoro, Arifiyanto Baasith, Rafi Haqul Bernadhed, Bernadhed Chaniago, Annisa Silvia Fauzi, Moch Farid Guric, Bonaventura Isaac Kharisma, Rizqi Sukma Kusrini, Kusrini Majid Rahardi Muktafin, Elik Hari Mulia Sulistiyono Nurcholis, Muhammad Tofa Prasetya, Wahyu Sindu Putra, Elliantosya Anugrah Rohim, Sigid Nur Sasongko, Theopilus Bayu