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All Journal Al-Hijr: Journal of Adulearn World Journal International of Lingua and Technology Journal Markcount Finance International Journal of Educational Narratives Journal of Social Science Utilizing Technology Journal of Social Science Utilizing Technology International Journal of Educatio Elementaria and Psychologia Research Psychologie, Orientation et Conseil Journal of Loomingulisus ja Innovatsioon Research of Scientia Naturalis Journal of Biomedical and Techno Nanomaterials Journal of Multidisciplinary Sustainability Asean
Pong, Ming
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Other

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Real-Time Sensing of Airborne Pollutants Using IoT-Integrated Electrochemical Sensors Nampira, Ardi Azhar; Pong, Ming; Lek, Siri
Research of Scientia Naturalis Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v2i5.2383

Abstract

Air pollution poses a significant threat to public health, demanding effective real-time monitoring solutions. Traditional monitoring systems are often costly and sparsely located, limiting their spatial-temporal resolution. This study aimed to develop and validate a low-cost, IoT-integrated electrochemical sensor system for the real-time detection of key airborne pollutants. We fabricated electrochemical sensors for nitrogen dioxide (NO?), sulfur dioxide (SO?), and volatile organic compounds (VOCs), which were then integrated with a microcontroller and a wireless communication module. The system was calibrated and validated against reference instruments in both laboratory and field conditions. The developed sensors exhibited high sensitivity, good selectivity, and rapid response times (<60s). Field data demonstrated a strong correlation (R² > 0.92) with co-located reference-grade analyzers, and the IoT platform successfully provided continuous data visualization via a cloud dashboard. This study confirms that IoT-integrated electrochemical sensors provide a scalable and cost-effective solution for building dense, real-time air quality monitoring networks, offering significant potential for urban environmental management.
AI-Powered Digital Histopathology: Predicting Immunotherapy Response Using Deep Learning Judijanto, Loso; Chai, Som; Pong, Ming; Justam, Justam; Nampira, Ardi Azhar
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2379

Abstract

Immunotherapy has revolutionized cancer treatment, yet predicting which patients will respond remains a major clinical challenge. Current predictive biomarkers, such as PD-L1 expression, have limited accuracy and fail to capture the complex interplay of cells within the tumor microenvironment. Digital histopathology, the analysis of digitized tissue slides, combined with artificial intelligence (AI), offers a novel approach to identify complex morphological patterns that could serve as more robust predictive biomarkers. Objective: A deep learning model, specifically a convolutional neural network (CNN), was trained on a large, multi-center cohort of digitized tumor slides from patients with non-small cell lung cancer who had received ICI therapy. The model was trained to identify subtle morphological features and the spatial arrangement of tumor cells and tumor-infiltrating lymphocytes. The model’s predictive performance was rigorously validated on an independent, held-out test cohort, and its performance was compared to the predictive accuracy of PD-L1 staining. The AI-powered model successfully predicted immunotherapy response with a high degree of accuracy, achieving an area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort.
Design of Shrimp Skin-Based Nano-Biodegradable Material for Eco-Friendly Food Packaging Juwairiah, Juwairiah; Pong, Ming
Journal of Multidisciplinary Sustainability Asean Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijmsa.v2i3.2250

Abstract

Background. The problem of plastic waste from food packaging continues to increase and poses a serious threat to the environment. The development of biodegradable-based eco-friendly packaging materials is one of the solutions that is getting more and more attention, especially those that come from organic waste such as shrimp skins that are rich in chitin. Purpose. This research aims to design a nano-biodegradable material based on shrimp skin that can be used as environmentally friendly food packaging, by evaluating its mechanical strength, water resistance, biodegradability ability, and application effectiveness in real conditions. Method. The research uses laboratory experiment methods with a quantitative approach. The shrimp skin is extracted into chitosan, then modified into a nanoform using the ionic gelation technique. Performance tests include tensile strength analysis, water contact tests, biodegradation tests, as well as application case studies on fresh fruit packaging. Results. The developed material shows high mechanical strength, good water resistance, and decomposes perfectly in a humid soil environment in less than 30 days. Direct application to the fruit shows effectiveness in maintaining freshness and preventing microbial contamination. Conclusion. The design of nano-biodegradable material from shrimp skin has the potential to be an alternative solution to plastic in food packaging, with ecological benefits and added value from the use of marine organic waste.
Co-Authors Abar, Reza Oktiana Aldina, Fauzi Chai, Nong Chai, Som Dara, Sokha Fatimah Fahurian Iriana Bakti Judijanto, Loso Justam, Justam Juwairiah Juwairiah Kiat, Ton Krit, Pong Lek, Siri Nampira, Ardi Azhar Pakaja, Marina Rith, Vicheka Rukiyanto Rukiyanto, Rukiyanto Sari, Hindri Febri Ana Som, Rit Surbakti, Rudy Suyitno Suyitno Syunikitta, Mirwanti Teddy Oswari Thai, Aom Triyugo Winarko, Triyugo Tursunov, Dilshod Umboh, Satria Evans