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All Journal JAIS (Journal of Applied Intelligent System) JMBI UNSRAT (Jurnal Ilmiah Manajemen Bisnis dan Inovasi Universitas Sam Ratulangi) JPM : Jurnal Pendidikan Matematika Jurnal Absis : Jurnal Pendidikan Matematika dan Matematika Jurnal Pendidikan Teknik Mesin Undiksha E-JOINT (Electronica and Electrical Journal Of Innovation Technology) Al-Zayn: Jurnal Ilmu Sosial & Hukum Jurnal Abdimas Peradaban Jurnal Pendidikan Bahasa Arab L-Dhad
Aripin
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Religion Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Developing Sustainable Business Practices in SMEs: A Community Outreach Initiative for Environmental and Economic Resilience Aripin
Jurnal Abdimas Peradaban Vol. 3 No. 2 (2022): Jurnal Abdimas Peradaban
Publisher : Global Writing Academica Researching and Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/rvakqv70

Abstract

Small and medium enterprises (SMEs) play a vital role in the economy but often face challenges in implementing sustainable business practices. Constraints such as limited resources, access to information, and unsupportive regulations make it difficult for SMEs to switch to more environmentally friendly practices. In addition, community initiatives and local support are important factors in encouraging the adoption of sustainable practices. This study aims to explore effective strategies that can help SMEs switch to sustainable business practices. This study uses a qualitative approach by collecting data from various reliable sources and analyzing them in depth. The conclusion of the study shows that the implementation of sustainable business practices by SMEs can reduce negative impacts on the environment, such as reduced carbon emissions and better waste management. In addition, these practices also increase the efficiency and productivity of SME operations. The adoption of sustainability strengthens the long-term resilience of SMEs to market changes and environmental challenges. Positive impacts are also felt by local communities, including improved quality of life and economic stability. Support from proactive government policies, adequate infrastructure, and access to green financing are critical to the success of this transition.
A Non-Invasive Allergy Detection using Convolutional Neural Network Model Aripin; Badia, Giulia Salzano; Safira, Intan
(JAIS) Journal of Applied Intelligent System Vol. 10 No. 1 (2025): April 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v10i1.12783

Abstract

Skin allergy detection is critical to detect allergies that trigger serious reactions such as anaphylaxis, so people can avoid allergens and reduce the risk of complications such as anaphylactic shock. Therefore, early allergy detection screening is essential to determine the risk of allergies. This research aims to develop a system to detect skin allergies caused by food, through sensors applied to human skin using the Convolutional Neural Network (CNN) model. The research steps include literature studies, data acquisition, preprocessing, learning processes, and testing. The developed system uses a camera to capture allergic reactions on the skin. Data acquisition consists of two types of data, namely primary data and secondary data. Primary data acquisition is done by taking images of normal and allergic patient skin. Meanwhile, secondary data acquisition is obtained from Kaggle. The captured images are processed by image processing and analyzed using the CNN model. The image dataset consists of four classes, namely atopic, angioedema, normal skin, and urticaria. The CNN model consists of several layers, including convolutional layers, pooling, and fully connected layers. The results of the research showed that the prototype product can detect changes in the skin surface due to allergic reactions, such as redness or swelling, quickly and accurately. Testing the learning process with the CNN model resulted in an accuracy rate of 92%. Meanwhile, the accuracy results of testing prototype products on patients with skin allergies were 93%. It shows that the system can detect types of allergies on the skin accurately and efficiently. This system provides a practical and fast solution for the public to detect allergies, while contributing to the advancement of medical technology.Keywords - social robots, adaptive learning, reinforcement learning, human-robot interaction, sensor fusion, educational robotics
Co-Authors Ainur Rahma Anjani, Ratna Dewi Asti Nurul Puspita Badia, Giulia Salzano Gusti Hardiansyah Irfan Mahdi, Irfan Kalis, Maria Christiana Nurdiansyah, Rian PUJI LESTARI Rahayu, Diar Veni Rizqiyah, Aghniya Barkah Sabna Anggraini Safira, Intan Sofia Zuhro Zein Pulungan Sofiani, Julia Stefhant Haryanto, Stefhant Triana M, Neni Wanda Mulyaningsih Putri Yani, Adi