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All Journal Jurnal Restikom : Riset Teknik Informatika dan Komputer Tensile : Jurnal Pengabdian Kepada Masyarakat
Eneng Susilistia Agustini
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Computer Science & IT Energy Engineering Social Sciences Transportation Other

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SIMULASI PENGATURAN SUHU RUANGAN MENGGUNAKAN DHT22 BASE ON WOKWI DI SMK PGRI 1 KOTA SERANG Sri Rejeki; Ranti Imawati; Eneng Susilistia Agustini; Irfan Fathoni; Agus Suhendi
Tensile : Jurnal Pengabdian Kepada Masyarakat Vol 2 No 2 (2024): Juli 2024
Publisher : Teknik Mesin ,Universitas Pamulang Serang

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Abstract

Penelitian ini mengusulkan suatu solusi kreatif untuk meningkatkan kemahiran dan kenyamanan lingkungan kerja di tempat kerja guru melalui pemanfaatan inovasi sensor DHT22 dan mikrokontroler Arduino berbasis Wokwi. Pemanfaatan sensor DHT22 digunakan untuk mengukur suhu dan kelengketan secara real-time yang kemudian dikirim ke mikrokontroler Arduino untuk mengontrol kipas secara alami ketika suhu ruangan mencapai batas tertentu.Pendekatan ini bertujuan untuk menciptakan lingkungan kerja yang lebih nyaman bagi instruktur dengan reaksi cepat terhadap perubahan suhu. Selain itu, usaha ini mempertimbangkan penerapan kipas angin pada AC sebagai perluasan penggunaan. Penelitian ini diharapkan dapat memberikan dampak positif dan solusi cerdas untuk meningkatkan kualitas lingkungan kerja di tempat kerja guru, dan akan disebarkan ke siswa SMA, SMK atau MA untuk meningkatkan kesadaran inovasi. Kata Kunci : sensor suhu, DHT22, kontroler Arduino, kenyamanan lingkungan kerja, efisiensi energi, monitoring. 
Klasifikasi Rhinosinusitis Menggunakan Modifikasi VGG16 Anissa, Thia; Ita Mubarokah; Eneng Susilistia Agustini
Jurnal RESTIKOM : Riset Teknik Informatika dan Komputer Vol 7 No 2 (2025): Agustus
Publisher : Program Studi Teknik Informatika Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/restikom.v7i2.470

Abstract

Rhinosinusitis is an inflammatory disease affecting the mucosal lining of the nasal cavity (rhinitis) and paranasal sinuses (sinusitis), posing a significant public health challenge in Indonesia due to its high clinical and economic burden. This study aims to develop an advanced diagnostic method to assist healthcare professionals in accurately detecting sinusitis, thereby reducing diagnostic bias and improving patient outcomes. The proposed method involves a modified VGG16 architecture, where the traditional fully connected layers are replaced with Global Average Pooling (GAP) to minimize overfitting and computational complexity. By retaining the depth advantages of VGG16 while enhancing efficiency, this approach is tailored for medical image analysis. The dataset comprises 659 thermal images, evenly split between normal and sinusitis cases, which were preprocessed through cropping, global thresholding, and masking to improve feature extraction. The modified model incorporates additional convolutional layers (Conv3-4, Conv4-4, Conv5-4, and Conv5-5) to capture intricate spatial features, further boosting classification performance. Experimental results demonstrate an impressive accuracy of 95.67%, outperforming the standard VGG16 model, which achieved only 68.34% accuracy and exhibited overfitting. The study also highlights the effectiveness of thermal imaging as a non-invasive, cost-efficient alternative to conventional diagnostic methods like CT scans or MRIs.
Co-Authors Agus Suhendi Anissa, Thia Irfan Fathoni Ita Mubarokah Ranti Imawati Sri Rejeki