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All Journal G-Tech : Jurnal Teknologi Terapan Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Pengabdian Masyarakat Bangsa
Anissa, Thia
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Pemanfaatan Teknologi Industrial Internet of Things (IIoT) untuk Meningkatkan Produktivitas dan Kualitas di Industri Manufaktur Widodo, Amin; Anissa, Thia; Mubarokah, Ita
Jurnal Pengabdian Masyarakat Bangsa Vol. 2 No. 9 (2024): November
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v2i9.1623

Abstract

Pengabdian kepada Masyarakat (PKM) dengan judul "Pemanfaatan Teknologi Industrial Internet of Things (IIoT) untuk Meningkatkan Produktivitas dan Kualitas di Industri Manufaktur" dilaksanakan di SMK 1 Pasundan Kota Serang. Tujuan utama kegiatan ini adalah memperkenalkan konsep serta penerapan teknologi IIoT kepada siswa, khususnya dalam konteks dunia industri manufaktur. Teknologi IIoT sendiri memiliki potensi besar untuk mendukung optimalisasi operasional, pemeliharaan prediktif, serta meningkatkan efisiensi dan kualitas produksi melalui pemantauan otomatis berbasis sensor dan data real-time. Kegiatan ini melibatkan dosen dan mahasiswa dari Program Studi Sistem Komputer Universitas Pamulang. Narasumber memberikan materi mengenai manfaat teknologi IIoT, termasuk bagaimana sistem ini memungkinkan integrasi perangkat fisik dengan sistem digital untuk memonitor, menganalisis, dan mengendalikan proses industri secara efisien. Teknologi ini juga dapat membantu meningkatkan kualitas produk dengan pemantauan data yang akurat dan responsif. Metode yang digunakan dalam PKM ini mencakup sesi sosialisasi, pelatihan, serta demonstrasi penerapan IIoT. Selain itu, kegiatan ini juga melibatkan instalasi perangkat keras dan perangkat lunak yang relevan, termasuk pengenalan dan penggunaan sensor-sensor IIoT yang memungkinkan pemantauan dan pengendalian otomatis di sektor industri manufaktur. Hasil dari kegiatan PKM ini menunjukkan peningkatan antusiasme dari siswa dan guru dalam memahami penerapan IIoT di industri manufaktur. Pengetahuan dan keterampilan mereka tentang teknologi industri terkini meningkat signifikan. Dengan adanya kegiatan ini, diharapkan siswa SMK Pasundan 1 semakin siap untuk berinovasi dan menghadapi tantangan di era Revolusi Industri 4.0, serta mampu menerapkan teknologi IIoT untuk meningkatkan produktivitas dan kualitas di berbagai sektor industri.
ABO-Vision: Automatic Blood Type Detection with YOLOv4-Tiny and Morphological Image Processing Mubarokah, Ita; Anissa, Thia; Irfa'i, Ahmad Khumedillah
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7475

Abstract

Blood is a red-colored fluid in the human body that plays a crucial role in maintaining the immune system. According to the ABO system, blood is classified into four main types: A, AB, B, and O. This classification is essential for facilitating blood transfusions. Currently, blood type determination is still performed manually by healthcare professionals, who observe the presence or absence of clumping (agglutination) in the blood when it reacts with specific antigens.Numerous studies have been conducted to support and enhance healthcare services, particularly as technological advancements continue to grow rapidly across various fields. In the medical field, these advancements have led to the development of increasingly sophisticated medical devices, including blood type detection tools. These devices typically use manual optical sensors to read blood agglutination by detecting changes in light intensity. However, such devices are not fully automated and still require human intervention, making them prone to human error. Today, automated blood type detection systems utilizing cameras and smartphones—integrated with various image processing methods and Artificial Intelligence (AI) are being increasingly developed. Therefore, this study focuses on the development of a blood type detection model that combines image processing and Deep Learning (DL) to support an intelligent, fast, and efficient healthcare system, achieving a detection accuracy of 98%.
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 Eneng Susilistia Agustini Irfa'i, Ahmad Khumedillah Ita Mubarokah MUBAROKAH, ITA Widodo, Amin