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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor JPM17: Jurnal Pengabdian Masyarakat Jurnal Teknologi Elektro INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Eksplora Informatika Dinamisia: Jurnal Pengabdian Kepada Masyarakat Konvergensi Jurnal ULTIMATICS KOMPUTIKA - Jurnal Sistem Komputer Jurnal Teknik Industri Terintegrasi (JUTIN) ABIDUMASY : Jurnal Pengabdian kepada Masyarakat Civitas Ministerium: Jurnal Pengabdian Kepada Masyarakat Journal of Information Technology and Cyber Security Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Fajar Astuti Hermawati
Universitas 17 Agustus 1945 Surabaya
Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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PENERAPAN TEKNOLOGI TEPAT GUNA DI UKM JAMU CUKA REMPAH Widiasih, Wiwin; Satoto, Handy Febri; Hermawati, Fajar Astuti; Faizah, Nur; Syanindita, Previa
ABIDUMASY Vol 4 No 02 (2023): ABIDUMASY : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/abidumasy.v4i02.5051

Abstract

Pada masa pandemi Covid-19 banyak UKM terdampak, termasuk yang dialami oleh UD. Intansari Raya yaitu penjualan menurun hingga 60%. UD. Intansari Raya merupakan sebuah UKM di Wage Kecamatan Taman kabupaten Sidoarjo yang memiliki produksi jamu cuka rempah, berdiri sejak Tahun 2012 dan beranggotakan tujuh pekerja. Produk tersebut dinilai unik karena berciri khas dan mampu mengangkat kearifan lokal Indonesia dalam hal jamu atau herbal yang memiliki banyak manfaat. UKM tersebut telah memiliki IUMK dan NIB, selain itu juga berkomitmen dalam pengelolaan dan pemantauan dampak lingkungan yang terjadi akibat aktivitas usaha dan bersedia diawasi oleh instansi yang berwenang dengan dibuktikan memiliki SPPL. Permasalahan yang dihadapi saat ini yaitu dalam proses produksi masih sangat sederhana serta alat fermentor diperlukan pengadaan untuk meningkatkan produktivitas karena jumlahnya sangat terbatas. Rak juga dibutuhkan oleh UD. Intansari Raya untuk tempat penyimpanan bahan bakuan hasil produksi. Solusi yang ditawarkan pada kegiatan ini yaitu diadakan penerapan TTG dengan melakukan pengadaan alat fermentor, tong, selang dan aerator. Dengan diterapkan TTG dan pengadaan alat fermentor, UKM dapat menaikkan jumlah produksi. Sedangkan dengan ditambahkan rak juga dapat menampung raw material dan hasil produksi.
Stowage Planning System for Ferry Ro-Ro Ships Using Particle Swarm Optimization Method Hermawati, Fajar Astuti; Mulya, Jalu Prasetya
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 7 No 2 (2023): August 2023
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v7i2.20562

Abstract

Stowage planning involves distributing cargo on board a ship, including quantity, weight, and destination details. It consists of collecting cargo manifest data, planning cargo location on decks, and calculating stability until the vessel is declared safe for sailing. Finding the ideal solution to real-world situations in this stowage planning problem is challenging and frequently requires a very long computing period. The Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms known for its efficient performance. PSO has been extended to complex optimization problems due to its fast convergence and easy implementation. In this study, the Particle Swarm Optimization (PSO) method is implemented to automate stowage arrangements on ships considering three factors (width, length, and weight of the vehicle). This system was evaluated with KMP Legundi vehicle manifest data and four load cases of 12 different vehicle types that can be loaded on Ferry / Ro-Ro Ships. It provides complete vehicle layouts and allows interactive changes for stowage planners, ensuring speed and accuracy in arranging ship cargo.
Explainable Artificial Intelligence Analysis of Transfer Learning Models for Alzheimer’s Disease MRI Classification Salsabila, Dea Amanda; Sari, Ghaluh Indah Permata; Hermawati, Fajar Astuti
Journal of Information Technology and Cyber Security Vol. 4 No. 1 (2026): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.133060

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder that leads to cognitive decline and requires early and accurate diagnosis to slow disease progression. Magnetic resonance imaging (MRI) is widely used to detect structural brain changes associated with Alzheimer’s disease; however, manual interpretation of MRI scans is time-consuming and subject to observer variability. Deep learning approaches have shown strong potential in automated MRI analysis, but their black-box nature limits clinical trust and interpretability. This study proposes a transfer learning–based deep learning framework for Alzheimer’s disease classification, complemented by explainable artificial intelligence (XAI) techniques to analyze model predictions. A pretrained VGG16 model is employed to classify MRI images into four cognitive impairment categories: no impairment, very mild impairment, mild impairment, and moderate impairment. To enhance transparency, Grad-CAM, Saliency Maps, and Guided Grad-CAM are applied to visualize brain regions that contribute most to model predictions. Experimental results demonstrate that the proposed approach achieves 95.41% accuracy, indicating that a well-balanced network architecture combined with integrated explainability techniques leads to effective, interpretable classification. The visual explanations highlight clinically meaningful brain regions that align with known Alzheimer’s disease–related structural changes. These findings suggest that combining deep transfer learning with explainable artificial intelligence can provide accurate and interpretable decision support for Alzheimer’s disease diagnosis. This study is limited by the use of a single publicly available dataset and two-dimensional MRI slices, which may affect generalizability across clinical environments.
Hybrid V-Net And Swin Transformer Deep Learning Model For Brain Tumor Segmentation in Low-Quality MRI Scan Hermawati, Fajar Astuti; Pramudya, Andre
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.4494

Abstract

Brain tumor segmentation from low-quality magnetic resonance imaging (MRI) remains a challenging task due to noise, resolution variation, and low contrast between tumor and healthy tissues. Improving segmentation accuracy is essential to support more precise diagnosis and treatment planning. This study proposes a hybrid deep learning model that integrates V-Net and Swin Transformer architectures for automatic brain tumor segmentation in multimodal MRI images. The MICCAI BraTS 2020 dataset was used, consisting of T1, T1c, T2, and FLAIR sequences with corresponding segmentation labels. The preprocessing pipeline includes resampling, skull stripping, intensity normalization, and data augmentation. V-Net is employed to extract local spatial features from 3D volumetric data, while the Swin Transformer captures global spatial relationships through a self-attention mechanism. Postprocessing procedures such as thresholding, morphological refinement, and false-positive removal are applied to enhance segmentation quality. The proposed hybrid model achieves Dice scores of 0.8635 for Whole Tumor (WT), 0.7179 for Tumor Core (TC), and 0.8073 for Enhancing Tumor (ET), with additional evaluation using precision, recall, and IoU further confirming its effectiveness. These results highlight the model’s potential to improve automated brain tumor segmentation in low-quality MRI images and support its applicability as an efficient AI-assisted diagnostic tool in clinical practice.
Generating Synthetic B-Mode Fetal Ultrasound Images Using CycleGAN-Based Deep Learning Hermawati, Fajar Astuti; Hardiansyah, Bagus; Andrianto, Andrianto
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.282

Abstract

B-mode ultrasound (USG) is a key imaging modality for fetal assessment, providing a noninvasive approach to monitor anatomical development and detect congenital anomalies at an early stage. However, portable ultrasound devices commonly used in low-resource healthcare settings often yield low-resolution images with significant speckle noise, reducing diagnostic accuracy. Furthermore, the scarcity of labeled medical data, caused by privacy regulations such as HIPAA and the high cost of expert annotation, poses a significant challenge for developing robust artificial intelligence (AI) diagnostic models. This study proposes a CycleGAN-based deep learning model enhanced with a histogram-guided discriminator (HisDis) to generate realistic synthetic B-mode fetal ultrasound images. A publicly available dataset from the Zenodo repository containing 1,000 grayscale fetal head images was utilized. Preprocessing included normalization, histogram equalization, and image resizing, while the architecture combined two ResNet-based generators and a dual discriminator configuration integrating PatchGAN and histogram-guided evaluation. The model was trained using standard optimization settings to ensure stable convergence. Experimental results demonstrate that the proposed HisDis module accelerates convergence by 18 epochs and reduces the Fréchet Inception Distance (FID) by 23.6 percent from 1580.72 to 1208.49 compared with the baseline CycleGAN. Statistical analysis revealed consistent pixel-intensity distributions between the original and synthetic images, with entropy from 7.16 to 7.40. At the same time, visual assessment confirmed that critical anatomical structures, including the brain midline and lateral ventricles, were well preserved. These results indicate that the CycleGAN-HisDis model produces statistically and visually realistic fetal ultrasound images suitable for medical data augmentation and AI-based diagnostic training. Furthermore, this approach holds potential to enhance diagnostic reliability and clinical education in healthcare settings with limited imaging resources. Future work will focus on clinical validation and generalization across diverse fetal ultrasound datasets.
Perancangan UI/UX Aplikasi Pemesanan Guna Mengurangi Antrian Pelanggan di Coffe Shop AMPM Reborn dengan Metode Design Thinking Maanary, Michellino Apriliandio; Putri, Erni Puspanantasari; Hermawati, Fajar Astuti
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 9 No. 1 (2026): January
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v9i1.52444

Abstract

AMPM Reborn, a coffee shop in Sidoarjo, faces recurring long queues during weekends, especially on Saturdays between 19:00–23:00. Customer waiting times reach 20–30 minutes, with queues extending up to 24 people, leading to reduced customer comfort and decreased service efficiency. This study aims to design a UI/UX website-based ordering system as a digital solution to streamline the ordering process and reduce physical queues. The research adopts the Design Thinking approach through the stages of empathize, define, ideate, prototype, and test, ensuring that the developed design aligns with user needs. A prototype was created using Figma and evaluated through usability testing to assess its effectiveness and ease of use. The results present an intuitive and efficient interface design that is expected to minimize waiting times and enhance overall customer satisfaction at AMPM Reborn.
Co-Authors ., Tigor Aidil Primasetya Armin Andrianto Andrianto Balok Hariadi, Balok Elsen Ronando Erni Puspanantasari Putri, Erni Puspanantasari Evi Triandini Fathonah, Nenden Siti Fatonah, Nenden Siti Handayani Tjandrasa hardiansyah, bagus Herry Widhiarto I Ketut Putu Suniantara Indasyah, Enny Jamila, Siti Nur Jaya, Viko Adi Kurnianingtyas, Yuniar Luvia Friska Narulita Maanary, Michellino Apriliandio Mangambali, Hermawan Ali Mulya, Jalu Prasetya Nanik Suciati Nur Faizah Pramudya, Andre Prasetyo, Cahyo Tri Pratama, Achmad Yogie Pratama, Bima Agung Putra, Ressa Ardiansya Reza Sarwo Widagdo Roenadi Koesdijarto Salsabila, Dea Amanda Sari, Ghaluh Indah Permata Sidqon, Mohammad Suanto, Edy Syanindita, Previa Tanjung, Christian Adikusuma Valantino Ateng Pamolango Windari, Diana Ayu Wiwin Widiasih