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All Journal Jurnal Informatika dan Teknik Elektro Terapan Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Unnes Journal of Mathematics Journal Of Artificial Intelligence And Software Engineering
Ulfah, Julia
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

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MODEL MATEMATIKA UNTUK PENYAKIT DIABETES TANPA FAKTOR GENETIK DENGAN PERAWATAN Ulfah, Julia; Kharis, Muhammad; Chotim, Moch
Unnes Journal of Mathematics Vol 3 No 1 (2014)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v3i1.3280

Abstract

Diabetesmellitus merupakan penyakit degenerative yang disebabkan oleh hypokinetic (berkurangnya aktivitas fisik) dan merupakan penyebab kematian nomor enam dari seluruh kematian pada semua kelompok umur. Diabetes mellitus adalah penyakit gangguan metabolic menahun yang lebih dikenal dengan pembunuh manusia secara diam-diam atau “Silent killer”. Diabetes juga dikenal sebagai “Mother of Disease” karena merupakan induk atau ibu dari penyakit-penyakit lainnya seperti hipertensi, penyakit jantung dan pembuluh darah, stroke, gagal ginjal, dan kebutaan. Dalam tulisan ini akan dikaji model matematika untuk penyakit degenerative diabetes mellitus tanpa factor genetic dengan pengaruh perawatan. Dalam artikel ini model yang digunakan untuk pendekatan dalam kasus ini berbentuk SEIIT. Analisa yang dilakukan meliputi penentuan titik ekuilibrium model dan analisa terkait kestabilan di sekitar titik ekuilibrium. Simulasi diberikan berdasarkan nilai-nilai parameter yang terkait dalam model matematika yang menggambarkan kondisi pada setiap kelas subpopulasi. Diharapkan hasil yang diperoleh dapat memberikan gambaran tentang adanya pengaruh perawatan terhadap individu penderita diabetes tanpa factor genetik.
Internet of Things and Artificial Neural Network Application for Optimizing Spirulina Cultivation with Palm Oil Mill Effluent Ula, Munirul; Fajriana, Fajriana; Ulfah, Julia
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 1 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i1.22389

Abstract

This study aims to optimize algae biomass production by utilizing Palm Oil Mill Effluent (POME) as a nutrient source, employing Internet of Things (IoT) technology and Artificial Neural Networks (ANN) for predictive modeling and system control. POME, an organic waste from the palm oil industry, was used as an organic liquid fertilizer to enhance the efficiency and sustainability of algae cultivation. The system was designed to monitor and control key environmental parameters such as pH, temperature, salinity, and dissolved oxygen in real-time during a one-month trial in July 2024. ANN-based models were used to predict and adjust environmental conditions, leading to significant improvements in algae growth and resource efficiency. The results indicate that POME can serve as an effective and eco-friendly nutrient source, contributing to both reduced industrial waste and sustainable biomass production. This integrated approach supports circular economy principles and sustainability goals, with potential applications in bioresource production and waste management. Future research will focus on large-scale system testing, optimization for various algae species, and long-term sustainability assessment.
IMPLEMENTASI METODE DETEKSI TEPI CANNY UNTUK MENGHITUNG JUMLAH UANG KOIN DALAM GAMBAR MENGGUNAKAN OPENCV Ulfah, Julia; Nurdin, Nurdin
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 3 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3.3147

Abstract

Edge detection is often used in image processing to find the edge boundaries of image objects and this is important for selecting the edges of image parts. The image edge detection used in this study counts coins in an image, so that it can easily pass through the human eye but not by a computer. In a computer system, it is necessary to provide programs and test data so that the computer can read and count coins like a human. The purpose of this study is to count coins in an image using Canny edge detection and Gaussian filtering methods. The images used in this study are in the form of *jpg and tested using the Visual Studio Code application using OpenCV and Python. The results of this study indicate that the results of the number of coins read from the images inputted to the system can be read properly, with a data accuracy of 90%. Canny's edge detection method can be used to count the number of coins in an image.Keywords: Pengolahan Citra; Deteksi tepi Canny; Gaussian filtering; OpenCV;
Analisis Perbandingan Kinerja Algoritma You Only Look Once (YOLOv8) Dan Single Shot Detector (SSD) dalam Pengenalan Nominal Uang Kertas Ulfah, Julia; Ula, Munirul; Fajriana, Fajriana; Nurdin, Nurdin
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.7471

Abstract

The advancement of technology in the field of image recognition has significantly facilitated and improved the effectiveness of object detection in computer-based banknote recognition systems. This study aims to automatically identify banknotes based on their denominations, with the objective of minimizing human errors—such as lack of concentration, fatigue, and other factors—and enabling its application in ATMs and automated payment systems. This research compares the accuracy levels and detection success rates between the YOLO and SSD algorithms in recognizing the denominations of banknotes. The YOLO model operates by dividing the image into grids and predicting bounding boxes along with object classes in a single step, resulting in fast and consistent detection. In contrast, the SSD model employs a multi-scale approach by utilizing feature maps from multiple levels to generate predictions. The parameters used in this study include 7 classes of Indonesian banknotes: Rp1,000, Rp2,000, Rp5,000, Rp10,000, Rp20,000, Rp50,000, and Rp100,000. A total of 353 images were used in the dataset, and three images from each class were selected for testing purposes. The results of the study indicate a significant performance difference. The YOLO algorithm achieved a 100% accuracy rate under both normal and low-light conditions, while the SSD algorithm achieved an accuracy rate of 87.2% under normal lighting and 91.4% under low-light conditions.
Co-Authors Chotim, Moch Fajriana, Fajriana Muhammad Kharis Munirul Ula Nurdin Nurdin Nurdin Nurdin