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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Abdi Masya Teknika STTKD: : Jurnal Teknik, Elektronik, Engine
Rofi’i, Mohammad
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Utilization of Spiking Neural Network (SNN) in X-Ray Image for Lung Disease Detection Ningtias, Diah Rahayu; Rofi’i, Mohammad; Pramudita, Brahmantya Aji
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10671

Abstract

The large number of cases of lung disease means that doctors have difficulty in making initial diagnoses, making them prone to misdiagnoses. One type of lung disease that is included in the vulnerable category is pneumonia. Early detection of the condition of the lungs affected by bacterial pneumonia can be carried out by screening using the X-Ray examination modality, namely Digital Radiography (DR). However, in practice, the diagnosis process on Citra DR takes a long time because it requires competent medical personnel (specialists). A system is needed that can help medical personnel to speed up the process of diagnosing lung disease and get accurate results so that misdiagnosis does not occur. The aim of this research is to utilize the Spiking Neural Network (SNN) method for classifying lung disease from DR images. The system was created using MATLAB with the initial step of creating a read data program, namely reading DR image secondary data in .jpg format taken from Kaggle.com. This research uses DR image data totaling 200 images. Next, standardize the size to 50 x 50 pixels. Then segmenting the image divides the gray level histogram into two different parts of the image automatically without requiring user assistance to enter threshold values ​​for normal and pneumonia images. Then convert the image to 1 dimension and create a manual program for the training data using 50 normal images and 50 pneumonia images. Lastly, create a program to test the data using 100 normal images and 100 pneumonia images. Based on the results of data testing, a confusion matrix was obtained from 200 images with sensitivity of 87%, specificity of 69%, precision of 73.7288%, recall of 69%, and accuracy of 78%
MONITORING KUALITAS AIR BERBASIS IoT (INTERNET OF THINGS) UNTUK MENINGKATKAN PRODUKTIVITAS NELAYAN DI KABUPATEN DEMAK Ningtias, Diah Rahayu; Rofi’i, Mohammad; Zulfa, Nely
Abdi Masya Vol 5 No 2
Publisher : Pusat Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52561/abdimasya.v5i2.406

Abstract

Pond farmers in Demak Regency, especially in Tambakbulusan Village, face challenges in maintaining the optimal quality of brackish water ponds. Uncontrolled fluctuations in temperature, pH, and salinity hurt the growth of brackish water biota, including shrimp and milkfish. Therefore, it is necessary to develop technology and innovation, such as an IoT (Internet of Things)-based water quality monitoring tool that allows farmers to monitor pond conditions in real time from a distance. This monitoring uses a website that is integrated with localhost so that special treatment can be carried out more quickly. In making this IoT-based monitoring tool, pH, temperature, and salinity levels were tested. The results obtained were that the pH, salinity, and temperature levels in pond water were still within normal limits. Community service activities in Tambakbulusan Village, Karangtengah District, Demak Regency have succeeded in increasing the productivity of brackish water pond farmers for milkfish and shrimp. In the future, it is hoped that there will be follow-up activities, namely the addition of fresh water when there is an increase in salinity levels in brackish water ponds automatically, so that the community feels more helped.
OPTIMASI ALGORITMA RANDOM FOREST MENGGUNAKAN PSO UNTUK KLASIFIKASI KANKER PAYUDARA DENGAN CITRA MAMMOGRAMS Salwa Alexita, Alfreda Cecio; Kusumaningtyas, Pramesti; Rofi’i, Mohammad
Teknika STTKD: : Jurnal Teknik, Elektronik, Engine Vol 11 No 1 (2025): TEKNIKA STTKD: JURNAL TEKNIK, ELEKTRONIK, ENGINE
Publisher : Sekolah Tinggi Teknologi Kedirgantaraan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56521/teknika.v11i1.1346

Abstract

This research focuses on improving breast cancer classification through a combination of Random Forest and Particle Swarm Optimization (PSO) algorithms. Being the most common cancer among women worldwide, breast cancer requires an effective diagnostic screening method. Traditional methods such as manual examination and X-ray imaging are time-consuming and prone to errors. This research applies machine learning techniques, specifically Random Forest, for image classification based on mammograms. The methodology involves data collection, image preprocessing (including image resize, grayscale, and image segmentation using Sobel Edge Detection and Adaptive Thresholding), feature extraction via Local Binary Pattern (LBP), and classification via Random Forest optimized with PSO. PSO helps to identify the optimal hyperparameters and improves the accuracy of the Random Forest model. Model evaluation is done using confusion matrix which includes accuracy, precision, and recall values. The testing experiment showed that the PSO-optimized Random Forest model achieved an accuracy of 88.37%, outperforming the standard Random Forest model which achieved 86.05%. This shows that PSO significantly improves classification accuracy. This research contributes to the development of an easy-to-use diagnostic tool to assist specialists in accurately identifying breast cancer stages, and suggests future investigations should incorporate additional machine learning algorithms and utilize higher-standard DICOM images to improve training and testing data.
Co-Authors Brahmantya Aji Pramudita Diah Rahayu Ningtias, Diah Rahayu Kusumaningtyas, Pramesti Salwa Alexita, Alfreda Cecio zulfa, nely