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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Abdi Masya Teknika STTKD: : Jurnal Teknik, Elektronik, Engine Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Rofi’i, Mohammad
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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Other

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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

Petani tambak di kabupaten Demak khususnya di Kelurahan Tambakbulusan menghadapi tantangan dalam menjaga kualitas air tambak air payau secara optimal. Fluktuasi suhu, pH, dan salinitas yang tidak terkontrol dengan baik memberikan dampak negatif pada pertumbuhan biota air payau, yaitu meliputi udang serta bandeng. Oleh sebab itu diperlukan pengembangan teknologi dan inovasi misalnya alat monitoring kualitas air berbasis IoT (Internet of Things) memungkinkan petani untuk memantau kondisi tambak secara real-time dari jarak jauh. Pemantauan ini menggunakan website yang terintegrasi dengan localhost sehingga memungkinkan perlakukan khusus secara lebih cepat. Pada pembuatan alat monitoring berbasis IoT ini dilakukan uji tingkat pH, suhu, dan salinitas. Hasil yang didapatkan adalah tingkat pH, salinitas, dan suhu pada air tambak sejauh ini masih dalam batas normal. Kegiatan pengabdian kepada masyarakat di Kelurahan Tambakbulusan, Kecamatan Karangtengah, Kabupaten Demak ini telah berhasil meningkatkan produktivitas petani tambak air payau untuk bandeng dan udang. Kedepan diharapakn terdapat kegiatan lanjutan yaitu penambahan air tawar ketika terjadi kenaikan kadar salinitas di tambak air payau secara otomatis, sehingga masyarakat lebih merasa terbantu.
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.
Waterbath Calibrator with Nine Channels Sensor Rofi’i, Mohammad; Syaifudin, Syaifudin; Titisari, Dyah; Utomo, Bedjo
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 1 No. 1 (2019): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v1i1.235

Abstract

There is one fundamental thing that says electrosurgery is dangerous because of the lack of understanding of the monopolar technology in ESU can produce a larger current and will spread more widely throughout the body compared to bipolar. So in this study, the objective of this research is to develop a monopolar electrosurgery unit equipped with an additional mixed-mode and also equipped with a power selection. The contribution of this research is designing power management and adding several modes for the surgical process. The module is calibrated using the ESU Analyzer. This module comes with a choice of low, medium, and high power. And there are also several additional modes including blend 1 and blend 2. After the measurement, the value of the voltage at the inverter input shows the value for blend 1 mode, low 80 V with an error of 0.84%, Medium 90 V with an error of 0.84%, High 104 V with an error of 0.81. %. The measurements show an error of less than 1% for Blend 1 and also in Blend 2, while a cut is less than 3%. The results of this study can be implemented to minimize errors due to a lack of power regulation and mode selection during operation for electrosurgery equipment
Co-Authors Brahmantya Aji Pramudita Diah Rahayu Ningtias, Diah Rahayu Dyah Titisari, Dyah Kusumaningtyas, Pramesti Salwa Alexita, Alfreda Cecio Syaifudin Syaifudin, Syaifudin Utomo, Bedjo zulfa, nely