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All Journal Justek : Jurnal Sains Dan Teknologi Journal of Computer System and Informatics (JoSYC) Teknika STTKD: : Jurnal Teknik, Elektronik, Engine Abdi Teknoyasa Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Kusumaningtyas, Pramesti
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Pemeliharaan Alat Centrifuge Dan Ultrasonic Scaler Dental di Rsud Dr. Soewondo Kendal Harsoyo, Imam Tri; Kusumaningtyas, Pramesti; Wahyudi, Bayu; Ningtias, Diah Rahayu
Abdi Teknoyasa Volume 5, No. 1, Juli 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/abditeknoyasa.v5i1.5603

Abstract

Sesuai dengan Permenkes no.15  tahun 2023 bahwa kegiatan pemeliharaan alat kesehatan pada fasilitas pelayanan kesehatan bertujuan untuk menjamin tersedianya alat kesehatan sesuai dengan standar pelayanan, persyaratan mutu, keamanan, manfaat, keselamatan dan laik guna mendukung penyelenggaraan pelayanan kesehatan yang aman dan bermutu. Tujuan dari kegiatan pengabdian masyarakat yang dilakukan adalah untuk membantu pihak IPSRS RSUD Dr. Soewondo Kendal dalam melaksanakan kegiatan manajemen pemeliharaan peralatan elektromedik. Dalam penyelenggaraan kegiatan pemeliharaan, diketahui bahwa terdapat unit centrifuge dan ultrasonic scaler dental yang mengalami kerusakan. Trobleshooting alat dilakukan dengan mencari sumber kerusakan kemudian memperbaiki kerusakan tersebut. Informasi kerusakan digali dengan wawancara dengan user, kemudian dilakukan cek fisik, cek kelistrikan dan uji fungsi. Dari proses tersebut diketahui bahwa kerusakan centrifuge terjadi pada rangkaian dimmer dan motor universal, sedangkan pada dental scaler kerusakan terdapat di trafo osilator dan selenoid valve. Setelah dilakukan langkah perbaikan dan uji fungsi, alat harus tetap dikalibrasi oleh pihak yang berwenang agar kelaikan alat dapat dipastikan. Hasil akhir dari PKM ini menunjukan adanya peningkatan usia pakai peralatan elektromedik untuk menunjang pelayanan kesehatan di RSUD Dr. Soewondo Kendal.  
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.
Deteksi Dini Kanker Payudara Menggunakan Citra Ultrasonografi Berbasis Convolutional Neural Networks dan Particle Swarm Optimization Azahra, Serina; Kusumaningtyas, Pramesti; Rofi'i, Mohammad
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.6637

Abstract

The increasing prevalence of breast cancer, one of the cancers with the highest incidence rate in the world, demands the development of effective early detection methods to increase patients' chances of recovery and reduce treatment costs. The main challenge in early detection of breast cancer is the identification of early symptoms that are often not felt, so many cases are only detected at an advanced stage. This study aims to develop a breast cancer early detection model using ultrasound images based on the convolutional neural networks (CNN) artificial neural network method optimized with the particle swarm optimization (PSO) algorithm. CNN is used to extract complex features from medical images, while PSO optimizes model parameters to improve accuracy. The dataset consists of 1,607 images classified into normal, benign, and malignant categories, through the process of model training and validation. The results showed that the integration of particle swarm optimization and convolutional neural networks resulted in an accuracy of 90.67%, higher than the convolutional neural networks method alone at 87.33%. In addition, the CNN-PSO model also excels in precision, sensitivity, and F1-score value. This research provides an effective diagnostic technology solution for primary healthcare facilities, with implications for improved early detection and reduced delayed diagnosis. This technology can be applied more widely through training of health workers and equitable distribution of diagnostic devices to improve service accessibility.
Rancang Bangun Alat Pengukur Tinggi dan Berat Badan Secara Wireless Untuk Menentukan Status Gizi Wibowo, Nadif Adhi; Harsoyo, Imam Tri; Kusumaningtyas, Pramesti; Yuniarti, Christina Ary; Rofii, Muhammad
Justek : Jurnal Sains dan Teknologi Vol 8, No 3 (2025): September
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v8i3.32011

Abstract

Nutritional status is a key indicator in assessing individual health that can be determined through anthropometric measurements, particularly height and weight. This study uses a design approach that aims to design and implement a wireless system for measuring nutritional status based on Body Mass Index (BMI) using a TOF400C sensor and a Load Cell controlled by an ESP32 microcontroller. The TOF400C sensor measures height based on the Time-of-Flight principle of infrared light, while the Load Cell measures weight with the HX711 module. Data from both sensors is processed by the ESP32 to calculate BMI and categorize nutritional status according to WHO standards. Test results on 10 adult subjects showed an accuracy of 99.6% for the height measuring device and 98.3% for the weight measuring device compared to standard devices, as well as real-time results displayed on OLED and TFT screens. This system is expected to be a practical and efficient solution for digital nutritional status monitoring.
An IoT-based Baby Scales for Stunting Monitoring in Indonesia Sofie, Mohamad; Olla, Patrisius Kusi; Kusumaningtyas, Pramesti; Chambali, Fani Maduratni
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 5 No. 4 (2023): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v5i4.191

Abstract

The Indonesian government has tried to do prevention stunting case by monitoring and recording the toddlers growth. The problems are the process of recording and entering toddler growth data is still done manually. This is not effective if done over a large area such as in Indonesia because it takes a long time to collect data and has a large possibility of recording errors. The aim of this research is to design an IoT-based baby scale by adding Node MCU ESP32 in to digital baby scales; so the data read by the scales recorded directly in the central data base. The scales are connected to the data base and web page via a WiFi network. The measurement data will be processed in the cloud to obtain a nutritional status classification. It makes the process of determining nutritional status faster. Based on the research conducted, the use of IoT-based baby scales can simplify the process of measuring toddlers' weight and height. The results of unit testing show that the tool is running well with the level of accuracy are 0,3% still within tolerance limits. The next research is expected to add location features, resumes per area and improve access levels on web pages for data security.
Co-Authors Azahra, Serina Chambali, Fani Maduratni Diah Rahayu Ningtias, Diah Rahayu Harsoyo, Imam Tri Mohamad Sofie, Mohamad Nurwijayanti Patrisius Kusi Olla, Patrisius Kusi Rofi'i, Mohammad Rofii, Muhammad Rofi’i, Mohammad Salwa Alexita, Alfreda Cecio Wibowo, Nadif Adhi yuniarti, christina ary