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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Biosains Pascasarjana JFA (Jurnal Fisika dan Aplikasinya) Journal of Information Systems Engineering and Business Intelligence Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Magister Pendidikan IPA Darmabakti Cendekia: Journal of Community Service and Engagements Indonesian Journal of Electrical Engineering and Computer Science Indonesian Applied Physics Letters ABSYARA: Jurnal Pengabdian Pada Masyarakat International Conference on Industrial Revolution for Polytechnic Education Medika Respati : Jurnal Ilmiah Kesehatan Jurnal Abmas
Riries Rulaningtyas
Biomedical Engineering, Faculty Of Science And Technology, Universitas Airlangga, Surabaya, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

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Multimodal deep learning from sputum image segmentation to classify Mycobacterium tuberculosis using IUATLD assessment Saurina, Nia; Chamidah, Nur; Rulaningtyas, Riries; Aryati, Aryati
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9250

Abstract

Tuberculosis (TB) continues to be a major global health issue, especially in areas with limited resources where diagnostic tools are often insufficient. Traditional TB detection methods are slow and lack sensitivity, particularly for early-stage or low bacterial load cases. This study introduces a new multimodal deep learning model that integrates sputum image segmentation across RGB, hue, saturation, and value (HSV), and CIELAB color channels, using the YOLOv8 model for real-time detection and segmentation. The model uses the International Union Against Tuberculosis and Lung Disease (IUATLD) grading scale for accurate Mycobacterium tuberculosis (MTB) classification. Our approach shows high accuracy (92.24%) and precise forecasting (mean absolute percent error (MAPE) of 0.23%), greatly enhancing diagnostic speed and reliability. This research offers a novel method for classifying MTB using a multimodal deep learning model that integrates sputum image segmentation across RGB, HSV, and CIELAB color channels. By using the YOLOv8 model for real-time bounding box detection and segmentation, and the IUATLD grading scale for classification, our method achieves high accuracy and precision in identifying TB bacteria. Our findings indicate that this multimodal deep learning approach significantly improves diagnostic accuracy and speed, providing a reliable tool for early TB detection.
Design Of Autofocus Microscope With Histogram Method For Tuberculosis Bacteria Observation Kholil, Mohammad; Rulaningtyas, Riries; Winarno, Winarno
Indonesian Applied Physics Letters Vol. 1 No. 1 (2020): Indonesian Applied Physics Letters - June 2020
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v1i1.21331

Abstract

This research was conducted to design an autofocus microscope with a histogram method that can observe Tuberculosis (TB) bacteria. The bacteria observed were preparations or phlegm preparations which had been stained with Ziehl Neelsen. The microscope is designed to be equipped with a program to control the focus motor that moves the microscope tube and the program to digitally display the image and histogram of TB bacteria. Histograms are analyzed based on intensity values spread between 0-255 and the entropy value is sought. The measurement results that have been carried out as many as 20 times the field of view of the TB bacteria show that the most focused areas have the highest entropy value with an accuracy level ranging from 81.90476% to 100% at 1000 times the magnification.
Tubule Formation Segmentation Of Histopathological Image Of Breast Cancer By Using Clustering Method Waasilah, Hadiyyatan; Rulaningtyas, Riries; Winarno, Winarno; Rahaju, Anny Setijo
Indonesian Applied Physics Letters Vol. 1 No. 1 (2020): Indonesian Applied Physics Letters - June 2020
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v1i1.21338

Abstract

Histopathological assessment is one of the examinations that allows the classification of breast cancer based on its level. Histopathological assessment factors are based on tubule formation, nuclear pleomorphism, and the mitotic count. This study only focused on tubule formation. The tubule formation was represented by a lumen surrounded a  nucleus. The segmentation of tubule histopathology of breast cancer method was using a combination of k-means clustering and graph cut. The image data used in this study were 15 images of breast cancer histopathology preparations using 5 variations in the number of clusters (k) in the k-means clustering method. The best results of tubule formation segmentation using k = 4, with an average value of balanced accuracy was 81.08% and the most optimal balanced accuracy results was 94.34%.
PID-Based Design of DC Motor Speed Control Irhamni, Irfan; Rulaningtyas, Riries; Yunardi, Riky Tri
Indonesian Applied Physics Letters Vol. 2 No. 1 (2021): Indonesian Applied Physics Letters - June 2021
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v2i1.28297

Abstract

DC motor is an easy-to-apply motor but has inconsistent speed due to the existing load. PID (Proportional Integral Differential) is one of the standard controllers of DC motors. This study aimed to know the PID controller's performance in controlling the speed of a DC motor. The results showed that the PID controller could improve the error and transient response of the system response generated from DC motor speed control. Based on the obtained system response data from testing and tuning the PID parameters in controlling the speed of a DC motor, the PID controller parameters can affect the rate of a DC motor on the setpoint of 500, 1000, 1500: Kp = 0.05, Ki = 0.0198, Kd = 0.05.
Application of ANFIS-based Non-Linear Regression Modelling to Predict Concentration Level in Concentration Grid Test as Early Detection of ADHD in Children Rahma, S.T., M.Si., Osmalina Nur; Harahap, Akila Firdausi; Rahmatillah, Akif; Putra, Alfian Pramudita; Rulaningtyas, Riries; Thifal, Quinolina; Sumanang, Delfina Amarissa; Ittaqillah, Sayyidul Istighfar
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): Indonesian Applied Physics Letters - June 2023
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.48153

Abstract

Concentration is the main asset for students and serves as an indicator of successful learning implementation. One of the abnormal disturbances that can occur in a child's concentration development is attention deficit hyperactivity disorder (ADHD). The prevalence of ADHD in Indonesia in 2014 reached 12.81 million people due to delayed management in addressing ADHD. Therefore, early detection of ADHD is necessary for prevention. ADHD detection can be done by testing the level of concentration using a concentration grid. However, a method is needed that can be applied to uncooperative young children who are not familiar with numbers. Therefore, research was conducted with an innovative approach using a combination of EEG-ECG to classify concentration levels. The data used in this study were primary data from 4 participants with 5 repetitions. The data were processed in the preprocessing stage, which involved noise filtering and Butterworth filtering. The features used in this study were BPM (beats per minute), alpha, theta, and beta EEG signals, which would later become inputs for the Adaptive Neuro-Fuzzy Inference System (ANFIS). The output shows that the combination of EEG-ECG has the potential to predict concentration test results. Using BPM, alpha, theta, and beta signals can serve as parameters for predicting the concentration grid test values using ANFIS effectively. In the ANFIS model with 4 features, an accuracy of 99.997% was obtained for the training data and 80.2142% for the testing data. This result could be developed for early detection of ADHD based on concentration levels so the learning implementation could be more effective.
Classification of Pneumonia from Chest X-ray Images Using Keras Module TensorFlow Arisgraha, S.T., M.T., Franky Chandra Satria; Rulaningtyas, Riries; Kusumawardani, Miranti Ayudya
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): Indonesian Applied Physics Letters - June 2023
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.48241

Abstract

Pneumonia is a respiratory disease caused by bacteria and viruses that attack the alveoli, causing inflammation of the alveoli. This study aims to examine the ability of the Convolutional Neural Network (CNN) model to classify pneumonia and normal x-ray images. The method used in this research is to construct a CNN model from scratch by compiling layers one by one with the help of the Keras TensorFlow module, which consists of a Convolution layer, MaxPooling layer, Flatten layer, Dropout layer, and Dense layer. Data used in this research is from Guangzhou Women and Children Medical Center, Guangzhou, China. The total data used is 200 images divided into 160 test data, 20 training data, and 20 validation data. From the results of the research conducted, the model has the fastest processing speed of 9.6ms/epoch with a total of 20 epochs. The model has the highest accuracy value of 77% in the training process and an accuracy value of 80% in the testing process. The highest sensitivity value is 1.54 in training and 1.6 in testing. The highest specificity value is 0.77 in training and 0.8 in testing. It can be said that the model can do good classification.
Innovative electrotherapy for low back pain management in health centers Soegianto Soelistiono; Suryani Dyah Astuti; Khusnul Ain; Riries Rulaningtyas; Suhariningsih Suhariningsih; Winarno Winarno
Jurnal Abmas Vol. 25 No. 2 (2025): Jurnal Abmas
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/abmas.v25i2.88671

Abstract

Low Back Pain (LBP) is a common musculoskeletal complaint among the elderly, primarily due to static posture or poor ergonomics. At the primary care level, such as community health centers (puskesmas), LBP management often faces challenges, including limited equipment and low understanding among healthcare workers regarding electrical therapy technologies. This community service activity aimed to enhance the capacity of basic physiotherapy services in primary care facilities by conducting technological training and providing innovative electrostimulator devices based on magnetic electrodes. The implementation methods included education on the basic principles of body electricity and the device’s functionality, hands-on workshops for participants on device usage and electrode placement, and evaluation through pre-tests and post-tests. The results demonstrated a significant improvement in participants’ understanding of the device’s working principles and operational procedures. The response to the device’s use was highly positive, and the electrostimulator has begun to be integrated into basic physiotherapy services at Puskesmas Teja. This activity proves that collaboration between applied education and technological innovation can strengthen the capacity of primary care services to manage LBP effectively and safely, particularly in the elderly population.   Abstrak Nyeri punggung bawah (Low Back Pain/LBP) merupakan keluhan muskuloskeletal yang umum terjadi pada lansia, terutama akibat postur statis atau ergonomi yang buruk. Pada tingkat pelayanan primer seperti puskesmas, penanganan LBP sering menghadapi kendala berupa keterbatasan alat dan rendahnya pemahaman tenaga kesehatan terhadap teknologi terapi listrik. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kapasitas layanan fisioterapi dasar di fasilitas pelayanan primer melalui pelatihan teknologi dan pemberian alat elektrostimulator inovatif berbasis elektroda magnetik. Metode pelaksanaan meliputi edukasi prinsip dasar listrik tubuh dan fungsi alat, pelatihan langsung penggunaan dan pemasangan elektroda, serta evaluasi melalui pre-test dan post-test. Hasil kegiatan menunjukkan peningkatan signifikan pada pemahaman peserta terhadap prinsip kerja dan prosedur operasional alat. Respons terhadap penggunaan alat sangat positif dan alat elektrostimulator mulai diintegrasikan ke dalam layanan fisioterapi dasar di Puskesmas Teja. Kegiatan ini membuktikan bahwa kolaborasi antara pendidikan terapan dan inovasi teknologi mampu memperkuat kapasitas layanan primer dalam menangani LBP secara efektif dan aman, terutama pada populasi lansia. Kata kunci: elektroda magnetik; elektrostimulator; fisioterapi primer; nyeri punggung bawah 
Co-Authors Abidah Alfi Maritsa Alfian Pramudita Putra Ama, Fadli Amillia Kartika Sari Amirul Amalia Amma, Fadli Andriyan B. Suksmono Anny Setijo Rahaju, Anny Setijo Aryati Aryati Budi Dwi Satoto David Buntoro Kamandjaja Dewi, Rosa Amalia Eko Budi Khoendori Endah Purwanti Fitriyatul Qulub Franky Chandra Satria Arisgraha G. A. Putri Saptawati Hanif Assyarify Harahap, Akila Firdausi I Made Mas Dwiyana Prasetya Wibawa Imam Utoyo Irhamni, Irfan Ittaqillah, Sayyidul Istighfar Katherine Katherine Katherine Kho Kezia Sarahsophia Immanuel Ryadi Khouliya Zalda Khusnul Ain Khusnul Ain Kusumawardani, Miranti Ayudya Lina Choridah Miranda Syafira Widyananda Moh. Imam Utoyo Moh. Imam Utoyo Mohammad Kholil, Mohammad Muhammad Fadhel Maulama Muqmiroh, Lailatul Nafisa Rahmatul Laili Alami Nia Saurina Nita Lutfiyah Nur Chamidah Osmalina Nur Rahma Pramudita, Alfian Pramulen, Aji Sapta Prihartini Widiyanti Rahmatillah, Akif Rifai Chai Soegianto Soelistiono Solly Aryza Suhariningsih Sumanang, Delfina Amarissa Suprayitno Suprayitno Suryani Dyah Astuti Tati L. R. Mengko Thifal, Quinolina Ummi Hanifah Waasilah, Hadiyyatan Wahyudi Setiawan Wahyudi Setiawan Wardhani, Inten Firdhausi Weni Endahing Warni Winarno Winarno Winarno, Winarno Yunardi, Riky Tri Yusrinourdi Muhammad Zuchruf