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R. Rizal Isnanto
Computer Science Department, Semarang State University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Transportation Other

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Journal : Scientific Journal of Informatics

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Pneumothorax Detection System in Thoracic Radiography Images Using CNN Method Fardana, Nouvel Izza; Isnanto, R. Rizal; Nurhayati, Oky Dwi
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.16635

Abstract

Purpose: This research aims to develop an automatic pneumothorax detection system using Convolutional Neural Networks (CNN) to classify thoracic radiography images. By leveraging CNN's effectiveness in identifying medical abnormalities, the system seeks to enhance diagnostic accuracy, reduce evaluation time, and minimize subjective interpretation errors. The output will provide a predicted label of "pneumothorax" or "non-pneumothorax," facilitating faster clinical treatment and improving diagnostic services while supporting radiologists in making more accurate and efficient decisions for this critical condition. Methods: This research employs an experimental deep learning approach using Convolutional Neural Networks (CNN) to detect pneumothorax in thoracic radiography images. The CNN model is trained on an annotated dataset with preprocessing steps, including zooming, brightness adjustment, flipping and format adjustment, followed by performance evaluation using accuracy, precision, recall, and F1 score metrics. Result: The results showed that the CNN model detected pneumothorax with 79.59% accuracy, a loss of 1.3056, and 1,092 correct predictions out of 1,372 test data. Precision was 51.12%, recall 78.62%, and F1 score 61.96%, confirming the system's potential, though further optimization is needed. Novelty: The novelty of this research lies in developing an automated pneumothorax detection system using a CNN architecture, improving diagnostic accuracy and efficiency. Despite high accuracy, precision and recall can be improved. Future research can focus on optimizing the model and applying data augmentation techniques.
Optimization of Coronary Heart Disease Risk Prediction Using Extreme Learning Machine Algorithm (Case Study: Patients of Dr. Soeselo Hospital) Iswanti, Arie; Isnanto, R. Rizal; Widodo, Catur Edi
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24746

Abstract

Purpose: Coronary heart disease (CHD) is the leading cause of death globally, with 17.8 million deaths reported by the WHO in 2021. Early detection remains a major challenge due to low public awareness and dependence on manual diagnostic procedures. These limitations necessitate the development of automated and accurate predictive models. This study aims to construct a CHD risk prediction model using the Extreme Learning Machine (ELM) algorithm. The research addresses a critical limitation in existing models, namely, poor performance on minority classes (CHD stages 2–4), caused by data imbalance. To overcome this, oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are applied. The objective is to improve classification performance, particularly in high-risk categories, and to enhance the model’s generalisation capability for real-world implementation. Methods: This research implements the Extreme Learning Machine (ELM) algorithm to achieve optimal prediction results. The data used in this study as the initial database of patients consists of gender, age, height, weight, whether they have diabetes or not, the number of cigarettes consumed daily, and blood pressure. The data will be the main component in building the heart disease prediction system. The prediction classes are: no heart disease, stage 1 heart disease, stage 2 heart disease, stage 3 heart disease, and stage 4 heart disease. The total number of dataset are 521 data points, with 70% of the training data amounting to 364 patients, and 30% of the test data amounting to 157 patients. The data collection process uses patient data from RSUD Dr. Soeselo, Tegal Regency, Central Java, for the years 2023 and 2024. Result: The research successfully developed and evaluated an Extreme Learning Machine (ELM) algorithm for Coronary Heart Disease (CHD) risk prediction using patient data from Dr. Soeselo Hospital. The model achieved an overall accuracy of 82% on the dataset of 157 patients, demonstrating a promising capability for automated risk assessment. Novelty: This predictive model can be utilised in the medical field to facilitate the early detection of heart disease or other risks. This model will soon be introduced in hospitals in the Tegal Regency and City area, Central Java.
Co-Authors Achmad Hidayatno Adi Dhama Kameswara Adi Mora Tunggul Adian Fatchur R Adian Fatchur Rochim Adrian Khoirul Haq Adrianus Stephen, Adrianus Afrizal Mohamad Riand Aghus Sofwan agung setiawan Agung Wicaksono Ahmad Bahauddin Ahmad Fashiha Hastawan Ajub Ajulian Zahra Macrina Ali, Sarifa Isna Ali, Sarifa Isna Alwin Indra Fatra Aminullah Ruhul Aflah Anang Paramita Wahyadyatmika Andino Maseleno Andre Lukito Kurniawan, Andre Lukito Angga Setiawan Anggie Salsa Saputra Antonius Dwi Hartanto Antonius Hendry Setyawan Ardian Wijaya Arfriandi, Arief Arie Firmansyah Permana Aris Triwiyanto Aris Triwiyatno Bagus Hario Setiadji Basuki Rahmat Masdi Siduppa Bondhan Tunjung Bowo Leksono Budi Setiyono Budi Warsito Candra Laksono Catur Edi Widodo Causa Prima Wijaya Chairunnisa Adhisti Prasetiorini Chandra Yogatama Chauhan, Rahul Darmawan Surya Kusuma Dela Nurlaila Dewi Lestari Dian Wijayanto Dictosendo Noor Pambudi Rahayu Didik Supriyadi, Didik Djoko Windarto Donny Zaviar Rizky Dony Bagus Rudiyanto Dyah Kusuma Mauliyani, Dyah Kusuma Eko Didik Widianto Eliezer, Petrick Jubel Enda Wista Sinuraya Endang Purbowati Endriawan Endriawan Eskanesiari Eskanesiari Fachrul Rozy Fachry Abda El Rahman Fajar Adi Nugroho Fara Mantika Dian Febriana, Fara Mantika Fardana, Nouvel Izza Febry Santo Ferry Hadi Fifiana Wisnaeni Fikri Ahmad Affandi Habiba, A. Herdhian Cahya Novanto Herjuna Dony Anggara Putra, Herjuna Dony Anggara Heru Prastawa Ilina Khoirotun Khisan Iskandar Imam Santoso Irwan Andaltria Iswanti, Arie Kholid, Kholid Kodrat Imam Satoto Kurnia, Dita Juni Lasmedi Afuan Lathifah Alfat, Lathifah Lukas Aditratika M. Azwar A. G. N. M. Ikhsan Mulyadi M. Wirdan Syahrial Maman Somantri Maria Fitriana Mario Christy Sinuraya Martha Irene Kartasurya Meet Shah, Meet Meidiana Dwidiyanti Melly Arisandi Muhammad Satriya Utama Mukharrom Edisuryana Munawar Agus Riyadi Mutiara Shabrina Nanang Trisnadik Nani Purwati Natanael Benino Tampubolon, Natanael Benino Novettralita, Ucky Pradestha Nugroho Arif Widodo Nur Arifin Akbar Nur Rizky Rosna Putra Nurul Ifan Purba Oky Dwi Nurhayati Patel, Raj Praseti, Agung Budi Praseti, Agung Budi Prasetijo, Aging Budi Pringgo Budi Utomo R. Edith Indera Bagaskara R. G Alam Nusantara P.H, R. G Alam R. Mh. Rheza Kharis Rachmad Arief Setiawan Ragil Aji Prastomo Rahmat Gernowo Raidah Hanifah Raithatha, Bhavya Ramchandani, Paras Relung Satria D Rico Eko Wibowo Rizky Parlika, Rizky Rody Verdika Cahyadi RR. Ella Evrita Hestiandari Saputra, I Gede Dharma Setyowati, Ro'fah Shabrina Mihanora Sharma, Ansh Shriyal, Harsh Siboro, Septihadi Klinsman Sompura, Jayesh Sudjadi Sudjadi Sumardi . Suseno, J.Endro Teguh Dwi Prihartono Theodora Anita Fidelia Tito Tri Pamungkas Tri Murwanto Tri Prasetyo Wahyul Amien Syafei Widyati, Dian Ami Yuli Christiyono Yuli Christyono Yuli Syarif Zaka Bil Fiqhi