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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Komputasi Jurnal Sains dan Informatika Sebatik Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal of Electronics and Communications Systems Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Andi Farmadi
Universitas Lambung Mangkurat
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Materials Science & Nanotechnology Mathematics

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Journal : Journal of Electronics, Electromedical Engineering, and Medical Informatics

 1 
Implementation of Particle Swarm Optimization Feature Selection on Naïve Bayes for Thoracic Surgery Classification Shalehah; Muhammad Itqan Mazdadi; Andi Farmadi; Dwi Kartini; Muliadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.305

Abstract

Thoracic surgery is among the operations that are most often performed on patients with lung cancer. Naive Bayes is one of the data mining classification techniques that may be used to handle thoracic surgery data. Therefore, the goal of this study is to assess the precision of all research models using Naive Bayes with and without Particle Swarm Optimization. This study's methodology includes the dataset used, the Naive Bayes algorithm theory, the particle swarm optimization algorithm, test validation using split validation, and performance assessment using the confusion matrix and AUC evaluation approaches. In this inquiry, secondary data are retrieved via the UCI Repository website. Thoracic surgery weight optimization accuracy is increased using particle swarm optimization. The test results of the Naive Bayes technique utilizing the thoracic surgery dataset showed the highest accuracy of 81.91% at a ratio of 80:20 and an AUC value of 0.620. The highest accuracy score is 93.62% with an AUC value of 0.773 at a ratio of 90:10, with three characteristics, namely PRE6, PRE14, and PRE17, having zero weight. This accuracy score was achieved when Particle Swarm Optimization was used to refine feature selection for attribute weighting. As a consequence, Naïve Bayes accuracy in thoracic surgery has increased as a result of attribute weighting on feature selection utilizing Particle Swarm Optimization. In turn, this research contributes to increasing the precision and efficiency with which thoracic surgical data are processed, which benefits lung cancer diagnosis in both speed and accuracy.
An Approach to ECG-based Gender Recognition Using Random Forest Algorithm Arif, Nuuruddin Hamid; Faisal, Mohammad Reza; Farmadi, Andi; Nugrahadi, Dodon; Abadi, Friska; Ahmad, Umar Ali
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.363

Abstract

Human-Computer Interaction (HCI) has witnessed rapid advancements in signal processing research within the health domain, particularly in signal analyses like electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). ECG, containing diverse information about medical history, identity, emotional state, age, and gender, has exhibited potential for biometric recognition. The Random Forest method proves essential to facilitate gender classification based on ECG. This research delves into applying the Random Forest method for gender classification, utilizing ECG data from the ECG ID Database. The primary aim is to assess the efficacy of the Random Forest algorithm in gender classification. The dataset employed in this study comprises 10,000 features, encompassing both raw and filtered datasets, evaluated through 10-fold cross-validation with Random Forest Classification. Results reveal the highest accuracy for raw data at 55.000%, with sensitivity at 46.452% and specificity at 63.548%. In contrast, the filtered data achieved the highest accuracy of 65.806%, with sensitivity and specificity at 67.097%. These findings conclude that the most significant impact on gender classification in this study lies in the low sensitivity value in raw data. The implications of this research contribute to knowledge by presenting the performance results of the Random Forest algorithm in ECG-based gender classification.
Gender Classification on Social Media Messages Using fastText Feature Extraction and Long Short-Term Memory Sa’diah, Halimatus; Faisal, Mohammad Reza; Farmadi, Andi; Abadi, Friska; Indriani, Fatma; Alkaff, Muhammad; Abdullayev, Vugar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.407

Abstract

Currently, social media is used as a platform for interacting with many people and has also become a source of information for social media researchers or analysts. Twitter is one of the platforms commonly used for research purposes, especially for data from tweets written by individuals. However, on Twitter, user information such as gender is not explicitly displayed in the account profile, yet there is a plethora of unstructured information containing such data, often unnoticed. This research aims to classify gender based on tweet data and account description data and determine the accuracy of gender classification using machine learning methods. The method used involves FastText as a feature extraction method and LSTM as a classification method based on the extracted data, while to achieve the most accurate results, classification is performed on tweet data, account description data, and a combination of both. This research shows that LSTM classification on account description data and combined data obtained an accuracy of 70%, while tweet data classification achieved 69%. This research concludes that FastText feature extraction with LSTM classification can be implemented for gender classification. However, there is no significant difference in accuracy results for each dataset. However, this research demonstrates that both methods can work well together and yield optimal results.
Comparison of the Adaboost Method and the Extreme Learning Machine Method in Predicting Heart Failure Muhammad Nadim Mubaarok; Triando Hamonangan Saragih; Muliadi; Fatma Indriani; Andi Farmadi; Rizal, Achmad
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.440

Abstract

Heart disease, which is classified as a non-communicable disease, is the main cause of death every year. The involvement of experts is considered very necessary in the process of diagnosing heart disease, considering its complex nature and potential severity. Machine Learning Algorithms have emerged as powerful tools capable of effectively predicting and detecting heart diseases, thereby reducing the challenges associated with their diagnosis. Notable examples of such algorithms include Extreme Learning Machine Algorithms and Adaptive Boosting, both of which represent Machine Learning techniques adapted for classification purposes. This research tries to introduce a new approach that relies on the use of one parameter. Through careful optimization of algorithm parameters, there is a marked improvement in the accuracy of machine learning predictions, a phenomenon that underscores the importance of parameter tuning in this domain. In this research, the Heart Failure dataset serves as the focal point, with the aim of demonstrating the optimal level of accuracy that can be achieved through the use of Machine Learning algorithms. The results of this study show an average accuracy of 0.83 for the Extreme Learning Machine Algorithm and 0.87 for Adaptive Boosting, the standard deviation for both methods is “0.83±0.02” for Extreme Machine Learning Algorithm and “0.87±0.03” for Adaptive Boosting thus highlighting the efficacy of these algorithms in the context of heart disease prediction. In particular, entering the Learning Rate parameter into Adaboost provides better results when compared with the previous algorithm. Our research findings underline the supremacy of Extreme Learning Machine Algorithms and Adaptive Improvement, especially when combined with the introduction of a single parameter, it can be seen that the addition of parameters results in increased accuracy performance when compared to previous research using standard methods alone.
Analysis of Important Features in Software Defect Prediction Using Synthetic Minority Oversampling Techniques (SMOTE), Recursive Feature Elimination (RFE) and Random Forest Ghinaya, Helma; Herteno, Rudy; Faisal, Mohammad Reza; Farmadi, Andi; Indriani, Fatma
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.453

Abstract

Software Defect Prediction (SDP) is essential for improving software quality during testing. As software systems grow more complex, accurately predicting defects becomes increasingly challenging. One of the challenges faced is dealing with imbalanced class distributions, where the number of defective instances is significantly lower than non-defective ones. To tackle the imbalanced class issue, use the SMOTE technique. Random Forest as a classification algorithm is due to its ability to handle non-linear data, its resistance to overfitting, and its ability to provide information about the importance of features in classification. This research aims to evaluate important features and measure accuracy in SDP using the SMOTE+RFE+Random Forest technique. The dataset used in this study is NASA MDP D", which included 12 data sets. The method used combines SMOTE, RFE, and random forest techniques. This study is conducted in two stages of approach. The first stage uses the RFE+Random Forest technique; the second stage involves adding the SMOTE technique before RFE and Random Forest to measure the accurate data from NASA MDP. The result of this study is that the use of the SMOTE technique enhances accuracy across most datasets, with the best performance achieved on the MC1 dataset with an accuracy of 0.9998. Feature importance analysis identifies "maintenance severity" and "cyclomatic density" as the most crucial features in data modeling for SDP. Therefore, the SMOTE+RFE+RF technique effectively improves prediction accuracy across various datasets and successfully addresses class imbalance issues.
Classification of Lung Disease in X-Ray Images Using Gray Level Co-Occurrence Matrix Method and Convolutional Neural Network Nurcahyati, Ica; Saragih, Triando Hamonangan; Farmadi, Andi; Kartini, Dwi; Muliadi, Muliadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.457

Abstract

The lungs are a very important part of the human body, as they serve as a place for oxygen exchange. They have a very complex task and are susceptible to damage from the polluted air we breathe every day, which can lead to various diseases. Lung disease is a very common health problem that can be found in everyone, but there are still many people who do not pay attention to their lung health, making them vulnerable to lung disease. One of the methods used to detect lung disorders is by examining images obtained from X-rays. Image processing is one of the techniques that can also be used for lung disease identification and is most commonly used in medical images. Therefore, the purpose of this research is to implement image processing to determine the accuracy of lung disease identification using deep learning algorithms and the application of feature extraction. In this research, there are two experiments conducted consisting of the application of the classification method, namely Convolutional Neural Network and Gray Level Co-Occurrence Matrix feature extraction with CNN. The results show that the CNN model gets a precision of 0.92, recall of 0.92, f1-score of 0.92, and average accuracy of 0.92. The combination of the GLCM method with CNN produces a precision of 0.87, recall of 0.87, f1-score of 0.87, and average accuracy of 0.87. The results of this study indicate that the use of CNN in the lung disease classification model based on X-ray images is superior to the GLCM-CNN method.
Baby Cry Sound Detection: A Comparison of Mel Spectrogram Image on Convolutional Neural Network Models Junaidi, Ridha Fahmi; Faisal, Mohammad Reza; Farmadi, Andi; Herteno, Rudy; Nugrahadi, Dodon Turianto; Ngo, Luu Duc; Abapihi, Bahriddin
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.465

Abstract

Baby cries contain patterns that indicate their needs, such as pain, hunger, discomfort, colic, or fatigue. This study explores the use of Convolutional Neural Network (CNN) architectures for classifying baby cries using Mel Spectrogram images. The primary objective of this research is to compare the effectiveness of various CNN architectures such as VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152 in detecting baby needs based on their cries. The datasets used include the Donate-a-Cry Corpus and Dunstan Baby Language. The results show that AlexNet achieved the best performance with an accuracy of 84.78% on the Donate-a-Cry Corpus dataset and 72.73% on the Dunstan Baby Language dataset. Other models like ResNet-50 and LeNet-5 also demonstrated good performance although their computational efficiency varied, while VGG-16 and VGG-19 exhibited lower performance. This research provides significant contributions to the understanding and application of CNN models for baby cry classification. Practical implications include the development of baby cry detection applications that can assist parents and healthcare provide.
The Enhancing Diabetes Prediction Accuracy Using Random Forest and XGBoost with PSO and GA-Based Feature Selection Dzira Naufia Jawza; Mazdadi, Muhammad Itqan; Farmadi, Andi; Saragih, Triando Hamonangan; Kartini, Dwi; Abdullayev, Vugar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.626

Abstract

Diabetes represents a global health concern classified as a non-communicable disease, impacting more than 422 million people worldwide, with the number expected to increase each year. This study aims to evaluate the performance of the Random Forest and Extreme Gradient Boosting (XGBoost) classification algorithms on the diabetes disease dataset taken from Kaggle. To improve prediction accuracy, feature selection was carried out using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which are expected to filter the most relevant features. The study results showed that the Random Forest model without feature selection yielded an Area Under Curve (AUC) value of 0.8120, while XGBoost achieved an AUC of 0.7666. After applying feature selection with PSO, the AUC increased to 0.8582 for Random Forest and 0.8250 for XGBoost. The use of feature selection with GA gave better results, with an AUC of 0.8612 for Random Forest and 0.8351 for XGBoost. These results indicate that the increase in accuracy after feature selection using PSO ranges from 5.7% to 7.6%, while the increase with GA ranges from 6.1% to 8.9%, with GA providing more significant results. This study contributes to improving the accuracy of diabetes disease classification, which is expected to support the diagnosis process more quickly and accurately.
Comparative Analysis of YOLO11 and Mask R-CNN for Automated Glaucoma Detection Fayyadh, Muhammad Naufaldi; Saragih, Triando Hamonangan; Farmadi, Andi; Mazdadi, Muhammad Itqan; Herteno, Rudy; Abdullayev, Vugar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1266

Abstract

Glaucoma is a progressive optic neuropathy and a major cause of irreversible blindness. Early detection is crucial, yet current practice depends on manual estimation of the vertical Cup-to-Disc Ratio (vCDR), which is subjective and inefficient. Automated fundus image analysis provides scalable solutions but is challenged by low optic cup contrast, dataset variability, and the need for clinically interpretable outcomes. This study aimed to develop and evaluate an automated glaucoma screening pipeline based on optic disc (OD) and optic cup (OC) segmentation, comparing a single-stage model (YOLO11-Segmentation) with a two-stage model (Mask R-CNN with ResNet50-FPN), and validating it using vCDR at a threshold of 0.7. The contributions are fourfold: establishing a benchmark comparison of YOLO11 and Mask R-CNN across three datasets (REFUGE, ORIGA, G1020); linking segmentation accuracy to vCDR-based screening; analyzing precision–recall trade-offs between the models; and providing a reproducible baseline for future studies. The pipeline employed standardized preprocessing (optic nerve head cropping, resizing to 1024×1024, conservative augmentation). YOLO11 was trained for 200 epochs, and Mask R-CNN for 75 epochs. Evaluation metrics included Dice, Intersection over Union (IoU), mean absolute error (MAE), correlation, and classification performance. Results showed that Mask R-CNN achieved higher disc Dice (0.947 in G1020, 0.938 in REFUGE) and recall (0.880 in REFUGE), while YOLO11 attained stronger vCDR correlation (r = 0.900 in ORIGA) and perfect precision (1.000 in G1020). Overall accuracy exceeded 0.92 in REFUGE and G1020. In conclusion, YOLO11 favored conservative screening with fewer false positives, while Mask R-CNN improved sensitivity. These complementary strengths highlight the importance of model selection by screening context and suggest future research on hybrid frameworks and multimodal integration
Co-Authors Abdilah, Muhammad Fariz Fata Abdullayev, Vugar Achmad Rizal Adi, Puput Dani Prasetyo Ahdyani, Annisa Salsabila Ahmad Bahroini Ahmad Faris Asy’arie Ahmad Juhdi Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Tajali Akhmad Yusuf Ando Hamonangan Saragih Ardiansyah Sukma Wijaya Arif, Nuuruddin Hamid Arifin Hidayat Aris Pratama Azizah, Siti Roziana Bachtiar, Adam Mukharil Bahriddin Abapihi Bedy Purnama Deni Sutaji Dita Amara Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Dzira Naufia Jawza Efendi Mohtar Erdi, Muhammad Evi Nadya Prisilla Faisal Murtadho Fathul Hadi Fatma Indriani Fayyadh, Muhammad Naufaldi Fitria Agustina fitria Friska Abadi Ghinaya, Helma Gita Malinda Hertono, Rudy Heru Candra Kartika Heru Kartika Chandra I Gusti Ngurah Antaryama Ichsan Ridwan Irwan Budiman Irwan Budiman Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Kamil, Hawariul Keswani, Ryan Rhiveldi Khairunnisa Khairunnisa Lisnawati M. Apriannur Miftahul Muhaemen Muhammad Alkaff Muhammad Halim Muhammad Itqan Mazdadi Muhammad Khairin Nahwan Muhammad Nadim Mubaarok Muhammad Reza Faisal, Muhammad Reza Muhammad Ridha Maulidi Muhammad Rusli Muliadi Muliadi Muliadi Muliadi Aziz muliadi muliadi Muliadi Muliadi Muliadi Muliadi Musyaffa, Muhammad Hafizh Mutiara Ayu Banjarsari Nafis Satul Khasanah Ngo, Luu Duc Noryasminda Nugraha, Muhammad Amir Nugrahadi, Dodon Nurcahyati, Ica Nurlatifah Amini P., Chandrasekaran Patrick Ringkuangan Pirjatullah Pirjatullah Pirjatullah Priyatama, Muhammad Abdhi RACHMAT HIDAYAT Radityo Adi Nugroho Raidra Zeniananto Ramadhan, As`'ary Rifki Izdihar Oktvian Abas Pullah Rifki Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rudy Herteno Rusdiani, Husna Sabur, Baharuddin Salsabila Anjani Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sa’diah, Halimatus Setyo Wahyu Saputro Shalehah Srihardjanti, Rurien Suci Permata Sari Syahputra, Muhammad Reza Tajali, Ahmad Ulya, Azizatul Umar Ali Ahmad Wijaya Kusuma, Arizha Winda Agustina YILDIZ, Oktay