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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Epsilon: Jurnal Matematika Murni dan Terapan Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (Mediteg) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Journal of Data Science and Software Engineering Jurnal Teknik Ilmu dan Aplikasi Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Saragih, Triando Hamonangan
Universitas Lambung Mangkurat
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Journal : Journal of Electronics, Electromedical Engineering, and Medical Informatics

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Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine Muhamad Fawwaz Akbar; Muhammad Itqan Mazdadi; Muliadi; Triando Hamonangan Saragih; Friska Abadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.328

Abstract

In the current digital era, sentiment analysis has become an effective method for identifying and interpreting public opinions on various topics, including public health issues such as COVID-19 vaccination. Vaccination is a crucial measure in tackling this pandemic, but there are still a number of people who are skeptical and reluctant to receive the COVID-19 vaccine. This public perception is largely influenced by, including information received from social media and online platforms. Therefore, sentiment analysis of the COVID-19 vaccine is one way to understand the public's perception of the COVID-19 vaccine. This research has the purpose to enhance the classification performance in sentiment analysis of COVID-19 vaccines by implementing Information Gain Ratio (IGR) and Particle Swarm Optimization (PSO) on the Support Vector Machine (SVM). With a dataset of 2000 entries consisting of 1000 positive labels and 1000 negative labels, validation was performed through a combination of data splitting with an 80:20 ratio and stratified 10-Fold cross-validation. Applying the basic SVM, an accuracy of 0.794 and an AUC value of 0.890 were obtained. Integration with Information Gain Ratio (IGR) feature selection improved the accuracy to 0.814 and an AUC of 0.907. Furthermore, through the combination of SVM based on PSO and IGR, the accuracy significantly improved to 0.837 with an AUC of 0.913. These results demonstrate that the combination of feature selection techniques and parameter optimization can enhance the performance of sentiment classification towards COVID-19 vaccines. The conclusions drawn from this research indicate that the integration of IGR and PSO positively contributes to the effectiveness and predictive capability of the SVM model in sentiment classification tasks.
Implementation of Random Forest and Extreme Gradient Boosting in the Classification of Heart Disease using Particle Swarm Optimization Feature Selection Ansyari, Muhammad Ridho; Mazdadi, Muhammad Itqan; Indriani, Fatma; Kartini, Dwi; Saragih, Triando Hamonangan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.322

Abstract

Heart disease is a condition that ranks as the primary cause of death worldwide. Based on available data, over 36 million people have succumbed to non-communicable diseases, and heart disease falls within the category of non-communicable diseases. This research employs a heart disease dataset from the UCI Repository, consisting of 303 instances and 14 categorical features. In this research, the data were analyzed using the classification methods XGBoost (Extreme Gradient Boosting) and Random Forest, which can be applied with PSO (Particle Swarm Optimization) as a feature selection technique to address the issue of irrelevant features. This issue can impact prediction performance on the heart disease dataset. From the results of the conducted research, the obtained values for the XGBoost (Extreme Gradient Boosting) model were 0.877, and for the Random Forest model, it was 0.874. On the other hand, in the model utilizing Particle Swarm Optimization (PSO), the obtained AUC values are 0.913 for XGBoost (Extreme Gradient Boosting) and 0.918 for Random Forest. These research results demonstrate that PSO (Particle Swarm Optimization) can enhance the AUC of heart disease prediction performance. Therefore, this research contributes to enhancing the precision and efficiency of heart disease patient data processing, which benefits heart disease diagnosis in terms of speed and accuracy.
Implementation of Monarch Butterfly Optimization for Feature Selection in Coronary Artery Disease Classification Using Gradient Boosting Decision Tree Siti Napi'ah; Triando Hamonangan Saragih; Dodon Turianto Nugrahadi; Dwi Kartini; Friska Abadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.331

Abstract

Coronary artery disease, a prevalent type of cardiovascular disease, is a significant contributor to premature mortality globally. Employing the classification of coronary artery disease as an early detection measure can have a substantial impact on reducing death rates caused by this ailment. To investigate this, the Z-Alizadeh dataset, consisting of clinical data from patients afflicted with coronary artery disease, was utilized, encompassing a total of 303 data points that comprise 55 predictive attribute features and 1 target attribute feature. For the purpose of classification, the Gradient Boosting Decision Tree (GBDT) algorithm was chosen, and in addition, a metaheuristic algorithm called monarch butterfly optimization (MBO) was implemented to diminish the number of features. The objective of this study is to compare the performance of GBDT before and after the application of MBO for feature selection. The evaluation of the study's findings involved the utilization of a confusion matrix and the calculation of the area under the curve (AUC). The outcomes demonstrated that GBDT initially attained an accuracy rate of 87.46%, a precision of 83.85%, a recall of 70.37%, and an AUC of 82.09%. Subsequent to the implementation of MBO, the performance of GBDT improved to an accuracy of 90.26%, a precision of 86.82%, a recall of 80.79%, and an AUC of 87.33% with the selection of 31 features. This improvement in performance leads to the conclusion that MBO effectively addresses the feature selection issue within this particular context.
Implementation of SMOTE and whale optimization algorithm on breast cancer classification using backpropagation Erlianita, Noor; Itqan Mazdadi, Muhammad; Saragih, Triando Hamonangan; Reza Faisal, Mohammad; Muliadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.334

Abstract

Breast cancer, which is characterized by uncontrolled cell growth, is the primary cause of mortality among women worldwide. The unchecked proliferation of cells leads to the formation of a mass or tumor. Generally, the absence of timely and efficient treatment contributes to this phenomenon. To prevent breast cancer, one of the strategies involves the classification of malignant and non-malignant types. For this particular investigation, the Breast Cancer Wisconsin dataset (original) comprising 699 instances with 11 classes and 1 target attribute was utilized. Synthetic Minority Oversampling (SMOTE) was employed to balance the dataset, with the Backpropagation classification algorithm and the Whale Optimization Algorithm (WOA) serving as optimization techniques. The main objectives of this study were to analyze the impact of the backpropagation method and SMOTE, examine the effect of the backpropagation method in conjunction with WOA, and assess the outcome of using the backpropagation method and SMOTE after incorporating WOA. The evaluation of the study's findings was performed using a confusion matrix and the Area Under the Curve (AUC) metric. The research outcomes based on the application of backpropagation yielded an accuracy rate of 96%, precision of 94%, recall of 95%, and an AUC of 96%. Subsequently, upon implementing SMOTE and WOA, the performance of the backpropagation method improved, resulting in an accuracy rate of 99%, precision of 97%, recall of 97%, and an AUC of 98%. This notable enhancement in performance suggests that the utilization of SMOTE and WOA effectively enhances accuracy. However, it is important to note that the observed improvements are relatively modest in nature.
Comparison of CatBoost and Random Forest Methods for Lung Cancer Classification using Hyperparameter Tuning Bayesian Optimization-based Zamzam, Yra Fatria; Saragih, Triando Hamonangan; Herteno, Rudy; Muliadi; Nugrahadi, Dodon Turianto; Huynh, Phuoc-Hai
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.v6i2.382

Abstract

Lung Cancer is a disease that has a high mortality rate and is often difficult to detect until it reaches a very severe stage. Data indicates that lung cancer cases are typically diagnosed late, posing significant challenges to effective treatment. Early detection efforts offer potential for better recovery chances. Therefore, this research aims to develop methods for the identification and classification of lung cancer in the hope of providing further knowledge on effective ways to detect this condition at an early stage. One approach under scrutiny involves employing machine learning classification techniques, anticipated to serve as a pivotal tool in early disease detection and enhancing patient survival rates. This study involves five stages: data collection, data preprocessing, data partitioning for training and testing using 10-fold cross validation, model training, and analysis of evaluation results. In this research, four experiments consist of applying two classification methods, CatBoost and Random Forest, each tested using default hyperparameter and hyperparameter tuning using Bayesian Optimization. It was found that the Random Forest model using hyperparameter tuning Bayesian Optimization outperformed the other models with accuracy (0.97106), precision (0.97339), recall (0.97185), f-measure (0.97011), and AUC (0.99974) for lung cancer data. These findings highlight Bayesian Optimization for hyperparameter tuning in classification models can improve clinical prediction of lung cancer from patient medical records. The integration of Bayesian Optimization in hyperparameter tuning represents a significant step forward in refining the accuracy and effectiveness of classification models, thus contributing to the ongoing enhancement of medical diagnostics and healthcare strategies.
Application Of SMOTE To Address Class Imbalance In Diabetes Disease Classification Utilizing C5.0, Random Forest, And SVM M. Khairul Rezki; Mazdadi, Muhammad Itqan; Indriani, Fatma; Muliadi, Muliadi; Saragih, Triando Hamonangan; Athavale, Vijay Annant
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.v6i4.434

Abstract

The implementation of SMOTE to tackle class imbalance in classification frequently results in suboptimal outcomes, owing to the intricacy of the dataset and the multitude of attributes at play. Consequently, alternative classification models were explored through experimentation to gauge their precision. This research aims to compare the precision of C5.0, Random Forest, and SVM classification models both with and without SMOTE. The methodology encompasses dataset selection, an overview of classification algorithms (C5.0, Random Forest, SVM), SMOTE technique, validation via split validation, preprocessing involving min-max normalization, and execution evaluation utilizing confusion matrices and AUC analysis. The dataset was sourced by Kaggle, specifically to rectify class imbalance in a diabetes dataset using SMOTE, consisting of 768 instances, with 268 samples for diabetic cases and 500 samples for non-diabetic cases. Prior to SMOTE application, the classification precision for C5.0, Random Forest, and SVM were 0.714, 0.733, and 0.746 respectively, with corresponding AUC values of 0.745, 0.824, and 0.799. Post-SMOTE, the precision depicts for the same techniques were 0.603, 0.727, and 0.727, with AUC values of 0.734, 0.831, and 0.794 respectively. It can be inferred that there's minimal impact post-SMOTE across the three classification models due to potential overfitting on the dataset, leading to excessive reliance on synthesized data for minority classes, resulting in diminished model execution, precision, and AUC scores.
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.
A Comparative Study: Application of Principal Component Analysis and Recursive Feature Elimination in Machine Learning for Stroke Prediction Hermiati, Arya Syifa; Herteno, Rudy; Indriani, Fatma; Saragih, Triando Hamonangan; Muliadi; Triwiyanto, Triwiyanto
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.v6i3.446

Abstract

Stroke is a disease that occurs in the brain and can cause both vocal and global brain dysfunction. Stroke research mainly aims to predict risk and mortality. Machine learning can be used to diagnose and predict diseases in the healthcare field, especially in stroke prediction. However, collecting medical record data to predict a disease usually makes much noise because not all variables are important and relevant to the prediction process. In this case, dimensionality reduction is essential to remove noisy (i.e., irrelevant) and redundant features. This study aims to predict stroke using Recursive Feature Elimination as feature selection, Principal Component Analysis as feature extraction, and a combination of Recursive Feature Elimination and Principal Component Analysis. The dataset used in this research is stroke prediction from Kaggle. The research methodology consists of pre-processing, SMOTE, 10-fold Cross-Validation, feature selection, feature extraction, and machine learning, which includes SVM, Random Forest, Naive Bayes, and Linear Discriminant Analysis. From the results obtained, the SVM and Random Forest get the highest accuracy value of 0.8775 and 0.9511 without using PCA and RFE, Naive Bayes gets the highest value of 0.7685 when going through PCA with selection of 20 features followed by RFE feature selection with selection of 5 features, and LDA gets the highest accuracy with 20 features from feature selection and continued feature extraction with a value of 0. 7963. It can be concluded in this study that SVM and Random Forest get the highest accuracy value without PCA and RFE techniques, while Naive Bayes and LDA show better performance using a combination of PCA and RFE techniques. The implication of this research is to know the effect of RFE and PCA on machine learning to improve stroke prediction.
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.
1D and 2D Feature Extraction Based on AAC and DC Protein Descriptors for Classification of Acetylation in Lysine Proteins using Convolutional Neural Network Faisal, Mohammad Reza; Adawiyah, Laila; Saragih, Triando Hamonangan; kartini, Dwi; Herteno, Rudy; Lumbanraja, Favorisen Rosyking; Handayani, Lilies; Solechah, Siti Aisyah
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.v6i4.458

Abstract

Post-Translational Modification (PTM) denotes a biochemical alteration observed in an amino acid, playing crucial roles in protein activity, functionality, and the regulation of protein structure. The recognition of associated PTMs serves as a fundamental basis for understanding biological processes, therapeutic interventions for diseases, and the development of pharmaceutical agents. Using computational approaches (in silico) offers an efficient and cost-effective means to identify PTM sites swiftly. The exploration of protein classification commences with extracting protein sequence features that are subsequently transformed into numerical features for utilization in classification algorithms. Feature extraction methodologies involve using protein descriptors like Amino Acid Composition (AAC) and Dipeptide Composition (DC). Yet, these approaches exhibit a limitation by neglecting crucial amino acid sequence details. Moreover, both descriptor techniques generate a limited number of 1-dimensional (1D) features, which may not be ideal for processing through the Convolutional Neural Network (CNN) classification method. This investigation presents a novel approach to enhance feature diversity through protein sequence segmentation techniques, employing adjacent and overlapping segment strategies. Furthermore, the study illustrates the organization of features into 1D and 2D formats to facilitate processing through 1D CNN and 2D CNN classification methodologies. The findings of this research endeavour highlight the potential for enhancing the accuracy of acetylation classification in lysine proteins through the multiplication of protein sequence segments in a 2D configuration. The highest accuracy achieved for AAC and DC-based feature extraction methods is 77.39% and 76.75%, respectively.
Co-Authors AA Sudharmawan, AA Abadi, Friska Abdul Latief Abadi Abdullayev, Vugar Achmad Rizal Adawiyah, Laila Afifa, Ridha Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Tajali Aida, Nor Ajwa Helisa Al Ghifari, Muhammad Akmal Alamudin, Muhammad Faiq Alfita Rakhmandasari Amelia Aditya Santika Andi Farmadi Andi Farmadi Anshari, Muhammad Ridha Anshory, Muhammad Naufal Ansyari, Muhammad Ridho Athavale, Vijay Anant Athavale, Vijay Annant Bachtiar, Adam Mukharil Bachtiar, Adam Mukharil Difa Fitria Dina Arifah Diny Melsye Nurul Fajri Diny Melsye Nurul Fajri Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Dzira Naufia Jawza Erdi, Muhammad Erlianita, Noor Faisal, Mohammad Reza Fatma Indriani Fatma Indriani Favorisen R. Lumbanraja Febrian, Muhamad Michael Friska Abadi Haekal, Muhammad Haekal, Muhammad Hafizah, Rini Hermiati, Arya Syifa Herteno, Rudy Huynh, Phuoc-Hai Ichwan Dwi Nugraha Indriani, Fatma Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Ivan Sitohang Jumadi Mabe Parenreng Keswani, Ryan Rhiveldi Lilies Handayani M. Khairul Rezki Mafazy, Muhammad Meftah Mariana Dewi Muhamad Fawwaz Akbar Muhammad Al Ichsan Nur Rizqi Said Muhammad Alkaff Muhammad Darmadi Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Ikhwan Rizki Muhammad Itqan Mazdadi Muhammad Mursyidan Amini Muhammad Nadim Mubaarok Muhammad Reza Faisal, Muhammad Reza Muhammad Rofiq Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Musyaffa, Muhammad Hafizh Nafiz, Muhammad Fauzan Noryasminda Nugraha, Muhammad Amir Nurcahyati, Ica Nurlatifah Amini Okta Muthia Sari Purwoko, Agus Putra, Aditya Maulana Perdana Radityo Adi Nugroho Rahmat Ramadhani Rahmat Ramadhani Rahmatullah, Satrio Wibowo Ramadhani, Rahmat Ratna Septia Devi Regina Reza Faisal, Mohammad Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Safitri, Yasmin Dwi Said, Muhammad Al Ichsan Nur Rizqi SALLY LUTFIANI Salsha Farahdiba Saputro, Setyo Wahyu Siena, Laifansan Siti Aisyah Solechah Siti Napi'ah Suci Permata Sari Sulastri Norindah Sari Tajali, Ahmad Totok Wianto Vivi Nur Wijayaningrum Wahyu Caesarendra Wayan Firdaus Mahmudy Winda Agustina Yanche Kurniawan Mangalik YILDIZ, Oktay Yusuf Priyo Anggodo Zamzam, Yra Fatria