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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jiko (Jurnal Informatika dan komputer) Journal of Computer Science and Informatics Engineering (J-Cosine) Journal of Electronics, Electromedical Engineering, and Medical Informatics
Irwan Budiman
Lambung Mangkurat University
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Using Social Media Data to Monitor Natural Disaster: A Multi Dimension Convolutional Neural Network Approach with Word Embedding Mohammad Reza Faisal; Irwan Budiman; Friska Abadi; Muhammad Haekal; Mera Kartika Delimayanti; Dodon Turianto Nugrahadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4525

Abstract

Social media has a significant role in natural disaster management, namely as an early warning and monitoring when natural disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, non-eyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new features by combining data from all word embedding techniques. The classification model is made using three Convolutional Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the 2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique produces better average accuracy.
Prediction of Post-Operative Survival Expectancy in Thoracic Lung Cancer Surgery Using Extreme Learning Machine and SMOTE Ajwa Helisa; Triando Hamonangan Saragih; Irwan Budiman; Fatma Indriani; Dwi Kartini
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.25973

Abstract

Lung cancer is the most common cause of cancer death globally. Thoracic surgery is a common treatment for patients with lung cancer. However, there are many risks and postoperative complications leading to death. In this study, we will predict life expectancy for lung cancer patients one year after thoracic surgery The data used is secondary data for lung cancer patients in 2007-2011. There are 470 data consisting of 70 death class data and 400 survival class data for one year after surgery. The algorithm used is Extreme learning machine (ELM) for classification, which tends to be fast in the learning process and has good generalization performance. Synthetic Minority Over-sampling (SMOTE) is used to solve the problem of imbalanced data. The proposed solution combines the benefits of using SMOTE for imbalanced data along with ELM. The results show ELM and SMOTE outperform other algorithms such as Naïve Bayes, Decision stump, J48, and Random Forest. The best results on ELM were obtained at 50 neurons with 89.1% accuracy, F-Measure 0.86, and ROC 0.794. In the combination of ELM and SMOTE, the accuracy is 85.22%, F-measure 0.864, and ROC 0.855 on neuron 45 using a data division proportion of 90:10. The test results show that the proposed method can significantly improve the performance of the ELM algorithm in overcoming class imbalance. The contribution of this study is to build a machine learning model with good performance so that it can be a support system for medical informatics experts and doctors in early detection to predict the life expectancy of lung cancer patients.
IMPLEMENTATION OF INFORMATION GAIN AND PARTICLE SWARM OPTIMIZATION UPON COVID-19 HANDLING SENTIMENT ANALYSIS BY USING K-NEAREST NEIGHBOR Riana Riana; Muhammad I Mazdadi; Irwan Budiman; Muliadi Muliadi; Rudy Herteno
JIKO (Jurnal Informatika dan Komputer) Vol 6, No 1 (2023)
Publisher : JIKO (Jurnal Informatika dan Komputer)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v6i1.5260

Abstract

In early 2020, a new virus from Wuhan, China, identified as the coronavirus or COVID-19, shocked the entire world. (Coronavirus Disease 2019). The government has made various attempts to combat this outbreak, despite the fact that the government's involvement in combating Covid-19 has many benefits and disadvantages. One of the most commonly debated subjects on Twitter is the Indonesian government's response to the Covid-19 virus. This research compares the k-nearest neighbor classification technique, Information Gain feature selection with the K-Nearest Neighbor classification algorithm, and Information Gain feature selection and Particle Swarm Optimization optimization with the K-Nearest Neighbor classification algorithm. Comparisons are performed to determine which method is more accurate. Because it is frequently used for text and data categorization, the K-Nearest Neighbor algorithm was selected. The K-Nearest Neighbor algorithm has flaws, including the ability to be fooled by irrelevant characteristics and being less than ideal in finding the value of k. The selection of the Information Gain feature could indeed solve this issue by decreasing Terms that are less important and to optimize the K-Nearest Neighbor categorization, an optimization method with the Particle Swarm Optimization algorithm is employed to maximize the K-Nearest Neighbor classification. According to the results of this research, the K-Nearest Neighbor categorization with Information Gain feature selection and Particle Swarm Optimization optimization is better than the K-Nearest Neighbor model without selecting features and without optimization and is better than the K-Nearest Neighbor model with Information Gain selecting features, notably 87,33% with a value of K 5.
Classification of Natural Disaster Reports from Social Media using K-Means SMOTE and Multinomial Naïve Bayes Nor Indrani; Mohammad Reza Faisal; Irwan Budiman; Dwi Kartini; Friska Abadi; Septyan Eka Prastya; Mera Kartika Delimayanti
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 7 No 1 (2023): June 2023
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v7i1.503

Abstract

Disasters can occur anytime and anywhere. Floods and forest fires are two types of disasters that occur in Indonesia. South Kalimantan Province is an area that frequently experiences floods and forest fires. The dataset used for previous research's flood and forest fire disaster data is unbalanced. Unbalanced data conditions can complicate the classification method in carrying out the classification process. The sampling method for the data level approach that can be used to solve imbalance problems is oversampling, one of the derivatives of oversampling, namely SMOTE. The K-Means SMOTE method is a modification of SMOTE. One Naïve Bayes model often used in text classification is Multinomial Naïve Bayes. Multinomial Naïve Bayes has a good performance in classifying text. The research results on flood disaster data using K-Means SMOTE with Multinomial Naïve Bayes yielded an f1 score of 66.04%, and forest fire disaster data using K-Means SMOTE with Multinomial Naïve Bayes produced an f1 score of 66.31%.
Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory Kevin Yudhaprawira Halim; Dodon Turianto Nugrahadi; Mohammad Reza Faisal; Rudy Herteno; Irwan Budiman
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26354

Abstract

Gender classification by computer is essential for applications in many domains, such as human-computer interaction or biometric system applications. Generally, gender classification by computer can be done by using a face photo, fingerprint, or voice. However, researchers have demonstrated the potential of the electrocardiogram (ECG) as a biometric recognition and gender classification. In facilitating the process of gender classification based on ECG signals, a method is needed, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). Researchers use these two methods because of the ability of these two methods to deal with sequential problems such as ECG signals. The inputs used in both methods generally use one-dimensional data with a generally large number of signal features. The dataset used in this study has a total of 10,000 features. This research was conducted on changing the input shape to determine its effect on classification performance in the LSTM and Bi-LSTM methods. Each method will be tested with input with 11 different shapes. The best accuracy results obtained are 79.03% with an input shape size of 100×100 in the LSTM method. Moreover, the best accuracy in the Bi-LSTM method with input shapes of 250×40 is 74.19%. The main contribution of this study is to share the impact of various input shape sizes to enhance the performance of gender classification based on ECG signals using LSTM and Bi-LSTM methods. Additionally, this study contributes for selecting an appropriate method between LSTM and Bi-LSTM on ECG signals for gender classification.
LSTM and Bi-LSTM Models For Identifying Natural Disasters Reports From Social Media Mohammad Reza Faisal; Rahmi Yunida; Muliadi; Fatma Indriani; Friska Abadi; Irwan Budiman; Septyan Eka Prastya
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.v5i4.319

Abstract

Natural disaster events are occurrences that cause significant losses, primarily resulting in environmental and property damage and in the worst cases, even loss of life. In some cases of natural disasters, social media has been utilized as the fastest information bridge to inform many people, especially through platforms like Twitter. To provide accurate categorization of information, the field of text mining can be leveraged. This study implements a combination of the word2vec and LSTM methods and the combination of word2vec and Bi-LSTM to determine which method is the most accurate for use in the case study of news related to disaster events. The utility of word2vec lies in its feature extraction method, transforming textual data into vector form for processing in the classification stage. On the other hand, the LSTM and Bi-LSTM methods are used as classification techniques to categorize the vectorized data resulting from the extraction process. The experimental results show an accuracy of 70.67% for the combination of word2vec and LSTM and an accuracy of 72.17% for the combination of word2vec and Bi-LSTM. This indicates an improvement of 1.5% achieved by combining the word2vec and Bi-LSTM methods. This research is significant in identifying the comparative performance of each combination method, word2vec + LSTM and word2vec + Bi-LSTM, to determine the best-performing combination in the process of classifying data related to earthquake natural disasters. The study also offers insights into various parameters present in the word2vec, LSTM, and Bi-LSTM methods that researchers can determine.
Comparison of Industrial Business Grouping Using Fuzzy C-Means and Fuzzy Possibilistic C-Means Methods Mega Lestari; Dwi Kartini; Irwan Budiman; Mohammad Reza Faisal; Muliadi Muliadi
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2548

Abstract

The industrial business sector plays a role in the development of the economic sector in developing countries such as Indonesia. In this case, many industrial businesses are growing, but the data has not been processed or analyzed to produce important information that can be processed into knowledge using data mining. One of the data mining techniques used in this research is data grouping, or clustering. This research was conducted to determine the comparison results of the Cluster Validity Index on Fuzzy C-Means and Fuzzy Possibilistic C-Means methods for clustering industrial businesses in Tanah Bumbu Regency. In each process, 5 trials were conducted with the number of clusters, namely 3, 4, 5, 6, and 7, and for the attributes used: Male Labor, Female Labor, Investment Value, Production Value, and BW/BP Value. Furthermore, this study will evaluate the Cluster Validity Index, namely the Partition Entropy Index, Partition Coefficient index, and Modified Partition Coefficient Index. This research provides the best performance results in the Fuzzy C-Means method with the results of the Cluster Validity Index on the Partition Entropy Index of 0.21566, Partition Coefficient Index of 0.88078, and Modified Partition Coefficient Index of 0.82117, and the best number of clusters is 3 with the labels of low competitive industry clusters, medium competitive industry clusters, and highly competitive industry clusters.
Co-Authors Ajwa Helisa Dodon Turianto Nugrahadi Dwi Kartini, Dwi Fatma Indriani Friska Abadi Kevin Yudhaprawira Halim Mega Lestari Mera Kartika Delimayanti Muhammad Haekal Muhammad I Mazdadi Muhammad Reza Faisal, Muhammad Reza Muliadi Muliadi Muliadi Nor Indrani Rahmi Yunida Riana Riana Rudy Herteno Saragih, Triando Hamonangan Septyan Eka Prastya Septyan Eka Prastya