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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Teknologi Informasi dan Ilmu Komputer Telematika JUITA : Jurnal Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Sebatik Journal of Computer Science and Informatics Engineering (J-Cosine) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Mnemonic Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Masyarakat Tekno
Friska Abadi
Universitas Lambung Mangkurat
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Library & Information Science

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Identification of Social Media Posts Containing Self-reported COVID-19 Symptoms using Triple Word Embeddings and Long Short-Term Memory Amalia, Raisa; Faisal, Mohammad Reza; Indriani, Fatma; Budiman, Irwan; Mazdadi, Muhammad Itqan; Abadi, Friska; Mafazy, Muhammad Meftah
Telematika Vol 17, No 1: February (2024)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v17i1.2774

Abstract

The COVID-19 pandemic has permeated the global sphere and influenced nearly all nations and regions. Common symptoms of this pandemic include fever, cough, fatigue, and loss of sense of smell. The impact of COVID-19 on public health and the economy has made it a significant global concern. It has caused economic contraction in Indonesia, particularly in face-to-face interaction and mobility sectors, such as transportation, warehousing, construction, and food and beverages. Since the pandemic began, Twitter users have shared symptoms in their tweets. However, they couldn't confirm their concerns due to testing limitations, reporting delays, and pre-registration requirements in healthcare. The classification of text from Twitter data about COVID-19 topics has predominantly focused on sentiment analysis regarding the pandemic or vaccination. Research on identifying COVID-19 symptoms through social media messages is limited in the literature. The main objective of this study is to identify symptoms using word embedding techniques and the LSTM algorithm. Various techniques such as Word2Vec, GloVe, FastText, and a composite approach are used. LSTM is used for classification, improving upon the RNN technique. Evaluation criteria include accuracy, precision, and recall. The model with an input dimension of 147x100 achieves the highest accuracy at 89%. This study aims to find the best LSTM model for detecting COVID-19 symptoms in social media tweets. It evaluates LSTM models with different word embedding techniques and input dimensions, providing insights into the optimal text-based method for COVID-19 detection through social media texts.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN BIBIT PADI BERKUALITAS PADA LAHAN RAWA MENGGUNAKAN METODE DEMATEL DAN MFEP Ulya, Azizatul; Muliadi, Muliadi; Herteno, Rudy; Farmadi, Andi; Abadi, Friska
Sebatik Vol. 28 No. 1 (2024): June 2024
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v28i1.2291

Abstract

Bidang pertanian merupakan salah satu sektor penting di Indonesia. Sebagian besar masyarakat Indonesia bergantung pada sektor ini sebagai petani padi. Adapun tujuan penelitian ini adalah mengetahui hasil akurasi perankingan alternatif bibit padi pada lahan rawa menggunakan metode metode  Decision Trial Making And Evaluation Laboratory (DEMATEL) dan Multifactor Evaluation Process (MFEP). Data yang sudah dikumpulkan kemudian akan dianalisis untuk mendapatkan atribut apa saja yang akan dimasukkan dalam perancangan sistem untuk metode membobotan dan perankingan menggunakan metode DEMATEL dan MFEP. Hasil pembobotan yang didapatkan dengan metode Dematel yaitu umur tanaman adalah 0,2277, tinggi tanaman adalah 0,1961, anakan produktif tanaman adalah 0,1921, ketahanan terhadap hama adalah 0,1294, ketahanan terhadap penyakit adalah 0,0896 dan ketahanan terhadap genangan air adalah 0,1652. Jika nilai bobot dijumlahkan maka hasilnya sama dengan 1. Kesimpulan bahwa Nilai bobot kriteria menggunakan metode Dematel pada pemilihan bibit padi pada lahan rawa yang terdiri dari 6 kriteria, yaitu umur tanaman adalah 0,2277, tinggi tanaman adalah 0,1961, anakan produktif tanaman adalah 0,1921, ketahanan terhadap hama adalah 0,1294, ketahanan terhadap penyakit adalah 0,0896 dan ketahanan terhadap genangan air adalah 0,1652. Nilai perbandingan hasil pemilihan bibit padi pada lahan rawa menggunakan metode Dematel dan MFEP dengan keputusan pihak Balai Penyuluhan Pertanian (BPP) berdasarkan perhitungan akurasi yang didapatkan dari MAE (Mean Absolute Error) adalah 80,42%.
Uji Sensitivitas Metode Aras Dengan Pendekatan Metode Pembobotan Kriteria Sahnnon Entropy Dan Swara Pada Penyeleksian Calon Karyawan Halimah, Halimah; Kartini, Dwi; Abadi, Friska; Budiman, Irwan; Muliadi, Muliadi
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 4 No. 2 (2020)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v4i2.194

Abstract

Penelitian ini melakukan uji sensitivitas metode Additive Ratio Assessment (ARAS) pada penyeleksian calon karyawan dengan pendekatan pembobotan kriteria menggunakan metode Shannon Entropy dan metode Stepwise Weight Assessment Ratio Analysis (SWARA) yang bertujuan untuk mengukur seberapa sensitif metode ini jika diterapkan pada sebuah kasus pengambilan keputusan. Data yang digunakan ialah data penyeleksian calon karyawan. Uji Sentitivitas pada penelitian ini digunakan untuk mengetahui metode yang lebih sensitif saat diterapkan pada suatu kasus. Metode perangkingan menggunakan ARAS karena metode perangkingan ini memiliki fungsi utilitas dan nilai optimalisasi. Metode Shannon Entropy bobot kriteria diperoleh berdasarkan perhitungan data alternatif penyeleksian karyawan, sedangkan metode SWARA bobot kriteria diperoleh dari pakar atau si pengambil keputusan. Hasil penelitian ini menunjukkan bahwa metode yang paling sensitif dengan kasus penyeleksian calon karyawan adalah metode SWARA-ARAS yang pemberian bobotnya berdasarkan pakar atau si pengambil keputusan dengan hasil sebesar 91,24203% lebih tinggi dibandingkan metode Shannon Entropy-ARAS yang hasil sebesar 74,75263%.
Feature Selection Using Firefly Algorithm With Tree-Based Classification In Software Defect Prediction Maulida, Vina; Herteno, Rudy; Kartini, Dwi; Abadi, Friska; Faisal, Mohammad Reza
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.v5i4.315

Abstract

Defects that occur in software products are a universal occurrence. Software defect prediction is usually carried out to determine the performance, accuracy, precision and performance of the prediction model or method used in research, using various kinds of datasets. Software defect prediction is one of the Software Engineering studies that is of great concern to researchers. This research was conducted to determine the performance of tree-based classification algorithms including Decision Trees, Random Forests and Deep Forests without using feature selection and using firefly feature selection. And also know the tree-based classification algorithm with firefly feature selection which can provide better software defect prediction performance. The dataset used in this study is the ReLink dataset which consists of Apache, Safe and Zxing. Then the data is divided into testing data and training data with 10-fold cross validation. Then feature selection is performed using the Firefly Algorithm. Each ReLink dataset will be processed by each tree-based classification algorithm, namely Decision Tree, Random Forest and Deep Forest according to the results of the firefly feature selection. Performance evaluation uses the AUC value (Area under the ROC Curve). Research was conducted using google collab and the average AUC value generated by Firefly-Decision Tree is 0.66, the average AUC value generated by Firefly-Random Forest is 0.77, and the average AUC value generated by Firefly-Deep Forest is 0, 76. The results of this study indicate that the approach using the Firefly algorithm with Random Forest classification can work better in predicting software damage compared to other tree-based algorithms. In previous studies, tree-based classification with hyperparameter tuning on software defect prediction datasets obtained quite good results. In another study, the classification performance of SVM, Naïve Bayes and K-nearest neighbor with firefly feature selection resulted in improved performance. Therefore, this research was conducted to determine the performance of a tree-based algorithm using the firefly selection feature.
LSTM and Bi-LSTM Models For Identifying Natural Disasters Reports From Social Media Yunida, Rahmi; Faisal, Mohammad Reza; Muliadi; Indriani, Fatma; Abadi, Friska; Budiman, Irwan; Prastya, Septyan Eka
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.
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.
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.
Impact of a Synthetic Data Vault for Imbalanced Class in Cross-Project Defect Prediction Putri Nabella; Rudy Herteno; Setyo Wahyu Saputro; Mohammad Reza Faisal; Friska Abadi
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.v6i2.409

Abstract

Software Defect Prediction (SDP) is crucial for ensuring software quality. However, class imbalance (CI) poses a significant challenge in predictive modeling. This study delves into the effectiveness of the Synthetic Data Vault (SDV) in mitigating CI within Cross-Project Defect Prediction (CPDP). Methodologically, the study addresses CI across ReLink, MDP, and PROMISE datasets by leveraging SDV to augment minority classes. Classification utilizing Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF), also model performance is evaluated using AUC and t-Test. The results consistently show that SDV performs better than SMOTE and other techniques in various projects. This superiority is evident through statistically significant improvements. KNN dominance in average AUC results, with values 0.695, 0.704, and 0.750. On ReLink, KNN show 16.06% improvement over the imbalanced and 12.84% over SMOTE. Similarly, on MDP, KNN 20.71% improvement over the imbalanced and a 10.16% over SMOTE. Moreover, on PROMISE, KNN 13.55% improvement over the imbalanced and 7.01% over SMOTE. RF displays moderate performance, closely followed by LR and DT, while NB lags behind. The statistical significance of these findings is confirmed by t-Test, all below the 0.05 threshold. These findings underscore SDV's potential in enhancing CPDP outcomes and tackling CI challenges in SDV. With KNN as the best classification algorithm. Adoption of SDV could prove to be a promising tool for enhancing defect detection and CI mitigation
Optimization of Backward Elimination for Software Defect Prediction with Correlation Coefficient Filter Method Muhammad Noor; Radityo Adi Nugroho; Setyo Wahyu Saputro; Rudy Herteno; Friska Abadi
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.v6i4.466

Abstract

Detecting software defects is a crucial step for software development not only to reduce cost and save time, but also to mitigate more costly losses. Backward Elimination is one method for detecting software defects. Notably Backward Elimination may remove features that may later become significant to the outcome affecting the performance of Backward Elimination. The aim of this study is to improve Backward Elimination performance. In this study, several features were selected based on their correlation coefficient, with the selected feature applied to improve Backward Elimination final model performance. The final model was validated using cross validation with Naïve Bayes as the classification method on the NASA MDP dataset to determine the accuracy and Area Under the Curve (AUC) of the final model. Using top 10 correlation feature and Backward Elimination achieve an average result of 86.6% accuracy and 0.797 AUC, while using top 20 correlation feature and Backward Elimination achieved an average result of 84% accuracy and 0.812 AUC. Compare to using Backward Elimination and Naïve Bayes respectively the improvement using top 10 correlation feature as follows: AUC:1.52%, 13.53% and Accuracy: 13%, 12.4% while the improvement using top 20 correlation feature as follows: AUC:3.43%, 15.66% and Accuracy: 10.4%, 9.8%. Results showed that selecting the top 10 and top 20 feature based on its correlation before using Backward Elimination have better result than only using Backward Elimination. This result shows that combining Backward Elimination with correlation coefficient feature selection does improve Backward Elimination’s final model and yielding good results for detecting software defects.
Co-Authors A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA Abdullayev, Vugar Achmad Zainudin Nur Adi Mu'Ammar, Rifqi Ahmad Juhdi Amalia, Raisa Andi Farmadi Andi Farmandi Arif, Nuuruddin Hamid budiman, irwan Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Emma Andini Faisal, Mohammad Reza Fathmah, Siti Fatma Indriani Halimah Halimah Halimah Herteno, Rudy Indriani, Fatma Irwan Budiman Irwan Budiman M Kevin Warendra Mafazy, Muhammad Meftah Mera Kartika Delimayanti Muhamad Fawwaz Akbar Muhammad Alkaff Muhammad Azmi Adhani Muhammad Denny Ersyadi Rahman Muhammad Haekal Muhammad Itqan Mazdadi Muhammad Khairin Nahwan Muhammad Mirza Hafiz Yudianto Muhammad Nazar Gunawan Muhammad Noor Muhammad Reza Faisal, Muhammad Reza Muhammad Sholih Afif Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Nabella, Putri Nor Indrani Nugrahadi, Dodon Nurlatifah Amini Nursyifa Azizah Prastya, Septyan Eka Putri Nabella Radityo Adi Nugroho Rahman Hadi Rahman Rahmat Ramadhani Rahmina Ulfah Aflaha Reina Alya Rahma Rinaldi Riza Susanto Banner Rizal, Muhammad Nur Rizky, Muhammad Hevny Rudy Herteno Rudy Herteno SALLY LUTFIANI Saragih, Triando Hamonangan Sarah Monika Nooralifa Sa’diah, Halimatus Septyan Eka Prastya Setyo Wahyu Saputro Setyo Wahyu Saputro Siti Napi'ah Tri Mulyani Ulya, Azizatul Umar Ali Ahmad Vina Maulida, Vina Wahyu Dwi Styadi Yunida, Rahmi