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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) JURNAL SISTEM INFORMASI BISNIS Epsilon: Jurnal Matematika Murni dan Terapan Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Mimbar Sekolah Dasar POSITIF 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 Jurnal Sains dan Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Pengembangan Riset dan Observasi Teknik Informatika Journal of Computer Science and Informatics Engineering (J-Cosine) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Formil (Forum Ilmiah) Kesmas Respati Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (Mediteg) Altasia : Jurnal Pariwisata Indonesia Jurnal Mnemonic Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Data Science and Software Engineering Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Dwi Kartini, Dwi
Unknown Affiliation
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Public Health Social Sciences Transportation

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Automated Detection of COVID-19 Cough Sound using Mel-Spectrogram Images and Convolutional Neural Network Nafiz, Muhammad Fauzan; Kartini, Dwi; Faisal, Mohammad Reza; Indriani, Fatma; Saragih, Triando Hamonangan
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.v9i3.26374

Abstract

COVID-19 disease is known as a new disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant. The initial symptoms of the disease commonly include fever (83-98%), fatigue or myalgia, dry cough (76-82%), and shortness of breath (31-55%). Given the prevalence of coughing as a symptom, artificial intelligence has been employed as a means of detecting COVID-19 based on cough sounds. This study aims to compare the performance of six different Convolutional Neural Network (CNN) models (VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152) in detecting COVID-19 using mel-spectrogram images derived from cough sounds. The training and validation of these CNN models were conducted using the Virufy dataset. Audio data was processed to generate mel-spectrogram images, which were subsequently employed as inputs for the CNN models. The AlexNet model, utilizing an input size of 227x227, exhibited the best performance with the highest Area Under the Curve (AUC) value of 0.930303. This study provides compelling evidence of the efficacy of CNN models in detecting COVID-19 based on cough sounds through the utilization of mel-spectrogram images. Furthermore, the study underscores the impact of input size on model performance. The primary contribution of this research lies in identifying the CNN model that demonstrates the best performance in COVID-19 detection based on cough sounds. Additionally, this study establishes the fundamental groundwork for selecting an appropriate CNN methodology for early detection of COVID-19.
Dimensionality Reduction Using Principal Component Analysis and Feature Selection Using Genetic Algorithm with Support Vector Machine for Microarray Data Classification Kartini, Dwi; Badali, Rahmat Amin; Muliadi, Muliadi; Nugrahadi, Dodon Turianto; Indriani, Fatma; Saputro, Setyo Wahyu
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/mr7x9713

Abstract

DNA microarray is used to analyze gene expression on a large scale simultaneously and plays a critical role in cancer detection. The creation of a DNA microarray starts with RNA isolation from the sample, which is then converted into cDNA and scanned to generate gene expression data. However, the data generated through this process is highly dimensional, which can affect the performance of predictive models for cancer detection. Therefore, dimensionality reduction is required to reduce data complexity. This study aims to analyze the impact of applying Principal Component Analysis (PCA) for dimensionality reduction, Genetic Algorithm (GA) for feature selection, and their combination on microarray data classification using Support Vector Machine (SVM). The datasets used are microarray datasets, including breast cancer, ovarian cancer, and leukemia. The research methodology involves preprocessing, PCA for dimensionality reduction, GA for feature selection, data splitting, SVM classification, and evaluation. Based on the results, the application of PCA dimensionality reduction combined with GA feature selection and SVM classification achieved the best performance compared to other classifications. For the breast cancer dataset, the highest accuracy was 73.33%, recall 0.74, precision 0.75, and F1 score 0.73. For the ovarian cancer dataset, the highest accuracy was 98.68%, recall 0.98, precision 0.99, and F1 score 0.99. For the leukemia dataset, the highest accuracy was 95.45%, recall 0.94, precision 0.97, and F1 score 0.95. It can be concluded that combining PCA for dimensionality reduction with GA for feature selection in microarray classification can simplify the data and improve the accuracy of the SVM classification model. The implications of this study emphasize the effectiveness of applying PCA and GA methods in enhancing the classification performance of microarray data.
Effectiveness of SMOTE in Enhancing Adult Autism Spectrum Disorder Diagnosis Predictive Performance With Missforest Imputation And Random Forest Musyaffa, Muhammad Hafizh; Saragih, Triando Hamonangan; Nugrahadi, Dodon Turianto; Kartini, Dwi; Farmadi, Andi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.66

Abstract

Autism Spectrum Disorder (ASD), originally described by Leo Kanner in 1943, is a complex developmental condition that manifests through social, emotional, and behavioral challenges, often including speech delays and difficulties in interpersonal interactions. Despite significant advancements in diagnostic criteria over the years, accurate diagnosis of ASD in adults remains challenging due to limited access to comprehensive datasets and inherent methodological constraints. The Autism Screening Adult dataset used in this study exemplifies these issues, as it contains missing values and exhibits a marked class imbalance, both of which can adversely affect model performance. To address these challenges, we proposed a framework that integrates Random Forest classification with MissForest imputation and the Synthetic Minority Over-sampling Technique (SMOTE). MissForest effectively imputes missing data by employing an iterative random forest approach that preserves the underlying structure of the data without relying on strict parametric assumptions. Meanwhile, SMOTE generates synthetic samples for the minority class, thereby balancing the dataset and reducing prediction bias. Experimental evaluation through 10-Fold Cross Validation demonstrated that the application of SMOTE significantly enhanced model performance. Notably, the overall accuracy improved from 70.17% to 79.32%, and the AUC-ROC increased from 47.13% to 85.84%, indicating a robust improvement in the model’s ability to distinguish between positive and negative cases. These results underscore the critical importance of addressing data imbalance and missing values in predictive modeling for ASD. The promising outcomes of this study provide a solid foundation for developing more reliable diagnostic tools for adult ASD, and future research may further refine feature selection and incorporate additional data sources to optimize performance even further.
Improving nutrient prediction models with polynomial and ratio features and mRMR selection Indriani, Fatma; Budiman, Irwan; Kartini, Dwi; Handayani, Lilies
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9189

Abstract

Due to limited space and regulations, food labels often lack information on micronutrients, i.e., vitamins and minerals. Accurately predicting missing these micronutrient data is essential yet challenging. This study explores the feasibility of using machine learning to predict these missing nutrients based on a limited reported nutrient (protein and carbs). Using the Tabel Komposisi Pangan Indonesia (TKPI) dataset, we evaluated the performance of 12 diverse classifiers to predict binary classes ("low" or "high") for 13 target micronutrients. Random forest emerged as the best performing classifier with an average accuracy of 0.7421 across all target nutrients. Additionally, we introduced feature engineering techniques by incorporating polynomial and ratio features to enhance model performance. Minimum redundancy maximum relevance (mRMR) feature selection was then applied to identify the most informative features. This approach boosted the average accuracy of the random forest classifier to 0.7591. These findings highlight the efficacy of feature engineering and selection in enhancing nutrient prediction models, demonstrating the potential to improve consumer knowledge about unknown nutrients in food.
Co-Authors A.A. Ketut Agung Cahyawan W Abadi, Friska Abdullayev, Vugar Adawiyah, Laila Adin Nofiyanto, Adin Ahdyani, Annisa Salsabila Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Aida, Nor Ajwa Helisa Al Habesyah, Noor Zalekha Andi Farmadi Andi Farmadi Anshari, Muhammad Ridha Ansyari, Muhammad Ridho Antoh, Soterio Arfan Eko Ari Widodo Aryastuti, Nurul Azizah, Siti Roziana Bachtiar, Adam Mukharil Badali, Rahmat Amin Budiman, Irwan Daduk Merdika Mansur Dalimunthe, Gallang Perdhana Deni Kurnia Diana Sari Dike Bayu Magfira, Dike Bayu Dina Arifah Dita Amara Dodon Turianto Nugrahadi Dzira Naufia Jawza Faisal, Mohammad Reza Faisal, Mohammad Reza Fathmah, Siti Fatma Indriani Fatma Indriani Favorisen R. Lumbanraja Fitra Ahya Mubarok Friska Abadi Halimah Halimah Helma Herlinda Herteno, Rudy Ihsan, Muhammad Khairi Indriani, Fatma Irwan Budiman Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Jhondy Baharsyah Lestari, Mega Lilies Handayani Mafazy, Muhammad Meftah Mahmud Mahmud Maya Yusida Mera Kartika Delimayanti Miftakhul Huda Muhammad Fauzan Nafiz Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muhammad Syahriani Noor Basya Basya Muliadi Muliadi Muliadi Muliadi . Muliadi . Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi, M Muliadi, Muliadi Musyaffa, Muhammad Hafizh Nafiz, Muhammad Fauzan Nor Indrani Nurcahyati, Ica Nurdiansyah Nurdiansyah Nurul Chamidah P., Chandrasekaran Padhilah, Muhammad Pirjatullah Pirjatullah Radityo Adi Nugroho Radityo Adi Nugroho Rahmat Hidayat Rahmat Ramadhani Reina Alya Rahma Riadi, Putri Agustina Rizky, Muhammad Hevny Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rusdiani, Husna Safitri, Yasmin Dwi Said, Muhammad Al Ichsan Nur Rizqi Salsha Farahdiba Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sari, Fitri Eka Septyan Eka Prastya Shalehah Siena, Laifansan Siti Aisyah Solechah Sulastri Norindah Sari Sule, Ernie Tisnawati Tri Mulyani Triando Hamonangan Saragih Vina Maulida, Vina Wahyu Caesarendra Wijaya Kusuma, Arizha Yevis Marty Oesman YILDIZ, Oktay Yuyus Suryana