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All Journal ComEngApp : Computer Engineering and Applications Journal Fountain of Informatics Journal JOIV : International Journal on Informatics Visualization METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi
Novitasari, Dian C Rini
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Computer Science & IT Engineering

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KLASIFIKASI KUALITAS UDARA MENGGUNAKAN METODE EXTREME LEARNING MACHINE (ELM) Jannah, Rachma Raudhatul; Sholahuddin, Muhammad Zulfikar; Haq, Dina Zatusiva; Novitasari, Dian C Rini
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3066

Abstract

Air quality is a critical factor affecting both ecological and human well-being. Air pollution is a global epidemic that poses a threat to human health and the environment. High population density resulting from industrial expansion and the increased number of motor vehicles are two primary causes of declining air quality in metropolitan areas. Air pollutants include surface ozone (O3), dust particles (PM 10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO). Researchers have begun exploring the use of Extreme Learning Machine (ELM) to classify air quality. The ELM method assesses air quality as either very good or poor. In this study, we compare datasets to evaluate the effectiveness of hidden node parameters using the split method. Our tests indicate that the split method impacts accuracy, sensitivity, and specificity. The ideal model with a 70:30 split ratio and 15 hidden nodes achieved a 90% success rate.  
Enhancing Covid-19 Diagnosis: Glrlm Texture Analysis And Kelm For Lung X-Ray Classification Novitasari, Dian C Rini; Ramadanti , Alvin Nuralif; Haq, Dina Zatusiva
Fountain of Informatics Journal Vol. 9 No. 1 (2024): Mei 2024
Publisher : Universitas Darussalam Gontor

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Abstract

Abstrak This study aims to diagnose COVID-19 using GLRLM feature extraction, known for its high accuracy, and optimize Kernel Extreme Learning Machine (KELM) with Genetic Algorithm (GA) for improved computational efficiency, along with Principal Component Analysis (PCA) for data reduction. The gamma values in KELM are optimized using GA, yielding the best solution function. Results reveal that at angles of 0°, 45°, and 135°, the optimal gamma value with KELM is 1, while at 90°, GA determines it to be 1.35. This adjustment demonstrates the critical role of gamma values in achieving optimal performance. Performance analysis of various classification methods demonstrates that GLRLM-PCA-Optimized KELM outperforms others, achieving an accuracy exceeding 97%, particularly notable at 90° angles. This study shows that the importance of hyperparameter optimization in enhancing classification accuracy, revealing a significant improvement of over 1% compared to non-optimized models. Kata kunci: COVID-19, GLRLM, KELM, Feature Reduction, PCA   Abstract Penelitian ini bertujuan untuk mendiagnosis COVID-19 menggunakan ekstraksi fitur GLRLM yang dikenal dengan akurasi tinggi, dan mengoptimalkan Kernel Extreme Learning Machine (KELM) dengan Algoritma Genetika (GA) untuk meningkatkan efisiensi komputasi, bersama dengan Principal Component Analysis (PCA) untuk reduksi data. Nilai gamma dalam KELM dioptimalkan menggunakan GA, menghasilkan fungsi solusi terbaik. Hasil penelitian menunjukkan bahwa pada sudut 0°, 45°, dan 135°, nilai gamma optimal dengan KELM adalah 1, sedangkan pada 90°, GA menentukan nilainya menjadi 1,35. Penyesuaian ini menunjukkan peran penting nilai gamma dalam mencapai kinerja optimal. Analisis kinerja berbagai metode klasifikasi menunjukkan bahwa GLRLM-PCA-KELM yang Dioptimalkan mengungguli yang lain, mencapai akurasi lebih dari 97%, terutama mencolok pada sudut 90°. Studi ini menyoroti pentingnya optimasi hyperparameter dalam meningkatkan akurasi klasifikasi, mengungkapkan peningkatan signifikan lebih dari 1% dibandingkan dengan model KELM konvesional. Keywords: COVID-19, GLRLM, KELM, Feature Reduction, PCA
Leukaemia Identification based on Texture Analysis of Microscopic Peripheral Blood Images using Feed-Forward Neural Network Puspitasari, Wahyu Tri; Haq, Dina Zatusiva; Novitasari, Dian C Rini
Computer Engineering and Applications Journal Vol 11 No 3 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.893 KB) | DOI: 10.18495/comengapp.v11i3.412

Abstract

Leukaemia is very dangerous because it includes liquid tumour that it cannot be seen physically and is difficult to detect. Alternative detection of Leukaemia using microscopy can be processed using a computing system. Leukemia disease can be detected by microscopic examination. Microscopic test results can be processed using machine learning for classification systems. The classification system can be obtained using Feed-Forward Neural Network. Extreme Learning Machine (ELM) is a neural network that has a feedforward structure with a single hidden layer. ELM chooses the input weight and hidden neuron bias at random to minimize training time based on the Moore Penrose Pseudoinverse theory. The classification of Leukaemia is based on microscopic peripheral blood images using ELM. The classification stages consist of pre-processing, feature extraction using GLRLM, and classification using ELM. This system is used to classify Leukaemia into three classes, that is acute lymphoblastic Leukaemia, chronic lymphoblastic Leukaemia, and not Leukaemia. The best results were obtained in ten hidden nodes with an accuracy of 100%, a precision of 100%, a withdrawal of 100%.
Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data Novitasari, Dian C Rini; Fatmawati, Fatmawati; Hendradi, Rimuljo; Nariswari, Rinda; Saputra, Rizal Amegia
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1501

Abstract

Diabetic Retinopathy (DR) is a complication of diabetes mellitus, which attacks the eyes and often leads to blindness. The number of DR patients is significantly increasing because some people with diabetes are not aware that they have been affected by complications due to chronic diabetes. Some patients complain that the diagnostic process takes a long time and is expensive. So, it is necessary to do early detection automatically using Computer-Aided Diagnosis (CAD). The DR classification process based on these several classes has several steps: preprocessing and classification. Preprocessing consists of resizing and augmenting data, while in the classification process, CSVM method is used. The CSVM method is a combination of CNN and SVM methods so that the feature extraction and classification processes become a single unit. In the CSVM process, the first stage is extracting convolutional features using the existing architecture on CNN. CSVM could overcome the shortcomings of CNN in terms of training time. CSVM succeeded in accelerating the learning process and did not reduce the accuracy of CNN's results in 2 class, 3 class, and 5 class experiments. The best result achieved was at 2 class classification using CSVM with data augmentation which had an accuracy of 98.76% with a time of 8 seconds. On the contrary, CNN with data augmentation only obtained an accuracy of 86.15% with a time of 810 minutes 14 seconds. It can be concluded that CSVM was faster than CNN, and the accuracy obtained was also better to classify DR.
Co-Authors Fatmawati Fatmawati Haq, Dina Zatusiva Jannah, Rachma Raudhatul Puspitasari, Wahyu Tri Ramadanti , Alvin Nuralif Rimuljo Hendradi Rinda Nariswari Rizal Amegia Saputra Sholahuddin, Muhammad Zulfikar