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All Journal ComEngApp : Computer Engineering and Applications Journal Fountain of Informatics Journal JOIV : International Journal on Informatics Visualization BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi INTEK: Informatika dan Teknologi Informasi ILKOMNIKA: Journal of Computer Science and Applied Informatics Algoritme Jurnal Mahasiswa Teknik Informatika
Novitasari, Dian C Rini
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Analisis Indeks Pembangunan Manusia Di Jawa Timur Tahun 2022-2023 Berdasarkan Indikator Menggunakan Metode Fuzzy C-Means Hartanto, Salsabila; Adzan, M. Sailul; Haq, Dina Zatusiva; Novitasari, Dian C Rini
INTEK : Jurnal Informatika dan Teknologi Informasi Vol. 7 No. 2 (2024)
Publisher : Program Studi Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37729/intek.v7i2.5358

Abstract

Indeks Pembangunan Manusia (IPM) termasuk faktor perkembangan suatu negara. Khususnya di Jawa Timur yang dengan nilai IPM rendah dibandingkan dengan IPM provinsi lainnya karena IPM di Jawa Timur memiliki indikator yang berpengaruh pada nilai IPM rendah, yaitu Tingkat pengangguran Terbuka, Angka Harapan Hidup, Gini Rasio, dan Upah Minimum Regional. Penelitian ini bertujuan mengelompokkan Kabupaten/Kota di Jawa Timur dengan menerapkan algoritma Fuzzy C-Means. Dihasilkan cluster pada tahun 2023 dengan silhouette 0,7742 dengan 3 cluster. Berdasarkan kualitas IPM, bahwasannya mengungkapkan bahwa kota/kabupaten di Provinsi Jawa Timur terdiri dari 3 cluster : Cluster 0 (rendah) yang memiliki 23 kabupaten/kota, Cluster 1 (tinggi) yang memiliki 5 kabupaten/kota, dan Cluster 2 (sedang) dengan memiliki 10 kabupaten/kota. Terdapat perubahan pada Indeks Pembangunan Manusia di tahun 2022 dan 2023 yakni Jember, Kota Kediri, dan Kota Blitar.
Identifikasi Penyakit Anemia menggunakan Metode Support Vector Machine (SVM) Berdasarkan Hemoglobin Darah Wulandari, A’isyah; Wahyuni, Sri; Haq, Dina Zatusiva; Novitasari, Dian C Rini
Jurnal Algoritme Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.8767

Abstract

In the world, the number of people infected with anemia is still very high, especially in the Asian region, reaching 48.7 percent. Anemia or anemia occurs due to a lack of blood pressure below normal values. If many people experience blood shortages, there will be many people who suffer from anemia. So it can be seen that variable Then the variable Y shows that the anemia class can be grouped into two parts, namely class 1 which states that they are infected with anemia and class 0 which states that they are not infected with anemia. This research aims to identify anemia using the Support Vector Machine (SVM) method which can be used in the analysis process with approaches from various types of kernels including; Linear, Radial Basis Function (RBF), Polynomial, and Sigomid to determine the level of accuracy, sensitivity and specificity in anemia. This research can show that the best classification of anemia using a linear kernel produces an accuracy value of 99.3 percent. The results obtained from this study indicate that the SVM method with a linear kernel is highly effective in identifying and classifying cases of anemia.
Implementation of LSTM Method on Tidal Prediction in Semarang Region Ambadar, Panreshma Rizkha; Novitasari, Dian C Rini; Farida, Yuniar; Hafiyusholeh, Moh; Setiawan, Fajar
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8932

Abstract

Semarang is the capital of the Central Java province, located in the north and directly adjacent to the Java Sea. Having an almost flat land condition with a slope of about 0-2%, Semarang City has the opportunity to experience tidal flooding. The occurrence of tides does not have a fixed period. So, it is necessary to predict the height of the tide and the ebb of the seawater. Thus, this research aims to predict tides in the Semarang area using the LSTM method. The data used is tidal data in Semarang waters from 2020 to 2024. The advantage of the LSTM method is its ability to effectively remember time series data or data with long-term dependence. LSTM can store past information using special cells contained in its structure. This research on tidal prediction using the LSTM method with 70% training data trial batch size 32 and epoch 200 obtained the smallest error value, namely the MAE value of 0.0388 and MAPE of 0.0313 which is the best LSTM result.
Peningkatan Identifikasi PCOS dengan KELM melalui Seleksi Fitur LDA dan Deteksi Outlier LOF Ambadar, Panreshma Rizkha; Novitasari, Dian C Rini; Hamid, Abdulloh
ILKOMNIKA Vol 7 No 1 (2025): Volume 7, Number 1, April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i1.727

Abstract

Polycystic Ovary Syndrome (PCOS) merupakan kelainan yang terjadi pada organ reproduksi perempuan. Kelainan ini mempunyai dampak implikasi klinis yang beragam dan serius, diantaranya pada bagian reproduksi, metabolisme, hingga gangguan psikologis. Identifikasi yang tepat sangat penting untuk meningkatkan penanganan. Penelitian ini bertujuan untuk menguji efektivitas metode Kernel Extreme Learning Machine (KELM) dalam mengidentifikasi PCOS setelah penghapusan outlier dengan Local Outlier Factor (LOF) dan seleksi fitur menggunakan Linear Discriminant Analysis (LDA). Dalam penelitian ini, metode KELM mengidentifikasi kelainan PCOS dengan klasifikasi berdasarkan data rekam medis pasien. Penelitian ini juga melibatkan pengolahan data dengan LOF untuk menangani data outlier dan seleksi fitur terbaik menggunakan LDA guna meningkatkan akurasi identifikasi kelainan PCOS. Berbagai uji coba dilakukan, untuk mengoptimalkan hasil identifikasi kelainan PCOS. Hasil penelitian menunjukkan bahwa ketiga kombinasi dari metode LOF, LDA, dan KELM memperoleh nilai akurasi sebesar 100% dengan eliminasi 10% data outlier dan 10 fitur utama. Hal ini yang menunjukkan kombinasi ketiga metode ini mampu meningkatkan kualitas deteksi dan identifikasi kelainan PCOS.
LONG-SHORT TERM MEMORY (LSTM) FOR PREDICTING VELOCITY AND DIRECTION SEA SURFACE CURRENT ON BALI STRAIT Pramesti, Diah Devi; Novitasari, Dian C Rini; Setiawan, Fajar; Khaulasari, Hani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (860.674 KB) | DOI: 10.30598/barekengvol16iss2pp451-462

Abstract

The strategic role of the Bali Strait as a connection between the islands of Java and Bali is growing in line with the increase in the economy and tourism of the two islands. Therefore, it is necessary to have a further understanding of the condition of the waters in the Bali strait, one of which is ocean currents. This study aims to predict future ocean currents based on 30-minute data in the Bali Strait in the range of 16 May 2021 to 9 June 2021 obtained from the Perak II Surabaya Maritime Meteorological Station. In this study, the Long Short Term Memory method was used. The parameters used are hidden layer, batch size, and learn rate drop. Based on the parameters used, the results showed that the smallest MAPE value was 18.64% for U ocean current velocity data and 5.29% for V ocean current velocity data.
PREDICTION OF THE ELECTRIC POWER BY OSCILLATING WATER COLUMN WAVE POWER PLANTS ON BAWEAN ISLAND USING LSTM Putri, Risma Madurahma; Hakim, Lutfi; Novitasari, Dian C Rini; Asyhar, Ahmad Hanif; Setiawan, Fajar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2287-2300

Abstract

The demand for electricity in Indonesia continues to increase in line with population growth and the expansion of economic development. This increase is not matched by the diminishing electricity resources, as fossil fuels, which are non-renewable, are being used. Therefore, there is a need for renewable energy sources that can be utilized as long-term electricity resources. The abundant marine areas in Indonesia make it a potential source of alternative energy, one form of its utilization is the Ocean Wave Power Plant using the Oscillating Water Column (OWC) method. Bawean Island in Gresik is one of the regions that has this potential, while also facing long-standing electricity supply limitations that have resulted in uneven electricity distribution among the community. The problem does not stop at power generation but also extends to the transmission system between supply and demand. This research is conducted to predict the electricity generated by the ocean wave power plant to help avoid mismatches when supplying electricity. This study uses time series data from January 1st, 2021, to May 5th, 2024, which includes wave height, length, period, and amplitude. Electricity prediction based on these parameters can be performed using deep learning-based methods that can effectively process sequential time series data, such as the Long Short Term Memory (LSTM) method, by experimenting with the number of neurons, epochs, and batch sizes. The best prediction results for the variables of height, length, period, and amplitude of the waves obtained MAPE values of 0.3657%, 0.1637%, 0.0888%, and 0.3480%, respectively. The electricity prediction results from the best parameters obtained a MAPE of 0.3549%.
Co-Authors Abdulloh Hamid Adzan, M. Sailul Ahmad Hanif Asyhar Ambadar, Panreshma Rizkha FAJAR SETIAWAN Fatmawati Fatmawati Hani Khaulasari Haq, Dina Zatusiva Hartanto, Salsabila Jannah, Rachma Raudhatul Lutfi Hakim Moh. Hafiyusholeh Pramesti, Diah Devi Puspitasari, Wahyu Tri Putri, Risma Madurahma Ramadanti , Alvin Nuralif Rimuljo Hendradi Rinda Nariswari Rizal Amegia Saputra Sholahuddin, Muhammad Zulfikar Sri Wahyuni Wulandari, A’isyah Yuniar Farida, Yuniar