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All Journal ComEngApp : Computer Engineering and Applications Journal IPTEK The Journal for Technology and Science Fountain of Informatics Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Informatika Universitas Pamulang METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi INTEK: Informatika dan Teknologi Informasi Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika JATI (Jurnal Mahasiswa Teknik Informatika) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Algoritme Jurnal Mahasiswa Teknik Informatika
Haq, Dina Zatusiva
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Transportation Other

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FETAL HEALTH RISK STATUS IDENTIFICATION SYSTEM BASED ON CARDIOTOCOGRAPHY DATA USING EXTREME GRADIENT BOOSTING WITH ISOLATION FOREST AS OUTLIER DETECTION Sari, Firda Yunita; Rini Novitasari, Dian Candra; Hamid, Abdulloh; Haq, Dina Zatusiva
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1711-1724

Abstract

Premature birth and birth defects contribute significantly to infant mortality, highlighting the need for early identification of fetal health risks. This study uses XGBoost for fetal health classification, integrating IForest for outlier detection to improve model performance. By varying the contamination percentage, learning rate (η), maximum depth, and n_estimator, the best results were achieved at CP = 8%, η = 0.01, max_depth = 7, and n_estimator = 100, which resulted in 100% accuracy, sensitivity, and specificity with a calculation time of 0.36 seconds. IForest effectively reduced the dataset from 2126 to 1956 samples by removing outliers, improving accuracy by 3.76%, and reducing computation time by 0.51 seconds. These findings suggest that IForest improves classification efficiency while maintaining high predictive performance, supporting early identification of fetal health risks to aid timely medical intervention.
Leukaemia Identification based on Texture Analysis of Microscopic Peripheral Blood Images using Feed-Forward Neural Network Puspitasari, Wahyu Tri; Haq, Dina Zatusiva; Novitasari, Dian Candra Rini
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 3 (2022)
Publisher : Universitas Sriwijaya

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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%.
PREDIKSI HARGA BERAS PREMIUM TAHUN 2024 MENGGUNAKAN METODE GRADIENT BOOSTED TREES REGRESSION Andriyani , Mayrisa; Nurwilda , Siti; Haq, Dina Zatusiva; Novitasari, Dian C Rini
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.14859

Abstract

Food needs are a special concern among the community. Every year the growth of Indonesian society increases so that the amount of food needed increases, especially rice which is the staple food of Indonesian society. Regarding this, the public needs information regarding forecasting rice prices for future needs. Therefore, this research aims to predict rice prices using the Gradient Boosted Trees Regression method. This method was chosen because of its ability to produce accurate predictions by minimizing errors through an ensemble approach. Evaluation is seen from the R-Squared and Root Mean Square Error (RMSE) values. The results of research using the Gradient Booster Trees Regression model obtained an R-Squared value of 0.9047 and an RMSE value of 0.0473, which indicates that the model has a high level of accuracy in predicting rice prices. The results of the dataset testing are divided into 80 percent training data and 20 percent for testing data. Based on this research, model testing was carried out by displaying decision tree visualization, using a sample of 50 decision trees.
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.  
Co-Authors Abdillah, Rifqi Abdulloh Hamid Adzan, M. Sailul Andriyani , Mayrisa C. R. Novitasari, Dian Chastine Fatichah Hartanto, Salsabila Jannah, Rachma Raudhatul Muhammad Syaifulloh Fattah Muhtadin Billah, Muhammad Novitasari, Dian C Rini Nurwilda , Siti Pratama, Moch Deny Puspitasari, Wahyu Tri Putri Wulandari Ramadanti , Alvin Nuralif Rasyid Al-Hadi, Daud Sari, Dian Candra Rini Novita Sari, Firda Yunita Sholahuddin, Muhammad Zulfikar Sri Wahyuni Wulandari, A’isyah