Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal ComEngApp : Computer Engineering and Applications Journal JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Dina Zatusiva Haq
UIN Sunan Ampel Surabaya
Computer Science & IT Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Leukaemia Identification based on Texture Analysis of Microscopic Peripheral Blood Images using Feed-Forward Neural Network Wahyu Tri Puspitasari; Dina Zatusiva Haq; Dian C Rini Novitasari
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

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%.
Implementasi Metode Firefly Algorithm-Extreme Learning Machine (FA-ELM) untuk Peramalan Cuaca Maritim pada Jalur Penyeberangan Ketapang - Gilimanuk Putri Wulandari; Dina Zatusiva Haq; Dian Candra Rini Novitasari
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 10, No 2 (2022)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v10i2.49964

Abstract

Cuaca merupakan fenomena yang dinamis. Dalam beberapa tahun terakhir, atmosfer bumi selalu berubah. Keadaan laut berdampak pada kegiatan di pelabuhan, seperti cuaca di laut, angin kencang, pasang surut, dll. Hujan deras menyebabkan kabut menutupi visibilitas kapten, angin kencang, dan ketinggian ombak adalah beberapa persyaratan sebelum keberangkatan transportasi laut. Untuk mengurangi risiko kecelakaan, diperlukan peramalan cuaca maritim dalam beberapa jam ke depan. Penelitian ini, meramalkan parameter cuaca maritim, yaitu, kecepatan angin dan tinggi gelombang di tiga titik untuk jam berikutnya berdasarkan tiga jam sebelumnya menggunakan algoritma Extreme Learning Machine yang telah dioptimalkan bobotnya menggunakan Firefly Algorithm.
Co-Authors Dian C Rini Novitasari Dian Candra Rini Novitasari Putri Wulandari Wahyu Tri Puspitasari