Article Per Year (5 Year)
p-Index From 2020 - 2025
1.055
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Rekayasa elektrika
Jusak Jusak
Universitas Dinamika
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
A novel fern-like lines detection using a hybrid of pre-trained convolutional neural network model and Frangi filter Heri Pratikno; Mohd Zamri Ibrahim; Jusak Jusak
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.23319

Abstract

Full ferning is the peak of the formation of a salt crystallization line pattern shaped like a fern tree in a woman’s saliva at the time of ovulation. The main problem in this study is how to detect the shape of the salivary ferning line patterns that are transparent, irregular and the surface lighting is uneven. This study aims to detect transparent and irregular lines on the salivary ferning surface using a comparison of 15 pre-trained convolutional neural network models. To detect fern-like lines on transparent and irregular layers, a pre-processing stage using the Frangi filter is required. The pre-trained convolutional neural network model is a promising framework with high precision and accuracy for detecting fern-like lines in salivary ferning. The results of this study using the fixed learning rate model ResNet50 showed the best performance with an error rate of 4.37% and an accuracy of 95.63%. Meanwhile, in implementing the automatic learning rate, ResNet18 achieved the best results with an error rate of 1.99% and an accuracy of 98.01%. The results of visual detection of fern-like lines in salivary ferning using a patch size of 34×34 pixels indicate that the ResNet34 model gave the best appearance
Model Identifkasi Sinyal Jantung Pertama (S1) dan Sinyal Jantung Kedua (S2) pada Janin Ira Puspasari; Jusak Jusak; Weny Indah Kusumawati; Ekasari Oktarina
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1146.416 KB) | DOI: 10.17529/jre.v16i1.14991

Abstract

Process of identifying fetal heart sound signals is imperative in recognizing congenital heart function that caused by many factors, such as hereditary factors and food intake of pregnant mothers. This study developed a method for processing heart signals to separate normal fetal phonocardiogram signals from noise by utilizing the Complete Empirical Mode Decomposition (CEEMD) algorithm which is integrated with the Pearson Distance metric. Heart signals that have been separated from noise are then processed using the Shannon Energy equation in order to sharpen the intensity of the first heart signal (S1) and the second heart signal (S2), but at the same time suppress the intensity of the residual noise in the signal. Based on the experiment results from 75 normal fetal heart sound cycles, the model that has been developed is able to identify the S1 signal and S2 signal, the time duration of T11 (S1-S1), and the time duration of T12 (S1-S2). Average duration of T11 and T12 acquired in this research can possibly be used as a reference for measuring the normal duration of fetal heart sound signals.
Classification of Secondary School Destination for Inclusive Students using Decision Tree Algorithm Rizal Prabaswara; Julianto Lemantara; Jusak Jusak
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5081

Abstract

Inclusive student education has become one of the most important agendas of UNESCO and the Indonesian government. Developing an inclusive education for children is critical to adapt to their abilities while attending school. However, most parents and educators who help students select their future secondary school after finishing primary school are often unaware of their real potential. The problem is mainly because the decision is not based on objective assessments such as IQ, average, and mental scores. In this study, our objective is to create a school-type decision support system using data mining as a factor-analytic approach to extract rules for the knowledge model. The system uses some variables as the basic principles for building school-type classification rules using the ID3 decision tree method. This system can also assist educators in making decisions based on existing graduate data. The evaluation showed that the proposed system produced an accuracy of 90% by allocating 75% of the data for training and 25% for testing. The accuracy value from the evaluation phase stated that the ID3 decision tree algorithm performs well. This system can also dynamically create new decision trees based on newly added datasets. More research is expected to result in a more variable and dynamic system that can have a more accurate result for the inclusive student classification of secondary school.
Model Identifkasi Sinyal Jantung Pertama (S1) dan Sinyal Jantung Kedua (S2) pada Janin Ira Puspasari; Jusak Jusak; Weny Indah Kusumawati; Ekasari Oktarina
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i1.14991

Abstract

Process of identifying fetal heart sound signals is imperative in recognizing congenital heart function that caused by many factors, such as hereditary factors and food intake of pregnant mothers. This study developed a method for processing heart signals to separate normal fetal phonocardiogram signals from noise by utilizing the Complete Empirical Mode Decomposition (CEEMD) algorithm which is integrated with the Pearson Distance metric. Heart signals that have been separated from noise are then processed using the Shannon Energy equation in order to sharpen the intensity of the first heart signal (S1) and the second heart signal (S2), but at the same time suppress the intensity of the residual noise in the signal. Based on the experiment results from 75 normal fetal heart sound cycles, the model that has been developed is able to identify the S1 signal and S2 signal, the time duration of T11 (S1-S1), and the time duration of T12 (S1-S2). Average duration of T11 and T12 acquired in this research can possibly be used as a reference for measuring the normal duration of fetal heart sound signals.
Co-Authors Eka Sari Oktarina Heri Pratikno Ira Puspasari Julianto Lemantara Mohd Zamri Ibrahim Rizal Prabaswara Weny Indah Kusumawati