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INDONESIA
Indonesian Applied Physics Letters
Published by Universitas Airlangga
ISSN : -     EISSN : 27453502     DOI : http://dx.doi.org/10.20473/iapl.v1i2.23444
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Electrical & Electronics Engineering Materials Science & Nanotechnology Physics
Indonesian Applied Physics Letter is an multi-disciplinary international journal which publishes high quality scientific and engineering papers on all aspects of research in the area of applied physics and wide practical application of achieved results. The field of IAPL, which can be described as encounter of material science, theoretical science, computational, instrumentation, biomedical, geophysics and applied physics, has become distinguishable integrated discipline of research-based endeavor.
Arjuna Subject : Fisika dan Astronomi - Fisika dan Astronomi (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 4 No. 1 (2023): June" : 5 Documents clear
On the Design of Maze Wanderer Robot Mohamad Abdulhamid
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.45450

Abstract

This paper focuses on designing, programming and implementing a maze wonderer robot that takes the commands todictate its direction of motion based on the obstacle avoidance. A program is to be written to give the robot its intelligence. Thepaper will as well be restricted to a motorized car which will be given the intelligence to be able to navigate through a given maze.
IMPLEMENTATION OF FAST FOURIER TRANSFORM (FFT) FOR INFANT CRYING DETECTION Latifah Listyalina; Evrita Lusiana Utari; Mario Warran Wizando
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.46916

Abstract

Babies cry based on the discomfort felt by the baby which is a reflex such as when a hungry baby will suck his hand and then he will start crying, hunger can be interpreted from the baby's crying. At each of the baby's cries, when each crying pattern was responded to with the solution applied to the previous baby, each baby would stop crying. For this reason, to carry out this solution, a research was carried out to identify the pattern recognition of the sound of a baby's cry using the fast fourier transform (FFT) method with several different frequency ranges. The voice recording process is stored in digital form in the form of frequency-based sound spectrum waves, where signals that were previously in the time domain will be changed in the frequency domain. The sounds that will be distinguished in this study include the sounds of crying babies, adults, and colliding objects. This can be obtained through several stages, namely sound sample recording, sampling, signal cutting, frame blocking, final normalization, hamming window, and finally the FFT calculation process. From these series of stages, the results of the frequency range of baby crying are 101-1863 Hz, for adults the frequency range is 101-1376 Hz and for the sound of colliding objects 101-2233 Hz.
Application of ANFIS-based Non-Linear Regression Modelling to Predict Concentration Level in Concentration Grid Test as Early Detection of ADHD in Children Sayyidul Istighfar Ittaqillah; Delfina Amarissa Sumanang; Quinolina Thifal; Akila Firdausi Harahap; Akif Rahmatillah; Alfian Pramudita Putra; Riries Rulaningtyas; Osmalina Nur Rahma, S.T., M.Si.
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.48153

Abstract

Concentration is the main asset for students and serves as an indicator of successful learning implementation. One of the abnormal disturbances that can occur in a child's concentration development is attention deficit hyperactivity disorder (ADHD). The prevalence of ADHD in Indonesia in 2014 reached 12.81 million people due to delayed management in addressing ADHD. Therefore, early detection of ADHD is necessary for prevention. ADHD detection can be done by testing the level of concentration using a concentration grid. However, a method is needed that can be applied to uncooperative young children who are not familiar with numbers. Therefore, research was conducted with an innovative approach using a combination of EEG-ECG to classify concentration levels. The data used in this study were primary data from 4 participants with 5 repetitions. The data were processed in the preprocessing stage, which involved noise filtering and Butterworth filtering. The features used in this study were BPM (beats per minute), alpha, theta, and beta EEG signals, which would later become inputs for the Adaptive Neuro-Fuzzy Inference System (ANFIS). The output shows that the combination of EEG-ECG has the potential to predict concentration test results. Using BPM, alpha, theta, and beta signals can serve as parameters for predicting the concentration grid test values using ANFIS effectively. In the ANFIS model with 4 features, an accuracy of 99.997% was obtained for the training data and 80.2142% for the testing data. This result could be developed for early detection of ADHD based on concentration levels so the learning implementation could be more effective.
Android Application for Initial Screening of Atrial Fibrillation Using The Dempster Shafer Method Rizky Widya Rachmawati; Endah Purwanti; Marcella Aurelia Yatijan; Yudi Her Oktaviono; M. Arief Bustomi
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.48159

Abstract

Atrial Fibrillation (FA) is one of the most common types of heart rhythm abnormalities found in clinical practice in Indonesia. Atrial fibrillation can cause a stroke risk 5 times. Meanwhile, stroke cases themselves tend to rise and become one of the main causes of death every year. The cause of the high number of FA cases is the lack of public knowledge and awareness/sensitivity to the early symptoms of the disease. The purpose of this study was to design an android device for early screening of suspected FA through examination of pulse, complaints/symptoms and disease risk factors. The dempster shafer method is used as a decision making tool for suspected FA or not FA. The results of the detection system performance test, obtained a sensitivity of 93.5%, a specificity of 89.7%, and an accuracy of 91.7%. The test results of the android application design, namely Visual Design and User Interaction, Functionality, Stability and Performance and Overall Satisfaction, show a "good" response in all aspects.
Classification of Pneumonia from Chest X-ray Images Using Keras Module TensorFlow Franky Chandra Satria Arisgraha, S.T., M.T.; Riries Rulaningtyas; Miranti Ayudya Kusumawardani
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.48241

Abstract

Pneumonia is a respiratory disease caused by bacteria and viruses that attack the alveoli, causing inflammation of the alveoli. This study aims to examine the ability of the Convolutional Neural Network (CNN) model to classify pneumonia and normal x-ray images. The method used in this research is to construct a CNN model from scratch by compiling layers one by one with the help of the Keras TensorFlow module, which consists of a Convolution layer, MaxPooling layer, Flatten layer, Dropout layer, and Dense layer. Data used in this research is from Guangzhou Women and Children Medical Center, Guangzhou, China. The total data used is 200 images divided into 160 test data, 20 training data, and 20 validation data. From the results of the research conducted, the model has the fastest processing speed of 9.6ms/epoch with a total of 20 epochs. The model has the highest accuracy value of 77% in the training process and an accuracy value of 80% in the testing process. The highest sensitivity value is 1.54 in training and 1.6 in testing. The highest specificity value is 0.77 in training and 0.8 in testing. It can be said that the model can do good classification.

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