INDONESIAN JOURNAL OF APPLIED PHYSICS
https://jurnal.uns.ac.id/ijap
<hr /><table class="data" style="height: 181px; width: 100%;" width="100%" bgcolor="#f0f0f0"><tbody><tr style="height: 18px;" valign="top"><td style="height: 18px; width: 24.4484%;" width="20%">Journal title</td><td style="height: 18px; width: 75.5516%;" width="80%"> <a href="/ijap/index" target="_blank"><strong>Indonesian Journal of Applied Physics</strong></a></td></tr><tr style="height: 18px;" valign="top"><td style="height: 18px; width: 24.4484%;" width="20%">Initials</td><td style="height: 18px; width: 75.5516%;" width="80%"> <strong>IJAP</strong></td></tr><tr style="height: 18px;" valign="top"><td style="height: 18px; width: 24.4484%;" width="20%">Frequency</td><td style="height: 18px; width: 75.5516%;" width="80%"> <strong>Two issues per year (April and October) </strong></td></tr><tr style="height: 18px;" valign="top"><td style="height: 18px; width: 24.4484%;" width="20%">DOI</td><td style="height: 18px; width: 75.5516%;" width="80%"><strong> Prefix 10.13057 by <img src="/public/site/images/mohtaryunianto/Crossref_Logo_Stacked_RGB_kecil1.png" alt="" /></strong></td></tr><tr style="height: 18px;" valign="top"><td style="height: 18px; width: 24.4484%;" width="20%">Online ISSN</td><td style="height: 18px; width: 75.5516%;" width="80%"> <a href="https://issn.brin.go.id/terbit/detail/1424155941" target="_blank"><strong>2477-6416</strong></a></td></tr><tr style="height: 18px;" valign="top"><td style="height: 18px; width: 24.4484%;" width="20%">Print ISSN</td><td style="height: 18px; width: 75.5516%;" width="80%"> <a href="https://issn.brin.go.id/terbit/detail/1317119256" target="_blank"><strong>2089-0133</strong></a></td></tr><tr style="height: 18px;" valign="top"><td style="height: 18px; width: 24.4484%;" width="20%">Editor-in-chief</td><td style="height: 18px; width: 75.5516%;" width="80%"> <a href="https://www.scopus.com/authid/detail.uri?authorId=53463958000" target="_blank"><strong>Prof. Nuryani, Ph.D</strong></a> </td></tr><tr style="height: 18px;" valign="top"><td style="height: 18px; width: 24.4484%;" width="20%"> <span>Publisher</span></td><td style="height: 18px; width: 75.5516%;" width="80%"><p> <strong style="background-color: #ffffff;"><a href="https://fisika.mipa.uns.ac.id/" target="_blank">Department of Physics</a>, <a href="https://uns.ac.id/en/" target="_blank">Sebelas Maret University</a></strong></p><p><strong>in collaboration with <a href="https://drive.google.com/file/d/1FJp0jypEYC7L16JPv_pkuTZiHgW_R8RA/view?usp=sharing" target="_blank">Physical Society of Indonesia (LoA</a>, <a href="https://www.fisika.or.id/Pages/read/15" target="_blank">website)</a></strong></p></td></tr><tr style="height: 19px;" valign="top"><td style="height: 19px; width: 24.4484%;" width="20%"> </td><td style="height: 19px; width: 75.5516%;" width="80%"><p><strong> <br /></strong></p></td></tr></tbody></table><hr /><p align="justify"><strong>Indonesian Journal of Applied Physics</strong>, a journal provides rapid publication and important research in fields of physics, <span>This journal covers research in the following areas: <span>Materials physics, <span>Theoretical & Computational physics, <span>Instrumentation, and <span>Geophysics Tracks</span></span></span></span></span> ( <a href="/ijap/about/editorialPolicies#focusAndScope" target="_blank">read more..</a>.). We accept submission from all over the world. All submitted articles shall never be published elsewhere, original and not under consideration for other publication.</p><p align="justify">To implement the quality assurance of the journal, the editorial board members were invited from various countries, such as Indonesia, France, Mexico, Malaysia, Japan, India, China, Australia, and Finland. Meanwhile, the authors are from many countries such as Indonesia, Japan, the United States, Finland, the United Kingdom, Australia, Malaysia, Pakistan, Turkey, Mesir, India, China, Timor Leste, Taiwan, and Nigeria). Peer reviewers who have worked in the editorial process come from many countries, such as Indonesia, Egypt, Bangladesh, Mexico, Turkey, Malaysia, India, and the United Kingdom.</p><p>IJAP has been accredited by National Journal Accreditation (ARJUNA) Managed by Ministry of Research, Technology, and Higher Education, Republic Indonesia with First Grade ( <a href="https://sinta.kemdikbud.go.id/journals/profile/1710" target="_blank">SINTA 1 </a>) since year 2023 to 2026 according to the decree No. 0041/E5.3/HM.01.00/2023</p>Department of Physics, Sebelas Maret Universityen-USINDONESIAN JOURNAL OF APPLIED PHYSICS2089-0133<p>This is an open-access journal in accordance with the <a href="https://creativecommons.org/licenses/by-sa/4.0/" target="_blank">Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)</a> license. <br /><br />This permits users to:</p><p><strong>Share</strong> — copy and redistribute the material in any medium or format<br /><strong>Adapt</strong> — remix, transform, and build upon the material for any purpose, even commercially.</p><p>Under the following terms:</p><p><strong>Attribution</strong> — You must give <a href="https://creativecommons.org/licenses/by-sa/4.0/" target="_blank">appropriate credi</a>t, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</p><p><strong>ShareAlike</strong> — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.</p><p><strong>No additional restrictions</strong> — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.</p>A Weighted Average of Multiple Inversions of Rayleigh Wave Dispersion Curve Using Particle Swarm Optimization for Geotechnical Site Characterization
https://jurnal.uns.ac.id/ijap/article/view/77921
<p class="Keywords">Shear wave velocity is an important parameter in geotechnical engineering for studying liquefaction, finding bedrock for the basement of a building, and figuring out the presence of subsurface cavities. This study aims to develop and evaluate the accuracy of the multiple inversions by the Particle Swarm Optimization (MI-PSO) algorithm with a weighted average solution. This algorithm is applied to Rayleigh wave dispersion data for geotechnical site characterization. Two synthetic models, the HVL model and the complex model (i.e., a combination of models with LVL and HVL characteristics), are used to conduct algorithm tests. These synthetic models replicate subsurface characteristics that are frequently encountered in geotechnical cases. Synthetic data tests show that the MI-PSO algorithm with a weighted average solution works excellently. The MI-PSO technique with a weighted average solution resolves the model better than the conventional average solution. When applied to two field data sets, the MI-PSO algorithm with a weighted average solution can delineate target models that are consistent with the qualitative interpretation based on the observed dispersion curve characteristics.</p>Jamhir SafaniRezki WirawanAl Rubaiyn RubaiynMohd NawawiToshifumi Matsuoka
Copyright (c) 2023 INDONESIAN JOURNAL OF APPLIED PHYSICS
2023-11-022023-11-0213234736110.13057/ijap.v13i2.77921Experimental Study of Storing Electrical Energy Generated by an Acoustic Energy Harvester Into a Supercapacitor
https://jurnal.uns.ac.id/ijap/article/view/67671
<p class="Abstract">Acoustic energy harvester is a device used to convert environmental noise into electrical energy. Many researches on acoustic energy harvesting have been carried out, but most of them have not yet reached the stage of storing the electrical energy produced. This paper presents an experimental study of storing electrical energy generated by an acoustic energy harvester into a supercapacitor. The acoustic energy harvester in this study used a 4-inch woofer loudspeaker as a noise converter into electricity, equipped with a straight cylindrical resonator, a cylindrical housing, and an electric current rectifier unit. The supercapacitor used has a specification of 100F/2.7V. Experiments were carried out by using several variations of the sound frequency with three variations of sound pressure level (SPL) namely 90 dB, 95 dB, and 100 dB, and by measuring the supercapacitor voltage in a charging time of 60 minutes. It was found that the supercapacitor voltage reached 368 mV which was obtained from noise sound with an SPL of 100 dB and a frequency of 54 Hz which gave an initial charging electric current of about 12 mA. In the last five minutes of charging, the increase in supercapacitor voltage was still linear with time at a rate of about 5.2 mV/min. Therefore, the supercapacitor voltage can still significantly increase if the charging continues.</p>Ikhsan SetiawanBagas Wahyu WibowoRizki Dwi Prasetya
Copyright (c) 2023 INDONESIAN JOURNAL OF APPLIED PHYSICS
2023-11-022023-11-0213233924610.13057/ijap.v13i2.67671Pneumonia Classification Based on GLCM Features Extraction using K-Nearest Neighbor
https://jurnal.uns.ac.id/ijap/article/view/77120
<p class="Abstract">Pneumonia has been detected using Machine learning. The stages in this study began with preprocessing in 4 stages: resizing, cropping, filtering using a high pass filter, and Adaptive Histogram Equalization. The feature extraction process continued with 22 Gray Level Co-occurrence Matrix (GLCM) features and classification using K-Nearest Neighbor (KNN). The image used was 150 data sets for training on the classification of 3 classes with a ratio of 50:50:50 while training on two classes was 50 bacterial pneumonia and 50 viral pneumonia. The most optimal training data accuracy results were obtained using the angle direction on the GLCM, namely 135o with the KNN classification (k = 3). For the classification of two classes Using 40 data sets, an accuracy of 91% was obtained, while testing for three classes with 60 data sets was 83.3%.</p>Suharyana SuharyanaFuad AnwarArmylia Chandra DewiMohtar YuniantoUmi SalamahRifai Chai
Copyright (c) 2023 INDONESIAN JOURNAL OF APPLIED PHYSICS
2023-11-022023-11-0213232533810.13057/ijap.v13i2.77120First-Principle Investigation of La0.7Ba0.3Mn(1-x)FexO3 Structural Properties Using CASTEP
https://jurnal.uns.ac.id/ijap/article/view/77031
<p>We conducted first-principles Density Functional Theory (DFT) calculations using the CASTEP software package to investigate the crystal structure and mechanical properties of Fe<sup>3+</sup>-doped La<sub>0.7</sub>Ba<sub>0.3</sub>MnO<sub>3</sub> material at the Mn<sup>3+</sup> site, with doping concentrations ranging up to 50%. Through geometry optimization, we simulated the X-ray diffraction (XRD) pattern. We observed that the doping of Fe did not result in a shift in the peak positions of the diffraction pattern. However, it led to an increase in intensity at the [012] peak and the splitting of peaks [104] and [110]. Regarding the mechanical properties, we examined the elastic constants and observed a reduction in the Bulk, Shear, and Young's modulus values. The Shear and Bulk modulus and Poisson's ratio indicated that La<sub>0.7</sub>Ba<sub>0.3</sub>Mn<sub>(1-x)</sub>Fe<sub>x</sub>O<sub>3</sub> becomes less ductile with increased Fe<sup>3+</sup> doping content. Furthermore, we performed calculations for the Debye temperature, which revealed a decrease in the thermal conductivity of the La<sub>0.7</sub>Ba<sub>0.3</sub>Mn<sub>(1-x)</sub>Fe<sub>x</sub>O<sub>3</sub> material.</p><p class="Abstract"> </p>Sitti Ahmiatri SaptariSarah AuliaRyan RizaldyAnugrah Azhar
Copyright (c) 2023 INDONESIAN JOURNAL OF APPLIED PHYSICS
2023-11-022023-11-0213231332410.13057/ijap.v13i2.77031Graphene as an Active Material for Supercapacitors: A Machine Learning Approach
https://jurnal.uns.ac.id/ijap/article/view/76678
<p class="Abstract">Graphene is a promising material for supercapacitors due to its unique properties, which influence the device's supercapacitor. This study aims to investigate the key factor of graphene properties in supercapacitors (, with the goal of improving their performance. Also, we observe the machine learning models for predicting capacitance of supercapacitor including four algorithms of machine learning: Linear Regression (LR), lazy IBK, Decision Table (DT), and Random Forest (RF). Machine learning model showed that the RF model demonstrated the highest correlation value of 0.745, surpassing other models. Also, the study revealed that graphene has a high specific surface area and highly porous structure, which enhanced the high capacitance values. Finally, these machine learning models are suitable to apply in materials sciences field for understanding the materials properties in supercapacitor.</p>Anif JamaluddinAnnisa Dwi NursantiAnafi Nur'ainiRekyan Regasari M PutriMuhammad Usama Arshad
Copyright (c) 2023 INDONESIAN JOURNAL OF APPLIED PHYSICS
2023-11-022023-11-0213230531210.13057/ijap.v13i2.76678