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All Journal Jurnal Ilmu dan Inovasi Fisika Jurnal Pendidikan Fisika
Chrisnanto, Julian Evan
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Computer Science & IT Education Electrical & Electronics Engineering Energy Environmental Science Materials Science & Nanotechnology Physics

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WEB-BASED INTERACTIVE LEARNING MEDIA DEVELOPMENT USING P5.JS ON LIGHT AND OPTICAL DEVICE MODULE FOR HIGH SCHOOL COMPETENCY Safarina, Sania Salwa; Chrisnanto, Julian Evan; Adiperdana, Budi; Prawiradilaga, Muhammad Galih
Jurnal Pendidikan Fisika Vol 11, No 2 (2024): Jurnal Pendidikan Fisika
Publisher : Universitas negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpf.v11i2.21129

Abstract

This research was conducted with the aim of making an interactive learning module for light and optical devices adapted for school students, to improve the performance of education in Indonesia in the Program for International Student Assessment (PISA). By integrating simulation and interactive media using p5.js, students’ understanding and engagement in the subject matter can be improved. The development process includes a comprehensive literature review, interactive object design, simulation creation, and analytical validation of the media by students and learning practitioners. The research also performed analytical calculations and compared the values with the simulated value, then get a fairly small coefficient correlation which is around 0,000427 - 0,021912; 0,003426 - 0.243858; 0,003298 - 0,082615; and 0,001004 - 0,183618 for the convex mirror, concave mirror, convex lens, and concave lens respectively. The effectiveness of the module was evaluated through pretest and posttest, practicality assessment by students, and review by learning practitioners at SMP Muhammadiyah 10 Bandung. The results of this research showed significant improvement in students’ understanding and highlighted the practicality of the module in conveying complex scientific concepts. The research also highlighted the potential of interactive learning media in overcoming educational challenges, especially in science education, and suggests its broader application to improve learning outcomes in a variety of science fields.
HYBRID SARIMA-LSTM MODEL FOR PREDICTING EROSION IN BUTTERFLY VALVE Dzulfiqar, Azhar Aiman; Chrisnanto, Julian Evan; Adiperdana, Budi
JIIF (Jurnal Ilmu dan Inovasi Fisika) Vol 9, No 1 (2025)
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jiif.v9i1.61007

Abstract

In the oil and gas industry, butterfly valves often undergo erosion due to being placed in fluid flow with high pressure and high temperature.  Erosion on butterfly valves can result in huge losses so it requires early anticipation. To overcome these problems, this research proposes a hybrid SARIMA-LSTM model to predict the mass erosion of butterfly valves under several opening conditions. The results show that the SARIMA-LSTM model has superior performance compared to the conventional LSTM and SARIMA model with MSE values at valves opening 20  – 90  reaching 1E-06; 1E-06; 6.2E-05; 2.34E-04; 1.35E-07; and 1E-06 respectively. The hybrid SARIMA-LSTM model successfully identifies the non-linear characteristics of the erosion data by identifying the residual value resulting from the difference between the SARIMA model prediction and the actual data. This study also reveals that the combination of SARIMA and LSTM models significantly affects the performance of the LSTM model. This study also successfully used the SARIMA-LSTM model to predict the erosion mass value for the next 30 time-steps. Through this study, it is known that the SARIMA-LSTM hybrid model has the possibility to be applied to the oil and gas industry to help the process of observing the erosion mass on the butterfly valve.  Keywords: butterfly valve, seasonal autoregressive integrated moving average (SARIMA), long-short term memory (LSTM), erosion mass, time-series forecasting. 
CBDR-CNN Approach for Rapid Identification of XRD Data: A Preliminary Study Chrisnanto, Julian Evan; Fadillah, Nurfauzi; Faizal, Ferry
JIIF (Jurnal Ilmu dan Inovasi Fisika) Vol 8, No 1 (2024)
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jiif.v8i1.49575

Abstract

In this study, we present a novel approach combining Content-Based Data Retrieval (CBDR) and 1-dimensional Convolutional Neural Networks (1D-CNN) for crystal structure analysis of powder materials by using X-Ray Diffraction (XRD) data. The introduction sets the background by highlighting the importance of X-ray diffraction analysis and the limitations of conventional approaches in dealing with complex crystal structures. To overcome this challenge, researchers have explored artificial intelligence techniques, specifically CNN for crystal structure classification based on XRD image graph represented as 2-theta versus intensity values. The aims of this study are: implementing CBDR method on CNN model for crystal structure classification; simulating CBDR-CNN model for crystal structure classification; verifying CBDR-CNN model in crystal structure classification. Each class for CNN model such as crystal system, class material, sub-class material, and space-group achieved accuracies 99.86%, 99.99%, 99.95%, and 99.82% respectively. The results and discussion section presents the results of the CBDR-CNN model. The CBDR model effectively retrieved the most similar XRD spectrum data from the dataset based on the query properties, including Miller indices and peak position. The model effectively reduced the scope potential candidate materials, sub-materials, and space-groups. The 1D-CNN model showed high accuracy in predicting crystal properties such as material, sub-material, space-group, and crystal system. In conclusion, the CBDR-CNN approach potential revolutionizes XRD data analysis and crystal system prediction, which promotes progress in computer-aided materials study.
Co-Authors Adiperdana, Budi Dzulfiqar, Azhar Aiman Fadillah, Nurfauzi Faizal, Ferry Prawiradilaga, Muhammad Galih Safarina, Sania Salwa