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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 55 Documents
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Fuzzy-Based Adaptive Switching Time Determination for VRLA Batteries Based on Discharge–Recovery Characteristics Soelistiono , Soegianto; Rahmadani, Muhammad Azzam
Indonesian Applied Physics Letters Vol. 6 No. 2 (2025): Volume 6 No. 2 – December 2025
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v6i2.84886

Abstract

Valve Regulated Lead-Acid (VRLA) batteries are widely used in energy storage systems due to their reliability and low cost; however, their energy utilization is strongly affected by discharge patterns and recovery behavior. Recent studies have shown that dynamic battery switching can improve extractable energy compared to static configurations, yet the switching time is commonly treated as a fixed parameter, despite experimental evidence indicating that the optimal switching interval depends on battery capacity and operating conditions. This paper proposes a fuzzy-based framework for adaptive switching time determination in VRLA battery systems, where switching duration is treated as an explicit control variable inferred from discharge–recovery characteristics. Key indicators, including voltage drop rate, voltage recovery magnitude, and relative internal resistance, are incorporated as inputs to a Mamdani-type fuzzy inference system, while the switching time is defined as the fuzzy output. The proposed approach enables adaptive adjustment of switching duration without relying on detailed electrochemical models. Simulation-based analysis is conducted to qualitatively evaluate the behavior of the proposed method in comparison with fixed switching strategies. The results demonstrate that fuzzy-based adaptive switching produces smoother switching time evolution and more stable voltage trends, indicating improved utilization of discharge–recovery dynamics. This study establishes a conceptual foundation for adaptive switching time control and provides a basis for future experimental validation and real-time implementation in intelligent battery management systems.
Physical Characterization in the Fabrication of Taper Structured Fiber Optic Sensors Parastuti, Frazna; Hikmawati, Dyah; Trilaksana, Herri
Indonesian Applied Physics Letters Vol. 6 No. 2 (2025): Volume 6 No. 2 – December 2025
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v6i2.84887

Abstract

In a fiber optic sensor with a taper structure, the sensor geometry is the main parameter that can affect the sensitivity of the sensor. Fiber taper fabrication is carried out using the pulling and heating method continuously and simultaneously. Based on this fabrication process, the optical fiber is pulled using an autograph until it enters the plastic deformation area. Mechanical properties analysis showed that the optical fiber had a stress of 50.24 MPa and an elastic modulus of 2.17 GPa. It was also found that the optical fiber experienced elongation up to 10.90%. The results of the digital microscope test showed that the taper process succeeded in reducing the diameter of the optical fiber by 19.77%. The optical power test shown in the form of output voltage proves that the taper process causes many evanescent waves to come out of the waveguide so that the voltage decreases up to 46.40%. Besides the ease of processing, the advantage of fabrication using this method is that the mechanical properties are measured in real-time, making this method reproducible on a mass scale.
CHARACTERIZATION SCAFFOLD 3D-PRINTING PLA WITH HYDROXYAPATITE-CHITOSAN-AgNPs COATING TO TREAT MANDIBULAR OSTEOMYELITIS Malini, Mirza Hema; Hanan, Annisa; Ady, Jan; Aminatun
Indonesian Applied Physics Letters Vol. 6 No. 2 (2025): Volume 6 No. 2 – December 2025
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v6i2.84889

Abstract

Osteomyelitis The mandible can cause pathological fractures of the bones and interfere with the function of the mandible, so it is necessary to perform resection for the removal of the infected part. As a result of resection, it is necessary to carry out mandibular reconstruction using Scaffold. The study aims to discuss the effect of PLA surface modification with HA-Chitosan-AgNPs coating on morphological structure, porosity, compressive strength, hydrophilicity properties and antibacterial properties and determine the best sample variation from the characterization carried out. Scaffold made from PLA 3D-printing material using the Fused Deposition Modelling (FDM). The variation in composition (wt%) of hydroxyapatite-chitosan-AgNPs used as coatings was A (100:0:0), B (90:10:0), C (90:7:3), D (90:5:5), E (90:3:7), and F (90:0:10). The results obtained are Scaffold PLA with HA-chitosan-AgNPs coating has pores interconnected with rough surface walls. The porosity value varies between 40%-53%. HA-Kitosan-AgNPs as a surface modification are also able to increase compressive strength, antibacterial properties, and hydrophilicity Scaffold The PLA with the best sample variation is indicated by sample D (90:5:5). Based on these results, it shows that Scaffold PLA with HA-Kitosan-AgNPs coating is potentially used as a mandibular reconstruction.
Integrative Machine Learning for Optimizing Brushless DC Motor Discharge in Horizontal Photovoltaic Systems Yuspian, Galang Perwiradhani; Yhuwana, Yhosep Gita Yhun
Indonesian Applied Physics Letters Vol. 6 No. 2 (2025): Volume 6 No. 2 – December 2025
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v6i2.84890

Abstract

This study explores the integration of machine learning to optimize the discharge of Brushless DC (BLDC) motors in horizontal photovoltaic (PV) systems, designed to maximize solar radiation capture with the assistance of Maximum Power Point Tracking (MPPT) technology to enhance battery charging efficiency. Using the Random Forest algorithm, the research develops a predictive model to analyze the relationship between PV input power, battery status, and BLDC motor speed, achieving power classification accuracy of 74% and speed prediction with an R-squared value of 0.9124 and low error rates. System testing, which includes PV modules, batteries, BLDC motors, and MPPT, demonstrates successful integration under various operational conditions, while a PyQt5-based interface enhances user accessibility through interactive features. The findings make a significant contribution to renewable energy management, support electric vehicle efficiency, extend operational range, and reduce environmental impact.
Machine Learning-Based Prediction of Distance Coverage (DC) in Electric Motorcycle Under Full Throttle Usage Pattern Pambudi, Henri Setyo; Soelistiono , Soegianto
Indonesian Applied Physics Letters Vol. 6 No. 2 (2025): Volume 6 No. 2 – December 2025
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v6i2.84891

Abstract

The development of electric vehicles (EVs) in Indonesia is accelerating following government policies aimed at reducing greenhouse gas emissions. Despite their benefits, the adoption of electric motorcycles remains limited due to concerns about battery life and charging station availability. This study proposes a machine learning-based model to predict distance coverage (DC) based on the state of charge of the battery (SoC) for electric motorcycles, specifically under a full throttle dominant usage pattern. The research employs multiple regression and classification algorithms, including Linear Regression, Random Forest Regression, and Support Vector Regression (SVR) for prediction, along with Random Forest Classifier, Logistic Regression, and K-Nearest Neighbors (KNN) Classifier for travel classification. The results demonstrate that Linear Regression outperforms other models for DC prediction, achieving an R2 value of 0.9818, while the Random Forest Classifier achieves 98% accuracy in classifying travel distances. A graphics user interface (GUI)-based software was developed to integrate these models, enabling real-time prediction and travel classification for users. The findings indicate that ML-based DC prediction can enhance user confidence and optimize battery usage in electric motorcycles.

Page 6 of 6 | Total Record : 55

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