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Hybrid Catenary-Battery Trains for Non-Electrified Sections and Emergency Use Muhammad Nizam; Hari Maghfiroh; Mufti Reza Aulia Putra; Anif Jamaluddin; Inayati Inayati
Automotive Experiences Vol. 8 No. 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.13440

Abstract

The hybrid catenary”“battery system offers a promising solution for railways operating in non-electrified sections and during emergencies, ensuring uninterrupted operation, enhanced safety, environmental sustainability, and cost efficiency. This study addresses the challenge of determining an appropriate battery size and introduces a novel rule-based Energy Management Strategy (EMS) with coasting mode to minimize energy consumption while meeting operational requirements. The novelty of this work lies in (i) a straightforward sizing method based on worst-case emergency scenarios and (ii) the integration of coasting-mode operation into a rule-based EMS for hybrid catenary”“battery trains. Simulation results show that the proposed approach achieves up to 12.56% energy savings on 3% gradient tracks while fully supplying auxiliary loads, compared with baseline operation that provides only partial coverage. These results demonstrate a practical and scalable framework for designing efficient, reliable, and resilient railway transport systems.
Influence of Mixing Time to Crystal Structure and Dielectric Constant of Ba0,9Sr0,1TiO3 Dianisa Khoirum Sandi; Agus Supriyanto; Anif Jamaludin; Yofentina Iriani
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 5, No 02 (2015): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v5i02.290

Abstract

Barium Strontium Titanate (Ba1-xSrxTiO3) or BST has been synthesized using solid state reaction method. Raw materials of BST were BaCO3, SrCO3, and TiO2. Those materials were mixed, pressed, and sintered at temperature 1200oC for 2 h. Mixing time of raw materials was varied to identify its effects on crystal structures and dielectrics constant of Ba0.9Sr0.1TiO3 using X-Ray Diffraction (XRD) and LCR meter instrument, respectively. The results of XRD showed that crystals structure of Ba0.9Sr0.1TiO3 is tetragonal. Lattice parameter of Ba0.9Sr0.1TiO3 for 6 h of mixing time is a = b = 3.988 Å and c = 3.998 Å. Lattice parameter of Ba0.9Sr0.1TiO3 for 8 h of mixing time is a = b = 3.976 Å and c = 4.000 Å. Crystalline size of Ba0.9Sr0.1TiO3 was calculated using Scherrer equation. Crystalline size, crystallinity, and dielectric constant of Ba0.9Sr0.1TiO3 for 6 h of mixing time is 38 nm, 96%, and 115 at frequency 1 KHz, respectively while their value for 8 h of mixing time is 39 nm, 96%, and 196 at frequency 1 KHz, respectively. Thus it can be concluded that mixing time affects the lattice parameters of Ba0.9Sr0.1TiO3 crystal. The longer mixing time causes crystalline size, crystallinity, and dielectrics constant increase.
Graphene as an Active Material for Supercapacitors: A Machine Learning Approach Anif Jamaluddin; Annisa Dwi Nursanti; Anafi Nur'aini; Rekyan Regasari M Putri; Muhammad Usama Arshad
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 13, No 2 (2023): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v13i2.76678

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.
IoT-based mobile data acquisition for pH and temperature monitoring as an enrichment material for high school energy transformation book Amalia Hidayah; Maulana Firmansyah; Dwi Teguh Rahardjo; Anif Jamaluddin
Journal of Environment and Sustainability Education Vol. 4 No. 2 (2026)
Publisher : Education and Development Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62672/joease.v4i2.123

Abstract

Monitoring pH and temperature is critical in various fields, yet integrating such technical concepts into high school education remains a challenge. This study aims to develop and validate an IoT-based mobile data acquisition system as a practical case study for an enrichment book on the topic of energy transformation for high school students. Using a Research and Development (R&D) approach, an IoT device consisting of a pH sensor, a K-type thermocouple, and a NodeMCU ESP8266 was built. An accompanying enrichment book was developed using Canva and Heyzine Flipbook. The IoT system demonstrated good accuracy, achieving R² values for pH measurement of 0.9526 (acidic) and 0.8843 (basic). The enrichment book was validated by experts and practitioners, receiving high feasibility ratings of 92.58% and 83.01%, respectively. These findings indicate that the developed enrichment book is a highly feasible supplementary teaching resource. This study demonstrates the significant potential of integrating hands-on IoT projects into science curricula to enhance students' understanding of abstract concepts like energy transformation.
Research Trends in Science Literacy in Education: A Bibliometric Analysis for the Period 2022-2025 Diky Nurkhoerudin; Daru Wahyuningsih; Anis Nazihah; Anif Jamaluddin
Unnes Science Education Journal Vol. 15 No. 2 (2026): August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/usej.v15i2.48414

Abstract

Scientific literacy has become a key competency in contemporary science education, particularly amid the post-pandemic landscape marked by accelerating digitalization, expanding access to information, and growing demand for evidence-based decision-making. Although literature on scientific literacy continues to expand, understanding of recent research developments remains limited. This study aims to examine research trends, intellectual structures, and emerging themes in scientific literacy research within educational contexts from 2022-2025. A bibliometric approach was adopted using data retrieved from the Scopus database through a systematic search and screening process guided by the PRISMA framework, with publications analyzed via Microsoft Excel and VOSviewer to assess publication growth, geographical and institutional contributions, citation performance, and keyword co-occurrence networks. The results indicate a substantial increase in publication output after 2023, with the United States, Indonesia, and China as leading contributors, reflecting the global scope of the field and disparities in national research capacities. Institutional analysis reveals broad participation across universities and research organizations, while citation and keyword analyses point to inquiry-based learning, scientific reasoning, critical thinking, and emerging themes including socioscientific issues and technology-enhanced education. These findings signal a shift toward competency-based frameworks emphasizing reasoning and evidence evaluation, informing curriculum development and future research.