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p-Index From 2021 - 2026
0.817
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This Author published in this journals
All Journal Journal of Engineering and Applied Technology Control Systems and Optimization Letters Indonesian Journal of Applied Science and Engineering
Hajidah Salsabila Allissa Fitri
Universitas Negeri Yogyakarta
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Portable Construction Maps (PCM) using location fingerprint positioning algorithm for construction worker safety Annurdien Rasyid; Ahsan Firdaus; Dwi Setiawan; Hajidah Salsabila Allissa Fitri; Pradana Setialana
Journal of Engineering and Applied Technology Vol 3, No 1 (2022): (March)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jeatech.v3i1.43666

Abstract

The existence of a work from home policy does not seem to have a substantial impact on construction workers. Cases of work accidents on infrastructure projects during the Covid-19 pandemic experienced a significant increase. As reported by BPJamsostek data that the number of work accident insurance claims in the first semester (January-June) 2020 reached 108,573 cases. An increase of 128% over the same period in the previous year.  One of the steps from the construction side to minimize the occurrence of work accidents is by marking dangerous locations or limiting them with a yellow line. The supervision is carried out by the Occupational Health and Safety division which supervises every worker by using hearing and sight senses. However, this supervision is deemed less effective and efficient considering the number of work accidents that continue to increase over time. Therefore, a worker location monitoring system based on An indoor positioning system called Portable Construction Maps (PCM) Using Location Fingerprint Positioning Algorithm for Construction Worker Safety was developed. The development of this system uses the algorithm Location Fingerprint as a method for estimating the location of workers in a construction building. Each worker will bring a device called the Worker Tag, in which there is a microcontroller (Espressif 32) with WiFi and Bluetooth module, which is used to capture wifi and bluetooth signals and calculate the Received signal strength indication(RSSI) which will be sent to the server to be processed using the location fingerprint algorithm. In addition, in the worker tag there is also a Passive Buzzer that is used to alert workers if they enter a dangerous area.
Scientific Literacy Instruments in Science Education: A Meta-Analytic Study on Internal Consistency Hajidah Salsabila Allissa Fitri; Alfian Ma’arif; Casphama Jovansyah Chaidir; Zafrullah Zafrullah; Nurdin Munthe; Sumalee Chuchai; Shazia Aslam
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.338

Abstract

Scientific literacy is a central goal of science education and is commonly assessed using diverse instruments across educational levels and contexts. However, internal consistency estimates are sample dependent and may vary across studies. This study conducted a reliability generalization meta analysis of Cronbach’s alpha coefficients reported for scientific literacy instruments. A systematic search of the Scopus database identified empirical studies reporting Cronbach’s alpha for scientific literacy instruments. In total, 19 studies providing 20 reliability estimates, comprising 10,163 participants, were included. Reliability generalization and moderator analyses were conducted using mixed effects models in jamovi. The pooled Cronbach’s alpha was 0.814 (95% CI [0.776, 0.853]), suggesting high average internal consistency; however, the extreme heterogeneity (I² = 99.42%) indicates that this estimate represents an overall average rather than a stable or universally generalizable reliability value. Moderator analyses showed that education level, instrument type, study design, item group, and geographical region were not significant moderators of reliability, indicating that these characteristics did not explain the substantial variability across estimates. Publication bias assessment suggested a low risk of bias, although sensitivity analysis showed that the pooled estimate was influenced by the exclusion of low alpha values. For instrument developers, these findings emphasize the need to establish reliability evidence for specific target populations and contexts rather than relying on a pooled reliability coefficient. Future research should investigate additional methodological and contextual factors that may explain the substantial heterogeneity.
From Molecules to Models: A Bibliometric Analysis of Visualization in AI-Driven Chemistry with Emerging Educational Implications Hajidah Salsabila Allissa Fitri; Alfian Ma’arif; Casphama Jovansyah Chaidir; Jaslin Ikhsan
Indonesian Journal of Applied Science and Engineering Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/ijase.v4i2.156

Abstract

The rapid advancement of artificial intelligence has significantly transformed chemical research, particularly in molecular modeling, reaction prediction, and visualization. However, the role of visualization within AI-driven chemistry remains fragmented and underexplored. This study addresses this gap through a bibliometric analysis of research at the intersection of artificial intelligence, chemistry, and visualization. Data were collected from the Scopus database in April 2026 using a structured search query and screened following PRISMA guidelines. The final dataset consisted of 1,069 journal articles, which were analyzed using Biblioshiny and VOSviewer to examine publication trends, conceptual structures, and thematic evolution. To complement the bibliometric findings, eight additional articles related to IBM RXN for Chemistry were qualitatively reviewed to explore the role of emerging AI platforms in chemical visualization. The results reveal a substantial increase in research output after 2019, reflecting the rapid expansion of AI applications in chemistry. The field is dominated by themes related to machine learning, molecular modeling, computational chemistry, and analytical techniques. Temporal analysis indicates a progression from early computational approaches to advanced AI-based methods, including deep learning and predictive modeling. Rather than emerging as an independent research theme, visualization is primarily embedded within machine learning, molecular modeling, retrosynthesis, and computational chemistry workflows, where it supports the interpretation of complex chemical data and predictive outputs. The analysis of IBM RXN-related studies further highlights the emergence of integrated platforms that combine molecular design, reaction prediction, and retrosynthesis within interactive visualization environments. Overall, AI-driven chemistry is undergoing a transition toward platform-based and predictive visualization systems. These findings contribute to understanding how visualization supports knowledge generation in contemporary chemistry, although citation-based indicators for the most recent publications should be interpreted cautiously due to the inclusion of records indexed up to April 2026.
Mapping Research Trends of Technology Enhanced Project-Based Learning for Sustainable Chemistry Education Hajidah Salsabila Allissa Fitri; Alfian Ma’arif; Jaslin Ikhsan; Zafrullah Zafrullah; Shazia Aslam; Casphama Jovansyah Chaidir; Elysa Putri Agustin; Rasamimanana Joronavalona
Indonesian Journal of Applied Science and Engineering Vol. 3 No. 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/ijase.v3i2.173

Abstract

The integration of project-based learning, sustainability education, and educational technology has gained increasing attention in chemistry education as educators seek innovative approaches to address global sustainability challenges and develop students’ twenty first century competencies. However, the development of research in this area has not been systematically mapped within the Scopus indexed literature. Therefore, this study aimed to analyze publication trends, leading contributors, thematic development, and research gaps in technology enhanced project-based learning for sustainable chemistry education. A bibliometric approach was employed using English language literature retrieved from the Scopus database on May 11, 2026. The search process identified 485 English language publications, including articles, conference papers, and review papers published between 2010 and 2026. Bibliometric analyses were conducted using Biblioshiny in RStudio and VOSviewer to examine publication trends, collaboration networks, keyword co-occurrence patterns, and thematic development. The results indicate a substantial increase in research output, particularly after 2020, with 162 publications recorded in 2025, representing the highest annual output in the dataset. Indonesia emerged as the most productive contributor, accounting for 29.07% of the total publications, while Finland, the United States, and China demonstrated greater citation influence. Keyword analysis identified project-based learning, students, sustainability, engineering education, and green chemistry as dominant research themes. Thematic evolution revealed a shift from technology adoption and instructional innovation toward sustainability oriented learning and competency development. Emerging topics such as critical thinking, design thinking, and twenty first century skills have gained increasing attention in recent years. The findings suggest that although the Scopus indexed literature has experienced rapid growth, the integration of artificial intelligence, learning analytics, and adaptive educational technologies remains limited. This study provides a systematic mapping of the Scopus indexed, English language literature and offers directions for future research on technology enhanced sustainable chemistry education.
Co-Authors Ahsan Firdaus Alfian Ma’arif Annurdien Rasyid Casphama Jovansyah Chaidir Casphama Jovansyah Chaidir Dwi Setiawan Elysa Putri Agustin Jaslin Ikhsan Nurdin Munthe Rasamimanana Joronavalona Setialana, Pradana Shazia Aslam Sumalee Chuchai Zafrullah Zafrullah