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Alfian Ma'arif
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Jl. Empu Sedah No. 12, Pringwulung, Condongcatur, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
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INDONESIA
Indonesian Journal of Applied Science and Engineering
Published by Peneliti Teknologi Teknik Indonesia
ISSN : 30309174     EISSN : 30309174     DOI : 10.59247/ijase
Core Subject :
The Indonesian Journal of Applied Science and Engineering is a peer-reviewed, open-access academic journal. The journal is dedicated to advancing knowledge and disseminating high-quality research at the intersection of applied science, engineering, education, and social science, with a strong emphasis on the development and application of knowledge for the benefit of society. The journal serves as a multidisciplinary scholarly platform for academics, researchers, educators, and practitioners to share original work that bridges theoretical foundations with practical applications across diverse fields. The journal recognizes that solving complex real-world problems increasingly requires collaboration across disciplines — from engineering systems and applied technologies to educational innovation and social transformation. Contributions that demonstrate interdisciplinary thinking, innovation, and societal impact are especially encouraged. The journal publishes original research articles, technical reports, systematic literature reviews, expert opinions, and critical responses covering Engineering and Technology, Education and Learning Sciences, and Social Science and Humanities.
Arjuna Subject : -
Articles 26 Documents
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Design Of Electrical Lighting Installation in a Simple House with Solar Panels Ardiansyah Fajrin; Ahmad Raditya Cahya Baswara
Indonesian Journal of Applied Science and Engineering Vol. 2 No. 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/ijase.v2i2.172

Abstract

Solar energy is a potential renewable energy source to meet the electricity demand in Indonesia, especially in remote areas. This research aims to design and implement an electric lighting system in a simple residential house using solar panels with a capacity of 20 Watt Peak (WP). The main focus of this research is on the use of polycrystalline solar panels, inverter systems, batteries and solar charge controllers (SCC) to meet the lighting needs of simple residential houses using LED lights. The research involved testing the solar panels for five consecutive days, recording voltage, current and power data every 30 minutes from 09:00 to 16:00 WIB. The tests were carried out in a range of weather conditions, including sunny, cloudy and variable. Load tests were also carried out with the inverter to assess the performance of the inverter in converting DC to AC and supplying power to the load (2 3W LED lights). The test results showed that the highest average power of the solar panel was achieved on the first day, which was 12.305 W under sunny weather conditions. On the third day, the average power dropped to 10.376W due to cloudy weather conditions. The inverter tests showed that the inverter was able to produce a stable output voltage (around 222 V) and a consistent output current (0.1 A) despite the drop in input voltage. The SCC output power analysis also showed good energy transfer efficiency, with the highest average power on the second day being 9,413 W. In conclusion, the designed solar power generation system is able to work effectively and efficiently in meeting the lighting needs of a simple residential house.
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.
Common LMS Log Indicators for Predicting Student Success: A Systematic Literature Review Rugaya Tuanaya; Erna Andriyanti; Edi Istiyono
Indonesian Journal of Applied Science and Engineering Vol. 3 No. 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/ijase.v3i1.175

Abstract

This systematic review synthesizes research on common Learning Management System (LMS) log indicators used to predict student success, addressing challenges of variability and interpretability in leveraging digital behavioral data to forecast academic outcomes. Following PRISMA guidelines, a literature search and screening process using Google Scholar as the primary data source, identified 125 empirical studies for narrative synthesis; no formal meta-analysis was performed, so the ranges reported below reflect the spread of individually reported study findings rather than pooled or averaged effect sizes. The reviewed studies indicate that behavioral indicators such as timeliness of assignment submissions, submission frequency, and regularity of study intervals are repeatedly associated with academic performance, with individual studies reporting correlations as high as r=0.60–0.88, although predictive strength varies considerably by educational context. Tree-based learning models such as Random Forest and Gradient Boosting frequently offer a favorable balance between accuracy (75–96% across studies) and interpretability, while deep learning architectures such as LSTM and GRU are reported to achieve higher accuracy (89–97% in some studies) in capturing non-linear temporal patterns, though these figures are not directly comparable across studies because they were obtained using different evaluation metrics, datasets, and validation procedures. Weekly data aggregation and early engagement metrics (weeks 1–3) are associated with improved timely identification of at-risk students in several studies. Cognitive engagement metrics (e.g., content completion, quiz attempts) are reported to outperform simple frequency indicators in a number of studies, and interpretability techniques such as SHAP values suggest that modifiable behavioral features tend to carry more predictive weight than fixed demographic data. These findings underscore the significant potential of LMS log analysis in understanding student learning behavior. However, the results also highlight methodological differences between studies and limitations in the applicability of the results to other contexts. Therefore, a standardized framework is needed to translate LMS data into applicable, transparent, and equitable educational interventions.
Digital Administrative Service Quality and Student Satisfaction in Higher Education: A Bibliometric Analysis Resky Nuralisa Gunawan; Dwi Mahardiyati; Lamanisa Rasid Saputra; Indah Sari Hamid
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.v3i2.176

Abstract

The rapid digitalization of higher education has transformed administrative service delivery and increased the importance of digital service quality in shaping student experiences and satisfaction. However, research on digital administrative service quality remains dispersed across higher education, service management, educational technology, and information systems, making it difficult to identify its development and thematic structure. This study aims to examine the development, characteristics, and thematic evolution of research on digital administrative service quality and student satisfaction in higher education. Bibliometric and science-mapping analyses were conducted using 36 Scopus-indexed documents published between 2015 and 2025, identified through a structured search conducted on June 27, 2025. Bibliographic data were analysed using descriptive bibliometric techniques and VOS viewer to examine publication trends, document types, subject areas, author and country contributions, keyword co-occurrence, and temporal patterns. The results show that journal articles represented the largest proportion of publications (63.9%), followed by conference papers (19.4%) and book chapters (11.1%). Social Sciences constituted the largest subject area (28.8%), followed by Computer Science (21.2%) and Engineering (10.6%). Keyword co-occurrence analysis identified higher education, students, satisfaction, service quality, and e-learning as prominent themes, while artificial intelligence and chatbot-related terms appeared as more recent themes in the dataset. The temporal pattern therefore suggests emerging attention toward AI-enabled and increasingly interactive digital services, although this pattern should be interpreted cautiously given the limited number of documents analysed. The study provides a preliminary bibliometric overview of the research intersection between digital administrative service quality and student satisfaction and identifies potential directions for future empirical research on usability, accessibility, responsiveness, trust, artificial intelligence, and student experience in higher education.
AI-Driven Innovation Management: A Bibliometric and Science Mapping Analysis Nurfitri Ramadana; Desy Dwi Putri; Lamanisa Rasid Saputra; Indah Sari Hamid; Resky Nuralisa Gunawan; Nadya Viranti Khamsiah
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.v4i2.177

Abstract

The rapid development of artificial intelligence (AI) has transformed innovation management by enabling organizations to enhance decision-making, knowledge management, business processes, and the development of innovative products, services, and business models. As the number of publications continues to increase and the research becomes increasingly multidisciplinary, studies on AI-driven innovation management have expanded across various disciplines, creating a need for a more systematic mapping of the field's development, thematic structure, and emerging topics. This study aims to map the evolution of AI-driven innovation management and identify its intellectual and thematic structure through bibliometric and science mapping analysis. The study employed a bibliometric approach using scientific publications indexed in the Scopus database. Data were collected on 5 July 2026 using the TITLE-ABS-KEY search field and predefined keywords related to AI-driven innovation, artificial intelligence, innovation management, and digital innovation. From an initial 180 documents, 80 English-language journal articles published between 2019 and 2026 were selected through a screening process and analyzed using descriptive bibliometric analysis and VOSviewer-based science mapping. The results indicate a substantial increase in publications since 2024, with 2025 recording the highest number of publications, totaling 43 articles. The data for 2026 represent a partial year because data collection was conducted in July and should therefore be interpreted with caution. China and India were the leading contributing countries, with 11 publications each. Business, Management and Accounting was the dominant subject area, accounting for 43% of the total publications. Keyword analysis indicates that artificial intelligence occupies a central position and is strongly connected with innovation, digital transformation, machine learning, knowledge management, and project management. The overlay visualization identifies several relatively recent themes, including generative AI, Industry 5.0, AI-driven business model innovation, leadership, and knowledge acquisition and transfer. The study concludes that AI-driven innovation management is evolving from a primarily technology-oriented focus toward a more strategic, knowledge-oriented, and interdisciplinary research domain. These emerging themes represent promising research areas based on the current bibliometric patterns and warrant further investigation.
Item Analysis of an English Achievement Test Using Rasch Model and Classical Test Theory Rezkilaturahmi Rezkilaturahmi; Nur Hidayanto Pancoro Setyo Putro; Guldana Begimbetova; Oscar Ndayizeye; Muthmainnah Muthmainnah; Dinda Khaira Latifa
Indonesian Journal of Applied Science and Engineering Vol. 3 No. 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/ijase.v3i1.180

Abstract

This study aimed to evaluate the quality of English test items developed by the English Teachers Working Group (MGMP) in Muna District, Indonesia. The study specifically examined the psychometric characteristics of the items in terms of item difficulty, discrimination, distractor effectiveness, and item fit using Classical Test Theory (CTT) and Item Response Theory (IRT). This study employed a quantitative psychometric approach. The data consisted of responses to an English achievement test developed and administered by English teachers participating in the MGMP in Muna District. Item quality was initially examined using Classical Test Theory based on item difficulty, discrimination index, and distractor effectiveness. Further analysis was conducted using the Rasch model of Item Response Theory with the Quest program to evaluate item difficulty and item fit based on INFIT Mean Square (MNSQ) statistics. The results of both approaches were used to determine whether individual items were acceptable, required revision, or should be rejected. The analysis revealed variation in the psychometric quality of the test items. The CTT analysis showed differences in item difficulty, discrimination, and distractor effectiveness, while the Rasch analysis revealed variation in item difficulty and item fit. The combined findings indicated that some items demonstrated acceptable psychometric characteristics, whereas others required revision. In addition, several items could not be evaluated using Quest. These findings indicate that although more than half of the items demonstrated acceptable psychometric characteristics, a substantial proportion still requires improvement before being used as a high-quality English assessment.

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