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Alfian Ma'arif
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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.
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Articles 5 Documents
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Search results for , issue "vol. 4 no. 2 (2026)" : 5 Documents clear
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.
Does Educational Level Moderate the Relationship Between Mathematical Resilience and Mathematical Problem-Solving Ability? A Meta-Regression Analysis Asma Khoirunnisa; Heri Retnawati; Risky Setiawan; Samsul Hadi; Hanifah Mar'atush Shalihah; Xu Wenxin; Kamola Omonova; Ibnu Rafi
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.v4i2.157

Abstract

The relationship between mathematical resilience and mathematical problem-solving ability is well established; however, the extent to which this relationship varies across educational levels and learning contexts remains unclear. Extending previous meta-analytic evidence, this study examined educational level and the COVID-19 learning period as potential moderators. The quantitative synthesis included 31 primary studies published between 2017 and 2022, yielding 40 effect sizes: 11 from high-school studies, 17 from university studies, and 12 from junior-high-school studies. A random-effects model produced an overall pooled Fisher’s (z) of 0.76,95% CI [0.64,0.88], (t(38)=13.10),(p<.001), equivalent to approximately (r=.64). Substantial heterogeneity was observed, (τ=0.32),95, and (I^2=81.83, with a 95% prediction interval from (z=0.10) to (z=1.42), equivalent to approximately (r=.10) to (r=.89). The pooled effects were positive and significant for high school, (z=0.831), university, (z=0.793), and junior high school, (z=0.659). However, educational level did not significantly moderate the relationship, (Q_m (2)=1.57),(p=.457). The COVID-19 period was also not a significant moderator within the high-school, (F_m (1,9)=0.046),(p=.836), university, (F_m (1,15)=3.984),(p=.064), or junior-high-school subgroup, (F_m (1,10)=0.480),(p=.504). These findings indicate that mathematical resilience is consistently associated with stronger problem-solving performance across educational levels, although substantial variation remains within each subgroup. Resilience-supportive mathematics instruction should therefore be implemented across educational stages and accompanied by adequate instructional and contextual support.
Packaging Quality Control in a Small-Scale Tempeh Industry Using Pareto Analysis and Control Charts Ardhiansyah Pradana; Safinta Nurindra Rahmadhia; Ainul Mardhiah; Ali Jebreen
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.v4i2.165

Abstract

Tempeh is a traditional Indonesian fermented food with high economic value and is widely consumed by the public. In the tempeh industry, packaging quality is a crucial factor as it protects the product during fermentation, storage, and distribution. Packaging defects such as torn packaging or loose seals can reduce product quality and increase the risk of contamination. This study aims to analyze the level of packaging defects and evaluate the stability of the tempeh packaging process using a Statistical Quality Control (SQC) approach. The study was conducted at a tempeh industry in Yogyakarta using observation data for seven days of production. The analysis was conducted using check sheets, Pareto diagrams, and c-charts. The results showed that during the observation period, 179 packaging defects were found out of a total production of 3,670 kg. The 700 g packaging size had the highest number of defects at 71 defects (39.7%), followed by the 425 g packaging with 64 defects (35.8%), while the 150 g and 270 g packaging each contributed 14.5% and 10.0% of the total defects, respectively. Pareto analysis showed that 700g and 425g packaging contributed 75.5% to the total defects, making them a top priority in the quality improvement program. The c-chart analysis showed a CL value of 25.57, a UCL of 40.74, and a LCL of 10.40. All observation points were within the control limits, indicating that the packaging process was in statistically controlled conditions. However, the relatively high number of defects indicated the need for continuous improvement efforts, especially in large-sized packaging. This study demonstrated that the application of the SQC method can be used effectively to identify quality improvement priorities and evaluate the stability of the packaging process in small and medium-scale tempeh industries.
Mapping Inclusive Education Research in Indonesia: A Bibliometric Analysis of Publication Trends, Research Themes, and Future Directions Rahayu Nurfadilla; Zafrullah Zafrullah; Fauzia Harun; Qorri Azizah; Sharafina Mardhotillah Parciagla; Dina Auliatil Wahyuni
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.v4i2.167

Abstract

Inclusive education plays a crucial role in ensuring equitable access to learning opportunities for all students regardless of their backgrounds and abilities. Although research on inclusive education has grown substantially, a comprehensive understanding of its development, thematic structure, and emerging trends in Indonesia remains limited. Therefore, this study aims to map the landscape of inclusive education research in Indonesia using a bibliometric approach. This study contributes to the literature by providing insights into publication trends, institutional contributions, dominant research themes, and future research directions within the Indonesian context. A total of 123 Scopus-indexed articles affiliated with Indonesian researchers were analyzed using Bibliometrix/Biblioshiny in R Program and VOSviewer to examine publication characteristics, collaboration patterns, thematic structures, and keyword evolution, providing insights into the Scopus-visible landscape of inclusive education research in Indonesia. The results indicate a significant growth in publication output and reveal that Universitas Pendidikan Indonesia, Universitas Negeri Yogyakarta, Universitas Djuanda, Universitas Negeri Jakarta, and Universitas Negeri Malang are the most productive institutions in this field. The thematic analysis identified three dominant research themes, namely Pedagogical Capacity Development, Learner Participation Dynamics, and Equity Governance Frameworks. Furthermore, overlay visualization revealed “Disability Policy” and “Madrasah” as emerging topics with strong novelty potential, indicating a shift toward policy-related and institutional perspectives in inclusive education research. In conclusion, inclusive education research in Indonesia has experienced substantial development and thematic diversification, with future studies encouraged to further explore disability-related policies and inclusive practices in madrasah settings.
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.

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