The key conclusions from the systematic literature review of 34 chosen scientific publications are summarized in this section (Riski, 2026). The forms, models, and strategies of artificial intelligence implementation used in Indonesian learning processes; the effect of AI implementation on Indonesian learning quality; and the obstacles, constraints, and prospects for the advancement of AI implementation in Indonesian learning were the four primary research focuses into which the analysis was conducted thematically.

The goal of presenting the results in tabular form is to offer a methodical, comparative, and structured mapping of research patterns across different methodological and geographic contexts. This method provides a solid analytical basis for more discussion in the following section by making it possible to identify dominating patterns, thematic variations, and correlations among pertinent variables. Table 2 displays an analysis of the distribution of articles:

The rapid digital transformation of the education sector is reflected in the expanding trend of research on the application of AI in Indonesian education. Several publications that explicitly look at the use of AI in Indonesian education were found based on the findings of the literature selection process carried out through a comprehensive literature review. These articles provide a thorough overview of the research landscape of AI in education in Indonesia by covering a variety of time periods, educational levels, AI technologies used, and topics of study. The results from each study were examined using the chronological, thematic, and technological aspects employed to address RQ1, as shown in the accompanying Table 3.

Figure 2 shows a fluctuating development trend throughout the observation period. The initial value was 1, then decreased to 0 before gradually increasing to 2. After that, there was a significant spike to 9, although there was a slight decrease to 8 in the following period. The trend increased again and peaked at 12, the highest value during the observation period. However, after reaching this peak, there was a very sharp decline, returning to 2 in the final period. This pattern indicates a phase of strong growth in the middle of the period, followed by a drastic decline at the end, so further analysis is needed to identify the factors influencing this change.

The research trend on the use of artificial intelligence (AI) in Indonesian education has significantly increased in recent years, according to Table 3 analysis results. Particularly between 2023 and 2025, the number of publications increased dramatically, peaking at 12 pieces in 2025. In comparison, there were comparatively few research conducted at previous times like 2020 and 2022. This pattern suggests that AI research in Indonesian education is developing at an exponential rate, in keeping with the increased focus on digital transformation and the use of technology in education around the world.

Figure 3 illustrates the geographical distribution of the articles reviewed in this study across various regions of Indonesia. Most articles originated from Java, totaling 21, and were published in major cities such as Jakarta, Yogyakarta, Surabaya, Bandung, and Malang, indicating that Java remains a central hub for research and publication activities. Four articles from Manado, Makassar, and West Sulawesi were from Sulawesi, while four from Padang, Bukittinggi, and other areas came from Sumatra. Java contributed eight articles in total, whereas Kalimantan, Papua, and Bali each produced three, showing a limited representation from these regions. Additionally, two articles were of multi-location or national scope. This distribution highlights a high concentration of research activity in Java, but studies from other parts of Indonesia also contribute to a diverse geographic landscape of the research analyzed.

Language learning covering English, Indonesian, and Arabic—remains the primary focus of AI in education research in Indonesia. Significant attention has also been given to the acceptance and deployment of AI, using models like TAM and UTAUT. Other focus areas include learning innovation, students’ use of AI, and science and math instruction. However, topics such as education policy, educational system administration, and AI ethics are largely underexplored. This suggests that, although strategic and policy-oriented studies have yet to reach their full potential, AI research in Indonesia continues to emphasize practical aspects of learning.

In terms of educational levels, most studies are conducted at the higher education level, accounting for more than half of all examined papers. This indicates that universities are leading initiatives in AI research and application in education. Conversely, there is relatively little research at the primary, junior secondary, and senior secondary levels, revealing a gap that needs to be addressed in AI adoption within primary and secondary education through research and policy efforts, despite some cross-level studies. The most common techniques are qualitative approaches and literature reviews or systematic literature reviews (SLR). This points to AI education research in Indonesia still being exploratory and conceptual. Nonetheless, there is a growing use of quantitative methods, especially for assessing user acceptability and perceptions of AI, such as surveys employing SEM-PLS and technology adoption models like TAM and UTAUT. Yet, experimental studies directly measuring the effectiveness of learning with AI are scarce. This highlights the need for more experimental research to generate empirical evidence. Overall, trends and developments in AI research within Indonesian education are still in their early stages but have shown promising progress. The rising number of publications, the diversity of topics, and the variety of methodologies suggest an active and dynamic field. Nonetheless, gaps remain in distribution across educational levels, a scarcity of experimental studies, and limited research on ethics and policy. Future efforts should focus on fostering more comprehensive, cross-level, and evidence-based research to facilitate more effective and sustainable AI integration into Indonesia’s education system.

Incorporating artificial intelligence into the educational process is not just a reflection of technological adoption; it also entails choosing forms, models, and approaches that are specific to Indonesian educational contexts and demands. Educational academics and practitioners have employed a variety of strategies, such as the usage of AI-based chatbots, intelligent tutoring systems, adaptable platforms, and the incorporation of machine learning algorithms to create more individualized and successful learning experiences. The increasing efforts to use AI’s capabilities to address learning issues at many levels and situations of Indonesian education are reflected in the variety of forms and implementation tactics. In order to address RQ2, the following Table 4 summarizes the findings from the examined literature according to the type of AI technology employed, its learning application model, and the tactics used.

The synthesis results in Table 4 show that the use of artificial intelligence (AI) in Indonesian education has various forms, models, and techniques that can be grouped into several main groups. AI implementation is not limited to a single technology but rather integrates multiple methodologies, including learning analytics, generative AI, adaptive learning, and intelligent tutoring systems. This indicates that the AI ecosystem in Indonesian education is growing in a multi-faceted way by utilising technology adjusted to educational levels, subject contexts and learning needs.

Adaptive learning and personalized learning is the first and the most common. Here AI is used to change content, learning pace and learning routes for individual students. Platforms such as Novo Learning (A1) and other AI-based systems can utilize pretests and data on student interaction to identify learning gaps, and then offer adaptive automated feedback. This is also supported by theoretical techniques such as UTAUT (A19, A29) which indicate that users’ adoption of AI is highly influenced by perceived utility and system quality. Adaptive learning is widely used in language learning, math and throughout the education spectrum from primary to post-secondary with the aim to provide a more personalized and effective learning experience.

The other kind of implementation that’s growing fastest is generative AI and chatbots especially with things like ChatGPT, Grammarly and Photomath. These technologies are used in a wide range of educational activities, including automatic language correction, conversation simulations, student task completion, and teacher-developed modules (A3, A10, A16, A21). The implementation tactics are often useful and are embedded directly into teaching and learning activities, including helping with draft writing, giving immediate feedback and increasing work efficiency. AI learning experiences are further enhanced by gamification and the integration of visual media (e.g. Canva and Quizzes). The third category includes learning analytics, automated assessment, and AI-generated educational materials. AI is used to make learning materials more interactive, e.g. automated presentations, animated movies and apps based on image recognition (A6, A7, A13). Learning analytics, on the other hand, is designed for deep analysis of student data to inform pedagogical decision making such as providing real time feedback and data driven assessment (A4, A11). Automated assessment is also a key strategy for improving evaluation efficiency, since AI can conduct evaluations quickly, without bias, and according to complex rubrics.

Finally, the use of AI is also progressing through intelligent tutoring systems (ITS) and multi-modal AI techniques that combine different technologies such as NLP, AR/VR, chatbots and intelligent recommendation systems. This approach enables more immersive, interactive and adaptive learning experiences. Examples are the use of virtual tutors (Cicibot), VR/AR in remote learning, and AI-based personalized guidance systems (A9, A22, A24). The implementation plan is strongly focused on the use of data to enable richer learning experiences, improve human-technology interaction, and provide personalized advice. Thus, the use of AI in Indonesia is not only technological but also pedagogical, transforming learning paradigms to be smarter, more flexible, and student-centered.

An essential component of assessing how much the use of artificial intelligence actually raises the standard of education in Indonesia is impact measurement. Improvements in student learning outcomes, instructional effectiveness, student motivation and engagement, and teachers’ capacity to oversee technology-based learning are just a few of the ways that numerous studies have tried to quantify this influence. According to current research, the effects of implementing AI in education are not consistent and are impacted by a number of variables, including the kind of technology employed, infrastructure preparedness, user proficiency, and the institutional setting in which AI is applied. The following table summarizes the literature analysis results based on the assessed impact indicators, the direction of the findings, and their implications for the quality of education in Indonesia in order to address RQ3.

The synthesis results in Table 5 show that the application of artificial intelligence (AI) in Indonesian education often improves learning outcomes. Significant gains in students’ language proficiency, conceptual understanding, writing skills and mathematical ability have been reported in many studies (A1, A2, A3, A16). Some research even showed quantitative gains, such as 35% gains in understanding (A11) and about 20% contributions of AI to exam performance (A12). This means AI can provide fast, accurate and data-driven feedback which leads to more effective and adaptable learning processes.

AI has been seen to increase student motivation, engagement, and learning interaction. The use of technologies such as chatbots, gamification and AR/VR leads to more interesting, dynamic and immersive learning experiences (A8, A9, A18). In addition, AI increases students’ trust in the learning process and encourages collaborative learning (A17, A22). Such high levels of student engagement and satisfaction indicate that AI, as well as a tool for cognitive support, acts as a catalyst for more fulfilling and pleasurable learning experiences.

AI is one of the main differentiating advantageous factors in individualized learning between technology-based learning and traditional ones. The AI allows tailoring the learning style, pace and content to the needs of each individual student (A4, A14, A24). Adaptive learning systems, intelligent tutoring and personalized recommendation systems have been shown to increase student happiness and effectiveness of learning. Additionally, this customization fosters student independence and motivates them to explore and comprehend content more profoundly and in context.

Artificial intelligence also plays a big role in the effectiveness of teaching and learning 21st-century skills. AI can improve efficiency: assessment processes can be speeded up, administrative work can be automated and teachers can develop lesson plans faster and more systematically (A4, A10, A30). AI is also an agent of 21st-century skills such as critical thinking, creativity, collaboration, digital literacy and technology adaptability (A8, A20, A31). However, some studies also warn that critical thinking skills may deteriorate if the use of AI is not combined with adequate pedagogical measures. Thus, the use of AI in education should be purposeful and carefully managed.

Even though Indonesia’s use of AI in education is making encouraging strides, there are still a number of obstacles and constraints that need to be addressed in order to achieve full and long-lasting AI integration. These difficulties include regional differences in technology infrastructure, educators’ lack of digital proficiency, the scarcity of AI content created locally, and concerns about data protection and ethics when using AI-based systems in classrooms. However, these difficulties actually offer stakeholders significant chances to promote innovation, fortify technology-based education regulations, and create an AI ecosystem that is more inclusive and pertinent to Indonesia’s educational requirements. As shown in the following table, findings from the literature were thoroughly examined in order to address RQ4 based on the dimensions of problems, identified constraints, and future development prospects in Table 6.

The uneven preparation of infrastructure is still the main obstacle to the integration of artificial intelligence (AI) in education in Indonesia, based on the synthesis results presented in Table 6. Most of the studies (A1, A4, A5, A11, A20, A32, A34) indicate an unequal distribution of technology between urban and rural areas, a gap in internet access and limited equipment. The 3 T (least developed, frontier and outermost) countries still face big challenges, but this tends to concentrate the use of AI in more digitally advanced places. The widespread use of AI in the country’s educational system is further complicated by several technological challenges, such as unstable connections, platform restrictions and implementation costs.

Digital skills and AI literacy remain significant barriers to human resource proficiency, particularly for instructors and lecturers. Many educators struggle with a steep technical learning curve (A13), are inexperienced with AI (A10, A34), and are reluctant to change (A19, A29). There is also concern that AI may undermine students’ ability to think critically, or replace teachers (A16, A28). It is also important to engage in continuous training, capacity building and the development of AI literacy to ensure that AI is used as a learning support tool and not as a replacement for the pedagogical function of instructors. AI also raises important considerations around data privacy and ethics. Several studies have raised concerns about algorithmic bias, data security, and possible violations of academic integrity, such as plagiarism and overreliance on technology (A12, A24, A25, A28). Moreover, learning might become less personal and there might be less social contact between students and teachers (A8, A22). The lack of a comprehensive ethical framework and explicit data protection norms indicate that stronger regulations are still required to guarantee responsible, equitable and transparent use of AI in Indonesian education.

However, Indonesia possesses a broad range of potential for the development of AI in education. The use of AI can improve the quality of learning through automated assessment, building intelligent tutoring systems, personalized learning, and the integration of AR and VR technologies (A2, A9, A18, A31). In addition, there are opportunities to develop AI-based local contexts that are more relevant to Indonesian characteristics, such as language, culture, and citizenship education (A5, A13, A26). However, to fully exploit these possibilities and to promote an inclusive and lasting AI-based reform of education, comprehensive national policies, a precise implementation roadmap and cooperation between the government, educational institutions and the technology industry are required.

The remarkable increase in the number of scholarly articles discussing the use of artificial intelligence (AI) in education in Indonesia is one of the most startling conclusions of this thorough literature assessment. Only four articles were found during the initial era (2020–2022), but this number rose dramatically to 14 during the 2023–2024 period and peaked at 16 during the 2025–2026 period, accounting for 47.1% of the 34 articles examined. This exponential development pattern is not just a statistical phenomena; rather, it represents a substantial change in scholarly focus toward the role of AI in transforming education (Khatun et al., 2024; Zhong & Zhao, 2025).

This boom is directly related to the surge of popularity of generative AI, in particular ChatGPT by OpenAI, which was released publicly in late 2022. This technology has directly inspired academics, particularly in Indonesia, to study, document and evaluate the effects of AI in educational contexts, besides changing the way the general public interacts with AI. The wave of publications after 2023 can be called the “second wave” of AI adoption in Indonesian education, because the first wave was mainly conceptual and experimental. But this positive development is undergirded by an imbalance that needs to be corrected. Geographically, the island of Java is the focus of 61.8% of the studies (21 out of 34 papers) which include major cities such as Jakarta, Yogyakarta, Surabaya and Semarang. Kalimantan, Papua and Bali only contribute 8.8% of the total research output, while Sulawesi and Sumatra only contribute 11.8%. This pattern indicates that academic discussions on AI in education in Indonesia are still inclined to be very urban-biased and Java-centric.

This gap is even more pronounced when education levels are taken into account. The subject of higher education is addressed in 22 articles, which is 64.7% of all publications. There are many fewer studies at the primary, junior secondary and senior secondary levels. The situation demonstrates that universities are pioneering the investigation and use of AI because they have better human resources, digital infrastructure and research capabilities. Conversely, primary and secondary education levels are almost entirely lacking in the literature currently being published, especially in contexts with poor digital infrastructure.

This discrepancy is not merely an academic problem, but has serious policy implications. Although urban and higher education environments remain dominant in the research agenda, the policy suggestions below will likely be less relevant to most Indonesian schools, particularly those located in 3 T areas (underdeveloped, frontier and outermost regions). In order to broaden the scope of AI implementation studies to all regions in Indonesia, a concerted effort to promote more geographically and educationally inclusive AI research needs to be undertaken such as cooperative research incentives between the major universities and educational institutions in the regions and support from the Ministry of Education, Culture, Research and Technology.

The results of RQ2 show the progress of AI in education in Indonesia in several aspects which can be grouped into five major categories, namely learning analytics and automated assessment (5 articles), intelligent tutoring system (ITS) with multimodal AI approaches (4 articles), generative AI and chatbots (10 articles), adaptive learning and personalized learning (7 articles), and AI for learning media development (6 articles). This diversity highlights the large space of AI applications but also suggests tendencies that are worth investigating further. Generative AI and chatbots (particularly ChatGPT) are the most popular implementation type with more than one-third of all reviewed publications. Applications include aiding instructors in creating lesson plans (A10), helping students complete projects and essays (A17, A21), automating grammar correction (A3, A26), and designing visual content with AI (A16). While these applications have been shown to improve information accessibility and efficiency, the bulk of implementations are still at what one could call a “surface level use”, utilizing AI as a tool for daily chores and not as an integrated component of well-designed pedagogical frameworks.

Actually, AI’s actual educational potential goes well beyond just speeding up job completion (B. George & Wooden, 2023; Srinivasa et al., 2022). Learning outcomes have been demonstrated to be significantly impacted by deeply integrated adaptive learning systems, such as intelligent tutoring systems that adapt to individual learning styles. AI-driven personalization is a major predictor of satisfaction and continuing usage intention among Indonesian students, according to Study A14 (Novianti et al., 2026). This conclusion is statistically significant, although it is still infrequently used as a reference in actual implementation.

This problem indicates the contradiction between the amount of technology at disposal and the depth of its application in the educational process. Many institutions are adopting AI as a reaction to trends, with no prior pedagogical planning about how AI should be incorporated into curriculum, evaluation and teacher-student interaction. That’s why AI is more of a performance enhancer than a transformer of the learning experience. To move past this superficial application, a paradigm shift is needed from simply “using AI” to “pedagogically integrating AI.” This means that the selection of AI tools should be guided by well-defined learning goals, consistent with the desired competences, and systematically evaluated for impact. Such an approach requires availability of technology but also the ability of educators to create relevant AI-based learning scenarios a skill that is still severely lacking among Indonesian lecturers and teachers.

The results of RQ3 and RQ4 together create a paradox that is the most complicated topic in the discussion of AI implementation in Indonesia: while AI has been shown to significantly improve a number of aspects of learning quality, its unplanned application actually poses a serious risk to academic integrity, critical thinking abilities, and the preparedness of human resources in education.

From the standpoint of advantages, the numerous research examined offer reasonably solid empirical proof that AI improves learning results. Using a MobileNetV2-based mobile application to learn about ornamental plants improved student comprehension by up to 35%, according to Study A11 (Setyawan & Purbohadi, 2025). Through adaptive and customized feedback, the AI-based NovoLearning platform successfully improves students’ English language proficiency, as Study A1 showed. When compared to classes without AI intervention, organized AI deployment can improve academic achievement by roughly 20–35% overall.

AI has been demonstrated to improve student motivation and engagement in addition to academic results. A more engaging and entertaining learning environment is produced through the use of gamification technology, interactive chatbots, and media based on augmented reality (AR) and virtual reality (VR). When learning was combined with AI tools, more than 75% of students in the examined trials reported higher levels of engagement. The results of Study A22 (Al Yakin et al., 2025) show that the virtual AI tutor “Cicibot” greatly enhanced pre-service teachers’ verbal and nonverbal expression and effectively promoted collaborative learning.

Another aspect of influence that consistently appears in all research is teaching efficiency. Significant time savings are reported by educators who utilize AI for automated assessment, content creation, and data administration; many studies show operational efficiency benefits of up to 52.9%. Theoretically, this “saved” time could be used for more pedagogically significant tasks like in-depth conversations, personalized coaching, and fostering students’ creativity.

But this story of advantages must be viewed in conjunction with a number of grave dangers that have also been noted in the literature. According to Study A12 (Adiyono et al., 2025), the usage of ChatGPT in test situations in Indonesia has led to a worrying dependence: while it increases question-answering efficiency, it also weakens critical thinking abilities and jeopardizes academic integrity. AI has made it more difficult to enforce standards of academic honesty, according to almost 50% of lecturers surveyed in many studies; nonetheless, most institutions still lack explicit processes to handle this issue.

The literature refers to this threat to kids’ cognitive ability as “cognitive offloading,” and it is becoming a more pressing issue. Students’ capacity for autonomous reasoning may deteriorate if they grow accustomed to using AI to produce writing, respond to inquiries, and even solve logical puzzles. According to Study A28 (Ninghardjanti et al., 2026), adopting AI without metacognitive awareness does not greatly enhance critical thinking abilities and, in the absence of intentional pedagogical intervention, may even worsen them. Beyond scholarly issues, difficulties with human resource preparedness also show up as a significant obstacle. The majority of Indonesian educators, particularly those working in elementary and secondary education and outside of Java, have never employed AI tools in an organized way for teaching. Current training initiatives are typically irregular, unsustainable, and lack standardized training curriculum. Therefore, pedagogical capability to maximize AI continues to be a significant barrier even in cases where infrastructure is available.

Algorithmic ethics and data privacy have likewise not gotten enough attention. The management of students’ personal data, potential biases in AI recommendation systems, and accountability for decisions made by algorithms are all significantly impacted by the usage of commercial AI platforms by educational institutions and students. Although the recently passed Personal Data Protection Law (UU PDP) still needs more precise implementing regulations for education, Indonesia currently lacks data privacy laws especially designed for the education sector.

Technology alone cannot overcome this benefit-risk conundrum. What is needed is a framework for the appropriate application of AI that coherently incorporates pedagogical, ethical, regulatory, and capacity-building aspects. At least four essential components should be present in such a framework. First, teacher certification programs and ongoing professional development incorporate AI literacy proficiency standards for educators. These criteria should cover more than just tool usage; they should also include pedagogical skills in creating AI-based learning.

Second, the Ministry of Education, Culture, Research, and Technology released official ethical guidelines for the use of AI in education. These recommendations include explicit limitations on the use of AI in assessment, attribution of AI-generated content, and safeguards for student data. Third, instructional design that prioritizes enhancement over replacement. Instead of taking the place of thought itself, AI should be positioned as a tool that improves students’ cognitive capacities by encouraging inquiry, analysis, and innovation. This necessitates a change in the way projects, assignments, and tests are created to make them resistant to “AI-enabled cheating” while still being pertinent to 21st-century skills.

Fourth, a national plan for implementing AI in the education sector. Current government initiatives like the government AI Strategy 2020–2045 and Making Indonesia 4.0 continue to be cross-sectoral and macro-level. A more operational roadmap with quantifiable goals, precise budgetary allotments, and evaluation procedures involving stakeholders at all levels from the school level to the ministerial level is required. Therefore, the dichotomy between the advantages and disadvantages of implementing AI in Indonesia is not insurmountable. Rather, it can act as a springboard for developing a more developed, inclusive, and accountable AI-based education ecosystem that prioritizes social justice and pedagogical ideals in addition to utilizing cutting-edge technology.

This study is subject to several methodological limitations that should be acknowledged. First, the final synthesis was based on a relatively small corpus of 34 articles, drawn exclusively from the Scopus and Sinta databases and restricted to English-language, open-access publications. This selection process, while methodologically rigorous under the PRISMA protocol, excluded a substantial body of Indonesian-language literature (54 articles were removed on language grounds alone) as well as studies with restricted access, potentially omitting relevant grey literature, institutional reports, and regionally focused studies that could have enriched the geographic and educational-level coverage of the review. The predominance of higher education studies (64.7%) and Java-based research sites (61.8%) in the included literature therefore reflects not only actual patterns in the field but also possible selection bias inherent to the database and language restrictions applied.

Second, the review relied primarily on self-reported outcomes and cross-sectional, largely descriptive or correlational study designs rather than longitudinal or experimental evidence, which constrains the strength of causal claims that can be made about AI’s impact on learning outcomes such as the frequently cited 20–35% performance gains. Many of the primary studies also varied considerably in sample size, measurement instruments, and operational definitions of constructs such as “engagement” or “learning quality,” making direct comparison and aggregation across studies inherently imprecise. As a qualitative-thematic synthesis rather than a meta-analysis, this review is further limited in its ability to statistically quantify the magnitude or consistency of effects across the reviewed literature, and its conclusions should therefore be interpreted as indicative of broad patterns rather than as precise, generalizable estimates.

Building on the gaps identified in this review, future research should prioritize expanding the geographic and educational-level scope of inquiry beyond Java-based higher education institutions. Studies situated in primary and secondary schools, in regions outside Java, and particularly in underdeveloped, frontier, and outermost (3 T) areas are urgently needed to produce a more representative picture of AI adoption across Indonesia’s diverse educational landscape. Future work would also benefit from including Indonesian-language and grey literature sources, as well as adopting broader database coverage, to reduce the selection bias associated with English-only, Scopus/Sinta-restricted searches.

In terms of methodology, future studies should move beyond descriptive and correlational designs toward longitudinal and experimental or quasi-experimental research capable of establishing more robust causal evidence of AI’s effects on learning outcomes, critical thinking, and academic integrity over time. There is also a need for research that goes beyond documenting the presence of AI tools to examining how deep pedagogical integration rather than superficial task automation can be designed, implemented, and evaluated in Indonesian classrooms. Finally, future research should engage more directly with policy and ethics, including empirical evaluation of teacher AI-literacy training programs, data privacy frameworks, and the practical implementation of a national AI-in-education roadmap, so that evidence generated by scholars can more directly inform the regulatory and institutional frameworks called for in this review’s conclusions.

The author would like to express his deepest gratitude and appreciation to the Indonesian Endowment Fund for Education (LPDP), Ministry of Finance of the Republic of Indonesia, for the financial support provided through the Indonesian Education Scholarship (BPI) under the Doctoral Program Scholarship for Indonesian Lecturers (PDDI), which has enabled the author to complete his doctoral studies and produce this scientific publication. This scholarship support not only significantly contributed to the smooth running of the research process but also opened up opportunities for the author to actively contribute to the development of science. The author realizes that without the support of LPDP, BPI, and PDDI, this research would not have been possible, therefore, his sincere gratitude is extended to all parties who have provided this opportunity and trust.

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