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All Journal Jurnal Mantik Tematik : Jurnal Teknologi Informasi Komunikasi Al-Hijr: Journal of Adulearn World Journal International Inspire Education Technology (JIIET) Computing Jurnal Informatika Journal Research of Social Science, Economics, and Management Kinanti : Jurnal Karya Insan Pendidikan Terpilih Journal of Humanities Research Sustainability Maneggio Technologia Journal Journal of Pedagogi INTERNATIONAL JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS (INJOSS) Indonesian Journal of Education (INJOE) Scientechno: Journal of Science and Technology Journal of World Future Medicine, Health and Nursing Journal Emerging Technologies in Education International Journal of Educational Narratives International Journal of Language and Ubiquitous Learning
Rustiyana, Rustiyana
Universitas Bale Bandung, Indonesia
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Evaluasi Kebijakan "Kurikulum Merdeka" dari Perspektif Tata Kelola Pemerintahan: Peran Dinas Pendidikan Kabupaten Bandung Barat dalam Pendampingan Sekolah Dasar Rustiyana Rustiyana
Kinanti : Jurnal Karya Insan Pendidikan Terpilih Vol. 4 No. 1 (2026): Vol 4 No 1 (2026): Jurnal Kinanti (Karya Insan Pendidikan Terpilih)
Publisher : Dinas Pendidikan Kabupaten Bandung Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62518/xeyqfr88

Abstract

Implementasi Kurikulum Merdeka di Indonesia menandai pergeseran paradigma pendidikan yang fundamental, bergerak dari pendekatan berbasis konten yang kaku menuju arsitektur pembelajaran yang fleksibel, berdiferensiasi, dan berpusat pada karakteristik esensial peserta didik. Penelitian ini bertujuan untuk mengevaluasi secara komprehensif pelaksanaan kebijakan Kurikulum Merdeka pada jenjang Sekolah Dasar (SD) di Kabupaten Bandung Barat (KBB) dengan menggunakan kacamata tata kelola pemerintahan (educational governance). Menggunakan desain evaluasi kebijakan kualitatif yang mengadopsi model implementasi George C. Edward III (Komunikasi, Sumber Daya, Disposisi, dan Struktur Birokrasi) serta sembilan prinsip dasar Good Governance, analisis ini menyoroti interaksi fungsional antara Dinas Pendidikan Kabupaten Bandung Barat, pengawas sekolah, dan Komunitas Penggerak Pendidikan Daerah (KPPD) dalam mengeksekusi pendampingan struktural. Temuan penelitian memanifestasikan sebuah paradoks tata kelola: di satu sisi, Dinas Pendidikan KBB telah menunjukkan intensi strategis yang sangat responsif di tingkat makro—seperti pembentukan Tim Pengembang Kurikulum (TPK) jenjang SD dan peluncuran inisiatif pendampingan kolaboratif "Ngajang Ka Sakola". Namun di sisi lain, pada tataran birokrasi akar rumput (street-level bureaucracy), terjadi defisit kompetensi yang parah. Data empiris dari studi kasus SDN Garuda di KBB mengungkap bahwa mayoritas tenaga pendidik masih berjuang dengan kompetensi pedagogis yang rendah dalam penyusunan modul ajar, tingkat literasi digital yang sangat minim, serta disorientasi kognitif dalam menerapkan instrumen asesmen berdiferensiasi. Kegagalan operasional ini secara kausalitas berakar pada model pelatihan dari dinas yang masih bersifat teoretis-konvensional, tanpa diiringi pendampingan teknis pasca-pelatihan (hands-on mentoring). Sebagai intervensi strategis, laporan ini merekomendasikan restrukturisasi radikal terhadap tata kelola pelatihan guru melalui adopsi model desain instruksional ADDIE dan kerangka evaluasi Kirkpatrick level 2 dan 3, serta transformasi peran pengawas sekolah dari figur pengendali administratif menjadi fasilitator dan coach klinis guna mengamankan mutu pendidikan di tingkat dasar.
The Integration of Artificial Intelligence in Indonesian Language Learning: Pedagogical Opportunities and Risks Muh. Safar; Rustiyana Rustiyana
Journal of Pedagogi Vol. 2 No. 6 (2025): Journal of Pedagogi-December
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/30c69d53

Abstract

The rapid development of artificial intelligence (AI) has significantly transformed educational practices, including Indonesian language learning. AI offers various pedagogical opportunities such as personalized learning, enhanced literacy skills, and continuous feedback, while simultaneously posing risks that may undermine the core values of language pedagogy. This study aims to critically analyze the pedagogical opportunities and risks of AI integration in Indonesian language learning. A Systematic Literature Review (SLR) was conducted following PRISMA guidelines, drawing on reputable scholarly articles related to AI, language pedagogy, and education within the Indonesian context. The findings indicate that AI can enrich Indonesian language learning when positioned as a supportive tool within a reflective pedagogical framework. However, risks such as students’ cognitive dependency, decline in authentic language production, digital literacy gaps, and linguistic and cultural biases must be carefully addressed. This study concludes that the effectiveness of AI integration largely depends on teachers’ pedagogical competence, critical instructional approaches, and institutional policies governing AI use. The results provide a conceptual foundation for developing responsible AI-based practices and policies in Indonesian language education.
The Algorithmic Self: Rethinking Consciousness And Personal Identity In The Era Of Brain-Computer Interfaces Arief Budi Pratomo; Rustiyana Rustiyana
Journal of Humanities Research Sustainability Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jhrs.v2i5.2648

Abstract

Background. The rapid integration of Brain-Computer Interfaces (BCIs) into human cognitive processes has initiated a profound transformation in how consciousness and personal identity are conceptualized. As neural data become digitized, the boundaries between human cognition and machine computation blur, leading to the emergence of what can be described as the algorithmic self a hybrid consciousness co-produced by biological and artificial systems. Purpose. This research aims to examine how BCIs reshape the phenomenology of selfhood, agency, and memory by mediating the interaction between neural intention and algorithmic feedback. Method. The study employs a qualitative phenomenological design complemented by neuroscientific literature analysis, focusing on participants using non-invasive BCIs for communication, learning, and rehabilitation. Data were collected through in-depth interviews, reflective diaries, and neuro-ethical discourse mapping to identify cognitive and existential shifts in participants’ self-perception. Results. The findings reveal that BCI users experience fragmented yet extended forms of consciousness, where identity is continuously negotiated between embodied experience and algorithmic prediction. Participants reported increased cognitive augmentation but also existential dissonance, expressing uncertainty over the locus of agency and authorship of thought. Conclusion. The study concludes that the algorithmic self represents a new stage in human consciousness an emergent, co-dependent identity formed through neural–digital symbiosis. These findings call for an interdisciplinary rethinking of personhood, ethics, and autonomy in the age of neurotechnology.
Digital Amnesia And Algorithmic Memory: Reconstructing The Past In The Age Of Big Data Archives Thabo Mokoena; Elchin Mammadov; Rustiyana Rustiyana
Journal of Humanities Research Sustainability Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jhrs.v2i4.2651

Abstract

Background. The exponential growth of digital data and algorithmic curation has transformed how societies construct, preserve, and remember the past. The phenomenon of digital amnesia the tendency to outsource memory to digital systems reveals a paradox of modern knowledge: while more information is archived than ever before, human capacity for contextual recollection diminishes. Purpose. This study investigates how algorithmic mechanisms within big data archives reconstruct historical narratives and shape collective memory in the digital age. The research aims to analyze the epistemological and ethical implications of algorithmic memory, focusing on how automated retrieval, ranking, and personalization systems mediate historical knowledge and cultural continuity Method. A qualitative multi-case analysis was conducted on digital archival platforms and algorithmic recommendation systems using interpretive content analysis and critical data studies methodology. Results. The findings show that algorithmic archives not only preserve information but actively curate and reinterpret history through patterns of visibility and omission. The findings indicate that memory in the age of big data is not neutral but performative constructed through computational decisions that privilege certain narratives while marginalizing others. Conclusion. The study concludes that the digital era demands a critical redefinition of archival literacy, emphasizing the need for transparency, human oversight, and ethical design in algorithmic systems. Understanding digital amnesia thus becomes essential to safeguarding cultural memory and ensuring that the reconstruction of the past remains plural, accountable, and inclusive.
The Post-Human Author: Deconstructing Narrative Identity And Creativity In Ai-Generated Literary Works Wirdatul Khasanah; Li Wei; Rustiyana Rustiyana
Journal of Humanities Research Sustainability Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jhrs.v2i6.2652

Abstract

Background. The emergence of artificial intelligence as a creative agent has fundamentally disrupted the human-centered paradigm of authorship in literary production. Recent advances in generative models such as GPT and other neural language systems have blurred the boundaries between human intention, machine output, and narrative authenticity. Purpose. This study aims to deconstruct the notion of the “post-human author” by examining how AI-generated literary works redefine narrative identity, creativity, and the ontology of authorship. Employing a qualitative meta-analytical method combined with post-structuralist textual analysis, the research synthesizes existing literature and conducts interpretive readings of selected AI-generated texts. Through Derridean deconstruction and Foucault’s concept of the “author-function,” this study explores how algorithmic creativity challenges the metaphysics of originality and intentionality. Method. Employing a qualitative meta-analytical method combined with post-structuralist textual analysis, the research synthesizes existing literature and conducts interpretive readings of selected AI-generated texts. Through Derridean deconstruction and Foucault’s concept of the “author-function,” this study explores how algorithmic creativity challenges the metaphysics of originality and intentionality. Results. The findings reveal that AI-generated literature destabilizes the humanist framework of creative agency , producing hybrid narratives where authorship becomes distributed, contingent, and collaborative between human and machine. However, this post-human creativity also exposes ethical and philosophical tensions related to authorship, ownership, and meaning-making. Conclusion. The study concludes that literary creation in the age of AI demands a reconfiguration of aesthetic and epistemic assumptions about what it means to “create,” inviting a new hermeneutics of reading that acknowledges the co-agency of the artificial and the human.
AI-BASED ADAPTIVE LEARNING SYSTEMS TO SUPPORT THE MERDEKA CURRICULUM: PERSONALIZING MATHEMATICS LEARNING AT THE JUNIOR HIGH SCHOOL LEVEL Rustiyana Rustiyana; Peter Wapun; João Lima; Haji Ahmad Makie
Journal International Inspire Education Technology Vol. 4 No. 3 (2025)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/jiiet.v4i3.1085

Abstract

Indonesia’s Kurikulum Merdeka (Merdeka Curriculum) demands personalized mathematics learning, but teachers in heterogeneous junior high school classrooms lack scalable tools. This creates a critical “implementation gap” between the policy’s differentiation ideals and classroom reality. This study aimed to design, develop, and evaluate a bespoke AI-Based Adaptive Learning System (ALS) natively aligned with the Merdeka Curriculum’s “Fase D” framework to enhance student achievement. A mixed-methods, quasi-experimental pre-test/post-test control group design (N=435) was employed over 14 weeks. The treatment group (n=232) used the ALS. Effectiveness was measured by an ANCOVA on mathematics achievement scores, supplemented by usability surveys (SUS) and qualitative data from teachers and students. The ALS group demonstrated significantly superior mathematics achievement (F(1, 432) = 121.4, p < .001, \eta_p^2 = .219). Qualitative data confirmed the system enabled “Targeted Differentiation” for teachers and provided “Personalized Pacing and Safety” for students, reducing peer anxiety. The context-aligned ALS is a highly effective, usable (SUS=79.5), and feasible tool. It successfully provides the scalable, individualized support required to operationalize the pedagogical philosophy of the Kurikulum Merdeka.
AI-DRIVEN SIMULATION OF DENGUE FEVER OUTBREAKS IN URBAN JAVA BASED ON CLIMATE VARIABILITY AND HUMAN MOBILITY DATA Vann Sok; Sokha Dara; Thabo Mokoena; Rustiyana Rustiyana
Scientechno: Journal of Science and Technology Vol. 4 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i1.2860

Abstract

Dengue fever outbreaks in urban areas of Java, Indonesia, have become a significant public health concern, with increasing frequency due to climate variability and human mobility patterns, where the spread of dengue is influenced by environmental conditions such as temperature and rainfall as well as human movement within urban centers, making an understanding of these factors crucial for effective disease control and prevention. This study aims to simulate and predict dengue fever outbreaks in urban Java using AI-driven models based on climate variability and human mobility data, with the research seeking to identify the key factors that contribute to the transmission dynamics of dengue fever in urban settings and to evaluate the effectiveness of predictive models in managing potential outbreaks. The study employs machine learning techniques, including Random Forest and Artificial Neural Networks, to analyze climate data consisting of temperature, rainfall, and humidity alongside human mobility data collected from mobile phone tracking and demographic information, where the data is processed to create a simulation model of dengue fever transmission that is validated against historical outbreak data. The results show that the AI-driven model successfully simulated dengue fever outbreaks, demonstrating a high correlation between climate conditions, human mobility, and the spread of the disease, and indicating that increased mobility during the rainy season significantly amplified the risk of outbreaks in high-density urban areas. Overall, the findings conclude that AI-driven simulations offer a promising approach to understanding and predicting dengue fever outbreaks in urban Java, and by incorporating climate and mobility data, the model provides valuable insights for early warning systems and targeted public health interventions.
DEEP LEARNING APPROACHES FOR PREDICTING DEFORESTATION PATTERNS AND BIODIVERSITY HOTSPOT LOSS IN SUMATRA Rithy Vann; Ming Kiri; Aaraf Sharma; Rustiyana Rustiyana
Scientechno: Journal of Science and Technology Vol. 4 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i1.2861

Abstract

Deforestation in Sumatra, Indonesia, represents a critical environmental challenge that has led to the degradation of biodiversity hotspots and poses serious threats to both local ecosystems and global climate stability, driven largely by rapid forest conversion into agricultural land, illegal logging, and extensive land-use changes, making accurate prediction of deforestation patterns essential for effective conservation planning. This study applies deep learning approaches to predict deforestation patterns in Sumatra while simultaneously assessing their impacts on biodiversity hotspots, with the objective of developing a model capable of identifying areas at high risk of deforestation and estimating potential biodiversity losses. The research employs deep learning algorithms, specifically Convolutional Neural Networks and Recurrent Neural Networks, to analyze satellite imagery, historical deforestation data, land-use changes, and biodiversity hotspot maps, enabling the model to capture both spatial and temporal trends in deforestation dynamics. The results demonstrate that the proposed deep learning model achieves a high prediction accuracy of 92 percent in identifying deforestation hotspots and successfully highlights key biodiversity-rich areas that are highly vulnerable to rapid forest loss, with agricultural expansion and infrastructure development emerging as the dominant drivers of deforestation in these regions. Overall, the findings confirm that deep learning provides a powerful and reliable tool for predicting deforestation patterns and assessing biodiversity hotspot degradation, offering valuable evidence-based insights for policymakers and conservation practitioners to prioritize protection efforts and design targeted interventions aimed at mitigating further environmental damage in Sumatra.
AUTONOMOUS SYSTEMS IN INDUSTRY 5.0: ENHANCING HUMAN ROBOT COLLABORATION AND SAFETY IN INDONESIAN MANUFACTURING Lucas Lima; Tiago Costa; Li Wei; Rustiyana Rustiyana
Scientechno: Journal of Science and Technology Vol. 4 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i2.2890

Abstract

Industry 5.0 represents a fundamental shift toward a more human-centric paradigm in manufacturing by emphasizing enhanced collaboration between humans and robots, where autonomous systems are designed not only to optimize efficiency but also to improve safety and support workers in performing more complex and value-added tasks. In the Indonesian manufacturing context, the adoption of autonomous technologies is accelerating as industries seek to remain competitive; however, empirical evidence regarding their effectiveness in improving human-robot collaboration and workplace safety remains limited. This study addresses this gap by exploring the role of autonomous systems in Industry 5.0 and examining how integrated safety protocols and collaboration strategies can enhance both operational efficiency and occupational safety. Employing a mixed-methods approach, the research combines qualitative insights from interviews with industry experts and quantitative data derived from experimental implementations of autonomous robotic systems in Indonesian manufacturing environments. The findings demonstrate that the deployment of adaptive safety systems significantly strengthens human-robot collaboration, resulting in a 30% reduction in workplace accidents and a 20% increase in production efficiency. These results indicate that well-designed autonomous systems can effectively minimize risks while enabling workers to interact more confidently and productively with robots, thereby supporting the conclusion that Industry 5.0 technologies hold substantial potential for improving safety standards and overall performance in Indonesian manufacturing settings.
INTEGRATING DIGITAL TWINS AND SYSTEMIC AI FOR PREDICTIVE MAINTENANCE OF NATIONAL CRITICAL INFRASTRUCTURE Lucas Wong; Sofia Lim; Rohan Kumar; Rustiyana Rustiyana
Scientechno: Journal of Science and Technology Vol. 4 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v4i3.2891

Abstract

National critical infrastructure, including energy, transportation, and communication systems, plays a vital role in sustaining modern society, yet failures within these systems can trigger severe economic, environmental, and security consequences. Conventional maintenance approaches often lack the capability to anticipate failures in complex and large-scale infrastructures. Recent advancements in Digital Twin technology and Artificial Intelligence (AI) provide innovative opportunities to enhance predictive maintenance and infrastructure resilience. This study aims to integrate Digital Twins with systemic AI to optimize predictive maintenance strategies for national critical infrastructure by leveraging real-time data and intelligent prediction mechanisms. The research employs a combined framework in which sensor-generated data from infrastructure components are continuously synchronized with Digital Twin models and analyzed using machine learning algorithms to monitor system conditions, simulate operational behavior, and predict potential failures. The proposed framework was implemented in a case study of a national energy grid to evaluate its effectiveness. The results indicate that the integrated system significantly improved predictive maintenance performance, achieving a 30% reduction in unplanned downtime and a 25% decrease in maintenance costs through accurate failure prediction and timely intervention. These findings demonstrate that the integration of Digital Twins and systemic AI offers a robust, scalable, and efficient solution for enhancing reliability, resilience, and sustainability in the management of national critical infrastructure.
Co-Authors Aaraf Sharma Achmad Sanusi Ahmed Shah Ahmet Demir Ali Khan Ali Reza Aom Thai Arief Budi Pratomo Arief Hidayat Asep Didik Setia Permana Aung Myint Ava Lee Baatar Tserendorj Baran Akbulut Charlotte Thomsen COMPUTING INFORMATIKA Dadan Herdiansah Daeng Arifin Diana Salendra Dito Anurogo Dyah Maharani Elchin Mammadov Eliagus Telaumbanua Ethan Tan Fatemeh Hashemi Fatima Malik Haji Ahmad Makie Iin Almeina INFORMATIKA, COMPUTING Jamil Khan João Lima Karmini, Ni Wayan Khilda Nistrina Kusnadi Leila Mahdavi Li Na Li Wei Loso Judijanto Lucas Lima Lucas Wong Marcus Tan Maryana Maryana, Maryana Ming Kiri Muh. Safar Munkhzul Ganbat N Dede Khoeriah Nandar Hlaing Nenden Sri Rahayu Nong Chai Novianti Indah Putri Nurul Saila Park Jihoon Peter Wapun Puja Permana, Ridwan Rafiullah Amin Razia Khan Rit Som Rithy Vann Rohan Kumar Roshonah, Adiyati Fathu Rosmalina ROSMALINA ROSMALINA, ROSMALINA Roya Zahir Rusdi Rusdi Sandhy Dwi A Sari Mariahma Nova Sipayung Sauca Ananda Pranidana Selenge Batbold Shazia Akhtar Shela Tansyah, Lorenza Siri Lek Sofia Lim Sokha Dara Suharya, Yaya Sukiman Sulastry Pakpahan Sun Wei Thabo Mokoena Tiago Costa Triyugo Winarko Vann Sok Wirdatul Khasanah Yang Xiang Yudi Herdiana Yusuf Muharam Zara Ali Zaw Min Oo Zen Munawar