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Strategies for Teaching Language to Students with Special Needs Nuryanti, Titik; Lim, Sofia; Tan, Ethan; Purnama, Yulian
Journal International of Lingua and Technology Vol. 3 No. 3 (2024)
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/jiltech.v3i3.779

Teaching language to students with special needs presents unique challenges, requiring tailored strategies to address diverse learning abilities and disabilities. Despite the growing emphasis on inclusive education, there is limited research on effective language teaching methods for this population. This study explores strategies for teaching language to students with special needs, focusing on their impact on language acquisition and engagement. The research aims to identify and evaluate effective strategies for teaching language to students with special needs, focusing on their impact on language proficiency, engagement, and inclusivity. It seeks to provide practical recommendations for educators to create supportive and effective learning environments. A mixed-methods approach was employed, involving 50 students with special needs and their teachers. Data were collected through classroom observations, teacher interviews, and pre- and post-tests to measure language proficiency. Qualitative data were analyzed using thematic analysis, while quantitative data were analyzed using statistical software. The findings revealed that multisensory approaches, visual aids, and individualized instruction significantly improved language proficiency and engagement among students with special needs. Teachers reported that these strategies enhanced students' confidence and participation. However, challenges such as resource limitations and lack of training were also identified. Tailored strategies, such as multisensory approaches and individualized instruction, are effective in teaching language to students with special needs. Educators should prioritize inclusive practices and professional development to address challenges and enhance language learning outcomes.

The Impact of Cognitive-Behavioral Therapy on Decreased Symptoms of PTSD in Victims of Natural Disasters Permatananda, Pande Ayu Naya Kasih; Tan, Ethan; Lim, Sofia
World Psychology Vol. 4 No. 1 (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/wp.v4i1.800

Post-traumatic stress disorder (PTSD) is a significant mental health issue faced by individuals who have experienced natural disasters. Survivors of such events often endure long-lasting psychological effects, including flashbacks, hyperarousal, and emotional numbness, which hinder their ability to reintegrate into normal life. Cognitive-behavioral therapy (CBT) has emerged as an effective intervention for PTSD, offering tools for individuals to reframe distressing thoughts and regulate emotional responses. This study investigates the impact of CBT on the symptoms of PTSD in victims of natural disasters. The primary aim is to evaluate whether CBT can reduce the severity of PTSD symptoms in affected individuals. A quasi-experimental design was used, with 100 participants who were victims of a recent natural disaster. Participants underwent 8 weeks of CBT sessions, with pre- and post-assessment using the PTSD Checklist for DSM-5 (PCL-5). The results indicated a significant reduction in PTSD symptoms, with a marked decrease in both intrusive thoughts and hyperarousal behaviors. The findings suggest that CBT is an effective therapeutic approach for alleviating PTSD symptoms in disaster survivors. The study concludes that integrating CBT into post-disaster mental health care programs can significantly improve the well-being of disaster victims.

Get to know artificial intelligence in Epidemiology: Predicting and Controlling Communicable and Non-Communicable Diseases Lim, Sofia; Tan, Jaden; Ajani, Anggra Trisna
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 1 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i1.1902

The increasing burden of both communicable and non-communicable diseases (NCDs) presents significant challenges for public health worldwide. The application of artificial intelligence (AI) in epidemiology has emerged as a promising tool for predicting, monitoring, and controlling the spread of these diseases. This study aims to explore the role of AI in enhancing epidemiological practices and improving public health outcomes. The research employs a systematic review methodology, analyzing 60 peer-reviewed articles on the integration of AI technologies in disease prediction and control. The findings indicate that AI, particularly machine learning (ML) algorithms, has demonstrated remarkable success in predicting disease outbreaks, identifying high-risk populations, and optimizing resource allocation. AI-driven tools have been effectively utilized in both communicable diseases, such as influenza and COVID-19, and NCDs, including diabetes and cardiovascular diseases. The study concludes that AI holds substantial potential for transforming epidemiological practices, offering more accurate forecasts and efficient interventions. However, challenges such as data privacy concerns and resource limitations in low-income settings need to be addressed. The research highlights the need for continued investment in AI technologies to strengthen global disease prevention and control efforts.

Blockchain for Social Trust: Rebuilding Transparency in Public Sector Transactions through DLT Astawa, I Putu; Prasetio, Rachmat; Lim, Sofia
Journal of Social Science Utilizing Technology Vol. 3 No. 2 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v3i2.2290

Background. The erosion of public trust in government institutions has become a critical global concern, driven largely by persistent issues of corruption, inefficiency, and opaque administrative processes. Amid this trust deficit, blockchain technology—especially Distributed Ledger Technology (DLT)—has emerged as a promising tool to rebuild transparency, accountability, and citizen engagement in the public sector. Purpose. This study aims to examine how blockchain can be strategically implemented to restore social trust by enhancing transparency in public sector transactions. Method. This study uses a qualitative method supported by several case studies, this study analyzes the initiative of real world blockchain adoption in countries such as Estonia, the United Arab Emirates, and Indonesia. Data was collected through analysis of policy documents, expert interviews, and comparative evaluation of the DLT -based public administration framework. Results. The findings indicate that blockchain’s immutable and decentralized architecture significantly mitigates information asymmetry, reduces opportunities for fraud, and enables real-time auditing of government activities. Moreover, smart contract integration allows for automatic enforcement of public service agreements, further reinforcing institutional integrity. However, the study also highlights critical challenges such as legal uncertainties, technological literacy gaps, and resistance to institutional change that may hinder effective implementation. Conclusion. In conclusion, while blockchain is not a panacea for all governance issues, it presents a powerful foundation for restoring social trust when embedded within a broader ecosystem of legal reform, digital literacy, and civic participation. This research contributes to the growing discourse on digital governance by offering a conceptual and empirical basis for blockchain-enabled transparency in the public sector.

Blockchain for Social Trust: Rebuilding Transparency in Public Sector Transactions through DLT Astawa, I Putu; Prasetio, Rachmat; Lim, Sofia
Journal of Social Science Utilizing Technology Vol. 3 No. 2 (2025)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v3i2.2290

Background. The erosion of public trust in government institutions has become a critical global concern, driven largely by persistent issues of corruption, inefficiency, and opaque administrative processes. Amid this trust deficit, blockchain technology—especially Distributed Ledger Technology (DLT)—has emerged as a promising tool to rebuild transparency, accountability, and citizen engagement in the public sector. Purpose. This study aims to examine how blockchain can be strategically implemented to restore social trust by enhancing transparency in public sector transactions. Method. This study uses a qualitative method supported by several case studies, this study analyzes the initiative of real world blockchain adoption in countries such as Estonia, the United Arab Emirates, and Indonesia. Data was collected through analysis of policy documents, expert interviews, and comparative evaluation of the DLT -based public administration framework. Results. The findings indicate that blockchain’s immutable and decentralized architecture significantly mitigates information asymmetry, reduces opportunities for fraud, and enables real-time auditing of government activities. Moreover, smart contract integration allows for automatic enforcement of public service agreements, further reinforcing institutional integrity. However, the study also highlights critical challenges such as legal uncertainties, technological literacy gaps, and resistance to institutional change that may hinder effective implementation. Conclusion. In conclusion, while blockchain is not a panacea for all governance issues, it presents a powerful foundation for restoring social trust when embedded within a broader ecosystem of legal reform, digital literacy, and civic participation. This research contributes to the growing discourse on digital governance by offering a conceptual and empirical basis for blockchain-enabled transparency in the public sector.

The Contribution of Kalam in Resolving Contemporary Theological Controversies: A Study of Rational Debates Mufron, Ali; Lim, Sofia; Chan, Rachel; Jasafat, Jasafat
Journal of Noesantara Islamic Studies Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnis.v2i1.1845

The discipline of Kal?m (Islamic theology) has historically served as a framework for addressing theological controversies, utilizing rational discourse to harmonize scriptural interpretation with intellectual inquiry. In the modern era, theological debates concerning issues such as faith, ethics, and the reconciliation of science and religion have intensified, necessitating renewed exploration of Kal?m as a method for resolving these challenges. This study examines the contribution of Kal?m in addressing contemporary theological controversies through rational debates and intellectual engagement. A qualitative approach was employed, combining historical analysis and textual study of classical Kal?m works with case studies of modern applications in theological discourse. Data were collected through critical analysis of primary texts and interviews with contemporary theologians and scholars actively engaging in rational debates on theological issues. The findings demonstrate that Kal?m provides a robust intellectual foundation for navigating contemporary theological controversies. Its emphasis on rational argumentation fosters constructive dialogue between traditional Islamic perspectives and modern intellectual paradigms. The study concludes that Kal?m remains a vital tool for resolving contemporary theological controversies, bridging the gap between tradition and modernity.

Development of an Aptamer-Based Electrochemical Biosensor for Early Detection of Prostate Cancer Markers Lim, Sofia; Tan, Marcus; Tan, Ethan
Journal of Biomedical and Techno Nanomaterials Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v1i4.1811

Prostate cancer is a leading malignancy in men, where early detection is critical for effective treatment. Current diagnostic methods, such as PSA tests, have limitations in sensitivity and specificity. To develop an aptamer-based electrochemical biosensor for the early detection of prostate cancer markers, aiming to improve diagnostic accuracy and speed. The study involved the design and optimization of aptamers through SELEX, integration with electrochemical sensors, and validation using prostate cancer cell lines and clinical samples. Instruments used include electrochemical workstations, HPLC, and mass spectrometry for characterization and evaluation. The developed biosensor demonstrated a detection limit of 0.1 ng/mL for PSA, with a response time of less than 10 minutes. High reproducibility was achieved with a coefficient of variation below 5%, and the biosensor showed significant specificity and stability in detecting PSA in various samples. The aptamer-based electrochemical biosensor offers a promising tool for the early detection of prostate cancer markers, providing higher sensitivity and specificity compared to traditional methods. Further clinical validation is necessary to confirm its efficacy and reliability in broader applications.

AI-Assisted Personalized Vaccine Design Using Multi-Omics Cancer Data Zaman, Khalil; Akhtar, Shazia; Lim, Sofia; Nampira, Ardi Azhar
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2381

The development of personalized cancer vaccines represents a promising frontier in oncology, yet traditional approaches struggle with the complexity and volume of multi-omics data. This study addresses this challenge by introducing an AI-assisted framework for the design of personalized vaccines. The primary objective was to leverage machine learning models to identify and prioritize neoantigens from integrated genomic, transcriptomic, and proteomic data of cancer patients. The methodology involved a deep learning pipeline to analyze multi-omics datasets, predicting tumor-specific mutations and their immunogenicity. This was followed by an algorithm to select the most potent neoantigen peptides for vaccine formulation, optimizing for both MHC binding affinity and T-cell activation potential. Our results demonstrate that the AI-driven approach significantly improved the speed and accuracy of neoantigen identification compared to conventional methods. The framework successfully predicted a set of high-quality vaccine candidates for individual patients, which showed strong in silico binding to patient-specific MHC molecules. We conclude that this AI-assisted methodology provides a powerful and scalable solution for personalized vaccine design, accelerating the translation of multi-omics data into clinically actionable immunotherapies.

The Effect of Artificial Intelligence in Adaptive Learning on Improving Student Understanding in Elementary School Almeina Loebis, Iin; Lim, Sofia
Journal of Multidisciplinary Sustainability Asean Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijmsa.v2i2.2240

Background. Advances in artificial intelligence (AI) technology have presented various innovative opportunities in the world of education, especially in the development of adaptive learning systems. The diverse understanding of elementary school students and the need for appropriate learning approaches make AI-based learning a promising alternative to improving learning effectiveness. Purpose. This study aims to determine the effect of the application of artificial intelligence in adaptive learning systems on improving student understanding at the elementary school level. The main focus is to see how much this system contributes in accommodating differences in learning styles and students' ability to understand the subject matter. Method. The research method used was a pseudo-experiment with a pretest-posttest control group design. The study population consisted of grade V students at an elementary school in Indonesia, with purposive sampling techniques to determine the experimental and control groups. The instrument is in the form of a concept understanding test and observation of the learning process. Results. The results showed that students who learned with AI-based adaptive systems experienced a significant increase in understanding compared to the control group. The average posttest score of the experimental group was higher with a more even increase. Case studies also show higher learning engagement and increased student motivation. Conclusion. The conclusion of this study states that the application of AI in adaptive learning has great potential in improving student understanding, especially with a personalized approach to material and adjusted learning speed. This technology is able to effectively answer the challenge of differentiating learning at the elementary level.

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