Supervision Techniques Research Articles

Academic Supervision at SMA Negeri 18 Palembang

Minister of National Education Regulation Number 13 of 2007 explains the ability of school principals to understand the Competency Standards of School Principals. The competence of the school principal based on the Minister of National Education Regulation consists of four components. The competency of the principal is an absolute ability to be mastered and practiced by the principal to lead and guide his subordinates within the scope of the school. One form of application is to carry out supervision competencies. This competency is a real action by the principal to improve the professionalism of teachers. The content of the activity is to approach using appropriate supervision techniques and then follow up. Looking at the results of the analysis of the supervision program in general at SMAN 18 Palembang, it should be followed up in order to improve the quality of educators such as: teachers have not developed and implemented an active learning process, teachers’ understanding of creative learning activities has not been maximized, and learning indicators and programs have not been developed holistically. It is an obligation for the principal to carry out comprehensive and systematic supervision and detail the opportunities for activities to be held along with the feedback from the supervision activities carried out. It is expected to boost the quality of learning for teachers and students in the classroom.

CorMatcher: A corners-guided graph neural network for local feature matching

Local feature matching is crucial for various computer vision tasks like Structure from Motion (SfM) and visual localization. In human visual matching, individuals typically scan image pairs to detect and match prominent corners, then refine matches by leveraging the geometric structure information from these corners. Inspired by this observation, we propose CorMatcher, a corners-guided detector-free network that investigates the guiding role of prominent corners in local feature matching. The concept of CorMatcher is utilizing the corners-images interaction module (CIIM) and deep supervision technique (DST) to replicate the aforementioned human behaviors, thus generating more human-intuitive features for accurate feature matching. CIIM imitates human behavior that first observes corners to guide the matching process, and then extend the matching space from corners to entire images. Technically, In particular, CIIM first integrates global information across all keypoints to ensure overall perception. Then, CIIM extracts reliable corners, performs feature enhancement among them, and propagates the enhanced features of corners to entire images. Finally, CIIM interacts features of all keypoints to ensure that the global information is comprehensively considered to establish final matches. In addition, we apply DST to the corners, rather than all keypoints, to supervise matching results after each feature enhancement process, thus simulating the human behavior that appraises matching results after each observation. Comprehensive experiments demonstrate that CorMatcher exhibits superior performance compared to several benchmarks.

UB-FineNet: Urban building fine-grained classification network for open-access satellite images

Fine classification of city-scale buildings using satellite imagery is a crucial research area with significant implications for urban planning, infrastructure development, and population distribution analysis. However, the task faces great challenges due to low-resolution overhead images acquired from high-altitude space-borne platforms and the long-tailed sample distribution of fine-grained urban building categories, leading to a severe class imbalance problem. To address these issues, we propose a deep network approach to the fine-grained classification of urban buildings using open-access satellite images. A Denoising Diffusion Probabilistic Model (DDPM) based super-resolution method is first introduced to enhance the spatial resolution of satellite images, which benefits from domain-adaptive knowledge distillation. Then, a new fine-grained classification network with Category Information Balancing Module (CIBM) and Contrastive Supervision (CS) technique is proposed to mitigate the problem of class imbalance and improve the classification robustness and accuracy. Experiments on Hong Kong data set with 11 distinct building types revealed promising classification results with a mean Top-1 accuracy of 60.45%, which is on par with street-view image based approaches. A comprehensive ablation study demonstrates that the CIBM and CS modules improve Top-1 accuracy by 2.6% and 3.5%, respectively, over the baseline approach. In addition, these modules can be easily integrated into other classification networks, achieving similar performance improvements. This research advances urban analysis by providing an effective solution for detailed classification of buildings in complex mega-city environments using only open-access satellite imagery. The proposed technique can serve as a valuable tool for urban planners, aiding in the understanding of economic, industrial, and population distribution within cities and regions, ultimately facilitating informed decision-making in urban development and infrastructure planning. Data and code will be publicly available at https://github.com/ZhiyiHe1997/UB-FineNet.

Peran Supervisi Pendidikan dalam Meningkatkan Kualitas Pembelajaran Pasca Pandemi Covid-19

The COVID-19 pandemic has brought major challenges to education systems around the world, forcing a sudden shift to online and hybrid learning. This research aims to describe the importance of educational supervision in improving the quality of learning after the Covid-19 pandemic. Educational supervision in improving the quality of learning is the focus of this research. The method used is library research with a qualitative approach by collecting data sourced from books, articles and published research results. The discussion of this research covers various topics, including the concept of educational supervision, the objectives of educational supervision, the implementation of educational supervision, approaches to educational supervision, principles of educational supervision, techniques of educational supervision in improving the quality of learning. The results of this research show that educational supervision Effective learning plays an important role in helping teachers adapt new technology, overcome learning gaps, and support the mental and emotional well-being of teachers and students as an effort to increase learning effectiveness. In addition, this research found that a holistic approach in supervision, which considers physical and emotional aspects , and social, are critical to creating a learning environment that is inclusive and supports students' holistic development.

Global explanation supervision for Graph Neural Networks.

With the increasing popularity of Graph Neural Networks (GNNs) for predictive tasks on graph structured data, research on their explainability is becoming more critical and achieving significant progress. Although many methods are proposed to explain the predictions of GNNs, their focus is mainly on "how to generate explanations." However, other important research questions like "whether the GNN explanations are inaccurate," "what if the explanations are inaccurate," and "how to adjust the model to generate more accurate explanations" have gained little attention. Our previous GNN Explanation Supervision (GNES) framework demonstrated effectiveness on improving the reasonability of the local explanation while still keep or even improve the backbone GNNs model performance. In many applications instead of per sample explanations, we need to find global explanations which are reasonable and faithful to the domain data. Simply learning to explain GNNs locally is not an optimal solution to a global understanding of the model. To improve the explainability power of the GNES framework, we propose the Global GNN Explanation Supervision (GGNES) technique which uses a basic trained GNN and a global extension of the loss function used in the GNES framework. This GNN creates local explanations which are fed to a Global Logic-based GNN Explainer, an existing technique that can learn the global Explanation in terms of a logic formula. These two frameworks are then trained iteratively to generate reasonable global explanations. Extensive experiments demonstrate the effectiveness of the proposed model on improving the global explanations while keeping the performance similar or even increase the model prediction power.

Automated monitoring innovations for efficient and safe construction practices

As construction projects increase in complexity, there are growing challenges with conventional monitoring methods in terms of efficiency, safety, and competitiveness. Traditional supervision techniques are labour-intensive, intermittent, and prone to errors. Hence, this study statistically evaluates the potential advantages of photogrammetry, sensors, and algorithms to enable continuous automated monitoring. The results of this survey were analysed to compare manual and automated monitoring systems. The outcome shows that the Malaysian construction industry is aware of Automated Monitoring Innovations for Efficient and Safe Construction Practices. The top ranked factor was Photogrammetry which had a relative importance index (RII) of 0.821 for straightforward site monitoring and 0.812 for accelerated 3D BIM modelling. The RII for sensors to track labourers, apparatus, and progress in real time was 0.82, while the RII for hazard anticipation was 0.796. Automation achieved a reduction in fatigue by 0.784, labour intensity by 0.792, and time demands by 0.768, as measured by the RII. A conceptual framework was developed that incorporates measurable improvements in schedules, safety, and quality control. Automated solutions, as opposed to human examinations which are prone to error, provided exhaustive geographical data and ongoing surveillance notwithstanding obstacles pertaining to cost, cybersecurity, privacy, and integration. Construction monitoring must incorporate new technology, strategic change management, data investments, and supporting regulations to increase profitability, safety, and efficiency as competition and complexity increase.

Fusing Conditional Submodular GAN and Programmatic Weak Supervision

Programmatic Weak Supervision (PWS) and generative models serve as crucial tools that enable researchers to maximize the utility of existing datasets without resorting to laborious data gathering and manual annotation processes. PWS uses various weak supervision techniques to estimate the underlying class labels of data, while generative models primarily concentrate on sampling from the underlying distribution of the given dataset. Although these methods have the potential to complement each other, they have mostly been studied independently. Recently, WSGAN proposed a mechanism to fuse these two models. Their approach utilizes the discrete latent factors of InfoGAN for the training of the label models and leverages the class-dependent information of the label models to generate images of specific classes. However, the disentangled latent factor learned by the InfoGAN may not necessarily be class specific and hence could potentially affect the label model's accuracy. Moreover, the prediction of the label model is often noisy in nature and can have a detrimental impact on the quality of images generated by GAN. In our work, we address these challenges by (i) implementing a noise-aware classifier using the pseudo labels generated by the label model, (ii) utilizing the prediction of the noise-aware classifier for training the label model as well as generation of class-conditioned images. Additionally, We also investigate the effect of training the classifier with a subset of the dataset within a defined uncertainty budget on pseudo labels. We accomplish this by formalizing the subset selection problem as submodular maximization with a knapsack constraint on the entropy of pseudo labels. We conduct experiments on multiple datasets and demonstrate the efficacy of our methods on several tasks vis-a-vis the current state-of-the-art methods. Our implementation is available at https://github.com/kyrs/subpws-gan

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Photovoltaic systems are prone to breaking down due to harsh conditions. To improve the reliability of these systems, diagnostic methods using Machine Learning (ML) have been developed. However, many publications only focus on specific AI models without disclosing the type of learning used. In this article, we propose a supervised learning algorithm that can detect and classify PV system defects. We delve into the world of supervised learning-based machine learning and its application in detecting and classifying defects in photovoltaic (PV) systems. We explore the various types of faults that can occur in a PV system and provide a concise overview of the most commonly used machine learning and supervised learning techniques in diagnosing such systems. Additionally, we introduce a novel classifier known as Extra Trees or Extremely Randomized Trees as a speedy diagnostic approach for PV systems. Although this algorithm has not yet been explored in the realm of fault detection and classification for photovoltaic installations, it is highly recommended due to its remarkable precision, minimal variance, and efficient processing. The purpose of this article is to assist technicians, engineers, and researchers in identifying typical faults that are responsible for PV system failures, as well as creating effective control and supervision techniques that can minimize breakdowns and ensure the longevity of installed systems.

STRATEGI SUPERVISI KEPALA SEKOLAH DALAM MENINGKATKAN KINERJA GURU PENDIDIKAN AGAMA ISLAM

One of the programs that can be held to improve the quality of learning is the implementation of assistance to teachers or better known as supervision. The Principal as an educational leader in the school has a duty in the field of supervision. and the purpose of this study is to determine the Principal's Supervision Strategy in improving the performance of Islamic Education teachers at SDN 08 Indralaya.
 The type of research used in conducting this research is a type of field research, which is a research process that produces qualitative descriptive data. Data collection techniques with observation, interviews, and documentation. While data analysis techniques through data reduction, data presentation, conclusion drawing. As well as data validity techniques by triangulating sources, techniques and time.
 The results of the study, namely, the Principal's Supervision Strategy in Improving the Performance of Islamic Education Teachers at SDN 08 Indralaya is good and in accordance with academic supervision techniques starting from planning the preparation of academic supervision programs and preparing schedules for the implementation of academic supervision. The preparation of this program was carried out at the beginning of the year and assisted by several senior teachers. Furthermore, the implementation of academic supervision using individual supervision techniques through class visits, class observations and group supervision techniques such as meetings and group discussions to supervise teacher performance at SDN 08 Indralaya and the last stage is the evaluation of academic supervision carried out at the end of each semester with the aim of measuring the success rate of a strategy or academic supervision technique carried out.

Robust explanation supervision for false positive reduction in pulmonary nodule detection.

Lung cancer is the deadliest and second most common cancer in the United States due to the lack of symptoms for early diagnosis. Pulmonary nodules are small abnormal regions that can be potentially correlated to the occurrence of lung cancer. Early detection of these nodules is critical because it can significantly improve the patient's survival rates. Thoracic thin-sliced computed tomography (CT) scanning has emerged as a widely used method for diagnosing and prognosis lung abnormalities. The standard clinical workflow of detecting pulmonary nodules relies on radiologists to analyze CT images to assess the risk factors of cancerous nodules. However, this approach can be error-prone due to the various nodule formation causes, such as pollutants and infections. Deep learning (DL) algorithms have recently demonstrated remarkable success in medical image classification and segmentation. As an ever more important assistant to radiologists in nodule detection, it is imperative ensure the DL algorithm and radiologist to better understand the decisions from each other. This study aims to develop a framework integrating explainable AI methods to achieve accurate pulmonary nodule detection. A robust and explainable detection (RXD) framework is proposed, focusing on reducing false positives in pulmonary nodule detection. Its implementation is based on an explanation supervision method, which uses nodule contours of radiologists as supervision signals to force the model to learn nodule morphologies, enabling improved learning ability on small dataset, and enable small dataset learning ability. In addition, two imputation methods are applied to the nodule region annotations to reduce the noise within human annotations and allow the model to have robust attributions that meet human expectations. The 480, 265, and 265 CT image sets from the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset are used for training, validation, and testing. Using only 10, 30, 50, and 100 training samples sequentially, our method constantly improves the classification performance and explanation quality of baseline in terms of Area Under the Curve (AUC) and Intersection over Union (IoU). In particular, our framework with a learnable imputation kernel improves IoU from baseline by 24.0% to 80.0%. A pre-defined Gaussian imputation kernel achieves an even greater improvement, from 38.4% to 118.8% from baseline. Compared to the baseline trained on 100 samples, our method shows less drop in AUC when trained on fewer samples. A comprehensive comparison of interpretability shows that our method aligns better with expert opinions. A pulmonary nodule detection framework was demonstrated using public thoracic CT image datasets. The framework integrates the robust explanation supervision (RES) technique to ensure the performance of nodule classification and morphology. The method can reduce the workload of radiologists and enable them to focus on the diagnosis and prognosis of the potential cancerous pulmonary nodules at the early stage to improve the outcomes for lung cancer patients.

Remote sensing based forest cover classification using machine learning

Pakistan falls significantly below the recommended forest coverage level of 20 to 30 percent of total area, with less than 6 percent of its land under forest cover. This deficiency is primarily attributed to illicit deforestation for wood and charcoal, coupled with a failure to embrace advanced techniques for forest estimation, monitoring, and supervision. Remote sensing techniques leveraging Sentinel-2 satellite images were employed. Both single-layer stacked images and temporal layer stacked images from various dates were utilized for forest classification. The application of an artificial neural network (ANN) supervised classification algorithm yielded notable results. Using a single-layer stacked image from Sentinel-2, an impressive 91.37% training overall accuracy and 0.865 kappa coefficient were achieved, along with 93.77% testing overall accuracy and a 0.902 kappa coefficient. Furthermore, the temporal layer stacked image approach demonstrated even better results. This method yielded 98.07% overall training accuracy, 97.75% overall testing accuracy, and kappa coefficients of 0.970 and 0.965, respectively. The random forest (RF) algorithm, when applied, achieved 99.12% overall training accuracy, 92.90% testing accuracy, and kappa coefficients of 0.986 and 0.882. Notably, with the temporal layer stacked image of the Sentinel-2 satellite, the RF algorithm reached exceptional performance with 99.79% training accuracy, 96.98% validation accuracy, and kappa coefficients of 0.996 and 0.954. In terms of forest cover estimation, the ANN algorithm identified 31.07% total forest coverage in the District Abbottabad region. In comparison, the RF algorithm recorded a slightly higher 31.17% of the total forested area. This research highlights the potential of advanced remote sensing techniques and machine learning algorithms in improving forest cover assessment and monitoring strategies.

Progress and clinical translation in hepatocellular carcinoma of deep learning in hepatic vascular segmentation.

This paper reviews the advancements in deep learning for hepatic vascular segmentation and its clinical implications in the holistic management of hepatocellular carcinoma (HCC). The key to the diagnosis and treatment of HCC lies in imaging examinations, with the challenge in liver surgery being the precise assessment of Hepatic vasculature. In this regard, deep learning methods, including convolutional neural networksamong various other approaches, have significantly improved accuracy and speed. The review synthesizes findings from 30 studies, covering aspects such as network architectures, applications, supervision techniques, evaluation metrics, and motivations. Furthermore, we also examine the challenges and future prospects of deep learning technologies in enhancing the comprehensive diagnosis and treatment of HCC, discussing anticipated breakthroughs that could transform patient management. By combining clinical needs with technological advancements, deep learning is expected to make greater breakthroughs in the field of hepatic vascular segmentation, thereby providing stronger support for the diagnosis and treatment of HCC.

STS-TransUNet: Semi-supervised Tooth Segmentation Transformer U-Net for dental panoramic image.

In this paper, we introduce a novel deep learning method for dental panoramic image segmentation, which is crucial in oral medicine and orthodontics for accurate diagnosis and treatment planning. Traditional methods often fail to effectively combine global and local context, and struggle with unlabeled data, limiting performance in varied clinical settings. We address these issues with an advanced TransUNet architecture, enhancing feature retention and utilization by connecting the input and output layers directly. Our architecture further employs spatial and channel attention mechanisms in the decoder segments for targeted region focus, and deep supervision techniques to overcome the vanishing gradient problem for more efficient training. Additionally, our network includes a self-learning algorithm using unlabeled data, boosting generalization capabilities. Named the Semi-supervised Tooth Segmentation Transformer U-Net (STS-TransUNet), our method demonstrated superior performance on the MICCAI STS-2D dataset, proving its effectiveness and robustness in tooth segmentation tasks.

Academic Supervision Strategies in Problems Solving Teacher Performance On Language Learning

The purpose of this research is to determine academic supervision strategies in problems solving teacher performance on language learning. The research approach used is qualitative research. The research subjects were madrasah supervisors, madrasah heads, and teachers. The research was carried out on the supervision activities of madrasa supervisors at MI Darussalam Sambiroto Baron Nganjuk. The research results show that problems with teacher performance on language learning include low teacher professionalism due to the mismatch between teachers' academic qualifications and their professional duties. There are teachers whose academic qualifications are non-Madrasah Ibtidaiyah Teacher Education and are forced to become class teachers and teach general subjects. This condition results in less effective learning management and teachers experience difficulties in planning, implementing and assessing of language learning. Efforts to solve teacher performance problems on language learning are carried out by supervision with coaching and mentoring by the MI head or madrasah supervisor. The academic supervision strategies that has been implemented is class visits and regular teacher meetings. Apart from that, MI heads or madrasah supervisors can apply clinical supervision techniques to solve teacher performance problems on language learning.

Supervisi Penerapan Metode Ummi dalam Meningkatkan Kemampuan Membaca dan Menghafal Al-Qurân

Implementing a learning method requires guidance and supervision. In many cases, teachers are often required to implement a new method without any guidance and supervision. There is one application of learning methods that provides guidance and supervision, namely the application of the Ummi learning method in improving the ability to read and memorize the Al-Qur'an. This is worth studying. For this reason, this research is aimed at revealing how to implement supervision activities by focusing on three things, namely: steps, techniques and factors inhibiting and supporting these supervision activities. The research was carried out in two locations: SMPN 1 Sampang and SDN 1 Dalpenang Sampang. This research uses a qualitative approach with data collection techniques: non-participant observation, in-depth interviews, and documentation of related sources. The research results obtained are first, the stages of implementing supervision in both institutions include daily, weekly and monthly supervision. Second, the technique for implementing Ummi method supervision in both institutions is in the form of individual and group techniques. Individual supervision techniques include class visits, individual meetings and document observation, while group techniques include school visits, evaluation meetings and teaching demonstrations. Third, the supporting factors for implementing the Ummi method of supervision in both institutions are support from school principals and parents who encourage supervisors to continue to supervise, supervisors' activeness and teacher welfare as well as adequate facilities. The inhibiting factors are the lack of supervision time and the lack of supervisors which is not commensurate with the number of existing groups. The results of the research have implications for supervisors being able to emphasize the objectives of supervision, so that supervision activities are not understood as a form of supervision but as mentoring and guidance activities.

Supervisory Alliance as a Moderator of the Effects of Behavioral Rehearsal on TF-CBT Fidelity: Results from a Randomized Trial of Supervision Strategies.

Exposure is an important element of treatment for many evidence-based treatments but can be challenging to implement. Supervision strategies to support exposure delivery may be an important tool to facilitate the use of exposure techniques; however, they must be considered and used in the context of the supervisory alliance. The present study examined relations between supervisory alliance and fidelity to the trauma narrative (TN; i.e., imaginal exposure) component of Trauma-Focused Cognitive Behavioral Therapy. We also examined how supervisory alliance moderated the effect of behavioral rehearsal use in supervision on TN fidelity. We analyzed data from a randomized controlled trial, in which forty-two supervisors and their clinicians (N = 124) from 28 Washington State community-based mental health offices participated. Clinicians were randomized to receive one of two supervision conditions-symptom and fidelity monitoring (SFM) or SFM with behavioral rehearsal (SFM + BR). Supervisory alliance alone did not predict delivery (i.e., occurrence) or extensiveness of delivery of the trauma narrative. Client-focused supervisory alliance moderated the effectiveness of behavioral rehearsal-as client-focused alliance increased, the odds of delivering the TN also increased significantly. Future research should further investigate how to appropriately match supervision techniques with supervisory dyads and explore the interplay of alliance with supervision techniques a supervisor might employ.

Supervisi Dalam Pendidikan

Education is one of the important pillars in the development of society and the country. To ensure a quality learning experience, educational supervision becomes an inevitable component in the education system. This article outlines various aspects of educational supervision, including the types, techniques, types, and processes involved in the process of supervising and improving educational practice. The approach in this article uses a qualitative approach with literature study data collection techniques. The type of supervision based on the supervisor's work attitude is supervision that is corrective, prefentive, constructive, creative. Educational supervision techniques are carried out individually and in groups. The types of educational supervision that can be used are autocratic, democratic, laisses faire, coercive, and training and guidance. The educational supervision process includes the planning, implementation, evaluation, and follow-up stages.

A Qualitative Exploration of Shifts to Community Supervision Work During the COVID-19 Pandemic

With the onset of the COVID-19 pandemic, community supervision agencies were forced to incorporate drastic changes to traditional supervision techniques, which shifted the daily roles of probation officers (POs). Research on the pandemic identifies rapid adaptations to supervision strategies and increased demands on POs during the pandemic. Using qualitative data from interviews with POs across the United States, the goal of this study was to examine the experiences of POs working during the COVID-19 pandemic. Results suggest POs had to alter their supervision approach, both physically and ideologically. POs discussed shifts to both remote supervision in place of in-person work coupled with a need to use an “agent of change” approach given limited access to formal, punitive responses to noncompliance, with a priority on the highest risk/need clients. Findings suggest several implications for practice, including factors to consider regarding community correction reform efforts in a post-pandemic world.

Distant Supervision-based Relation Extraction for Literature-Related Biomedical Knowledge Graph Construction

Background: The task of relation extraction is a crucial component in the construction of a knowledge graph. However, it often necessitates a significant amount of manual annotation, which can be time-consuming and expensive. Distant supervision, as a technique, seeks to mitigate this challenge by generating a large volume of pseudo-training data at a minimal cost, achieved by mapping triple facts onto the raw text. Objective: The aim of this study is to explore the novelty and potential of the distant supervisionbased relation extraction approach. By leveraging this innovative method, we aim to enhance knowledge reliability and facilitate new knowledge discovery, establishing associations between knowledge from specific biomedical data or existing knowledge graphs and literature. Methods: This study presents a methodology to construct a biomedical knowledge graph employing distant supervision techniques. Through establishing links between knowledge entities and relevant literature sources, we methodically extract and integrate information, thereby expanding and enriching the knowledge graph. This study identified five types of biomedical entities (e.g., diseases, symptoms and genes) and four kinds of relationships. These were linked to PubMed literature and divided into training and testing datasets. To mitigate data noise, the training set underwent preprocessing, while the testing set was manually curated. Results: In our research, we successfully associated 230,698 triples from the existing knowledge graph with relevant literature. Furthermore, we identified additional 205,148 new triples directly sourced from these studies. Conclusion: Our study markedly advances the field of biomedical knowledge graph enrichment, particularly in the context of Traditional Chinese Medicine (TCM). By validating a substantial number of triples through literature associations and uncovering over 200,000 new triples, we have made a significant stride in promoting the development of evidence-based medicine in TCM. The results underscore the potential of using a distant supervision-based relation extraction approach to both validate and expand knowledge bases, contributing to the broader progression of evidence-based practices in the realm of TCM.

Supervisi Sebagai Upaya Pengawas Untuk Meningkatkan Kinerja Guru Dalam Implementasi Kurikulum Merdeka

The quality of education is a key aspect in the development of a nation. In Indonesia, the implementation of the "Kurikulum Merdeka" in 2020 is a significant step in keeping up with global developments. Amidst this change, teachers play a central role in delivering this curriculum. They need to adapt, integrate technology, and understand the individual needs of students. Educational supervision becomes a crucial tool for enhancing teacher performance, especially in the implementation of "Kurikulum Merdeka." Educational supervision assists supervisors and teachers in collaborating, providing feedback, and identifying areas for improvement. The implementation of "Kurikulum Merdeka" requires oversight that involves various aspects such as teaching, assessment, and the use of information technology. This research uses a qualitative descriptive analysis approach and focuses on how academic supervision contributes to improving teacher performance in the implementation of "Kurikulum Merdeka." The study employs a literature review approach, utilizing previous research as a data source. The research results indicate that intensive and continuous academic supervision, coupled with teacher mentoring, can enhance teacher performance in addressing the challenges of "Kurikulum Merdeka." As a recommendation, there is a need for increased supervision intensity, the development of supervision techniques, and training in differentiated teaching to support teacher performance in implementing "Kurikulum Merdeka." This way, the quality of education in Indonesia can continue to improve.