Validation Data Research Articles

Analisis Penerapan Marketing Mix Syariah Dalam Akad Rahn Tasjily Untuk Meningkatkan Jumlah Anggota Baitul Maal Wat Tamwil (Studi Kasus di BMT UGT Nusantara Cabang Pembantu Kencong)

This research aims to explore more deeply the suitability of the application of the Rahn Tasjily concept in the BMT UGTN Capem Kencong with the DSN-MUI Fatwa and explore and analyze the application of Sharia Marketing Mix in the Rahn Tasjily contract in an effort to increase the number of members in the BMT UGTN Kencong sub-branch. This type of research uses a descriptive qualitative approach. Researchers used primary data obtained through interviews, observation and documentation. Data validity analysis uses triangulation techniques. The results of the research concluded that the concept of implementing the Rahn Tasjily contract at the BMT UGT Nusantara Kencong sub-branch was in accordance with the DSN-MUI fatwa Number 68/DSN-MUI/III/2008. BMT has actually implemented indicators in the 7P sharia marketing mix which consists of Product, Price, Place, Promotion, Process, People and Physical Evidence thus increasing Rahn Tasjily's membership in 2021 to around 529 members, while in 2022 it will be 628 members. There are also findings that BMT applies the Customer Centrism theory (consumer-oriented), which means that activities prioritize customers so that members become loyal to BMT.

COLLABORATIVE BASED TOURISM GOVERNANCE MODEL IN SUSTAINABLE TOURISM DEVELOPMENT

Tourism has great potential to improve the economy, create jobs, and support the development of various regions. However, improper tourism management can cause problems such as environmental degradation, excessive tourism and unequal economic benefits. Therefore, it is essential that collaborative-based tourism management can be effective in developing sustainable tourism. This research aims to identify collaborative-based tourism governance research in tourism development. The output of this research is to provide recommendations for a collaborative-based tourism governance model to impact tourism development in the Tenjolaya sub-district, Bogor Regency. The research method used was descriptive qualitative with 28 informants from the sub-district government and the Tenjolaya tourism awareness group. After the data was collected using data collection techniques through observation, interviews, field notes, analytical memos, and document elicitation, the researcher continued the analysis of the data obtained and then confirmed it using data validity techniques. The research results show that tourism governance in Tenjolaya has implemented collaborative governance even though it is still local, so every indicator still needs to be optimally fulfilled using the good tourism governance model and the public-private partnership model. Thus, tourism governance in Tenjolaya must focus on good tourism collaboration so that tourism goals in Tenjolaya can be achieved based on fulfilling indicators that support good tourism governance, good tourism development and good tourism sustainability.

Network Pharmacology to Reveal the Mechanism of Fufang Banmao Capsule for Treating Unresectable Primary Liver Cancer and Clinical Data Validation.

Fufang Banmao capsule (FFBM), a traditional Chinese medicine, has been used to treat primary liver cancer (PLC) for several years. However, the bioactive ingredients, and mechanism of FFBM for treating PLC remains unclear. Our objective is to utilize network pharmacology to investigate these aspects and subsequently validate their effectiveness through clinical data. The FFBM ingredients were obtained from the HERB database and screened for bioactive ingredients using the SwissTargetPrediction database. The PharmMapper and GEO database were used to acquire targets and differentially expressed genes (DEGs) for FFBM and PLC, respectively. Common targets were identified using Venn diagrams, followed by enrichment and protein-protein interaction (PPI) analysis. Furthermore, the Cytoscape software was utilized to identify Hub genes and construct the ingredienttarget- pathway network. Subsequently, patients diagnosed with unresectable PLC who underwent transcatheter arterial chemoembolization (TACE) at our hospital between January 2008 and December 2019 were retrospectively collected. Finally, Cox analysis was conducted to reveal the role of FFBM in the treatment of unresectable PLC. FFBM had 232 targets, and PLC had 1582 DEGs. HSP90AA1 and SRC were identified as crucial targets. Alpha-santalol, glycyrrhizin, and morroniside were identified as the top three bioactive ingredients. Enrichment analysis revealed a significant connection between FFBM utilization for treating PLC and multiple pathways, such as chemical carcinogenesis, PI3K-AKT, Rap1, FoxO, MAPK, and VEGF pathway. Clinical data revealed that consuming FFBM significantly improved the prognosis of unresectable PLC with a hazard ratio of 0.69. Our study identified the bioactive ingredients of FFBM and its potential mechanisms for treating PLC. Additionally, we validated the effectiveness through clinical data.

Analisis Pembiayaan Murabahah Pada Baitul Maal Wat Tamwil Dalam Proses Jual Beli Usaha Mikro, Kecil, Dan Menengah (Studi Kasus Pada Bmt Usaha Gabungan Terpadu Nusantara Capem Kencong)

This research aims to determine the impact of implementing Murabahah financing in the buying and selling process for Micro, Small and Medium Enterprises (MSMEs) at BMT UGT Nusantara. This research uses a descriptive qualitative approach with the type of field research. The location that is the object of this research is BMT UGT Nusantara Capem Kencong. Researchers used primary data obtained through interviews, observation and documentation. Data validity analysis uses triangulation techniques. The results of the research conclude that Murabahah financing at BMT UGT Nusantara Capem Kencong has a good impact on the economy, especially on the surrounding MSMEs, which can be proven by several developments that have occurred in MSMEs through indicators including increasing income, increasing trading facilities (ruko), and increasing the number of buyer

Deep Learning Models for Dental Conditions Classification Using Intraoral Images

This paper presents the digitalization of dentistry medical records to support the dentist in the patient examination process. A dentist uses manual input to fill out the evaluation form by drawing and labeling each patient’s tooth condition based on their observations. Consequently, it takes too long to finish only one examination. For time efficiency, using AI-based digitalization technology can be a promising solution. To address the problem, we made and compared several classification models to recognize human dental conditions to help doctors analyze patient teeth. We apply the YOLOv5, MobileNet V2, and IONet (proposed CNN model) as deep learning models to recognize the five common human dental conditions: normal, filling, caries, gangrene radix, and impaction. We tested the ability of YOLO classification as an object detection model and compared it with classification models. We used a dataset of 3.708 intraoral dental images generated by various augmentation methods from 1.767 original images. We collected and annotated the dataset with the help of dentists. Furthermore, the dataset is divided into three parts: 90% of the total dataset is used as training and validation data, then divided again into 80% training data and 20% validation data. 10% of the total dataset will be used as testing data to compare classification performance. Based on our experiments, YOLOv5, as an object detection model, can classify dental conditions in humans better than the classification model. YOLOv5 produces an 82% accuracy testing value and performs better than the classification model. MobileNet V2 and IONet only get 80% and 70% testing accuracy. Although statistically, there is not much of a difference between the test accuracy values for YOLOv5 and MobileNet v2, the speed in classifying dental objects using YOLOv5 is more efficient, considering that YOLOv5 is an object detection model. There are still challenges with the deep learning technique used in this research, but these can be addressed in further development. A more complex model and the enlargement of more data, ensuring it is varied and balanced, can be used to address the limitations.

EAR DISEASE CLASIFICATION USING DEEP LEARNING WITH XCEPTION AND MOBILENET-V2 ARCHITECTURE

Hearing loss is a significant global health problem, with a high prevalence in Indonesia. Limited access to ENT specialists, especially in remote areas, causes delays in diagnosis and treatment of ear diseases. This research aims to develop an early diagnosis system for ear diseases using deep learning. The proposed method applies Xception and MobileNet-V2 Convolutional Neural Network (CNN) architecture with hyperparameter optimization using Bayesian Optimizer. The dataset consists of 1,101 images covering 20 types of ear diseases, collected using an endoscope ear cleaning kit at Mataram University Hospital. The dataset was divided into 60% training data, 20% validation data, and 20% test data. Xception recorded the best performance with accuracy, precision, recall, and f1-score of 0.911, 0.166, 0.166, and 0.151, respectively. The best model performance was obtained on MobileNet-V2 with the application of Bayesian Optimizer, resulting in the best hyperparameters at Unit Dense 174, Dropout Rate 0.2, and LXceptionearning Rate 0.003. This scenario resulted in an increase in accuracy, precision, recall, and f1-score compared to the scenario without hyperparameter search of 0.004, 0.010, 0.018, and 0.012, respectively. This research demonstrates the potential of deep learning in improving early diagnosis of ear diseases.

Urban Traffic Identification by Comparing Machine Learning Algorithms

The Internet of Things (IoT) applied to intelligent transport systems has become a key element for understanding the way traffic flow behaves in cities, which helps in decision-making to improve the management of the transport system by monitoring and analyzing network traffic in real time, all with the aim of daily benefiting users of the city’s road infrastructure. Traffic volume estimation in real time, with high effectiveness, may help mobility management and improve traffic flow. Moreover, machine-learning algorithms have shown effectiveness in various scientific fields and have provided a significant platform for achieving intelligent applications. Therefore, we applied various machine learning algorithms to classify the vehicular traffic status in the traffic network of two cities with more than 2 million inhabitants. It was first necessary to establish, from the attributes provided by the datasets, the object class from the LOS (Level of Services) thresholds proposed by the National Academies of Sciences, Engineering, and Medicine, for the basic segments of highways in an urban area. We then selected the attributes of interest using the Recursive Feature Elimination Method (RFE) to reduce the dimensionality of the data, and applied the DT, RF, ET, KNN, and MLP algorithms to train and classify the level of vehicular congestion, defining various volumes of training and validation data. The results show the high effectiveness of the algorithms, highlighting the MLP algorithm as the one that provides the highest effectiveness on average for the evaluated datasets, with a mean precision of 99.5%.

Annual Variations of PM10, PM2.5 and PM1.0 Fraction at an Urban Site in Ansan, Korea

This study examines particulate matter (PM) concentrations, specifically PM10, PM2.5, and PM1.0, at an urban site in Ansan, South Korea. Three BAM-1020 continuous monitors measured PM10, PM2.5, and PM1.0 hourly from January to December 2021, with data acquisition rates of 97.1%, 97.3%, and 93.4%, respectively. Average annual concentrations were 45.7±48.4 µg/m³ (PM10), 26.4±22.7 µg/m³ (PM2.5), and 18.5±14.5 µg/m³ (PM1.0). PM10 was 91% of the annual air quality standard, while PM2.5 exceeded this standard by 176%, indicating severe pollution. Statistical analyses using probability density function (PDF) and cumulative distribution function (CDF) showed 98.5% of data converged at 150 µg/m³ (PM10), 75 µg/m³ (PM2.5), and 50 µg/m³ (PM1.0), with the remaining 1.5% corresponding to approximately 131 hours, annually. In 2021, all six observed yellow dust events occurred in spring (March to May). PM10, PM2.5, and PM1.0 concentrations increased from January to March, peaked in March, decreased until September, and rose again. The coarse fraction ratio increased stepwise from February to May, likely due to yellow dust and episodes of high PM concentration in the spring. The PM1.0 concentration was higher in summer and fall compared with that in spring and winter. The PM1.0/PM2.5 ratio was highest in summer and fall even though the annual PM1.0concentration was lowest, suggesting a higher secondary formation rate of fine particles during these periods. This underscores the need for further scientific investigation on PM1.0. The study provides insights into the simultaneous observation and distribution characteristics of PM10, PM2.5, and PM1.0, highlighting the mass concentration, distribution, and ratio of PM1.0. These findings are crucial for understanding PM distribution in Ansan, an industrial complex, and can serve as essential data for chemical composition comparisons, data validation, public health strategies, and cost-benefit analyses for regional PM improvement.

Investigation of wettability and IFT alteration during hydrogen storage using machine learning

Reducing the environmental impact caused by the production or use of carbon dioxide (CO2) and other greenhouse gases (GHG) has recently attracted the attention of scientific, research, and industrial communities. In this context, oil production and enhanced oil recovery (EOR) have also focused on using environmentally friendly methods. CO2 has been studied as a significant gas in reducing harmful environmental effects and preventing its release into the atmosphere. This gas, along with methane (CH4) and nitrogen (N2), is recognized as a ‘cushion gas’. Given that hydrogen (H2) is considered a green and environmentally friendly gas, its storage for altering wettability (contact angle (CA) and interfacial tension (IFT)) has recently become an intriguing topic. This study examines how H2 can be utilized as a novel cushion gas in EOR systems. In this research, the role of H2 and its storage in altering wettability in the presence of other cushion gases has been investigated. The performance of H2 in changing the CA and IFT with other gases has also been compared using machine learning (ML) models. During this process, ML and experimental data were used to predict and report the values of IFT and CA. The data used underwent statistical and quantitative preprocessing, processing, evaluation, and validation, with outliers and skewed data removed. Subsequently, ML models such as Random Forest (RF), Random Tree, and LSBoost were implemented on training and testing data. During this process of modeling and predicting IFT and CA, the hyperparameters were optimized using Bayesian algorithms and random search (RS) methods. Finally, the results and performance of the modeling were evaluated, with the LSBoost modeling method using Bayesian optimization reporting R2 values of 0.998614 for IFT and 0.986999 for CA.

THE INFLUENCE OF ARTIFICIAL INTELLIGENCE IN THE MEDIA INDUSTRY IN INDONESIA

The influence and use of Artificial Intelligence (AI) in the mass media industry in Indonesia has begun to be applied. Both online media and television. In fact, mass media in Indonesia also produce content in the form of audio, video, text and presenters using AI. The use of this technology does make it easier for media workers. However, it has various shortcomings and problems that arise as a logical consequence of the presence of AI technology. This research was conducted using a qualitative approach with a descriptive analysis style. The results show that AI technology has shortcomings in terms of accuracy, verification, and validation of data collected from the digital universe. On the other hand, it has no legal justification in Indonesia. For this reason, special regulations for the use of AI in the Indonesian mass media are needed. Thus, the public as the main recipient of information is not harmed and maintains the image of the media industry as the most trusted institution in the country.

Upper limb movement control strategy of top-tier elite male badminton players when smashing for different distances

PurposeMastering the skill of smashing in a game can be quite challenging. Unfortunately, it’s not always easy to control the distance of the smash. The present study investigated the impact of different smashing distances on muscle activation patterns and kinematics of the upper limbs.Material and methodsTen collegiate athletes from a top-ranked team in Korea participated in the present study with a mean age, weight, and height of 21 ± 1 years, 71.9 ± 9.1 kg, and 1.78 ± 2.24 m, respectively. Three valid data points for each subject at three different distances were analyzed using a one-way analysis of variance (ANOVA).ResultsThe results demonstrated that with increasing smashing distance, the internal rotation angle of the elbow and wrist joints decreased, while the angular velocity of flexion and internal rotation of the shoulder, elbow, and wrist joints increased. Muscle activation increased for all muscles except the biceps brachii (BICLO) with increasing distance. The greatest changes in activation levels were observed in the anterior deltoid (DELTA), extensor carpi radialis (ECR), and flexor carpi ulnaris (FCU).ConclusionsDuring tasks with different smashing distances, muscle activation in the upper limbs (except the biceps) increased with increasing distances. Additionally, the internal rotation angle of the upper limbs decreased with increased distances, while the angular velocity of flexion and internal rotation of the shoulder, elbow, and wrist joints increased.

Validated patient reported outcome measures in speech-language pathology: A scoping review of adult practice

Purpose Patient reported outcome measures (PROMs) are increasingly used to inform value-based healthcare. Within speech-language pathology (SLP), there is no synthesis of validated PROMs to guide professional practice. This scoping review systematically identifies and evaluates condition-specific PROMs across adult SLP practice. Method A literature search was performed to identify studies published until 18th February 2022 from MEDLINE, Embase, CINAHL, PsycInfo, Scopus, Cochrane Collaboration, and Web of Science. s and full texts were screened in Covidence. Relevant studies that validated PROMs in English were extracted and assessed using the “Checklist to operationalise measurement characteristics of PROMs” by two independent reviewers. Result Ninety-seven articles provided validation data for 71 PROMS across seven SLP practice areas. These included voice (n = 18), swallowing (n = 14), language (n = 11), fluency (n = 8), speech (n = 4), laryngectomy (n = 3), and cognitive-communication (n = 2). No PROMs were identified for augmentative and alternative communication (AAC) (n = 0). Quality ratings were variable on the Francis et al. checklist. Conclusion A range of validated PROMs were identified to guide SLP practice in measuring patient perceptions across a range of practice areas in adults. Opportunities for further development in SLP practice areas with limited PROMs, such as speech, cognitive-communication, and AAC are also highlighted.

Development of an Inconsistent Responding Scale for the Big Five Inventory-2

The Big Five Inventory − 2 (BFI-2) is a commonly used self-report assessment of normal personality trait domains (Extraversion, Agreeableness, Conscientiousness, Negative Emotionality, Open-Mindedness) and facets. To date, however, no direct measures of response distortion have been developed for it to identify potentially invalid responses. Such distortions (e.g., careless or random responding) can adversely impact data quality. The current study developed and provided initial validation data for an inconsistent responding scale within the BFI-2 to identify careless responders using two large undergraduate samples and a community sample. To create the scale, we first identified highly correlated BFI-2 item pairs in one undergraduate sample (N = 1,461) and then computed a total score by summing the absolute differences of these item pairs. This scale, the Detection of Response Inconsistency Procedure (DRIP), differentiated randomly generated and genuine data and generally correlated as expected with personality domains and other inconsistent responding scales across samples. The DRIP also incrementally predicted random data beyond a composite of items with exceptionally high or low base rates of endorsement from the Comprehensive Infrequency/Frequency Item Repository. We provide recommendations for DRIP cut scores that can detect careless responding while balancing sensitivity and specificity.

Application of Bivariate Statistical Method for Landslide Susceptibility Assessment along Srinagar-Bandipora Highway, North Kashmir Himalaya

The present study aims to create and evaluate a map indicating the potential of landslides along the Srinagar-Bandipora highway in the North Kashmir Himalaya region. Therefore, the study utilized the Bivariate Statistical Relative effect (RE) method to establish the correlation between casual factors (CFs) contributing to landslides and the actual landslide incidences. Additionally, fifteen different CFs were analyzed within a Geographic Information System (GIS) framework to assess the impact of these factors on landslides. Further, a comprehensive GIS-based inventory map was compiled, incorporating both primary and secondary data sources, to identify landslide locations. A landslide susceptibility zonation (LSZ) map was developed by integrating all identified landslide CFs along with their respective effect function values. Moreover, the results revealed that 21.06% of the study area was classified as having a very high to high susceptibility to landslides. To validate the accuracy of the produced map, 30% of landslide validation data was employed and the receiver operating characteristics (ROC) curve method was utilized, yielding an excellent result with an area under curve (AUC) value of 0.919 for the study area. Consequently, the findings of this study hold significant potential for mitigating landslide-related hazards and guiding land use planning projects.

The Influence of Social Influence and Perceived Usefulness on Intention to Spread E-WOM Mediated by Attitude

This study aims to analyze the influence of Social Influence, Hedonic, Perceived Usefulness, Perceived Easy to Use, and Attitude on E-WOM. A total of 238 valid data have been obtained to conduct research. The data obtained will be analyzed using the SmartPLS data analysis method to test direct and indirect relationships between variables. The results of this study show that Social Influence, Hedonic, Perceived Usefulness, and Perceived Easy to Use have a significant effect on buyer Attitude. Attitude, Hedonic, and Perceived Easy to Use have a significant effect on E-WOM. These findings provide a reference that goods sold in E-commerce or physical stores need to pay attention to Social Influence, Hedonic, Perceived Usefulness, Perceived Easy to Use, and Attitude in order to create E-WOM.

Gravimetric and SIM-Headspace GC-MS for Residual Organic Solvents Detection in Halal and Wholesomeness Food Analysis

The demand for halal foods and beverages is increasing globally. While most halal analysis focuses on porcine, this study focuses on assessing residual organic solvents to ensure their halal compliance and wholesomeness, following several Malaysian standards and guidelines. A significant challenge in this study was the volatility of the residual solvents during the preparation of standards and quality control. To address this issue, a gravimetric technique was employed and effectively minimized the difference between theoretical (1,000 ppm) and actual (710 – 892 ppm) concentrations of the residual organic standard stock solution, except for acetone (588 ppm). The aim of this study was to establish a validated, reliable, and accurate method using SIM-headspace GC-MS to identify and quantify residual organic solvents for halal and wholesomeness analysis. Confirmation of each residual organic solvent was achieved by comparing the obtained spectra with the NIST 11 spectral database, containing 70,832 compounds, with similarity ranging from 80.9% to 96.6%, except for acetonitrile at 52.2%. The validation parameters were carried out according to ISO 17025:2017, the Center for Drug Evaluation and Research, and the European Guidelines. The parameters included recovery ranging from 95.65% to 95.68%, precision from 10.08% to 19.65% RSD, linearity between 0.996 to 0.999, limit of detection from 0.01 to 0.08 ppm, and limit of quantification from 0.02 to 0.24 ppm. Uncertainty considerations were limited to recovery, precision, and linearity, as other uncertainties were negligible based on the bottom-up approach using in-house validation data. This combination of gravimetric and SIM-headspace GC-MS techniques has provided valuable insights for discussions and collaborations among halal authorities worldwide to establish a consensus analytical methodology for halal and wholesomeness assessment.

Machine Learning Algorithm to Predict Atrial Fibrillation Using Serial 12-Lead ECGs Based on Left Atrial Remodeling.

We hypothesized that analysis of serial ECGs could predict new-onset atrial fibrillation (AF) more accurately than analysis of a single ECG by detecting the subtle cardiac remodeling that occurs immediately before AF occurrence. Our aim in this study was to compare the performance of 2 types of machine learning (ML) algorithms. Standard 12-lead ECGs of patients selected by cardiologists between January 2010 and May 2021 were used for ML model development. Two ML models (single ECG and serial ECG) were developed using a light gradient boosting machine-learning algorithm. Model performance was evaluated based on the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and F1 score. We trained the ML models on 415 964 ECGs from 176 090 patients. When testing the 2 ML models using external validation data sets, the performance of the serial-ML model was significantly better than that of the single-ML model for predicting new-onset AF (single- versus serial-ML model: sensitivity 0.744 versus 0.810; specificity 0.742 versus 0.822; accuracy 0.743 versus 0.816; F1 score 0.743 versus 0.815; area under the receiver operating characteristic curve 0.812 versus 0.880; P<0.001). The Shapley Additive Explanations analysis ranked P-wave duration and amplitude among the top 10 ECG parameters. An ML model based on serial ECGs from an individual had greater ability to predict new-onset AF than the ML model based on a single ECG. P-wave morphologies were associated with future AF prediction.

Strategies for Integrating Multicultural Education and Religious Education to Strengthen the Multicultural Character of Elementary School Students

Religious education and multicultural education in elementary schools are considered very important in addressing the increasing issue of moral decay among children. This study aims to describe the strategy of integrating the interconnection of religious and multicultural education in strengthening the character of elementary school students. This study uses a qualitative approach of the case study type with the objects of the principal, teachers, and students. Research data were collected through observation, interviews, and documentation. The collected data were then analyzed through data reduction, data presentation, and conclusion drawing. Researchers use triangulation techniques and triangulation sources to obtain data validity. This study found that integrating the interconnection of religious education and multicultural education strengthens the character of elementary school students by integrating-interconnecting certain values in the curriculum and the learning process, namely equality, unity, justice, kinship, tolerance, and love of peace. The results of this study not only provide theoretical contributions to character formation through religious and multicultural education and offer practical insights for elementary schools in forming students' characters through the integration of religious and multicultural education.

Analisis Historis, Metode Dan Karakteristik Pendidikan Tasawuf Di Ma’had Aly IqnᾹ’ Ath-ThᾹlῙbin Pondok Pesantren Al-Anwar, Sarang, Rembang

This research aims to examine: (1) the history of the establishment of Ma'had Aly Iqnā' Ath-Thālibīn Pondok Pesantren Al-Anwar, Sarang, Rembang (2) Taṣawuf education at Ma'had Aly Iqnā' Ath-Thālibīn Pondok Pesantren Al-Anwar, Sarang, Rembang (3) the characteristics of Taṣawuf education at Ma'had Aly Iqnā' Ath-Thālibīn Pondok Pesantren Al-Anwar, Sarang, Rembang. The research approach used in this study is descriptive qualitative. The research setting was conducted at Ma'had Aly Iqnā' Ath-Thālibīn Pondok Pesantren Al-Anwar, Sarang, Rembang. The data collection method uses documentation, interview, and observation methods. For data validity techniques used are source triangulation techniques. While the data analysis technique that researchers obtained, using data collection, data reduction, and data verification. The results of the study conclude The learning method follows the tradition of the salaf pesantren and emphasizes social practice. Its characteristics include the use of the book by Masyayikh PP. Al-Anwar, sanadan kitab, syarh kitab as a final assignment, and the application of Sufism in social life.

Implementation of Inclusive Education Programs in Reading Practice for Low-Grade Slow Learners in Elementary Schools

Education is a fundamental entitlement granted to children, aimed at preparing forthcoming generations. However, it's imperative to acknowledge that not all children grasp education at the same pace, often referred to as slow learners. Additionally, the lack of specialized mentorship in primary schools exacerbates this issue. Therefore, it's crucial for teachers to employ effective strategies to address these challenges. This study seeks to outline the utilization of an approach method in supplementary reading sessions tailored for slow learners. Employing a descriptive qualitative research design, data was gathered through observations, in-depth interviews, and document analysis. The research, structured as an intrinsic case study, involved two class teachers and slow learner students in grades one and two at SDN Wiropaten. Data analysis comprised data reduction, display, and inference drawing, with data validity ensured through source and technique triangulation, along with crosschecking. Findings indicate the effectiveness of the approach method in catering to the needs of slow learner students in lower grades. Several programs and outcomes stemming from this approach were identified. Grade one initiatives encompassed building rapport with slow learners, fostering trust, facilitating social interaction, and implementing follow-up strategies. Grade two initiatives included assigning independent tasks, engaging in random student participation (such as alphabet memorization), word games, and guided reading exercises.