Publication Search

The professional competence of Islamic Education (PAI) teachers plays a decisive role in determining the quality of learning at the elementary school level. In practice, many teachers still encounter challenges in mastering subject content, developing systematic teaching materials, applying innovative learning strategies, and integrating educational technology into classroom activities. These conditions indicate the need for structured professional development efforts through collaborative forums. This study aims to analyze the role of the Islamic Education Teachers Working Group (KKG PAI) in maximizing the professional competence of PAI teachers in public elementary schools in Panti District, Pasaman Regency, and to identify the supporting and inhibiting factors influencing its implementation. This research employed a qualitative approach with a case study design. Data were collected through in-depth interviews, participatory observation, and documentation involving the KKG supervisor, the head of KKG PAI, and member teachers. Data analysis was conducted using an interactive model consisting of data reduction, data display, and conclusion drawing. The findings reveal that KKG PAI functions as a strategic platform for collaborative lesson planning, subject-matter enrichment, peer reflection, and technology-based instructional training. Supporting factors include strong teacher commitment and collaborative culture, while limitations in facilities and technological disparities remain challenges. Strengthening the sustainability and management of KKG programs is essential to enhance teachers’ professional competence and improve the overall quality of Islamic education learning.

Learning Islamic jurisprudence (fiqh taharah) is an important foundation in developing correct worship habits in children. This community service research examines the implementation of Islamic jurisprudence (fiqh taharah) learning at Majlis Taklim Al Falah Tangerang with the aim of improving knowledge, practical skills in ablution, and the capacity of teachers in teaching children. The method used is an asset-based community development (ABCD) approach and a participatory-educational approach that combines short theory sessions, demonstrations, repeated practice, and non-written practical evaluations. Data were collected through observation, interviews, direct practice, and formative evaluations during the activities. The results show an increase in participants' conceptual understanding of hadas and najis (impurity) and improved ablution practical skills, characterized by enthusiasm, the ability to imitate the ablution sequence more accurately, and a positive response to practical learning methods. In addition, the program encourages teachers' awareness to implement skills-based evaluation and enrich learning methods according to children's characteristics. In conclusion, Islamic jurisprudence (fiqh taharah) learning at Majlis Taklim is more effective when it is practice-oriented, participatory, and sustainable; Follow-up recommendations include routine ablution practices, pedagogical training for teachers, and the provision of simple media to support learning.

Flower disease detection is a common challenge in modern agriculture. Various factors, such as changes in leaf color, shape, petal structure, and environmental conditions, make it difficult to achieve high accuracy with conventional models. Transfer learning is an effective solution to improve model performance in image detection, especially when available data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The process included data processing, increasing the data volume, model training, and result verification. The results showed that the EfficientNet-B0 model provided the highest accuracy of 97.2%, significantly better than the CNN model created from scratch with an accuracy of 85.1%. This study proves that the transfer learning method is very effective in improving the accuracy of flower disease detection. These results confirm that transfer learning is effective for detecting plant diseases with higher accuracy, especially when the dataset is limited.  

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

This study aims to analyze the implementation of the Merdeka Curriculum in Fiqh subjects and how its application affects the improvement of students' understanding. Using the library research method, this study reviews various recent literature in the form of scientific articles, accredited journals, academic books, and relevant policy documents. The results of the study show that the Merdeka Curriculum is conceptually aligned with the principles of Islamic education, especially in terms of character development, freedom of learning, and student-centered learning. However, the implementation of this curriculum in Fiqh learning still faces a number of challenges, such as teachers' limited understanding of the new curriculum approach, a lack of project-based contextual teaching materials, and difficulties in conducting authentic assessments on aspects of worship and morals. Technical challenges such as the availability of facilities and access to technology also hamper the effectiveness of learning. This study emphasizes the need for continuous teacher training, the development of open-access learning resources, and improved infrastructure support in order to strengthen the implementation of the Merdeka Curriculum. Overall, this curriculum has significant potential to improve students' understanding of Fiqh if it is supported by appropriate and collaborative implementation strategies.

Primary Healthcare Facilities (Fasilitas Kesehatan Tingkat Pertama, FKTP) represent the first level of contact in the healthcare system and play a central role in infection prevention and control. Despite mandatory Infection Prevention and Control (IPC) training in Indonesia, evidence regarding its effectiveness in improving cognitive abilities among primary healthcare workers remains limited. This study aimed to evaluate the effectiveness of IPC training in enhancing the cognitive abilities of healthcare workers in FKTP. A quasi-experimental study with a one-group pretest–posttest design was conducted involving 91 healthcare workers who participated in IPC training across three cohorts in 2024. The training was delivered online through a Learning Management System and consisted of structured learning modules accompanied by a pre-test and a final quiz. Cognitive improvement was assessed using paired samples t-tests, while the magnitude of training impact was evaluated using Cohen’s dz effect size. The results showed statistically significant improvements in cognitive scores across all cohorts (p < 0.001), with mean score increases ranging from 16.10 to 23.35 points. Effect size analysis revealed large to very large effects, with an overall Cohen’s dz of 1.19, indicating substantial and practically meaningful cognitive gains. In conclusion, IPC training was effective in improving cognitive competence among FKTP healthcare workers. These results reinforce the value of well-structured training programs as an essential component of efforts to strengthen infection prevention capacity in primary healthcare settings.

This community service activity aims to strengthen the role of higher education institutions in preserving local culture through the revitalization of cultural arts learning based on local wisdom, particularly traditional Nias carving art. The main problems faced by the partners include the limited availability of contextual cultural arts learning, minimal integration of traditional art practices into university courses, and students’ low understanding of the philosophical values embedded in Nias carving motifs. The implementation method employs a participatory–educational approach consisting of preparation, socialization, theoretical and practical training, intensive mentoring, and evaluation stages. This activity involves students and lecturers as participants as well as agents of cultural preservation. The results indicate a significant improvement in participants’ knowledge of the symbolic meanings of Nias carving motifs, their basic skills in designing and drawing carving motifs, and their appreciative attitudes toward the preservation of local cultural arts. This activity contributes to the strengthening of cultural arts learning in higher education and has the potential to serve as a sustainable model of community service based on local culture.

The skill mismatch between university graduates and technology industry requirements remains a significant challenge in Indonesia. PT Vinix Seven Aurum requires an assessment tool to objectively identify the initial competencies of MBKM and internship program participants. This research aims to design a web-based self-assessment platform that helps students measure their skill gaps against industry standards through radar chart visualization and personalized learning recommendations. The UI/UX design applies the Design Thinking method with empathize, define, ideate, prototype, and test phases, utilizing Figma for wireframe and high-fidelity prototype development. Data collection was conducted through observation, interviews, literature studies, and usability testing with 10 respondents. The results demonstrate good usability with a 100% completion rate across all features. The VINIX Skill Radar platform provides five assessment categories, a 1-10 rating scale system, radar chart visualization, gap analysis, and learning recommendations. This system enhances students' self-awareness of their competencies and supports effective mapping of training program participants' capabilities.

Digital literacy is important in education in the era of Society 5.0, because teachers are required to be facilitators in supporting students to become digital learners. To support the success of students in becoming digital learners, the role of teachers in the use of technology during learning activities also contributes. The existence of these demands makes the use of virtual reality media in the learning environment an alternative that can be done to create learning activities that meet the demands in the Society 5.0 era. The purpose of this service activity is to improve the digital literacy of IGI Pekanbaru. as well as introduce virtual reality-based learning media as a renewable learning media innovation to support student learning activities in the digital era. In this service, the methods include presentation, demonstration, and practice. The results showed that teacher training in making VR learning media can improve their digital literacy. This training can also help teachers create interactive and interesting technology-based collaborative learning media for modern learning.

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.

Raining activities on making ketupat weaving for adolescents are important to implement as an effort to preserve Indonesia’s local cultural heritage. Ketupat weaving is a traditional skill that contains cultural, social, and philosophical values, which are at risk of fading among younger generations. This community service program was specifically designed for adolescents with the aim of providing hands-on experience and practical skills in traditional ketupat weaving. The training was conducted through several stages, including preparation, direct practice, guidance, and evaluation. Participants were actively involved in the entire process, starting from selecting materials, learning basic weaving techniques, to completing the final woven ketupat forms. The results of the activity indicate positive outcomes, including the improvement of participants’ traditional crafting skills, increased awareness of cultural values embedded in ketupat weaving, and strengthened social interaction among adolescents. In addition, the training contributed to fostering a sense of cultural pride and responsibility in preserving local traditions. Overall, this community service activity demonstrates that practical and participatory cultural training can serve as an effective medium for cultural transmission, character development, and social engagement among adolescents, while supporting the sustainability of local cultural heritage in the modern era.

The use of technology-based learning media, such as Smart TVs, has great potential in improving the quality of learning in elementary schools (SD). Smart TVs can support interactive learning, enrich teaching materials, and increase student learning motivation. However, in reality, many elementary schools still face limited supporting facilities so that their use is not optimal. This study analyzes the use of Smart TV in elementary schools with limited infrastructure, teacher readiness, and its impact on the learning process. The analysis method was carried out qualitatively through the study of school conditions, observations, and interviews with teachers. The results of the study show that the limitations of the internet network, the lack of supporting devices, and the lack of teacher training are the main factors that hinder the effectiveness of the use of Smart TVs. The impact of these limitations is the low integration of technology in learning, limited access to digital materials, and the suboptimal role of teachers as facilitators. Therefore, policy support from the government, improving school facilities, and teacher training programs are needed so that the use of Smart TVs can run optimally. The conclusion of the study emphasizes that Smart TV has the potential to become an innovative learning medium in elementary schools if supported by adequate infrastructure facilities and teacher competence in accordance with the demands of the digital era.

The rapid development of technology in today's era requires every profession to align with existing developments. The teaching profession is no exception. Teachers must be able to utilize technology in the learning process to create an engaging learning environment for students. To support this capability, training is essential. This community service activity aims to provide training to teachers at Bancong  Elementary School in Wonoasri District, Madiun Regency, to familiarize themselves with and be able to use the GeoGebra application. This training also aims to improve teachers' competency in utilizing digital technology as a learning medium. The GeoGebra application is an effective application for solving mathematics problems in elementary schools, particularly in geometry, particularly plane figures. The implementation method for this activity includes preparation, training, and mentoring. The training was provided in the form of workshops and hands-on practice using GeoGebra-based mathematics learning media. The activity proceeded orderly and smoothly, and the training participants understood the material well, both theoretically and in practice.

Public speaking is an essential communication skill that plays a crucial role in enhancing self-confidence, delivering ideas effectively, and fostering social participation. However, many women in rural areas still lack opportunities to develop this skill due to limited access to training, gender stereotypes, and minimal experience in public forums. These challenges lead to low levels of women’s involvement, particularly housewives who are members of PKK, in community activities and decision-making processes at the local level. This study aims to examine the impact of public speaking training on improving the knowledge, self-confidence, and social participation of PKK mothers in Nagari Sumpur Kudus. The research employed a descriptive qualitative method with a participatory approach through interactive lectures, speaking practice simulations, reflective discussions, participatory observations, and qualitative evaluations. Data were analyzed using data reduction, data display, and conclusion drawing based on field notes, observations, and participants’ feedback. The findings indicate that the training successfully improved participants’ understanding of basic public speaking techniques, including intonation, eye contact, body language, and structured idea delivery. Furthermore, the participants experienced significant growth in self-confidence, courage to speak in front of groups, and motivation to continuously practice their communication skills. The interactive and supportive learning atmosphere also strengthened collaboration and solidarity among PKK members. Thus, public speaking training not only enhances individual competencies but also contributes to women’s empowerment and their active participation in community development at the village level.  

Digital transformation has significantly reshaped human resource management (HRM) through the adoption of Human Resource Information Systems (HRIS), artificial intelligence (AI), big data analytics, e-learning platforms, and remote work technologies. Although these innovations improve efficiency and decision-making, they also generate ethical challenges related to data privacy, algorithmic bias, transparency, and employee monitoring. This article examines the role of professional ethics in HRM within the context of digital transformation, highlighting both emerging challenges and potential opportunities. This study employs a conceptual research approach supported by a comprehensive literature review of scholarly works on HRM, professional ethics, and digitalization. The analysis focuses on core ethical principles such as integrity, fairness, responsibility, professionalism, and confidentiality, and evaluates their implementation in digital HR practices. The findings indicate that unethical use of digital technologies may lead to discrimination, reduced employee trust, and violations of individual rights, particularly through biased AI-based recruitment systems and opaque performance evaluation mechanisms. However, digital transformation also offers opportunities to strengthen ethical HR governance. The use of ethical data management, algorithmic audits, digital transparency, and e-learning-based ethics training can enhance accountability and fairness in HR processes. The study concludes that integrating professional ethics with digital HRM is essential for developing human-centered, sustainable, and trustworthy organizations in the digital era.

The rapid growth of e-commerce platforms in Indonesia, particularly Tokopedia, has resulted in a large volume of consumer reviews containing valuable information regarding customer perceptions and satisfaction. However, manual analysis of such reviews is inefficient and prone to subjectivity, necessitating an automated approach based on machine learning. This study aims to classify the sentiment of sports product reviews on Tokopedia into positive, negative, and neutral categories by applying Logistic Regression, Support Vector Machine (SVM), and Random Forest using the Term Frequency–Inverse Document Frequency (TF-IDF) approach. The data were collected through web scraping of Indonesian-language sports product reviews and processed through several preprocessing stages, including data cleaning, case folding, tokenization, stopword removal, and stemming. Feature representation was performed using TF-IDF to transform textual data into numerical vectors, after which the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the application of TF-IDF significantly improves the performance of all models, with SVM consistently achieving the most optimal performance compared to Logistic Regression and Random Forest. These findings demonstrate that classical machine learning algorithms combined with TF-IDF remain highly effective for sentiment analysis of Indonesian-language text. The implications of this study are expected to assist sellers in understanding customer opinions, support consumers in making informed purchasing decisions, and serve as a foundation for the development of sentiment analysis and recommendation systems on e-commerce platforms.

Digital-based learning evaluation is an important effort to improve the effectiveness and efficiency of the assessment process. However, learning evaluation practices at MTsN 2 Kota Tangerang are still dominated by conventional methods and the limited use of simple digital applications. This community service activity aims to strengthen digital-based learning evaluation practices through training on the use of the Zep Quiz application for teachers. The activity employed a participatory and applicative approach, including observation, focus group discussions, training and mentoring, and program evaluation. The training was conducted in a hybrid format and focused on introducing, developing, and analyzing learning evaluations using Zep Quiz. The results indicate that teachers improved their understanding and skills in designing and operating digital-based learning evaluations independently.Teachers also demonstrated high enthusiasm and active participation throughout the activity. Although technical challenges such as varying levels of digital literacy and limited internet access were encountered, these issues were addressed through direct mentoring. Therefore, the Zep Quiz training was effective in strengthening digital-based learning evaluation practices at MTsN 2 Kota Tangerang.

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

Sudden Infant Death Syndrome (SIDS) is a sudden and unexpected death in infants that is often associated with the prone sleeping position. This study aims to develop an automated monitoring system capable of detecting SIDS risk factors using the YOLOv8 algorithm and to analyze the effect of data augmentation on model performance. The dataset consists of two classes, baby-lying-on-back (supine) and baby-lying-on-stomach (prone), which were processed through model training and evaluation using precision, recall, F1-score, and mAP metrics. The model was trained under two scenarios, without data augmentation and with data augmentation. The results show that the model without augmentation achieved a precision of 90%, recall of 85%, F1-score of 86%, and mAP50 of 93.7%. After applying augmentation, performance improved to a precision of 90%, recall of 87%, F1-score of 88%, and mAP50 of 95.1%. These findings indicate that augmentation increases detection accuracy and enhances model generalization, including robustness against variations in lighting and camera angles. Furthermore, testing with image and video inputs revealed that the non-augmented model exhibited a tendency toward overfitting, particularly in favor of the baby-lying-on-stomach, whereas the augmented model successfully classified both classes accurately. The developed system is also equipped with an alarm feature and early-warning notifications via Telegram to smartphone when a prone position is detected for a certain duration. Overall, the results demonstrate that YOLOv8 with data augmentation is effective for an automated, non-invasive monitoring system for infants, making it suitable for detecting and preventing potential SIDS risk factors.

This study conducts sentiment analysis of Indonesian user reviews of the CapCut application using IndoBERT and compares two evaluation schemes: a single 80/20 train–test split and stratified 5-fold cross-validation (k=5). A total of 1,048,575 reviews were collected from the Google Play Store through web scraping and labeled into three sentiment classes based on rating: negative (1–2), neutral (3), and positive (4–5). After preprocessing—cleaning, case folding, banned-word removal, normalization—and duplicate removal, 517,962 reviews were retained. IndoBERT Base P1 was fine-tuned using fixed hyperparameters (batch size 32, learning rate 2e-5, up to 4 epochs, early stopping patience 2), while undersampling was applied to the training set to address class imbalance. Performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC, supported by confusion matrix and ROC-curve visualizations. The single split achieved an accuracy of 0.756, whereas cross-validation produced a mean accuracy of 0.740. Across both schemes, the positive class achieved the best performance (F1-score 0.850; ROC-AUC 0.918–0.919), while the neutral class remained the most challenging (precision 0.198–0.206; F1-score 0.280–0.283). Overall, cross-validation is recommended for reporting because it reduces dependence on a single partition and provides a more representative estimate across multiple splits.