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The transformation of marketing strategies from conventional methods to digital marketing has become an important factor in the development of the beauty industry, especially for Make Up Artists (MUAs) in Bandung. This study aims to analyze the impact of digital marketing through social media on increasing customer interest and the number of MUA service users, with a case study of Iwan Haadi, a professional MUA in Bandung who is active on Instagram (@iwanhaadimakeup) and TikTok (@iwanhaadi). This research employs a descriptive qualitative method with a case study approach, using interviews, observations of social media activities, and documentation of digital promotional content. The results show that before implementing digital marketing, MUAs generally relied on conventional promotions such as word of mouth and collaborations with wedding organizers. However, technological developments have encouraged many MUAs to shift to social media to expand their promotional reach. Based on observations of Iwan Haadi and 20 other MUAs in Bandung, around 85% have utilized social media particularly Instagram and TikTok to showcase their portfolios and client testimonials. The implementation of digital marketing strategies has proven to increase brand awareness and the number of customers by up to 65% over the past two years. In conclusion, digital marketing has a significant influence on increasing customers and competitiveness among MUAs in Bandung. Consistent and creative use of social media is the key to attracting customer interest and strengthening a professional image in the modern beauty industry.

Business is an economic activity carried out by individuals or organizations to produce and sell goods or services with the aim of making a profit. The NSH Group Store is a business that sells carpets, pillows, bolsters, and dolls located in the Sei Dadap I/II Plantation, Sei Dadap District, Asahan Regency, North Sumatra 21225. The NSH Group Store was established in 2016 and is owned by Mrs. Siti Komariah Siregar. Among the challenges faced by the NSH Group Store owner are irregular stock procurement. Sales transaction processes still use conventional methods, reducing efficiency and time effectiveness, and potentially leading to data errors. Supply Chain Management is a series of approaches used to efficiently integrate suppliers so that goods can be distributed in the right quantities, locations, and at the right time, with the aim of minimizing overall system costs. A bolster pillow is a pillow that can function as both a pillow and a bolster. Bolster pillows are oval and long, so they can be hugged while sleeping. The benefits of a bolster pillow include maintaining a proper sleeping position, reducing pressure on joints, helping reduce aches, improving sleep quality, and improving overall health. Therefore, by implementing Supply Chain Management (SCM), data processing will be faster and more accurate.

The calculation of work volume and construction costs is a fundamental aspect of project management, as errors in the estimation process can directly affect the preparation of the project budget. Many projects still use conventional methods for estimation, which are based on 2D working drawings with the assistance of Microsoft Excel, as seen in the Sungai Raya Religious Court Building project. This method is considered prone to calculation errors and less efficient due to the considerable amount of time required. With the advancement of technology, Building Information Modeling (BIM) has emerged, enabling automatic and integrated calculation of work volumes and construction costs through a three-dimensional digital model. This study aims to examine the implementation of BIM in the Sungai Raya Religious Court Building project and to compare the results of work volume and construction cost calculations between the BIM method using Autodesk Revit and the conventional method based on the project’s Bill of Quantity (BoQ). The research method was conducted by modeling the structural elements of the building, including pile caps, tie beams, columns, beams, floor slabs, and reinforcements. The results of work volume and construction cost calculations obtained from Autodesk Revit were then compared with the project’s BoQ as the conventional method. Based on the analysis, an average difference of 6.3% in work volume and 5.6% in construction cost was found, with the Autodesk Revit calculations showing slightly lower values compared to the project’s BoQ.

Radiographic examination plays a crucial role in visualizing internal body structures for diagnostic purposes. One of the radiographic assessments frequently performed is the Acromioclavicular (AC) joint projection, which is used to evaluate abnormalities such as joint widening, subluxation, and dislocation. This study aimed to compare the image quality of the AC joint using the Anteroposterior (AP) projection with a 3-kg load and without load. The study was conducted in the Radiology Laboratory of STIKES Borneo Nusantara using a conventional X-ray system with a quantitative descriptive case-study approach. Data were collected through observation and questionnaires administered to 10 research subjects, including radiographers and patient participants. The findings demonstrated that the AP projection with a 3-kg load produced clearer visualization of the AC joint, particularly in widening of the joint space and separation between the humeral head and glenoid cavity. The average image quality score using load was 3.5 (good), compared with 2.9 (poor) for the projection without load. The study concludes that applying a 3-kg load improves anatomical visualization of the AC joint and is recommended for cooperative patients to enhance diagnostic accuracy.

This study analyzes online fraud as one of the most common forms of cybercrime in Indonesia, which has expanded alongside rapid advances in information and communication technology. These crimes utilize digital platforms such as social media, online marketplaces, and fraudulent websites to deceive victims for unlawful financial gain. The research aims to examine online fraud from a criminological perspective by identifying its causes, patterns, and relevance to routine activity theory and differential association theory. A normative juridical method is employed, using statutory, conceptual, and case-based approaches, with qualitative and descriptive analysis. The findings show that online fraud reflects a shift from conventional fraud to digital-based crimes, driven by low public awareness of cybersecurity, easy access to technology, and weak online supervision. Several fraud schemes were identified, including online investment scams, phishing, and identity impersonation. This study highlights the need for an integrated approach that goes beyond law enforcement by emphasizing digital literacy, public education, and cross-sector collaboration to reduce cybercrime in Indonesia.

Labor pain is a significant challenge faced by in-partum mothers, with a prevalence of 70% in Indonesia, and 40% of women reporting severe pain (Central Statistics Agency, 2022). Conventional pharmacological methods often do not provide optimal results and can cause side effects. As a non-pharmacological alternative, oxytocin massage has shown promise in reducing labor pain intensity by stimulating the endogenous production of oxytocin, a natural analgesic hormone. This study aimed to assess the effect of oxytocin massage on labor pain intensity in in-partum mothers at Manado Medical Center Hospital. A pre-experimental design with a one-group pre-test post-test approach was used. The sample consisted of 30 in-partum mothers who met the inclusion criteria. Pain intensity was measured using the Numeric Rating Scale (NRS) before and after a 30-minute oxytocin massage administered by trained health workers. Data were analyzed using a paired t-test. The results showed a significant reduction in pain intensity from an average of 7.23 (SD=1.08) before the massage to 4.63 (SD=1.11) afterward, with a decrease of 2.6 points. The proportion of mothers experiencing severe pain decreased from 50% to 20%, while mild pain increased from 16.7% to 46.7%. The paired t-test revealed a highly significant difference with t=10.274 (df=29), p=0.000 (p<0.05). In conclusion, oxytocin massage is an effective non-pharmacological intervention that can significantly reduce labor pain and improve the birth experience. It can be incorporated into standard maternity nursing protocols to enhance maternal care.

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 examines the implementation of an Islamic Religious Education (PAI) learning model based on Project-Based Learning (PjBL) and collaborative approaches in shaping students’ noble character at SD Negeri Besuk Kidul, Besuk District. The study is grounded in the research gap that PAI learning in elementary schools is often still dominated by conventional, teacher-centered methods that emphasize cognitive achievement while providing limited space for the internalization of moral values through authentic learning experiences. This research aims to analyze how PjBL and collaborative learning are designed and implemented in PAI instruction and to explore their contribution to students’ character development. A qualitative approach with a descriptive case study design was employed. The research subjects included the school principal, a PAI teacher, and elementary school students. Data were collected through observation, in-depth interviews, and documentation, and analyzed using an interactive data analysis model. The findings reveal that the integration of project-based and collaborative learning in PAI instruction enhances students’ noble character, particularly in terms of honesty, discipline, responsibility, cooperation, respect, and social awareness. Students actively engage in learning activities, apply Islamic values in real-life contexts, and demonstrate positive behavioral changes both in and outside the classroom. These results indicate that Project-Based Learning and collaborative approaches constitute an effective and meaningful strategy for strengthening character education in PAI at the elementary school level.

Mosque-based community economic empowerment activities have significant potential to improve community welfare, particularly for small businesses in the mosque's immediate vicinity. However, implementation is still largely conventional and lacks a data-driven approach, resulting in suboptimal beneficiary identification and activity evaluation. This community service initiative aims to promote a data-driven approach to mosque-based community economic empowerment through sharia bazaar activities. Implementation methods include initial observation, outreach and education for mosque managers and business owners, technical assistance for sharia entrepreneurship, and activity evaluation. The results demonstrate an increased public understanding of the importance of data use in determining beneficiaries, managing bazaar activities, and developing businesses based on sharia economic principles. The outcomes of this initiative include improved data management literacy, a simple data collection format for sharia bazaar activities, and recommendations for developing a mosque-based data collection system. It is hoped that this initiative will be the first step in building a sustainable, transparent, and data-driven community economic empowerment model within the mosque environment.

Innovation in biology learning is increasingly necessary, as conventional instructional practices are often dominated by memorization-based methods that limit students’ active engagement and critical thinking development. Along with rapid advances in science and technology, digital applications offer new opportunities to enhance teaching and learning processes. One such application is Canva, a digital design platform that can be utilized as an effective learning media to support teachers and students in biology education. This study aims to describe the implementation of Canva-based learning media through a training program conducted at SMA Islam Terpadu Daarul Rahman and to examine its perceived usefulness for both teachers and students. The training involved teachers in developing biology learning materials using Canva by integrating visual elements, images, and videos to present complex biological concepts more clearly. The results of the training indicate that Canva is considered practical, user-friendly, and efficient by teachers in designing learning materials. Teachers reported that the application helped them present content in a more attractive and structured manner. Furthermore, students benefited from the use of Canva-based materials, as they demonstrated better understanding of the subject matter and increased interest in learning biology. The integration of visual and multimedia elements also supported the development of students’ critical thinking skills by encouraging analysis and interpretation of biological phenomena. Overall, the use of Canva as a learning medium shows potential to enhance the quality of biology learning and foster more meaningful learning experiences.

This study focuses on the development of pumpkin shortcake (Curcubita moschata) as a dessert innovation, utilizing the potential of Indonesia’s local food resources, which are highly nutritious and widely available. Pumpkin is well known for its high dietary fiber content that supports digestion, as well as its antioxidant properties that may reduce the risk of cancer and chronic diseases. While traditional shortcakes are commonly made with strawberries, this innovation aims to substitute conventional ingredients with pumpkin, thereby not only enhancing nutritional value but also supporting the local farming economy. The main objectives of this research are to identify the production process and formulation of pumpkin shortcake, evaluate consumer acceptance of the product, and analyze packaging and presentation strategies that enhance consumer appeal. The production process involved selection, washing, steaming, and pureeing of pumpkin, followed by weighing, mixing, molding, baking, cooling, filling, and cutting. The third experiment successfully produced a product that met all sensory criteria, including color, taste, texture, and aroma. A hedonic test involving 35 untrained panelists indicated that pumpkin shortcake with 25% liquid milk and 75% pumpkin (Code 731) was the most preferred, particularly in terms of taste and color, while the 50% milk and 50% pumpkin formulation (Code 281) was most favored for aroma and texture. The findings conclude that the formulation of pumpkin shortcake with 25% liquid milk and 75% pumpkin is the most accepted by consumers. Attractive packaging and presentation are also emphasized as essential strategies to enhance product appeal and market value.

Work volume calculations are essential for project cost estimation. Many projects, such as the Laboratory Building of the Faculty Engineering at Tanjungpura University, calculate work volumes conventionally. Conventional calculation considered less efficient and prone to errors. Building Information Modeling (BIM) provides a solution that produces more accurate and efficient calculations than conventional methods. This research aims to compare structural work volume results produced by BIM using Autodesk Revit against conventional methods and project’s BOQ. This research also describes the benefits and challenges of BIM implementation based on the researcher’s experience applying BIM with Autodesk Revit in work volume calculation. The comparison between BIM and conventional method shows a maximum difference of 2% across all work items. Meanwhile, the comparison between BIM and the BOQ shows significant differences: 81% in column formwork area, 24% in grade beam/beam concrete volume, 25% in column reinforcement weight, 25% in steel beam weight, and 10% in the steel plate weight. This research proves that BIM implementation produces more accurate and efficient calculations and serves as an effective BOQ cross-check tool. Based on the researcher’s experience in implementing BIM with Autodesk Revit, challenges found in procurement aspects, modeling aspects, and model dependency on reference drawings.    

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

This study aims to develop an interactive digital learning media based on Wordwall, named TANGKAS (Tantangan Asyik Ngulik Pecahan Kelas Empat Seru), as a practice tool for addition and subtraction of fractions in fourth-grade elementary students. The development process employed the ADDIE model, which includes the Analyze, Design, Develop, Implement, and Evaluate stages. The research subjects consisted of 29 fourth-grade students at SDN Ngemplak I Baureno. The needs analysis revealed that learning activities were still dominated by conventional methods with limited use of digital media, resulting in low student engagement. To address this issue, TANGKAS was developed using a maze chase game design to enhance motivation and support engaging fraction practice. The validation results indicated a material validity score of 94% (highly valid) and a media validity score of 80% (valid). The practicality aspect obtained 81.8% from students, both categorized as highly practical. The effectiveness test showed an improvement in learning outcomes with N-Gain scores of 0.6 for fraction addition and 0.54 for fraction subtraction, both classified as moderate. Therefore, TANGKAS is proven to be feasible, practical, and effective as an interactive game-based learning media to support students’ understanding of mathematics in elementary school.

This quasi-experimental study examines the effectiveness of Artificial Intelligence (AI)-assisted learning in enhancing critical thinking skills among undergraduate history students. The study involved 60 students divided into experimental and control groups. The experimental group received AI-supported instruction integrating adaptive learning modules, scaffolded source-analysis prompts, and guided argumentative discussions facilitated by conversational AI tools, while the control group followed conventional lecture-based instruction. Data were collected using a validated critical thinking test, classroom observation protocols, and semi-structured interviews. Quantitative data were analyzed using paired and independent sample t-tests, while qualitative data were examined through Miles and Huberman’s interactive analysis model. Results indicate statistically significant improvements in critical thinking scores in the experimental group compared to the control group. Thematic findings reveal enhanced sourcing, contextualization, corroboration, and evidence-based argumentation skills. However, minor risks of over-reliance on AI highlight the need for instructional scaffolding and ethical guidance. The findings suggest that AI can function as a cognitive scaffold that strengthens historical thinking and metacognitive awareness when implemented within a structured pedagogical framework.

This study aims to analyze the effect of the implementation of the Intelligent Tutoring Sistem (ITS) based on Artificial Intelligence (AI) on improving the understanding of mathematical concepts, especially in fractional and basic geometry materials, in Class V students of SD Negeri 2 Badran, Temanggung Regency. The research method used was a quasiexperimental experiment with a Non-equivalent Control Group Design. The research sample consisted of 48 students who were divided into two groups, namely the experimental group (n=24) who received learning with the help of AI-based ITS, and the control group (n=24) who received conventional learning with lecture methods and practice questions. The research instrument is in the form of a test of understanding of mathematical concepts that has been validated by experts and tested for reliability. Data were analyzed using parametric statistical tests of the Independent Sample t-test and N-Gain Score to measure the improvement. The results showed that there was a significant difference in understanding of mathematical concepts between the experimental group and the control group. The average post-test score of the experimental group (82.45) was significantly higher than that of the control group (70.12) with a p< value of 0.05. N-Gain analysis showed that the improvement in conceptual understanding in the experimental group was in the "moderate" category (g=0.56), while the control group was in the "low" category (g=0.32). These findings indicate that AI-based ITS is effective in improving students' understanding of mathematical concepts. The advantages of the system lie in its ability to provide instant feedback, personalize materials according to learning pace, and present interactive materials, thus helping to better construct students' conceptual understanding. It is recommended that schools consider the integration of ITS technology as a supplementary tool in mathematics learning at the elementary level.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

The main objective of the Citizenship Education (PKn) course in higher education is to develop students into individuals with nationalist, participatory, and critical characters towards national dynamics. Conventional learning approaches that are still dominant in higher education, such as one-way lectures and memorization of materials, are considered less able to encourage active participation and the development of critical thinking patterns of students in the Citizenship Education (PKn) course. This study aims to identify the effectiveness of the application of innovative learning methods in improving students' activeness and critical thinking skills. Using a descriptive qualitative approach, data were collected through classroom observations, interviews with lecturers and students, and analysis of lecture documents from three study programs at Merdeka University of Malang. The results of the study showed that the application of learning strategies such as Project Based Learning, role playing, utilization of interactive multimedia, collaborative discussions, and nationality-based simulations were able to significantly increase students' participation and critical understanding. This method is relevant to the needs of learning in the era of globalization that demands digital literacy, cross-disciplinary collaboration, and contextual problem solving. Based on these findings, this study recommends the integration of innovative methods into the Civics curriculum in higher education, pedagogical training for lecturers, and the provision of technological infrastructure that supports the implementation of competency-based learning in the era of globalization.

The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.