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The production of breast milk is essential for promoting optimal growth and developmental outcomes in infants throughout the first six months of life. Nevertheless, several studies have indicated that some breastfeeding mothers face challenges in producing adequate milk, often influenced by nutritional status, psychological conditions, and environmental support. The katuk plant (Sauropus androgynus), a well-known local herb, has long been recognized for its potential as a natural galactagogue that can stimulate breast milk production. The innovation of processing katuk leaf extract into biscuits offers a more practical, convenient, and potentially acceptable alternative for daily consumption among the community. The present study aimed to examine the impact of katuk leaf biscuits on breast milk production among breastfeeding mothers within the jurisdiction of Kuta Baro Health Center, Aceh Besar District. A pre-experimental design utilizing a one-group pretest–posttest method was implemented, involving 20 breastfeeding mothers who fulfilled the established inclusion criteria. Data collection was carried out through the administration of questionnaires and by measuring breast milk volume using a calibrated measuring cup.The study findings indicated that before the intervention, most participants had relatively low breast milk production, averaging 69.1 ml. After a seven-day intervention, the mean breast milk volume showed a significant increase to 246.5 ml, with a p-value of 0.000 (p < 0.05), the findings indicate that katuk leaf biscuits exert a significant influence on increasing breast milk production. Overall, this study underscores the potential of locally sourced, herbal-based food innovations as an effective and sustainable approach to enhancing breast milk production and supporting the success of exclusive breastfeeding programs.

This study examines the price elasticity of fresh cow’s milk on the demand for raw materials at Anisa Yoghurt Company using a demand theory approach. Milk is the main input in yoghurt production, making price fluctuations potentially influential on production decisions. The objective of this research is to analyze the responsiveness of raw milk demand to price changes and to identify the elasticity characteristics of milk demand in a small-scale food processing business. This study employs a quantitative case study method using primary data collected through weekly observations of milk prices and quantities purchased over a three-month period, from September to November. The analysis applies the midpoint elasticity formula to measure price elasticity of demand. The findings indicate that the demand for raw milk is consistently inelastic across the observed periods, as shown by elasticity values with absolute magnitudes less than one. Both price decreases and increases result in relatively small changes in the quantity demanded, indicating low sensitivity of demand to price fluctuations. These results suggest that raw milk is an essential production input, and purchasing decisions are driven more by production needs than by short-term price changes. The study implies that yoghurt producers should prioritize supply stability and production efficiency rather than adjusting purchase volumes in response to minor price changes. This research contributes empirical evidence on demand elasticity in dairy-based agro-industries, particularly at the small enterprise level.

This study aims to analyze the effectiveness of waste management in preventing fires at the Alak Landfill (TPA) in Kupang City using a qualitative case study approach. Data was collected through interviews, observations, and documentation involving officials from the Environmental and Sanitation Office, landfill workers, waste pickers, and surrounding communities. The findings indicate that the effectiveness of waste management remains low. In terms of input, financial resources, infrastructure, and human resources are insufficient to support optimal operations. During the process stage, waste collection and transportation are hindered by low public compliance, limited vehicles, and minimal waste segregation. Final waste processing still relies on the open dumping method, which increases waste accumulation, methane generation, and fire risks. In terms of output, various mitigation efforts have not successfully reduced the recurring fire incidents due to limited facilities, inadequate compaction, and the absence of gas monitoring systems. The results highlight the need for increased budget allocation, improved facilities, enhanced human resource capacity, and the implementation of safer and more sustainable waste management methods to reduce fire risks at the Alak Landfill.

This research investigates the development of IT-based Automatic Identification System (AIS) data surveillance models supporting maritime safety through integration of advanced information technology, maritime engineering principles, and human factors optimization. AIS technology generates vast real-time vessel movement data creating unprecedented opportunities for safety enhancement through systematic surveillance, collision risk detection, traffic pattern analysis, and incident prevention, yet effectiveness depends critically on intelligent data processing algorithms, reliable IT infrastructure, and competent personnel capable of interpreting surveillance outputs and taking appropriate actions. Through qualitative analysis involving maritime safety authorities, vessel traffic service (VTS) operators, port authorities, marine engineers, IT specialists, data scientists, and maritime training institutions, this study examines how IT-based surveillance models incorporating pattern recognition, anomaly detection, predictive analytics, and crew-centered interfaces can transform maritime safety management from reactive incident response toward proactive risk prevention. Results demonstrate that intelligent AIS surveillance can identify 75-90% of high-risk situations 15-45 minutes before critical events, reduce collision risks by 60-80%, improve traffic management efficiency by 35-55%, and enhance crew situational awareness by 45-65% when integrated with appropriate training programs developing personnel competencies in data interpretation, system operation, and coordinated response. Key implementation challenges include data quality and completeness issues, computational infrastructure requirements, algorithm development complexity, personnel competency gaps requiring substantial training investments, organizational coordination barriers, and privacy/security concerns. Findings reveal that successful AIS surveillance implementation requires holistic sociotechnical approaches integrating IT systems engineering, maritime domain expertise, and human capability development through coordinated design, deployment, and training strategies. This research contributes to maritime safety literature by providing integrated frameworks for IT-based surveillance systems incorporating technical capabilities, operational requirements, and human factors supporting evidence-based safety management.

Education in the modern era requires students not only to master cognitive knowledge, but also to have the ability to think critically, analytically, creatively, and problem solving skills independently. Therefore, the application of the Problem Based Learning (PBL) model and the Learning Strategy for Improving Thinking Ability (SPPKB) is considered relevant and ideal in the context of Christian Religious Education (PAK) to integrate faith values with the reality of daily life. This study uses a literature study method by examining scientific journals, books, and academic articles to analyze the definition, essence, characteristics, and steps of PBL which include the introduction of authentic cases, problem solving, investigation planning, independent learning, solution synthesis, presentation of results, and reflection. PBL has proven to be effective in increasing learning motivation, collaboration, and internalization of Christian moral values, although it has limitations in the relatively long time requirement and the demands of teacher competence as facilitators. Meanwhile, SPPKB emphasizes intellectual stimulus, exploration of ideas, data processing, in-depth analysis, and metacognitive reflection to form a systematic and independent mindset in dealing with religious ethical issues. These two student-centered learning strategies complement each other in forming students who are dynamic, reflective, critical, and responsible as Christian personalities through flexible and contextual learning adaptations in PAK.

Social and environmental responsibility as part of business ethics. Corporate Social Responsibility (CSR) is an important strategy in building and maintaining a company's image in the public eye. This study aims to analyze how the CSR strategy implemented by PT. Kompas Gramedia through the Kampung Koran program maintains the company's image. This study uses a descriptive qualitative approach. Data collection techniques were carried out through in-depth interviews and documentation. The theory used is the CSR strategy from Coombs and Holladay which includes five stages: scanning, conducting, creating, communicating, and evaluating. In addition, the image formation theory from John M. Tiedemann was also used to analyze the process of forming a company's image. The results show that the Kampung Koran Program of PT. Kompas Gramedia demonstrates a planned and participatory CSR strategy. Through scanning and monitoring as well as conducting formative initiatives, the company is able to map community needs and design programs based on local potential. At the create CSR stage, the focus is directed at empowering housewives with skills in processing used newspapers, then strengthened through communicate CSR with communicative narrative techniques that touch public emotions, and is periodically evaluated at the evaluation stage to maintain relevance and sustainability. This process contributes directly to the formation of the company's image. Starting with a creative idea to recycle newspapers, a positive perception emerged that the program brings tangible benefits.

Halal certification is a primary requirement for Muslims when choosing food. This requirement is stated in Surah Al-Baqarah, verse 168 of the Quran, which commands Muslims to consume food that is halal and wholesome. Halal certification is assessed not only by the ingredients used but also by the entire production process, from processing and raw material use, transportation, to sales and consumption methods. In Indonesia, the development of halal products continues to grow rapidly in line with the large Muslim population. However, public understanding of halal certification, particularly for food and beverage products, remains relatively low. Based on this situation, this study examines the impact of halal certification on the marketing strategies of MSMEs in the food sector. This research seeks to answer two main questions: first, to what extent do MSMEs understand the importance of halal certification? and second, does the existence of halal certification influence consumer purchasing interest? The study used a library method, reviewing a variety of relevant literature, and an empirical approach to obtain data directly from the public.

One of the most profitable horticultural crops, but it spoils quickly due to its high water content. Processing chili peppers with a disc mill into chili powder is a great way to increase the added value of the product while extending its shelf life. The purpose of this study was to determine how the blade rotation speed and amount of material affected the production time of chili powder in an FFC15 Disc Mill. The blade rotation speed varied between 2100 rpm and 3500 rpm, and the amount of material varied between 5% and 500 g. The data were analyzed using statistical analysis to determine the effect of each component and their relationship with each other. The results showed that the blade rotation speed and the amount of material had a significant effect on the production time of chili powder. However, the relationship between these two factors did not have a significant effect. The best operating conditions were achieved with 500 grams of material and a speed of 3500 rpm, which resulted in the fastest production time of 4.367 minutes.

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.

Business ventures are activities carried out by individuals or organizations involving the production, sale, purchase, or exchange of goods and services, with the aim of generating profit. A basic necessities store (commonly known as a “sembako” store in Indonesia) sells daily staple needs, especially the nine essential commodities (sembako), which include items such as rice, sugar, cooking oil, eggs, salt, and other key food ingredients. UD. Putri 2, located in Dusun 1A, Sumber Harapan Village (21261), Tinggi Raja Subdistrict, Asahan Regency, was established in 2018 and has since become an essential part of the local community. This has required UD. Putri 2 to constantly monitor their stock inventory. However, the company still faces inefficiencies in managing sales data processing, which often leads to inventory shortages. When the supply of goods is insufficient to meet customer demand, customers may turn to other stores. If this occurs repeatedly, the store risks losing profit due to the unavailability of goods. Supply Chain Management (SCM) refers to the integrated processes and production activities starting from the acquisition of raw materials from suppliers, the value-adding processes that turn raw materials into finished products, the inventory storage process, and the distribution of finished goods to retailers and consumers. The implementation of SCM can optimize inventory management of staple goods, minimize inventory costs, and improve supply chain efficiency at UD. Putri 2.

This study addresses the urgent need for digital transformation at Percetakan Rajawali, a printing company currently reliant on manual order processes, which leads to service delays, recording errors, and lack of transparency. The research aims to design and implement a web-based online ordering information system to facilitate customer orders, accelerate confirmation and order tracking, and improve the efficiency of order data management. Adopting the linear and sequential Waterfall methodology, the study progressed through stages of system analysis, design, coding, testing, and maintenance. The key findings include the successful design of a comprehensive system featuring product catalogs, an online ordering module with real-time cost calculation, integrated payment management, and an admin dashboard for order processing. The system is designed with user-centered and secure principles, expected to significantly enhance operational efficiency, service speed, and customer satisfaction by providing an integrated, transparent, and accessible online ordering platform.

The performance of the Indonesian National Team (Timnas) in the 2026 World Cup qualifications has triggered massive and diverse responses on social media, particularly on platform X. This study aims to identify and classify public sentiment regarding Timnas Indonesia's performance into positive, negative, and neutral categories using a data mining approach. Text data was processed through pre-processing stages, term weighting using TF-IDF, and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class distribution imbalance. The classification algorithm employed was Multinomial Naïve Bayes. Model performance evaluation was conducted by comparing two training-testing data split scenarios: 90:10 and 80:20 ratios. The results indicate that public opinion is dominated by negative sentiment at 73.2%, reflecting public disappointment. In terms of model performance, the 90:10 ratio scenario yielded the best accuracy of 80%, outperforming the 80:20 ratio which recorded an accuracy of 75%. These findings demonstrate that combining Multinomial Naïve Bayes with the SMOTE technique is effective in handling imbalanced text data and is capable of accurately mapping public perception.

Communication is a fundamental aspect of human life. However, individuals with hearing and speech impairments often face barriers in communicating with the general public. The Indonesian Sign System (SIBI) serves as a communication solution for the deaf and speech-impaired community in Indonesia, yet public understanding of SIBI remains limited. To address this issue, this study aims to develop an automatic translation model from SIBI sign language into Indonesian text by utilizing Deep Learning technology, specifically the Convolutional Neural Network (CNN) algorithm. CNN was chosen for its ability to effectively recognize visual patterns, making it suitable for processing hand gesture images in sign language. This research involved collecting and classifying a dataset of hand images based on the alphabet or words in SIBI, which were then used to train the CNN model. The designed CNN model was built to accurately classify hand signs and translate them into Indonesian text. The results of this study have the potential to serve as a supportive solution for inclusive communication between the deaf community and the wider public, and can be further developed for contextual sentence translation. Keywords: Indonesian Sign System (SIBI), CNN, Deep Learning, Automatic Translation, Inclusive Communication

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

Behind the success of the Free Nutritious Meal Program (MBG), there are several problems related to the health factors of the program targets, namely, there are several cases of allergies that occur in schools, inadequate understanding of allergen management owned by food processing vendors, and the high cost of laboratory tests and the process that takes a long time. So, to overcome these problems, an application is proposed that can help detect allergens in food products using data mining and machine learning approaches. SVM and AdaBoost algorithms each have advantages that can be used to help build an optimal allergen detection model. This research uses a cross-validation model validation method with a value of K = 10 to help improve the performance of the model built. In this study, from the entire fold, an average accuracy value of 98.74% was obtained. To evaluate the model built, this research has also conducted several new data inputs, and in each new data input, the accuracy value is obtained above 99%. This indicates that the model built, namely the combination of SVM and AdaBoost algorithms with the cross-validation model validation method, produces high accuracy, so this model can greatly assist the allergen detection process in food products.

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.  

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.

The rapid development of information technology encourages companies to optimize administrative sistems, including employee payroll processes, to ensure greater operational effectiveness and data accuracy. PT Infokom Internusa still utilizes manual and semi-computerized methods in payroll management, which may lead to calculation errors, data duplication, data inconsistency, and delays in report generation. These issues can affect overall organizational performance and decision-making efficiency. This study aims to design and develop a Java-based Employee Payroll Information Sistem to improve efficiency, accuracy, transparency, and security in payroll processing. The research employs the Waterfall software development model, which consists of requirement analysis, sistem design using Unified Modeling Language (UML), implementation using the Java programming language and MySQL database, and comprehensive sistem testing. The results indicate that the developed sistem is capable of automating salary calculations, allowances, deductions, tax computations, and report generation in an integrated and structured manner. The sistem also reduces manual errors, minimizes redundancy, and accelerates payroll administration processes. Therefore, the proposed information sistem can significantly enhance the performance, productivity, and service quality of the HR and finance departments at PT Infokom Internusa.

The incidence of degenerative diseases, which are part of Non-Communicable Diseases (NCDs), remains a major cause of death worldwide and in many regions of Indonesia. Meanwhile, efforts to prevent degenerative diseases affecting the adolescent age group are still rare, such as the establishment of a Youth Posyandu, including in Sungai Duren Village. There is no health post that provides youth care services in Sungai Duren Village, while there are many teenagers living there (students under 21 years old) with various lifestyles that are at risk of degenerative diseases. This study aims to analyze the increase in adolescent knowledge about CERDIK between before and after the establishment of the Youth Posyandu in order to prevent degenerative diseases early. This study is a quasi-experimental study involving 30 adolescents by measuring the dependent variable of knowledge. The research instrument used is a questionnaire. Computerized data processing and analysis were conducted through descriptive statistical analysis and T-test analysis. The results showed a significant difference in respondents' knowledge of CERDIK before and after the establishment of the Youth Posyandu in Sungai Duren Village (p=0.000). This study indicates that structured health education can improve adolescents' understanding of CERDIK and can be used as an effective promotive and preventive strategy.

This study was conducted to address contamination risks in Refill Drinking Water Depot (DAMIU) operations when sanitation, equipment maintenance, and quality control are not consistently implemented. This emphasizes the critical importance of systematic risk monitoring and review processes to ensure water quality and safety. The research aims to analyze the implementation of risk monitoring procedures, identify operational risks at each processing stage, and evaluate the effectiveness of risk mitigation strategies at Depot Isi Ulang Air Galon Barokah. A descriptive qualitative approach was employed, utilizing data collection techniques including in-depth interviews, direct field observations, and documentation analysis. The findings reveal that monitoring and risk review processes are conducted routinely, encompassing raw water quality checks, equipment sanitation protocols, and customer gallon container inspections. No significant risks were identified during the study period, as potential hazards such as microbiological contamination and equipment malfunction were effectively prevented through timely filter replacement and daily equipment cleaning procedures. Overall, the depot's risk management system operates effectively and consistently maintains water quality standards, ensuring product safety for consumers.