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Design and construction of gym angkasa management website system in margoyoso village to improve customer service
Full text linkThis study aims to design and implement a website-based gym management system at Angkasa Gym located in Margoyoso Village to improve customer service and operational efficiency. The current system is still manual, resulting in inefficiency and risk of errors in managing member registration, scheduling training sessions, and financial transactions. The proposed system is developed using the Waterfall model with stages of needs analysis, system design, implementation, testing, and maintenance. The Laravel framework was chosen because it supports the development of modern and secure web applications. The final system has key features such as user registration and login, trainer management, transaction processing, and review submission. Based on black box testing, all system functionality runs as expected. The results of the study indicate that the web-based system is able to significantly improve gym operational efficiency and improve user service experience. This study can be the basis for further development, such as automatic notifications, implemented in other gyms, and adaptation to the mobile application version
Improving V2V Communication Reliability in Dynamic Vehicular Networks: A Software-Defined Radio-Based Approach
Full text linkSmart Transportation Systems (STS) leverage Vehicle-to-Vehicle (V2V) communication to enhance road safety, traffic efficiency, and urban mobility. However, ensuring reliable V2V communication remains challenging due to signal power instability, environmental interference, and scalability limitations. This study explores the optimization of V2V communication using Software Defined Radio (SDR) technology, which offers a cost-effective and adaptable approach for real-time signal processing. An SDR-based V2V communication system was developed using GNU Radio and HackRF One, with signal power calibration conducted through comparative measurements involving a Spectrum Analyzer across varying distances (3-15 meters) and environmental conditions. Performance evaluation focused on Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) under different vehicle speeds (20-40 km/h). Results indicate that increasing distance leads to signal degradation, with BER reaching 36.83% and SNR dropping to -3.17 dB, emphasizing the need for adaptive signal optimization techniques. While SDR-enabled calibration provided accuracy in signal measurements, environmental factors such as multipath interference and atmospheric attenuation significantly impacted communication reliability. Despite its flexibility, the system exhibited high BER and limited communication range, necessitating further enhancements through adaptive modulation schemes, machine learning-based power control, and hybrid 5G-DSRC integration. The study highlights SDR's potential for improving V2V communication while addressing key limitations in urban mobility networks. Future research should focus on enhancing scalability, security, and energy efficiency through advanced signal processing techniques. This study contributes to developing next-generation STS by providing empirical insights into SDR-based V2V communication optimization, supporting safer and more efficient transportation systems
Fuzzy logic and application as a game-based fire disaster mitigation simulation
Full text linkFire is a disaster that can occur at any time and place, particularly in high-rise buildings with a high level of risk. This study developed an educational game called "Fire Escape", built using Unity 3D and integrated with Mamdani fuzzy logic to provide an interactive learning experience about fire evacuation procedures. This study adopted a Research and Development (R&D) approach utilizing the ADDIE model comprising the stages of Analysis, Design, Development, Implementation, and Evaluation.The game applies a fuzzy logic system to generate action recommendations based on player parameters such as health points (HP), time, and distance. The development results show that the game successfully integrates educational and entertainment aspects through realistic 3D simulation, intuitive controls, and tiered learning scenarios. The implementation of Mamdani fuzzy logic enables adaptive decision-making, enhancing the player’s learning experience. This game can be an effective alternative learning medium to improve public understanding and preparedness for fire emergencies in high-rise buildings
Scale up business strategy through the development of the frozen food products industry at ubi manis restaurant linggarjati
No full textThe development of the restaurant business in 2024 is growing very rapidly, so that the competition in the restaurant business is becoming increasingly competitive. To anticipate a decline in revenue and business continuity, a strategy is needed to anticipate these conditions. Ubi Manis Restaurant wants to find other business alternatives, besides serving guests who come to the restaurant. Food products at Ubi Manis Restaurant have great potential to be developed into a small-scale frozen food industry. Bitter ballen is one of the snack products that has the potential to become frozen food. The research method used is qualitative with thematic analysis methods, to analyze data with the aim of identifying patterns, or to find themes through the data that has been collected. The result of the research that is expected through the frozen bitter ballen product development strategy is that the Ubi Manis restaurant will be able to expand the scale of business, especially in the Kuningan, Majalengka and Cirebon areas, so that the existence of the Ubi Manis restaurant can be sustainable
Analyzing reading preferences based on gender and education with decision tree method
No full textThis study aims to analyze the suitability of book genre selection with gender and education level. A classification method using a decision tree algorithm with four different criterion parameters is used to examine reading preferences based on various demographic factors, namely Gain Index, Information Gain, Gini Index, and accuracy. Data was obtained from a dummy dataset involving 120 records with three main attributes. The results show variations in accuracy depending on the criteria selected, with the highest accuracy rate achieved being 78.57%
Comparison of naïve bayes and support vector machine methods for jkt48 music video comment classification
No full textThe research was conducted to discuss the classification of comments on music video JKT48 "Magic Hour" in YouTube using method Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). YouTube monitors viewer emotion by adjective comments Adjectives are the descriptive powers of human communication we use to help personify how different types, i.e. different "personalities" flavors and depths reflect artistic expressions The place where interactivity meets with digital marketing signifying a shared contribution to music lore In this work, we study the comparison of The Support Vector Machine (SVM) and Naive Bayes Classifier in terms of Accuracy, Precision & Recall. This Project includes data pre-processing, collecting the data by YouTube API and build classification models which involves Support Vector Machine and Naive Bayes Classifier. SVM displayed more stable performance than NBC, showing consistent results across different data split ratios. SVM achieved its highest accuracy of 93.42% at an 80:20 ratio, with precision and recall rates reaching 92.57% and 93.42%, respectively
Effectiveness and safety of the monoclonal antibody drug lecanemab (Leqembi) in reducing beta-amyloid plaques in alzheimer's dementia: a literature review
Full text linkDementia is a syndrome characterized by cognitive decline, behavioral changes, and impaired self-care, with Alzheimer's disease (AD) being the most common cause. The global prevalence of AD is rising and is expected to reach 152 million cases by mid-century, imposing significant public health and economic burdens, particularly in low- and middle-income countries. AD is marked by synapse loss and neuronal atrophy, beginning in the hippocampus and spreading across the cerebral cortex due to β-amyloid plaque and neurofibrillary tangle accumulation, which disrupt neuronal communication and survival. Current treatments, such as memantine and cholinesterase inhibitors, provide only temporary symptom relief without stopping disease progression. Literature was searched using search engines such as Google Scholar, Science Direct, ResearchGate, and NCBI. Inclusion and exclusion criteria were applied, resulting in 27 relevant references that explored monoclonal antibody-based therapies and multidisciplinary interventions for AD management. Lecanemab has been shown to reduce amyloid accumulation effectively. However, its use is associated with risks such as amyloid-related imaging abnormalities with edema (ARIA-E) and hemorrhage ARIA-H, particularly in ApoE ε4 carriers. Despite these concerns, recent meta-analyses suggest that lecanemab is generally well-tolerated and offers potential as a cost-effective treatment for AD. Monoclonal antibody therapies, such as lecanemab, provide hope for slowing AD progression. Further research is crucial for developing more effective treatments. A multidisciplinary approach that integrates pharmacological therapies with advanced technologies may offer a more effective strategy for managing AD in the future
Corn sales analysis using linear regression and svm methods to improve production planning
Full text linkThis research aimed to analyze and predict corn sales at UD Muara Kasih to improve production planning accuracy. The study used historical corn sales data collected over a specific period, covering 42 data entries from January 2021 to December 2024. The dataset included variables such as sales date, quantity sold, selling price per ton, total sales value, weather conditions, market demand (in tons), and the number of laborers. Prior to model training, the data underwent comprehensive preprocessing involving data cleaning, feature extraction, and normalization to ensure its quality and readiness for analysis. Two predictive models were applied: Linear Regression and Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. Simulation data for 2024 and 2025 were generated based on the monthly averages derived from the historical dataset. The results showed that the Linear Regression model produced more stable predictions with a lower Root Mean Squared Error (RMSE) of 255.84 compared to the SVM model’s RMSE of 256.42. While the SVM model showed greater responsiveness to seasonal variations, the Linear Regression model was identified as the most suitable for the given dataset. The predictive models developed in this study are expected to support UD Muara Kasih in making more accurate and informed production decisions in the future
Machine Learning Techniques for Classifying Indonesian Foods and Drinks by Nutritional Profiles
Full text linkLocal ingredients and Indonesia's diverse culinary traditions play an important role in shaping people's health and eating habits. Understanding the nutritional profile of Indonesian food is crucial to promoting healthier food choices. This study aims to classify Indonesian food and beverages based on their nutritional content, with a focus on calories, protein, fat, and carbohydrates. To achieve this, a dataset of 1,346 food items was preprocessed using normalization techniques to improve model performance. Each food item was categorized as High Protein, High Fat, or High Carbohydrate based on its dominant macronutrient content. Five machine learning models which are K-Nearest Neighbors, Decision Trees, Support Vector Machines, Random Forest, and Multilayer Perceptron-were used and compared. Among these models, the Support Vector Machine achieved the highest classification accuracy of 99.1%. These findings demonstrate the potential of machine learning in nutrition research, providing a basis for developing data-driven dietary recommendations tailored to individual nutritional needs. This research bridges traditional dietary research with modern computational approaches, offering insights for public health initiatives and personalized nutrition planning
Online reservation system development and digital payment integration in car wash business: Case study of car wash sniper
Full text linkThis study aims to develop a website-based online reservation system at Car Wash Sniper with digital payment integration to improve operational efficiency and customer convenience. The system design method uses a waterfall model approach that includes needs analysis, system design, implementation, testing, and maintenance. Black box testing is also carried out to test the suitability of the system with the design that has been developed. To support the payment process, this system is integrated with Midtrans as a payment gateway that provides various payment options such as bank transfers, e-wallets, and credit cards safely and in real-time. The results of the study show that this system is able to optimize the reservation process, reduce queues, and increase customer satisfaction. The novelty of this research by developing a system that is integrated with digital payments, making transactions more practical, efficient, and transparent. This system can be an innovative solution for business actors in the car wash industry to improve efficiency and service quality