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Performance Analysis of Long Short-term Memory (LSTM) Model for Remaining Useful Life Prediction on Turbofan Engine
Full text linkAccurate Remaining Useful Life (RUL) prediction is critical for the predictive maintenance and operational safety of aircraft turbofan engines. This research develops and evaluates a stacked Long Short-Term Memory (LSTM) network for RUL prediction using the NASA C-MAPSS FD001 dataset as a fundamental case study. A systematic data preprocessing pipeline was employed, including sensor selection, RUL value clipping at 130 cycles, and feature normalization to prepare the data for modeling. The LSTM model was trained with regularization techniques and an EarlyStopping callback to ensure robustness and prevent overfitting. Evaluation results on the unseen test data show the final model achieved a solid and competitive performance with a Root Mean Squared Error (RMSE) of 15.22 and a PHM08 Score of 311.20. These results demonstrate that a well-configured LSTM architecture provides a reliable baseline for engine prognostic tasks, exhibiting strong generalization capabilities on new data
Programming the 8031 Minimum System in Proteus Simulator using the C: Issues and Solutions
Full text linkAn essential required course in electrical engineering, computer science, and informatics is the microprocessor. Students may consider using Proteus software in cases wherein microprocessor trainers are unavailable. Yet, the simulation of the 8031 microprocessor-based minimum system circuit that Proteus executes fails to operate correctly, despite the fact that the source code and circuit wiring comply to programming and circuit theory standards. This is in contrast to other microcontroller-based minimum system circuits that it can be simulated successfully and as intended. This research aims to get hints in programming the 8031 minimum system circuit simulated using Proteus. The problem was investigated and analyzed by observing the parameters that become the properties of each element in the circuit, especially the RAM, then comparing them with the specifications of the microprocessor. The experimental results showed that some adjustments on the program code were necessary either written using assembly language or C program code
Applying the design thinking methodology in ui/ux development: A case study of justeatss.id e-commerce platform
Full text linkThis study demonstrates the effectiveness of the Design Thinking approach in developing the UI and UX of the Justeatss.id e-commerce website. The process followed five stages including empathizing, defining, ideate, prototype, and test which are focusing on user needs as the foundation for design. Qualitative data from interviews and usability testing identified key issues such as difficult navigation, unappealing visual design, and checkout obstacles. High-fidelity prototypes were developed to create an intuitive, engaging, and responsive interface. Evaluation using the System Usability Scale (SUS), User Acceptance Testing (UAT), and the Customer Satisfaction Index (CSI) showed significant improvements in usability, functionality, and overall user satisfaction, with the website receiving an "Excellent" SUS score, a perfect UAT score, and 100% CSI rating. These results highlight that a user-centered Design Thinking approach can effectively optimize aesthetics, functionality, and emotional satisfaction, providing practical insights for enhancing e-commerce platform performance
Implementation of digital human avatar virtual assistant with augmented generation retrieval technology in interactive systems for nutrition education
Full text linkIn today's digital era, artificial intelligence (AI) based chatbots utilizing Large Language Models (LLM) have become a promising innovation for nutrition education. The integration of Natural Language Processing (NLP) technology with digital animation systems creates new opportunities in developing interactive applications in the context of Indonesian public health, with nutritional challenges in Indonesia showing 21.5% of toddlers experience stunting and 12.2% of adults face obesity, indicating an urgent need for accessible and comprehensive nutrition education. This research aims to develop the GiziAI website that integrates Retrieval Augmented Generation (RAG) technology with digital human avatars to provide nutrition education to Indonesian society. The research method implementing the Nusantara 2.7B Indo Chat large language model, ChromaDB as vector database, Three.js for 3D rendering, ElevenLabs for text-to-speech, and Rhubarb for lip synchronization, with React JS, Flask, MySQL, and LangChain frameworks. Evaluation was conducted using LangSmith to measure model response time, BERTScore to measure answer accuracy, and black box testing for website functionality. Research results show that the RAG system significantly improves model performance with precision increase of 71.5%, recall 60.6%, and F1-score 65.8%, while GPU usage accelerates response by 13.5% compared to CPU. Black box testing shows all website features function as expected
Design and implementation of an IoT-based real-time water level monitoring system of belik river at yogyakarta city
Full text linkFlooding is a serious threat in the Belik River area, Yogyakarta, especially during high rainfall and blocked water flow, while the existing Early Warning System (EWS) managed by BPBD Yogyakarta is still not functioning optimally. This study aims to design and implement a real-time water level monitoring system that can be used as an early warning system for flooding. The research method uses a Waterfall approach that includes the stages of requirements, design, implementation, testing, and operation and maintenance. The developed system is based on the Internet of Things (IoT) using HC-SR04 ultrasonic sensors and NodeMCU ESP32 microcontrollers connected to Firebase for data storage, visualized through a website, and equipped with automatic notifications via the Telegram application. The system was implemented at two points on the Belik River, Yogyakarta, with water level data recorded every minute for 3 hours over three days. The data obtained was pre-processed by averaging the sensor readings and ignoring abnormal data to improve stability. Evaluations were carried out on sensor accuracy, data transmission stability, real-time display on the website, and Telegram notification speed. The results of the study showed an average measurement error of 1.27%, with a tendency for the error to increase at distances greater than 300 cm. This system has proven capable of providing rapid information to the Regional Disaster Management Agency (BPBD) and the surrounding community to take early anticipatory measures against potential flooding, thereby helping to reduce the impact of losses and speed up emergency response
Web based IoT monitoring system for ultrasonic water flow measurement using ESP32-S3 and cloud database
Full text linkEfficient water management is crucial for ensuring sustainable resource utilization and reducing water losses in both industrial and domestic applications. This study presents the design and implementation of a smart water monitoring system based on an ultrasonic flow meter, which enables accurate, real-time measurement of water flow without physical contact with the medium. The proposed system integrates ultrasonic sensors with a microcontroller-based data acquisition unit and wireless communication to transmit flow rate, volume, and consumption data to a cloud-based monitoring platform. The system was tested in various flow conditions to evaluate accuracy, stability, and energy efficiency. Experimental results demonstrate that the ultrasonic flow meter achieved a measurement accuracy of ±1% compared to a reference turbine flow meter, while maintaining minimal power consumption. Furthermore, the integration of Internet of Things (IoT) capabilities allows remote monitoring, anomaly detection, and data logging for long-term analysis. The results indicate that this ultrasonic-based monitoring system provides a reliable and non-invasive solution for smart water management, offering potential applications in household metering, agricultural irrigation, and industrial fluid monitoring
Analysis of the musculoskeletal pain risk profile through observation of work posture in minimarket employees
Full text linkMusculoskeletal pain refers to the discomfort experienced in the musculoskeletal system as a result of various pain-inducing factors. Musculoskeletal pain is a leading cause of disability and absenteeism in the workplace. The primary causes of declining health among workers include occupational injuries (29.5%), overtime (25.9%), and ergonomic factors (13.7%), significantly influence occupational health and can adversely affect organ function. The study was conducted through observational approach with a cross-sectional design and primary data collected using the Nordic Body Map (NBM) questionnaire and the Rapid Entire Body Assessment (REBA). The study population is mini market employees and sample comprises were employees who fulfil inclusion and exclusion criteria as many as 50 respondents by using non-probability sampling technique. The results of study were obtained that the majority of workers belong to the age group of under 35 years, as many as 49 individuals (98%) and more than half of the respondents are predominantly female, accounting for 52% of the total. Furthermore, the study indicates that the dominant "Very Painful" predilection criterion is most frequently reported in the back and waist of the respondents, accounting for 40% of the total respondents. Next, the majority of minimarket workers exhibit a moderate level of risk in their work posture, with 21 individuals (42%) falling into this category. Based on the results, it is recommended that respondents engage more actively in physical exercise and massage therapy
Activity-based function point complexity of use case diagrams for software effort estimation
Full text linkThis study proposes a Function Point Analysis (FPA) based software development effort estimation methodology integrated with Use Case Diagrams. These methods include identifying actor activities, classifying those activities into FPA categories, and calculating Unadjusted Function Points (UFP). Followed by the calculation of Technical Complexity Factors (TCF) and Adjusted Function Points (AFP), this study aims to produce more accurate man-hours estimates. Results show a UFP of 162 TCF of 11, AFP of 123.12, and an estimated effort of 1846.8 hours worked, while the actual effort is 1228 hours. Evaluation of estimates using the metrics Mean Magnitude of Relative Error (MMER) 0.34, Mean Magnitude of Relative Error (MMRE) 0.50, Mean Absolute Error (MAE) 618.80, Mean Balanced Relative Error (MBRE) 0.50, Mean Inverse Balanced Relative Error (MIBRE) 0.34, and Root Mean Squared Error (RMSE) 618.80, showed sufficient precision despite the overestimation. The study suggests the need for adjustments in TCF calculations and considering development environment factors in more detail to improve estimation accuracy. These findings are essential in improving the precision of effort estimation methodologies in software development, particularly in projects that use Use Case Diagrams as the primary framework
Improving car price prediction performance using stacking ensemble learning based on ann and random forest
Full text linkDetermining the right selling price for a car can be a challenge for car sales companies. The selling price of a car is highly influenced by car characteristics such as brand, type, year of production, fuel type, and mileage. Therefore, the research aims to develop a more accurate model of car price prediction model by using a stacking ensemble technique that combines Random Forest and ANN. Random Forest is effective in handling outliers and reducing the risk of overfitting, while ANN has the advantage of capturing complex nonlinear patterns. The results show that the stacking ensemble model combining ANN and Random Forest can predict car sales prices by achieving an R2 value of 0.97. The results of this study can help distributors in selling cars make the right decisions regarding the sales price of cars. To improve the generalization of the model, future research is recommended to try a combination of different ensemble methods and the use of larger and more diverse datasets
Implementation of promotion mix in increasing sales at Janji Jiwa Coffee Volume 841
No full textThis research aims to identify the application of the promotion mix at Janji Jiwa Coffee Shop Volume 841. This research uses descriptive qualitative research and data source collection with observation and in-depth interview method. The informant is the manager and owner of Janji Jiwa Coffee Shop Volume 841. Findings of this research indicate that Janji Jiwa Coffee Shop Volume 841 applies four promotion mix variables from the five existing variables. (i) Advertising using paid promote, billboards and banners. (ii) Personal selling by fostering good relations by coming directly to the house. (iii) Direct marketing, in collaboration with grabfood and gofood. (iv) Sales promotion with bonus vouchers, free coffee milk vouchers, and shopeepay cashback vouchers