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The influence of service quality, product quality, and product price on consumer satisfaction at teras malioboro 1 yogyakarta
No full textThis research aims to determine the effect of service quality, product quality, and product price on consumer satisfaction at Teras Malioboro 1 Yogyakarta. Teras Malioboro 1 Yogyakarta is located on Jalan Margo Mulyo, Ngupasan, Gondomanan, Yogyakarta City. This study uses primary data with a questionnaire method where the number of respondents is 100 respondents selected using purposive sampling technique with a quantitative approach carried out using multiple linear regression analysis with normality test, multicollinearity test, heteroscedasticity test, determination coefficient test (R²), t-Test, and f-Test. The results of the tests carried out indicate that service quality has a significant effect (α = 5%), product quality does not have a significant effect (α = 5%) and product price has a significant effect (α= 5%) on consumer satisfaction at Teras Malioboro 1 Yogyakarta with a significant (α = 5%). With the percentage obtained from the determination coefficient test is 80.2% and proven by the researcher's observation with the existence of good service quality, product quality, and product prices in a purchase, it will create satisfaction for its consumers and play an important role in forming consumer satisfaction
Eye disease classification using deep learning convolutional neural networks
Full text linkThis study begins with the analysis of the growing challenge of accurately diagnosing eye diseases, which can lead to severe visual impairment if not identified early. To address this issue, we propose a solution using Deep Learning Convolutional Neural Networks (CNNs) enhanced by transfer learning techniques. The dataset utilized in this study comprises 4,217 images of eye diseases, categorized into four classes: Normal (1,074 images), Glaucoma (1,007 images), Cataract (1,038 images), and Diabetic Retinopathy (1,098 images). We implemented a CNN model using TensorFlow to effectively learn and classify these diseases. The evaluation results demonstrate a high accuracy of 95%, with precision and recall rates significantly varying across classes, particularly achieving 100% for Diabetic Retinopathy. These findings highlight the potential of CNNs to improve diagnostic accuracy in ophthalmology, facilitating timely interventions and enhancing patient outcomes. For future research, expanding the dataset to include a wider variety of ocular diseases and employing more sophisticated deep learning techniques could further enhance the model's performance. Integrating this model into clinical practice could significantly aid ophthalmologists in the early detection and management of eye diseases, ultimately improving patient care and reducing the burden of ocular disorders
Early Detection of Diabetes Using Random Forest Algorithm
Full text linkDiabetes is one of the most chronic and deadly diseases. According to data from WHO in 2021, there were approximately 422 million adults living with diabetes worldwide, and this number is expected to continue to increase in the future due to various factors. Many studies have been conducted for early detection of diabetes by focusing on improving accuracy. However, a big problem in diabetes prediction is the selection of the right classification algorithm. This study aims to improve the accuracy of early detection of diabetes by implementing the Random Forest algorithm model. This research was conducted with the stages of data collection, data preprocessing, split data, modeling, and evaluation. This research uses the Pima Indian Diabetes data set. The results showed that the diabetes early detection model using the Random Forest algorithm produced an accuracy of 87%. This research shows that by using the Random Forest algorithm model, the performance of early detection of diabetes can be improved. However, there is still room for optimization of this performance, which is recommended for further research to carry out feature selection, data balancing, more complex model building, and exploring larger data
IoT-based implementation of rickshaws for real-time monitoring and measuring the weight of cattle
Full text linkIn the era of modern agriculture that is increasingly dependent on technology, livestock management has become crucial to increasing efficiency and productivity. An important aspect in livestock management is providing appropriate feed to fattening cattle. Manual monitoring of feed weight is often complex and prone to errors, which can have a significant impact on operational efficiency and result in losses. Accuracy in monitoring feed weight is the key to maintaining optimal health and growth of cattle. Internet of Things (IoT) technology is emerging as an innovative solution to overcome these challenges. The use of Angkong load cells, a tool connected to IoT, allows automatic monitoring of feed weight with a high level of precision. The test results show an error rate close to zero, with a Mean Absolute Percentage Error (MAPE) of around 0.158%, making the Angkong load cell a reliable tool. With this capability, farmers can monitor cow feed weight in real-time with minimal error rates. This not only increases the operational efficiency of the farm but also optimizes the health and growth of livestock more efficiently, having a positive impact on overall farm productivity. The aim of this research is to monitor the amount of feed given to cows with an adequate level of accuracy. Rickshaw load cells can be well suited for this use due to their ability to handle relatively large weights with fairly good accuracy, but do not necessarily have the level of precision required in laboratory measurements or the pharmaceutical industry
The Influence of Determining the K-Value on Improving the Diabetes Classification Model using the K-NN Algorithm
Full text linkDiabetes mellitus is still an important health problem globally, so it requires an efficient classification model to help determine a patient's diagnosis. This study aims to determine the K-value on the accuracy performance of the diabetes classification model using the K-Nearest Neighbors (K-NN) algorithm. This research utilizes a simulated dataset generated through interaction with ChatGPT, we investigate various K-values in the K-NN model and assess its accuracy using a confusion matrix. Based on experiments, we found that the K-NN classification model with a K=6 obtained an optimal accuracy of 97.62%. Thus, our findings highlight the important role of selecting optimal K-values in improving the performance of diabetes classification models
Relationship between healthy house and smoking habits with afb (+) pulmonary tuberculosis cases at the singotrunan public health center, Banyuwangi district
No full textSingotrunan Public Health Center has 65 cases of AFB (+) pulmonary TB, which means increased by 22.6% from the previous year. Healthy house and smoking habits are known to be some of the risk factors for pulmonary TB cases. This study aims to analyze the relationship between healthy house and smoking habits with AFB (+) Pulmonary Tuberculosis cases in the working area of the Singotrunan Public Health Center, Banyuwangi District. The method used is analytic research with a case-control type. This research was conducted on 28 samples, consist of 14 from the case group and 14 from the control group in the working area of the Singotrunan Health Center. Data were obtained from assessments using observation sheets and interviews using questionnaires. The data obtained were analyzed univariably using tabulation and bivariably using the Chi Square test using the Odds Ratio value to determine the dynamics of the independent and dependent variables. The results of the Chi Square test showed that there is a significant relationship between healthy houses (pvalue = 0.022) and smoking habits (pvalue = 0.002) with AFB pulmonary TB cases (+). The Odds Ratio value shows that unhealthy homes have a 6.6 times higher risk and smoking increases the risk 15 times higher for being diagnosed with AFB (+) Pulmonary TB. In conclusion, healthy homes and smoking habits have the potential to increase the risk of AFB (+) Pulmonary TB in the working area of the Singotrunan Public Health Center, Banyuwangi District
Utilization of eye tracking technology to control lights at operating room
Full text linkThe development of technology for control systems is increasing, especially to help people with disabilities and facilitate the performance of health workers. Where it is required to maintain the level of sterilization of equipment in hospitals. Eye tracking technology in the last few decades has developed very rapidly. This control system using eye tracking technology can be done with eye movements for those who experience mobility problems. This research aims to develop a light control system through eye activity using the Mediapipe framework from Google. In this study, 2 lamps (A and B) were used, each with a light intensity of 10W. In lamp A, the light intensity can be controlled by turning the light on or off using the blink of the right eye and the blink of the left eye, while lamp B can adjust the intensity of the light by opening both eyes (right and left). Research on a lighting control system using the eye tracking method with an image processing system has been successfully carried out. All data generated is based on activity, distance, eye position on the camera and differences in participant backgrounds. Apart from that, a system that can work well means consistent results are obtained. However, based on distance, the system can read with precision at distances of 50 cm and 60 cm
Performance analysis of amd ryzen 5 4600h mobile processor undervolting using AMD APU tuning utility on cinebench R23
Full text linkIn an effort to optimize laptop performance for gaming and high-demand applications without costly hardware upgrades, this research investigates the impact of CPU undervoltage using the AMD Ryzen Mobile 4600H processor. Undervolting, the process of reducing the CPU's voltage supply, is proposed as a strategy to enhance performance by lowering operational temperatures, potentially allowing for more efficient processing. This study uses the AMD APU Tuning Utility to adjust voltage settings and assesses performance changes using a series of benchmarks. Initial findings indicate that undervoltage can indeed have beneficial effects. The most significant data point from the research is the comparison of Cinebench R23 scores before and after applying undervolting settings. From a baseline score of 6835 points, system performance increased to 7880 points in the optimal undervolting scenario, an improvement of 1045 points. This shows a noticeable enhancement in processing efficiency. However, the study also reveals some complexities in undervolting, such as an initial drop in performance in the first configuration before gains are realized in subsequent adjustments. Efficiency values varied across different settings, starting with a decrease (-0.41) and culminating in a substantial gain (+1.54) by the fourth configuration. These results suggest that while undervolting can improve performance, the outcomes depend significantly on finding the right voltage balance, highlighting the nuanced nature of CPU voltage manipulation for performance optimization
Classification of water quality based on dissolved solids and turbidity parameters with the utilization of total dissolved solids sensor and turbidity sensor
Full text linkClean water quality is essential for public health, but its scarcity is increasing amid population growth and industrialization. Monitoring turbidity and total dissolved solids (TDS) is essential to determine the quality of clean water. This study addresses the urgent need for accurate and reliable water quality monitoring to test the applicability of TDS and turbidity sensors in taking measurements, aiming to develop efficient monitoring solutions for public health and sustainable water management. The TDS sensor operates according to the principle of electrical conductivity, with a range of 0 to 1000 ppm and an accuracy of ±10%. The turbidity sensor detects water turbidity by determining the level of turbidity particles. The ESP32 microcontroller integrates Wi-Fi and USB capabilities. The hardware and software design ensures accurate sensor readings, which are critical to successful water quality measurement and monitoring. The test results show satisfactory accuracy of the TDS sensor with an average error of 0.09% and good accuracy of the turbidity sensor with an average error of about 1.536%. Concerning the above two parameters, in this study, among 15 water samples, seven were clean, meeting the standard, while eight water samples were dirty, exceeding the limit, making them unsafe for human consumption
Improved convolutional neural network model for leukemia classification using EfficientNetV2M and bayesian optimization
Full text linkLeukemia is a health condition in which the body produces too many abnormal white blood cells or leukocytes. Leukemia can affect both children and adults. Early diagnosis of leukemia faces significant challenges, as diagnostic methods are time consuming, require experienced medical experts, and are expensive. Previous studies have been conducted using deep learning approaches, but it is still rare to find a model that shows the best performance and uses optimization methods to classify leukemia diseases. Therefore, a Convolutional Neural Network (CNN) model with EfficientNetV2M architecture and Bayesian Optimization is proposed as the main method assisted by ImageDataGenerator in preprocessing. This study shows a significant impact of Bayesian optimization with good Accuracy, Precision, Recall and F1-Score results of 91.37%, 93.00%, 87.00%, 89.00%, respectively, which are expected to improve the performance of the model in previous studies in classifying leukemia diseases