Sinkron : jurnal dan penelitian teknik informatika
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Raw Material Weighing Application Through Visual-Based RS-232 Cable Port
No full textOfficers who record incoming weighing data using a manual weighing machine experience difficulties when interacting with the weighing device. It is difficult to press the buttons, the storage memory cannot be more than three digits, and the display is difficult for officials to understand which can hinder the performance of recording the scales. Lack of capacity to store scale data on
Officers who record incoming weighing data using a manual weighing machine experience difficulties when interacting with the weighing device. It is difficult to press the buttons, the storage memory cannot be more than three digits, and the display is difficult for officials to understand which can hinder the performance of recording the scales. Lack of capacity to store scale data on machine, so it can only store a maximum of 3 data scales. Inflexible on-machine data storage system. That is, the data scales that have been stored cannot be moved apart from within the machine itself. The large size of the machine is enough to take up space. So it is necessary to design a signal connection path from the scales to the computer via cable. With a computerized weighing application through the RS-232 communication port, where data input can be done using a visual-based weighing application. This data is then processed and produces an accurate report according to the data recorded by the scales. The testing process is carried out by entering data on the scales 19 times along with the check-in and check-out process for each incoming truck of raw materials for transportation. The testing process is carried out so that the application can run properly.
machine, so it can only store a maximum of 3 data scales. Inflexible on-machine data storage system. That is, the data scales that have been stored cannot be moved apart from within the machine itself. The large size of the machine is enough to take up space. So it is necessary to design a signal connection path from the scales to the computer via cable. With a computerized weighing application through the RS-232 communication port, where data input can be done using a visual-based weighing application. This data is then processed and produces an accurate report according to the data recorded by the scales. The testing process is carried out by entering data on the scales 19 times along with the check-in and check-out process for each incoming truck of raw materials for transportation. The testing process is carried out so that the application can run properly
Effect Effect of Gradient Descent With Momentum Backpropagation Training Function in Detecting Alphabet Letters
No full textThe research uses the Momentum Backpropagation Neural Network method to recognize characters from a letter image. But before that, the letter image will be converted into a binary image. The binary image is then segmented to isolate the characters to be recognized. Finally, the dimension of the segmented image will be reduced using Haar Wavelet. One of the weaknesses of computer systems compared to humans is recognizing character patterns if not using supporting methods. Artificial Neural Network (ANN) is a method or concept that takes the human nervous system. In ANN, there are several methods used to train computers that are made, training is used to increase the accuracy or ability of computers to recognize patterns. One of the ANN algorithms used to train and detect an image is backpropagation. With the Artificial Neural Network (ANN) method, the algorithm can produce a system that can recognize the character pattern of handwritten letters well which can make it easier for humans to recognize patterns from letters that are difficult to read due to various error factors seen by humans. The results of the testing process using the Backpropagation algorithm reached 100% with a total of 90 trained data. The test results of the test data reached 100% of the 90 test data
Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods
No full textTomato Leaf Disease is one of the common things for farmers in growing tomatoes. Tomatoes are one of the popular crops that can grow in low and high areas but are susceptible to disease. For this reason, farmers take precautions by looking at the characteristics and texture of tomato leaves. However, this requires more time and money and a long process. One of the efforts that can be made is to classify tomato leaf diseases. This research aims to classify using the Support Vector Machine and K-Nearest Neighbor methods. The dataset used is tomato leaf image data with 4 classes of leaves affected by disease and 1 healthy leaf. We evaluate and analyze all models using 5-Fold, 10-Fold, and 20-Fold Cross Validation with accuracy, precision, and recall for the best accuracy. The best results of this study are accuracy in the SVM method of 0.953 or 95.3%, Precision of 0.953 or 95.3%, and Recall of 0.953 or 95.3% with 10-Fold Cross-Validation. Compared to the K-NN method, it only obtained an accuracy of 0.907 or 90.7%, a Precision of 0.908 or 90.8%, and a Recall of 0.907 or 90.7% with 10-Fold Cross-Validation
Blockchain Technology For Circular Economy In Plastic Bank
No full textWith the use of blockchain technology, this research sought to understand the applications, benefits, and limitations faced by circular economy-based businesses. This research was conducted at the Plastic Bank Company, which used a digital conference room to allow interviews that could not be conducted in person, as well as the researcher's residence for online data gathering and document review. Five management members of the Plastic Bank Company comprise the sample population. The information used is first-hand information derived from interview findings. In order to acquire data, several methods including interviews, document analysis, and observation were applied and tested by Triangulation. The findings of this study revealed: 1) Companies with a circular economy may employ blockchain technology to change supply chain operations, tracking, and tracing. 2) Blockchain technology has benefits for businesses based on the circular economy, including easier distribution management, less duplicate papers, increased cost effectiveness, and the ability to turn plastic trash into digital cash. 3) The general public is still unaware of the use of blockchain technology for businesses that rely on the circular economy. Furthermore, the company's success is constrained on a small scale due to the absence of finance from affiliated parties. Therefore, in order to grow the use of technology on a big scale, this circular-based economy firm for plastic banks has to strengthen its performance and efforts
Comparison of the K-Means Algorithm and C4.5 Against Sales Data
No full textIn general, the process of collecting and grouping data requires a long process. And if it has to be grouped manually it takes a very long time. Therefore, data mining is a solution for clustering data - a lot of data to classify it. In this research conducted at CV.Togu - Togu On Medan Branch, data mining is applied using the K-Means process model and the C4.5 algorithm which provides a standard process for using data mining in various fields used in classification because the results of this method easy to understand and easy to interpret. . The K-means method is a non-herarical method which is an algorithmic technique for grouping items into k clusters by minimizing the distance of the SS (sum of square) to the cluster centroid. In the K-means method, the number of clusters can be determined by the researcher himself. And the testing methods used to measure cluster quality are the Silhouette Coefficient and the Elbow Method. Based on the research conducted, there are significant differences before and after using the two methods. The results of the K-Means algorithm will be compared with the results of the C4.5 algorithm in the form of rules (decision trees). This research produces data on goods that have the highest level of sales/behavio
Classification of Stroke Opportunities with Neural Network and K-Nearest Neighbor Approaches
No full textStroke is one of the deadly diseases. This is illustrated in stroke deaths in Indonesia which reached a death rate of 131.8 cases. Some of the things that cause a stroke to become a disease with the highest mortality rate are related to transitions in human life in 4 aspects, namely epidemiology, demography, technology, and economics, socio-culture. Of the many influencing aspects, one of the transition points of human life in the technological aspect can be an alternative solution and prevention. Aspects of technology with the utilization of data can be used as a preventive measure for stroke. One approach is to use data mining techniques, which can provide an initial picture regarding the chances of getting a stroke so that it can be used as an early warning for patients. With so many techniques in data mining, this study used a classification or grouping approach using 2 algorithms, namely K-Nearest Neighbor and one of the Neural Network groups, namely Multi-Layer Perceptron. This research will focus on finding the accuracy and best results of the two algorithms in classifying. The final result of this study is that the K-Nearest Neighbor algorithm has a better accuracy of 95% compared to the Multi-Layer Perceptron which produces an accuracy of 88
The Black Box Testing of the "Hybrid Engine" Application Using Boundary Value Analysis Technique: -
No full textThe "Hybrid Engine" application is an introduction to a hybrid engine that is packaged attractively and can be accessed online, this application is very important for conveying information about hybrid engines, if an error occurs in the functional application there can be misunderstandings about the information conveyed. Therefore it is necessary to test to ensure the quality of the application that has been produced. Testing is an evaluation process of assessing the functional quality of software to check whether the software meets the expected process or not. Functional processes that have not been maximized can cause inequalities in the data information to be displayed. Applications that have been designed must go through the testing stages to ensure the level of functional quality. Of the several types of black box testing methods, one of them is Boundary Value Analysis. The method tests the maximum and minimum number of digits to produce a valid value and is easy enough to test "hybrid engine" applications. The first stage carried out in this research is to identify the functionality to be processed and ensure that the maximum and minimum number of digits matches the predetermined system arrangement. The result of applying the method used is that the quality of the application is under its function, and can be utilized properly by the user. The results of the Boundary Value Analysis test show that the application is following the expected system and instructions with a success percentage of 78.245615%
Gold Price Prediction Using the ARIMA and LSTM Models
No full textFor some investors who are interested in investing for the long term, gold is one of the promising options because the price of gold has recently continued to increase. In the current condition, gold investors generally use instinct and guesswork in investing in gold because there is a benchmark gold price based on world market prices. Many empirical studies identify factors that affect gold prices to forecast them. Factual and econometric analysis recommend different informative factors. This study investigates the influence of gold prices and five supporting variables in the form of economic indicators, namely crude oil price, federal funds effective rate, consumer price index, effective exchange rate and S&P 500 stock market index between 2002 and 2022. Models were built using ARIMA and LSTM methods, evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE). With a dataset allocation of 80% for training data and 20% for testing data, the comparison of actual gold prices with the predicted values of each model shows that LSTM has the best performance compared to the ARIMA (0,1,1) model where the LSTM model has an RMSE value of 8.124 and a MAPE value of 0.023. The models also show that economic indicators affect the ounce price of gold
COMPARISON OF LASSO AND ADAPTIVE LASSO METHODS IN IDENTIFYING VARIABLES AFFECTING POPULATION EXPENDITURE
No full textAbstract: Since 2019, the difference in the increase in per capita expenditure of the population has continued to decline, and the most significant was only IDR 18,464 in 2021, indicating that the level of consumption of the population has not improved significantly, and the turnover of the community's economy is also not good. Multiple linear regression is more appropriate than other types of linear regression because it considers the influence of more than one independent variable on the dependent variable. However, problems may arise in the use of multiple linear regression, such as multicollinearity. To overcome this problem, other methods such as LASSO and adaptive LASSO should be used. Both methods have the ability to overcome multicollinearity between independent variables, thereby reducing the risk of misestimation. Nevertheless, the LASSO and Adaptive LASSO methods have differences in selecting important variables, so it is necessary to compare which method is better in terms of identifying influential variables. Based on the MSE and R-square comparison values, it is concluded that the Adaptive LASSO method model is the best model with a lower MSE value and a higher R-square value of 93%. The variable selection results of the Adaptive LASSO model are population size, number of households, average number of household members, constant price GDP, confirmed cases of COVID-19, human development index, percentage of the poor population, university student participation rate, and open unemployment rate
Analysis of User Adoption Levels of JAKI Application Using the Government Adoption Model (GAM)
No full textThis study delves into an analysis of the adoption patterns within the Jakarta Today e-government application (JAKI) through the dual lenses of the Government Adoption Model (GAM) and the Structural Equation Model (SEM). Encompassing JAKI users aged 17 years and above, the research encapsulates a substantial sample size of 384 individuals. The research findings underscore the pivotal role of key factors in driving e-Government adoption within the context of JAKI. Notably, Perceived Service Response, Perceived Trust, Perceived Uncertainty, Perceived Security, and Privacy collectively wield a significant and affirmative impact on the Adoption of e-Gov. However, intriguingly, factors including Perceived Awareness, Computer-self Efficacy, Availability of Resources, Perceived Ability to Use, Perceived Compatibility, Perceived Functional Benefit, Perceived Image, Perceived Information Quality, and Multilingual Option do not exert a notable influence on the Adoption of e-Gov. These insights proffer invaluable guidance for the Jakarta City Government, facilitating an enhanced understanding of user perceptions and needs. By meticulously addressing the determinative factors that engender a favorable adoption environment, the government stands poised to elevate the efficacy and reach of its e-government service, thus fostering greater citizen engagement and interaction with the JAKI application