Implementation of Random Search Algorithm with FSSRS (Fixed Step Size Random Search) for Applicating the Patrol System Based on Mobile Computing

Environmental security is very influential for the sustainability of human life. In order for environmental security to remain in a safe condition, a system is needed that can control the environment, such as patrolling at every point to ensure that the environmental conditions are safe. However, it is felt that this is not enough if the patrol system is not assisted by tools or systems that are digitalized and integrated with community service officers, such as firefighters, ambulances, and police, and are easy for officers to use when conducting patrols. So, it is necessary to schedule patrols to several points with different routes for each activity so that it is not easily read by unwanted parties in terms of crime. In order for the system to obtain patrol scheduling in a timely and efficient manner, an appropriate and efficient algorithm is needed, the algorithm is random search with FSSRS (Fixed Step Size Random Search) which can suggest random and precise patrol scheduling. From the results of training using four iterations, namely 50, 100, 150, and 200, the best value was produced in the 200th iteration. Data was taken from the results of a case study survey with eight patrol points using coordinates at each point. So, it can be concluded that the FSSRS algorithm is effectively used to randomize patrol points and can be implemented in the application patrol system

Lean UX: Applied PSSUQ to Evaluate Less-ON UI/UX Analysis and Design

Indonesian start-up growth continues to show upward growth. Behind the upward movement of change are success statistics that develop contradictions. According to start-up statistics, about 90% of start-ups fail. Up to 75% of unicorn start-ups believe his excellent UI/UX design can boost start-up valuations and additional investment capital. Less-On is a tutoring provider that acts as an intermediary between teachers and students. This research is done by integrating the process of Lean UX methodology into each process present in each phase of software development. The results obtained from this research are final prototypes that have been validated in terms of criticism and suggestions through questionnaires in the form of lessons on start-up branding. The positive user experience and excellent usability will help further the development of the tutor booking application prototype. This plays an important role in the acceptance, satisfaction and efficiency of using this Less-ON application. The user interface has excellent usability for users tested using the PSSUQ, with an overall average score of 2.136, indicating system usability, information quality, interface quality, and overall Satisfaction-based demonstrates that the system is highly acceptable

Web-Based Counseling Skills Evaluation Information System Using Design Science Research Methodology (DSRM) Approach

This research presents the development of a web-based counseling skills evaluation information system using the Design Science Research Methodology (DSRM) approach. The DSRM approach was utilized to design and develop an effective and efficient information system that meets the requirements of the counseling profession. The research discusses the six stages of DSRM, which include problem identification, solution design, construction, evaluation, communication, and reflection, and how they were used to develop the system. The evaluation stage involved conducting empirical studies to assess the system's effectiveness in supporting counseling skills evaluation. The article concludes that the DSRM approach was effective in developing a web-based counseling skills evaluation information system that meets the needs of the counseling profession. This web using PHP, MySQL and Youtube API. The testing software using blackbox and beta testing. the final results of the study show the level of success of the system in facilitating the process of assessing and evaluating basic counseling skills

The Evaluation of Computer Science Curriculum for High School Education Based on Similarity Analysis

The government is currently developing regulations to regulate education curriculum For High School Students. In this regulation, curriculum standards have been created that can be developed by educators in schools. Computer science teachers at the school level develop a curriculum that has been set as a standard curriculum. However, measurable evaluation to optimize the development of the new curriculum has not been available yet. This research proposes a form of evaluation that can be used as a benchmark by analyzing the similarity of curriculum content developed by teachers using a text mining approach. This is conducted by comparing computer science documents with applicable documents, namely knowledge field documents. It is expected that the results of optimizing competency development in the computer science curriculum can be achieved better. The average similarity checking performances using Cosine Similarity and Word2Vec are 40.9850 and 97.3558 respectively. Meanwhile, in the process of fulfilling the knowledge sector, with Cosine Similarity an average percentage of 40.98% was obtained, and with Word2Vec an average percentage of 97.36% was obtained. The results of this trial will be used as a basis for measurable evaluation of teacher contributions to be able to develop the curriculum better according to the applicable curriculum. The results of this evaluation are also used by the government to make future curriculum evaluations more measurable and the standards used are clear and help facilitate curriculum development in schools

Pay Later Risk Management: A Review of FMECA and Potential Customer Prediction Frameworks Through the Application of Machine Learning

The development of technology continues to develop and gradually change the way people buy such as on online shopping sites. The increase in internet use, especially in the use of E Commerce, has given birth to great potential in the market, especially in Indonesia. These changes prompted the birth of various payment methods. One of them is Pay Later. 27% of the 3560 samples decided to use Pay Later with all the conveniences offered. However, the development of Pay Later is not synchronized with good risk management. The use of Pay Later, which is not targeted at the right consumers, causes PT. XYZ suffered losses due to 22.37% of users defaulting on Pay Later installments. The purpose of this study is to reduce Pay Later default users by answering what factors cause consumers to default. To support this study, the authors used FMECA, Cause Effect Diagrams and conducted tests using Machine Learning to improve company efficiency. Through critical matrix analysis, the author gets 3 priority failure modes, Users default, users disappear, and users experience payment delays. In solving the problems in this study, the authors provide recommendations in the form of a new framework in the form of analyzing the best Pay Later offers by analyzing consumer behavior patterns in an E Commerce by utilizing Machine Learning. However, future research will need to be conducted correlation analysis and static testing in testing attribute correlation before testing algorithms when building machine learning models. The authors also suggested comparing using other methods to improve risk management in this study

Stock Price Prediction using Prophet Facebook Algorithm for BBCA and TLKM

Stocks are an investment instrument that is starting to be in great demand by the public today. However, stock prices are fluctuating, making people feel doubts about when they are going to invest. To overcome these doubts, we need a way to predict stock prices. This study aims to predict stock price fluctuations using Facebook's Prophet Algorithm to help people decide their investment in stock. The research object used is BBCA and TLKM stock price data in the form of a time series from 03 May 2021 to 28 April 2022 with stock price testing data for the next week, namely 01 May 2022 to 07 May 2022. From the training and testing process done, a prediction is produced that is very close to the original value. Using the RMSE, MSE and MAE measurements, we get RMSE 49.6, MSE 2462.1 and MAE 37.5 for BBCA and RMSE stocks, namely 21.3, MSE 456.5 and MAE 19.2 for TLKM shares. The conclusion is that Facebook's Prophet Algorithm is suitable for predicting stock prices

Enhancing Soil-Transmitted Helminth Detection in Microscopic Images Using the Chain Code for Object Feature Extraction

Soil-Transmitted Helminth (STH) infections are a grave global health issue, which involves particularly in countries that are developing with insufficient sanitation and limited access to healthcare. With better intestinal helminth egg detection technology, health facilities in areas with limited resources can identify and treat these infections more promptly. It is necessary to create a strong framework and an effective method to solve this challenge. The outcomes of this study could assist in parasite infection discovery and public health. Chain code-based feature extraction strategy can also be the foundation for the development of comparable approaches for diagnosing various parasitic diseases. Overall, the neural network design used in this study makes the model that is produced a good model that assigns well to never-before-seen data. The significance of image processing technologies in the medical field is shown by this study

Website Evaluation of DISPERDAG Section of the Average Price of Standard Needs Using the WEBQUAL 4.0 Method

DISPERDAG of Madiun City is one of the organizations that has utilized technology. The community can easily access and get price updates for basic necessities at Pasar Besar City of Madiun. The Internet helps support strong governance management, which benefits its effectiveness. Its functions include making it easier to get information, access public services, communicate with the public, and others. DISPERDAG aims to continue to provide quality services. The quality of services provided to the public must complement the implementation of website-based services so that the public continues to use SISKAPERBAPO services. To provide the best service to users, government institutions should prioritize service quality. Therefore, the success of the service quality of the SISKAPERBAPO website has not been known as long as the website is implemented. This is because there has never been an evaluation of website quality based on user satisfaction. It goes without saying that a website that is useful for assisting users in obtaining information must maintain quality in terms of information delivery and user interaction. Good service quality is offered from the user's point of view as well as the service provider's point of view

Business Intelligence Based on Kimball Nine-Steps Methodology for Monitoring the Feasibility of Goods in Market

Consumer protection is of great concern to the Government of Indonesia through the supervision of goods on the market by the Department of Industry and Trade in each province. The Bali Provincial Office of Industry and Trade has transactional data from various data sources but has not been able to optimize it to support monitoring of goods circulating in the market. This research designs and implements a Data Warehouse-based Business Intelligence system using the Kimball Nine-Steps Methodology and Pentaho BI tools, to facilitate the storage and processing of goods data on the market, and monitor their feasibility. The results of this research show that the system can assist the Bali Province Industry and Trade Office in monitoring the feasibility of goods circulating in the market through the selection process for determining query priorities and query modes, as well as supporting decision-making processes and determining business strategies

Prediction of Planning Value School Shopping Income Budget with Multiple Linear Regression

The School Expenditure Budget Plan or RAPBS is the pillar of school management for allocating the revenue budget and use of school funds to meet all school needs for one year. However, there are problems that occur in the management of the RAPBS, namely the difficulty of grouping the RAPBS data annually, making it difficult to predict the budget for the coming year. This research was conducted to study and implement the Multiple Linear Regression algorithm in predicting the value of data on income and expenditure budget plans which are a reference in planning future budgets. To support predictions of planned school budgets and income, BUMS data, Aid data, School Program Cost data, Original School Revenue data, Other Sources data, and Total Budget data are used. The prediction system method used consists of the planning stage, the analysis stage, the modeling stage, interface design, and implementation using the PHP and MySQL programming languages for database management and system testing and analysis. The results of testing the data analysis using the multiple linear regression method with SPSS software have a 100% result according to the manual calculations performed