Assessment of Students in Online Industrial Practice Activities Using Machine Learning Based on Mobile Application

All of the learning in the pandemic era uses online learning including practical in the industry that should do all of the students to apply their knowledge.  The practical industry online is very difficult to assement students that the assessment is given from both company and the university. Companies have many parameters to assessment and each company has different parameters. This study uses 14 parameters that are generally used in assessment for practical students and the university side using 10 parameters. The problem is that every parameter has a different weight than it makes it confusing to give marks with manual assessment. This research uses machine learning to fix this problem based on mobile applications for the user interfaces. The result of testing this application had an average accuracy for assessment students based on parameters of companies and universities that is 83,3%

User Experience Based Mobile Application Design for Boat Loaning at Marine Tourism in Indonesia

Boat loaning in marine tourism in Indonesia significantly impacts the continuity of tourism activities. It is because some nautical tourism destinations in Indonesia use ships as a means of transportation that cannot be separated from tourism activities. The problem is, there is a lack of information availability; for example, event information, destination, and access for boat loaning. That makes it difficult for tourists to be able to enjoy marine tourism. Therefore, the purpose of this study is to design a prototype of a mobile application that can help overcome the problem of limited information and access to marine tourism transportation, and be able to answer the needs of tourists regarding tourist information using the user experience as a design system. The user experience method will be applied in application design testing, to obtain development aspects in the ship transportation ordering application, according to user needs. The results of this study are the prototype design based on user experience. The final result shows 87.5% response agree with this prototype design. This study applies the method of boat loaning by utilising cooperation between agents and ship providers to provide Ease of access to information on marine tourism transportation to prospective tourists

Blockchain-based data sharing for decentralized tourism destinations recommendation system

One thing that tourists need to plan their tourism activities is a recommendation system. The tourism destinations recommendation system in this study has three primary nodes, namely user, server, and sensor. Each node requires the ability to share data to produce recommendations that the user expects through their mobile devices. In this paper, we propose the data-sharing system scheme uses a blockchain-based decentralized network that each node can be connected directly to each other, to support the exchange of data between them. The block architecture used in the blockchain network has three main parts, namely block information, hashes, and data. Each type of node has a different structure and direction of data communication. Where the user node sends destination assessment data to the server node, then the server node sends data from the machine learning process to the user node. The sensor sends dynamic data about popularity, traffic, and weather to the user node as consideration for finalizing the generating recommendations process. In the process of sending data, each node in the blockchain network goes through several functions, including hashing, block validation, chaining block, and broadcast. We conduct web-based experiments and analysis of the data-sharing system to illustrate the system works. The experimental results show that the system handles data circulation with an average time of mine is 84.5 ms in sending multi-criteria assessment data from the user and 119.1 ms in sending data of machine learning result from the server

Mobile-based Recommendation System for the Tour Package Using the Hybrid Method

Although it has been a lot of research recommendation of the tourist attraction, there has been no research that discusses the recommendations of tour packages from a collection of travel in the past. Therefore, in this study it is important to conduct a related study 1) The development of a mobile recommendation system using the Hybrid Method. 2) Test system accuracy in providing tour package recommendations.The study is using CBR stages in providing travel package recommendations from a collection of travel in the past. There are 4 stages of the process: Retrieve, Reuse, Revise, and Retain. In this study the main focus on the retrieve stage using the method hybrid method. The hybrid method of the mobile recommendation system is the combination of the Naive Bayes method, Bayes Theorem, and Dempster Shafer. Where Naive Bayes is used for calculating the probability of continuous criteria such as age and frequency of visits. The Bayes theorem is used for calculating the probability such as country, gender, and visiting purpose. To determine the mass value of the combination of evidence using the Dempster Shafer method. Based on system accuracy test, stated that the total system accuracy in giving recommendation is 95% consisting of 2 kinds of accuracy is 46% full accuracy and 49% of half accuracy. While the error rate of the system in providing tour package of 5%.</p