Solikhin Solikhin, Septia Lutfi, Purnomo Purnomo, Hardiwinoto Hardiwinoto
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A machine learning approach in Python is used to forecast the number of train passengers using a fuzzy time series model
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Prediction of passenger train using fuzzy time series and percentage change methods
Solikhin Solikhin, Septia Lutfi, Purnomo Purnomo, Hardiwinoto Hardiwinoto
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Membangun Sistem Smart Trash Menggunakan Mikrokontroler Motor Servo Panjerino
Yuda Hirmawan1, Eko Riyanto2, Solikhin Solikhin3*
Abstract
To cultivate good behavior and care for the environment, SD Negeri 2 KuwasenJepara promotes proper waste disposal, but in reality, there are still many students who don't do it. The purpose of this research is to build a smart trash can to socialize waste disposal in an attractive way for students. We use a manual trash can that is integrated with the Arduino Uno. This smart trash system is able to open automatically when it detects movement within <50 cm and vice versa, and can emit a "Thank you for not littering" sound. The performance test results show that the ultrasonic sensor device opens and closes within 3.07 seconds at a distance of 15 centimeters and 3.06 seconds at a distance of 30 centimeters. The feasibility test of the tool obtained a score of ≥76% and an ease of use score of 87.7%.
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JSON and MySQL Databases for Spatial Visualization of Polygon and Multipolygon Data in Geographic Information Systems: A Comparative Study
M. Zakki Abdillah1*, Devi Astri Nawangnugraeni2, Solikhin Solikhin3, Toni Wijanarko Adi Putra4
Abstract
Purpose: Spatial data is used to display digital maps. Geographic information systems' access performance depends on spatial data formats. This study compared JSON and MySQL database data display speeds. Open-source RDBMSs work with various programming languages. JSON displays data in text format. The purpose of this study is to select spatial data for polygon and multipolygon Geographic Information Systems (GIS).
Design of study: access speed to the GIS determined the method. This study evaluated how effectively JSON and MySQL displayed digital maps in GIS using two types of geographical data. JSON was in the server directory, and MySQL was on the database server. To measure performance, these two spatial data sets were compared using the same server parameters. Testers employed various tools, operating systems, devices, and browsers.
Result: JSON data is stored on a live server and is easier to access while having more data. This test compares file size and speed on three online devices. This test generates JSON as the fastest geographic data, with an average access time of 3.9 seconds and 8.5 MB loaded. MySQL, which averages 9.7 seconds, loads 6.3 MB of files. Despite its larger file size, JSON is faster for spatial data, according to tests.
Originality: Its comparison of JSON and MySQL databases based on its application for geographical data display in GIS is unique. This test offers geographic data in JSON faster than MYSQL. JSON can be used to choose location data that GIS can readily access.
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