ComTech: Computer, Mathematics and Engineering Applications

Prediction Model for Tourism Object Ticket Determination in Bangkalan, Madura, Indonesia

Fifin Ayu Mufarroha, Akhmad Tajuddin Tholaby, Devie Rosa Anamisa, Achmad Jauhari — Tue, 14 Nov 2023 00:00:00 +0000

One of the regencies in Madura, namely Bangkalan, with its local wisdom and beautiful landscapes has the potential to become a tourism center. However, there may be a decrease in the number of visits caused by some factors. The research used the time series method to build a prediction model for tourist attraction entrance tickets. The model development aimed to estimate the number of tourist attraction visits in the future. The right model was needed to get the best prediction results. Least square, Holt-Winter, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Rolling were chosen as the models. Data collection related to the number of tourist objects was carried out directly at the Tourism Office to obtain valid data. Using data on visitors to tourist attractions in Bangkalan Regency from 2015 to 2019, the results of measuring errors using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) are obtained. The error measurement results show that the Holt-Winter model has the lowest error rate of 5% and RMSE of 307,1198. Based on these calculations, the Holt-Winter model is the best model for determining tourist attraction entrance tickets. The ranking of the error measurement results from the highest to the lowest are Holt-Winter, Rolling, SARIMA, and Least Square methods.

Smart Shrimp Farming Using Internet of Things (IoT) and Fuzzy Logic

Michael Johan, Suharjito — Tue, 14 Nov 2023 00:00:00 +0000

In the case of ponds with Litopenaeus Vannamei shrimp, water quality parameters play a significant role in shrimp growth. Leveraging technology enhances water quality to optimize growth and survivability in the shrimp farming industry. The research aimed to empower local farmers with smart shrimp farming technologies, including Information Technology (IT), such as the Internet of Things (IoT), and Fuzzy Logic. The research also involved a comparison between Litopenaeus Vannamei shrimp in two different aquariums: one serving as a control group and the other implementing IoT and Fuzzy Logic for a period of 30 days. The initial Litopenaeus Vannamei shrimp stocking was 135 shrimps for control aquariums and 132 for experimental aquariums. Then, the research used Arduino ESP 8266, Raspberry Pi 3, and SciKit-Fuzzy library to record and process the data. Through the application of IoT and Fuzzy Logic, the research successfully increases survivability by 6%, specific growth rate by 28%, and length by 8% in 30 days compared to conventional methods. The results highlight the potential use of technology in Litopenaeus Vannamei shrimp farming. The proposed system’s hardware and software architecture can be easily scaled to accommodate the needs of Litopenaeus Vannamei shrimp farmers with multiple ponds, offering flexibility and adaptability.

The One-Dimensional (1D) Numerical Model: An Application to Oxygen Diffusion in Mitochondria Cell

Tue, 14 Nov 2023 00:00:00 +0000

The first model of oxygen transport was formulated by August Krogh. However, the investigations conducted have yet to yield a complete analytical model and a widely applicable solution for One-Dimensional (1D) network construction. The research sought to provide numerical and analytical solutions for the oxygen transfer model in mitochondrial cells to enable researchers to estimate the molecular dynamics and diffusion characteristics in mitochondrial cells. The oxygen diffusion process in mitochondria was modeled with ID numerical models. The numerical models used to solve the equations were explicit and implicit. The explicit model consisted of Forward Time Center Space (FTCS) and DuFort-Frankel. Meanwhile, the implicit model had Crank-Nicholson and Laasonen. The numerical solutions of the explicit and implicit were divided into four scenarios with a variation of Δt and compared with the analytical solutions. The results show that the Laasonen method is the best in describing the diffusion process. The best scenario with the lowest slope value and small Root Mean Square Error (RMSE) value is scenario 2 (Δt = 3,33E-4 s and Δx = 2,00E-5 cm). The numerical model and analytical solution show that the time required to reach a steady state is 0,7 s. It indicates oxygen exchange in two sides of the mitochondrial cell after 0,7 s.

Fuzzy C-Means in Content-Based Document Clustering for Grouping General Websites Based on Their Main Page Contents

Sri Probo Aditiyo, Eni Sumarminingsih, Rahma Fitriani — Tue, 14 Nov 2023 00:00:00 +0000

The research aimed to use Fuzzy C-Means clustering in content-based document clustering to classify general websites based on their content. The data used were a table ranking of the most visited websites for Indonesia, taken from https://dataforseo.com/top-1000-websites/ on September 24^th, 2022. The research was conducted with two different cases using Fuzzy C-Means clustering, which had two different iteration parameter values, namely 100 and 200 in maximum iteration. The research results on Fuzzy C-Means clustering in content-based document clustering are based on the two cases. These different maximum iteration parameters result in a different amount of website name data in the cluster. They are formed in the first and second clusters only. However, in the other clusters, the numbers are all the same. The results of the cluster research are validated using the silhouette coefficient, with case no. 1 and no. 2 values being 0,977783879 and 0,977788457. The use of Fuzzy C-Means clustering in content-based document clustering has an excellent performance when this method is applied to group general websites based on their content. With that result, content-based clustering can be also applied in other cases. Hence, the results can be considered to be applied to other cases for content-based clustering in the future.

Mobile-Based Car Diagnostic Application Using Onboard Diagnostic-II Scanner

Wed, 06 Dec 2023 00:00:00 +0000

Mobile applications today serve as versatile tools across diverse sectors, enhancing human productivity through specialized software on electronic devices. Implementation of the mobile application can also be applied to vehicles, with inspection and checking functions assisted by the Onboard Diagnostic-II (OBD-II) scanner. The research aimed to develop an integrated mobile application that utilized the OBD-II scanner and Data Acquisition System (DAS) to monitor vehicle health and provide timely service reminders. Vehicle information was taken by the DAS process into a Diagnostic Trouble Code (DTC) from the vehicle itself. The method applied the waterfall model, which consisted of communication, planning, modeling, construction, and evaluation. The problem analysis and requirements gathering for developing the application involves the interview method and Google Forms-generated questionnaires with 101 responses. Then, the research used OBD-II series ELM327 and ELM 327 IC devices for testing. The research results in an application developed for vehicle diagnostics using a recommendation system through notifications that provide vehicle health information and service time reminders to users. This application consists of eight modules, with the main module being able to provide recommendations for vehicle owners. These recommendations are helpful for users to maintain the health of their vehicles regularly. Further research is recommended to enhance the development of the application, aiming to create a more comprehensive user interface.

K-Means Clustering to Identity Twitter Build Operate Transfer (BOT) on Influential Accounts

Wed, 13 Dec 2023 00:00:00 +0000

Twitter is a popular social media with hundreds of millions of users, but some are not human. About 48 million accounts are created by Build Operate Transfer (BOT), which represents up to 15% of all accounts. BOTs are created for various purposes, one of which is to post information about news automatically. However, BOTs have also been abused, such as spreading hoaxes or influencing public perception of a topic. The research aimed to determine which Twitter accounts were identified as BOT accounts based on predefined attributes. The research used tweet data from 213 Twitter accounts. The accounts used as test data were accounts that had influence. After that, the data were clustered using k-means using the attributes of retweets + replies count, followers count, account age, friends count, status count, digits count in name, username length, name similarity, name ratio, and likes count. The results show the optimal number of clustering at k = 3 on the Sum of Squared Errors (SSE) evaluation and the Elbow method and the best quality and cluster power at k = 2 on the silhouette coefficient. It shows that the clustered accounts with the highest number of members on each attribute are places for accounts with high BOT scores from several aspects of the BOT score type.

Editorial Page and Table of Content

Evaristus Didik Madyatmadja — Tue, 14 Nov 2023 00:00:00 +0000