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ComTech: Computer, Mathematics and Engineering Applications
Published by Universitas Bina Nusantara
ISSN : 20871244     EISSN : 2476907X     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Engineering Mathematics
The journal invites professionals in the world of education, research, and entrepreneurship to participate in disseminating ideas, concepts, new theories, or science development in the field of Information Systems, Architecture, Civil Engineering, Computer Engineering, Industrial Engineering, Food Technology, Computer Science, Mathematics, and Statistics through this scientific journal.
Arjuna Subject : -
Articles 1,585 Documents
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The Influencing factors of Female Passenger Background in Online Transportation with Perceived Ease of Use Meyliana Meyliana; Surjandy Surjandy; Erick Fernando; Firman Anindra
ComTech: Computer, Mathematics and Engineering Applications Vol. 10 No. 1 (2019): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v10i1.5713

Abstract

This research aimed to explore the correlation or influence factors between the background of female passengers with perceived ease of use factors in Online Transportation Application (OTA). This research was explanatory and descriptive (causal) research. The respondents were the female users of OTA. The total of respondents was 408 people. SPSS applications were used to process the data. Then, the cross-tabulation was to find the correlation or influence factors. In the end, the researchers find 19 factors that are essential for future research.
The Development of Furniture Assembly Instruction Based on Augmented-Reality Hugo Adrian Hartanto; Irena Krishanti Makrie; Violitta Yesmaya; Permana Gustav Halfian
ComTech: Computer, Mathematics and Engineering Applications Vol. 10 No. 2 (2019): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v10i2.5853

Abstract

This research utilized Augmented Reality (AR) technology along with interactive multimedia applications to improve visual guidance and assist nonexpert users in assembling furniture. Rather than paperbased or video instructions, AR enhanced learning and manipulative-interactive approach. This research used the Waterfall model for development. It was divided into a couple of stages. It consisted of specifying the problem and requirements, designing the system, implementing and integrating it, and testing the system. Identifying problems and requirements was conducted through an online questionnaire and related works study. The graphics tool used was 3D Studio Max. Unity3d was used to develop the system with the software library from Qualcomm’s Vuforia for the AR implementation. The results show the mobile application called ARsembly. It serves as the early stage of concept model as it only assists the simple assembly. Through system testing performed by both researchers and end-users, ARsembly is proven to be useful and helpful in learning and performing furniture assembly.
The Proposed Maintenance Task for Plastic Injection Machine Using Reliability and Risk Centered Maintenance (RRCM) method in Manufacturing Industry Liza Nafiah Maulidina; Fransiskus Tatas Dwi Atmaji; Judi Alhilman
ComTech: Computer, Mathematics and Engineering Applications Vol. 10 No. 2 (2019): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v10i2.5900

Abstract

The objective of this research was to determine the optimal maintenance time interval for the selected critical components and the total cost of maintenance of a plastic injection machine. In determining the critical components, a risk matrix was used, and three components were selected, namely, hydraulic hose, barrel, and motor. Using the Reliability and Risk Centered Maintenance (RRCM) method, the researchers got a proposed maintenance policy and the total maintenance cost. Based on the result, it shows that there are seven proposed maintenance tasks with three scheduled oncondition tasks and four scheduled restoration tasks with an average maintenance interval of two months. The total maintenance cost proposed is IDR91.595.318. The cost is smaller compared to the actual maintenance costs of the company.
The Alternative of Sensor Placement in Multi-Story Buildings through the Metric Dimension Approach: A Representation of Generalized Petersen Graphs Asmiati; Akmal Junaidi; Ahmad Ari Aldino; Arif Munandar
ComTech: Computer, Mathematics and Engineering Applications Vol. 13 No. 2 (2022): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v13i2.7268

Abstract

In a public facility or private office where many people can get together, a fire detection device is a mandatory tool as an emergency alarm in the facility. However, the expense of the installation of the device is a troublesome matter. So, optimization is needed to minimize the number of these devices. The way to implement is to select the appropriate position to place the devices in public facilities. The research discussed the placement of the sensors in multi-story buildings. The multi-story buildings could be represented as cube composition graphs with the number of rooms, and the connectivity between the floor and its rooms was equal. The concept of this multi-story building was modeled into a generalized Petersen graph where a vertex represented a room, and an edge was the connectivity of rooms. The basis obtained on that metric dimension was represented as a sensor placed on the building. Then, the optimization of device placement was seen as determining the metric dimensions of the Petersen graph. In the research, the alternative sensor placements were computed using the graph metric dimension approach implemented in Python. The research successfully implements the metric dimension of  to  using Python code to obtain the alternative of its basis. A basic alternative indicates the location of the device placement like fire detectors, network access points, or other sensors inside a building.
Dynamic Time Warping Techniques for Time Series Clustering of Covid-19 Cases in DKI Jakarta Meicheil Yohansa; Khairil Anwar Notodiputro; Erfiani
ComTech: Computer, Mathematics and Engineering Applications Vol. 13 No. 2 (2022): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v13i2.7413

Abstract

The number of positive cases of Covid-19 in DKI Jakarta has contributed to the national issues, reaching 25% of the total cases in Indonesia. The research examined and modeled the distribution pattern of Covid-19 positive cases in DKI Jakarta based on 44 districts spreading over six administrative areas. The data were regarding positive Covid-19 cases in DKI Jakarta for the past year, from April 2020 to April 2021. The research related to the pattern of positive Covid-19 distribution in 44 districts was carried out by time series clustering through Dynamic Time Warping (DTW) distances and agglomerative hierarchical methods. Then, the effectiveness of the clustering process is evaluated by comparing the predicted value of Covid-19 cases between clustering and non-clustering forecast results at the city level for the next 14 days through the Autoregressive Integrated Moving Average (ARIMA) model. The results group 44 districts into 6 optimal clusters based on the pattern of positive cases of Covid-19 in each district. The highest distribution rate is in cluster A, and the lowest is in cluster F. Geographical characteristics are also indicated by clusters A, B, E, and F. Then, the results show that the Mean Average Percentage Error (MAPE) value of the clustering model ranges from 16% to 20%. The difference between MAPE values to the non-clustering model implies that the forecasting accuracy is not far apart, which is in the round of 5%−6%.
Modeling Automatic Room Temperature and Humidity Monitoring System with Fan Control on the Internet of Things Fivtatianti Hendajani; Arif Mughni; Ire Puspa Wardhani; Abdul Hakim
ComTech: Computer, Mathematics and Engineering Applications Vol. 13 No. 2 (2022): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v13i2.7433

Abstract

The Internet of Things (IoT) aims to expand the benefits of being connected to the Internet network continuously. It functions as a control system that has been widely applied in various fields because in certain case people are not allowed in certain rooms for security reasons. The research aimed to create a temperature and humidity monitoring system using as many fan controls as expected by utilizing IoT. The model used input in the form of temperature and humidity sensors. The output was a motor driver that drove a fan and used a microcontroller as the main processor. IoT-based systems consisted of hardware and software. Hardware included NodeMCU ESP8266 V3, DHT22 sensor, L298N motor driver module, fan, and computer. Meanwhile, the microcontroller software was made using Arduino IDE. From the test results, the system model works well. Fan control is set manually based on desired room temperature and humidity monitoring based on IoT. A mobile phone can also monitor temperature and humidity and control fans. The DHT22 sensor can read temperature and humidity every two seconds so that the resulting data is stable to display. Then, the L298N motor driver can adjust the fan speed with Pulse Width Modulation (PWM) using analog data ranging from 1 to 1.024.
Comparison of Adaptive Holt-Winters Exponential Smoothing and Recurrent Neural Network Model for Forecasting Rainfall in Malang City Novi Nur Aini; Atiek Iriany; Waego Hadi Nugroho; Faddli Lindra Wibowo
ComTech: Computer, Mathematics and Engineering Applications Vol. 13 No. 2 (2022): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v13i2.7570

Abstract

Rainfall forecast is necessary for many aspects of regional management. Prediction of rainfall is useful for reducing negative impacts caused by the intensity of rainfall, such as landslides, floods, and storms. Hence, a rainfall forecast with good accuracy is needed. Many rainfall forecasting models have been developed, including the adaptive Holt-Winters exponential smoothing method and the Recurrent Neural Network (RNN) method. The research aimed to compare the result of forecasting between the Holt-Winters adaptive exponential smoothing method and the Recurrent Neural Network (RNN) method. The data were monthly rainfall data in Malang City from January 1983 to December 2019 obtained from a website. Then, the data were divided into training data and testing data. Training data consisted of rainfall data in Malang City from January 1983 to December 2017. Meanwhile, the testing data were rainfall data in Malang City from January 2018 to December 2019. The comparison result was assessed based on the values of Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The result reveals that the RNN method has better RMSE and MAPE values, namely RMSE values of 0,377 and MAPE values of 1,596, than the Holt-Winter Adaptive Exponential Smoothing method with RMSE values of 0,500 and MAPE values of 0,620. It can be concluded that the non-linear model has better forecasting than the linear model. Therefore, the RNN model can be used in modeling and forecasting trend and seasonal time series.
Implementation of Fuzzy Mamdani Method to Classify Public Health Level in North Sumatra Province Sagita Charolina Sihombing; Agus Dahlia
ComTech: Computer, Mathematics and Engineering Applications Vol. 13 No. 2 (2022): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v13i2.7753

Abstract

Health is one of the most important fields aspects in people’s lives. Public health is influenced by several factors, such as environmental factors and lifestyle. Every year, the government makes a program to improve people’s health and welfare. Therefore, the government needs to obtain information about the classification of public health levels to monitor the progress of the programs that have been carried out. The research created an interface application to classify the level of public health in North Sumatra Province. The research classified the public health level using data from Badan Pusat Statistik (BPS) in 2018. Next, the application was made using the Matlab 2013b GUI. Classification of the level of public health was carried out using the Fuzzy Mamdani method that generated public health classification levels based on the inputted indicators. Then, the level of health classification was divided into four parts: low, lower middle, upper middle, and high. The result shows that for the 2018 data, some cities/ districts are still at low health levels. However, many cities or districts have good health levels. The final result can be used as a reference for the government to analyze which cities/districts should be paid attention to improve their health and welfare.
An Improved Weighted Median Algorithm for Spatial Outliers Detection Zerlita Fahdha Pusdiktasari; Rahma Fitriani; Eni Sumarminingsih
ComTech: Computer, Mathematics and Engineering Applications Vol. 13 No. 2 (2022): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v13i2.7821

Abstract

A spatial outlier is an object that significantly deviates from its surrounding neighbors. The median algorithm is one of the spatial outlier methods, which is robust. However, it assumes that all spatial objects have the same characteristics. Meanwhile, the Average Difference Algorithm (AvgDiff) has accommodated the differences in spatial characteristics, but it does not use statistical tests to determine the status of an object, whether it is an outlier or not. The research developed an improved version of the median algorithm and AvgDiff, called the Weighted Median Algorithm (WMA) which combined the advantages of the two methods. From the median algorithm, WMA adopted median and statistical test concepts. Meanwhile, from AvgDiff, WMA adopted the concept of using differences in objects’ spatial characteristics as weights. A combination of the two advantages was innovated by calculating WMA’s neighborhood score using a weighted median. Then, a simulation was conducted to analyze the accuracy of the method. The result confirms that when objects have heterogeneous spatial characteristics, WMA performs better than the median algorithm. The accuracy of WMA is not much higher than AvgDiff, but the use of WMA can prevent a serious false detection problem. The methods can be applied to an incidence rate of Covid-19 data in East Java.
K-Nearest Neighbors Method for Recommendation System in Bangkalan’s Tourism Devie Rosa Anamisa; Achmad Jauhari; Fifin Ayu Mufarroha
ComTech: Computer, Mathematics and Engineering Applications Vol. 14 No. 1 (2023): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v14i1.7993

Abstract

The more tourist objects are in an area, the more challenging it is for local governments to increase the selling value of these attractions. The government always strives to develop tourist attraction areas by prioritizing the beauty of tourist attractions. However, visitors often have difficulty in determining tourist objects that match their criteria because of the many choices. The research developed a tourist attraction recommendation system for visitors by applying machine learning techniques. The machine learning technique used was the K-Nearest Neighbor (KNN) method. Several trials were conducted with a dataset of 315 records, consisting of 11 attributes and 21 tourist attractions. Based on the dataset, the preprocessing stage was previously carried out to improve the data format by selecting data where the data were separated based on existing criteria, then calculating the closest distance and determining the value of k in the KNN method. The results are divided into five folds for each classification method. The highest system accuracy obtained at KNN is 78% at k=1. It shows that the KNN method can provide recommendations for three tourist attraction classes in Bangkalan. Applying the KNN method in the recommendation system determines several alternative tourist objects that tourists can visit according to their criteria in natural, cultural, and religious tourist objects.

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