ComTech Binus

ComTech Binus ComTech: Computer, Mathematics and Engineering Applications is an interdisciplinary and open access

ComTech is a semiannual journal, published in June and December. ComTech is an interdisciplinary and open access journal covering Computer, Mathematics, and Engineering Applications. ComTech has been accredited by DIKTI under the decree number 3/E/KPT/2019 (SINTA 2) and indexed by CrossRef, ASEAN Citation Index, Directory of Open Access Journals (DOAJ), Science and Technology Index 2 (SINTA2), Ga

rda Rujukan Digital (Garuda), Microsoft Academic Search, Google Scholar, Academic Resource Index (ResearchBib) and Indonesian Research Repository (Neliti)

Please visit our website: https://journal.binus.ac.id/index.php/comtech/index

📝 Featured Article, ComTech: Computer, Mathematics and Engineering Applications 📝Modeling Automatic Room Temperature and...
17/10/2024

📝 Featured Article, ComTech: Computer, Mathematics and Engineering Applications 📝

Modeling Automatic Room Temperature and Humidity Monitoring System with Fan Control on the Internet of Things

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.

Keywords: automatic room temperature, humidity monitoring system, fan control, Internet of Things (IoT)

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https://journal.binus.ac.id/index.php/comtech/issue/view/403

📝 Features Article, ComTech: Computer, Mathematics and Engineering Applications 📝Dynamic Time Warping Techniques for Tim...
16/10/2024

📝 Features Article, ComTech: Computer, Mathematics and Engineering Applications 📝

Dynamic Time Warping Techniques for Time Series Clustering of Covid-19 Cases in DKI Jakarta

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%.

Keywords: Dynamic Time Warping (DTW), time series, Covid-19

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/7413

💻Featured Article💻K-Means Clustering to Identity Twitter Build Operate Transfer (BOT) on Influential AccountsAbstractTwi...
15/10/2024

💻Featured Article💻

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

Abstract
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.

Keywords: K-Means clustering, Twitter accounts, Build Operate Transfer (BOT), influential accounts

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/10620

Mobile-Based Car Diagnostic Application Using Onboard Diagnostic-II ScannerAbstractMobile applications today serve as ve...
14/10/2024

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

Abstract
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.

Keywords: mobile-based application, car diagnostic, Onboard Diagnostic-II Scanner

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/9138

Fuzzy C-Means in Content-Based Document Clustering for Grouping General Websites Based on Their Main Page ContentsAbstra...
12/10/2024

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

Abstract
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 24th, 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.

Keywords: Fuzzy C-Means, content-based document clustering, general websites

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/9732
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The One-Dimensional (1D) Numerical Model: An Application to Oxygen Diffusion in Mitochondria CellAbstractThe first model...
11/10/2024

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

Abstract
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.

Keywords: mitochondria cell, One-Dimensional (1D) numerical model, oxygen diffusion

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/9705

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Smart Shrimp Farming Using Internet of Things (IoT) and Fuzzy LogicAbstractIn the case of ponds with Litopenaeus Vanname...
10/10/2024

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

Abstract
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.

Keywords: smart shrimp farming, Internet of Things (IoT), Fuzzy Logic

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/8981
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Prediction Model for Tourism Object Ticket Determination in Bangkalan, Madura, IndonesiaAbstractThe research used the ti...
09/10/2024

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

Abstract
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.

Keyword: prediction model, tourism object ticket, ticket determination

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/7992
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Psychological Stress Detection Using Transformer-Based ModelsAbstract:Stress is a significant mental health problem that...
08/10/2024

Psychological Stress Detection Using Transformer-Based Models

Abstract:
Stress is a significant mental health problem that results in a lack of concentration. It has been more widely identified through social media since people who are under stress usually post about their physical pain and tiredness. However, stress assessment through social media by professionals can be expensive and time-consuming. The research aimed to produce a stress detection system trained using a Twitter dataset to predict stress using the user’s input sentence. The experiments that were done in the research used transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT (RoBERTa). The research involved data pre-processing, model training, and model evaluation to ensure high-quality train data. Since the data were imbalanced, data trimming was performed in pre-processing to select data randomly until the balance matched. This process ensured the model’s effectiveness in the training and evaluation stages. The features used in these experiments were features from each pre-trained model. In evaluating the model, accuracy, loss, and F1 score were used as metrics. In the result, for BERT, accuracy reaches 0.848 with an F1 score of 0.847. Meanwhile, RoBERTa has an accuracy of 0.837 and 0.834. The results prove that BERT and RoBERTa can be used to classify stress with accuracy and an F1 score above 0.8. The experiment result shows that the BERT deep learning model can detect stress using the Twitter datasets.

Keywords: stress detection, transformer-based model, Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT (RoBERTa)

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/11105
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Water Quality Monitoring System Based on the Internet of Things (IoT) for Vannamei Shrimp FarmingAbstract:As Internet of...
07/10/2024

Water Quality Monitoring System Based on the Internet of Things (IoT) for Vannamei Shrimp Farming

Abstract:
As Internet of Things (IoT) technology develops, water quality monitoring systems for Vannamei shrimp farms have become more inventive and straightforward. The prototype IoT system monitors and controls the pool using sensors that can measure water quality parameters, such as temperature, pH, and salinity. The research aimed to design an automated water quality monitoring system for Vannamei shrimp aquaculture. The research used the E-4052C sensor, DS18B20 sensor, and DFRobot V1.0 sensor as data transmitting hardware (transmitter) and the receiving hardware microcontroller NodeMCU ESP32 as data processing, management, and control system tools. Then, the system used a Wi-Fi network to transfer data from the microcontroller to the Message Queue Telemetry Transport (MQTT) server as a data cloud. Several software programs, including Telegram, Node-Red, and ThingSpeak, help Android devices display real-time data. Test results for the accuracy of the sensor’s reading on water pH are 99.71, with an error rate of 0.29%. Meanwhile, the accuracy of the temperature sensor is 98.03 with an error rate of 1.7%. On the other hand, the accuracy of the salinity sensor is 99.49, with an error rate of 0.41%. The results indicate that all sensors have excellent performance. The real-time monitoring display and Android Telegram notification functions are good, and the automatic water quality monitoring tool is successfully operating in the Vannamei shrimp pool in Pamandati, South Konawe District, Southeast Sulawesi ssProvince, Indonesia.

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/10657
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Multiple Classifier System for Handling Imbalanced and Overlapping Datasets on Multiclass ClassificationAbstract:The per...
04/10/2024

Multiple Classifier System for Handling Imbalanced and Overlapping Datasets on Multiclass Classification

Abstract:
The performance of classification models suffer when the dataset contains imbalanced and overlapping data. These two conditions are already challenging separately and even more complex if they occur together. In the research, an ensemble method called a Multiple Classifier System was proposed to address these issues by combining K-Nearest Neighbour and Logistic Regression. The Synthetic Minority Oversampling Technique (SMOTE) method was also applied to balance the dataset. The One Versus One (OVO) decomposition technique helped the multiclass classification process. A simulation with 18 scenarios proves that the MCS-SMOTE model can handle these problems by providing good performance. The model’s performance is also tested using empirical data on Poverty in West Java in 2021. Empirical data also show that the proposed method performs well, with an accuracy rate of 80.09%, an F1 score of 0.782, and a G-Mean of 0.242. The areas with the highest poverty rates are Bogor, Bekasi City, Bandung City, Bekasi Regency, and Depok City, located near DKI Jakarta, the capital city. Based on existing predictor variables, poor households in West Java are more likely to occur when they do not have access to credit, the number of household members is more than three, multiple families live in one building, and the head of the household has not graduated from elementary school.

Keywords: Multiple Classifier System (MCS), imbalanced datasets, overlapping datasets, multiclass classification

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/11295
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Performance of Fuzzy C-Means (FCM) and Fuzzy Subtractive Clustering (FSC) on Medical Data ImputationAbstractMissing valu...
03/10/2024

Performance of Fuzzy C-Means (FCM) and Fuzzy Subtractive Clustering (FSC) on Medical Data Imputation

Abstract
Missing values or incomplete data are frequently encountered in medical records. These issues will be a serious problem if the data must be provided completely for analysis. The research aimed to prove the performance of the Fuzzy Subtractive Clustering (FSC) and Fuzzy C-Means (FCM) methods for solving imputation problems. Both methods were implemented using medical data. It had been conducted using K-Means as a crisp clustering approach for imputation. In the research, fuzzy clustering—a distinct methodology—was applied. The primary research contribution was the suggested fuzzy logic imputation method, which took uncertainty under consideration. The data sample consisted of patients who were at least 40 years old and had a history of hypertension, diabetes, heart disease, stroke, or chronic kidney disease. The test was carried out by taking random portions of data from the entire medical record. The randomization technique used a probability of 10%–50%. The results of the ANOVA test show that the p-value is greater than ∝(=0.05). It means that the imputed value does not differ from the original value, whether implemented in the FSC or FCM method. The algorithm’s performance is evaluated using the Pearson correlation coefficient. According to the t-test results, the FCM method has a higher correlation coefficient than the FSC method. It implies that FCM is superior to FSC.

Keyword: Fuzzy C-Means (FCM), Fuzzy Subtractive Clustering (FSC), medical data imputation

Read full article: https://journal.binus.ac.id/index.php/comtech/article/view/11002
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