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CommIT (Communication & Information Technology)
Published by Universitas Bina Nusantara
ISSN : 19792484     EISSN : 24607010     DOI : -
Core Subject : Science,
Computer Science & IT
Journal of Communication and Information Technology (CommIT) focuses on various issues spanning: software engineering, mobile technology and applications, robotics, database system, information engineering, artificial intelligent, interactive multimedia, computer networking, information system audit, accounting information system, information technology investment, information system development methodology, strategic information system (business intelligence, decision support system, executive information system, enterprise system, knowledge management), e-learning, and e-business (e-health, e-commerce, e-supply chain management, e-customer relationship management, e-marketing, and e-government). The journal is published in affiliation with Research Directorate, Bina Nusantara University in online and free access mode.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 478 Documents
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Emotion Intensity Value Prediction with Machine Learning Approach on Twitter Rindy Claudia Setiawan; Andry Chowanda
CommIT (Communication and Information Technology) Journal Vol. 17 No. 2 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i2.8503

Abstract

Recognizing the intensity of the emotions is a paramount task for an affective system. By recognizing the intensity of the emotions, the system can have better human-computer interaction. The research explores several machine learning approaches with several different feature extraction method combinations to solve the emotion intensity prediction task while also analyzing and comparing it with several previous related papers. The research uses the dataset provided through theWASSA 2017 and SemEval 2018 competition. The dataset utilizes four of the eight basic emotions that Plutchik defines (anger, fear, joy, and sadness). The total data result in 19,736 rows of entry, with a total of 10,715 (54.3%) for training, 1,811 (9.17%) for validation, and 7,210 (36.53%) for testing. Three feature extraction methods are used and compared: N-gram, TFIDF, and Bag-of-Words. Meanwhile, machine learning algorithms are Linear Regression, Ridge Regression, KNearest Neighbor for Regression, Regression Tree, and Support Vector Regression (SVR). The results show that SVR with TF-IDF features has the best result of all attempted experiments, with a Pearson correlation score of 0.755 for all data and 0.647 for gold labels data. The final model also accepts newly seen data and displays the corresponding emotion label and intensity.
Factors on Mobile Application User Satisfaction in the Largest Indonesian Internet Service Provider (ISP) Yashella Tirana; Sfenrianto Sfenrianto
CommIT (Communication and Information Technology) Journal Vol. 17 No. 2 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i2.8518

Abstract

Many users complain about using the largest mobile Internet Service Provider (ISP) application in Indonesia, MyIndihome, such as difficulties in verifying, logging in, and changing cell phone numbers and emails. With these complaints, the satisfaction of the MyIndihome application users decreases. The research aims to determine the effect of information quality, system quality, service quality, ease of use, usefulness, and chatbot effectiveness on user satisfaction with MyIndihome. Chatbot effectiveness is a novelty of the research because it has not been studied in previous research. The research applies a quantitative approach. Then the sampling technique used is probability sampling, and the method is simple random sampling with 417 respondents. Data collection techniques are carried out by distributing online questionnaires, and the data are statistically processed with SmartPLS and analyzed by Structural Equation Model (SEM). After carrying out several stages of testing from validity tests, reliability tests, and structural models, the results show that information quality, system quality, ease of use, usability, and chatbot effectiveness have a significant effect on user satisfaction. However, the service quality has no effect. These results can help companies to increase user satisfaction with the MyIndihome application. They can increase the variables that influence user satisfaction with the MyIndihome application.
Classifying Customer Attributes with Importance Performance Analysis and Fuzzy Kano Elia Oey; Nyimas Revita Permaisuri Putri; Benyamin Suwito Rahardjo
CommIT (Communication and Information Technology) Journal Vol. 17 No. 2 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i2.8534

Abstract

Analyzing what consumer needs remains every day’s challenge for every business. Every business entity requires continuous effort as consumers become more demanding and have more access to product/service offerings, leading to more competitive market dynamics and the necessity for more innovative ways of offering products/services. The research aims to recommend a set of customer attributes for the studied company and analyze the selected attributes using a combination of Importance Performance Analysis (IPA) and fuzzy Kano. The research is a case study of a company selling gift vouchers for individual and corporate consumers. The research combines literature study and affinity diagram workshop to identify the required consumer attributes, which are analyzed using the integration of IPA and fuzzy Kano. The results suggest that the studied company should concentrate on several attributes, such as A7-simple requirement during the purchasing process, A10-no administration fee during purchase, A14-cross promotion with various sister brands, and A15-no minimum purchase. The attributes fall under “concentrate here” in the IPA grid while at the same time, those are considered as “effective improving area” in the fuzzy Kano grid. The studied company is also recommended to keep their good work on the attribute of A5-expiry date longer than one year so that it remains their competitive attribute and does not fall into the other inferior quadrants.
Understanding Participation in Value Co-Creation and Acceptance of iPosyandu by Extending UTAUT among Community Health Workers Azmii Lathifah; Utomo Sarjono Putro; Fedri Ruluwedrata Rinawan; Santi Novani; Valid Hasyimi; Adhya Rare Tiara
CommIT (Communication and Information Technology) Journal Vol. 17 No. 2 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i2.8567

Abstract

Digitalization is inevitable, including in the health sector. The iPosyandu, a mobile digital platform, is introduced to help the report of Community Health Workers (CHWs) and monitor the Pos Pelayanan Terpadu (Posyandu - Integrated Healthcare Center) data online. Unfortunately, CHWs still report data manually using paper, which takes a long time to store because some are still reluctant to change to digital services. Therefore, it is necessary to study CHWs’ intention to create new values and accept technology to sustain the application. The research aims to determine the factor influencing the intention to participate in value co-creation and use iPosyandu by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) among CHW. A Partial Least Square-Structural Equation Modelling (PLS-SEM) is conducted with a cross-sectional survey involving 222 CHWs in Purwakarta, Indonesia. The research finds that effort expectancy and perceived policy support significantly affect the intention to participate in value co-creation and usage of iPosyandu. The findings highlight that the critical role of intention to participate in value co-creation significantly affects the intention to use iPosyandu. The findings also suggest that policymakers and application developers should increase the use of iPosyandu by improving the effort systems, providing policy support, and facilitating CHWs to cocreate the value of the application to encourage them to use iPosyandu.
Classification of Deepfake Images Using a Novel Explanatory Hybrid Model Sudarshana Kerenalli; Vamsidhar Yendapalli; Mylarareddy Chinnaiah
CommIT (Communication and Information Technology) Journal Vol. 17 No. 2 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i2.8761

Abstract

In court, criminal investigations and identity management tools, like check-in and payment logins, face videos, and photos, are used as evidence more frequently. Although deeply falsified information may be found using deep learning classifiers, block-box decisionmaking makes forensic investigation in criminal trials more challenging. Therefore, the research suggests a three-step classification technique to classify the deceptive deepfake image content. The research examines the visual assessments of an EfficientNet and Shifted Window Transformer (SWinT) hybrid model based on Convolutional Neural Network (CNN) and Transformer architectures. The classifier generality is improved in the first stage using a different augmentation. Then, the hybrid model is developed in the second step by combining the EfficientNet and Shifted Window Transformer architectures. Next, the GradCAM approach for assessing human understanding demonstrates deepfake visual interpretation. In 14,204 images for the validation set, there are 7,096 fake photos and 7,108 real images. In contrast to focusing only on a few discrete face parts, the research shows that the entire deepfake image should be investigated. On a custom dataset of real, Generative Adversarial Networks (GAN)-generated, and human-altered web photos, the proposed method achieves an accuracy of 98.45%, a recall of 99.12%, and a loss of 0.11125. The proposed method successfully distinguishes between real and manipulated images. Moreover, the presented approach can assist investigators in clarifying the composition of the artificially produced material.
Web Server Load Balancing Mechanism with Least Connection Algorithm and Multi-Agent System Afiyah Rifkha Rahmika; Zulkifli Tahir; Ady Wahyudi Paundu; Zahir Zainuddin
CommIT (Communication and Information Technology) Journal Vol. 17 No. 2 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i2.8872

Abstract

Demands for information over the Internet massively increase through the continuous expansion of website applications. Therefore, generating powerful and efficient server architecture for web servers is a must to satisfy Internet users and avoid the overloaded system. The research focuses on developing a new mechanism for load balancing to distribute incoming HTTP requests in website applications by combining the Least Connection algorithm and Multi-Agent System (LC-MAS). The proposed mechanism distributes the request based on load condition and the fewest number of active connections. The research applies virtualization technology to build servers on this proposed mechanism. The architecture is built inside a physical server with Proxmox as virtualization management and Linux Debian 7.11 as an operating system. Then, the research is tested in two scenarios (LCMAS and LC) using 500, 1,000, and 1,500 requests. The performance of this proposed mechanism is measured through the values of average response time, throughput, and error percentage. The results show that the proposed mechanism (LC-MAS) distributes the workload more equally than LC, with an average response time for 1,500 requests of 1338.8 milliseconds, 20.07% error, and 125 transactions per second. The LC-MAS makes the website application performance much better when the request increases. The LC-MAS helps in the utilization of system resources and improves system robustness.
Effect of Students’ Activities on Academic Performance Using Clustering Evolution Analysis Djoni Haryadi Setiabudi; Michael Santoso
CommIT (Communication and Information Technology) Journal Vol. 17 No. 2 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i2.9053

Abstract

Educational data mining is a technique to evaluate educational process of university students, especially in their early stages. Most preliminary studies focus on observing courses undertaken by students from one semester to the next to predict their success rate. However, besides studying, many students are also involved in non-academic activities, which tends to affect their grades. Therefore, the research aims to determine the effect of student activities on grades while taking into account their academic activities. The method used for clustering is K-Means. Data are collected by observing students’ activity patterns in lectures. The research is conducted in two study programs at Petra Christian University: Business Management and Architecture. The results show that the K-Means method gives good results. The clusters formed from the data show non-homogenous groups and produce insights from several groups. The results show a tendency for students’ performance to increase along with the number of activities and points earned. Most students have increased activities during busy times in the third, fourth, fifth, and sixth semesters. The peak is between the fifth and sixth semesters. Then, it starts to decrease in the seventh and eighth semesters. Therefore, students’ activities in the Business Management study program affect performance significantly. Meanwhile, in the Architecture study program, it has an insignificant effect on performance.
Program Evaluation and Review Technique (PERT) Analysis to Predict Completion Time and Project Risk Using Discrete Event System Simulation Method Yudistira, I Gusti Agung Anom; Nariswari, Rinda; Arifin, Samsul; Abdillah, Abdul Azis; Prasetyo, Puguh Wahyu; Susyanto, Nanang
CommIT (Communication and Information Technology) Journal Vol. 18 No. 1 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i1.8495

Abstract

The prediction of project completion time, which is important in project management, is only based on an estimate of three numbers, namely the fastest, slowest, and presumably time. The common practice of applying normal distribution through Monte Carlo simulation in Program Evaluation and Review Technique (PERT) research often fails to accurately represent project activity durations, leading to potentially biased project completion prediction. Based on these problems, a different method is proposed, namely, Discrete Event Simulation (DES). The research aims to evaluate the effectiveness of the simmer package in R in conducting PERT analysis. Specifically, there are three objectives in the research: 1) develop a simulation model to predict how long a project will take and find the critical path, 2) create an R script to simulate discrete events on a PERT network, and 3) explore the simulation output using the simmer package in the form of summary statistics and estimation of project risk. Then, a library research with a descriptive and exploratory method is used for data collection. The hypothetical network is used to obtain the numerical results, which provide the predicted value of the project completion, the critical path, and the risk level. Simulation, including 100 replications, results in a predicted project completion time and a standard deviation of 20.7 and 2.2 weeks, respectively. The DES method has been proven highly effective in predicting the completion time of a project described by the PERT network. In addition, it offers increased flexibility.
Object Detection Model for Web-Based Physical Distancing Detector Using Deep Learning Chowanda, Andry; Sariputra, Ananda Kevin Refaldo; Prananto, Ricardo Gunawan
CommIT (Communication and Information Technology) Journal Vol. 18 No. 1 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i1.8669

Abstract

The pandemic has changed the way people interact with each other in the public setting. As a result, social distancing has been implemented in public society to reduce the virus’s spread. Automatically detecting social distancing is paramount in reducing menial manual tasks. There are several methods to detect social distance in public, and one is through a surveillance camera. However, detecting social distance through a camera is not an easy task. Problems, such as lighting, occlusion, and camera resolution, can occur during detection. The research aims to develop a physical distancing detector system that is adjusted to work with Indonesian rules and conditions, especially in Jakarta, using deep learning (i.e., YOLOv4 architecture with the Darknet framework) and the CrowdHuman dataset. The detection is done by reading the source video, detecting the distance between individuals, and determining the crowd of individuals close to each other. In order to accomplish the detection, the training is done with CSPDarknet53 and VGG16 backbone in YOLOv4 and YOLOv4 Tiny architecture using various hyperparameters in the training process. Several explorations are made in the research to find the best combination of architectures and fine-tune them. The research successfully detects crowds at the 16th training, with mAP50 of 71.59% (74.04% AP50) and 16.2 Frame per Second (FPS) displayed on the web. The input size is essential for determining the model’s accuracy and speed. The model can be implemented in a web-based application.
Modeling Emotion Recognition System from Facial Images Using Convolutional Neural Networks Kusno, Jasen Wanardi; Chowanda, Andry
CommIT (Communication and Information Technology) Journal Vol. 18 No. 2 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i2.8873

Abstract

Emotion classification is the process of identifying human emotions. Implementing technology to help people with emotional classification is considered a relatively popular research field. Until now, most of the work has been done to automate the recognition of facial cues (e.g., expressions) from several modalities (e.g., image, video, audio, and text). Deep learning architecture such as Convolutional Neural Networks (CNN) demonstrates promising results for emotion recognition. The research aims to build a CNN model while improving accuracy and performance. Two models are proposed in the research with some hyperparameter tuning followed by two datasets and other existing architecture that will be used and compared with the proposed architecture. The two datasets used are Facial Expression Recognition 2013 (FER2013) and Extended Cohn-Kanade (CK+), both of which are commonly used datasets in FER. In addition, the proposed model is compared with the previous model using the same setting and dataset. The result shows that the proposed models with the CK+ dataset gain higher accuracy, while some models with the FER2013 dataset have lower accuracy compared to previous research. The model trained with the FER2013 dataset has lower accuracy because of overfitting. Meanwhile, the model trained with CK+ has no overfitting problem. The research mainly explores the CNN model due to limited resources and time.

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