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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 11 Documents
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Search results for , issue "Vol. 16 No. 2 (2022): CommIT Journal" : 11 Documents clear
Modified Multi-Kernel Support Vector Machine for Mask Detection Muhammad Athoillah; Evita Purnaningrum; Rani Kurnia Putri
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.7873

Abstract

Indonesia is one of the countries most affected by the Coronavirus pandemic with millions confirm cases. Hence, the government has increased strict procedures for using face masks in public areas. For this reason, the detection of people wearing face masks in public areas is needed. Face mask detection is a part of the classification problem. Thus Support Vector Machine (SVM) can be implemented. SVM is still known as one of the most powerful and efficient classification algorithms. The research aims to build an automatic face mask detector using SVM. However, it needs to modify it first because it only can classify linear data. The modification is made by adding kernel functions, and a Multi-kernel approach is chosen. The proposed method is applied by combining various kernels into one kernel equation. The dataset used in the research is a face mask image obtained from Github. The data are public datasets consisting of faces with and without masks. The results present that the proposed method provides good performance. It is proven by the average value. The values are 83.67% for sensitivity, 82.40% for specificity, 82.00% for precision, 82.93% for accuracy, and 82.77% for F1-score. These values are better than other experiments using single kernel SVM with the same process and dataset.
Political Communication Patterns through Social Media: A Case of an Indonesian Presidential Staff Twitter Account Addin Khaerunnisa Juswil; Sanny Nofrima; Herdin Arie Saputra
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.7889

Abstract

Given the current technological developments, social media has become a necessity and a new tool that can complement services. Social media can influence the understanding of political communication and its impact on the public. Every element of society uses social media for various purposes, including the government. One who is quite active on Twitter and has repeatedly drawn controversy is the Presidential Chief of Staff, Retired General Moeldoko. The research investigates the pattern of political communication carried out by the Presidential Chief of Staff by focusing on his Twitter account and using the components of the effectiveness of political communication and media effects. The research applies a qualitative approach supported by the NVivo 12 Plus software. The data are collected using the NCapture for NVivo feature on the Presidential Chief of Staff’s Twitter account (@Dr Moeldoko). The results show that Moeldoko’s communication through Twitter is generally for the media or fellow government agencies. His communication is also a way to provide information about government programs. In addition, the research also finds that the alleged ineffectiveness in Twitter management is based on a decrease in account activity. It can be concluded that his pattern of communication is general, vertical, and not participatory.
Integrated Information System of Material Resource Planning and Supply Chain Procurement: A Case Study of XYZ Company Santo Wijaya; Marta Hayu Raras Sita Rukmika Sari; Yeyeh Supriatna
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.7945

Abstract

Human errors are inevitable due to the mass customization and complex supply chain, which must be considered in the Material Resource Planning (MRP) explosion calculation and the Supply Chain Procurement (SCP) distribution of sales orders. Such errors lead to SCP problems with the subject company. The research presents the design and implementation of an integrated information system of MRP and SCP. The objective is to solve the issues in the subject company by designing and implementing an Integrated Information System (IIS) framework with an open-source software platform, which utilizes a generic Bill-of-Materials (BoM) explosion algorithm to calculate the MRP of customers’ sales orders. Then, an algorithm is also proposed to calculate SCP distribution to enable the framework in the implementation stage. The research subject is the process business in Indonesia’s local Original Equipment Manufacturer (OEM) for automotive components. The name of the company is concealed to be XYZ Company. The calculation is introduced in the testing phase to illustrate the algorithm’s mechanism. This approach ensures a valid calculation of efficient supply chain processes. The combined approaches in the subjected business process yield satisfactory results. It reduces significant issues to 12.5% for the stated problems. Hence, the design objective is achieved.
Comparison of Supervised Learning Methods for COVID-19 Classification on Chest X-Ray Image Faisal Dharma Adhinata; Nur Ghaniaviyanto Ramadhan; Arif Amrulloh; Arief Rais Bahtiar
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.7970

Abstract

The Coronavirus (COVID-19) pandemic is still ongoing in almost all countries in the world. The spread of the virus is very fast because the transmission process is through air contaminated with viruses from COVID-19 patients’ droplets. Several previous studies have suggested that the use of chest X-Ray images can detect the presence of this virus. Detection of COVID-19 using chest X-Ray images can use deep learning techniques, but it has the disadvantage that the training process takes too long. Therefore, the research uses machine learning techniques hoping that the accuracy results are not too different from deep learning and result in fast training time. The research evaluates three supervised learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Random Forest, to detect COVID-19. The experimental results show that the accuracy of the SVM method using a polynomial kernel can reach 90% accuracy, and the training time is only 462 ms. Through these results, machine learning techniques can compensate for the results of the deep learning technique in terms of accuracy, and the training process is faster than the deep learning technique. The research provides insight into the early detection of COVID-19 patients through chest X-Ray images so that further medical treatment can be carried out immediately.
Automatic Fish Identification Using Single Shot Detector Arie Vatresia; Ruvita Faurina; Vivin Purnamasari; Indra Agustian
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.8126

Abstract

The vast sea conditions and the long coastline make Bengkulu one of the provinces with a high diversity of marine fish. Although it is predicted to have high diversity, data on the diversity of marine fish on the Bengkulu coast is still very limited, especially in the process of fish species detection. With the development and expansion of computer capabilities, the ability to classify fish can be done with the help of computer equipment. The research presents a new method of automating the detection of marine fish with a Single Shot Detector method. It is a relatively simple algorithm to detect an object with the help of a MobileNet architecture. In the research, the Single Shot Detector used is six extra convolution layers. Three of the extra layers can generate six predictions for each cell. The Single Shot Detector model, in total, can generate 8,732 predictions. The research succeeds in identifying seven from ten genera of marine fish with a total dataset of 1,000 images, with 90% training data and 10% validation data. Each fish genus has 100 images with different shooting angles and backgrounds. The results show that the Single Shot Detector model with MobileNet architecture gets an accuracy value of 52.48% for the identification of 10 genera of marine fish.
Observing Pre-Trained Convolutional Neural Network (CNN) Layers as Feature Extractor for Detecting Bias in Image Classification Data Amadea Claire Isabel Ardison; Mikhaya Josheba Rumondang Hutagalung; Reynaldi Chernando; Tjeng Wawan Cenggoro
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.8144

Abstract

Detecting bias in data is crucial since it can pose serious problems when developing an AI algorithm. The research aims to propose a novel study design to detect bias in image classification data by using pretrained Convolutional Neural Network (CNN) layers as a feature extractor. There are three datasets used in the research with varying degrees of complexity, those are low, medium, and high complexity. There are Modified National Institute of Standards and Technology (MNIST) Digits, batik collections (Parang, Megamendung, and Kawung), and Canadian Institute for Advanced Research (CIFAR-10) datasets. Then, the researchers make a baseline workflow and substitute a step-in feature extraction with a convolution using the first pre-trained CNN layer and each of its kernels. Then, the researchers evaluate the effect of the experiments using accuracy. By observing the effect of the individual kernel, the research can better make sense of what happens inside a CNN layer. The research finds that color in the image is an essential factor when working with CNN. Furthermore, the proposed study design can detect bias in image classification data where it is related to the color of the image. Detecting this bias early is important in helping developers to improve AI algorithms.
E-learning Acceptance Model in a Pandemic Period with an Expansion to the Quality of Work Life and Information Technology Self-Efficacy Aspects Weli Weli; Julianti Sjarief
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.8145

Abstract

The research is inspired by the COVID-19 pandemic which affects face-to-face learning and leads to the e-learning system. However, educational institutions and related parties are not prepared for this sudden change. So, it is interesting to research the students’ intentions related to learning during the pandemic in the framework of the Technology Acceptance Model (TAM). Specifically, the research aims to analyze the acceptance and satisfaction model of e-learning users amid the pandemic. The proposed model that predicts student intentions and satisfaction with e-learning is an expanded TAM with factors such as quality of work life and information technology self-efficacy. The research provides empirical evidence related to the quality of work balance and the ability to use information technology related to e-learning access, in addition to other factors in the TAM. The data are collected by distributing online questionnaires with a snowball sampling model. The sample includes students who voluntarily fill out the questionnaire from various Indonesian universities. Then, the structural equation model processes the data using a Partial Least Square (PLS) approach and analyzes it through the SmartPLS3 program. The results show that the variables of quality of work life and information technology self-efficacy, such as computers, the Internet, and communication, can explain the acceptance of elearning models, especially during a pandemic. As an implication of the results, the teachers should focus on elearning designs that facilitate access to learning material and student-teacher interactions to attract intentions and increase students’ satisfaction in using e-learning.
Leveraging COBIT 2019 to Implement IT Governance in SME Context: A Case Study of Higher Education in Campus A Diana Utomo; Mahaning Wijaya; Suzanna Suzanna; Efendi Efendi; Noviyanti Tri Maretta Sagala
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.8172

Abstract

Achieving business and Information Technology (IT) alignment has become the aspiration of most organizations nowadays. Small and Medium Enterprises (SMEs) are not exempt as they also thrive to survive in a competitive market using IT. However, implementing all IT control and management components will be excessive, with a lack of justifiable cost-benefit for SMEs. A tailored governance system based on the specificities of SMEs is necessitated to help the organization to focus on its main objectives and strategies. By leveraging COBIT 2019 design toolkit, the researchers support Campus A in establishing healthy governance and IT management. Both qualitative and quantitative approaches are applied to select relevant governance/ management objectives. The toolkit has been designed with a semi-automated quantitative approach in which users will get scoring for each objective based on the associated value inputted for each design factor. Through a series of discussions with the management team, it concludes the governance design and recommends several improvements to increase its capability level, from the current level of 1.05 (initial stage) to the desired level of 2.33 (repeatable stage). Then, since the toolkit is practical to use, it is also rigid by design with its predefined and protected formula. To some extent, the resulting score or importance level of certain governance/management objectives is questionable and lacks justification. Flags or indicators to ‘should-have’ governance/management objectives, regardless of the organization’s size and type, will be useful to prevent the omission of essential objectives.
Exploring the Best Parameters of Deep Learning for Breast Cancer Classification System Andry Chowanda
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.8174

Abstract

Breast cancer is one of the deadliest cancers in the world. It is essential to detect the signs of cancer as early as possible, to make the survival rate higher. However, detecting the signs of breast cancer using the machine or deep learning algorithms from the diagnostic imaging results is not trivial. Slight changes in the illumination of the scanned area can significantly affect the automatic breast cancer classification process. Hence, the research aims to propose an automatic classifier for breast cancer from digital medical imaging (e.g., Positron Emission Tomography or PET, X-Ray of Mammogram, and Magnetic Resonance Imaging (MRI) images). The research proposes modified deep learning architecture with five different settings to model automatic breast cancer classifiers. In addition, five machine learning algorithms are also explored to model the classifiers. The dataset used in the research is the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM). A total of 2,676 mammogram images are used in the research and are split into 80%:20% (2,141:535) for training and testing datasets. The results demonstrate that the model trained with eight layers of Convolutional Neural Networks (CNN) (SET-8) achieves the best accuracy score of 94.89% and 93.75% in the training and validation dataset, respectively.
An Explainable AI Model for Hate Speech Detection on Indonesian Twitter Muhammad Amien Ibrahim; Samsul Arifin; I Gusti Agung Anom Yudistira; Rinda Nariswari; Abdul Azis Abdillah; Nerru Pranuta Murnaka; Puguh Wahyu Prasetyo
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.8343

Abstract

To avoid citizen disputes, hate speech on social media, such as Twitter, must be automatically detected. The current research in Indonesian Twitter focuses on developing better hate speech detection models. However, there is limited study on the explainability aspects of hate speech detection. The research aims to explain issues that previous researchers have not detailed and attempt to answer the shortcomings of previous researchers. There are 13,169 tweets in the dataset with labels like “hate speech” and “abusive language”. The dataset also provides binary labels on whether hate speech is directed to individual, group, religion, race, physical disability, and gender. In the research, classification is performed by using traditional machine learning models, and the predictions are evaluated using an Explainable AI model, such as Local Interpretable Model-Agnostic Explanations (LIME), to allow users to comprehend why a tweet is regarded as a hateful message. Moreover, models that perform well in classification perceive incorrect words as contributing to hate speech. As a result, such models are unsuitable for deployment in the real world. In the investigation, the combination of XGBoost and logical LIME explanations produces the most logical results. The use of the Explainable AI model highlights the importance of choosing the ideal model while maintaining users’ trust in the deployed model.

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