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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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Understanding Business Intelligence and Analytics System Success from Various Business Sectors in Indonesia Sevenpri Candra; Ambi Nainggolan
CommIT (Communication and Information Technology) Journal Vol. 16 No. 1 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i1.7849

Abstract

Many studies have shown the impact of the Business Intelligence and Analytics (BI&A) system on decision-making. Many organizations have invested heavily in BI technology and the growth of analytical skills and made the BI&A system a strategic priority over the last eight years by citing it as the largest IT investment. The research aims to determine the relevant constructs contributing to the organization’s BI&A system success. Survey research is applied to collect quantitative data for the research questions. The questionnaire is developed in English, which is translated into bahasa Indonesia later. The research obtains 208 decision-makers who use and utilize the BI&A system in various business sectors in Indonesia to achieve this goal. Then, PLS-SEM is used for measurement validation and hypothesis testing. About 8 out of 11 hypothesized relationships between 7 success factors are significantly supported. The findings demonstrate that the model constructs significantly improve decision-making quality in the BI&A system environment. Service quality is found to be the highest predictor of system use. Meanwhile, information quality is the highest predictor of user satisfaction. The research presents practical implications for organizations to adopt the essential factors of BI&A system finding to realize organizational success. Moreover, organizations that have already implemented the BI&A system can use the research as a theoretical basis to measure the ability of the BI&A system to improve decision-making quality.
The Resistance to Adopting Online Marketplace: The Influence of Perceived Risk and Behavioral Control of Small and Medium Enterprises in Indonesia Dwipuspa Ramadhanti Santoso; Putu Wuri Handayani; Fatimah Azzahro
CommIT (Communication and Information Technology) Journal Vol. 16 No. 1 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i1.7858

Abstract

To date, the number of e-commerce applications in Indonesia keeps increasing. However, the adoption rate amongst Small and Medium Enterprises (SMEs) is still around 13%. Thus, the research aims to analyze the factors that affect small and medium enterprises’ resistance to adopting online marketplaces in Indonesia by applying the technological-organizationalenvironmental framework, the diffusion of innovations theory, and the innovation resistance model. The respondents are 356 owners of SMEs in Indonesia who have sold their products online through social media or marketplace. The research applies a quantitative approach using an online questionnaire and interview. Then, the covariance-based structural equation modeling method is applied to analyze the data. In addition, the research interviews five SME representatives to gain further understanding and information for the rejected hypothesis. The results show that perceived complexity, owners’ self-efficacy, and enabling conditions are proven to affect perceived behavioral control. Perceived complexity is also shown to affect perceived risk. On the other hand, government support does not affect perceived behavioral control. It is also found that perceived risk and behavioral control affect resistance to adopting online marketplaces. The findings provide recommendations to overcome this situation, such as the simplification of online marketplace features for sellers and improvements in the digital literacy of the owners of SMEs.
Understanding Mobile Payment Continuance in Indonesia: A Brand Equity Perspective Continuance Model Raden Roro Fosa Sarassina
CommIT (Communication and Information Technology) Journal Vol. 16 No. 1 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i1.7882

Abstract

As the Indonesian government promotes cashless transactions, more and more efforts are put to make people adopt and keep using electronic money, including mobile payment (m-payment). The research focuses on investigating factors that influence people’s intention to keep using m-payment using two robust theories in the continuance intention: Technology of Acceptance Model (TAM) and Expectation Confirmation Model (ECM). Despite the robustness, the two models do not consider the user’s judgement of the issuer’s brand influence, which is reflected through its brand equity. Then, to fill this gap, A Continuance Model-Brand Equity Perspective (CMBEP) is proposed. The research applies a quantitative approach to validate the model. The data are collected using an online questionnaire to m-payment users. Then, Structural Equation Modelling (SEM) with SmartPLS software test the four hypotheses. Based on the analysis of 420 respondents, it is found that all the hypothesis is supported, and the model is validated. It shows the strongest to weakest relationships: perceived usefulness to satisfaction, perceived ease of use to satisfaction, satisfaction to continuance intention, and brand equity to continuance intention. The findings shed light on m-payment issuers for not only focusing on creating satisfaction for their users but also building a brand with strong equity.
Securing Medical Records of COVID-19 Patients Using Elliptic Curve Digital Signature Algorithm (ECDSA) in Blockchain Andi Andi; Carles Juliandy; Robet Robet; Octara Pribadi
CommIT (Communication and Information Technology) Journal Vol. 16 No. 1 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i1.7958

Abstract

The rapid and dangerous spread of COVID-19 has forced governments in various countries to provide information on patients’ medical records to the public in the context of prevention. Meanwhile, patients’ medical records are vital and confidential because they contain patients’ privacy. Changing and falsifying a patient’s medical record leads to various dangerous consequences, such as mishandling which results in the patient’s death. From these problems, the research introduces a new model with a combination of blockchain technology and the Elliptic Curve Digital Signature Algorithm (ECDSA) to secure the medical records of COVID-19 patients. This model is an improvement from the model and framework proposed by previous researchers. The proposed model consists of two big parts (front and back end). Then, the simulations are carried out to measure and prove the level of security of blockchain technology in securing patient medical records. The research results show that the ECDSA algorithm can protect patients’ medical records from being opened by unauthorized parties. Then, blockchain technology can prevent changes or manipulation of patient medical records because the information recorded on the blockchain network is impossible to change and will be immutable. The research has successfully introduced a new model in securing patient medical records.
Mobile Application Characteristics and User Perspective in Smart Healthcare Service Applications Ni Ketut Dewi Ari Jayanti; Evi Triandini; Gde Sastrawangsa; Ni Wayan Deriani
CommIT (Communication and Information Technology) Journal Vol. 16 No. 1 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i1.8015

Abstract

The current COVID-19 pandemic has an impact on accelerating the digital transformation of various sectors in Indonesia, one of which is the health sector. Hence, many mobile applications of smart healthcare service have been developed, which can be a solution for people to get better health services. The aim of the research is to evaluate the mobile application (SpeedId and SpeedQ) for smart healthcare service based on the user perspective and the characteristics of the mobile application. The research is an analytic observational study that contains five stages of research. There are 64 respondents in the research using a questionnaire as data collection tool. Then, through a literature review, seven variables are determined, and a research model is proposed. The evaluation of the mobile application characteristics includes the operating system and network availability as independent variables and response time, responsiveness, and interface accessibility as dependent variables. Meanwhile, evaluating user perspective consists of respondents’ gender and education as independent variables and duration and ease of use as dependent variables. From the mobile application characteristics, the results show that operating system and network availability do not significantly affect all dependent variables. From the user perspective, gender only significantly affects the duration variable. Meanwhile, education significantly impacts the duration and the ease of use.
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.

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