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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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Mapping Irrigation Networks with Geographical Information Systems Using Satelite Imagery Data: A Case of Brebes Regency, Indonesia Abdurachman, Aulia Azhar; Arsyad, Muhammad Fahmi; Abdurahman, Edi; Napitupulu, Togar Alam; Legowo, Nilo
CommIT (Communication and Information Technology) Journal Vol 13, No 1 (2019): CommIT Vol. 13 No. 1 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i1.5075

Abstract

Water resources are important factors in food production. Those are very vital and strategic to meet food needs and food security. As water is scarce both in terms of volume and distribution throughout the year, reliable water management is needed. To support this water management, the accurate data is needed. However, the complete tabular data is not enough. It is because the existing tabular data does not provide the various activities and events based on time and place spatially and detail enough for planning purposes at the sub-district level. The researchers use high-resolution satellite imagery data that have been pre-processed with the geometric and radiometric corrections. They are used as one of the layers in the working map, so it is easier to provide the depiction of irrigation network objects, to find out the location of rice fields that have not been irrigated and the location of damaged irrigation networks. The depiction of the working map can also be used to map irrigation networks and their network conditions. Through this work, it has been shown that the researchers can map irrigation networks in detail for operational planning at a sub-district level with the help of technology, in particular for developing countries that is difficult or even impossible to do in the past.
Javanese Document Image Recognition Using Multiclass Support Vector Machine Sugianela, Yuna; Suciati, Nanik
CommIT (Communication and Information Technology) Journal Vol 13, No 1 (2019): CommIT Vol. 13 No. 1 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i1.5330

Abstract

Some ancient documents in Indonesia are written in the Javanese script. Those documents contain the knowledge of history and culture of Indonesia, especially about Java. However, only a few people understand the Javanese script. Thus, the automation system is needed to translate the document written in the Javanese script. In this study, the researchers use the classification method to recognize the Javanese script written in the document. The method used is the Multiclass Support Vector Machine (SVM) using One Against One (OAO) strategy. The researchers use seven variations of Javanese script from the different document for this study. There are 31 classes and 182 data for training and testing data. The result shows good performance in the evaluation. The recognition system successfully resolves the problem of color variation from the dataset. The accuracy of the study is 81.3%.
A Comparison of Machine Learning Algorithms in Manufacturing Production Process Rosalina, Rosalina
CommIT (Communication and Information Technology) Journal Vol 13, No 1 (2019): CommIT Vol. 13 No. 1 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i1.5177

Abstract

This research aims to improve the productivity and reliability of incoming orders in the manufacturing process. The unclassified data attributes of the incoming order can affect the order plan which will impact to the low productivity and reliability in the manufacturing process. In order to overcome the problem, machine learning algorithms are implemented to analyze the data and expected to help the manufacturing process in deciding the incoming order arrangement process. Four machine learning algorithms are implemented (Decision Tree, Nave Bayes, Support Vector Machine, and Neural Network). These machine learning algorithms are compared by their algorithm performance to the manufacturing process problem. The result of the research shows that machine learning algorithms can improve the productivity and reliability rate in production area up to 41.09% compared to the previous rate without any dataset arrangement before. The accuracy of this prediction test achieves 97%.
Factors That Influence Employees’ Intention to Use Enterprise Social Media as Knowledge Sharing Media Gunawan, Jeanifer; Gunawan, Fergyanto E.
CommIT (Communication and Information Technology) Journal Vol 13, No 2 (2019): CommIT Vol. 13 No. 2 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i2.5627

Abstract

Along with the widespread use of Enterprise Social Media (ESM) by various large companies in Indonesia, this research is conducted to discover what the factors that drive employees’ intention to use ESM as knowledge sharing media are, and what factor is the most dominant in driving employees’ intention. This research is a quantitative research which uses Innovation Diffusion Technology (IDT) and Extended Technology Acceptance Model (TAM) as the research model. Data collection in this research is conducted by the survey method. The questionnaires are distributed to 374 respondents. Based on the data collected, data processing and hypothesis testing are carried out using Partial Least Square Structural Equation Modelling (PLS-SEM). The result of this study indicates that relative advantage, compatibility, and perceived ease of use have a significant influence on perceived usefulness and perceived enjoyment. Meanwhile, perceived usefulness and perceived enjoyment have a significant influence on employees’ intention to use ESM. Furthermore, it is also found that the most dominant factor among those two variables is perceived enjoyment.
Segmentation of Tuberculosis Bacilli Using Watershed Transformation and Fuzzy C-Means Kurniawan, Rahadian; Muhimmah, Izzati; Kurniawardhani, Arrie; Kusumadewi, Sri
CommIT (Communication and Information Technology) Journal Vol 13, No 1 (2019): CommIT Vol. 13 No. 1 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i1.5119

Abstract

The easily transmitted Tuberculosis (TB) disease is attributed to the fact that Mycobacterium Tuberculosis (MTB) bacteria/viruses can be transmitted through the air. One of the methods to screen the TB disease is by reading sputum slides. Sputum slides are colored sputum samples of TB patients placed on microscopic slides. However, TB disease microscopic analysis has some limitations since it requires high accuracy reading and well-trained health personnel to avoid errors in the process of interpretation. Furthermore, the number of TB patients in the Primary Health Care (PHC) and the process of manual calculation of bacteria in a field of view often complicate the decision-making in the screening process conducted by the medical staffs. In this paper, the researchers propose the use of Watershed Transformation and Fuzzy C-Means combination to help solve the problem. The researchers collect the photo shooting of three PHC in Indonesia with 55 images of sputum from different TB patients. The assessed results of the proposed method are compared with the opinions of three Microbiology doctors. The comparison shows Cohen’s Kappa Coefficient value of 0.838. It suggests that the proposed method can detect Acid Resistant Bacteria (ARB) although it needs some improvement to achieve higher accuracy.
Lung Nodule Texture Detection and Classification Using 3D CNN Harsono, Ivan William
CommIT (Communication and Information Technology) Journal Vol 13, No 2 (2019): CommIT Vol. 13 No. 2 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i2.5995

Abstract

Following artificial intelligence implementation in computer vision field, especially deep learning, many Computer-Aided Diagnosis (CAD) tools are proposed to help to detect lung cancer by the scoring system or by identifying the characteristics of nodules. However, lung cancer is a clinical diagnosis which does not provide detailed information needed by radiologists and clinician to prevent unnecessary invasive diagnostic procedures compared to lung nodule texture detection and classification. Hence, to answer this problem, this research explores the steps needed to implement 3D CNN on raw thorax CT scan datasets and usage of RetinaNet 3D + Inception 3D with transfer learning. The 3D CNN CAD tools can improve the speed, performance, and ability to detect lung nodule texture instead of malignancy status done by previous studies. This research implements 3D CNN on Moscow private datasets acquired from NVIDIA Asia Pacific. The proposed method of data conversion can minimize information loss from raw data to 3D CNN input data. On training phase, after 100 epochs, the researchers conclude that the best-proposed model (3D CNN with transfer learning of pretrained LIDC public datasets weight) provides 22.86% of mean average precision (mAP) detection capability and 70.36% of Area Under the Curve (AUC) in Moscow private dataset lung texture detection tasks. It outperforms non-transfer learning 3D CNN model (trained from scratch) and 3D CNN with transfer learning of pre-trained ImageNet weight.
Information System Security of Indonesia Terrestrial Border Control Tjiptabudi, Fransiskus Mario Hartono; Bernardino, Raul
CommIT (Communication and Information Technology) Journal Vol 13, No 2 (2019): CommIT Vol. 13 No. 2 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i2.5529

Abstract

Today, Information Technology (IT) becomes an integral part of human life. IT boosts every sector, such as infrastructure, economics, agriculture, social, organization, and politics. The institutional systems are developed according to the specific business requirements, processes, flows, and security. Pos Lintas Batas Negara ‘integrated cross-border post’ (PLBN) is a designated authority consisting of the Custom, Immigration, and Quarantine (CIQ). Each section has a different Standard Operation Procedure (SOP). This research aims to develop a secure information system based on Confidentiality, Integrity, and Availability (CIA) concepts. The CIA is embedded in the ISO 27001 and McCumber Cube approach. The research focuses on the Secure Immigration Information System (SIIS). This research is conducted in the Wini immigration office. The researchers observe the immigration activities on the location, interview the immigration officers, and collect information. The researchers produce an effective, efficient, and security application prototype system.
Digital Forensics Study of a Cloud Storage Client: A Dropbox Artifact Analysis Satrya, Gandeva Bayu
CommIT (Communication and Information Technology) Journal Vol 13, No 2 (2019): CommIT Vol. 13 No. 2 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i2.5781

Abstract

The rapid development of cloud storage technology paired with the prevalence of smartphone usage presents wide-ranging challenges for digital forensics practitioners. Data are more easily uploaded and shared between multiple devices and across multiple platforms. So, the process has increased the opportunities for criminality. Criminality undertaken in cloud computing can be directly seen on logs stored on the cloud storage server, which records user activity. However, because of user privacy protection, these logs cannot be easily used as evidence in court. This issue emphasizes the need for a reliable means of identifying, acquiring, and preserving evidential data from the client-side. This study identifies the data artifacts of a user accessing Dropbox via smartphone (Android Lollipop and Android Nougat). The data are from performing several common activities such as installing, signing up, uploading, downloading, sharing, and others. About 14 artifacts are identified by documenting the Dropbox client database changing contents as these activities are carried out. This study increases knowledge of the artifacts that are leftover by Dropbox client on Android smartphones. The results propose this comparing and analyzing method can be used by digital forensics investigators in carrying out investigations and cyberlaw practitioners as guidance in criminal cases.
The Influence of Perceived Risk and Trust in Adoption of FinTech Services in Indonesia Meyliana, Meyliana; Fernando, Erick; surjandy, surjandy
CommIT (Communication and Information Technology) Journal Vol 13, No 1 (2019): CommIT Vol. 13 No. 1 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i1.5708

Abstract

The service level in community must be considered if it wants to continue to be used by the users. This research studies the adoption of Financial Technology (FinTech) services in the terms of trust and risk. The work employs the Technology Acceptance Model (TAM) theory as the theoretical basis combined with trust and perceived risk. The research method is quantitative. The data are analyzed by the Structural Equation Model (SEM) using Smart PLS V2.0. The researchers use a questionnaire in Google Form to collect the data. It is distributed online with the snowball data collection technique. As a result, 548 respondents are successfully gathered. The results indicate that the factor of users trusts influences perceived usefulness in the adoption to use FinTech services. However, the risk factor does not affect the use of FinTech services, which further does not influence the users’ attitude. The work contributes to the study of the adoption of FinTech services, which provides a view determining the users’ intention to use FinTech services in Indonesia.
Diseases Classification for Tea Plant Using Concatenated Convolution Neural Network Krisnandi, Dikdik; Pardede, Hilman F.; Yuwana, R. Sandra; Zilvan, Vicky; Heryana, Ana; Fauziah, Fani; Rahadi, Vitria Puspitasari
CommIT (Communication and Information Technology) Journal Vol 13, No 2 (2019): CommIT Vol. 13 No. 2 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i2.5886

Abstract

Plant diseases can cause a significant decrease in tea crop production. Early disease detection can help to minimize the loss. For tea plants, experts can identify the diseases by visual inspection on the leaves. However, providing experts to deal with disease identification may be very costly. The machine learning technology can be implemented to provide automatic plant disease detection. Currently, deep learning is state-of-the-art for object identification in computer vision. In this study, the researchers propose the Convolutional Neural Network (CNN) for tea disease detections. The researchers focus on the implementation of concatenated CNN, namely GoogleNet, Xception, and Inception-ResNet-v2, for this task. About 4727 images of tea leaves are collected, comprising of three types of diseases that commonly occur in Indonesia and a healthy class. The experimental results confirm the effectiveness of concatenated CNN for tea disease detections. The accuracy of 89.64% is achieved.

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