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Fergyanto F. Gunawan
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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 6 Documents
 1 
Search results for , issue "Vol. 13 No. 2 (2019): CommIT Journal" : 6 Documents clear
Information System Security of Indonesia Terrestrial Border Control Fransiskus Mario Hartono Tjiptabudi; Raul Bernardino
CommIT (Communication and Information Technology) Journal Vol. 13 No. 2 (2019): CommIT Journal
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.
Factors That Influence Employees’ Intention to Use Enterprise Social Media as Knowledge Sharing Media Jeanifer Gunawan; Fergyanto E. Gunawan
CommIT (Communication and Information Technology) Journal Vol. 13 No. 2 (2019): CommIT Journal
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.
Digital Forensics Study of a Cloud Storage Client: A Dropbox Artifact Analysis Gandeva Bayu Satrya
CommIT (Communication and Information Technology) Journal Vol. 13 No. 2 (2019): CommIT Journal
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.
Diseases Classification for Tea Plant Using Concatenated Convolution Neural Network Dikdik Krisnandi; Hilman F. Pardede; R. Sandra Yuwana; Vicky Zilvan; Ana Heryana; Fani Fauziah; Vitria Puspitasari Rahadi
CommIT (Communication and Information Technology) Journal Vol. 13 No. 2 (2019): CommIT Journal
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.
Lung Nodule Texture Detection and Classification Using 3D CNN Ivan William Harsono
CommIT (Communication and Information Technology) Journal Vol. 13 No. 2 (2019): CommIT Journal
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.
Hydroponic Nutrient Control System Based on Internet of Things Herman Herman; Demi Adidrana; Nico Surantha; Suharjito Suharjito
CommIT (Communication and Information Technology) Journal Vol. 13 No. 2 (2019): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i2.6016

Abstract

The human population significantly increases in crowded urban areas. It causes a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods without using soil. It uses nutrient-enriched mineral water as a nutrition solution for plant growth. Traditionally, hydroponic farming is conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient temperature. In this research, the researchers propose a system that measures pH, TDS, and nutrient temperature values in the Nutrient Film Technique (NFT) technique using a couple of sensors. The researchers use lettuce as an object of experiment and apply the k-Nearest Neighbor (k-NN) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed k-NN algorithm achieves 93.3% accuracy when it sets to k = 5.

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