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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 478 Documents
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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.
Editorian Page and Table of Content Fergyanto E. Gunawan
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

Abstract

Fish Classification System Using YOLOv3-ResNet18 Model for Mobile Phones Suryadiputra Liawatimena; Edi Abdurachman; Agung Trisetyarso; Antoni Wibowo; Muhamad Keenan Ario; Ivan Sebastian Edbert
CommIT (Communication and Information Technology) Journal Vol. 17 No. 1 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i1.8107

Abstract

Every country in the world needs to report its fish production to the Food and Agriculture Organization of the United Nations (FAO) every year. In 2018, Indonesia ranked top five countries in fish production, with 8 million tons globally. Although it ranks top five, the fisheries in Indonesia are mostly dominated by traditional and small industries. Hence, a solution based on computer vision is needed to help detect and classify the fish caught every year. The research presents a method to detect and classify fish on mobile devices using the YOLOv3 model combined with ResNet18 as a backbone. For the experiment, the dataset used is four types of fish gathered from scraping across the Internet and taken from local markets and harbors with a total of 4,000 images. In comparison, two models are used: SSD-VGG and autogenerated model Huawei ExeML. The results show that the YOLOv3-ResNet18 model produces 98.45% accuracy in training and 98.15% in evaluation. The model is also tested on mobile devices and produces a speed of 2,115 ms on Huawei P40 and 3,571 ms on Realme 7. It can be concluded that the research presents a smaller-size model which is suitable for mobile devices while maintaining good accuracy and precision.
Tweets Emotions Analysis of Community Activities Restriction as COVID-19 Policy in Indonesia Using Support Vector Machine Abi Nizar Sutranggono; Elly Matul Imah
CommIT (Communication and Information Technology) Journal Vol. 17 No. 1 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i1.8189

Abstract

With the rising number of COVID-19 cases in Indonesia, the government has implemented the Imposition of Restrictions on Emergency Community Activities (Pemberlakuan Pembatasan Kegiatan Masyarakat - PPKM) as Indonesia’s COVID-19 policy. Several controversies and protests have colored the implementation of this emergency policy. Some netizens on Twitter voice their opinions about the policy in their tweets. Emotions in tweets can be recognized through text-based emotion detection or emotion analysis. However, textbased emotion detection is a challenging task. One of the main issues in classifying text with a machine learningbased approach deals with the feature dimensions. As a result, appropriate methods for accurately identifying emotion based on the text are required. The research studies an emotions analysis task on Indonesians’ PPKMrelated tweets to understand their emotional state while implementing the PPKM. The machine learning classification algorithms used are Support Vector Machine (SVM) and random forest. The total number of tweets is 4,401. The results show that SVM with linear kernel function combined with the TF-IDF and Chi-Square methods outperforms other classifiers with an accuracy of 0.7528. The accuracy value is higher than those obtained by previous studies. Moreover, the results of the emotion classification on PPKM tweets reveal that most Indonesians are unhappy with the implementation of the PPKM policy.
Design of Business Process Management in Waste Bank Application Based on BMC and SWOT Analysis Irfan Fandi; Emil Kaburuan
CommIT (Communication and Information Technology) Journal Vol. 17 No. 1 (2023): CommIT Journal (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i1.8205

Abstract

Waste is one of the world’s problems that is challenging for a country or a city to do good waste management. Currently, many waste bank application providers are running to produce benefit and value for the community. However, many of them do not run optimally due to improper business processes. The research discusses the design and development of a waste bank application provider using a Business Model Canvas (BMC) and Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis as tools to help design a waste bank application for the provider company. The research applies a qualitative descriptive research with a qualitative approach. The sample data are taken from the waste bank application provider, PT Kompis Creative Solution using direct interviews with managers of waste bank application providers. Then, observation of the waste bank system, identification of problems, and literature studies are aimed to find information and references related to the research. The analysis indicates that the waste bank business process can be included in the BMC which can provide a neat picture for PT Kompis Creative Solution. Each of the BMC block can be the starting point for the company to determine the most important criteria. Then, the company can develop a digital waste bank strategy using the SWOT matrix to design a future strategy and minimize the risks. With the role of the waste bank application, it is expected that it improves the bank’s business processes and increases public enthusiasm to know and contribute to the mutually beneficial waste bank business.
Comparison of the Performance Results of C4.5 and Random Forest Algorithm in Data Mining to Predict Childbirth Process Muhasshanah Muhasshanah; Mohammad Tohir; Dewi Andariya Ningsih; Neny Yuli Susanti; Astik Umiyah; Lia Fitria
CommIT (Communication and Information Technology) Journal Vol. 17 No. 1 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i1.8236

Abstract

Technology advancements in the world of information have made it easier for many people to process data. Data mining is a process of mining more valuable information from large data sets. The research aims to determine the difference between the C.45 and random forest algorithms in data mining to predict the childbirth process of pregnant women. It compares the accuracy of the performance results of the C4.5 and random forest algorithms to predict the delivery process for pregnant women. Then, experimental research is conducted to classify the childbirth process in Situbondo, Indonesia, by applying the C.45 and the random forest algorithm in the data mining. The decision tree J48 algorithm is used for the C4.5 algorithm in the research. Both algorithms are compared for their error classification and accuracy level. The research uses 1,000 data for training and 200 data for testing. The results show the accuracy of implementing the C4.5 and random forest algorithms with data mining using 10-fold cross-validation, generating 96% and 95% as correctly classified data. Then, the Relative Absolute Error for both algorithms has the same result. It is 15%. The C4.5 algorithm has a better result than the random forest algorithm by comparing the performance results. Further research can add more data to improve the accuracy of the analysis results by using another algorithm.

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