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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. 17 No. 1 (2023): CommIT Journal" : 11 Documents clear
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.
Development of a Target-Based Configurable Business-to-Business Model for Electronic Wholesale Information System in East Java Liliana Liliana; Felix Handani; Bambang Prijambodo; Maria Christabella Wariky
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.v17i1.8296

Abstract

Until the end of 2021, most of the electronics wholesale market in East Java has found obstacles in contract administration and monitoring of consumers’ sales targets managed conventionally in contract management and sales target monitoring aspects. This phenomenon becomes complicated because most wholesaler systems have specific rules of management. The research discusses information systems that can be configured for various business processes in the wholesale sale of electronic goods. The case-study approach is applied by analyzing, designing, constructing, and validating the new proposed system based on three case studies with different business processes. The result of the analysis phase explains minimum sales value, the number of item variants in sales, verification rules for sales supervisors, and the configuration of user access rights that are highly flexible when applied. This system is developed as web-based to make easy access to multiple platforms to accommodate flexibility. The result summarizes that companies have the power of freedom to customize their target and help them to monitor wholesale store sales to meet sales target achievement. Alternatively, suppose companies create their new sub-companies with different targets and segments. In that case, they can easily configure target sales rules without spending significant money on developing another application.
Strategies to Improve Data Quality Management Using Total Data Quality Management (TDQM) and Data Management Body of Knowledge (DMBOK): A Case Study of M-Passport Application Rina Rahmawati; Yova Ruldeviyani; Puja Putri Abdullah; Fathurahman Ma'ruf Hudoarma
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.v17i1.8330

Abstract

M-Passport is a mobile application developed for Indonesians to request for passport online. The applicants independently input all required data using this application, so the quality of data entered must be considered to ensure the passport’s validity as an official state document. However, input errors increase the time needed for the interview process and make the data verification procedure inefficient. The research aims to assess the data quality of M-Passport for organizations to take deliberate actions to enhance the data quality. The research applies the Total Data Quality Management (TDQM) method and the Data Management Body of Knowledge (DMBOK). Six data quality dimensions are used. It consists of completeness, validity, accuracy, timeliness, uniqueness, and consistency. The measure phase is carried out on 17 entities in the M-Passport database through a query process in the production environment. Then, the analysis phase observes the problems based on the pre-determined dimensional classification groups. The result indicates that the average values of completeness, validity, accuracy, consistency, timeliness, and uniqueness are 99.20%, 99.41%, 100%, 90.68%, 78.52%, and 99.98%, respectively. According to the findings, timeliness and consistency are the lowest dimensions in fulfilling business rules. It indicates that organizations need to focus more on improving data quality in these dimensions. Then, based on the DMBOK, the research also generates recommendations for resolving technical and operational issues.
Smart Agriculture Water System Using Crop Water Stress Index and Weather Prediction Jason Timotius Purwoko; Taurean Orlin Wingardi; Benfano Soewito
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.v17i1.8435

Abstract

Water is essential for crops to grow well. However, overwatered or underwatered plants hinder growth and produce less fruit than plants with sufficient watering. Using the Internet of Things (IoT), agriculture can be controlled to achieve the best condition for plants to grow. The research aims to develop a watering system based on Crop Water Stress Index (CWSI), soil moisture content, and weather prediction. By evaluating CWSI and soil moisture content, the research makes a smart watering system that efficiently monitors water concentration in a plant. However, there is a flaw in the watering system that water from the rain makes the plant overwatered. So, using weather prediction can delay irrigation to save water and produce a better stress index result. Next, the research compares the watering system using four pots: (1) weather prediction, CWSI, and soil moisture watering system; (2) CWSI and soil moisture watering syste;, (3) soil moisture watering system; (4) manual irrigation watering system to get the best watering system by water consumption and CWSI. The results show a significant difference by using CWSI. It gets a 42.7754% smaller CWSI value by using CWSI value in making watering decisions. By adding weather prediction, the research saves water consumption by 21.9% compared to CWSI and soil moisture watering systems. These results show that weather prediction and CWSI are vital for IoT plant watering systems.
Nighttime Motorcycle Detection for Sparse Traffic Images Using Machine Learning Pov Vandeth; Jimmy Tirtawangsa; Hertog Nugroho
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.v17i1.8443

Abstract

Traffic accidents often occur at night. It is understandable, since at night, people have low visibility. Many efforts to develop tools to detect nearby vehicles to avoid crashes have been reported. However, most of them worked only on detecting cars. The research aims to detect motorcycles at night, to complement the previous studies, which mainly focused on cars. The research introduces four features which are extracted from the red pixel and edge map. The algorithm to extract the features has also been developed. They are applied to three commonly used classifiers: Artificial Neural Network (ANN), Decision Tree, and Support Vector Machine (SVM) classifiers to validate the effectiveness of the features. Since the public dataset related to the research is not available yet, the nighttime videos from YouTube have been collected. The datasets contain all the various levels of darkness. They are divided into an 80-20 ratio for training and testing sets to support the experiment and measure the validity of the proposed method. As the best result, the detection using ANN can detect motorcycle proposals with accuracy of 72.71%, precision of 65.10% and recall of 73.33%. Furthermore, during the experiment, the classification can perform consistently in 0.04 seconds per image. Therefore, the method is suitable for use in a real-time system.
Hand Symbol Classification for Human-Computer Interaction Using the Fifth Version of YOLO Object Detection Sugiarto Wibowo; Indar Sugiarto
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.v17i1.8520

Abstract

Human-Computer Interaction (HCI) nowadays mostly uses physical contact, such as people using the mouse to choose something in an application. However, there are certain problems that people face in using conventional HCI. The research tries to overcome some problems when people use conventional HCI using the computer vision method. The research focuses on creating and evaluating the object detection model for classifying hand symbols. The research applies the fifth version of YOLO with the architecture of YOLOv5m to classify hand symbols in real time. The methods are divided into three steps. Those steps are dataset creation consisting of 100 images in each class, training phase, and performance evaluation of the model. The hand gesture classes made in the research are ‘ok’, ‘cancel’, ‘previous’, ‘next’, and ‘confirm’, the dataset is made by the researchers custom. After the training phase, the validation results show 93% for accuracy, 99% for precision, 100% for recall, and 99% for F1 score. Meanwhile, in real-time detection, the performance of the model for classifying hand symbols is 80% for accuracy, 95% for precision, 84% for recall, and 89% for F1 score. Although there are differences, it still acceptable for the research and can be improved in future research.
Technology Readiness During the COVID-19 Pandemic: Lessons Learned from Indonesia Genoveva; Jhanghiz Syahrivar; Eka Srirahayu Ariestiningsih
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.v17i1.8068

Abstract

The COVID-19 pandemic since March 2020 has forced Indonesians to practice physical distancing and carry out their personal and professional activities from home. However, not everyone is ready to conduct their regular activities remotely. The issue is the readiness for digital technology. The research aims to analyze the Indonesian people’s readiness for digital technology during the pandemic. The research also investigates the impact of optimism and technology adoption on behavioral intention mediated by perceived ease of use. The research applies a quantitative study using an online questionnaire. The population of the research is people who use Internet technology for online learning, working from home, online shopping, and social activities during the COVID-19 pandemic. The research successfully gathered 327 respondents using purposive sampling. The research uses Structural Equation Model (SEM) method via SPSS AMOS software to analyze the data and generate findings. There are several findings. First, optimism has a positive relationship with perceived ease of use. Second, technology adoption relates with perceived ease of use positively. Third, perceived ease of use has a positive relationship with behavioral intention. Last, the empirical evidence for the mediation roles of perceived ease of use is inconclusive. The research also offers some managerial implications.

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