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ITEj (Information Technology Engineering Journals)
Published by Universitas Islam Negeri Siber Syekh Nurjati Cirebon
ISSN : 25482130     EISSN : 25482157     DOI : https://doi.org/10.24235/itej.v5i2
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics
ITEj (Information Technology Engineering Journals) is an international standard, open access, and peer-reviewed journal to discuss new findings in software engineering and information technology. The journal publishes original research articles and case studies focused on e-learning and information technology. All papers are peer-reviewed by reviewers. The scope of the system discussed is attached but not limited; Systems and software engineering Artificial Intelligence Technology (AI) and Machine Learning Internet of Thing and Big Data Smart Education systems and components Computer Vision Information Technology etc
Arjuna Subject : -
Articles 5 Documents
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Search results for , issue "Vol 8 No 2 (2023): December" : 5 Documents clear
Interaction Design For Implementation Multi-Window on Smartphone Aristides, Samuel; Lubis, Fetty Fitriyanti
ITEJ (Information Technology Engineering Journals) Vol 8 No 2 (2023): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v8i2.118

Abstract

The use of smartphones varies from person to person, and one of the uses that requires attention is multitasking using a smartphone. Multitasking is usually done on computers, but the increase in smartphones’ screen size and RAM capacity makes it an option for multitasking. The implementation of multitasking on smartphones still has weaknesses, and this final project aims to create an interaction design that overcomes these weaknesses. The method used is user-centered design with problem analysis through questionnaires and current implementation analysis, followed by determining needs, followed by creating low fidelity and high fidelity designs, and finally testing those designs. Two iterations of the design resulted in a final design that has a SUS score of 91 (Grade A), an ease of use score that is not lower than 6.3 out of 7, a task completion rate of 100%, and icons that are almost entirely easy to find with a time-to-locate time of 0-5 seconds. The final design can be implemented on Android, but implementation on iOS needs to wait for its software to support the proposed solution.
Comparison Of Sentiment Analysis Of Traveloka And Tiket.Com Applications On Twitter Using The Naive Bayes Method Agustiana, Nathifa; Pratiwi, Oktariani Nurul; Fakhrurroja, Hanif
ITEJ (Information Technology Engineering Journals) Vol 8 No 2 (2023): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v8i2.119

Abstract

The country of Indonesia has a strategic geographical position and is also said to be a country that is very rich in natural resources and cultural diversity. One of the supporters of economic growth in Indonesia is tourism. To support the potential of the tourism sector in Indonesia, many online travel agent applications have started to appear. Of the many OTAs, the top two applications were selected, namely the Traveloka and Tiket.com applications. This sentiment analysis requires data from Twitter. This research compares sentiment analysis on the Traveloka and Tiket.com applications in terms of price and service. The method used is naïve Bayes. The goal is to get sentiment information contained in a text with a positive or negative view. With this research, it is hoped that we can see a comparison of sentiment analysis between the Traveloka and Tiket.com applications and be able to find out the level of accuracy of naïve bayes on the Traveloka and Tiket.com applications. The price dataset that gets more positive sentiment is the Traveloka price of 97.2%. In the service dataset that has positive sentiment, Tiket.com is 46.9%. Then, the greatest accuracy was obtained after oversampling the Tiket.com price dataset by 73%, Traveloka prices by 94%, Ticket services by 87% and Traveloka services by 86%.
Design of a Soil Nutrient Measuring Device for NPK (Nitrogen, Phosphorus, Potassium) Case Study of Cayenne Pepper Based on Arduino Nano V3 ATmega328P Arafat, Agil Soni; Yuliati, Ari; Manova M, R. Yovi
ITEJ (Information Technology Engineering Journals) Vol 8 No 2 (2023): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v8i2.120

Abstract

Fertile soil electronic soil is essential for plant growth, and its quality significantly impacts crop yields. To help farmers measure the nutrient content of their soil more easily and accurately, innovative technology is required. In this case study, we present the design of a soil nutrient measuring device for Nitrogen, Phosphorus, and Potassium (NPK) that focuses on cayenne pepper. The device uses NPK sensors that can detect soil nutrients when inserted into the ground. The data collected by the sensors is sent to an ARDUINO Nano V3 ATMEGA328P, where it is processed and displayed as analog signals on a screen. The device can be customized according to the user's testing or cultivation needs, making it a useful tool for optimizing plant growth and crop yields.
Enhancing Urban Safety: The Role of Object Detection in Smart City Surveillance Systems Dharany, Kosal; Trinita, Mariam
ITEJ (Information Technology Engineering Journals) Vol 8 No 2 (2023): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v8i2.122

Abstract

Urban safety is a critical concern in the development and management of smart cities. This article explores the transformative role of object detection technology in enhancing surveillance systems within these urban environments. Object detection, a subset of computer vision, enables the automated identification and tracking of various objects, such as vehicles, pedestrians, and unusual activities, in real-time. By integrating advanced object detection algorithms with existing surveillance infrastructures, smart cities can significantly improve public safety and response times. This paper reviews the current state of object detection technologies, their applications in urban surveillance, and the benefits they offer, including increased situational awareness, crime prevention, and efficient emergency management. Additionally, the article discusses challenges and future directions for research and development in this field, emphasizing the importance of ethical considerations and data privacy in the deployment of these technologies. Through case studies and practical examples, we illustrate how object detection is reshaping urban safety and contributing to the creation of more secure and resilient smart cities.
A Comparative Analysis of Modern Object Detection Algorithms: YOLO vs. SSD vs. Faster R-CNN Aboyomi, Dalmar Dakari; Daniel, Cleo
ITEJ (Information Technology Engineering Journals) Vol 8 No 2 (2023): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v8i2.123

Abstract

In recent years, object detection has become a crucial component in various computer vision applications, including autonomous driving, surveillance, and image recognition. This study provides a comprehensive comparative analysis of three prominent object detection algorithms: You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Faster Region-Based Convolutional Neural Networks (Faster R-CNN). The background of this research lies in the growing need for efficient and accurate object detection methods that can operate in real-time. YOLO is known for its speed, SSD for its balance between speed and accuracy, and Faster R-CNN for its high detection accuracy, albeit at a slower pace. The methodology involves implementing these algorithms on a standardized dataset and evaluating their performance based on various metrics, including detection accuracy, processing speed, and computational resource requirements. Each algorithm is tested under similar conditions to ensure a fair comparison. The results indicate that while YOLO excels in real-time applications due to its high speed, SSD offers a middle ground with respectable accuracy and speed, making it suitable for applications requiring a balance of both. Faster R-CNN demonstrates superior accuracy, making it ideal for scenarios where detection precision is paramount, despite its slower performance. This comparative analysis highlights the strengths and weaknesses of each algorithm, providing valuable insights for researchers and practitioners in selecting the appropriate object detection method for their specific needs.

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