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Buana Information Technology and Computer Sciences (BIT and CS)
Published by Universitas Buana Perjuangan Karawang
ISSN : 27152448     EISSN : 27157199     DOI : https://doi.org/10.36805/bit-cs
Core Subject : Science,
Computer Science & IT
Buana Information Technology and Computer Science (BIT and CS) is a journal focusing on new technologies that handle IT research and management - including strategy, change, infrastructure, human resources, information system development and implementation, technology development, future technology, policies and national standards and articles that advance understanding and application of research approaches and methods. This journal publishes works from disciplinary, theoretical and methodological perspectives. It was designed to be read by researchers, scholars, teachers, and students in the area of Information Systems and Computer Science, as well as IT developers, consultants, software vendors, and senior IT executives who are looking for updates on current experiences and prospects related to information and communication technology contemporary.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 48 Documents
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Detecting Harmful Activity in Pilgrimage Using Deep Learning Musa Genemo
Buana Information Technology and Computer Sciences (BIT and CS) Vol 4 No 1 (2023): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v4i1.2929

Abstract

CCTV surveillance is the most extensively used intelligent latest innovation. The use of surveillance cameras has risen dramatically because of the convenience of monitoring from anywhere and the reduction of crime rates in public areas. In this paper, we introduce the idea of bad vibe activity detection from live videos to enhance the security and safety of pilgrims. The proposed bad vibes activity recognition model is intended to be addressed in the most efficient manner possible using cutting-edge technologies such as TensorFlow and Keras. TensorFlow was chosen because the project could be deployed to a mobile environment in the future with the possibility of extension of other areas such as airport security, bus stain, and public areas that may deserve special attention for security checks. We choose MediaPipe Holistic for employee bad vibe recognition in the model.
The Cardiovascular Disease Prediction Using Machine Learning Shivam Pandey
Buana Information Technology and Computer Sciences (BIT and CS) Vol 4 No 1 (2023): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v4i1.3060

Abstract

Because of technology developments, the ECG yields improved outcomes in the realm of biomedical science and research. The Electrocardiogram reveals basic the heart's electrical activity. Early detection of aberrant heart disorders is crucial for diagnosing cardiac problems and averting sudden cardiac deaths. Measurements on an electrocardiogram (ECG) among people with comparable cardiac issues are essentially equal. Analyzing the Electrocardiogram characteristics can help predict abnormalities. Medical professionals presently base the preponderance of their Electrocardiogram diagnosis on their unique particular areas of expertise, which places a substantial load on their shoulders and reduces their performance. The use of technology that automatically analyses ECGs as hospital personnel performs their duties will be advantageous. A suitable algorithm must be able to categories Input signal with uncertain awesome feature on just how much they approximate Input signal having known characteristics in order to speed up the identification of heart illnesses. A possibility of identifying a tachycardia is raised if this predictor can reliably recognize connections, and this technique may be helpful in lab settings. To accurately diagnose myocardial illness, a powerful machine learning technique should be used. Through using recommended method, the effectiveness of cardiovascular disease identification using ECG dataset was evaluated. The reliability, sensitivities, and validity obtained using the Svm algorithm were 99.314%, 97.60%, and 97.60% respectively.
A Philosophical Study of Agricultural Image Processing Techniques Valarmathi J; Kruthika V T
Buana Information Technology and Computer Sciences (BIT and CS) Vol 4 No 2 (2023): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v4i2.3712

Abstract

The development of agriculture in China has been substantially aided by the development of image processing technologies. It is simple for people to comprehend the significance of image processing technology for agricultural development by presenting the application status of image processing technology in agriculture and its impact on agricultural production value. This research examines how image processing technology is used in agriculture on the basis of that information. This study first examines how image processing technology is used in the world of agriculture. Second, this study applies both classic machine recognition technology and image processing technology to crop pest identification, analyses their effects, and highlights the application effect of image processing technology in the agricultural industry. The findings indicate that this approach has a recognition rate of 86%, 89%, 91%, 83%, 78%, and 79%, respectively. It is evident that the detection of crop diseases and insect pests is improved by the use of image processing technology.
IOT-Based Farmland Intrusion Detection System Emmanuel Ibam; Olutayo K. Boyinbode; Helen Aladesiun
Buana Information Technology and Computer Sciences (BIT and CS) Vol 4 No 2 (2023): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v4i2.4332

Abstract

As crop vandalization with conflicts between farmers and herdsmen become recurrent in Nigeria, existing farm intrusion prevention methods such as fence mounting and placement of farm guards can no longer guarantee farm security. This is because intruders either jump over the fence or attack guards on duty without visual evidence. Therefore, a complementary approach using computer technologies for effective detection is required. This paper presents an IoT-based farm intrusion detection model using RFID and image recognition technology. RFID sensor as well as cameras are placed at entrances of a fenced farmland for simultaneous identification. The sensor reads workers’ tags for identification, while cameras capture images of users for further identification as captured images are sent to Convolutional Neutral Network (CNN) for recognition. A user whose image cannot be recognized is flagged as an intruder and an intrusion alert with visual evidence is sent to the farm owner. The system showed a high level of effectiveness with an accuracy of 90%, Precision of 70%, and 80% Recall rate and effectively controlled the rate of illegal encroachment into farmland.
Using I-Hubs for Bridging The Gap of Digital Divide in Rural Kenya Samuel Lusweti; Kelvin Omieno
Buana Information Technology and Computer Sciences (BIT and CS) Vol 4 No 2 (2023): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v4i2.5165

Abstract

The world is moving towards digital economy where almost everything being done today is digitally controlled because necessity is the mother of innovation. Everybody is striving to attain digital stability as a lot of revenue is generated in the digital world. Digital divide therefore becomes so disadvantageous to people left without access to computers and the internet. In this paper, researchers discuss the role of Kenyan innovation hubs in closing the gap between those who have access to the internet and computers and those who do not. The paper discuss the World Bank projection of the GDP emanating from the use of ICTs and the challenges facing innovation. Government support plays a key role in ensuring that the people secluded from ICTs are able to access these services especially those in rural areas. This research found that in Kenya, innovation hubs have helped the citizens staying in rural areas to gain access to internet and develop their ideas and innovations as well as undergo mentorship. Nonetheless, a lot of support is needed from the Kenyan government through the launching of more innovation hubs especially in rural areas that can help improve the online business, innovation and thus increase the GDP from ICTs.
Saubhagya: An Online Food Donation Platform for Ending Hunger and Malnutrition in Sri Lanka G.H.T.R. Irushika; K.V.M. Wijesinghe; P.K.I. Udeshika; J.J. Sathsara; D. I. De Silva; R.R.P. De Zoysa
Buana Information Technology and Computer Sciences (BIT and CS) Vol 4 No 2 (2023): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v4i2.5235

Abstract

Hunger and malnutrition continue to be significant challenges in developing nations, including Sri Lanka. To address this issue, the research paper presents "Saubhagya," an online web application that provides a platform for social assistance. The platform allows individuals to donate food and groceries to needy organizations such as blind, deaf, orphanages, making it a user-friendly and effective solution. Users are required to register as food donators, needy people(organizations), partners and food collection agents. The system connects these user groups when necessary, ensuring a smooth and efficient process. One unique feature of "Saubhagya" is its live capability of tracking food collection and delivery using GPS and Google Maps. This feature ensures that food donations are delivered to the right organizations promptly, promoting transparency, accountability, and communication among users. The research paper aims to evaluate the effectiveness of "Saubhagya" in reducing hunger and malnutrition in Sri Lanka through user feedback and system performance metrics. If successful, the platform can be scaled to other developing nations facing similar challenges. This research demonstrates the potential of digital platforms in addressing social and environmental challenges. By leveraging technology, collective action can be harnessed to create positive social impact. "Saubhagya" represents a significant step forward in the fight against hunger and malnutrition, and it is hoped that it can inspire others to use technology to address pressing global issues.
Agglomerative Clustering of 2022 Earthquakes in North Sulawesi, Indonesia Afrioni Roma Rio; Berton Maruli Siahaan
Buana Information Technology and Computer Sciences (BIT and CS) Vol 4 No 2 (2023): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v4i2.5361

Abstract

This paper presents a cluster analysis of earthquake data in the surrounding region of North Sulawesi, Indonesia. The dataset comprises seismic data recorded throughout the year 2022, obtained from the BMKG earthquake repository. A total of 211 earthquakes were included in the analysis, with a minimum magnitude threshold of 2.5 and a maximum depth of 300 km. The agglomerative clustering technique, combined with the elbow method, was employed to determine the optimal and distinct number of clusters. As a result, four unique clusters were identified. Cluster 1 exhibited high magnitudes, with an average magnitude of 4.4, and shallow depths, averaging at 20 km. Cluster 2 also had high magnitudes, averaging at 4.4, but deeper depths, with an average of 199 km. Cluster 3 consisted of earthquakes with low magnitudes, averaging at 3.4, and shallow depths, averaging at 21 km. Lastly, Cluster 4 comprised earthquakes with low magnitudes, averaging at 3.4, but deeper depths, with an average of 136 km. Among the 211 earthquakes, 29 were assigned to Cluster 1, 39 to Cluster 2, 100 to Cluster 3, which had the highest population, and 43 to Cluster 4. This study provides valuable insights into the clustering patterns and characteristics of earthquakes in the region, contributing to a better understanding of seismic activity in North Sulawesi, Indonesia
Liver Disease Prediction Model Based on Oversampling Dataset with RFE Feature Selection using ANN and AdaBoost algorithms ahmed sami jaddoa; Samah J. Saba; Elaf A.Abd Al-Kareem
Buana Information Technology and Computer Sciences (BIT and CS) Vol 4 No 2 (2023): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v4i2.5565

Abstract

Liver disease counts are one of the most prevalent diseases all over the world and they are becoming very common these days and can be dangerous. Liver diseases are increasing all over the world due to different factors such as excess alcohol consumption, drinking contaminated water, eating contaminated food, and exposure to polluted air. The liver is involved in many functions related to the human body and if not functioned properly can affect the other parts too. Predication of the disease at an earlier stage can help reduce the risk of severity. This paper implemented oversampling dataset, feature selecting attributes, and performance analysis for the improvement of the accuracy of classification of liver patients in 3 phases. In the first phase, the z-score normalization algorithm has been implemented to the original liver patient data-sets that has been collected from the UCI repository and then works on oversampling the balanced dataset. In the second phase, feature selection of attributes is more important by using RFE feature selection. In the third phase, classification algorithms are applied to the data-set. Finally, evaluation has been performed based upon the values of accuracy. Thus, outputs shown from proposed classification implementations indicate that ANN algorithm performs better than AdaBoost algorithm with the help of feature selection with a 92.77% accuracy

Page 5 of 5 | Total Record : 48

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