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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 136 Documents
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Liver Disease Prediction Model Based on Oversampling Dataset with RFE Feature Selection using ANN and AdaBoost algorithms: Author's Country: Iraq
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
Examining Healthcare Profesional’s Acceptance of Electronic Medical Records System using Extended UTAUT2
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 5 No. 1 (2024): 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.v5i1.5723

Abstract

This study aims to analyze user acceptance of the Electronic Medical Record system using the extended Unified Theory of Acceptance and Use Technology 2 model at PKU Muhammadiyah Bantul hospital. The UTAUT 2 model was chosen because it is the latest technology acceptance model which is a unification, synthesis, or summary of the eight pre-existing technology acceptance models. The subjects in this study were PKU Muhammadiyah Bantul hospital employees who used an electronic medical record system specifically for outpatient care. The object of this study is user acceptance of using the RME system in health services. Data collection techniques in this study are using questionnaires and observation. This research is a type of quantitative analytic research with data analysis using descriptive analysis. Data processing in this study used Smart-PLS software version 4.0 with SEM-PLS data analysis. The results showed that the aspects of the extended UTAUT2 model that had a positive and significant effect on user acceptance were performance expectancy (t=1.816), while the aspects of effort expectancy (t=0.419), social influence (t=0.635), facilitating conditions (t=0.139), hedonic motivation (t=0.909), price value (t=.304) habit (t=1.458), trust (t=0.032) and perceived risk (1.365) have no effect on user acceptance of the EMR system. Gender and age moderation variables were found to have no effect on the relationship between variables.
T Taking Into Account Imprecision in the Modeling Voter in a Multi-Agent Environment
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 5 No. 1 (2024): 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.v5i1.5800

Abstract

An electoral system is a set of individuals considered as agents in a multi-agent system in which voters communicate with each other and with the environment. In such a system, it is often difficult to understand the behavior of an agent that we call a voter. This is why, in this paper, we use fuzzy set theory as an approach to model the behavior of an imprecise voter in an electoral environment. It will be just a question of presenting a model of a voter with fuzzy behavior using mathematical approaches in this environment considered as a multi-agent environment and to propose the algorithms as the tools of computer modeling.
Implementation Of Marker-Based Tracking Method On Augmented Reality In Multimedia Learning (Case Study Of STMIK Tegal)
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 5 No. 1 (2024): 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.v5i1.5898

Abstract

Introducing campus locations for new students or address seekers is an important activity. Multimedialearning is not only a tool for creating harmonious presentations and alternatives that combine visualand audio media; technology can be used for its tools. Augmented Reality (AR) is one of them.Augmented Reality is helpful as a combination of virtual and Reality devices that operate interactivelyin a realtime natural environment. Based Marker Tracking is a method used to make objects into twodimensions and three dimensions whose process begins with directing the marking object by the userusing the camera on the mobile device until the camera reads the object. Light intensity affects detectionsuccess, and distance calculation also becomes essential. If the marker is successfully detected, theapplication will convert it into a 3-dimensional object as the final result. In this study, a location searchwill be carried out for the STMIK TEGAL Campus Building using Augmented Reality based on theBased Marker Tracking method to produce the most ideal conditions to be able to display 3D objectsfrom the STMIK TEGAL Building, which is a distance of 15 to 25 cm with bright Light using Android,so that this application can be used to find the location of the STMIK TEGAL Building.
Detection of Diseases and Pests on The Leaves of Sweet Potato Plants sing Yolov4
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 5 No. 1 (2024): 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.v5i1.6065

Abstract

Sweet potato (Ipomea batats) is a root plant that can live in all weather, in mountainous areas and on the coast.. This plant is one of the important food crops in Indonesia, and makes Indonesia the second largest sweet potato producer after China. However, according to data from the Central Statistics Agency (BPS), sweet potato production in Indonesia in 2018 decreased by 5.63% when compared to production in 2017 which reached 1,914,244 tons (Gultom, 2021). Based on these data, it is important to conduct research on pest and disease detection in plants. Therefore, the author conducted a study related to this problem entitled Detection of Diseases and Pests on the Leaves of Sweet Potato Plants using Yolov4 with the aim of helping educate farmers in recognizing diseases on the leaves of sweet potato plants and how to overcome them. In this study the dataset was sweet potato leaves with a total of 1500 data divided into three classes, namely aspidomorpha, yellow spot and normal leaves with 4000 iterations. The best training results on 1500 data with 75% accuracy. The Yolov4 algorithm produces high accuracy in detecting diseases in the leaves of sweet potato plants.
Implementation of Orange Data Mining to Predict Student Graduation on Time at Pringsewu Muhammadiyah University
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 5 No. 1 (2024): 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.v5i1.6073

Abstract

Thel prolcelss olf molnitolring and elvaluating thel graduatioln olf Muhammadiyah Pringselwu Univelrsity (UMPRI) studelnts relally nelelds tol bel dolnel belcausel thel studelnt graduatioln ratel is an ellelmelnt olf accrelditatioln asselssmelnt that is velry impolrtant folr elach Study Prolgram. Data Mining can bel useld tol classify studelnt graduatioln accuracy. This study aims tol apply thel olrangel data mining applicatioln using thel K-Nelarelst Nelighbolr (K-NN), Delcisioln Trelel and Naivel Bayels moldells and will theln elvaluatel thel accuracy olf elach olf thelsel moldells. This relselarch was colnducteld at Pringselwu Muhammadiyah Univelrsity in selvelral batchels, theln studelnt data will bel analyzeld using thel olrangel data mining applicatioln using thel K-NN, Delcisioln Trelel and Naivel Bayels moldells. Thel data telsting prolcelss appliels K-Folld Crolss Validatioln (K=5), whilel thel elvaluatioln moldell useld is thel Colnfusioln Matrix and ROlC. Thel relsults olf thel colmparisoln olf thel threlel moldells arel as folllolws, K-NN has an accuracy ratel olf 75.7%, Delcisioln Trelel has an accuracy ratel olf 78.1%, and Naivel Bayels has an accuracy ratel olf 77.8%. Thelrelfolrel, folr classifying thel graduatioln ratel olf Muhammadiyah Univelrsity studelnts, Pringselwu relcolmmelnds thel Delcisioln Trelel moldell belcausel it has a belttelr lelvell olf accuracy than K-NN and Naivel Bayels.
A Detection of Malacca Woven Fabric Motifs Using the YOLOv4 Method
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 5 No. 1 (2024): 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.v5i1.6081

Abstract

Malacca is one of the districts that has a weaving culture and also produces woven cloth in East NusaTenggara. The large number of types of woven cloth from each Malacca tribe means that outsiders andeven native Malacca people are not yet familiar with typical Malacca motifs, therefore a system isneeded that can help make it easier for people to recognize the types of woven fabric motifs. Malaccawoven fabric in this study was used to detect the types of woven fabric motifs in Malacca district usingthe YOLOv4 method. The results of detecting Malacca woven fabric motifs correspond to each type ofwoven fabric. Apart from that, the Malacca woven fabric motif detection system with YOLOv4technology is an effective and efficient solution in recognizing Malacca woven fabric motifs. Malaccawoven fabric is classified into four classes with an impressive mAP score of 100%.
Apriori Algorithm and Market Basket Analysis to Uncover Consumer Buying Patterns: Case of a Kenyan Supermarket: Author's Country: Kenya
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 5 No. 2 (2024): 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.v5i2.6082

Abstract

This article presents a study on utilizing the Apriori algorithm and Market Basket Analysis (MBA) to reveal consumer buying patterns in supermarkets. The aim of this research is to explore the effectiveness of these data mining techniques in revealing valuable insights that can inform marketing strategies and enhance the overall shopping experience for customers. This study centered on improving customer loyalty within the supermarket setting through the utilization of cutting-edge information technology and programming applications, including Python. Specifically, the Apriori algorithm libraries of the Python language were employed to identify frequent item sets and derive 42 association rules, which shed light on product affinities and co-purchasing patterns. By deriving association rules from the frequent item sets, the study identified the significance of strategically placing frequently purchased products to enhance revenue generation. In conclusion, the application of the Apriori algorithm and Market Basket Analysis in this case of a Kenyan supermarket has proven to be a valuable approach for uncovering consumer buying patterns, providing a competitive edge in the dynamic retail industry.
Identification of Socio Economic Registration Data Using OCR Based Tesseract and Google Cloud Vision: Author's Country: Indonesia
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 5 No. 2 (2024): 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.v5i2.6258

Abstract

The Indonesian government program, called Socio-Economic Registration (Regsosek), aims to measure and monitor the socio-economic conditions of low-income people. One of the relevant data used for research is Regsosek. This method is used to analyze the influence of economic and social infrastructure on economic growth, analyze the socio-economic determinants of ownership of work accident insurance for informal workers, create a women's socio-economic vulnerability index (IKSEP), and study intercultural literacy from a social, economic and political perspective. The success of the government's Socio-Economic Registration program depends on the role of data collection officers or surveyors, who directly interact with the community to obtain information about Socio-Economic Registration (Regsosek) data collection. This method also has other obstacles that significantly affect the overall results of the survey, where the survey results must be entered manually by the surveyor from a form with handwritten data, after which it is entered into the website. This method is vulnerable to human error, where the handwriting is difficult to read, and mistakes are made during the data input. The technology that can be used to handle this problem is implementing the OCR method, where writing that was initially handwritten manually can be identified and converted into digital text that can be edited (editable text) and processed automatically. This research shows that the proposed method has good accuracy, with an Accuracy of 96.45%, CER 0.3%, and WER 4.30%.
SVM Ransomware Detection Using Machine Learning Algorithm: Author's Country: Nigeria
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 5 No. 2 (2024): 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.v5i2.6265

Abstract

With the advent and subsequent explosion of the internet, global connectivity has been achieved, and is on the rise. This provides a host of advantages such as connectivity and communication, information broadcast and transmission, amongst others. This however introduces a new set of challenges: the safety and protection of these communication channels amongst them. Information has always been power, and the widespread mature of information only results in the widespread attempts to procure it, sometimes via illegal channels. In view of this, this research aims at detecting Crypto-ransomware and locker ransomware. Data was collected from an open repository and cleaned. The cleaned data was then split into tests, train sets and validation which was used to train a number of ML models based on the: Random Forest algorithm, Support Vector Machine (SVM) and Gradient boosting algorithm. Ransomware is one of the well-known ways and frequent use which cyber-attackers use in infecting their victims, either through phishing or drive download. Attackers will create an email pretending to be from a genuine resource and send it to their targeted victims. However, this research illustrated how to combat crypto-ransomware and locker ransomware. Implementing the machine learning algorithm, the system can detect ransomware under 30’s, giving computer users over 90% assurance of their system for ransomware free.

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