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JISA (Jurnal Informatika dan Sains)
Published by Universitas Trilogi
ISSN : 27763234     EISSN : 26148404     DOI : https://doi.org/10.31326/jisa
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JISA (Jurnal Informatika dan Sains) is an electronic publication media which publishes research articles in the field of Informatics and Sciences, which encompasses software engineering, Multimedia, Networking, and soft computing. Journal published by Program Studi Teknik Informatika Universitas Trilogi aims to give knowledge that can be used as a reference for researchers and can be useful for society. Accredited “SINTA 4” by The Ministry of Research-Technology and Higher Education Republic of Indonesia, Free of Charge (Submission,Publishing). JISA (Jurnal Informatika dan Sains) is scheduled for publication in June and December (2 issue a year) This Journal accepts research articles in these following fields: Software Engineering: Web Development, Mobile Apps Development, Database Management System Multimedia: Augmented Reality, Virtual Reality, Game Development Networking: Cloud Computing, Internet of Things, Wireless Sensor Network, Mobile Computing Soft Computing: Data Mining, Data Warehouse, Data Science, Artificial Intelligence, Decision Support System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 187 Documents
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Evaluating the Performance of ETSI-ITS Multi-Stack Protocols for V2V Communication in VANETs: A Simulation Study Bintoro, Ketut Bayu Yogha; Geraldo, David
JISA(Jurnal Informatika dan Sains) Vol 7, No 1 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i1.2010

Abstract

The research evaluates various multi-stack protocols for Vehicular Ad-hoc Networks (VANETs), focusing on Vehicle-to-Vehicle (V2V) communication scenarios with Emergency Vehicle (EV) simulations. The study uses the ns-3 network and SUMO (Simulation of Urban MObility) traffic simulators to test these protocols in diverse scenarios, including fluctuating data rates and dense network conditions. By implementing the IEEE 802.11p protocol alongside vehicular message dissemination stacks compliant with ETSI (European Telecommunications Standards Institute) ITS (Intelligent Transport Systems) standards, the study performs simulation experiments with varying vehicle counts, ranging from 20 to 35. It employs two distinct data rate configurations while maintaining a constant transmission power of 23 dBm. The results indicate a decline in the average Packet Reception Ratio (PRR) as vehicle density increases, indicating heightened contention and interference. At the same time, there is an observed increase in average latency, contributing to increased message transmission and reception delays. The quantitative analysis demonstrates an inverse relationship between the average PRR and the total vehicle count when the SEND_CAM message is enabled. On the other hand, disabling SEND_CAM maintains a relatively consistent average PRR across scenarios. Additionally, a positive correlation between vehicle count and average latency underlines the impact of network congestion and interference on communication efficacy within VANETs. Despite suboptimal PRR values falling between 41% and 47%, latency performance remains satisfactory, with average latency durations ranging from 0.154 s to 0.187 s. Notably, the SEND_CAM parameter status shows negligible impact on protocol performance, suggesting that network density plays a more pivotal role. Finally, this study offers valuable insights into the trade-offs and challenges of multi-stack protocols in V2V communication within VANETs. Further optimization efforts are recommended to improve packet reception ratios, especially in high-vehicle-density environments, while maintaining acceptable latency levels. These findings contribute to the ongoing efforts to enhance the reliability and efficiency of communication protocols in VANETs, thus advancing the development of intelligent transportation systems. The study's quantitative protocol performance analysis under varying network conditions provides valuable guidance for optimizing V2V communication deployments in VANETs.
Improving Biology Learning Through Augmented Reality Technology in Indonesia: A Review Hallaby, Syarifah Fadiya; Syahputra, Ade
JISA(Jurnal Informatika dan Sains) Vol 7, No 1 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i1.2029

Abstract

Biology is always considered difficult either to teach or to learn. As a field of study that learning about living subjects, biology has very broad and abstract concepts. Many students find the abstract concepts of biology are hard to understand. Some students even feel that studying biology is a dull experience. This type of perception in students subsequently led to low academic achievement. Hence, an innovative and attractive learning approach is needed for teaching biology. Augmented Reality (AR) technology has a great possibility to improve biology learning experience due to its ability to concretize the abstract concepts in biology by providing natural interaction between virtual and real-time situations simultaneously. In the last decade, numerous research of implementing AR in education including biology learning have been studied globally and nationally. In order to evaluate the effectiveness of AR in improving biology learning especially for students in Indonesia, this literature review research is conducted. The reviewed articles are retrieved through Google Scholar database with the assigned criteria published between 2020 to 2024, AR implemented, biology learning, student perception, and/or achievement in biological studies. The analysis shows that implementing AR in biology learning increases students’ interest, learning motivation, involvement, collaboration, independent learning, knowledge retention, and achievement. However, the conflicted finding is reported regarding the influence of AR on students’ critical thinking ability. Technical problems related to downloading and distributing AR applications are the main challenge that has been reported when using AR in biology learning.
Sentiment Analysis of 2024 Presidential Candidates Election Using SVM Algorithm Alfonso, Michael; Rarasati, Dionisia Bhisetya
JISA(Jurnal Informatika dan Sains) Vol 6, No 2 (2023): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v6i2.1714

Abstract

Elections for presidential candidates are held every 5 years with various candidates, especially on Twitter, arguments about political matters often occur that many Twitter users participate in discussions about the election for presidential candidate. Therefore, this study focuses on sentiment analysis to infer user responses to the presidential election and validate it by looking for a correlation between electability survey results and Twitter sentiment data using Pearson Correlation. In sentiment analysis model, the 10-Fold Cross Validation method is used to find the best model from a dataset with a division of training data and test data with 90:10 split. Then the alphabetic data will be converted into numeric data using the TF-IDF weighting method. To validate the best model, Confusion Matrix is used to get the best f1-score. The model is using Support vector machine algorithm with the Gaussian RBF (Radial Basis Function) kernel. The results of the analysis are compared with the results of the news portal electability survey which contains the 3 candidates using Pearson Correlation. This study produces the best fold for each data on each presidential candidate with the f1-score to find the best model for each fold. In the Peason Correlation result, the higher positive sentiment of each presidential candidate, the higher electability survey data. For further research, research can be discuss about hyper tuning parameters and using other kernels on Support vector machine algorithm.
Implementation of Android-Based Tailoring Service Ordering Application with Geolocation Integration Santoso, Muhamad Agung; Sutopo, Joko
JISA(Jurnal Informatika dan Sains) Vol 6, No 2 (2023): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v6i2.1773

Abstract

 Basic needs are an aspect that cannot be separated from human life, and one of the important aspects of these basic needs is clothing which not only functions to protect the body but also plays a role in the world of fashion. To meet these fashion needs, tailoring services have a key role in providing clothes that suit consumer desires. While this need is important, customers often rely on conventional methods, such as word-of-mouth recommendations or seeking out a familiar tailor. Some tailors may have a sign in front of their house, but it is often difficult for potential customers to find and connect with them. Therefore, this research aims to implement a tailor service ordering application that is integrated with geolocation features and can make things easier for tailors and consumers. This application development uses the Java programming language and Android Studio as the framework and Firebase as the database. The result of this research is an application that allows customers to order tailor services via smartphone anywhere and anytime.
Detection of Hate-Speech Tweets Based on Deep Learning: A Review Miran, Ara Zozan; Abdulazeez, Adnan Mohsin
JISA(Jurnal Informatika dan Sains) Vol 6, No 2 (2023): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v6i2.1813

Abstract

Cybercrime, cyberbullying, and hate speech have all increased in conjunction with the use of the internet and social media. The scope of hate speech knows no bounds or organizational or individual boundaries. This disorder affects many people in diverse ways. It can be harsh, offensive, or discriminating depending on the target's gender, race, political opinions, religious intolerance, nationality, human color, disability, ethnicity, sexual orientation, or status as an immigrant. Authorities and academics are investigating new methods for identifying hate speech on social media platforms like Facebook and Twitter. This study adds to the ongoing discussion about creating safer digital spaces while balancing limiting hate speech and protecting freedom of speech.   Partnerships between researchers, platform developers, and communities are crucial in creating efficient and ethical content moderation systems on Twitter and other social media sites. For this reason, multiple methodologies, models, and algorithms are employed. This study presents a thorough analysis of hate speech in numerous research publications. Each article has been thoroughly examined, including evaluating the algorithms or methodologies used, databases, classification techniques, and the findings achieved.   In addition, comprehensive discussions were held on all the examined papers, explicitly focusing on consuming deep learning techniques to detect hate speech.
Design of Warehouse Information System for KCM Division Using Javascript Novel, Nurillah Jamil Achmawati; Fasha, Adinda
JISA(Jurnal Informatika dan Sains) Vol 7, No 1 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i1.1805

Abstract

Increasingly advanced technological developments force business people to be adaptive, especially when there is an increase in production. The KCM division, one of the PT XYZ business units operating in the media sector, experienced increased production. This causes the production equipment to be borrowed irregularly, and information on the warehouse of production equipment is also difficult to find. The Warehouse Information System is the solution to this problem. This research aims to create a warehousing information system that records all information on borrowing production equipment, fulfills warehousing information needs, and solves several existing warehousing problems. This research method is an experimental one-shot case study designed with the Framework for the Applications of System Thinking (FAST) and using simple, easily accessible, and free tools, namely Google Spreadsheet and Javascript coding. The results of this research are that the KCM division's warehousing information system that has been created has been tested to be 100% capable of meeting information needs for borrowing production equipment and resolving existing warehousing problems based on system tests that have been carried out. It is recommended that this system be further developed in future research to overcome minor errors in coding.
Automating the Extraction of Words and Topics in Indonesian Using the Term Frequency-Inverse Document Frequency Algorithm and Latent Dirichlet Allocation Mutawalli, Lalu; Zaen, Mohammad Taufan Asri; Zulkarnaen, Muhammad Fauzi
JISA(Jurnal Informatika dan Sains) Vol 7, No 1 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i1.2012

Abstract

Keyword extraction and topic modeling in the analysis of Gojek user reviews in Indonesian are very important. By understanding user preferences and needs through keyword extraction, as well as grouping user reviews into different topics through topic modeling, stakeholders can use the information to further improve services. This research uses TF-IDF and LDA approaches to analyze text data from Gojek user reviews and feedback. The data spans from Nov 5, 2021, to Jan 2, 2024, totaling 225,002 rows. Each row includes username, content, time, and app version. The focus is on content reviews. The average length is 8 words, with a maximum of 104 and a minimum of a few words. The variability indicates a non-normal distribution. Preprocessing is conducted to maintain topic analysis accuracy. The TF-IDF method is used to extract relevant keywords, while the LDA approach is used to model the topics in user reviews. The topic analysis reveals patterns in Gojek user reviews. The first topic discusses experience, services, and affordable pricing. The second emphasizes app usability and benefits. The third relates to promos, discounts, and vouchers. The fourth reflects positive evaluations of service quality. However, the fifth topic highlights high costs and app issues. The sixth underscores overall user satisfaction and service convenience. Testing on the topic model yielded a coherence level of 0.509, indicating that the model's topics demonstrate a good level of consistency in finding relevant topics from Gojek user review data. The use of a combination of TF-IDF and LDA in Indonesian text analysis, particularly in the context of Gojek user reviews, is an important step in enhancing understanding and utilization of text data to improve overall user experience. 
Network Intrusion Detection Based on Machine Learning Classification Algorithms: A Review Younis, Aqeel Hanash; Abdulazeez, Adnan Mohsin
JISA(Jurnal Informatika dan Sains) Vol 7, No 1 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i1.2056

Abstract

The worldwide internet continues to spread, presenting numerous escalating hazards with significant potential. Existing static detection systems necessitate frequent updates to signature-based databases and solely detect known malicious threats. Efforts are currently being made to develop network intrusion detection systems that can utilize machine learning techniques to accurately detect and classify hazardous content. This would result in a decrease in the overall workload required. Network Intrusion Detection Systems are created with a diverse range of machine learning algorithms. The objective of the review is to provide a comprehensive overview of the existing machine learning-based intrusion detection systems, with the aim of assisting those involved in the development of network intrusion detection systems.
DevOps, Continuous Integration and Continuous Deployment Methods for Software Deployment Automation Istifarulah, Mochamad Hanif Rifa'i; Tiaharyadini, Rizka
JISA(Jurnal Informatika dan Sains) Vol 6, No 2 (2023): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v6i2.1751

Abstract

In the fast-paced landscape of software development, the need for efficient, reliable, and rapid deployment processes has become paramount. Manual deployment processes often lead to inefficiencies, errors, and delays, impacting the overall agility and reliability of software delivery. DevOps, as a cultural and collaborative approach, plays a central role in orchestrating the synergy between development and operations teams, fostering a shared responsibility for the entire software delivery lifecycle. Continuous Integration is a fundamental DevOps practice that involves regularly integrating code changes into a shared repository, triggering automated builds and tests. Continuous Deployment complements Continuous Integration by automating the release and deployment of validated code changes into production environments. The purpose of this research is to create a software deployment automation system to make it easier and reliable for organizations to deploy software. In conclusion, the results of this research show that by adopting DevOps, Continuous Integration, and Continuous Deployment, organizations can achieve enhanced collaboration, shortened release cycles, increased deployment frequency, consistent deployment, and improved overall software quality.
Forecasting Blood Demand Using the Support Vektor Regression Method (Case Study: Blood Transfusion Unit-PMI Central Lombok) Apriati, Yati; Murniati, Wafiah; Saikin, Saikin; Fadli, Sofiansyah; Fahmi, Hairul
JISA(Jurnal Informatika dan Sains) Vol 6, No 2 (2023): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v6i2.1780

Abstract

Blood is an important component produced by the human body. Blood is also a very vital part of human survival. When blood levels in the human body are less than they should be, the way to overcome this is by donating blood or blood transfusion. The health facilities that organize blood donations, provide blood and distribute blood are called Blood Transfusion Units (UTD). UTD in carrying out its duties encountered several obstacles, such as blood only having a shelf life of 35 days from donation. If it has passed the expiration date, it cannot be used anymore for blood transfusions. Meanwhile, regarding the demand for blood, the need for blood is greater than those donating. Making it difficult for UTD if the demand occurs when the existing blood stock is not sufficient. And if the stock in UTD experiences an axcess, it can cause losses because the blood is wasted due to expiration. Apart form that. The problem is that in everyday life, many people’s need for blood is reduced. Many of their families intervened directly to find available donors. They even search on social networks or social media such as WhatsApp, Facebook, Instagram and others. And this shows that many of them lack donors. To anticipate these problems. So it is necessary to carry out research on forecasting blood demand using the Support Vektor Regression method at UTD PMI Central Lombok. The aim of this research is to forecast or predict the demand for blood at UTD PMI Central Lombok in the coming period. To reduce the impact of lack or excess blood. SVR is the application of Support Vektor Machine (SVM) in the case of regression to find the best dividing line in the regression function. The advantage of the SVR model is that it can handle overfiting problems in the data. The tests used to measure the best model are Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2). The results of this research shows that the best model is Support Vektor Regression (SVR) with a polynomial kernel and based on the tuning results, the parameters used are C=10, degree=1, epsilon=1. The SVR model using a polynomial kernel produces a MAPE value of 18.7502% and RMSE value of 0.6919, which means the model has very good predictive ability. Prediction accuracy was achieved with an R2 value of 0.9936 or 99.36% and an MSE value of 0.4787, which means that the prediction of blood demand data at UTD PMI Central Lombok using SVR with a polynomial kernel function had very good prediction accuracy. With predicted result in january for blood type A it was 1654, B was 920, O was 2205 and AB was 1104

Page 12 of 19 | Total Record : 187

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