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Brilliance: Research of Artificial Intelligence
Published by Information Technology and Science
ISSN : -     EISSN : 28079035     DOI : https://doi.org/10.47709
Core Subject : Science, Education,
Decision Sciences, Operations Research & Management Mathematics Other
Brilliance: Research of Artificial Intelligence is The Scientific Journal. Brilliance is published twice in one year, namely in February, May and November. Brilliance aims to promote research in the field of Informatics Engineering which focuses on publishing quality papers about the latest information about Artificial Intelligence. Submitted papers will be reviewed by the Journal and Association technical committee. All articles submitted must be original reports, previously published research results, experimental or theoretical, and colleagues will review. Articles sent to the Brilliance may not be published elsewhere. The manuscript must follow the author guidelines provided by Brilliance and must be reviewed and edited. Brilliance is published by Information Technology and Science (ITScience), a Research Institute in Medan, North Sumatra, Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 544 Documents
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Detection of Malware Threats in Internet of Things Using Deep Learning Nashrullah, Naufal; Wahyu, Ari Purno
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3869

Abstract

This paper examines the potential risks associated with the Internet of Things (IoT) as a new gateway for cyberattacks. The continuous access it provides to systems, applications, and services within organizations increases the likelihood of serious threats, such as software piracy and malware attacks, which can result in the theft of sensitive information and significant economic losses. To address these concerns, researchers have proposed the use of Deep Convolutional Neural Network (DCNN) to detect malware infections in IoT networks by analyzing color image visualization. The malware samples were obtained from the Android Malware dataset on Kaggle. The proposed deep learning method, namely the Deep Convolutional Neural Network, was employed to detect malware infections in IoT networks.
Analysis of 2G and 4G Network Quality in Solok City Septima, Uzma; Candra, Dikky; Vitria, Rikki; Alfarezi, Muhammad; Yolanda, Amelia
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3870

Abstract

Gunung Talang, Twin Lake, Gumanti Valley, Hiliran Gumanti and Pantai Cermin sub-districts in Solok Regency, which are dominated by hills and trees, are the cause of unstable signal quality, causing complaints from the public regarding this. So this study is to determine the network quality in Gunung Talang District, Twin Lakes, Gumanti Valley, Hiliran Gumanti, and Pantai Cermin on Telkomsel, XL and Indosat Operators. Checking is carried out using the drive test method  using TEMS Pocket on 2G and 4G networks. The quality of the 2G network from the measurement results was obtained for RxLevel all operators was bad and for RxQual was good except for Telkomsel operators.Meanwhile,on the 4G network, measurement results for RSRP and SINR for all operators are bad. Some areas that have problems are recommended for optimization, site passenger  and site addition. All of this happens because  of obstacles in the form of hills, cliffs, and tall trees at some point. From the results of the study, the quality of the Telkomsel operator's 4G network has an RSRP value of 46.16% and SINR of 61.06%. XL operators have an RSRP value of 42.7% and SINR which is 60.74%.Meanwhile, Indosat Ooredoo has an RSRP value of 43.2% and SINR of 74.6%. As for the 2G network, the quality of Telkomsel's 2G network operator has an RxLevel value of 47.8% and RxQual of 89.49%. XL operators have an RxLevel value of 58.34% and RxQual of 77.62%.Meanwhile,Indosat Ooredoo has an RxLevel value of 46.16% and RxQual is 80.56%.
Implementation of KNN and AHP-TOPSIS as Recommendation System for Mustahik Selection Aprianti, Winda; Permadi, Jaka; Rhomadhona, Herfia; Amelia, Noor
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3883

Abstract

The National Amil Zakat Agency (BAZNAS) has the task of managing zakat on a national scale, including zakat. The number of prospective zakat recipients is greater than the availability of zakat funds distributed, which has an impact on the need for a selection process for mustahik. In this research, to assist the mustahik selection process, KNN will be used to classify mustahik candidates who meet the requirements, AHP to obtain consistent weights, and TOPSIS to provide recommendations for the order of mustahik whose zakat will be distributed. The dataset used in the research consisted of 77 data consisting of the criteria for number of dependents, husband's job, wife's job, total income, total expenses, and acceptance status of mustahik candidates. The application of KNN produced 15 data that were declared worthy of being considered mustahik. In the next stage, using AHP, the weights for each criterion were obtained at 12.66%, 9.23%, 10.10%, 45.96% and 22.04%. These weights were used in the TOPSIS decision support system and the results obtained were that the 76th mustahik candidate was the first ranked candidate to be proposed as a mustahik. In this research, a system was also built using KNN and AHP-TOPSIS using the PHP programming language as a recommendation system tool.
Enhancing the Accuracy of Diabetes Prediction Using Feedforward Neural Networks: Strategies for Improved Recall and Generalization Setiawan, Herry; Firnanda, Ary; Khair, Ummul
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3888

Abstract

This study explores the development and evaluation of a neural network model for predicting diabetes based on clinical data. The model was built using the Keras API with TensorFlow backend. Key steps included data preprocessing, such as feature scaling with `StandardScaler` and splitting the data into training and testing sets. The neural network architecture consisted of an input layer, two hidden layers with ReLU activation functions, and an output layer with a sigmoid activation function, optimized using the Adam optimizer and binary crossentropy loss function. The model was trained over 50 epochs with a batch size of 10, incorporating a validation split of 20% to monitor performance on unseen data during training. The results demonstrated a high accuracy of approximately 97% on the test set, indicating the model's efficacy in predicting diabetes. Further analysis using a confusion matrix revealed a high count of true positives and true negatives, alongside minimal false positives and false negatives, confirming the model's robustness. These findings suggest that neural networks can be effectively employed for diabetes prediction, offering significant potential for integration into clinical decision support systems. However, further validation with larger and more diverse datasets, alongside considerations for data imbalance and model interpretability, is recommended to ensure generalizability and practical application in real-world healthcare settings.
Controlling System in Smart Agriculture for Automatic Monitoring of Plant Nutrition Agustina, Dian Resha; Febrianti, Maria Shusanti; Susanty, Wiwin; Hindayanti, Kadek Setiani
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4001

Abstract

Technological developments have had a major impact on the lifestyle of Indonesian people through sophisticated communication tools such as smartphones and gadgets. However, the use of this technology is still not optimal. In the Indonesian context, computer and information technology, especially Intelligent Systems, has changed the way people work, interact and live. One significant implementation of Smart Systems is Smart Agriculture, which uses information and communication technology to increase agricultural productivity. The application of Greenhouse technology and hydroponic methods in agriculture supports more efficient and productive plant growth. The use of TDS sensors to measure nutrient concentrations in water in the hydroponic method helps ensure optimal conditions. In this research, the automation of providing nutrition to lettuce plants has been carried out well according to predetermined conditions. The solution to solve the problem that occurs is to use an automatic nutrition system. This system can control the amount and frequency of nutrients provided accurately and efficiently, without depending on the skill and caution of the hydroponic farmer. In addition, this system also ensures that nutrients are provided consistently and safely for farmers and the environment. This technology utilizes sensors and automation systems to monitor nutritional conditions in the planting media in real-time and farmers can ensure that their plants receive the nutrients they need and have maximum yields. This system also helps farmers speed up the plant growth process and saves time and energy.
Semiotic Analysis of Twitter Logo Change to Logo X: User Interface (UI) Design and User Psychological Perspectives Yulianto, Arief; Putri, I Gusti Ayu Agung Aristi
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4037

Abstract

The development of a logo has become a visual identity for big brands. Some brands create logos to be the biggest promotional resource to attract the mindset of their customers. In the context of technology applications, a logo is not just a symbol, but also an integral part of the User Interface (UI) that influences user interaction and engagement. Twitter, as one of the largest social media platforms in the world, has undergone a significant change in its visual identity by replacing its iconic logo to a new logo known as X. This research uses a descriptive analysis research method with comparative analysis from the researcher himself and data approaches carried out with the 5W + 1H theory. Semiotic Theory using Roland Barthes theory which explains that the first stage of signification is the relationship of signifier (expression) and signified (content) in a sign to external reality. In addition, this analysis also looks at the UI Design side and the user psychology side with the Gestalt Psycholgy theory. The results and findings viewed in the context of UI design, these changes present new challenges and opportunities to create a consistent and engaging user experience. Users' psychological reactions were mixed, indicating that the logo change had a major impact on users' perception and engagement with the app. Through the 5W+1H concept and personal analysis, this research provides a comprehensive understanding of the meaning and impact of the logo change.
Application of the K-Nearest Neighbor Machine Learning Algorithm to Preduct Sales of Best-Selling Products Danny, Muhtajuddin; Muhidin, Asep; Jamal, Akhiratul
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4063

Abstract

The development of increasingly intense competition in the business world, accompanied by advances in information technology, has brought retail companies into a situation of tighter and more open competition. PT LG Innotek Indonesia is the only company that produces tuners in Indonesia. Looking at consumer demand, PT LG Innotek must improve product quality, and add products that consumers like and frequently purchase. For this reason, PT LG Innotek Indonesia needs an analysis that can help the company identify products that tend to sell well. This analysis can be carried out through the application of machine learning algorithms, especially the K-Nearest Neighbor method. The aim of this research is to find out how the KNN algorithm performs in predicting products that are selling well and not selling well at PT LG Innotek Indonesia. Based on the analysis results, prediction results were obtained with an accuracy level of 94.74% and an error rate of 5.26%. With this high level of accuracy and low error rate, it can be concluded that the K-Nearest Neighbor method is effectively used to predict sales of PT LG Innotek Indonesia's best-selling products.
Sentiment Analysis Of Indosat's Mobile Operator Services On Twitter Using The Naïve Bayes Algorithm Butsianto, Sufajar; Fauziah, Sifa; Naya, Candra; Maulana, Futuh
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4084

Abstract

Twitter is a social media that allows users to share information with others in real time. Information that is shared on Twitter is usually referred to as a tweet. Sentiment analysis is a branch of research in the text mining domain where the process of identifying and extracting sentiment data will usually be categorized based on its polarity, whether it is positive, negative or neutral. We can process data from opinions on Twitter using data mining techniques, namely classification. The algorithm that will be used in this research is the Naïve Bayes Algorithm. This research will also use the RStudio application. It is a computer programming language that allows users to program algorithms and use tools that have been developed through R by other users. R is a high-level programming language and is also an environment for data and graph analysis. Based on the experimental results, using a comparison of training data and test data of 20%: 80%, 40%: 60%, 60%: 40%, 80%: 20% and 90%:10%, the results of sentiment classification using the Naïve Bayes method are obtained. and using 10-fold cross validation obtained an average value of 85.00% accuracy and The decrease in machine learning performance occurs in the ratio of 80:20 or 1440 training data: 360 data testing, while the ratio of 20%:80% and 90%:10% has the same accuracy value, namely 85.41%.
Linear Regression Algorithm Analysis for Predicting Electrical Panel Painting Quality Susilo, Arif; Widodo , Edy; Rilvani, Elkin; Suryana, Syahro
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4096

Abstract

Industry is increasingly developing rapidly and has an impact on the emergence of competition between companies, both private and state, both companies engaged in manufacturing and service companies. Linear Regression is used to find out how the dependent/criterion variable can be predicted through independent variables or predictor variables, individually. Based on the results of the tests that have been carried out, the variables or attributes used in this research (minute and thinkness results) have a significant effect on this research. It is proven that using the linear regression algorithm is able to provide good results with a Root Mean Squared Error value of 0.273 +/- 0.000. This is because there is a correlation or functional relationship (cause - effect) between one variable (dependent or criterion) and another variable (independent or predictor). This testing process is carried out to identify stock needs using a linear regression algorithm
Sentiment Analysis on Social Media X (Twitter) Against ChatGBT Using the K-Nearest Neighbors Algorithm Arwan Sulaeman, Asep; Danny, Muhtajuddin; Butsianto, Sufajar; Pratama, Suria
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4105

Abstract

This research aims to analyze the public's response to ChatGPT through data obtained from Twitter. Apart from that, it is also to understand whether people's responses tend to be positive or negative towards ChatGPT, as well as to test the performance of the K-Nearest Neighbors (KNN) method in classifying sentiment patterns in tweet data. The sentiment analysis method is carried out by dividing public responses into positive and negative categories. Next, the performance of the K-Nearest Neighbors (KNN) method was tested with varying k values ??to classify sentiment patterns in tweet data. This testing includes dataset division, vectorization of text data using TF-IDF, initialization and training of the KNN model, and evaluation of model performance using metrics such as precision, recall, and f1-score. The results of sentiment analysis show that the majority of people's responses to ChatGPT are positive (74.3%), while 25.7% of responses are negative. Performance testing of the KNN model shows that the highest accuracy of 88% is achieved when the k value is 5. Evaluation of model performance also shows satisfactory levels of precision, recall and f1-score. Based on the research results, it was concluded that sentiment analysis and classification using KNN were effective in understanding people's responses to ChatGPT

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