Journal of Applied Informatics and Computing

Crack Detection in Building Through Deep Learning Feature Extraction and Machine Learning Approch

Afandi Nur Aziz Thohari, Aisyatul Karima, Kuwat Santoso, Roselina Rahmawati — Sun, 07 Jul 2024 00:00:00 +0000

Buildings with cracks are extremely hazardous because they have the potential to cause destruction. Numerous occupants of structures such as houses and buildings are at risk when cracks appear. There are numerous techniques for identifying fractures in structures, including visual inspection, tool use, and expert inspection. The present study employed computer vision, a form of artificial intelligence, to detect cracks in buildings. The main objective of this research is to construct a prototype capable of real-time monitoring of cracks in building walls. This research makes use of a methodology that combines machine learning and deep learning. Machine learning is employed in the classification process, whereas deep learning is utilized for the extraction of features. This research employs MobileNetV2 as its deep learning architecture and K-NN, Naive Bayes, SVM, XGBoost, and Random Forest as its machine learning classifiers. Test results show that when dividing the 80:20 dataset, XGBoost algorithms can produce the highest accuracy, sensitivity, and specificity values of 99%. Tests in the real environment are performed by deploying Raspberry Pi. Test results show that the prototype can detect cracks inthe structure surfaceat a distance of 10 meters in a bright environment. The crack detection process is carried out in real time at an average speed of 42fps.

IndoBERT Model Analysis: Twitter Sentiments on Indonesia's 2024 Presidential Election

Dwi Ismiyana Putri, Ari Nurul Alfian, Mardi Yudhi Putra, Putro Dwi Mulyo — Sun, 07 Jul 2024 00:00:00 +0000

Elections are one of the key moments in a country's democracy. Indonesian elections have a significant impact on regional and global politics. Twitter being one of the popular social media platforms becomes a powerful tool for political campaigns. This makes it an ideal source to analyze public opinion during the 2024 general election, particularly the upcoming Presidential Election (Pilpres). IndoBERT is the model chosen to analyse the sentiment from the dataset in this study using a zero-shot learning approach. Based on the evaluation results, the accuracy value of the 2024 presidential election classification is 0.60 (60%), tends to predict with a good value in the positive label of 0.74 (74%) for F1-Score. This model is considered quite good at predicting negative labels but the results are not too optimal with a value of 0.49 (49%). Confusion Matrix in this IndoBERT model is more likely to label tweets with positive things, by detecting negative labels quite well.

Optimization Chatbot Services Based on DNN-Bert for Mental Health of University Students

Sun, 07 Jul 2024 00:00:00 +0000

Attention to mental health is increasing in Indonesia, especially with the recent increase in the number of cases of stress and suicide among students. Therefore, this research aims to provide a solution to overcome mental health problems by introducing a chatbot system based on Deep Neural Networks (DNN) and BiDirectional Encoder Representation Transformers (BERT). The primary objective is to enhance accessibility and offer a more effective solution concerning the mental health of students. This chatbot utilizes Natural Language Processing (NLP) and Deep Learning to provide appropriate responses to mild mental health issues. The dataset, comprising objectives, tags, patterns, and responses, underwent processing using Indonesian language rules within NLP. Subsequently, the system was trained and tested using the DNN model for classification, integrated with the TokenSimilarity model to identify word similarities. Experimental results indicate that the DNN model yielded the best outcomes, with a training accuracy of 98.97%, validation accuracy of 71.74%, and testing accuracy of 71.73%. Integration with the TokenSimilarity model enhanced the responses provided by the chatbot. TokenSimilarity searches for input similarities from users within the knowledge generated from the training data. If the similarity is high, the input is then processed by the DNN model to provide the chatbot response. This integration of both models has proven to enhance the responsiveness of the chatbot in providing various responses even when the user inputs remain the same. The chatbot also demonstrates the capability to recognize various inputs more effectively with similar intentions or purposes. Additionally, the chatbot exhibits the ability to comprehend user inputs although there are many writing errors.

Forecasting Air Quality Indeks Using Long Short Term Memory

Sun, 07 Jul 2024 00:00:00 +0000

Exercise offers significant physical and mental health benefits. However, undetected air pollution can have a negative impact on individual health, especially lung health when doing physical activity in crowded sports venues. This study addresses the need for accurate air quality predictions in such environments. Using the Long Short-Term Memory (LSTM) method or what is known as high performance time series prediction, this research focuses on forecasting the Air Quality Index (AQI) around crowded sports venues and its supporting parameters such as ozone gas, carbon dioxide, etc. -others as internal factors, without involving external factors causing the increase in AQI. Preprocessing of the data involves removing zero values and calculating correlations with AQI and the final step performs calculations with the LSTM model. The LSTM model which adds tuning parameters, namely with epoch 100, learning rate with a value of 0.001, and batch size with a value of 64, consistently shows a reduction in losses. The best results from the AQI, PM2.5, and PM10 features based on performance are MSE with the smallest value of 6.045, RMSE with the smallest value of 4.283, and MAE with a value of 2.757.

Enterprise Architecture Model of the New Student Admission System at Stella Maris University Sumba

Dian Fransiska Ledi, Ardiyanto Dapadeda — Sun, 07 Jul 2024 00:00:00 +0000

This research aims to design an Enterprise Architecture (EA) model for the new student admission system at Stella Maris Sumba University. The background of this research is the need to improve efficiency, transparency, and integration in the new student admission process, which currently still faces various administrative and technical challenges. The research method used is qualitative which includes literature studies and in-depth interviews with relevant parties. The data obtained was analyzed to identify needs and design the right EA model. The purpose of this research is to create a system capable of automating the admission workflow, ensuring data security, and providing real-time access for application status tracking. The results showed that the proposed Enterprise Architecture model can improve operational efficiency, user satisfaction, and support the strategic decisions of Stella Maris Sumba University based on accurate and integrated data.

Comparison of Deep Learning Architectures in Identifying Types of Medicinal Plant Leaf Images

Sarah Salsabila, Aries Suharso, Purwantoro Purwantoro — Sun, 07 Jul 2024 00:00:00 +0000

This study focuses on the identification of 3500 images of medicinal plant leaves using Deep Learning CNN Transfer Learning models such as MobileNet, VGG16, DenseNet121, ResNet50V2, and NASNetMobile. The dataset used is the "Indonesian Herb Leaf Dataset 3500," consisting of 10 classes of medicinal plants. This research has the potential to efficiently and accurately recognize medicinal plants using machine learning workflow methods. The objective of this study is to compare the performance of these five methods in conducting plant identification. The testing phase involves various data handling schemes, dividing the data into two scenarios: 80:10:10 and 70:20:10. Performance comparison is also done between augmented and non-augmented data. The research findings indicate that MobileNet exhibits the best performance with an accuracy, precision, recall, and f1-Score of 98.86%. Accurate leaf identification supports further research on the properties and benefits of medicinal plants and can be applied in the development of decision support systems for plant recognition.

Taxpayer Awareness Classification Using Decision Tree and Naïve Bayes Methods

Moch Riyadi Maskur A, Arief Wibowo — Sun, 07 Jul 2024 00:00:00 +0000

Land and Building Tax (PBB) has a big influence on a region's PAD. Therefore, regions always strive to increase PBB income as much as possible. Many factors influence the increase in PBB, one of which is public awareness of taxes. Lack of public awareness of taxes causes PBB income to also decrease, and has implications for regional PAD. And conversely, if public awareness of taxes is high, PBB and PAD revenues will also increase. Therefore, a system is needed to measure public awareness of taxes in the region. If public awareness of taxes can be measured, then the relevant agencies can evaluate and map taxpayers in which sub-districts have high or below average awareness. There are several factors that influence taxpayer awareness, including ownership status, tax sector, assessment category, and the number of receivable payments over the past 5 years. By knowing the awareness of taxpayers, the relevant agencies can review the targets for achieving PBB revenue and issue warning letters to taxpayers whose awareness of PBB is lacking. This research uses decision tree and naïve Bayes methods to classify 666,580 datasets obtained from the Cianjur Regency Regional Revenue Management Agency. The stages are carried out by data collection, data preprocessing, training data labeling, classification process, and evaluation. The result of this research is a system that can predict whether taxpayers are aware or not in a sub-district and sub-district or rural area using decision trees and naïve Bayes. The accuracy obtained from the decision tree method was 93.73%, while the accuracy obtained from the naïve Bayes method was 85.61%.

Stock Market Index Prediction using Bi-directional Long Short-Term Memory

Muhammad Althaf Majid, Prilyandari Dina Saputri, Soehardjoepri Soehardjoepri — Sun, 07 Jul 2024 00:00:00 +0000

The IHSG (Indonesia Stock Exchange Composite Index) is a stock price index in the Indonesia Stock Exchange (BEI) that serves as an indicator reflecting the performance of company stocks through stock price movements. Therefore, IHSG becomes a reference for investors in making investment decisions. Advanced stock exchanges generally have a strong influence on other stock exchanges. Several studies have proven the influence of one global index on another. Global index is a term that refers to each country's index to represent the movement of its country's stock performance. Forecasting IHSG can be one of the analyses that help investors make wise decisions when investing. To obtain an IHSG forecasting model, an appropriate and suitable method is needed, especially for data that has a large amount. LSTM is a development of Recurrent Neural Network (RNN) which has the ability to remember information in a longer period of time, while Bi-LSTM is a development of LSTM which has the ability to remember information longer and can understand more complex patterns than LSTM. This research provide the IHSG forecasting based on global index factors. The results showed that the best Bi-LSTM model (6-9-1) had a better performance in predicting and forecasting JCI movements with a MAPE value of 0.572314% better than the best LSTM model (4-10-1) which had a MAPE of 0.74326%. With forecasting based on the Bi-LSTM model, it is expected to help investors in making decisions on the Indonesia Stock Exchange (IDX).

Implementation of K-Means, Hierarchical, and BIRCH Clustering Algorithms to Determine Marketing Targets for Vape Sales in Indonesia

Sun, 07 Jul 2024 00:00:00 +0000

In today's era, smoking is a common thing in everyday life. Along with the development of the times, an innovation emerged, namely the electric cigarette or vape. Electric cigarettes or vapes use electricity to produce vapor. The e-cigarette business is very promising in today's business world due to the consistent increase in market demand. However, determining the target buyer is one of the things that is quite important in determining the success of a business. In this analysis, the background of each region in Indonesia has different diversity; therefore, observation of data is needed to find out which regions in Indonesia have the potential to increase marketing based on profits (margins) to support the target market analysis process so that companies do not suffer losses and increase business success. In this study, the analysis will be carried out using vape quantity, margin, and purchasing power data in each region, which is processed using 3 algorithms: K-Means, Hierarchical, and BIRCH. The results of the clustering of the three algorithms produce two clusters. The K-means, Hierarchical, and BIRCH algorithms produce the same clusters: a potential cluster consisting of 18 cities and a non-potential cluster consisting of 45 cities. To see the performance of the model results, an evaluation was carried out using the Silhouette score, Davies Bouldin, Calinski Harabasz, and Dunn index, which obtained results of 0.765201, 0.376322, 315.949434, and 0.013554. From these results, it can be concluded that the clustering results are not too good and not too bad because the greater the Silhouette Score, Calinski Harabasz, and Dunn Index value, the better the clustering results while for Davies Bouldin the smaller the value means the better the clustering results.

Sentiment Analysis of Sirekap Application Users Using the Support Vector Machine Algorithm

Joko Setyanto, Theopilus Bayu Sasongko — Sun, 07 Jul 2024 00:00:00 +0000

In the current era of digitalization, various activities are conducted using technology to aid their execution, including the democratic process scheduled for February 2024. The Komisi Pemilihan Umum (KPU) is utilizing a mobile-based application called Sirekap. During the previous presidential and vice-presidential elections, there were many pros and cons regarding the Sirekap application. A significant number of negative reviews were expressed by the public towards this application. This study employs the SVM algorithm to perform sentiment analysis of Sirekap application users. Before building the model, several steps were undertaken, including data labeling, data preprocessing, splitting the dataset into training and testing data, and performing transformations using Count Vectorizer. Evaluation of the SVM model results shows quite good performance with an accuracy of 81%. For the negative class, the precision and recall values are 87% and 85%, respectively, while for the positive class, the precision and recall values only reach 66% and 70%, indicating a need for improvement in the model's identification of the positive class. Five-fold cross-validation was performed with an average cross-validation score of 79.6% and a standard deviation of 2.14%, indicating the model's consistency across various training data subsets. These findings suggest that the SVM model can effectively perform text classification tasks. Based on the negative word cloud, it can be concluded that the Sirekap application still has many shortcomings affecting the democratic process in February 2024.

Analysis of pH and Turbidity Sensor Outputs in Shrimp Ponds for Vannamei Shrimp Commodities

Musayyanah Musayyanah, Erdasetya Bayunugraha, Harianto Harianto, Heri Pratikno — Sun, 07 Jul 2024 00:00:00 +0000

Vannamei shrimp is a high-value, economically important brackish water aquaculture commodity that is easy to cultivate. Optimal growth of Vannamei shrimp can be achieved by monitoring water quality parameters such as pH and turbidity. The pH levels can be measured using a pH sensor, with a pH meter as a reference. Turbidity levels are measured with a turbidity sensor in NTU units, with a turbidity stick serving as a reference. Testing of these sensors was conducted from morning to noon over three days in the brackish water ponds of IBAP Banjar Kemuning, Sidoarjo, recording 100 data samples. The performance of both sensors fluctuated due to disturbances around the pond, prompting the use of the Moving Average (MA) filter method to improve accuracy. Applying MA with varying window sizes (wz) resulted in a performance increase of 0.24% in the morning and 0.1% at noon. Additionally, turbidity sensor testing indicated that the pond conditions were consistent with the turbidity measurements obtained using the turbidity stick.

Text Insertion and Encryption Using The Bit-Swapping Method in Digital Images

Kiswara Agung Santoso, Muhammad Fahmil Fakih, Ahmad Kamsyakawuni — Sun, 07 Jul 2024 00:00:00 +0000

Communication is an essential aspect of everyday life, involving the transmission of information through various media. Technological advances have made communication easier but have also increased privacy and data security risks. Several efforts are made to maintain the security of digital information, including coding information (cryptography) and hiding information (steganography). In this article, the author secures information through a combination of cryptography and steganography. To secure text data, we encrypt by exchanging bits between adjacent characters. Subsequently, the encrypted text is hidden within an image. The security analysis results show the successful reconstruction of the message from the stego image and the successful restoration of the message to its original form. The use of the bit swapping method in the text message encryption process has been proven to enhance the security level of the ciphertext, as indicated by the lower TPK calculation value of 0.33 compared to the TPK value in previous studies. Additionally, embedding the ciphertext into digital images has been demonstrated to further increase the security level of the message, evidenced by the NPCR calculation value of 0.0000109% and the UACI calculation value of 0.000000555%. These very small values indicate no significant changes.

Implementation of Identity Loss Function on Face Recognition of Low-Resolution Faces With Light CNN Architecture

Sun, 07 Jul 2024 00:00:00 +0000

Face recognition in low-resolution images has seen significant advancements over the past few decades. Although extensive research has been conducted to improve accuracy in these conditions, one of the main challenges remains the difficulty in identifying unique facial features in low-resolution images, leading to high error rates in identification. The use of Deep Convolutional Neural Networks (DCNN) for low-resolution face recognition is still limited. However, employing super-resolution models like REAL-ESRGAN can enhance recognition accuracy in low-resolution images. This study utilizes the Light CNN architecture and applies the margin-based identity loss function AdaFace on low-resolution datasets. The model is trained using the Casia-WebFace dataset and evaluated using the LFW and TinyFace test datasets. Based on the evaluation results on the LFW test data, the best model is Light CNN9-AdaFace, achieving the highest accuracy of 97.78% at 128x128 resolution. For images with the lowest resolution of 16x16, an accuracy of 83.37% was achieved using super-resolution techniques. On the TinyFace test data, the use of super-resolution resulted in performance metrics with a Rank-1 accuracy of 47.26%, Rank-5 accuracy of 55.25%, Rank-10 accuracy of 58.61%, and Rank-20 accuracy of 61.90% using the Light CNN9-AdaFace architecture.

Deployment of Kidney Tumor Disease Object Detection Using CT-Scan with YOLOv5

Hastyantoko Dwiki Kahingide, Abu Salam — Sun, 07 Jul 2024 00:00:00 +0000

Image processing plays a crucial role in identifying kidney tumors through CT-Scan images. Object detection technology, particularly YOLO, stands out for its speed and accuracy in facilitating more detailed analysis. Using Flask as a web framework offers optimal responsiveness, providing adaptive ease of use, especially in medical image processing. Evaluation of the model shows impressive results, with a mean Average Precision (mAP) of 0.987 for the 'kidney tumor' label. Detection on public data demonstrated high performance with accuracy, precision, recall, and F1-Score of 98.56%, 98.66%, 99.66%, and 99.16%, respectively. This study also utilized clinical data comprising 62 CT-Scan images. Evaluation of the clinical data revealed that YOLOv5 produced an accurate detection model with accuracy, precision, recall, and F1-Score of 95.16%, 96.72%, 98.33%, and 97.52%, respectively. The research shows that both public and clinical data models can accurately detect kidney tumors based on CT-Scan images. The deployment process using the Flask web-based platform allows direct interaction with users through an intuitive interface, enabling users to upload their CT-Scan images and quickly obtain detection results. These test results provide evidence that object detection using YOLOv5 achieves high accuracy in detecting both public and clinical datasets.

Web-Based Mapping of Crime-Prone Areas in Samarinda Seberang and Loa Janan Ilir Districts, Samarinda Citys

Syafei Karim, F.V. Astrolabe Sian Prasetya, Anisa Sundarti — Sun, 07 Jul 2024 00:00:00 +0000

The development of Geographic Information System (GIS) technology has provided significant benefits in various fields, including the monitoring of crime-prone areas. GIS is used to minimize the traces of these crimes. This study aims to map crime-prone areas in the Samarinda Seberang and Loa Janan Ilir Districts to identify which areas are potentially vulnerable, allowing for analysis for prevention and handling. The data used were collected from theft cases that occurred in these districts in 2019 and 2020. The research employs a scoring technique where each parameter is rated according to its classification. The results of the scoring process are then analyzed to determine the level of crime-prone areas, categorizing them as very vulnerable, vulnerable, or not vulnerable. Based on respondents' feedback, the application facilitates users in locating crime-prone areas, with 94.34% of responses indicating agreement or strong agreement. These results suggest that the application is feasible for implementation.

Predictive Analytics for IMDb Top TV Ratings: A Linear Regression Approach to the Data of Top 250 IMDb TV Shows

Meryatul Husna, Lampson Pindahaman Purba, Muhammad Eri Rinaldy, Arif Ridho Lubis — Sun, 07 Jul 2024 00:00:00 +0000

In the era of a growing entertainment industry, understanding audience preferences and predicting the financial performance of entertainment products such as films and television shows has become increasingly important. Previous research has demonstrated various approaches in understanding the factors that influence the financial performance of entertainment products. However, there is still a need for research to investigate other aspects of film and television show evaluation. This study aims to explore the contribution of linear regression in analysing the ratings and financial performance of IMDb's top TV shows. Through the incorporation of various data-informed and interpretative approaches, it is expected to gain a deeper understanding of the factors that influence the success of a television show. Using data from the Top 250 IMDb TV Shows, a predictive analysis was conducted to understand the relationship between the number of episodes and IMDb ratings. The results of the information showed a negative relationship between the number of episodes and IMDb rating, with the linear regression model predicting a decrease in IMDb rating as the number of episodes increases. Implications of this research include recommendations for content creators to consider both quality and quantity of content in the development of TV shows.

Tourist Perceptions Through Sentiment Analysis to Support Tourism Development in Maluku Province

Sun, 07 Jul 2024 00:00:00 +0000

Tourist perceptions obtained by sentiment analysis can provide an overview of tourism development in Maluku Province. This study aims to determine the perception of tourists towards destinations in Maluku based on the results of sentiment analysis. This research uses a quantitative approach by analyzing scrapping and snipping data from Facebook, Instagram, TikTok, Google Maps Review, and Trip Advisor. Sentiment analysis is done by comparing the accuracy level of the Random Forest, Naïve Bayes, and Support Vector Machine classification models. The results of the comparison of the three methods show that Random Forest has the best accuracy rate, which is 85%. The results of sentiment analysis both on the entire dataset and the results of analysis per district/city show that tourists' perceptions of tourist destinations in Maluku can be said to be good because they are dominated by negative sentiments. The existence of negative and neutral sentiments indicates that there is a need for improvement and improvement in the quality of tourist services in terms of human resources, transportation, accommodation, and infrastructure facilities.

Analysis of User Experience in the Design of the AMGM Lab Mobile Application Using the User Experience Questionnaire (UEQ) for Enhanced Efficiency

Indah Rahma Ilmiana, Chanifah Indah Ratnasari — Sun, 07 Jul 2024 00:00:00 +0000

AMGM mobile Lab application is a design application supporting documentation of water sample management data. There is a management support system in the form of an intranet. Nevertheless, access to the system is restricted to the office. Naturally, this is less effective since officers must first return to the office in order to enter sample data, and this cannot be done in real-time while they are out in the field. Thus, in order to facilitate these operations, mobile application development is required. User experience analysis is necessary to provide an inventive UX design, which is then required to satisfy laboratory requirements and the company's expectations for the application design. The objective of this study is to use user experience research as a foundation for future application development. In order to do user experience analysis, the User Experience Questionnaire (UEQ) approach is used. The test results reveal that the assessment falls into the excellent category for attractiveness (1.86) and dependability (1.82). The efficiency (1.85) and novelty (1.24) scales are categorized as good. The perspicuity (1.71) and stimulation (1.21) measures are categorized as above average. The mean of the entire scale exceeds 0.8, indicating that people assess all features positively.

Classification of COVID-19 Aid Recipients in Kasomalang District Using the K-Nearest Neighbor Method

Sun, 07 Jul 2024 00:00:00 +0000

The impact of the Coronavirus, also known as COVID-19, which emerged in 2019, has not only threatened public health but also affected the global economy, including Indonesia. The government has initiated various aid programs to assist the community during the COVID-19 pandemic. These aids are expected to alleviate the economic burden on the affected population. One such aid program is the Direct Cash Assistance (Bantuan Langsung Tunai/BLT) from the Village Fund, which has been distributed since the onset of COVID-19 in Indonesia. However, the distribution of BLT has encountered several issues, including misidentification of recipients and double or excessive distribution beyond the established criteria. To address these issues, data mining for the classification of aid recipients can be employed. This study uses the K-Nearest Neighbor (KNN) method for data mining classification to classify residents' data with new patterns, ensuring aid distribution aligns with the criteria and eliminating double recipients. The application of K-Nearest Neighbor to the population data in Kasomalang District yields optimal performance, with evaluation results showing an accuracy of 96%, precision of 0.98, recall of 0.96, and F1 score of 0.97 using the confusion matrix method.

Applying the Multi-Attribute Utility Theory (MAUT) to Accurately Determine Stunting Susceptibility Levels in Toddlers

Nur Oktavin Idris, Nurain Umasugi — Sun, 07 Jul 2024 00:00:00 +0000

Stunting is a condition of impaired growth and development in toddlers due to prolonged nutritional deprivation. In the Kota Timur Community Health Center, stunting is traditionally assessed based solely on body weight and height, neglecting other crucial factors such as socioeconomic status, maternal nutrition during pregnancy, history of illness, and dietary intake. This limited approach leads to inaccurate decision-making and misdiagnoses of stunting. This research applied the Multi-Attribute Utility Theory (MAUT) to identify stunting susceptibility levels in toddlers by integrating various determinants, including body weight, height, socioeconomic conditions, maternal nutrition during pregnancy, morbidity, and dietary intake. MAUT effectively integrates multiple criteria and manages data uncertainties through its utility concept, allowing for comparison across different alternatives to facilitate accurate decision-making. The results showed that Arbi, Manaf, and Aisyah were susceptible to stunting, with evaluation scores of 0.028, 0.288, and 0.299, respectively, while Daffa and Zayyan were not susceptible, with scores of 0.900 and 0.966, respectively. Therefore, the system utilizing MAUT to determine stunting susceptibility levels in toddlers can be adopted by health workers at the Kota Timur Community Health Center to enable efficient, quick, and accurate diagnosis by integrating multiple determinants of stunting susceptibility.

Social Media Analysis for Effective Information Dissemination and Promotions Using TOPSIS

Reka Hani Latifah Nurhasanah, Abdul Halim Anshor, Asep Muhidin — Sun, 07 Jul 2024 00:00:00 +0000

Social media has become an essential tool for spreading news and promotions. This research aims to evaluate the effectiveness of using social media as a strategy for disseminating information and promoting products using the TOPSIS method. Initial data was collected from a survey of social media users. The data was gathered through questionnaires distributed to various groups, including students, entrepreneurs, and office workers. The TOPSIS method was used to analyze the data and identify the most effective social media channels for information dissemination and promotion. The findings indicate that Facebook is the most effective platform for disseminating information, followed by Instagram and Twitter. Conversely, Instagram is the most effective platform for content promotion, followed by Facebook and YouTube. This study has significant implications for businesses and organizations that use social media for information dissemination and promotions. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to evaluate and rank platforms based on criteria such as reach, accessibility, topicality, ease of use, creativity, informativeness, adaptability, transactionability, and security. The results show that TikTok is the best social media platform with the highest preference score of 0.755, followed by Facebook in second place, Instagram in third place, Twitter in fourth place, Telegram in fifth place, and YouTube in sixth place.

Comparative Study of Web Server Performance Testing with and without Docker Based on Virtual Machines

Fajar Kurnia Ramadhan, Garno Garno, Arip Solehudin — Sun, 07 Jul 2024 00:00:00 +0000

Web server development is often hindered by the cost and resources required, as developing a web server typically requires a bare-metal server. Container technology, which allows for the development of multiple web servers on a single bare-metal server, has become popular. One of the most widely used containers is Docker. Docker reduces the need for costs and resources. Beyond the issues of cost and resource requirements, the performance of web servers also needs to be considered. The performance of web servers with and without Docker needs to be verified. This research aims to test the performance of two web servers, one using Docker and one not using Docker, utilizing the native hypervisor VMware ESXi. The web server performance test items in this study include CPU and RAM resource usage. The method for developing infrastructure systems uses SIDLC (System Infrastructure Development Life Cycle). Performance testing (Load Test) was conducted using Apache JMeter as a tool, with the manipulation of the number of threads predetermined. Resource usage information was monitored using Prometheus and Grafana. The research results show that with the same resources for each virtual machine, the CPU resource usage of Virtual Machine 2 (Undockerized) is less than that of Virtual Machine 1 (Dockerized). Meanwhile, RAM resource usage is not affected by the number of users on both virtual machines. Virtual Machine 2 (Undockerized) is better at handling HTTP requests. Virtual Machine 1 (Dockerized) can handle only 2,790 users, while Virtual Machine 2 (Undockerized) can handle more than 2,790 users without errors.

Pricing and Producer-Retailer Supply Chain Coordination: A Game Theory Approach

KF. Sunny Cahya Utama, Valeriana Lukitosari — Sun, 07 Jul 2024 00:00:00 +0000

Supply chain process is interdependent. Starting from procurement of raw materials, production, distribution, and finally the goods reaching consumers will influence each other. The costs of goods and services will be somewhat impacted by these social habits. Producers and merchants must comprehend these social behaviors in order to properly establish prices and the distribution of goods. This includes determining the prices of goods and services. Offering manufacturers who participate in cooperative advertising schemes money for a percentage of the costs connected with local advertising encourages retailers to launch additional promotional activities. The aim of this research is to investigate how cooperative pricing and advertising can improve supply chain coordination using consumer demand functions. A model based on game theory that takes the dynamics of power in the supply. A series of numerical simulations is presented to illustrate the optimal solution of channel members based on scenarios that illustrate, understand and compare the fundamental results of the game models. The results of this research are that retail price decisions are influenced by the level of competition and product differentiation. The results show that retail margins depend on local ( ) and national ( ) advertising effectiveness values. In addition, retailers can gain greater profits by setting higher prices in conditions of low price elasticity but must consider consumer sensitivity to price to maximize profits.

Comparison of EfficientNetB7 and MobileNetV2 in Herbal Plant Species Classification Using Convolutional Neural Networks

Seno Arnandito, Theopilus Bayu Sasongko — Sun, 07 Jul 2024 00:00:00 +0000

This study compares the performance of EfficientNetB7 and MobileNetV2 in classifying herbal plant species using Convolutional Neural Networks (CNNs). The primary objective was to automatically identify herbal plant species with high accuracy. Based on the evaluation results, both EfficientNetB7 and MobileNetV2 achieved approximately 98% accuracy in recognizing herbal plant species. While both models demonstrated excellent performance in precision, recall, and F1-score for most plant species, EfficientNetB7 showed a slight edge in some evaluation metrics. These findings provide valuable insights into the potential implementation of CNN architectures in automatic plant recognition applications, particularly for developing widely applicable web-based systems for herbal plant identification.

Computational Analysis of IT Governance Audit Using COBIT 4.1 Framework: A Customer Perspective

Vera Wati, Siska Febriani, Eka Yulia Sari — Wed, 10 Jul 2024 01:14:08 +0000

A company’s performance can be measured by the number and satisfaction of customers, which helps in maintaining customer relationships. Indicators such as customer satisfaction, perception of service, and loyalty can be derived from the Customer Perspective of the Balance Scorecard (BSC). Conducting an IT governance audit is essential to understand how customers perceive a service. The use of the COBIT 4.1 Framework for IT governance audits is recognized for its detailed process, both for business and governance purposes, to avoid vulnerabilities and threats, thereby increasing customer satisfaction. Effective IT governance plays a crucial role in enhancing customer satisfaction and achieving organizational success. This research aims to analyze IT governance audits from a customer perspective using the COBIT 4.1 framework, with a focus on aligning IT strategy with business goals to meet customer expectations. The research method involves key processes in PO8 (Manage Quality) and PO10 (Manage Project) to determine quality standards and influential budgets. Integration with computational techniques for data analysis and IT audit algorithms is carried out to build strong IT governance practices. The computational audit results show maturity levels of 2.59 for PO8 and 3.02 for PO10, indicating areas needing improvement in product quality management and project execution to better meet customer needs. These findings underscore the importance of integrating computational insights to optimize IT governance frameworks and improve organizational performance, especially in customer retention through enhanced project quality management.

Comparative Analysis of the Performance of Decision Tree and Random Forest Algorithms in SQL Injection Attack Detection

Alfatarizky Budi Aulianoor, Muhammad Koprawi — Thu, 25 Jul 2024 00:00:00 +0000

This study compares the performance of two machine learning algorithms the Decision Tree and Random Forest. SQL Injection attacks continue to threaten web applications because they exploit vulnerabilities by injecting malicious code into SQL statements executed on database servers. Therefore, machine learning algorithms are used to identify SQL Injection attacks. The dataset used is 33761 in the form of random query data input in a CSV tabular containing sentence and label columns. The research software used is Google Colaboratory and Microsoft Edge. The series of research conducted by Collect Data is data collection, Preprocessing handling missing values, deleting rows that contain duplicates, and the same query having different labels. Train and Test is used to build models and prepare test data, Build and Compile involves building Decision Tree and Random Forest models. The final step is to evaluate both algorithm models to determine which performs better. After conducting a series of research processes, the results of the Random Forest algorithm are slightly better than the Decision Tree algorithm, with an accuracy of 99.81%, precision of 99.79%, recall of 99.65%, and an average F1-score of 99.72%.

Comparison of LSTM Model Performance with Classical Regression in Predicting Gaming Laptop Prices in Indonesia

Agus Dewantoro, Theopilus Bayu Sasongko — Thu, 25 Jul 2024 00:00:00 +0000

The demand for gaming laptops has surged in the digital era, appealing to both professional gamers and the general public. Gaming laptops come equipped with advanced features such as powerful graphics, fast processors, and sleek designs, offering a portable solution for gaming enthusiasts. However, the price of gaming laptops varies due to factors like brand, hardware specifications, screen size, and additional features. Accurately predicting these prices can help consumers make informed purchasing decisions and assist manufacturers in setting competitive prices. This research proposes the use of the Long Short-Term Memory (LSTM) algorithm to predict gaming laptop prices, comparing its performance with classic regression algorithms such as Linear Regression and Multi-layer Perceptron. Utilizing a comprehensive dataset of gaming laptop prices and specifications in Indonesia, this study employs robust pre-processing and model optimization techniques. The results show that the LSTM model achieves a Root Mean Squared Error (RMSE) of 0.09011, a Mean Squared Error (MSE) of 0.00812, and an R² Score of 0.90016. In comparison, the Linear Regression model has an RMSE of 0.09075, an MSE of 0.00823, and an R² Score of 0.89873, while the Multi-layer Perceptron model has an RMSE of 0.09891, an MSE of 0.00978, and an R² Score of 0.87971. These results indicate that the Long Short-Term Memory algorithm outperforms other classic regression algorithms in this case. This study highlights the potential of LSTM in developing a robust price prediction model for gaming laptops, particularly in the Indonesian market, providing valuable insights for both consumers and manufacturers.

Classification Vehicle Tire Quality using Convolutional Neural Networks

Vila Rusantia Pratiwi, Nova Rijati — Thu, 25 Jul 2024 00:00:00 +0000

Tires are a very important component in a vehicle because they are related to driving safety. Defective tires often cause accidents ranging from minor to fatal accidents. Convolutional Neural Network (CNN) is a type of neural network that is used to detect and recognize objects in an image. CNN can imitate the image recognition system in the human visual cortex, making it suitable for identification and classification of image data. This research aims to develop and evaluate a CNN model that is able to classify vehicle tires as 'defective' or 'good'. Model uses a total of 1856 tire images from kaggle.com and is labeled 'defective' or 'good'. Dataset is split using four different data split ratios (60:40, 70:30, 80:20, and 90:10) to determine the optimal distribution that improves the generalization ability of the model. Model evaluation uses accuracy, precision and recall matrices, which are calculated based on the confusion matrix results from testing on 300 data samples. Research results show that the model achieves the best performance at a split ratio of 80:20, with an accuracy of 76.67%, precision of 77.33%, and recall of 76.32%.

Visit Recommendation Model: Cluster Analysis of Retail Sales Data using Recursive K-Means Clustering Method

Thu, 25 Jul 2024 00:00:00 +0000

In the context of retail distribution, this study employs recursive K-means clustering on retail sales data to optimize clusters of nearest-distance stores for salesperson route recommendations. This approach addresses the stochastic salesperson problem by generating effective routes, enhancing cost reduction, and improving service efficiency. The recursive K-means algorithm dynamically adjusts to continuous changes in store numbers, locations, and transaction data. Consequently, this research successfully developed a model that automatically re-clusters the data with each change, providing continuously updated and effective store recommendations.

Musical Instrument Classification using Audio Features and Convolutional Neural Network

Gst. Ayu Vida Mastrika Giri, Made Leo Radhitya — Thu, 25 Jul 2024 00:00:00 +0000

The classification of acoustic instruments is the subject of this research, which utilizes Convolutional Neural Networks (CNNs). We employ a dataset from Kaggle that includes audio recordings of the piano, violin, drums, and guitar. In the training set, the dataset comprises 700 samples of guitar, percussion, and violin and 528 samples of piano. The test set contains 80 samples of each instrument. Mel spectrograms, MFCCs, and other spectral and non-spectral characteristics are among the features that can be extracted using the librosa package. Three feature sets—spectral-only, non-spectral-only, and a combined set—are employed to evaluate the efficacy of CNN models—various CNNs configurations by adjusting the number of convolutional filters, learning rates, and epochs. The combined feature set achieves the highest performance, with a validation accuracy of 71.8% and a training accuracy of 76.9%. In contrast, non-spectral features achieve 68.4% validation accuracy, while spectral-only features achieve 69.3%. These findings demonstrate the advantages of employing a vast feature set for precise classification.