Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

Performance Comparison between Double Exponential Smoothing and Double Moving Average Methods in Seasonal Beef Demand

Bain Khusnul Khotimah, Setiani, Ana Yuniasti Retno Wulandari, Devie Rosa Anamisa — Fri, 01 Nov 2024 00:00:00 +0000

Beef demand relies on seasonal patterns because it depends on feed supplies, especially in the rural areas, that still rely on natural feeds. Beef supply is regulated by the government as it is one of the highly demanded commodities. It is a livestock product containing nutritional value to meet the protein needs of the community. The supply is influenced by several factors such as beef production, beef consumption, and the people's income level. In order to anticipate the increasing demand for beef, it is necessary to conduct a forecast to estimate the demand for meat in the future. In forecasting, various methods were examined to choose the method with the lowest error rate. This research compared the Mean Absolute Percentage Error (MAPE) resulted from Double Exponential Smoothing (DES) and Double Moving Average (DMA) methods. Based on the test results and analysis on beef supplies in Madura, it can be concluded that the method with the lowest MAPE value is Double Exponential Smoothing, i.e. 9.50% with an alpha parameter of 0.5. Meanwhile, the test using the Double Moving Average method to determine the best MAPE value, resulted the best time order of 2 with a MAPE value of 29.8408%. After finding the parameter with the lowest MAPE value, that parameter was used for the data testing. In the measurement, the data used for the testing were the data of 1-year, 2-year, 3-year, and 4-year period. Each method has a level of error value that increases the same; the number of data entered can affect the MAPE value. Therefore, the more data entered, the lower the error value.

Advancements in Cooperative Mobile Robots Control Strategies for Large-Scale Material Transport: Review

Hendi Wicaksono Agung — Fri, 01 Nov 2024 00:00:00 +0000

This paper explores groundbreaking advancements in control strategies for cooperative mobile robots used in large-scale material transport, a critical aspect of modern industrial, manufacturing, logistics, and construction sectors. It delves into the development of sophisticated systems that enable seamless coordination among multiple mobile robot systems. The research presents a novel hierarchical finite state automaton for dynamic mission adaptation and a null space-based control scheme for precise task execution and enhanced system resilience. The introduction of Mecanum wheels facilitates flexible movement and manipulation of materials, thereby increasing the operational efficiency and safety. Cutting-edge sensory technology, including LiDAR (Light Detection and Ranging), and the implementation of Robot Operating System are highlighted for their roles in enhancing autonomous navigation and intelligent operation. Additionally, the paper discusses the impact of centralized and decentralized control methods in ensuring safe cooperative object transport. The findings contribute to the vision of Industry 4.0 by promoting the integration of automation and robotic cooperation in complex environments and present a foundational blueprint for further research. Challenges for future work such as scalability, communication efficiency, collision avoidance, and energy efficiency are also considered, underscoring the need for ongoing development of robust and scalable robotic systems to address modern transport challenges.

Exploring Trust, Privacy, and Security in Cloud Storage Adoption among Generation Z: An Extended TAM Approach

Rio Guntur Utomo, Rahmat Yasirandi — Fri, 01 Nov 2024 00:00:00 +0000

The incorporation of cloud storage technology holds the promise of significantly enhancing efficiency in various sectors, particularly from the perspective of Generation Z, a demographic known for its meticulous consideration of technology acceptance factors, especially security. This research thoroughly examines the level of acceptance of cloud storage technology among Generation Z. By augmenting the Technology Acceptance Model (TAM) with five core factors and introducing three novel factors—Perceived Security, Perceived Privacy, and Trust—this study not only adheres to traditional acceptance models but also ventures into uncharted territories, marking a significant contribution to understanding technology acceptance. This study meticulously collected data from 408 Generation Z respondents who actively use cloud storage technology, employing an innovative questionnaire disseminated via an online platform. Through sophisticated PLS-SEM data analysis, the study confirmed the positive and significant impact of all tested hypotheses, underscoring the importance of attitudes, perceived benefits, and usability in fostering the intention to use cloud storage. Notably, the added dimensions of privacy and security emerged as critical in enhancing users' trust in cloud storage solutions. Furthermore, this study paves the way for future explorations into technology acceptance across diverse populations and settings, underscoring the critical role of security and privacy in shaping technology adoption decisions among emerging generations.

The Application of PROMETHEE Method in Determining Scholarship Recipients at University

Wahyu Prima, Firmansyah Putra, Sopi Sapriadi, Rahmatul Hayati — Fri, 01 Nov 2024 00:00:00 +0000

This study aims to use PROMETHEE method as a decision support system in determining the recipients of the Academic Achievement Improvement Scholarship at Universitas Dharmas Indonesia (UNDHARI). The methodological steps include problem identification, analysis, goal setting, and the application of PROMETHEE method. In this study, the criteria and alternatives have been identified to evaluate the scholarship recipients. The criteria weights are set, and the criteria preference types are determined. After obtaining the baseline data from the questionnaire assessments, pairwise preference values and multicriteria preference index values are calculated. Then, the rankings are compiled using Leaving Flow, Entering Flow, and Net Flow methods, resulting in the priority order of the scholarship recipients. The ranking results show that alternative 3 (IS) has the highest Net Flow value (0.30), while alternative 2 (AV) has the lowest Net Flow value (-0.35). Thus, the priority order from highest to lowest is IS, AV, RD, YM, and AV. In the context of Net Flow scores, these results indicate that alternative 3 (IS) has the greatest chance of receiving the academic achievement improvement scholarship. This study provides important insights for UNDHARI in the scholarship recipient determination process using the PROMETHEE method as a decision-making tool.

Security Analysis of Web-based Academic Information System using OWASP Framework

Rusydi Umar, Imam Riadi, Muhammad Ihya Aulia Elfatiha — Fri, 01 Nov 2024 00:00:00 +0000

The Academic Information System plays a crucial role in efficiently managing student, faculty, and campus administration data. However, system security needs to be a primary concern as it is vulnerable to cyber attacks. This research aims to analyze the security of the Academic Information System at the Muhammadiyah Business Institute Bekasi. The research method used is a comprehensive security analysis based on the OWASP framework. The study includes identifying potential vulnerabilities, penetration testing, and system improvement recommendations. Testing is conducted through simulated attacks based on the OWASP-released security risk list (OWASP Top Ten Most Critical Web Application Security Risks). The analysis results indicate that the system is vulnerable to Broken Authentication due to weak passwords, Sensitive Data Exposure due to URLs pointing to direct directories, and Security Misconfiguration due to open protocols. Furthermore, in CVSS scoring, Broken Authentication scored 4.8 (Medium), Sensitive Data Exposure and Security Misconfiguration scored 5.3 (Medium), Cross-Site Scripting scored 2.0 (Low) and Using Component with Known Vulnerabilities scored 2.0 (Low), while SQL Injection, XXE, Broken Access Control, Insecure Deserialization, and Insufficient Logging and Monitoring scored 0.0 (No Vulnerability). Recommendations for future system improvements include regularly updating the system to prevent new security vulnerabilities, better server configurations, and routine system monitoring to promptly anticipate suspicious activities.

Optimizing Social Media Promotion Strategy to Increase Customer Retention Rate (CRR) with GKG Customer Engagement

Yahya Nur Ifriza, Kholiq Budiman, Adi Satrio Ardiansyah — Fri, 01 Nov 2024 00:00:00 +0000

In the digital age, businesses are increasingly relying on social media platforms to engage with their customers and foster brand loyalty. This paper presents a comprehensive study aimed at optimizing social media promotion strategies to enhance Customer Retention Rate (CRR) while utilizing the GKG (Get Keep Growth) Customer Engagement framework. By examining the interplay between social media promotion tactics and customer engagement metrics, we investigate how businesses can leverage data-driven insights to improve customer retention. Our research showcases the importance of tailoring social media campaigns to individual customer preferences and behavior, ultimately leading to increased customer satisfaction and loyalty. The results of the analysis of the development of the Customer Retention Rate graph were produced on FMIPA social media with an average CRR of 71% in the base case. Through a combination of data analysis and case studies, we provide actionable recommendations for businesses seeking to maximize the effectiveness of their social media promotion efforts and elevate their CRR with GKG customer engagement.

Layout Generation: Automated Components Placement for Advertising Poster using Transformer-based from Layout Graph

Fri, 01 Nov 2024 00:00:00 +0000

In the digital era, graphic design plays an important role in a company's marketing strategy, especially advertising posters that can convey messages to the audience. However, the process of creating attractive and informative posters takes a long time, especially the component placement on the layout. This research aims to develop a layout generator system that automatically places components on the layout using one of the transformer-based models. The transformer-based model used is a Graph Transformer with edge features called SGTransformer, which accepts input data as a graph. SGTransformer consists of several graph transformer layers that will calculate the attention of node and edge features on the input layout graph. A layout graph describes the spatial relationship between components in a layout. The SGTransformer model was trained by using advertising poster datasets collected from social media. The performance of the model were evaluated using the evaluation metrics commonly used in the layout generation domain such as Alignment, Overlap, Max IoU, and FID. The scores obtained from each evaluation metric are 0.025, 1.274, 0.325, and 8.575 respectively. The model evaluation results show that SGTransformer can produce structured and more diverse layouts although there are still challenges such as overlap between components. Code and other materials will be released at https://github.com/syahdeee/Layout-Generator.

Improving Software Defect Prediction Using a Combination of Ant Colony Optimization-based Feature Selection and Ensemble Technique

Windi Eka Yulia Retnani, Muhammad 'Ariful Furqon, Juni Setiawan — Fri, 01 Nov 2024 00:00:00 +0000

Software defect prediction plays a vital role in enhancing software quality and minimizing maintenance costs. This study aims to improve software defect prediction by employing a combination of Ant Colony Optimization (ACO) for feature selection and ensemble techniques, particularly Gradient Boosting. This research utilized three NASA MDP datasets: MC1, KC1, and PC2, to evaluate the performance of four machine learning algorithms: Random Forest, Support Vector Machine (SVM), Decision Tree, and Naïve Bayes. The data preprocessing comprised handling class imbalance using SMOTE and converting categorical data into numerical representations. The results indicate that the integration of ACO and Gradient Boosting significantly enhances the accuracy of all four algorithms. Notably, the Random Forest algorithm achieved the highest accuracy of 99% on the MC1 dataset. The findings suggest that combining ACO-based feature selection with ensemble techniques can effectively boost the performance of software defect prediction models, offering a robust approach for early detection of potential software defects and contributing to improved software reliability and efficiency.

Aspect-level Sentiment Analysis on GoPay App Reviews Using Multilayer Perceptron and Word Embeddings

Henzi Juandri, Hasmawati, Bunyamin — Fri, 01 Nov 2024 00:00:00 +0000

The increasing use of smartphone in Indonesia has encouraged the development of digital wallet applications, one of which is GoPay. Nowadays, GoPay has gained significant popularity among the public in Indonesia. Therefore, this research conducts aspect-level sentiment analysis to analyze user reviews of the GoPay application in more detail and depth. The sentiment analysis process in this study utilizes the Multilayer Perceptron (MLP) with fastText and word2vec as word embeddings. The dataset used is GoPay application reviews, which consist of 15,000 reviews collected from Google Play Store. The dataset is categorized into three main aspects: Feature and functionality, App Interface, and User Satisfaction. The stages of the research include data preparation, data preprocessing, word embeddings, model training, and model testing and evaluation. This research explores the effect of fastText and word2vec as word embeddings on model performance. Furthermore, this research examines the application of oversampling techniques, such as SMOTE and Random Oversampling. Based on the experiments conducted, utilizing fastText as word embeddings in MLP with a balanced dataset resulted the best model performance, with an F1-Score of 97%, Recall of 96%, and Precision of 95% for category classification. Then, for sentiment classification, using fastText on MLP with a balanced dataset resulted in a value of 98% for each of the F1-score, Recall, and Precision metrics. This research validates that MLP is effective for aspect-level sentiment analysis, delivering strong evaluation results.

Predicting the Sentiment of Review Aspects in the Peer Review Text using Machine Learning

Setio Basuki, Zamah Sari, Masatoshi Tsuchiya, Rizky Indrabayu — Fri, 01 Nov 2024 00:00:00 +0000

This paper develops a Machine Learning (ML) model to classify the sentiment of review aspects in the peer review text. Reviewers use the review aspect as paper quality indicators such as motivation, originality, clarity, soundness, substance, replicability, meaningful comparison, and summary during the review process. The proposed model addresses the critique of the existing peer review process, including a high volume of submitted papers, limited reviewers, and reviewer bias. This paper uses citation functions, representing the author's motivation to cite previous research, as the main predictor. Specifically, the predictor comprises citing sentence features representing the scheme of citation functions, regular sentence features representing the scheme of citation functions for non-citation sentences, and reference-based representing the source of citation. This paper utilizes the paper dataset from the International Conference on Learning Representations (ICLR) 2017-2020, which includes sentiment values (positive or negative) for all review aspects. Our experiment on combining XGBoost, oversampling, and hyper-parameter optimization revealed that not all review aspects can be effectively estimated by the ML model. The highest results were achieved when predicting Replicability sentiment with 97.74% accuracy. It also demonstrated accuracies of 94.03% for Motivation and 93.93% for Meaningful Comparison. However, the model exhibited lower effectiveness on Originality and Substance (85.21% and 79.94%) and performed less effectively on Clarity and Soundness with accuracies of 61.22% and 61.11%, respectively. The combination predictor was the best for the 5 review aspects, while the other 2 aspects were effectively estimated by regular sentence and reference-based predictors.