IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
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Maintaining Query Performance through Table Rebuilding & Archiving
Full text linkDespite the system previously utilizing optimal query configurations and database settings, the transaction table in the database, which is undergoing significant numerical increases and notable queries and updates on each line, has seen a drop in query speeds simultaneous with data growth. This situation arises due to an increase in disk space in the database tablespace, which results from block fragmentation. At times, database engines do not detect this problem, thereby overlooking it in the database recommendation engine. Lacking an understanding of the fundamental issue, database engineers need analysis and strategies to maintain the query speed of the transaction table in the relational databas
Strategy Selection to Enhance Customer Data Quality using AHP: Case Study of General Insurance Company (PT XYZ)
Full text linkThis study examines strategies to enhance customer data quality at PT XYZ, a general insurance company, which is crucial for strategic decision-making and revenue growth. The importance of this topic lies in its potential to significantly improve operational efficiency and customer satisfaction. The Analytical Hierarchy Process (AHP) is utilized to select the optimal strategy from available alternatives. The research begins by establishing criteria and identifying alternative strategies to improve customer data quality. Expert evaluations are conducted based on four criteria: cost, time, security, and availability. Four experts are involved, chosen for their expertise in technology and organizational impact. The experts include a Senior IT Business Intelligence, a Data Analyst, a Unit Head of IT Infrastructure & Security Department, and a Division Head of Digital. The findings highlight that the strategy of Strengthening Collaboration Across Departments, with a value of 0.455, is the most effective. This strategy emphasizes interdepartmental cooperation to enhance data quality. The results underscore the long-term benefits of improved operational efficiency and customer satisfaction despite initial investment challenges, demonstrating the practical application of AHP in organizational settings.
Ensemble Method for Anomaly Detection On the Internet of Things
Full text link The internet of things generates various types of data traffic with a very large amount of data traffic which has an impact on security issues, one of which is an attack on the Internet of Things network. In the IoT data traffic flow, which contains various data, it turns out that the portion of attack data traffic is usually smaller than normal traffic. Therefore, the attack detection method must be able to recognize the type of attack on a very large data traffic flow and unbalanced data. High data dimensions and unbalanced data are one of the challenges in detecting attacks. To overcome the large data dimensions, Chi-square was chosen as a feature selection technique. In this study, the ensemble method is proposed to improve the ability to detect anomalies in unbalanced data. To produce an ideal detection method, a combination of several classification algorithms such as Bayes Network, Naive Bayes, REPtree and J48 is used. The CICIDS-2017 dataset is used as experimental data because it has a high data dimension which contains unbalanced data. The test results show that the proposed Ensemble method can improve the performance of anomaly detection for high-dimensional data containing unbalanced dat
The Adoption of Blockchain Technology the Business Using Structural Equation Modelling
Full text linkThere are many aspects of readiness that must be considered when implementing technological breakthroughs, the business sector is still relatively slow in adopting blockchain technology. However, considering that blockchain technology is still in its early stages of development and has many potential applications, it is necessary to conduct empirical studies on the factors influencing its application in the industry. The problem of this study is to develop an appropriate framework based on how well its features match the needs of the business sector. This research method uses data collection using online questionnaires to obtain information from 86 respondents. The current study also utilizes the Smart PLS 4 model to produce a structural hypothetical model. The results of this study find a significant influence on Revolutionary Innovation by enriching the literature on the relationship between Blockchain, Big Data and the Business Sector, which is expanded by adding new variables. The novelty of this research identifies potential utilization, analyzes internal and external factors, and identifies how blockchain disrupts the business sector. The purpose of this study is to assess how blockchain technology is currently used in the business sector for data provision as a theoretical information technology innovatio
Optimizing Clustering Models Using Principle Component Analysis for Car Customers
Full text link In the competitive business world, companies strategically utilize customer data to achieve goals, requiring a comprehensive understanding of various customer traits, behaviors and needs. Customer segmentation, an important strategy, requires grouping individuals based on various characteristics. The K-Means algorithm is widely used for customer data grouping connectivity because of its ease of implementation in Machine Learning. However, challenges arise in high-dimensional data, prompting the need for dimensionality reduction. Principal Component Analysis (PCA) is emerging as an effective method for data communication while minimizing information loss. Previous research emphasizes the success of PCA in improving analysis and clustering efficiency. This research contributes by integrating PCA into K-Means clustering to analyze customer segments in a car company. This empowers companies to attract new customers, implement targeted marketing, understand customer-company relationships, and increase expected profitability. PCA, which preserves 75% of the variation with 3 principal components, precedes the implementation of K-Means after normalization. Evaluation using the Elbow and Silhouette Score Method identified eight optimal clusters. The post-PCA K-Means model with optimal cluster selection produces a Silhouette Score of 0.7789.
Chicken Weight Prediction in Close House Farm Using Fuzzy Method
Full text linkThis study aims to predict the weight of chicken on a close house farm using the Fuzzy Logic method by implementing the LUKASEWICZ method. The data used in this study are the factors that affect the weight of the chicken including the number of chickens entering, the initial weight of the chicken, the temperature of the cage, the humidity of the cage, the quantity of water, the quantity of feed, and air circulation (wind speed) in the cage. The results of the calculation of Fuzzy with the łukasiEwicz method of these factors can be used to predict the chicken boboy during the harvest period and according to the weight set during the harvest period. The accuracy of the prediction value with the Absolute Percentage Error (MAPE) mean test shows the value of 5,3981%. It was concluded that the calculation of fuzzy with the łukasiewicz method can be used to predict the weight of chicken during the harvest period
Hyperparameter Optimization Techniques for CNN-Based Cyber Security Attack Classification
Full text linkAbstract The proliferation of cyber security attacks necessitates advanced and efficient detection methods. This study explores the application of Convolutional Neural Networks (CNNs) for classifying cyber security attacks using a comprehensive dataset containing various attack types and network traffic features. Emphasizing the role of hyperparameter optimization (HPO) techniques, this research aims to enhance the CNN model's performance in accurately detecting and classifying cyber attacks. Traditional machine learning approaches often need to catch up in capturing the complex patterns in such data, whereas CNNs excel in automatically extracting hierarchical features. Using the provided dataset, which includes attributes such as packet length, source and destination ports, protocol, and traffic type, we implemented various (HPO) techniques, including Grid Search, Random Search, and Bayesian Optimization, to identify the optimal CNN configurations. Our optimized CNN model significantly improved classification result. to baseline models without hyperparameter tuning. The results underline the importance of HPO in developing robust CNN models for cybersecurity applications. This study provides a practical framework for leveraging deep learning and optimization techniques to enhance cyber defense mechanisms, paving the way for future advancements in the field
Forecasting Pertalite Stock Expenditures Using Exponential Smoothing and Linear Regression
Full text linkIn the current industrial and business era, effective inventory management is essential for maintaining operational sustainability, particularly in the fuel industry. Pertalite, a popular fuel in Indonesia, with an octane number of 90, offers cleanliness, efficiency, and affordability. However, challenges arise in stock expenditure management due to inaccurate forecasting methods. Data mining, utilizing statistical and machine learning techniques, can identify patterns and trends for better stock forecasting. Recent studies highlight the effectiveness of exponential smoothing and linear regression in fuel demand forecasting. Exponential smoothing, which gives more weight to recent data, improves prediction accuracy, while linear regression analyzes the relationship between fuel stock and various independent variables. This study examines Pertalite fuel sales data from May 2022 to April 2024 from a Pertamina gas station in North Lampung. Results show that linear regression can predict trends, while exponential smoothing, using alpha values between 0.1 and 0.9, captures trends and variations over time. Both methods provide stable forecasts for specific months, demonstrating their utility in understanding Pertalite fuel sales patterns. The study underscores the importance of accurate forecasting in inventory management to meet market demands and maintain operational efficiency
Integrating Learning Management System and Clasification Learning Media Based On Two Dimension Animation
Full text linkThis research aims to combine the advantages of LMS technology with the potential of two-dimensional animated learning media in an educational context. This research explores the basic concept of a Learning Management System (LMS) and its advantages in providing a structured platform for delivering learning material, student-teacher interaction, and evaluating learning progress. LMS offers broad accessibility, student progress tracking, and the ability to facilitate collaborative learning. The research focus shifted to the potential of two-dimensional animation-based classification learning media. Two-dimensional animation offers interesting and engaging visualizations and can help students understand complex concepts better. In this context, this research integrates the visual power of animation with the learning structure provided by the LMS. This integration is expected to increase student engagement, facilitate a deeper understanding of concepts, and increase information retention. In addition, this combination is also expected to improve the overall quality of teaching by enabling the use of more innovative and interactive teaching methods. This research aims to contribute to our understanding of how learning technology can be integrated effectively to improve student's learning experiences. Thus, it is hoped that the results of this research can provide valuable insight into the development of better learning practices
Transformation of State Civil Apparatus Learning Task Administration Services through the J-SiLAKON Application
Full text link The emergence of the Covid-19 pandemic and the progress of industry 4.0. The developments encourage the government to digitize public administration services. The Jember Regency Personnel and Human Resources Development Agency (BKSDM) therefore launched a web-based application named J-SiLAKON (Jember Online Personnel Services System) for study assignments to increase effectiveness and efficiency in providing services, and to encourage good governance. This research examines the effectiveness of the transformation of ASN learning task administration services through J-SiLAKON at the Jember Regency Education Office. By Using a qualitative case study approach, data were collected through semi-structured interviews, passive observation, and primary documentation involving J-SiLAKON operators and staff from the Teachers and Education Personnel Division. The analysis of data involved condensation, presentation, and conclusion drawing, with triangulation to ensure data validity. Results showed that J-SiLAKON is easy to learn, control, understand, and use, thus improving users' proficiency in performing their duties. It provided significant benefits, including faster work, improved employee performance, increased productivity, and more effective administrative services. Overall, the J-SiLAKON application improves the efficiency, productivity and transparency of ASN study assignment administration services at the Education Office, meeting user expectations and improving the performance of the Education office