International Journal Software Engineering and Computer Science (IJSECS)

E-Archive Academic Data at SMK Negeri 2 Sekayu

Fitri Kurnia Handayani — 2024-04-01

Effective management of archival documents is vital in the educational sector as it plays a crucial role in decision-making and preserving historical records. Archives are a valuable resource that contains a wealth of information for any organization. However, the traditional method of physical document management at Sekayu 2 Negeri Vocational School has led to a significant accumulation of documents, making the process both ineffective and inefficient. This situation has created an urgent need for faster access to information. To address this issue, the school has developed an Electronic Archive for Academic Data that aims to streamline the archiving process, making it more efficient and effective. The primary objectives of this initiative are to facilitate faster retrieval of information and to reduce the physical storage space required for documents. The E-Archive for Academic Data at Sekayu 2 Negeri Vocational School was developed using the PHP programming language and the MySQL database. The introduction of this digital filing system is expected to significantly reduce the time needed to access archives and decrease the physical storage space required, thereby enhancing efficiency and effectiveness at SMK Negeri 2 Sekayu.

Classification of Potential Tsunami Disaster Due to Earthquakes in Indonesia Based on Machine Learning

Eri Mardiani — 2024-04-01

Earthquakes and tsunamis pose significant threats to Indonesia due to its unique geological positioning at the convergence of four tectonic plates. This study focuses on classifying the potential occurrence of tsunami disasters following earthquakes using various data mining methods, including k-Nearest Neighbor (kNN), Naïve Bayes, Decision Tree and Ensemble Method, and Linear Regression. The research employs a qualitative approach to systematically understand and describe the context of natural disasters, utilizing both primary and secondary data collection techniques. Performance evaluation metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, and Recall are utilized to assess the effectiveness of each method in predicting potential tsunami events. The findings reveal that the kNN method exhibits the highest performance, with an AUC of 94.4% and a precision of 82.8%, indicating robust predictive capabilities. However, misclassifications were observed, emphasizing the need for further refinement. Naïve Bayes also shows promising results with an AUC of 84.5% and precision of 78.6%. Decision Tree and Ensemble Method models, such as Random Forest and AdaBoost, demonstrate reasonable performance, with Random Forest achieving the highest AUC of 71.9%. Linear Regression is employed to explore the correlation between earthquake attributes and tsunami occurrence, revealing a weak relationship. Further research integrating advanced modeling approaches and additional earthquake attributes is recommended to enhance the predictive capabilities of tsunami risk assessment models. The study underscores the importance of employing diverse machine learning techniques and evaluating their performance metrics to refine the accuracy of tsunami prediction models, ultimately contributing to practical disaster preparedness and mitigation strategies.

Application of The SD-WAN Load Balancing Method in Managing Internet Bandwidth at IDN Bogor Vocational School

Dadang Iskandar — 2024-04-01

The rapid growth of SMK IDN since its establishment in 2016 has necessitated the addition of several branches. With the increasing number of branches, SMK IDN's administrative activities need to be centralized at SMK IDN Jonggol (Central), where the school leases and utilizes MPLS services to support interconnections between branches and the central office. However, there are considerations regarding using MPLS services as the number of SMK IDN branches continues to grow. Concerns from the SMK IDN IT team include issues related to the inflexibility of MPLS links, which can only be specifically used for interconnecting branches or private links that cannot simultaneously accommodate the internet needs of branches. In this situation, the SMK IDN IT team seeks an efficient solution that provides flexibility in interconnections from branches to the central office, with capabilities and stability equal to or better than before. In response to the challenges faced by SMK IDN, we propose the Load Balancing SDWAN solution, where SMK IDN only needs to use internet services available at the central office and branches. The Load Balancing SDWAN solution offers flexibility in terms of cost efficiency and link usage because links can simultaneously fulfill internet and interconnection needs for branches. In its implementation, the Load-balancing SDWAN method effectively addresses the challenges encountered by SMK IDN. Test results show that after implementing the Load Balancing SDWAN method, there is a significant improvement in internet access stability for users, with the ability to failover if there are issues with one internet link, as well as maintaining secure and private connections between the central office and branches. Another benefit of implementing the Load Balancing SDWAN method is budget efficiency, as SMK IDN can provide dedicated internet services per month at a 63% lower cost compared to renting MPLS links previously used

Classification of Hoax News Using the Naïve Bayes Method

Rama Qubra — 2024-04-01

The rampant dissemination of false and unsourced information, commonly known as hoaxes, has become a pervasive issue in the era of internet media. In the digital age, the widespread dissemination of false and unverified information has emerged as a critical concern within the realm of internet media. Hoax news can be used to influence elections, sway public opinion, and create political instability. The rapid evolution of information technology has contributed to the uncontrollable proliferation of hoax content, necessitating the development of intelligent systems for effective classification. This research focuses on implementing a robust classification system for identifying hoax news circulating through internet media. The method used in this program is the Naive Bayes method, specifically Naive Bayes Multinomial, which works with the assumption that each feature (word) is considered independent from the others. Text vectorization using CountVectorizer converts text into a numeric vector, which can be used by classification algorithms. This program uses a trained model to make predictions on testing data and calculate evaluation metrics such as accuracy, confusion matrix, and classification reports. By leveraging these methodologies, the study aims to enhance the accuracy and efficiency of distinguishing genuine news from deceptive hoaxes. The highest accuracy value obtained in this research was 94.73% with a division of 20% test data and 80% training data. True Negative (TN): 4555, False Positive (FP): 178 and False Negative (FN): 295, True Positive (TP): 3952

Implementation of Web Engineering in the Design and Development of the Website Portal for SMA Negeri 1 Martapura

Dewa Nihan Bhagaskara — 2024-04-01

This research aims to improve the quality of educational services at SMA Negeri 1 Martapura by developing a website portal that is more attractive and user-friendly. Even though SMA Negeri 1 Martapura has an accessible website, there are shortcomings in the interface and menu structure that are less attractive to users. To overcome these challenges, this research focuses on developing a new website portal using the PHP programming language and MySQL as a database. The resulting website portal is designed to improve user interaction with a more intuitive interface and more comprehensive features. By involving 53 permanent teachers (PNS/P3K), ten non-permanent teachers, and 953 active students, the development of the SMA Negeri 1 Martapura website portal aims to provide better services to the entire school community. With an attractive and easy-to-use website portal, it is hoped that it will improve the user experience in accessing information about schedules, activities, and various other educational services offered by SMA Negeri 1 Martapura. Overall, this research aims to positively contribute to increasing the effectiveness and efficiency of academic services at SMA Negeri 1 Martapura through sophisticated and user-friendly information technology.

The Impact of Technological Advancements on Higher Education: A Study of Generation Alpha’s Educational Prospects

Dan Coolsaet — 2024-04-01

The present-day environment is experiencing a rapid technological evolution that fundamentally shifts our understanding of knowledge into a more accessible and open entity. This transformative progression is redefining the practical application of competencies, concepts, and insights and exerting a profound impact on various facets of education. This paradigmatic transition, catalyzed by the pervasive influence of technology, is particularly pertinent in education, where its metamorphic contributions are conspicuously manifest. As the educational milieu continues to undergo metamorphosis, the future pedagogical and didactic methodologies will inevitably bear the indelible imprint of technological advancements. Concurrently, educators are confronted with the distinctive imperative of effectively engaging the emergent cohort of learners, commonly referred to as Generation Alpha, within the context of higher education. A pronounced entrepreneurial disposition characterizes Generation Alpha and is notably predisposed to embracing innovation and advancement, with a significant proportion of its members harboring aspirations of pursuing tertiary education. The present study undertakes a proactive stance in envisioning the educational dynamics and prospects that will define the forthcoming landscape of higher education, with a focal lens on the distinctive attributes of Generation Alpha. This entails a comprehensive inquiry into their favored pedagogical modalities, cognitive perspectives, and educational anticipations. The study embraces a robust theoretical framework anchored in the distinctive attributes of Generation Alpha, attributes invariably molded by the inexorable march of technological progress. In a complementary manner, the study derives insights from a triad of discrete empirical investigations conducted across diverse locales, including New Zealand, Iran, Iraq, and Jordan.

Implications of Deep Learning for Stock Market Forecasting

Supendi — 2024-04-01

This research explores the effectiveness of using deep learning in predicting stock market movements. This research uses rigorous methods to bring out the performance of deep learning models, compare them with traditional methods, and identify critical factors that influence stock market predictions. The research results show that deep learning models, especially LSTM and CNN-LSTM architectures, can achieve satisfactory levels of accuracy and outperform traditional methods by capturing patterns in complex stock market data. In addition, this research identifies external and internal factors that influence predictions of stock market movements. This research's practical and theoretical implications highlight the potential of deep learning in improving investment decision-making and understanding financial market dynamics. Recommendations for future research include exploration of advanced deep learning techniques, integration with traditional methods, emphasis on risk management strategies, continuous evaluation of model performance, and provision of training and education to encourage analysts and investors to adopt this technology. By implementing these recommendations, the potential of deep learning models in financial analysis can be optimized, ultimately improving market efficiency and investment returns.

Implementation of Augmented Reality to Enhance Alphabet Letter Understanding at Paud Bintang Jonggol

Sri Lestari — 2024-04-01

Implementing Augmented Reality (AR) in Early Childhood Education (PAUD) promises children an interactive and immersive learning experience. In this study, we evaluate the potential of AR in improving the quality of early childhood education. AR can help PAUD educators create a more engaging learning environment where children can learn through games that support the development of motor skills and conceptual understanding. However, challenges such as access to technology, adequate teacher training, and appropriate curriculum planning need to be addressed. Policy support, comprehensive teacher training, and selecting the proper AR application are critical factors in implementing AR in PAUD. This technology has great potential to change how children learn and better prepare them for the future.

Optimizing the 2024 Governor Election Quick Count with Extreme Gradient Boosting (XGBoost) to Increase Voting Prediction Accuracy

I Wayan Gede Suacana — 2024-04-01

This research aims to increase the accuracy of vote predictions in the Quick Count process in the 2024 Governor Election using the XGBoost algorithm. Quick Count is a fast method for obtaining estimates of election results based on some of the data that has been calculated. The XGBoost algorithm was chosen because it has proven effective in various applications, including predictive modeling. This research analyzes the implementation of the XGBoost algorithm in modeling vote predictions for Quick Count, especially in the context of the 2024 gubernatorial election. By using various evaluation metrics such as accuracy, precision, recall, and F1-score, this research provides a comprehensive understanding of the performance of the XGBoost model. The research results show that the XGBoost algorithm achieves high accuracy, precision, recall, and F1 score, demonstrating its ability to classify sounds accurately. The practical implications of this research are significant in improving the integrity of the democratic process by providing more reliable and transparent election results. Additionally, this research paves the way for developing more sophisticated Quick Count methods by leveraging insights from previous research on machine learning techniques and data security.

Optimizing E-commerce Inventory to prevent Stock Outs using the Random Forest Algorithm Approach

Achmad Ridwan — 2024-04-01

This research investigates the effectiveness of the Random Forest algorithm in optimizing e-commerce inventory management. In a digital business that continues to grow, inventory management is crucial for smooth operations and customer satisfaction. The Random Forest algorithm, a development of the CART method by applying bagging techniques and random feature selection, was tested to predict inventory. An experimental design is used to test the algorithm's performance algorithms performance, using data relevant to the observed inventory variables. The analysis involves evaluating the performance of algorithms in predicting and preventing stockouts. The results show that the Random Forest algorithm provides more accurate inventory predictions than traditional methods. Comparison with linear and rule-based regression reveals higher accuracy, making this algorithm a promising choice for e-commerce inventory management. These findings imply that the Random Forest Algorithm can be an effective solution in overcoming the complexity and fluctuations of digital markets. Practical recommendations include a deep understanding of the data, engagement of trained human resources, and training strategies for optimal use of these algorithms. This research also contributes to the literature by expanding understanding of the application of the Random Forest algorithm in various contexts, including forest basal area prediction, supply chain management, and backorder prediction. In conclusion, the Random Forest algorithm has great potential to improve e-commerce inventory management, opening up opportunities for broader application in the digital business world. Proactive adoption of these algorithms can have a positive impact on operational efficiency, customer satisfaction, and a company's competitiveness in an ever-evolving market.

Implementation of Academic Information System at Al-Inayah Pamulang Quran House, South Tangerang

Dadang Iskandar Mulyana — 2024-04-10

In today's digital era, technology profoundly influences various societal domains, notably education. Rumah Quran Al-Inayah Pamulang, a non-formal educational center in Tangerang Selatan, fosters Quran memorization and practice among Muslim children. However, its manual system requires enhancement for better accuracy and timeliness in managing information. A web-based academic information system is proposed, utilizing the waterfall model methodology to address this. This system aims to streamline data collection, monitor memorization progress efficiently, and ensure organized data storage. The Academic Information System of Rumah Quran Al-Inayah Pamulang is a digital platform facilitating student information management, activity scheduling, academic progress tracking, and parent communication.

Optimization of Product Placement on E-commerce Platforms with K-Means Clustering to Improve User Experience

Achmad Ridwan — 2024-04-10

This study delves into product placement strategies on E-commerce platforms using K-Means Clustering analysis. Employing an experimental methodology, data about products and user preferences were gathered to delineate product and user clusters. The K-Means Clustering analysis yielded three primary product clusters and four user preference clusters. These findings hold significant practical implications, empowering E-commerce platforms to refine user experience personalization, streamline sales efficiency, and bolster overall business performance. Platforms can positively influence sales conversion rates and user satisfaction by implementing targeted and adaptable product placement strategies. This research contributes not only to the theoretical comprehension of product placement in E-commerce but also furnishes actionable insights for stakeholders to optimize platform operations and deliver an enriched online shopping experience.

Purchasing Patterns Analysis in E-commerce: A Big Data-driven Approach and Methodological

Andik Adi Putra Riwayat — 2024-04-10

This research aims to analyze purchasing patterns in e-commerce using a Big Data-based approach and data analysis methods. Leveraging advanced technology and analytical methodology, this research explores consumer behavior, market trends, and factors influencing purchasing decisions in e-commerce. Through collecting transaction data from leading e-commerce platforms and applying rigorous data analysis techniques, this research identifies significant patterns and provides valuable insights for e-commerce companies. This research shows that big data has great potential in understanding consumer behavior and market trends in e-commerce. In contrast, sophisticated data analysis methods are essential in interpreting the large and complex data generated by e-commerce. The findings of this research significantly contribute to the development of the e-commerce industry by helping companies improve their marketing strategies and business decision-making. However, this research also needs help, as it requires data analysis skills and privacy issues. To overcome these challenges, collaboration between researchers, e-commerce companies, and governments is needed to develop the necessary data analysis expertise and ensure that consumer data is managed securely and ethically. Thus, this research provides a more holistic view of consumer behavior and market dynamics in e-commerce, assisting companies and policymakers in addressing challenges and opportunities in an ever-evolving landscape.