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Data Security Analysis with Advanced Firewall Filtering at PT PUSRI
No full textData and network security analysis are essential for ensuring the integrity, confidentiality, and
availability of organizational data. Among the various threats, sniffing attacks—where malicious
actors intercept and monitor data transmitted over a network—pose a significant risk to data
security. This study analyzes the network security performance of the IT Service Department of
PT—Palembang, focusing on the impact of sniffing attacks and the effectiveness of
countermeasures. The research involves a comprehensive evaluation of the existing security
infrastructure, testing for vulnerabilities to sniffing attacks, and implementing advanced security
mechanisms. These mechanisms include encryption protocols, network segmentation, and
intrusion detection systems. The analysis assesses the performance of these countermeasures in
mitigating risks and enhancing overall network security. Findings from this study reveal that the
proper implementation of security mechanisms significantly reduces the risk of sniffing attacks.
Encryption ensures the confidentiality of transmitted data, network segmentation limits
unauthorized access, and intrusion detection systems provide real-time threat identification.
Additionally, the research highlights the importance of proactive measures, such as training IT
staff on security best practices and implementing enhanced real-time monitoring systems. This
study not only evaluates the technical aspects of network security but also provides actionable
recommendations for sustainable improvements. By addressing both current vulnerabilities and
future preparedness, the analysis underscores the critical role of a multi-layered security approach
in safeguarding organizational dat
Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction
No full textIntegrating Big Data and Internet of Things (IoT) platforms is the focus of this research, which
aims to improve energy management. The problem statement is centered on the potential for
development through advanced technologies and the inefficiencies in traditional energy
management methods. The objectives are to analyze energy consumption patterns, develop an
innovative Home Energy Management System (HEMS) architecture, and offer energy-saving
solutions. Synthetic energy consumption data is generated, normalized, and divided into training
and testing sets from a methodological perspective. K-nearest neighbors, Decision Trees, Support
Vector Regression, and Random Forest are the machine learning models trained and evaluated.
The Random Forest model outperforms other models in terms of the accuracy of its predictions of
energy consumption. The integration of renewable energy sources with cutting-edge technology
to revolutionize energy management practices is the essence of novelty. In conclusion, this
investigation underscores the importance of utilizing advanced technologies to promote
sustainable energy management, providing practitioners and policymakers with practical insights
Coastline Change along Pebuahan Beach in Jembrana Bali
No full textMonitoring changes in coastlines over large areas will be more efficient using satellite imagery
rather than conducting field surveys. This research aims to measure the coastline transformation
that occurred over a period of time at Pebuahan Beach in Jembrana Regency. This research uses
the Digital Shoreline Analysis System (DSAS) program. Ex situ data for 2004 and 2019 obtained
from Landsat satellite imagery was used in this research. When compared coastline between 2004
and 2019, there was more extensive abrasion, reaching 300,577.17 m2, while the accretion that
occurred only reached 159,697.67 m2. The cause of abrasion at Pebuahan Beach is sediment
transport which tends to leave the beach to the west and sediment supply is trapped due to
obstruction at the Pengambengan Port
Design of Automatic Parking System of Lhokseumawe State Polytechnic Using RFID
No full textThe design of the barrier or portal system now in use predominantly relies on a manual mechanism;
however, the automatic barrier door of the Lhokseumawe State Polytechnic aims to create a device
that facilitates the opening and closing of the parking portal for users. This Portal system employs
RFID (Radio Frequency identifying) cards as identifying tags for vehicles entering and exiting.
This autonomous portal comprises several components, specifically an RFID (Radio Frequency
Identification) Tag, an RFID Reader, a Servo Motor, an Arduino Uno, and a Buzzer. The servo
motor is designed to elevate and lower the barrier door, while the buzzer is intended to detect
vehicles entering and exiting via unregistered RFID (Radio Frequency Identification) tags
Analysis of Traffic Accident Patterns Using Association Rule Mining
No full textThis study analyzed the levels of minor, moderate, and severe traffic accidents in the Palembang
Police area from 2015 to 2020 using association rule mining and the apriori algorithm. The study
established valuable insights into accident trends and contributing factors by leveraging traffic
accident data and determining variable relationships. With a minimum support threshold of 0.05
and a confidence value of 0.5, the processed data revealed 349 total incidents, categorized as
follows: 58 minor accidents (16.62%), 168 moderate accidents (48.14%), and 123 severe accidents
(35.24%). The findings highlight that moderate-level accidents form the majority, underlining the
need for targeted interventions in this category. The application of the apriori algorithm facilitated
the identification of frequent itemsets and rules that reveal patterns across accident variables, such
as road conditions, road functions, accident types, weather conditions, and victim statuses. This
study also demonstrated the practicality of the apriori algorithm in analyzing extensive datasets to
extract actionable insights. The processed rules can be a foundation for developing predictive
models or decision-making tools to mitigate accident risks. For example, analyzing variables at
different accident levels allows policymakers to identify critical factors contributing to accidents,
implement tailored safety measures, and prioritize infrastructure improvements. Furthermore, the
study emphasizes the potential of data-driven traffic management and accident prevention
approaches. By incorporating modern data mining techniques, stakeholders can transition from
traditional data recapitulation to predictive analytics, enabling proactive measures for public
safety. Future research can build upon this work by integrating real-time data sources, such as IoTbased
traffic monitoring systems, to enhance the prediction accuracy and scope of analysis. Further
exploration of mid- and low-confidence rules may provide insights into rare but critical patterns,
offering a more comprehensive understanding of accident dynamics. Overall, this research is
crucial to leveraging advanced computational methods for public safety and traffic accident
reduction, aligning with global efforts to improve road safety and minimize fatalities
Data-Driven Augmented Reality for Scout Password Recognition in Interactive Learning Environment
No full textScout password is an essential component of scout science, used for communication through secret
codes. Learning scout passwords has traditionally relied on conventional methods, where coaches
provide material and exercises during weekly meetings. Due to infrequent practice opportunities,
this approach often limits students' ability to engage deeply with the material. To address these
challenges, this research introduces a data-driven augmented reality (AR) application designed to
enhance the learning experience by making scout password recognition more interactive and
accessible. The proposed tool leverages AR technology to provide real-time visualization of
movements and auditory cues, creating an immersive and engaging learning environment. By
integrating data-driven insights into the AR application, the learning process is tailored to
individual student needs, ensuring effective understanding and retention of scout codes. The
development process follows the Multimedia Life Cycle methodology, providing a structured and
iterative approach to creating a user-friendly and impactful application. This innovative AR-based
tool aims to transform traditional scout learning methods, offering students a more dynamic and
interactive way to master scout password communication while addressing the limitations of
conventional teaching practices
Smart Home Security Using Facial Authentication
No full textSmart home security enhances security by allowing only authenticated individuals to enter the
home. The technology uses advanced algorithms to recognize faces, making them safer and
easier to access. The main objective is to improve privacy and security by using facial
recognition based on the LBPH technique. The proposed application allows users to keep track
of the happenings at their homes using mobile phones, Tablets, or PCs. The system records a
flow of people at the door and identifies their faces against the database of the allowed people.
If the face is recognized it allows the homeowner access but if the face is not recognized it will
sound a warning that there is intrusion. The new face is captured and compared to the existing
one and the homeowner then decides where to add the new face for storage
Exploring the Integration of STEAM Education in Senior High School Chinese Language Teaching-taking “Yiwu Mechanical and Electrical Technician High School” as a Case Study
No full textThis paper explores the integration of STEAM (Science, Technology, Engineering, Arts, and
Mathematics) education into senior high school Chinese language teaching, using Yiwu
Mechanical and Electrical Technician High School as a case study. The study investigates how
the incorporation of STEAM elements enhances students’ engagement, critical thinking, and
language skills by embedding creative and interdisciplinary approaches into traditional Chinese
language curricula. By adopting project-based learning, digital storytelling, and interactive
activities, the program seeks to foster a deeper appreciation for Chinese language and literature
while promoting skills relevant to the 21st century, such as problem-solving, collaboration, and
innovation. Qualitative data, including classroom observations, teacher interviews, and student
feedback, reveal that integrating STEAM education in language arts encourages students to
view language as a dynamic tool for exploration rather than a static subject. The study showed
that many teachers were willing to participate in relevant training even though they were
unfamiliar with STEAM education concepts. Chinese language teaching in senior high schools
based on the STEAM concept significantly enhances students' interest and learning outcomes,
and promotes the cultivation of interdisciplinary thinking and innovative abilities. Therefore, it
is important to continue to improve this teaching model to promote the reform of Chinese
language teaching
The Effects of Actor Popularity on Audience’s Desire to Watch Korean Dramas
No full textThe globalization of Korean culture, introduced to Asia from the mid-1990s to the mid-2000s, has
been prominently reflected in Korean drama series and pop music, which have become favored
media genres, particularly among Malaysian audiences. This study examines the factors
influencing Malaysian audiences’ motivation to watch Korean dramas, focusing on the impact of
actors’ popularity and other dramatic elements. Data collected from 189 participants were analyzed
using SPSS version 27 and the Sobel Test, employing regression and descriptive analyses. The
findings reveal a positive correlation between actor influence and audience viewing desire, with
good-looking actors significantly enhancing the appeal of Korean dramas. However, Malaysian
audiences prioritize compelling storytelling and plot over physical appearances. By analyzing
factors such as actor appeal and narrative quality, this research highlights strategies to promote
responsible media consumption, aligning with Sustainable Development Goal 12 (SDG 12).
Through its insights, the study encourages audience awareness of sustainable media consumption
and advocates for the entertainment industry to adopt practices that balance cultural influence with
sustainable production. These efforts aim to foster a more conscious and equitable media
ecosystem, contributing to long-term sustainability in global entertainment
Multidimensional Analysis of Booking Data in Hospitality Industry Using Data Warehousing Techniques
No full textUnderstanding customer behaviors is essential for optimizing pricing strategies, enhancing guest
experiences, and effectively meeting demand in the hospitality industry. This study presents the
development of a data warehouse system designed to analyze hotel booking behaviors. Using ETL
processes, reservation data from diverse sources is consolidated and standardized to enable
comprehensive analysis. Then, multidimensional analyses of booking frequency and transaction
value reveal key customer preferences and behavioral patterns. A real-world dataset comprising
119,390 records spanning from July 1, 2015, to August 31, 2017, was utilized to validate the
system. Multidimensional analyses revealed that 70% of bookings occurred during peak seasons,
with transaction values averaging 25% higher compared to off-peak periods. Additionally,
customers who booked via direct channels displayed a 20% higher retention rate. The results
validate the proposed system's capability to provide actionable intelligence, driving effective
business strategies and supporting predictive modeling in the hospitality industry