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Water Level Prediction of Riam Kanan Dam Using ConvLSTM, BPNN, Gradient Boosting, and XGBoosting Stacking Framework (CLBGXGBoostS)
No full textResearch focuses on developing a water level prediction framework for the Riam Kanan
Dam using an innovative stacking approach called ConvLSTM-BPNN-Gradient Boosting and
Stacking XGBoost (CLBGXGBoostS), which combines the strengths of Convolutional Long
Short-Term Memory (ConvLSTM), Backpropagation Neural Network (BPNN), and Gradient
Boosting. The study aims to evaluate the performance of the CLBGXGBoostS stacking
framework in predicting the water level of the Riam Kanan Dam using 5 years of historical
data. The results demonstrate that the CLBGXGBoostS framework provides more accurate
predictions compared to single models, as evidenced by the Root Mean Squared Error (RMSE)
values. CLBGXGBoostS achieves an RMSE of 0.0071, significantly lower than the RMSE of
the individual models ConvLSTM (0.1006), BPNN (0.2618), and Gradient Boosting (0.6905).
This research contributes to the development of a better water level prediction framework for
the Riam Kanan Dam, supporting more effective water resource management and serving as a
reference for future research in this field
Evaluating Technology (Social Media and Apps) And Blockchain for Cost- Savings and Efficiencies in Event Management
No full textThis literature review investigates the transformative impact of technology, particularly social
media, event-mobile apps, and blockchain on the event management industry. This exploration of
technology's influence on the industry reveals a profound shift in how events are planned,
marketed, and executed. It addresses two key research questions: the influence of apps and social
media on cost savings in event marketing and how blockchain technology enhances efficiencies,
specifically in ticketing and data security. Social media is shown to be a cost-effective marketing
tool that reduces expenses, though it comes with its own set of challenges, such as data
management and maintaining visibility in a crowded digital landscape. Event-mobile apps are
highlighted for their ability to reduce costs, streamline logistics, and improve attendee experiences,
but they face obstacles related to user adoption and potential obsolescence. This review also delves
into how blockchain technology revolutionizes the industry by addressing issues like ticket fraud
and manipulation through transparent and immutable transaction records. However, it introduces
concerns about data privacy and visibility. In summary, technology is reshaping the event
management landscape, offering cost reduction and operational efficiency, while simultaneously
presenting ongoing challenges and opportunities for innovation
Influencing Factor of Waste Generation towards Environmental Sustainability and Economic Viability
No full textIn today's rapidly evolving industrial landscape, the efficient management of industrial waste
has become an imperative for both environmental sustainability and economic viability. This
report explores the key factors that influence industrial waste generation and how the
application of information technology and MIS is important in the minimizing waste
management challenges. Industrial wates refer to those material which discarded or couldn’t
be used in manufacturing like excess raw material, broken or defective product. The
manufacturing process could generate a huge amount of waste materials, ranging from
dangerous chemicals to non-recyclable by products. This research utilizing both quantitative
approaches, collect primary data through questionnaire and data collection instruments will be
using Google form and qualitative approach, using secondary data from existing sources as
supporting resources. This report has examined the key factors influencing industrial waste
generation and emphasized the vital role of information technology and information systems
in addressing waste management challenges. In addition, the foundation of equipment
efficiency lies in regular maintenance protocols. Hence, implementing rigorous and proactive
maintenance practices is essential to ensure that equipment remains in top condition
A Comparative Study of Z-Score and Min-Max Normalization for Rainfall Classification in Pekanbaru
No full textData preprocessing plays a crucial role in enhancing the performance of machine learning
algorithms for classification tasks. Among the essential preprocessing stages is data normalization,
which aims to standardize data into a comparable range of values. This study focuses on
normalizing rainfall data in Pekanbaru from 2019 to 2023. The objective is to compare various
data normalization techniques, including Min-Max Normalization and Z-Score Normalization.
The comparison of these particular strategies is justified because they are widely applied and have
different approaches. Min-max normalization is an easy-to-implement technique that makes the
data sensitive to outliers by scaling it to a specific range, often from 0 to 1. However, Z-Score
Normalization, sometimes referred to as Standardization, standardizes the data by dividing by the
standard deviation and subtracting the mean, maintaining the shape of the distribution and making
it resistant to outliers. The findings demonstrate that applying normalization techniques effectively
enhances classification performance compared to using unnormalized data. Specifically, the
optimal classification performance is achieved through Z-Score Normalization, yielding accuracy,
sensitivity, and specificity rates of 74.59%, 82.48%, and 63.92%, respectively
Analyzing Factors That Influence the Indonesia’s Gen Z in Reducing Food Waste
No full textZero hunger is one of the goals that is still being realized in the Sustainable Development Goals (SDGs). With conditions in Indonesia, which currently occupies fourth place in the amount of food waste worldwide, with a weight reaching 20.93 tons per year. This is certainly a serious enough problem to realize sustainable development. Indonesia, which is currently dominated by Gen Z, certainly needs to pay more attention to this food waste so that it doesn't continue in the future. This problem makes it important to analyze the factors that influence Gen Z Indonesia in reducing food waste. This research aims to form a structural model that explains the factors that influence Gen Z in reducing food waste. The variables used in this research are the influence of social media content, millennial eating manners, food consumption efficiency, the role of social demographics, and commitment to reducing food waste. which was analyzed using the Structural Equation Modeling Partial Least Square (SEM-PLS) method. The research results show that the factors that influence Gen Z are the influence of social media content and the role of social demographics. Through this research, recommendations for activities related to efforts to reduce food waste based on SEM-PLS can be formulated to realize one of the goals of sustainable development
Deep Learning Techniques for Wind Speed Forecasting at Palembang Airport
No full textThe Sultan Mahmud Badaruddin (SMB) II Palembang Meteorological Station is a technical implementation unit (UPT) of the Meteorology, Climatology, and Geophysics Agency (BMKG) that plays a role in disseminating actual weather information, particularly at SMBII Palembang Airport. Various weather parameters are observed, one of which is wind speed. During the take-off and landing processes, wind speed is a crucial parameter used by airport personnel, including pilots and air traffic controllers (ATC). This study focuses on analyzingand evaluating three deep learning methods using the architectures of LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), and BiLSTM (Bidirectional Long Short Term Memory). Time series data such as air pressure, rainfall, humidity, and temperature are used as predictors. The data is sourced from the AWOS (Automatic Weather Observation System) device. After processing the data using deep learning methods with the architectures above, an analysis will be conducted to determine which architecture model is the most accurate based on the lowest loss error rate in forecasting wind speed at SMB II Palembang Airport. The results show that the GRU deep learning architecture has the lowest loss value compared to the LSTM and BiLSTM architectures so that it can produce better wind speed forecasts in the next 12 hours and 24 hours, with RMSE of 1.62 and 1.77, respectively
Literature Review on Acupuncture Treatment for Parkinson’s Disease
No full textParkinson’s Disease is a degradation of brain functions leads to deterioration in movement, sleep,
mental health, pain, memory, self-care, daily activity and other health issues. In modern medicine,
treatment of Parkinson’s Disease provided mostly involves symptom control which can lead to
side effects. Acupuncture treatment in Traditional Chinese Medicine has been researched to be
able to relieve the symptoms, reduce the side-effects caused by medication as well as slowing
down the progression of the disease. The objective of this thesis is to review the frequency of
acupoint selections as well as meridian used to treat Parkinson’s Disease. In this thesis, all the data
are collected from online databases based on the inclusion and exclusion criteria. The data
collected will be tabulated and calculated to conclude the result. There were total 46 journals
selected and the most frequently appeared acupoints are Bai Hui appeared 27 times, Tai Chong
appeared 25 times, He Gu appeared 24 times, San Yin Jiao appeared 21 times, Zu San Li appeared
20 times and Si Shen Cong appeared 20 times which are the top 6 acupoints. Meanwhile Tai Yang
Bladder of the Foot (BL) appeared 24 times and Governor Vessel (GV) appeared 20 times were
the top 2 meridians that most frequently used. The World Health Organisation (WHO) defines a
Sustainable Healthcare System as a system that improves, maintains or restores health, while
minimizing negative impacts on the environment and leveraging opportunities to benefit of the
health and well-being of current and future generations. In fact, acupuncture treatment can improve
the health and well-being of the Parkinson’s disease patient with long term cost savings as a
sustainable health care
Early Detection of Cardiovascular Disease Using Photoplethysmography (PPG) Signal Analysis
No full textPhotoplethysmography (PPG) signals have gained prominence in clinical diagnostics
for their non-invasive, cost-effective, and user-friendly applications in detecting cardiovascular
diseases (CVDs). This study leverages machine learning techniques to enhance the accuracy
of CVD detection from PPG data, addressing critical risk factors such as hypertension and
stress, which significantly contribute to elevated blood pressure and, consequently, to
cardiovascular disorders. The use of PPG provides a reliable approach for identifying
cardiovascular anomalies by monitoring essential parameters like blood pressure and heart rate.
In this work, we employ both machine learning and deep learning, specifically neural networks,
to assist clinicians in diagnosing CVD, achieving a high accuracy rate of 98% on the PPG-BP
dataset. The findings demonstrate the potential of PPG signals combined with advanced
algorithms to support early diagnosis and personalized treatment, ultimately reducing mortality
rates associated with cardiovascular diseases
Data-Driven Analysis of an Integrated Employee Database System for South Sumatra’s Tourism and Culture Department
No full textThe management of employee data in many governmental organizations remains entrenched in
outdated, manual processes that are inefficient and prone to errors. This issue was notably present
at the South Sumatra Province Culture and Tourism Office, where the existing method of using
Microsoft Excel and Word for handling employee records was becoming increasingly untenable.
The manual system not only required excessive administrative effort but also exposed the
organization to significant risks of data loss and errors, which are detrimental to effective human
resource management. To address these challenges, a web-based information system was
developed using the PHP programming language. This system was designed to automate and
streamline the entire process of managing employee data, from entry to retrieval and reporting.
The system includes several key components: a secure login page, a profile page for quick access
to important data, dedicated pages for managing specific types of information such as employee
details and position data, and a comprehensive reporting page for generating actionable insights
from the data collected. The results of implementing this new system were transformative. It
significantly reduced the time and effort required to manage employee data, improved the accuracy
of the data stored, and enhanced the security measures protecting sensitive information. The
system's user-friendly interface and robust functionality were well-received by the staff,
facilitating smooth adoption and integration into daily operations. The new web-based information
system has successfully modernized the administrative functions of the South Sumatra Province
Culture and Tourism Office. It has established a more reliable, efficient, and secure framework for
managing employee data, setting a strong example for similar advancements in other governmental
departments. Future recommendations include ongoing updates to the system and continuous
training for users to ensure it continues to meet the evolving needs of the organization
Classification of Mental Health Care Using the ELM, MLP, and CatBoost Stacking Framework
No full textMental health significantly impacts overall well-being, yet the increasing prevalence of
mental health issues presents challenges in their effective classification and treatment. Traditional
methods often fail to accurately handle complex, non-linear data, compromising the timeliness and
appropriateness of interventions. This study introduces an innovative mental health classification
framework, ELM-MLP-CatBoost Stacking, to address these deficiencies. The primary objective
is to enhance classification accuracy by integrating three advanced computational techniques: the
speed of the Extreme Learning Machine (ELM), the flexibility of the Multi-Layer Perceptron
(MLP) for modeling non-linear data, and the predictive refinement of CatBoost as a meta-model.
Our methodology involves a stacking approach where ELM and MLP models serve as base
learners with CatBoost integrating their outputs to optimize final predictions. Experimental results
demonstrate that the ELM-MLP-CatBoost Stacking framework substantially outperforms
traditional models, achieving a notable accuracy of 92.76%, an improvement over the MLP’s
92.64% and the ELM’s 69.59%. This framework enhances the reliability and efficiency of mental
health condition classifications and paves the way for further research into advanced diagnostic
tools. The novelty of this research lies in the synergistic combination of these models, setting a
new standard for accuracy and reliability in mental health diagnostics and establishing a robust
foundation for future advancements in the field