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Prototyping Disaster Preparedness Information System: A Case of Pandeglang District, Indonesia
Full text linkIn December 2018, a tsunami triggered by the eruption of Anak Krakatau Volcano (AKV) devastated the coastal area of Pandeglang, Indonesia, claiming hundreds of lives and leaving thousands missing. This tragedy underscores the critical importance of enhancing tsunami awareness through disaster preparedness and education. However, the lack of disaster preparedness in vulnerable areas, such as Pandeglang, remains a significant challenge. This is evident from the absence of early warning systems and evacuation initiatives at the time of the tsunami, highlighting the urgent need for improved disaster resilience in at-risk communities. This research aims to develop the disaster preparedness information system to equip society with sufficient knowledge and skill in case of the next disaster. The method this research uses is Soft Systems Methodology (SSM) to obtaining system requirements to the development of prototype. The prototype of a disaster preparedness information system was developed as a result. The system can be accessed using a smartphone or computer. This study introduces a novel approach by proposing a new prototype of disaster preparedness information specifically tailored for vulnerable areas in developing countries
Rainfall forecasting using triple exponential smoothing for rice cultivation in lamongan, jawa timur
Full text linkRice cultivation is a major agricultural activity that is heavily influenced by weather conditions. Extreme weather events, such as heavy rainfall, can cause farmers' productivity to decline. Rainfall forecasts are important for farmers to help them make the right decisions in managing their farming businesses. This research aims to predict rainfall in Lamongan Regency, East Java province, and provide valuable information to rice farmers to plan the optimal planting season. The method used in this study is Triple Exponential Smoothing (TES), an effective forecasting technique for processing time series data with seasonal patterns. Monthly rainfall data for the last five years formed the basis of the forecast, with data sourced from NASA's Power Data Access Viewer. The analysis results include a Mean Absolute Percentage Error (MAPE) value of 97.559% for rainfall. This rainfall forecast can assist farmers in increasing rice productivity and minimizing the risk of crop failure due to unpredictable weather conditions. With the rainfall weather forecast, farmers are expected to know the suitable months for rice cultivation so that productivity increase
Ev Battery Controller Tuning For Efficient Thermal Management Based On Grasshopper Algorithm And Particle Swarm Optimization Algorithm
Full text linkElectric Vehicles (EVs) offer low emissions and reduced fossil fuel dependence but require efficient battery thermal management to ensure performance and safety. This research aims for tuning proportional-derivative(PD), proportional-integral(PI) and proportional-integral-derivative (PID) controller for Electrical Vehicle (EV) Thermal Management System using Particle Swarm Optimization (PSO) and Grasshopper Optimization Algorithm method (GOA) method to optimize the compressor power consumption to contribute to the development of better EV battery thermal management systems. By minimizing and maximizing the factors involved in the challenges, optimization is the process of identifying the best way to make something as useful and effective as feasible. Simulation results show that GOA outperforms PSO for all controllers. Objective function values for GOA are lower, 1.6783 (PD), 0.8517 (PI), and 0.8114 (PID), compared to PSO, 1.7578, 0.8665, and 0.8254, respectively. Improvement percentages of GOA over PSO are 4.73% (PD), 1.70% (PI), and 1.65% (PID). The PID controller achieved the best performance overall, showing 51.65% improvement over PD and 4.91% over PI. The findings confirm that GOA is more effective than PSO in optimizing controller performance, and that PID is the most suitable for stable and efficient EV battery thermal management
The humanistic use of social media strategies for tourism and hospitality industry promotion
No full textThis study examines communication and promotional strategies in the hospitality and tourism sectors, emphasizing the strategic use of social media to achieve specific marketing objectives and to enhance relationships between tourism managers and tourists. In the digital era, social media functions as an essential tool that facilitates access to information about tourism destinations and hospitality services for prospective travelers and hotel guests. This research adopts a qualitative methodology, enriched by a humanistic perspective, to gain a deeper understanding of tourism participants' behaviors and expectations. The findings indicate that social media plays a crucial role in promoting tourism and hospitality businesses by fostering meaningful engagement and sustaining relationships between operators and stakeholders over both short- and long-term periods. Furthermore, the integration of social media into promotional efforts enables more personalized, accessible, and location-independent communication, thereby improving the overall effectiveness of tourism marketing strategies on a global scale
Analyzing the Impact of Effort Expectancy and Cognitive Attitudes on The Willingness to Accept ChatGPT
Full text linkThis study aims to analyze the impact of Effort Expectancy (EE) adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT) and Cognitive Attitude (CA) from the Theory of Reasined Action (TRA) model on Willingness to Accept (WA) adapted from TAM on ChatGPT. By understanding the relationship between these factors, we can identify effective strategies to increase user acceptance of ChatGPT technology. The research method used is quantitative with multiple linear regression calculations in SPSS. This study obtained 50 respondents with a total of 10 variables but there were 3 main variables. With the final result, Effort Expectancy has no significant effect on Willingness to Accept while Cognitive Attitude has a significant effect on Willingness to Accept. This suggests that users’ perceptions of how easy or difficult it is to use ChatGPT do not influence their decision to accept and use the technology. In this context, users may feel that ease of use is not a major factor influencing their acceptance of ChatGPT. This means that users’ cognitive attitudes—including their beliefs, perceptions, and understanding of the technology—play an important role in their decision to accept and use ChatGPT
Model of Integrated System for Feeding Catfish and Monitoring Pond Temperature Based on IoT
Full text linkIndonesia is renowned for its rich biodiversity, including a diverse array of ornamental and consumable fish species. These fish are widely cultivated in aquariums, ponds, and cages, with prices varying greatly depending on the species. However, the current method of manual fish feeding remains inefficient and time-consuming. To address this challenge, an automated fish feeding system has been developed to streamline and enhance the feeding process.This research project was conducted using a simulation approach to lay the groundwork for a future prototype. The simulation involves an IoT-based model capable of providing automated feeding for catfish and monitoring the temperature of their pond. The system is designed for future implementation at Botani UPI, where traditional manual feeding methods are still prevalent.The primary objectives of this research are to automate catfish feeding and monitor the temperature of their pond. Feed will be dispensed according to a predetermined schedule stored in a database, allowing for flexible adjustments via the internet.Testing has demonstrated the successful operation of the website, fulfilling its core functions: adjusting pond water temperature as needed, automatically providing catfish feed, and scheduling regular feeding intervals. Users can control these functions remotely, enabling effective pond monitoring and management without physically being present at the site.The success of this testing underscores the immense potential of the integrated system to elevate catfish farming productivity and efficiency. By maintaining optimal pond temperatures, the health and growth of catfish can be better sustained, while scheduled and accurate feeding helps minimize feed wastage and ensures adequate nutrition for the fish. Additionally, the system reduces the manual workload for farmers, allowing them to focus on other aspects of pond management. Overall, the implementation of this technology not only offers economic benefits through increased production yield but also promotes more sustainable and modern fish farming practices
Food and Beverage Product Efforts in Maintaining Food Quality in the Restaurant and Room Service of Hotel Chanti Semarang
No full textThe research in this thesis is motivated by the increasing development of the hotel industry in Indonesia. Competition in the hotel industry is increasingly fierce due to the many new hotels that have emerged to date. So hotel management should have a mature strategy to be able to compete with new industries and major competing industries. The problem contained in this thesis is in how to maintain consistency in food quality and overcome it complaint guests about the quality of food at Chanti Hotel. The aim of this final assignment is to maintain food consistency and overcome problems complaint guests about food quality, in this research using a qualitative descriptive method based on the author's experience during On The Job Training and also based on previous research sources. The result of this final assignment is how to maintain consistency in food in a hotel, especially in restaurant and room service, and how to overcome a problem complaint guests about the quality of the food. The conclusion of this thesis is that in maintaining consistency in the quality of our food we must comply with standardized recipes
Optimising SVM models in text mining to see the sentiments and user complaints of DANA mobile application through play store reviews
No full textDana is a mobile electronic wallet application available for download on Google Play Store. Users can rate and comment on this application directly through the review section on the platform. By utilizing these user reviews, research can be conducted to identify the main complaints experienced by Dana application users. This research uses Support Vector Machine (SVM) sentiment analysis to classify reviews and Latent Dirichlet Allocation (LDA) to map negative comment topics. LDA extracts several representative words or tokens that are grouped to form specific themes. The findings show that the most common sources of user complaints are related to transaction issues, premium features, and app updates. These insights can provide valuable input for developers to improve the overall quality and user experience of the Dana app
The Asthma Classification Using an Adaptive Boosting Model with SVM-SMOTE Sampling
Full text linkAsthma is a disease that affects the human respiratory tract, characterized by inflammation and narrowing of the respiratory tract such as wheezing, coughing, and shortness of breath. The causes of asthma can come from genetics, lifestyle, and a bad environment. Diagnosis made to asthma patients is very influential on the severity and treatment carried out. However, the diagnosis process may not be able to precisely determine asthma patients because the diagnosis is influenced by the classification of asthma based on the symptoms that appear. Therefore, this study proposes an asthma disease classification model that is optimized using a sampling method to balance the data. The proposed classification model uses the Adaptive Boosting algorithm with a sampling technique using SVM-SMOTE to help balance the data. The results obtained from the experiment achieved an accuracy of 98.60%. This result shows that the proposed model is more accurate and optimal in performing classification when compared to previous research
An Implementation of Loyalty Program Theory Based on Recency Frequency Monetary Score in Information Systems to Increase Customer Loyalty
No full textThis study aims to help online retail stores find the right strategy for treating customers through customer segmentation based on Recency, Frequency, and Monetary (RFM) Score. With a quantitative approach, this study uses the K-Means Clustering algorithm to group customers based on their RFM values and applies it within the Loyalty Program Theory framework. The results show that the Best Customers segment has the highest percentage at 26.3%, which emphasizes the importance of retaining high-value customers through exclusive loyalty programs such as VIP access and premium offers. In contrast, the Lost Customers segment at 24.8% requires attention through retargeting and discount programs to attract them back. This study proves that data-based customer segmentation and the implementation of relevant strategies can strengthen long-term relationships with customers, increase loyalty, and ultimately help the development of online retail businesses