Comparison of the Accuracy of Brown's and Holt's Double Exponential Smoothing in LQ45 Stock Price Forecasting

As of May 2022, 787 stocks are listed on the Indonesia Stock Exchange (IDX), and the number of stock indices in Indonesia to date is 38. One interesting and important stock index is the LQ45 index. Because this index is a very important reference index for investors, this research data focuses on stocks in the LQ45 index. There are two essential things in the forecasting process: the data and the right forecasting method. Two forecasting methods that can be used are Brown and Holt's Double Exponential Smoothing (DES). This study examines two methods with the lowest accuracy error in forecasting the LQ45 stock price data. Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the error. The analysis methods used to compare the MAPE of the two methods are the F test for variance similarity, Boxplot, t-test to test paired means with different cases of variance, and Wilcoxon signed rank test to test paired means nonparametric statistics. The result is that the MAPE average with Holt's DES method is smaller than the average MAPE with Brown's DES method. This is supported by the t-test for paired means with different cases of variance and also supported by the Wilcoxon signed exact rank test. Meanwhile, the MAPE standard deviation with Holt's DES method is smaller than the MAPE standard deviation with Brown's DES method. This is supported by the F test to test the variance similarity and is visually supported by a Boxplot diagram. From this study, LQ45 stocks with the smallest MAPE value accuracy are ICBP stocks. In general, based on the MAPE value, Holt's DES method is better than Brown's DES method in predicting the prices of stocks in the LQ45 index

Multinomial Naive Bayes Algorithm for Indonesian language Sentiment Classification Related to Jakarta International Stadium (JIS)

The research focuses on analysing public evaluations, particularly those on Google Maps, about the Jakarta International Stadium (JIS). The study aims to employ the multinomial Naive Bayes algorithm to ascertain the sentiment expressed in these reviews. The objective of this study was to employ the multinomial Naive Bayes method to analyse the reviews on Google Maps pertaining to the Jakarta International Stadium (JIS). The utilised data consists of 2971 public reviews on Google Maps specifically pertaining to Jakarta International Stadium (JIS). These reviews were acquired through web scraping using a data miner. The acquired data is next processed in the text preparation phase to generate a prepared dataset suitable for analysis. This preprocessing stage includes operations such as casefolding, stopword removal, tokenizing, and stemming. The study yielded an accuracy of 0.83, or 83%, when tested on 733 data points. Out of these, 292 positive data points were correctly anticipated, while 59 positive data points were incorrectly forecast. Additionally, 317 negative data points were correctly predicted, while 65 negative data points were incorrectly predicted. The conducted modelling is subsequently categorised using a novel dataset of 161 review data points, with the objective of discerning the sentiment expressed within the dataset. The analysis of the new dataset yielded 101 reviews with positive sentiment and 50 reviews with negative sentiment

Bayadome Geotours (BATOUR) Prototype for Geosite Management at Bayah Dome Geopark, Banten

The objective of this study is to create a technology-driven application prototype, named "Bayadome Geotours," as a cutting-edge solution to enhance geotourism governance and environmental conservation in the Bayah Dome Geopark, Banten. This research advances the utilisation of information and geospatial technology to improve visitor experiences and bolster local community involvement. It achieves this through an emphasis on needs analysis, prototype design, implementation, and testing. The Bayadome Geotours prototype is specifically engineered to offer a dynamic and engaging tourism encounter. Geospatial navigation capabilities enable users to digitally explore geosites, while an intuitive user interface assures accessibility for visitors with different levels of knowledge. This programme offers precise and comprehensive geological information, providing a novel method to enhance comprehension of the geological resources found in the Bayah Dome Geopark. Bayadome Geotours is a good example of the value of local community involvement in geotourism administration. This application serves as both a travel guide and a venue for the exchange of knowledge, local narratives, and cultural heritage. Engaging the public in sharing information fosters a stronger connection between tourists and the environment, resulting in a beneficial influence on the preservation of geosites and the overall management of destinations. Prototype testing conducted using a unit testing methodology demonstrates the successful execution of all system functionalities. The JEST tool's test results confirm that the Bayadome Geotours application is prepared for distribution to the general user base. Nevertheless, there are obstacles in the way of effectively managing and modernising the application, as well as achieving general acceptance, that must be addressed in order to guarantee the ongoing triumph of this prototype. However, Bayadome Geotours has created significant opportunities for advancing sustainable geotourism governance

Navigating Healthcare Challenges Text Analytics, Data Integration, and Decision-Making in the COVID-19 Era

In the context of the COVID-19 pandemic, Integrated Healthcare Systems have emerged as crucial components in effectively managing healthcare challenges. This study delves into the multifaceted role of integrated systems, with a particular focus on the pivotal aspects of text analytics. An exploration of various applications of text analytics unfolds, shedding light on its diverse utility within the healthcare landscape. Extensive reviews of problems encountered by different organizations and insights gleaned from research contribute to a comprehensive understanding of the challenges faced by Health and Human Services (HHS). These challenges, intricately linked to issues such as hospital strains and consumers' personal experiences, are thoroughly examined to provide actionable solutions. A key emphasis is placed on the indispensability of data integration, and the abstract discusses how various analytic approaches can be strategically employed within a well-integrated database system. The nuances of implementing an integrated model are scrutinized, highlighting the primary challenges that organizations, particularly HHS, may encounter. Subsequently, potential solutions are presented, leveraging the power of OLAP to construct a dashboard tailored to address the identified problems. Beyond the technical intricacies, the abstract explores the ramifications of an integrated approach on decision-making processes within HHS. The discussion extends to the acceleration of decision-making possibilities, underlining the imperative need for timely and informed actions in the face of healthcare challenges. In essence, this study provides a nuanced exploration of the role of Integrated Healthcare Systems during the COVID-19 pandemic, incorporating insights from text analytics, data integration, and analytic methodologies. The findings aim to contribute valuable perspectives to healthcare organizations, particularly HHS, as they navigate and mitigate the complexities posed by the ongoing global health crisis

Enhancing Online Food Delivery Systems through Comprehensive Text Analytics and Strategic Data Integration

Addressing challenges in the online food delivery system involves employing various data analytics techniques. Text Analytics, encompassing web analytics, social media analytics, stream analytics, and geospatial analytics, plays a pivotal role in managing and extracting valuable insights. The use of third-party systems by many companies to meet the demand for online food delivery presents issues related to control. Furthermore, information overload and poorly organized data contribute to observed problems. This research proposes effective data integration as a solution, facilitating strategic analytics for optimal system performance. Proper data sorting enables adaptive planning and priority shifts tailored to customer satisfaction. The framework of data integration is crucial in illustrating the comprehensive analysis of online food delivery systems. The report also delves into the challenges associated with implementing text analytics

Mapping Global Joy: Descriptive Analytics of Subjective Well-Being from the World Happiness Report

This article delves into the pursuit of happiness as a fundamental aspect of human existence, focusing on the exploration of factors contributing to subjective well-being (SWB). Drawing from the rich dataset obtained from the January 2019 release of the Gallup World Poll (GWP) and the World Happiness Report of 2019, spanning the years 2005 to 2018, our descriptive analytics offer a comprehensive analysis of global happiness. Through an examination of individuals' perceptions across diverse cultures and societies, this research elucidates key findings, revealing nuanced insights into the complex interplay of societal, economic, and personal elements that influence well-being on a global scale. This article contributes to a deeper understanding of the intricate fabric of happiness trends, patterns, and influencing factors, providing valuable insights for researchers and policymakers alike

Comparative Study of Classification Algorithms for Customer Decisions on Telecommunication Products Using Supervised Learning

Customers are the main goal of all business fields, without customers the company will not be able to continue or compete in the business field it is in, even though the company has brilliant products, if it does not have an increase in the number of customers the business will not be able to develop or even go bankrupt. Therefore, it is necessary to make observations and make applications that are able to predict customers who will subscribe so that companies can predict customers who will subscribe correctly without having to wait for confirmation from customers whose possibilities are still unknown. This can be very useful for any company because companies no longer need to look for random customers where it only takes time to find customers. PT. Telekomunikasi Indonesia with its product (Indihome) which is struggling to compete in the business world in the telecommunications and internet sector. Therefore research and development of this application are carried out so that PT. Indonesian telecommunications can get its customers quickly without having to spend a lot of money and effort. Making this application uses a classification method from machine learning technology based on customer historical data. The classification method has many strong algorithms for predicting variables that have more than 1 label. Some of the algorithms used are Logistic Regression, Random Forest Classifier, Support Vector Machine and Decision Tree which are provided by modules in the python programming language, namely SkLearn. The four algorithms will be tested with data balanced using the Oversampling method from the Smote algorithm to get optimal results in automatically predicting customers

Sentiment Analysis of the Use of Digital Banking Service Applications On Google Play Store Reviews Using Naïve Bayes Method

The development of the financial system is characterized by the emergence of digital banking service applications that are widely circulated and can be accessed for free. With so many applications, users often feel confused in choosing which applications are safe to use. Before downloading an application on the Google Play Store, users will usually look at ratings and reviews first. However, the title of the best application cannot be pinned if only seen from the rating and number of downloads. This research was conducted to analyze sentiment on user reviews of digital banking service applications on the Google Play Store using the NBC (Naïve Bayes Classifier) method. Research using the NBC algorithm produced an accuracy value of 81% on the classification of Allo Bank reviews and 78% on the classification of Line Bank review

Customer Value and Data Mining in Segmentation Analysis

Customer Value is the accessed value that a customer has to an organization. In Business, the customer is always right. This statement gives us the impression that all customers are viewed as equal in terms of potential value. Each customer is treated differently according to how much profit they can bring to a company. We use various Data Mining techniques to determine who are these customers and how we can acquire more customers like them who can bring more profit. A loyal customer will be treated differently than a customer that rarely do business with the organization. These customers are usually given bonus gifts and special offers as a form of thanks for their loyalty thus further strengthening that bond. Companies need a way to determine which of their hundreds of thousands of customers are deserving of this attention. Customer Value Segments are used in this specific situation

Development and Analysis of a Unified Mobile App for Coffee Shop Operations and Ordering Experience: A Proposal Review

This study extends the exploration of ordering apps in the context of coffee shop owners, specifically focusing on the utilization of popular apps like Grabfood and Foodpanda. With the increasing number of coffee shops adopting ordering apps, there arises a clear necessity for a coffee-focused app that can effectively address the unique demands of establishments. The objective of this study is to conduct a comprehensive review of a mobile app specifically designed to streamline the process of ordering coffee in advance, with a paramount emphasis on ensuring its reliability. By developing an app that caters to the specific needs of coffee shops, both owners and customers can benefit greatly. The app will serve as a dedicated platform, connecting coffee enthusiasts with quality coffee shops, while offering a seamless and convenient ordering experience. By providing a high-quality ordering system that encompasses the full range of customization options for beverages, the developed app is expected to significantly enhance the customer experience and ultimately boost sales for the coffee establishments listed on the platform. With a focus on reliability, the app will enable coffee shop owners to efficiently manage orders, minimize errors, and improve overall operational efficiency. Moreover, by fostering a user-friendly interface and intuitive design, the app will engage customers and encourage them to explore new coffee shops, further promoting the growth of the coffee industry. This study will contribute to the existing body of knowledge by highlighting the importance of tailored ordering apps for coffee shops and providing insights into the development and implementation of such apps. The findings will be valuable not only for coffee shop owners seeking to enhance their business operations but also for app developers looking to cater to the specific needs of the coffee industry. Ultimately, the study aims to bridge the gap between technology and the coffee business, fostering innovation and growth in the ever-evolving digital landscape