Erika Noor Dianti

Bio: Erika Noor Dianti is an academic researcher from State University of Semarang. The author has contributed to research in topics: The Internet & Market segmentation. The author has an hindex of 1, co-authored 3 publications receiving 4 citations.

Analysis of twitter sentiment in COVID-19 era using fuzzy logic method

Abstract: The sentiment is an assessment of attitudes towards certain events or things. Collecting opinion is known as a sentiment from existing data. This technique can also help analyze the opinions given by people in assessing certain objects. The best available source for gathering sentiment is the internet. In the era of the Covid-19 pandemic, many people access social media, especially Twitter to give their opinion on certain objects. Twitter is known as the social media that is accessed by users to post their opinions online. By using soft computing, especially fuzzy logic, it is possible to design, create and build bots that can analyze user opinions on Twitter. This model is used for data sentiment analysis on Twitter.

Improved logistics service quality for goods quality delivery services of companies using analytical hierarchy process

Abstract: Logistics plays a role in the smooth transaction between companies because it is a facilitator of buying and selling goods and services to fulfill the supply orders of consumer companies. This study aims to analyze how the impact of improved Logistic Service Quality (LSQ) for quality of goods delivery services by using LSQ dimensions from previous research. Sample data is obtained through the dissemination of questionnaires which are then processed quantitatively with convergent validity and reliability tests. Data processing with a sample count of 61 respondents. The results of this study show that there is the main dimension of logistic service quality in improving the quality of service, namely ordering condition, time, and information quality. Each comparison factor is tested for consistency using the Analytical Hierarchy Process (AHP), each of the main criteria has a consistency value of less than 0.1 so that the main criteria tested have a consistent comparison matrix and can be the basis of decision making for companies in choosing alternative criteria priorities.

Improvement business process model and notation on the drink distribution industries using six core element

Abstract: The development of distribution and market segmentation has become the company's background in improving business processes. The purpose of this research is to analyze the business processes of beverage companies using Business Process Management (BPM) modeling and improvised based on six core element management. In the analysis process, it is found that there is no stock forecasting system in forecasting sales stock that must be fulfilled. The results of the study show that the Business Process Management model is improved with the addition of a stock forecasting system, so that business processes become more controlled with the presence of a product stock inventory forecasting system in the company.

A Study on UCI Hepatitis Disease Dataset Using Soft Computing

Abstract: Accurate diagnosis for decision making in medical diagnosis is solicited for further treatment planning. Intelligent decision support system plays an important role for medical diagnosis as well as early detection of disease to survive. In intelligent model machine learning is achieved by searching a pattern in the available data set. For this reason, data preprocessing plays a vital role for better learning and analysis process. This work uses UCI Hepatitis disease data set. Missing data are managed by using multiple imputation. Feature extraction is done using rough set (RS) based techniques. Data preprocessing was the main focus to achieve better classification accuracy. Incremental Back Propagation Learning Network (IBPLN) and Levenberg Marquardt (LM) algorithms are used as classifier. The parameters – CCR, Sensitivity, Specificity and AUC are considered for performance prediction.

COVID-19 Vaccination and PPKM Policy with the Implementation of the Fuzzy Sugeno Method to Income Classification

Abstract: This study aims to determine the implementation of Fuzzy Sugeno in classifying textual data obtained from Twitter so as to determine the polarity of public opinion regarding PPKM policies and Covid-19 vaccinations. This study uses primary data via Twitter related to COVID-19 vaccination and PPKM policies in Indonesia starting from February 9, 2021 to January 17, 2022. There are several stages carried out, namely data collection, data pre-processing, data labeling, data weighting. , identification of membership functions, determination of fuzzy sets, formation of classification systems, and evaluation of classification results. The results of this study explain that Fuzzy Sugeno's performance in classifying tweets is quite good with an average accuracy of 89.13%. Meanwhile, public opinion regarding PPKM policies and Covid-19 vaccinations tends to be balanced with 36.92% of tweets classified as positive sentiments, 22.85% negative sentiments, and another 40.23% classified as neutral sentiments. In addition, the fuzzy set that is formed based on the data observation method is very well done because it is able to adjust the frequency of the data in each category. This really helps improve the performance of the built classification system.

Laptop selection decision support system according to buyer criteria with the simple additive weighting method

Abstract: Along with the development of increasingly modern times, so that all activities require gadgets, including laptops. However, it is often found among prospective laptop buyers who are still confused in determining a laptop to suit their needs, for that purpose the purpose of this study is to help people who want to buy a laptop when choosing or who are looking for a laptop to get the right one for their needs. To achieve this goal, a decision support system is needed. The Decision Support System that will be used is the SAW (Simple Additive Weighting) method because this method can filter out several existing alternatives and based on predetermined criteria so that later you get the best alternative. By using this SAW method, a matrix normalization process is needed, the weight value of each attribute, and finally a ranking process that will determine the optimal alternative. The results obtained in this study are to be able to provide laptop advice to prospective buyers based on the specifications of the prospective buyers' needs and with a 100% accuracy level based on calculations from the decision support system.

Fuzzy Sugeno Method for Opinion Classification Regarding Policy of PPKM and Covid-19 Vaccination

Abstract: The Indonesian government has implemented various interventions to overcome the impact of the Covid-19 pandemic, including those written in Minister of Home Affairs Instructions on PPKM (Community Activities Restrictions Enforcement) and Covid-19 vaccination policies. This policy are not at least reaping the pros and cons, so it is necessary to monitor public opinion to be able to provide solutions or become an evaluation of future policies. The aim of this study is to determine the polarity of public opinion regarding PPKM and Covid-19 vaccinations policies on Twitter, as well as to determine the implementation of FIS Sugeno in classifying textual data. There are several stages carried out, i.e. data collection, data pre-processing, data labeling, data weighting, identification of membership functions, determination of fuzzy sets, formation of a classification system, and evaluation of classification results. In this study, the performance of FIS Sugeno in classifying tweets was quite good with an average accuracy of 89.13%. Meanwhile, public opinion regarding the PPKM and Covid-19 vaccination policies tends to be balanced with 36.92% of tweets classified as a positive sentiments, 22.85% being negative sentiments, and another 40.23% belonging to neutral sentiments.

A unified deep neuro-fuzzy approach for COVID-19 twitter sentiment classification

Abstract: . Covid-19 braces serious mental health crisis across the world. Since a vast majority of the population exploit social media platforms such as twitter to exchange information, rapid collecting and analyzing social media data to understand personal well-being and subsequently adopting adequate measures could avoid severe socio-economic damage. Sentiment analysis on twitter data is very useful to understand and identify the mental health issues. In this research, we proposed a unified deep neuro-fuzzy approach for Covid-19 twitter sentiment classification. Fuzzy logic has been a very powerful tool for twitter data analysis where approximate semantic and syntactic analysis is more relevant because correcting spelling and grammar in tweets are merely obnoxious. We conducted the experiment on three challenging COVID-19 twitter sentiment datasets. Experimental results demonstrate that fuzzy Sugeno integral based ensembled classifiers succeed over individual base classifiers.