Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : IJIIS: International Journal of Informatics and Information Systems

Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm Hariguna, Taqwa; Baihaqi, Wiga Maulana; Nurwanti, Aulia
International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.13

Abstract

In an e-commerce Shopee, the process of selling and buying continues to run every day, and the comments given by consumers will increase more and more. Comments given by consumers will be the reference/review of a product that has been purchased by consumers. Consumers freely provide a review containing positive comments and negative comments in the Comments field listed on the Shopee e-commerce website. With the above problems, researchers will do a research with the method of sentiment analysis to distinguish classes in product review comments that include positive comment class or negative comment class using a combination of K-means and naive Bayes classifier. K-means used to determine the grouping of classes; naive Bayes classifier used to get the value of accuracy. The results obtained based on clustering K-means include getting 116 negative comments on product reviews and 37 negative comments product reviews. Accuracy results obtained from product review comment data of 77.12%. Thus, the accuracy value using K-means and naive Bayes classifier without manual data get a higher accuracy value is compared using K-means, Naive Bayes classifier, and manual data get results lower accuracy of 56.86%. From the results above the most comments is a negative comment of 116 data review comments product, from the results of the study can be concluded that one of the products of Spatuafa named high heels women know the Ribbon Ikat FX18 the condition of the product is not good enough due to the high negative comments compared to positive comments
COVID-19 Vaccination: A Retrospective Observation and Sentiment Analysis of the Twitter Social Media Platform in Indonesia Hananto, Andhika Rafi; Rahayu, Silvia Anggun; Hariguna, Taqwa
International Journal of Informatics and Information Systems Vol 5, No 1: January 2022
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v5i1.126

Abstract

Coronavirus (COVID-19) is a rapidly emerging and spreading infectious disease. To minimize the impact caused by the virus, it is necessary to have a vaccine. However, the existence of vaccinations for the Indonesian people has caused controversy so that it invites many people to give an opinion assessment, therefore people choose social media as a place to channel their opinions. In this study, a comparison was made with an observational infoveillance study by collecting data using a Python programming script (Python Software Foundation) to display posts related to the COVID-19 vaccine on Twitter as well as quantitative and qualitative analysis to identify trends and characterize the main themes discussed by twitter users on Twitter. Indonesia. Our research collects data through social media Twitter in the period August 2020 - March 2021. In this study we combine Retrospective Observation and Sentiment Analysis, with the aim of producing periodic timeline evaluations within a predetermined time frame. In this study author found that there was an interaction increase in positive posts due to officially reported developments, on the other hand we were quite difficult to understand the factors behind the emergence of negative posts but we made a conclusion based on the results of sentiment analysis that most of the negative posts were caused by lack of information and understanding of vaccines and vaccines. the COVID-19 outbreak itself.
Sales Transaction Data Analysis using Apriori Algorithm to Determine the Layout of the Goods Hariguna, Taqwa; Hasanah, Uswatun; Susanti, Nindi Nofi
International Journal of Informatics and Information Systems Vol 1, No 1: September 2018
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v1i1.19

Abstract

In a shop, usually apply a sales strategy in order. The sales strategy can be in the form of determining the layout of goods so that they are close to one another. Determining the layout of items can be based on items that are often purchased simultaneously. Searching for items that are often purchased together can be done using data mining techniques, which is processing data to become more useful information. Sales transaction data processing can be done using apriori algorithm. Apriori algorithm is the most famous algorithm for finding high-frequency patterns and generating association rules. From the results of the discussion and data analysis, there were 3 (three) association rules formed, namely "If you buy Milo Active 18 grm, then buy ABC Kopi Susu 31G" with support 0.36% and 75% confidence, "If you buy Dancow 1 + Honey 200 grm, then buy Ice Cream Corneto" wit H Support 0.36% and confidence 60%, "If you buy SIIP Roasted 6.5 grm, then buy Davos Strong 10 grm" with support 0.36% and 75% confidence. From the association's rules can be used as decision making to determine the layout of goods that are likely to be purchased simultaneously by the buyer
Integrating Big Data Technologies to Strengthen Network Security Awareness Hariguna, Taqwa
International Journal of Informatics and Information Systems Vol 5, No 3: September 2022
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v5i3.137

Abstract

The Network Security Alarm Warning System (NSAWS) is a sophisticated, real-time, large-scale database management system designed to enhance cybersecurity by analyzing user identity data and access rights within the amassed information to detect potential threats and issue timely alarm notifications. This paper explores methods to bolster network attack prevention in a Big Data framework, starting with an overview of prevalent internet technologies in our country and their current applications. It then elaborates on the architecture, deployment strategies, and operational methodologies of the NSAWS, emphasizing the integration of fundamental security infrastructures like cloud computing platforms and firewalls. Following this, the traditional NSAWS is analyzed, and a simulation platform is tested to evaluate its performance in identifying and alerting network security threats. The results indicate that the NSAWS platform demonstrates exceptional accuracy and stability in its warning capabilities, effectively safeguarding network security by swiftly addressing potential vulnerabilities and threats. This paper underscores the importance of leveraging advanced technologies and robust security frameworks to fortify network defenses against evolving cyber threats, highlighting the NSAWS as a vital tool in maintaining and enhancing network security in an increasingly digital and interconnected world.