Article Per Year (5 Year)
p-Index From 2019 - 2024
0.562
P-Index
This Author published in this journals
All Journal IJIIS: International Journal of Informatics and Information Systems
Taqwa Hariguna
Amikom University Purwokerto, Indonesia
Computer Science & IT

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Community Opinion Sentiment Analysis on Social Media Using Naive Bayes Algorithm Methods Taqwa Hariguna; Vera Rachmawati
International Journal of Informatics and Information Systems Vol 2, No 1: March 2019
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i1.11

Abstract

The election of Governor is an election event for the Regional Head for the future of the region and the country. The Central Java Governor election in 2018 was held jointly on 27 June 2018, which was followed by 2 candidate pairs of the governor. Its many responses from people through twitter's social media to bring up opinions from the public. Sentiment analysis of 2 research objects of Central Java Governor 2018 candidates with a total of 400 tweets with each candidate being 200 tweets. The used of tweets are divided into 3 classes: positive class, neutral class and negative class. In this study the classification process used the Naive Bayes Classifier (NBC) method, while for data preprocessing is using Cleansing, Punctuation Removal, Stopword Removal, and Tokenisation, to determine the sentiment class with the Lexicon Based method produces the highest accuracy in the Ganjar Pranowo dataset with an accuracy of 87,9545%, Precision value is 0.891%, Recall value is 0.88% and F-Measure is 0.851% while Sudirman Said dataset has an accuracy rate of 84.322%, Precision value of 0.867%, Recall value of 0.843% and F-Measure of 0.815%. From these results, we can conclude that the Ganjar Pranowo dataset was higher compared to Sudirman Said's dataset.
E-Government Asset Management Using the Extreme Programming Taqwa Hariguna; Mirra Tsamara
International Journal of Informatics and Information Systems Vol 2, No 1: March 2019
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i1.1

Abstract

Village Asset Management is a village property derived from the original property of the village, purchased or acquired for the expense of the village's income and expenditure budget (APBDes) or other legitimate acquisition of rights. In this study is the management of the village assets Kebumen Baturraden subdistrict and The problems acquired are the recording of village assets and the realization of the budget of the village assets that are still written so it takes A long time, Therefore the purpose in This research is to help and facilitate management performance in village Asset listing. Methods of collecting data using observations, interviews and Library studies. System development method using the Extreme programming method consisting of four phases of system development, namely planning, design, coding and test. The result of this research is a Village Asset Management E-Government using the Extreme Programming method of the Kebumen Village case study. The conclusion of this research is that it can simplify the management and realization of village assets budget due to the two interconnected reporting are reports of budget realization and village Asset report.
Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm Taqwa Hariguna; Wiga Maulana Baihaqi; Aulia Nurwanti
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
Sales Transaction Data Analysis using Apriori Algorithm to Determine the Layout of the Goods Taqwa Hariguna; Uswatun Hasanah; Nindi Nofi Susanti
International Journal of Informatics and Information Systems Vol 1, No 1: September 2018
Publisher : Bright Publisher

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
Co-Authors Aulia Nurwanti Mirra Tsamara Nindi Nofi Susanti Uswatun Hasanah Vera Rachmawati Wiga Maulana Baihaqi