cover
Contact Name
Taqwa Hariguna
Contact Email
taqwa@amikompurwokerto.ac.id
Phone
+62895422720524
Journal Mail Official
contact@ijiis.org
Editorial Address
Puri Mersi Baru, Jl.Martadireja II, Gang Sitihingil 3 Blok A No 2, Purwokerto Timur, Jawa Tengah
Location
Unknown,
Unknown
INDONESIA
IJIIS: International Journal of Informatics and Information Systems
Published by Bright Publisher
ISSN : -     EISSN : 25797069     DOI : https://doi.org/10.47738/ijiis
Core Subject : Science,
The IJIIS is an international journal that aims to encourage comprehensive, multi-specialty informatics and information systems. The Journal publishes original research articles and review articles. It is an open access journal, with free access for each visitor (ijiis.org/index.php/IJIIS/); meanwhile we have set up a robust online platform and use an online submission system to ensure the international visibility and the rigid peer review process. The journal staff is committed to a quick turnaround time both in regards to peer-review and time to publication.
Articles 8 Documents
Search results for , issue "Vol 2, No 2: September 2019" : 8 Documents clear
Naive Bayes Algorithm Using Selection of Correlation Based Featured Selections Features for Chronic Diagnosis Disease Santiko, Irfan; Honggo, Ikhsan
International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.14

Abstract

Chronic kidney disease is a disease that can cause death, because the pathophysiological etiology resulting in a progressive decline in renal function, and ends in kidney failure. Chronic Kidney Disease (CKD) has now become a serious problem in the world. Kidney and urinary tract diseases have caused the death of 850,000 people each year. This suggests that the disease was ranked the 12th highest mortality rate. Some studies in the field of health including one with chronic kidney disease have been carried out to detect the disease early, In this study, testing the Naive Bayes algorithm to detect the disease on patients who tested positive for negative CKD and CKD. From the results of the test algorithm accuracy value will be compared against the results of the algorithm accuracy before use and after feature selection using feature selection Featured Correlation Based Selection (CFS), it is known that Naive Bayes algorithm after feature selection that is 93.58%, while the naive Bayes without feature selection the result is 93.54% accuracy. Seeing the value of a second accuracy testing Naive Bayes algorithm without using the feature selection and feature selection, testing both these algorithms including the classification is very good, because the accuracy value above 0.90 to 1.00. Included in the excellent classification. higher accuracy results.
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 : Bright Publisher

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
Decision Support System to Determine the Achievement of Students Using Simple Multi-Attribute Rating Technique (SMART) Jahir, Abdul; Setiawan, Ito; Arta, Anisa Dayu
International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.12

Abstract

The problem is in determining the achievement of students by organizing the consultation between teachers. The purpose of this research is to assist the decision-making process of determining the achievement of students with SMART method implementation. The methods of collecting data are interviews, documentation, and observations. The method of system development used is the waterfall method by using the system design tools in the form of DFD and ERD. The software used in the creation of this application is Visual Studio and SQL server express. The results of this study are SMART ranking methods. The decision support process is more objective because it complies with predefined criteria.Decision Support System to Determine the Achievement of Students Using Simple Multi-Attribute Rating Technique (SMART)
Comparative Method of Weighted Product and TOPSIS to Determine The Beneficiary of Family Hope Program Suhartono, Didit; Sari, Tika
International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.16

Abstract

The Family Hope Program (PKH) is a government program that provides cash assistance to impoverished households. The implementation of PKH in Cimrutu Village has not been implemented optimally, namely prioritizing the targets of PKH participants who are not yet on targets. This happened because the officers in registering the poor were still using manual methods. To simplify the work and avoid miscalculation of data with the old system, a decision support system was built that could help make decisions on PKH recipients quickly and more accurately. The calculation method used is the Weighted Product (WP) method. Data collection methods used in this study were interviews and documentation. System development in this study uses waterfall through black-box testing. System design tools in the form of DFD and ERD. The software used in making this application is Visual Studio 2012, Xampp, and Crystal Reports. The programming language used is Java with its supporting database using MySQL. This decision support system is expected to be able to help officers in Cimrutu Village in selecting and determining communities that are eligible for PKH.
Product Review Sentiment Analysis by Artificial Neural Network Algorithm Astuti, Tri; Pratika, Irnawati
International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.15

Abstract

Buying and selling and marketing goods and services are now done online. The online store provides facilities that enable its customers to provide review related products offered. The number of reviews received by the store, online sometimes does not allow the store online to analyze one by one. Thus, it takes the help of machines to assist in the analysis of such sentiments. Analysis of the sentiments of the review the product is done to help the shop get a general overview related to the level of consumer satisfaction. In this study, the ANN algorithm will be used to analyze sentiment for review. A product ANN algorithm used because it can provide high accuracy performance. This research resulted in a reasonably high accuracy performance is 88.2%.
The Influence of the privacy concern and social advertising type on the attitude and behavior Wang, Chih-Chien; Yang, Yolande Yunhsiou; Lin, Ming-Ru
International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.89

Abstract

Nowadays social media can collect consumers' online behavior. The enterprises make the customized advertisement to achieve targeting marketing and close consumers' needs. With the right of privacy, the consumers pay attention to this kind of advertisement. In this study, we made the online questionnaire. Asking the privacy concern, and analyzing the advertising attitude and behavior in a different advertising situation. The result we found that customized advertising made consumers increase positive attitude, but made negative attitude on advertising behavior like click, share, etc. In addition, both male and female have different responses to customized advertisements and intimate products advertisements. The result can serve a reference for manufacturers to make advertising strategies in the future.
A Study of Influence Factors for Advertising on Messaging Applications Towards Mobile Buyer's Decision Making Paireekreng, Worapat; Osathanukroh, Jaruvarintra; Supasak, Chavanit
International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.90

Abstract

The advancement of information technology leads to development of a new business paradigm which is focused on innovative technology. M-commerce seems to be a crucial tool to develop a country rapidly. The combination of messaging application features and business model can build start-up business driven by these technologies. However, the appropriate accessing target group of each business issues to be a main issue in the messaging application usage. This research aims to investigate the influence factors related to advertising on messaging applications. The Mixed method; quantitative and qualitative methods were implemented to investigate such factors. The findings are that three main factors, demographic factors, m-commerce factors, and behavioral factors, affected the buying decision making. Whereas, the demographic factor such as marital status showed no differences in this study. The products such as information technology accessories, beauty products and fashion goods are an important business area for customers focused on m-commerce. In addition, it was found that education had a significant effect towards advertising on messaging applications. Furthermore, the derived influence factors and criteria for advertising on messaging applications were confirmed with online merchants in the focused group method. The main advantage of messaging applications is the ability to interact with merchants and get quick responses. The results can be a guidance for start-up businesses for sustainable development.
Improving Music Recommendation System by Applying Latent Topics of Lyrics Thwe, Khine Zar; Yukawa, Takashi
International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.91

Abstract

The proposed music recommendation system was developed by using various information filtering approaches based on user context and song context. This study proposes a music recommendation system with Latent Dirichlet Allocation (LDA) by using user listening behavior and analyzing a latent relationship of each song. As a consequence, small musical niche genres without listing history will become a member of their respective topic groups. Modeling topic analysis of LDA is utilized for songs lyric as well as the user action and, then song group preferences support the collaborative filtering recommendation engine. The system addresses the optimization of the cold start problem of adding new items in Collaborative Filtering by lyric analysis with LDA. Predicted ratings for user recalculated by combination matrix of song listening action with binary rating values ​​and latent topic group result of lyrics. In this analysis, a system proposition compared with two models, normal collaborative filtering and user defined genre group preference.

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