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All Journal International Journal of Evaluation and Research in Education (IJERE) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Adi Widya : Jurnal Pengabdian Masyarakat JMM (Jurnal Masyarakat Mandiri) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Community Empowerment Prima Abdika: Jurnal Pengabdian Masyarakat ABDIKOM
Artika Arista
Universitas Pembangunan Nasional Veteran Jakarta, Indonesia
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Text Mining for News Forecasting on The Turnback Hoax Website Rio Wirawan; Erly Krisnanik; Artika Arista
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1939

Abstract

News has been disseminated swiftly via the internet due to the rapid growth of information technology. The rapid spreading of news often confuses because the truth cannot be ascertained. Additionally, online social media is becoming increasingly popular, making it an excellent environment for propagating false information, including misinformation, phony reviews, advertising, rumors, political remarks, innuendo, etc. This study's specific goal is to classify data using a data mining approach model called text mining so that a system can automatically do the classification. As a result, the study will produce a dataset, which can then be used to create an application using data mining's ability to predict breaking news. An application was produced by employing data mining to forecast recent news. This study was able to classify data using a naive Bayes data mining approach model so that a system can automatically do the classification. The study produced an accuracy of 77% obtained with training data of 82%. From 994 contents, the classification of misleading content reached 33.9%, false content as many as 24.85%, imitation content was 13.48%, fake content reached 11.07%, manipulated content was 9.86%, parody content was 3.22%, satire content was 2.31%, and connection content as many as 1.31%. This study then visualizes the results using bar charts and word clouds. This work also produced datasets with the naïve Bayes method of news data and news that has been valid. Afterward, the dataset will be used in making applications to produce prototypes of computer program applications.
The Extension of the UTAUT2 Model: A Case Study of Indonesian SMEs Acceptance of Social Commerce Arista, Artika; Tjahjanto, Tjahjanto; Ernawati, Iin; Purabaya, Rudhy Ho; Abdullah, Engku Fadzli Hasan Syed
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1847

Abstract

An entirely updated e-commerce platform referred to as Social Commerce was developed in response to the rise in social media use. Social commerce integrates interactions between buyers and sellers made possible by social media platforms and Web 2.0 technology. It is frequently seen as a subfield of e-commerce. Social commerce has been successfully introduced in developing countries. Many businesses around the world are small and medium enterprises (SMEs). For instance, SMEs in Indonesia can contribute up to 60.34% of the country's GDP and have a substantial labor pool. Using social commerce as an e-commerce platform can significantly improve the operational efficiency of small and medium-sized enterprises (SMEs). However, little empirical research has specifically examined how SMEs embrace social commerce. Given the high level of concern, further research is required. Therefore, the current study experimentally examined how local SMEs in rural Indonesia introduced social commerce. The study was modeled using the Unified Theory of Technology Acceptance and Use (UTAUT) 2 model and several previous studies. SmartPLS 4 software was used to model and evaluate data using partial least squares structural equation modeling (PLS-SEM). The findings of the 114 samples showed that relative advantage, social support, facilitation conditions, and the government's support of social commerce influenced behavioral intention to use social commerce. Behavioral intention to use social commerce influences the actual use of social commerce. The findings of this study can help local governments and policymakers develop social trade promotion regulations to help potential SMEs and entrepreneurs gain long-term business support.
Big Mart Sales Data Visualization and Correlation Arista, Artika; Theresiawati, Theresiawati; Seta, Henki Bayu
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1780

Abstract

The amount of unprocessed data available every day is growing. This massive amount of data needs to be effectively assessed to give results that are extremely useful. In the present day, it is crucial for inventory management and demand forecasting to collect sales data for commodities or things, together with all their numerous dependent or independent parts. In a Big Mart Company, the use of sales forecasting is to estimate numerous goods that are readily available and supplied at multiple retailers in different towns. As the number of products and outlets increased drastically, it became increasingly difficult to forecast them manually. As a result, it is necessary to see to what extent the relationship between several variables, including price, popularity, time of day, outlet type, outlet location, etc., affects the appeal of a product. In this research, a data cleaning process was carried out, and data visualization using scatter plots, as well as finding Pearson correlations. The raw processing the data with study of a case big mart sales data is taken from the Kaggle website [https://www.kaggle.com/datasets/sandeepgauti/bigmart-sales]. The Pearson correlation test determines a lack of connection between the two Item_Weight and Item_Outlet_Sales variables. There is a strong but negative correlation where if Item_Visibility decreases, Item_Outlet_Sales also decreases. Positive relationships exist between the two Item_MRP and Item_Outlet_Sales variables. In addition to the correlation test, descriptive statistical analysis is also performed here. With this simple data processing, the raw data will be better organized and easier to analyze, read, and use.
Co-Authors Abdullah, Engku Fadzli Hasan Syed Anita Muliawati Anita Muliawati Bahtiar Saleh Abbas Erly Krisnanik Ermatita - Ernawati, Iin Henki Bayu Seta Henki Bayu Seta Ika Nurlaili Isnainiyah Ika Nurlaili Isnainiyah Khairun Nisa Meiah Ngafidin Kraugusteeliana Kraugusteeliana Muhammad Eka Purbaya Noor Falih Nurhafifah Matondang Rio Wirawan Rona Nisa Sofia Amriza Rudhy Ho Rudhy Ho Purabaya Rudhy Ho Purabaya Ruth Mariana Bunga Wadu Sarika Sarika Theresiawati Theresiawati Theresiawati Tjahjanto, Tjahjanto Tri Rahayu Tri Rahayu Zaidiah, Ati