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All Journal Jurnal Informasi dan Teknologi Brilliance: Research of Artificial Intelligence VIDHEAS: Jurnal Nasional Abdimas Multidisiplin
Arwan Sulaeman, Asep
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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Sentiment Analysis on Social Media X (Twitter) Against ChatGBT Using the K-Nearest Neighbors Algorithm Arwan Sulaeman, Asep; Danny, Muhtajuddin; Butsianto, Sufajar; Pratama, Suria
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4105

Abstract

This research aims to analyze the public's response to ChatGPT through data obtained from Twitter. Apart from that, it is also to understand whether people's responses tend to be positive or negative towards ChatGPT, as well as to test the performance of the K-Nearest Neighbors (KNN) method in classifying sentiment patterns in tweet data. The sentiment analysis method is carried out by dividing public responses into positive and negative categories. Next, the performance of the K-Nearest Neighbors (KNN) method was tested with varying k values ??to classify sentiment patterns in tweet data. This testing includes dataset division, vectorization of text data using TF-IDF, initialization and training of the KNN model, and evaluation of model performance using metrics such as precision, recall, and f1-score. The results of sentiment analysis show that the majority of people's responses to ChatGPT are positive (74.3%), while 25.7% of responses are negative. Performance testing of the KNN model shows that the highest accuracy of 88% is achieved when the k value is 5. Evaluation of model performance also shows satisfactory levels of precision, recall and f1-score. Based on the research results, it was concluded that sentiment analysis and classification using KNN were effective in understanding people's responses to ChatGPT
Association Rule to Increase Sales Using the Apriori Algorithm Method Ermanto, Ermanto; Halim Anshor, Abdul; Arwan Sulaeman, Asep; Winarni, Sri
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4185

Abstract

The Apriori algorithm is a data mining technique used to find relationship patterns between items in a transaction dataset. In this context, the Apriori algorithm will be used to identify products that are often purchased simultaneously by customers. By understanding these purchasing patterns, companies can design more effective marketing strategies, such as strategic product placement, bundling package offers, and special promotions. This research involves several stages, starting from collecting sales transaction data, data preprocessing, applying the Apriori algorithm, to interpreting the results. The transaction data used is taken from the sales database of a retail store during a certain period. After the data is processed, the Apriori algorithm is applied to identify frequent itemsets and form association rules. The results of this research show that there are several significant purchasing patterns, such as a combination of product A and product B which are often purchased together. By applying data mining using the a priori algorithm method, you can find out which products sell the most. From the results of manual calculations it was found that consumers who bought RB 1060 would buy RB 1099 with 81% confidence, whereas using WEKA it was found that consumers who bought RB 1060 would buy RB 1099 with a confidence value of 82%.
Pelatihan Internet of Things (IoT) untuk Smart Home dan Smart School di SMK Garuda Nusantara Danny, Muhtajuddin; Arwan Sulaeman, Asep; Maringan Hutauruk, Basar; Damuri, Amat
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 3 No. 2 (2025): Desember 2025
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v3i2.157

Abstract

The rapid development of digital technology, particularly the Internet of Things (IoT), requires vocational education institutions to align graduate competencies with the demands of Industry 4.0. SMK Garuda Nusantara has significant potential in developing technology-based human resources; however, limitations remain in teachers’ and students’ practical IoT skills as well as in the availability of supporting learning facilities. This community service program aims to enhance teachers’ and students’ competencies through IoT training integrated with smart home and smart school concepts. The implementation method consists of preparation, socialization, theoretical training, hands-on workshops, mentoring, and program evaluation. The training focuses on the use of microcontrollers, sensors, and the development of applied IoT prototypes such as automatic lighting systems, RFID-based attendance systems, and classroom environmental monitoring. The expected outcomes include improved practical skills of teachers and students, the development of IoT learning modules based on project-based learning, and the creation of simple smart home and smart school prototypes applicable in the school environment. This program also supports the implementation of the Merdeka Belajar Kampus Merdeka (MBKM) policy and contributes to the achievement of higher education Key Performance Indicators (IKU) through sustainable collaboration between universities and school partners.
Co-Authors . Ermanto Abdul Rohim, Abdul Andriani Aprila Hardi, Resty ARIF SUSILO Baehaqi Butsianto, Sufajar Damuri, Amat Dendy K. Pramudito Eko Putra, Fibi Halim Anshor, Abdul Hudan Permana, Rinaldy Indradewa, Rhian Maringan Hutauruk, Basar Muhtajuddin Danny Naya, Candra Pratama, Suria Setyawan, Wisnu Soejarminto, Yos Sri Winarni Triwibowo, Edi