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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

Designing an Chatbot with NLP Technology in a Website-Based New Student Admission Information System Fauzan, Muhammad Fathan; Imanda, Rahmi; Hasbi, Muhammad Adryan
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8489

Abstract

In the fast pace of digitalization, student admission information system websites face the challenge of providing responsive and quality services to applicants. One emerging solution is the use of chatbots, which enable automated interaction with customers. Technology continues to transform over time. At SMK Insan Teknologi (InTek), the service process is still manual, such as physical archives for student registration, incomplete information, and the absence of an official website. To improve administration and data access, a web-based information system is offered. While the Chatbot helps in interactive services and time efficiency to answer registrants' questions, NLP is used to make the conversation in the chat more natural and easy to understand by registrants. The results of testing the system show that the system functions properly in responding to messages sent through the chatbot on the website both from the message text according to the intent, as well as abstract text and not according to the pattern with an accuracy rate of 87,5%. It is hoped that this research can improve the quality of service and administrative efficiency at SMK Insan Teknologi and can be applied in other educational institutions.
Public Sentiment Analysis of the Free Meal Program: A Comparison of Naive Bayes and Support Vector Machine Methods on the Twitter (X) Social Media Platform Saleh, Muhammad Farhan; Imanda, Rahmi
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8895

Abstract

The problems of nutrition, including stunting, remain a challenge in Indonesia. Therefore, Prabowo and Gibran launched the 2024 Free Meal Program, which provides free lunch to every school child as well as pregnant mothers. This research analyzes public sentiment towards this program using data from X with Naïve Bayes and Support Vector Machine (SVM) methods. The data was analyzed through crawling, preprocessing, labeling, and feature extraction using TF-IDF. The results showed a predominance of positive sentiment towards the program, with SVM performing better in sentiment classification, achieving 86.42% accuracy compared to Naïve Bayes with 67.9%. The findings can guide policymakers in improving the communication strategy and implementation of the Free Meal Program to make it more impactful for Indonesians.