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Zarnelly
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Penerapan Metode Regresi Linier Untuk Prediksi Jumlah Orang Terlantar Di Provinsi Riau Windy Amelia Putri; M. Afdal; Permana, Inggih; Zarnelly
Jurnal Sistem Cerdas Vol. 6 No. 2 (2023)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v6i2.291

Abstract

Displaced people are residents who for some reason cannot meet their needs naturally, both spiritually, physically, and socially. The problem of displaced people occurs for various reasons and urbanization is one of them. Social Service is a government agency that plays a role in improving the quality of social welfare of individuals, groups, and communities. Many community services are carried out by Social Service and one of them is the repatriation of displaced persons. Based on data from the Riau Province Social Service, the number of displaced people in Riau Province from year to year has increased or decreased erratically. This is of course a problem that hinders the Riau Province Social Service in its internal processes such as determining strategies or policies to make decisions. Therefore, this research was conducted to overcome these problems. This research was conducted using the linear regression method with a MAPE result of 7.09% which will be implemented into a prediction application for the number of displaced people and aims to help the Riau Province Social Service get information on the number of displaced people in the next period. Based on the results of the Blackbox test, shows that all menus and features have run very well and obtained a User Acceptance Test (UAT) calculation value of 92%.
Sentiment Analysis ChatGPT Using the Multinominal Naïve Bayes Classifier (NBC) Algorithm Sri Rahayu, Dwi; Novita, Rice; Khairil Ahsyar, Tengku; Zarnelly
Jurnal Sistem Cerdas Vol. 7 No. 1 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i1.388

Abstract

Chatbots have become one of the popular solutions for improving customer service. One well-known chatbot is ChatGPT, a language model developed by OpenAI. As time goes by and more and more people use ChatGPT, sentiment analysis is needed about users' opinions about the ChatGPT service. Therefore, it is necessary to carry out sentiment analysis of the ChatGPT service on Twitter to find out how users respond to this chatbot service. In this research, the results showed positive sentiment of 57%, negative sentiment of 29% and neutral sentiment of 14%. Topics for each sentiment were also obtained and sentiment prediction results from 40% of the test data with results of 96% positive, 3.5% negative and 0.5% neutral with a test accuracy of 63%.
Co-Authors Inggih Permana Khairil Ahsyar, Tengku M. Afdal Rahayu, Dwi Sri Rice Novita Windy Amelia Putri