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All Journal Union: Jurnal Ilmiah Pendidikan Matematika Jurnal Teknoinfo Ainet : Jurnal Informatika Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Prosiding Sesiomadika Journal of Engineering and Information Technology for Community Service Jurnal Pengabdian kepada Masyarakat (Nadimas) CHAIN: Journal of Computer Technology, Computer Engineering and Informatics AI and Developmental Insights in Education (AIDIE)
Permata Permata
Universitas Teknokrat Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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The Potential of AI Chatbots as Learning Companions: Early Insights from Students’ Cognitive and Emotional Responses Adhie Thyo Priandika; Permata Permata
AI and Developmental Insights in Education Vol. 1 No. 1 (2025): AI and Developmental Insights in Education
Publisher : CV. FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aidie.v1i1.58

Abstract

This study examined the growing educational challenge of understanding how students cognitively and emotionally experience AI chatbots when used as learning companions. Using a convergent mixed-methods design, data were collected from 189 university students through Likert-scale measures of cognitive support, emotional response, and perceived effectiveness, along with 186 written reflections analyzed using thematic analysis. Quantitative results showed strong perceptions of cognitive clarity, positive emotional experiences, and significant associations among the three constructs, indicating that students who felt cognitively supported also viewed the chatbot as more effective. Qualitative themes reinforced these findings by revealing that students valued the chatbot’s step-by-step explanations and experienced a sense of emotional safety when asking questions. Integrated analysis demonstrated convergence across strands, highlighting the intertwined cognitive and emotional dimensions of chatbot-assisted learning. The study contributes early evidence that AI chatbots can function as supportive learning companions with meaningful implications for AI-enhanced education.
IMPLEMENTASI FORECASTING PADA PERENCANAAN SISTEM PEMESANAN BUKU LKS (LEMBAR KERJA SISWA) MENGGUNAKAN ALGORITMA REGRESI LINEAR. (STUDI KASUS: TOKO BUKU DARUL ULUM, PUNGGUR, LAMPUNG TENGAH Permata permata
Jurnal Data Mining dan Sistem Informasi Vol 3, No 2 (2022): Agustus 2022
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v3i2.2162

Abstract

Darul Ulum Bookstore is engaged in distributing LKS books to be sent to schools. The need for worksheets that support learning is one of the most important aspects of availability in the store. so it takes sufficient stock in the order at the beginning of the semester. In this case, the shop owner has difficulty in estimating the number of books to be ordered, so a calculation model is needed to estimate how many books will be ordered at the beginning of the semester. The Multiple Linear Regression method is one of the methods used to predict how many books will be ordered. This method uses the dependent variable and the independent variable as the basis by taking into account the initial stock of books for 2018 and 2019 as the independent variable (x) and the initial stock of 2020 as the dependent variable (y). The results of this study obtained a predictive accuracy value from each printing, namely for CV. Hasan Pratama with MAPE testing of 6.42% with very good indicators. CV. Pratama Mitra Aksara with MAPE testing of 23.52% the results of the indicators are feasible, and CV. Pilar Pustaka with MAPE testing of 6.75% the indicator results are very good. And visualization of predictive data using R-Markdown. Keywords: Linear Regression, Predicting, Interactive Website, R-Markdown
Co-Authors ., Rusliyawati Ade Dwi Putra Ade Surahman Alita, Debby Anggun Maylani Anissa Anggraini Annisa Anggraini Debby Alita Devi Utari Dina Amelia Eka Nurhana Faruq Ulum Feby Amanda Gunawan, Rakhmat Dedi Isnain, Auliya Rahman Lathifah L Lathifah Lathifah Mico Fahrizal Nirwana Hendrastuty Parjito Parjito Priandika, Adhie Thyo Rachmi Marsheilla Aguss Rakhmad Dedi Gunawan Rido Febryansyah Riska Amalia Praptiwi S. Samsugi SERLI WIDIASTUTI suaidah suaidah Temi Ardiansah Ulum, Faruk Very Hendra Saputra Wahyu Setiawan Wulan Destaria Rahmawati Yuri Rahmanto Zaenal Abidin Zulfa Nabila