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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Zonasi: Jurnal Sistem Informasi Jurnal IPTEK Bagi Masyarakat Brilliance: Research of Artificial Intelligence
Joy Nasten Sinaga
Universitas Mikroskil
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Analysis of Factors Affecting the Use of ChatGPT at Mikroskil University: A Study Based on the Extended UTAUT2 Model Sinaga, Joy Nasten; Panjaitan, Erwin Setiawan; Nurjanah, Sofinana
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.v4i1.3862

Abstract

This study aims to identify factors that influence students' intention and behavior in using ChatGPT at Mikroskil University. The method used in this study is the Unified Theory of Acceptance and Use of Technology (UTAUT2), with the addition of the curiosity variable. Other variables analyzed included Performance Expectations, Effort Expectations, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, and Habit. To collect data, the research team conducted a survey of active students of the Faculty of Informatics at the university. Data analysis was conducted using Structural Equation Modeling (SEM), a method that allows researchers to understand complex relationships between variables. The results showed that several factors had a significant influence on the intention and behavior of using ChatGPT. Hedonic Motivation, Habit, and Curiosity were found to be the main factors driving the intention to use ChatGPT. Facilitating Conditions also contributed to the use of the technology. On the other hand, some variables did not show a significant impact on usage intention. Performance Expectancy, Effort Expectancy, Social Influence, and Price Value fall into this category. These findings suggest that the factors that drive technology use may vary, and highlight the importance of curiosity and personal motivation in the adoption of new technologies in higher education settings.
Pelatihan Pemanfaatan Aplikasi Ms. Power Point Untuk Presentasi Pada SMA Methodist 6 Medan Tan, Hita; Br Sembiring, Salsalina; Nasten Sinaga, Joy
Jurnal IPTEK Bagi Masyarakat Vol 2 No 1 (2022)
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jibm.v2i1.188

Abstract

SMA Methodist 6 Medan adalah satuan pendidikan jenjang SMA di Jl. Sekolah No.402, Kota Medan, Sumatera Utara. Dalam menjalankan kegiatannya, Sekolah ini berada di bawah naungan Kementerian Pendidikan dan Kebudayaan. Tujuan sekolah ini adalah menghasilkan siswa berkompetensi tinggi dan mampu bersaing di era globalisasi sehingga setiap siswa harus memiliki kreatifitas dan kemampuan dalam bidang teknologi yang sangat diperlukan dalam dunia kerja. Pandemi juga menuntut siswa untuk selalu aktif dan produktif dalam mengembangkan potensinya. Siswa-siswi harus dipersiapkan sedini mungkin agar mampu dalam mempresentasikan materi, baik dalam menyiapkan materi maupun kemampuan melakukan presentasi dengan baik. Para siswa cenderung hanya menyusun konten presentasi menggunakan fitur seadanya, bahkan menggunakan template, penyusunan format dan animasi yang kurang cocok dengan topik yang akan dipresentasikan.
OPTIMALISASI LAYANAN FREELANCE MELALUI PENGEMBANGAN APLIKASI MARKETPLACE BERBASIS MOBILE Sinaga, Joy Nasten; Limbong, Ricky Paian
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 2 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Mei 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v7i2.24563

Abstract

Marketplace Freelance Service adalah platform yang menghubungkan freelancer sebagai penyedia jasa dengan klien sebagai pencari jasa, memungkinkan transaksi layanan secara online. Namun, terdapat tantangan dalam proses pembayaran honor yang kurang efisien, di mana freelancer sering kali tidak menerima honor tepat waktu, sementara klien merasa tidak nyaman membayar penuh di awal kontrak. Selain itu, freelancer tidak memiliki bukti otentik berupa Laporan Penghasilan yang merinci pendapatan mereka, yang penting sebagai referensi melamar pekerjaan atau untuk negosiasi gaji. Penelitian ini bertujuan mengembangkan Aplikasi Mobile Marketplace Freelance Service sebagai solusi atas permasalahan tersebut. Aplikasi ini dikembangkan menggunakan metode Rapid Application Development (RAD) dengan pemodelan berbasis React Native dan Node.js untuk aplikasi mobile, serta React.js dan Node.js untuk aplikasi back-end. Database dikelola menggunakan MySQL. Aplikasi yang dihasilkan mendukung tiga jenis pengguna: freelancer, klien, dan admin, sehingga menciptakan ekosistem layanan yang lebih efisien dan transparan
Analysis of Factors Affecting the Use of ChatGPT at Mikroskil University: A Study Based on the Extended UTAUT2 Model Sinaga, Joy Nasten; Panjaitan, Erwin Setiawan; Nurjanah, Sofinana
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.v4i1.3862

Abstract

This study aims to identify factors that influence students' intention and behavior in using ChatGPT at Mikroskil University. The method used in this study is the Unified Theory of Acceptance and Use of Technology (UTAUT2), with the addition of the curiosity variable. Other variables analyzed included Performance Expectations, Effort Expectations, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, and Habit. To collect data, the research team conducted a survey of active students of the Faculty of Informatics at the university. Data analysis was conducted using Structural Equation Modeling (SEM), a method that allows researchers to understand complex relationships between variables. The results showed that several factors had a significant influence on the intention and behavior of using ChatGPT. Hedonic Motivation, Habit, and Curiosity were found to be the main factors driving the intention to use ChatGPT. Facilitating Conditions also contributed to the use of the technology. On the other hand, some variables did not show a significant impact on usage intention. Performance Expectancy, Effort Expectancy, Social Influence, and Price Value fall into this category. These findings suggest that the factors that drive technology use may vary, and highlight the importance of curiosity and personal motivation in the adoption of new technologies in higher education settings.
Big Data Analytics for Predicting Depression Risk in Generation Z: Integrating Self-Organizing Maps and Long Short-Term Memory Sinaga, Joy Nasten; Nuraina, Nuraina; Sinaga, Frans Mikael; Kelvin, Kelvin; Nurhayati, Nurhayati
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38011

Abstract

Mental health issues among Generation Z are rising, with depression being one of the most significant challenges. Leveraging the capabilities of big data analytics and artificial intelligence, this study proposes a hybrid method combining Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) networks to predict depression risk based on behavioral data. The SOM algorithm is utilized for clustering high-dimensional input data to uncover hidden patterns, while the LSTM network is employed to capture sequential dependencies over time. Data were collected from various digital platforms, processed, and analyzed to train and validate the proposed model. Results show that the SOM-LSTM framework significantly improves the accuracy and reliability of early depression risk detection compared to conventional models. This study contributes a scalable and adaptable model for mental health prediction that can assist in timely interventions for Generation Z
Co-Authors Br Sembiring, Salsalina Ginting, Tri Wulandari Kelvin Kelvin Nuraina, Nuraina Nurhayati Nurhayati Nurjanah, Sofinana Panjaitan, Erwin Setiawan Ricky Paian Limbong Sinaga, Frans Mikael Tan, Hita