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All Journal Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Jurnal MediaTIK Artificial Intelligence in Educational Decision Sciences
Sul Fitriana
Universitas Negeri Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Elevating Entertainment Experience: Crafting an Interactive Streaming Platform with Waflixx Wahyu Hidayat M; Al Hikma; Sul Fitriana; Nur Ainung
Information Technology Education Journal Vol. 3, No. 3, September (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v3i3.5843

Abstract

Menyikapi perkembangan lanskap hiburan digital, pembuatan Waflixx muncul sebagai solusi untuk memenuhi permintaan yang meningkat terhadap platform streaming multimedia interaktif. Mengambil inspirasi dari tantangan yang dihadapi oleh layanan streaming tradisional, Waflixx bertujuan untuk merevolusi cara pengguna berinteraksi dengan konten digital. Dengan menawarkan fitur-fitur unik seperti urutan tonton yang dapat disesuaikan dan pilihan alur cerita, bersama dengan fungsionalitas kolaboratif seperti pesta nonton virtual, Waflixx membedakan dirinya dalam pasar streaming yang kompetitif. Selain itu, integrasi pengalaman mendalam melalui dukungan VR dan peta interaktif yang menampilkan lokasi syuting dunia nyata meningkatkan kedalaman keterlibatan pengguna. Melalui pengembangan perangkat lunak yang teliti, kurasi konten, dan desain UI/UX, Waflixx berusaha untuk menyajikan pengalaman hiburan yang personal dan interaktif, mengatasi kebutuhan kontemporer konsumen digital.
Analysis Of Data Security Systems In Business Organizations Veronika Asri Tandirerung; Fauziah; Sul Fitriana; Putri Rahayu; Dzaky Raihan Muharram
Journal of Embedded Systems, Security and Intelligent Systems Vol 4, No 2 (2023): November 2023
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v4i2.1124

Abstract

This research aims to find out the data security system at Graha Pena Makassar. Graha Pena Makassar is also called Fajar Building which was built in 2007. Graha Pena Makassar is an office building that is the main choice for investors for business development. This research was conducted by identifying potential security gaps and existing security infrastructure. This research method is observation, literature study and conducting interviews. The results showed that the data security system implemented by Graha Pena Makassar has not implemented technology or special applications for database security systems. The security system implemented is more on a physical security system.
Predicting Student Dependency on ChatGPT for Academic Tasks Using Naive Bayes Classification Risha Febrianti; Sul Fitriana; Asrafah; Stephen Amukune
Artificial Intelligence in Educational Decision Sciences Vol 1 No 2 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i2.23

Abstract

Purpose – This study aims to predict and classify the level of student dependency on ChatGPT in completing academic tasks using the Naive Bayes algorithm to support data-driven decision making in higher education.Methods – A quantitative survey approach was employed involving 254 active undergraduate students from the Department of Informatics and Computer Engineering at a public university in Indonesia. Data were collected through a Likert-scale questionnaire measuring five behavioral indicators: purpose of ChatGPT use, interaction frequency and duration, understanding of generated outputs, trust in AI responses, and learning independence. The collected data were cleaned, numerically encoded, and labeled into three dependency categories (low, medium, high). A Naive Bayes classification model was implemented using Orange Data Mining and evaluated under three data split scenarios: 90:10, 80:20, and 70:30.Findings – The results indicate that the 70:30 data split achieved the highest classification performance, with an AUC value of 0.973, accuracy of 85.3%, F1-score of 0.866, and precision of 0.909. These results demonstrate that the Naive Bayes algorithm is effective in identifying distinct patterns of student dependency on ChatGPT based on multidimensional behavioral data.Research limitations – This study is limited to a single academic program and relies on self-reported questionnaire data, which may constrain the generalizability of the findings across different educational contexts.Originality – This study provides empirical evidence on the application of probabilistic classification models to assess student dependency on generative AI, contributing to educational decision sciences by informing institutional policies on balanced and responsible AI use in higher education.
Analisis Efektivitas Mixed-Mode Learning dalam Menjawab Tantangan Era Metaverse pada Mahasiswa Universitas Negeri Makassar Wahyu Hidayat M; M Syahid Nur Wahid; Fauziah Fauziah; Sul Fitriana; Al Hikma
Jurnal MediaTIK Volume 7 Issue 2, Mei (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/mediatik.v7i2.2189

Abstract

Peningkatan teknologi dan perkembangan media digital telah membawa dampak signifikan pada dunia pendidikan, terutama dalam menghadapi tantangan era Metaverse. Metaverse menuntut adopsi pendekatan pembelajaran inovatif guna memenuhi kebutuhan mahasiswa yang semakin beragam dan dinamis. Salah satu pendekatan yang muncul sebagai solusi adalah Mixed-Mode Learning, yang mengintegrasikan pembelajaran daring dan tatap muka. Artikel ini bertujuan untuk menganalisis efektivitas Mixed-Mode Learning sebagai metodologi pembelajaran campuran dalam menjawab tantangan era Metaverse, khususnya pada mahasiswa Universitas Negeri Makassar. Metodologi penelitian ini melibatkan pengumpulan data kualitatif melalui penyebaran angket (Kuesioner) terhadap mahasiswa yang mengikuti program Mixed-Mode Learning pada Universitas Negeri Makassar. Hasil penelitian menunjukkan bahwa implementasi Mixed-Mode Learning secara positif memengaruhi efektivitas pembelajaran, memfasilitasi interaksi antara mahasiswa dan dosen, serta memberikan pengalaman belajar yang lebih menyeluruh dalam konteks Metaverse. Oleh karena itu, artikel ini menyajikan temuan-temuan yang mendukung penggunaan Mixed-Mode Learning serta implikasi praktis dan saran untuk pengembangan lebih lanjut untuk meningkatkan penerapan Mixed-Mode Learning di lingkungan pendidikan tinggi.
Co-Authors Al Hikma Asrafah Dzaky Raihan Muharram Fauziah Fauziah Fauziah M. Syahid Nur Wahid Nur Ainung Putri Rahayu Risha Febrianti Stephen Amukune Veronika Asri Tandirerung Wahyu Hidayat M Wahyu Hidayat M