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All Journal Indo-MathEdu Intellectuals Journal Prosiding SeNTIK STI&K
Wardhana, Indra Sari Kusuma
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics

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METAVERSE: TRANSFORMASI DUNIA DIGITAL DAN TANTANGAN MASA DEPAN Wardhana, Indra Sari Kusuma; Irawati, Diyah Ruri; Hakim, Abdul
Prosiding Seminar SeNTIK Vol. 8 No. 1 (2024): Prosiding SeNTIK 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Metaverse merupakan evolusi teknologi yang menggabungkan elemen realitas virtual (VR), augmented reality (AR), dan lingkungan digital untuk menciptakan ruang virtual yang imersif dan interaktif. Artikel ini membahas potensi metaverse dalam mengubah cara manusia berinteraksi, belajar, dan bekerja, sekaligus menganalisis tantangan yang dihadapi dalam pengembangannya. Dari segi transformasi digital, metaverse menawarkan peluang besar di berbagai sektor, termasuk pendidikan, bisnis, dan hiburan, dengan memungkinkan kolaborasi dan komunikasi tanpa batas geografis. Namun, penerapan teknologi ini tidak terlepas dari tantangan signifikan seperti keterbatasan infrastruktur teknologi, masalah privasi dan keamanan data, serta regulasi yang belum jelas. Artikel ini juga mengeksplorasi peran teknologi blockchain, cloud computing, dan kecerdasan buatan (AI) dalam mengatasi tantangan tersebut, serta pentingnya kolaborasi lintas sektor untuk menciptakan metaverse yang aman, inklusif, dan berkelanjutan. Dengan memadukan analisis teoretis dan studi kasus, artikel ini menawarkan pandangan komprehensif tentang masa depan metaverse dan dampaknya terhadap transformasi dunia digital.
Klasifikasi Tingkat Prestasi Mahasiswa Pada Mata Kuliah Penambangan Data Menggunakan Naïve Bayes Putri, Basmallah Ramadhani Aisyah; Mursidan, Almurozy; Wardhana, Indra Sari Kusuma
Indo-MathEdu Intellectuals Journal Vol. 7 No. 1 (2026): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v7i1.5050

Abstract

This study aims to classify the academic achievement levels of students in Data Mining courses using the Naïve Bayes algorithm and to evaluate the performance of the resulting classification model. This study uses a quantitative approach with academic data from 190 students, including assignment scores, mid-term exam scores, and final exam scores. The classification process was carried out by applying the Naïve Bayes algorithm, while model evaluation was performed using accuracy metrics, classification reports, and confusion matrices. The test results showed that the Naïve Bayes model produced an accuracy rate of 73.68%. Based on the classification report, classes B and B+ showed the best performance with recall values of 1.00 and f1-scores of 0.87 and 0.95, respectively. Confusion matrix analysis showed that most of the data in classes B and B+ were classified correctly. The results of this study indicate that the Naïve Bayes algorithm is quite effective in classifying students' academic achievement levels and has the potential to be used as an academic evaluation tool in learning decision-making.
Co-Authors Abdul Hakim Almurozy Mursidan Irawati, Diyah Ruri Putri, Basmallah Ramadhani Aisyah