Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal INTECOMS: Journal of Information Technology and Computer Science Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Aulia, Mutia Dwi
Unknown Affiliation
Computer Science & IT Languange, Linguistic, Communication & Media Library & Information Science

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Media Pembelajaran Interaktif Berbasis Multimedia Menggunakan Adobe Animate Untuk TK Permata Hati Amalia, Ghina; Aulia, Mutia Dwi; Rahmah, Andini; Rasiban, Rasiban
INTECOMS: Journal of Information Technology and Computer Science Vol 8 No 2 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v8i2.14519

Abstract

Pendidikan anak usia dini memiliki peran penting dalam membentuk dasar perkembangan kognitif, afektif, dan psikomotor anak. Namun, metode pembelajaran tradisional sering kali kurang efektif dalam menarik perhatian anak-anak, sehingga diperlukan media pembelajaran yang lebih interaktif dan inovatif. Penelitian ini bertujuan untuk mengembangkan media pembelajaran interaktif berbasis multimedia menggunakan Adobe Animate untuk mendukung proses belajar-mengajar di TK Permata Hati. Media pembelajaran ini dirancang dengan fitur interaktif, animasi, suara, serta permainan edukatif yang dapat meningkatkan minat dan pemahaman anak-anak terhadap materi pembelajaran. Metode penelitian yang digunakan meliputi observasi, studi literatur, dan pendekatan prototyping dalam pengembangan media. Pengujian dilakukan melalui tiga tahap, yaitu pengujian fungsionalitas, kelayakan, dan efektivitas. Hasil pengujian menunjukkan bahwa media pembelajaran interaktif berbasis Adobe Animate dapat meningkatkan keterlibatan siswa, memudahkan guru dalam menyampaikan materi, serta membantu anak-anak memahami konsep dengan cara yang lebih menarik dan menyenangkan. Dengan demikian, pengembangan media ini dapat menjadi solusi inovatif dalam meningkatkan kualitas pembelajaran di tingkat pendidikan anak usia dini.
Penerapan Algoritma Naive Bayes dalam Sistem Analisis Sentimen Media Sosial X terhadap Film Agak Laen Aulia, Mutia Dwi; Akbar, Yuma
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i3.1608

Abstract

This study examines the application of the Naïve Bayes algorithm for sentiment analysis on social media platform X (formerly Twitter) regarding the movie Agak Laen. In the digital era, understanding public opinion is highly important, and this film was chosen as a case study due to the large number of circulating reviews. Naïve Bayes was selected for its efficiency in text classification. The research process began with data collection using the TwCommentExport extension, followed by preprocessing to remove noise such as links and punctuation. The cleaned data were manually labeled into positive or negative sentiments. Subsequently, the data were transformed into numerical representations using TF-IDF feature extraction and trained with the Naïve Bayes algorithm. The dataset was divided into 70% training data and 30% testing data to evaluate the model’s performance. The experimental results demonstrated an accuracy of 75.73%. These findings indicate that Naïve Bayes is an effective method for analyzing movie sentiment, although further improvements in data processing or advanced classification techniques are still possible. This research is expected to provide insights into public responses to the film Agak Laen and serve as a reference for the film industry as well as researchers in understanding audience opinions more comprehensively.
Co-Authors Akbar, Yuma Amalia, Ghina Rahmah, Andini Rasiban