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All Journal ELINVO (Electronics, Informatics, and Vocational Education) Edu Komputika Journal Journal of Educational Research and Evaluation Journal of Information Engineering and Technology
Nurlayli, Akhsin
Department Of Electronic And Informatic Engineering Education, Faculty Of Engineering, UNY
Humanities Computer Science & IT Education Electrical & Electronics Engineering Engineering Public Health Social Sciences Other

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Analisis Tren Pertemuan Tatap Muka Terbatas dari Persepsi Warganet pada Twitter Menggunakan Topic Modeling Nurlayli, Akhsin; Dwiningrum, Siti Irene Astuti; Alwi, Ahmad Chafid; Raharjo, Amrih Setyo
Edu Komputika Journal Vol 9 No 2 (2022): Edu Komputika Journal
Publisher : Jurusan Teknik Elektro Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukomputika.v9i2.62076

Abstract

Salah satu dampak dari pandemi COVID-19 adalah pemberlakuan pembelajaran daring. Kegiatan pembelajaran daring banyak mengalami kendala, sehingga menyebabkan tidak tercapainya kompetensi pembelajaran dengan baik. Kemudian pemerintah memberlakukan program Pertemuan Tatap Muka Terbatas (PTMT), dan banyak tanggapan masyarakat terhadap program tersebut. Untuk mengetahui bagaimana persepsi masyarakat terhadap PTMT, kita dapat melakukan analisis terkait PTMT melalui media sosial. Karena media sosial merupakan salah satu media yang paling banyak digunakan oleh masyarakat selama pandemi untuk berkomunikasi, menyampaikan pendapat, mencari berita, dan lain-lain. Adapun analisis tren PTMT dari persepsi warganet yang dapat dilakukan adalah malalui topic modeling. Dengan mengambil data dari Twitter terkait PTMT menggunakan API (Application Programming Interface), selanjutnya memproses topic modeling dengan metode Latent Dirichlet Allocation (LDA), sebuah metode yang paling populer dan banyak digunakan pada penelitian text mining. Hasil pemodelan topik menghasilkan sepuluh klaster topik dengan nilai koheren 0,50344, dengan tiga topik yang paling banyak dibicarakan oleh warganet. Yakni tentang banyaknya tugas yang harus dikerjakan di luar jam sekolah, mereka sudah mencapai titik kebosanan menjalani sekolah dari rumah hingga menyebabkan kemalasan, dan banyak warganet yang membicarakan bahwa mereka akan melaksanakan kegiatan PTMT.
Advancing Elementary Education: A Personalized e-Math Approach Using Interactive Gamification Wati, Unik Ambar; Nurlayli, Akhsin; D, Siti Irine Astuti; Murrti, Rahayu Condro; Kurniawan, Hendra Listya; Fitriya, Yeni; Ramadhani, Deasy
Journal of Education Reseach and Evaluation Vol 9 No 2 (2025): May
Publisher : LPPM Undiksha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jere.v9i2.93789

Abstract

PISA's assessment criteria aim to gauge the efficacy of educational systems in equipping students with the skills to utilize mathematical knowledge across various life aspects, thereby nurturing them into constructive, engaged, and reflective citizens for the 21st century. Our research focus is on scrutinizing the current spectrum of research and advancements relevant to the integration of e-learning, gamification, and personalized learning methodologies within elementary mathematics education. The Game Development Life Cycle process encompasses an iterative methodology with six distinct phases: Initiation, Pre-Production, Production, Testing, Beta, and Release. The beta testing phase, which involved 265 respondents, was conducted using the System Usability Scale (SUS) to assess the usability of the new software. The SUS, a proven and efficient tool, consists of a 10-item questionnaire that gauge’s users' overall satisfaction with the product.  This result serves as a valuable feedback mechanism, guiding the development team on where to focus their efforts in refining the software's interface and functionality to enhance the overall user experience in subsequent iterations. The integration of e-learning, personalization, and gamification in elementary mathematics education offers promising benefits in terms of accessibility, engagement, and learning outcomes. However, challenges such as the digital divide and the need for teacher training must be addressed to maximize their potential. The presented game development methodology serves as a structured guide for creating effective personalized educational games, contributing to the ongoing discourse on innovative approaches to mathematics education.
Topik Modeling Penelitian Dosen JPTEI UNY Pada Google Scholar Menggunakan Latent Dirichlet Allocation Nurlayli, Akhsin; Nasichuddin, Moch. Ari
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 4 No. 2 (2019): November 2019
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (612.934 KB) | DOI: 10.21831/elinvo.v4i2.28254

Abstract

The mapping of research topics for lecturers is necessary to determine the research tendencies in a department or study program. This study aims to implement topic modeling in the publication titles of the Department of Electronics and Informatics Education Engineering of Universitas Negeri Yogyakarta (JPTEI UNY) lecturers taken from Google Scholar. The method used for topic modeling is the Latent Dirichlet Allocation (LDA). LDA is a generative probabilistic model for finding the semantic structure of a corpus collection based on the hierarchical bayesian analysis. After the topic modeling process, the results showed that JPTEI UNY lecturers tend to have four research clusters consisting of vocational education, system development, learning media, and vocational learning systems.
Analisis Perbandingan Metode Support Vector Machine dan Naïve Bayes pada Persepsi Publik terhadap Naturalisasi Pemain Timnas Sepak Bola Indonesia: Studi Kasus Sosial Media X Widodo, Yoga Sulistiyo; Nurlayli, Akhsin
Journal of Information Engineering and Technology Vol. 3 No. 2 (2025): September 2025
Publisher : Department of Electronics and Informatics Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jiety.v3i2.2162

Abstract

Pemerintah menerapkan kebijakan naturalisasi untuk meningkatkan prestasi Timnas Sepak Bola Indonesia, namun hal ini menimbulkan sentimen yang beragam di masyarakat.  Penelitian ini bertujuan untuk membandingkan performa algoritma Support Vector Machine (SVM) dan Naïve Bayes serta menganalisis persepsi publik terhadap kebijakan tersebut melalui data media sosial X.  Dengan menggunakan metodologi Cross Industry Standard Process for Data Mining (CRISP-DM), penelitian ini mengolah 10.454 data yang diambil dari Januari 2020 hingga Januari 2025.  Hasil evaluasi menunjukkan SVM lebih unggul dengan akurasi 0,80 dan F1-score 0,72, dibandingkan Naïve Bayes dengan akurasi 0,76 dan F1-score 0,66.  Analisis sentimen mengungkapkan bahwa persepsi publik secara keseluruhan cenderung negatif jika dibandingkan langsung dengan sentimen positif, meskipun sentimen netral sedikit lebih unggul.  Implikasi dari penelitian ini adalah memberikan wawasan berbasis data bagi PSSI untuk mengevaluasi dan merumuskan strategi komunikasi kebijakan naturalisasi yang lebih efektif dan sejalan dengan aspirasi publik.
Co-Authors Ahmad Chafid Alwi Amrih Setyo Raharjo D, Siti Irine Astuti Kurniawan, Hendra Listya Murrti, Rahayu Condro Nasichuddin, Moch. Ari Ramadhani, Deasy Siti Irene Astuti Dwiningrum Unik Ambarwati Widodo, Yoga Sulistiyo YENI FITRIYA