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All Journal Jurnal Ilmu Komunikasi Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) CogITo Smart Journal Technomedia Journal Jurnal Teknologi Informasi dan Pendidikan Jurnal Teknologi Informasi dan Multimedia SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Sinar Sang Surya: Jurnal Pusat Pengabdian kepada Masyarakat Jurnal PkM (Pengabdian kepada Masyarakat) Kresna: Jurnal Riset dan Pengabdian Masyarakat Prosiding Konferensi Nasional PKM-CSR Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Ticom: Technology of Information and Communication Telematika MKOM Jurnal Ticom: Technology of Information and Communication Jurnal Pengabdian Masyarakat Tekno
Utomo Budiyanto
Fakultas Teknologi Informasi, Universitas Budi Luhur
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Nursing Physics Public Health Social Sciences

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Journal : Technomedia Journal

 1 
Adaptive E-Learning System Berbasis Vark Learning Style dengan Klasifikasi Materi Pembelajaran Menggunakan K-NN (K-Nearest Neighbor) Agung Nugraha, Akmal; Budiyanto, Utomo
Technomedia Journal Vol 7 No 2 October (2022): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (531.648 KB) | DOI: 10.33050/tmj.v7i2.1900

Abstract

This research is about Adaptive E-Learning System Based on VARK (Visual, Aural, Read/Write & Kinesthetic) Learning Style With Classification of Learning Materials Using K-NN (K-Nearest Neighbor). The world of education today must follow technological developments, one of which is by utilizing learning using e-learning, one of the shortcomings in e-learning that currently exists is that most provide the same material to all students, in fact every student has a different learning style. different in absorbing learning material. This Adaptive E-Learning System adopts VARK Learning Style in classifying student learning styles into four classes (Visual, Aural, Read/Write & Kinesthetic). At the beginning of using e-learning students are required to fill out a questionnaire based on the VARK instrument and will be assigned to one of the four classes according to their learning style tendencies. Students will get material according to their class with the K-NN (K-Nearest Neighbor) classification method. In this study, the classification of learning materials used 60 learning materials as datasets with visual, aural, read/write & kinesthetic labels, with 48 training data and 12 testing data divided into 91% accuracy, 93% precision and 91% recall.
Analisis Sentimen Saran Pengguna Mandatory E-Learning Menggunakan Text Mining pada Learning Management System: Sentiment Analysis of User Suggestions for Mandatory E-Learning Using Text Mining on the Learning Management System Nur Syamsudin, Andi; Budiyanto, Utomo
Technomedia Journal Vol 9 No 3 (2025): February
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v9i3.2368

Abstract

Mandatory E-learning is a required training for Ministry of Finance’s employees through Kemenkeu Learning Center (KLC) as LMS, where text-based recapitulation reports for participant’s feedback are not available due to large volume of participant’s evaluation data. Sentiment analysis using text mining is necessary to classify the feedback into positive, negative, and neutral labels, enabling the recapitulation process to be automated, faster, and more accurate. Using Knowledge Discovery in Databases (KDD) framework, the process involves data selection and manual labeling, text preprocessing (data cleansing, case folding, stop word removal, stemming, tokenizing, filtering tokens by length), data transformation (TF-IDF weighting, cosine similarity measurement, and resampling using random undersampling/RUS to reduce majority label). Modeling phase compares the best combination of algorithms covers Support Vector Machine (SVM), Multinomial Naïve Bayes, K-Nearest Neighbor (KNN), and Random Forest using a 90:10 training-to-testing data ratio. This research show that SVM with cosine similarity is the best algorithm scenario, achieving accuracy, precision, recall, and f1-score for negative label of 97.01\%, 96.22\%, 95.82\%, and 96.02\%, respectively, within 48.71 seconds, which \textbf{can be leveraged} to improve quality of e-learning’s report faster, more accurate, and to be automated.
Co-Authors Abdul Nasir Abdul Rahman Achmad Solichin Agung Nugraha, Akmal Amalia, Anissa Amini, Safrina Andhika Putra Cahyono Angga Prasetyo Angga Prasetyo Anindya Putri Pradiptha Anissa Amalia Mulya Anissa Amalia Mulya Ari Sujiana ari Atik Ariesta Bijan Austin Pratama Diajeng Prastyanti Priyadi Fadillah, Arkan Faishal Zoelfiandi Faishal Zoelfiandi Farida Ariyani, Pipin Giri Sarah Mustika Hairil Fiqri Sulaiman Hardjianto, Mardi Hari Soetanto Helmy Yudhistira Putra Megananda Hervita Permata Sari Mirza Sutrisno Mulya, Anissa Amalia Nawiroh Vera Nico Nico Nico Nur Syamsudin, Andi Painem, Painem Pradiptha, Anindya Putri Retsya Andany Putri Rony, Zahara Tussoleha Rulli Sofansyah saputro, heru Sartana, Bruri Trya Subandi Subandi Sulaiman, Hairil Fiqri Sutrisno, Mirza Syihabudin Alawi Titin Fatimah Titin Fatimah Triyono, Gandung Triyono, Gandung Triyono Umaimah Wahid Wendi Usino Yani Prabowo Yunanto, Rinno Zulfikar, Muhammad Fahmi