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All Journal Letters in Information Technology Education (LITE) Algoritme Jurnal Mahasiswa Teknik Informatika
Christianto, Yudhi
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Computer Science & IT Education

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Project-Based Learning with Examples Non-Examples to Improve Digital Literacy through Short Movie Ads Erinda, Hayyu; Karina, Anggita Ajeng; Wijaya, Dandi Nurmansyah; Nikma, Irfianninza Risquillia; Yuliono, Gun; Ulum, Miftahul; Widayanti, Erma; Christianto, Yudhi
Letters in Information Technology Education (LITE) Vol 7, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study explores the effectiveness of the Project-Based Learning (PjBL) method combined with the Examples Non-Examples approach in enhancing students' digital literacy outcomes through the production of short movie advertisements. In an increasingly digital era, digital literacy has become an essential skill that students must master. PjBL offers a learning approach that actively engages students in real-world projects, while the Examples Non-Examples method provides concrete guidance through both positive and negative examples. This study employs the Classroom Action Research (CAR) model using Kurt Lewin's cycle, involving 36 students from class X.7 at SMAN 8 Malang. Data were collected through observation and documentation studies and analyzed descriptively, both qualitatively and quantitatively. The results indicate that the application of this method significantly improves students' understanding and skills in digital literacy, particularly in video editing, visual composition, and creativity. The study concludes that PjBL with the Examples Non-Examples approach is effective in enhancing students' digital skills and soft skills, and it offers a meaningful contribution to the development of digital learning methods.
Students Incubator Business: A Mentorship Program for Creating Promotional Media for Student Micro Businesses Using Canva Karina, Anggita Ajeng; Erinda, Hayyu; Wijaya, Dandi Nurmansyah; Nikma, Irfianinza Risqulia; Yuliono, Gun; Ulum, Miftahul; Widayanti, Erma; Christianto, Yudhi
Letters in Information Technology Education (LITE) Vol 7, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um010v7i22024p76-80

Abstract

The "Students Incubator Business" program at SMAN 8 Malang aims to enhance students' skills in managing micro businesses by creating effective promotional media using Canva. The program comprises several stages: participant registration, workshops, mentoring, project assessment, and evaluation. A total of 20 students with micro businesses participated in this program, which involved intensive training in graphic design and digital marketing. The results showed increased students' knowledge and skills, mainly using Canva to create attractive promotional media tailored to their target markets. Program evaluation indicated that 80% of participants successfully produced high-quality promotional media, while the rewards effectively boosted students' motivation and confidence. Despite the program's success, ongoing mentorship is needed, particularly in copywriting strategies, to enhance marketing effectiveness further. Overall, this program successfully supported students in developing their micro businesses by improving their digital marketing skills.
Analisis Komparasi Kinerja Model SVM-TF-IDF dan LSTM dengan Embedding BERT untuk Klasifikasi Tingkat Literasi Digital christianto, yudhi; Crysdian, Cahyo; Abidin, Zainal
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.13597

Abstract

This study compares the performance of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) with BERT embedding for classifying users’ digital literacy levels from textual digital footprints, dataset of 1,500 Indonesian-language texts from platform X was annotated by three experts into low, medium, and high literacy categories. After text preprocessing, TF-IDF features were applied to SVM and BERT tokenization to LSTM. Models were evaluated using 5-Fold Cross-Validation to ensure reliability. Results show that LSTM-BERT achieved the highest performance (F1-Score = 73.8%) compared to SVM (70.50%), with confusion-matrix analysis indicating better accuracy in detecting high-literacy texts. These findings confirm that contextual linguistic patterns effectively represent digital literacy levels and highlight the potential of deep-learning approaches for scalable, objective, and automated literacy assessment based on text data.
Co-Authors Cahyo Crysdian Erinda, Hayyu Karina, Anggita Ajeng Miftahul Ulum Nikma, Irfianinza Risqulia Nikma, Irfianninza Risquillia Widayanti, Erma Wijaya, Dandi Nurmansyah Yuliono, Gun Zainal Abidin