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All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Pengabdian Sains dan Humaniora
Angga Lisdiyanto
Universitas Pembangunan Nasional “Veteran” Jawa Timur
Computer Science & IT Education Other

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An Instant Online CV Creation Workshop Using Generative AI and a Web-Based Platform to Improve Digital Literacy and Job Readiness for Vocational High School Students Angga Lisdiyanto; Addien Haniefardy; Laqma Dica Fitrani; Agus Wibowo; Ikhwan Abdillah; Nurul Fuad; Winarti; Yerezqy Bagus; Dina Zatusiva Haq; Yoga Ari Tofan; Vinza Hedi Satria
Jurnal Pengabdian Sains dan Humaniora Vol. 5 No. 1 (2026): 2026 May Edition
Publisher : Fakultas Keguruan dan Ilmu Pendidikan-Universitas Timor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32938/jpsh.v5i1.10943

Abstract

Graduates of Vocational High Schools (SMK) consistently contribute the most to Indonesia's Open Unemployment Rate (TPT), reaching 8.63% as of August 2025. A major issue is students' limited ability to develop relevant digital personal branding aligned with modern recruitment standards, such as having an attractive and accessible online Curriculum Vitae (CV). This community service project (PkM) aims to equip 12th-grade students at SMK Al-Amin Mojowuku, Kedamean, Gresik, with skills to create instant website-based CVs using three free tools: generative AI (DeepSeek), image hosting service (ImgBB), and HTML publishing platform (Tiiny.host). Conducted offline on April 28, 2026, with 28 participants, the workshop employed project-based learning combined with AI-assisted learning. The activity involved needs analysis, module development, workshops through lectures and practical exercises, and output evaluation. Results showed all participants successfully published personal CV websites with various themes such as manga comics, anime, and floral motifs. Quantitative indicators included a 100% task completion rate, high active engagement, and positive feedback on material relevance. This activity effectively improved digital literacy, creativity and prepared students for digital-focused recruitment processes
Stock Price Prediction Using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Methods Riza Akhsani Setyo Prayoga; Fery Almas Ariansyah; Muhammad Falikhuddin Daffa; Laqma Dica Fitrani; Masti Fatchiyah Maharani; Angga Lisdiyanto; Steven Angkawidjaja
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.158

Abstract

This research aims to improve the accuracy of stock price prediction through the application of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) methods, focusing on stocks from the Composite Stock Price Index (CSPI) referred to as the IDX Composite. The research process includes comprehensive steps, including data collection and preprocessing, dataset creation with emphasis on stock closing prices, and division of the dataset into training and test data. The LSTM and GRU models were designed with a recurrent layer and a Dense layer and then trained for 100 epochs with a batch size of 32. Model evaluation was performed by comparing key metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) on the test set. The EPOCH-RMSE graph provides an overview of the changes in the RMSE value during training. The best result of the LSTM model was achieved at the 96th epoch with RMSE 40.36, MSE 1385.97, and MAE 30.09, while GRU achieved peak performance at the 92nd epoch with RMSE 37.33, MSE 908.29, and MAE 25.42. In conclusion, GRU can be considered as a more effective option in predicting JCI stock prices based on performance evaluation using various metrics such as RMSE, MSE, and MAE.
Co-Authors Addien Haniefardy Agus Wibowo Dina Zatusiva Haq Fery Almas Ariansyah Ikhwan Abdillah Laqma Dica Fitrani Laqma Dica Fitrani Masti Fatchiyah Maharani Muhammad Falikhuddin Daffa Nurul Fuad Riza Akhsani Setyo Prayoga Steven Angkawidjaja Vinza Hedi Satria Winarti Yerezqy Bagus Yoga Ari Tofan