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All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Ilmu Ekonomi, Pendidikan dan Teknik (IDENTIK)
Mellanda Kurniawati
Universitas Negeri Surabaya
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering

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Pengembangan Media Pembelajaran Berbasis Web Dengan Pendekatan Progressive Learning Untuk Meningkatkan Hasil Belajar Siswa Pada Elemen Pemrograman Web Di SMK PGRI 2 Sidoarjo Mellanda Kurniawati; Rindu Puspita Wibawa
Jurnal Ilmu Ekonomi, Pendidikan dan Teknik Vol. 3 No. 5 (2026): IDENTIK - September
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/identik.v3i5.1790

Abstract

Web programming instruction at SMK PGRI 2 Sidoarjo is still delivered through one way, conventional classroom sessions without a system that sequences mastery of learning materials, causing students to write code before grasping the underlying concepts and making it difficult for teachers to monitor individual progress. This study aimed to build a website based learning medium applying a progressive learning approach for the HTML topic within the Web Programming element, to examine its effect on learning outcomes, and to evaluate the resulting user experience. The medium was built through Research and Development using the ADDIE model, with React on the front end and Supabase on the back end, and was tested on 30 eleventh grade Software Engineering students using a one group pretest-posttest design. Expert validation of the medium, materials, test items, and deep learning lesson plan produced scores of 94.17%, 86.92%, 89.44%, and 94.07% respectively, all in the very valid category. The average score rose from 61.67 (pretest) to 86.67 (posttest); a paired samples t-test returned p < 0.001, and the average N-Gain was 0.658 (moderate). The User Experience Questionnaire showed all six dimensions falling between Above Average and Good. These findings indicate that a website based medium that locks progression until a minimum quiz score is met can meaningfully improve HTML learning outcomes while remaining pleasant to use.
Evaluating CNN-LSTM for Indonesian Hoax News Classification: A Comparative Analysis with Existing Models Martini Dwi Endah Susanti; Novi Eka Rahmawati; Rani Nur Rahmawati; Eka Yunizar Saimatus Sa'diyah; Mellanda Kurniawati
IJCONSIST JOURNALS Vol 8 No 1 (2026): 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.v8i1.182

Abstract

Abstract— The spread of Indonesian-language hoax news through digital platforms continues to increase along with the massive use of social media and online news portals. This condition poses a serious challenge to providing accurate and reliable information to the public, necessitating an effective AI-based system for identifying hoax news. This study aims to implement the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) algorithm in an Indonesian-language hoax news identification system. The CNN-LSTM method was chosen because it combines the advantages of CNNs in extracting local text features and LSTMs in modeling contextual word-order dependencies. The dataset used consists of a collection of Indonesian-language news articles that have been labeled as hoax or non-hoax, after text preprocessing stages including cleaning, tokenization, stopword removal, and padding. Model performance was evaluated using the Accuracy, Precision, Recall, and F1-Score metrics. The test results show that the CNN-LSTM model can classify hoax and non-hoax news with good accuracy and consistency, demonstrating the effectiveness of the hybrid approach for hoax detection. Based on these results, it can be concluded that the implementation of CNN-LSTM has the potential to be a reliable solution to support the Indonesian-language hoax news identification system. Keywords: hoax news, CNN-LSTM, text classification, deep learning, Bahasa Indonesia
Co-Authors Eka Yunizar Saimatus Sa'diyah Martini Dwi Endah Susanti Novi Eka Rahmawati Rani Nur Rahmawati Rindu Puspita Wibawa