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All Journal Al Ishlah Jurnal Pendidikan JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal Artificial: Informatika dan Sistem Informasi
Andi Wafda
Universitas Islam Indonesia
Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Other

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ASPECT-BASED SENTIMENT ANALYSIS ON TWITTER TWEETS ABOUT THE MERDEKA CURRICULUM USING INDOBERT Andi Wafda; Dhomas Hatta Fudholi; Jaka Nugraha
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5692

Abstract

The curriculum has changed once again with the introduction of the Merdeka Curriculum to address learning loss in the education sector. Its implementation has elicited various responses, such as support for granting teachers the freedom to innovate, focusing on essential materials, offering diverse learning methods, and fostering student creativity. However, criticism has also arisen, including issues related to teachers’ lack of understanding, parents' concerns, and the increased workload on students due to numerous projects. To improve educational policies, an in-depth analysis of these responses is essential. This study aims to analyze public sentiment toward the Merdeka Curriculum by applying Aspect-Based Sentiment Analysis (ABSA) using data from Twitter. The research focuses on four main aspects: Teaching Modules (MA), Education Reports (RP), the Merdeka Teaching Platform (PMM), and the Strengthening of the Pancasila Student Profile Projects (P5). Data were collected using specific and relevant keywords for each aspect, followed by preprocessing, labeling, and filtering based on sentiment and aspect. The final dataset comprised 2,359 valid tweets. The ABSA model was developed using IndoBERT with fine-tuning, then tested and evaluated. The results showed that the aspect classification model achieved an accuracy of 97%, F1 score of 97%, recall of 97%, and precision of 97%. Meanwhile, the sentiment classification model achieved an accuracy of 85%, F1 score of 85%, recall of 85%, and precision of 85%. This ABSA model is expected to assist in monitoring public responses and provide valuable insights for policy development, particularly within the context of the Merdeka Curriculum.
The Urgency of Developing Digital Teaching Modules with Luwu Cultural Context for Literacy Skills Muhammad Rusli Baharuddin; Fitriani Fitriani; Andi Wafda
AL-ISHLAH: Jurnal Pendidikan Vol 17, No 1 (2025): MARCH 2025
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v17i1.6068

Abstract

The lack of local cultural representation in teaching materials and literacy challenges in the digital era highlight the need for innovative instructional resources. Developing digital teaching modules that enhance literacy while reinforcing cultural identity is crucial. This study aims to develop a Luwu Culture-Based Digital Teaching Module to improve students' literacy skills. This study employs a Research and Development (RD) approach using the ADDIE model, which consists of analysis, design, development, implementation, and evaluation phases. Data collection involves qualitative and quantitative methods to assess validity, practicality, and effectiveness. Findings indicate that the module is highly valid, with an average validity score of 81%, demonstrating strong relevance, comprehensiveness, and clarity. The module’s practicality is rated as excellent, with a mean score of 88%, indicating ease of use, learning effectiveness, and efficiency in implementation. The integration of local cultural elements into literacy instruction provides a meaningful learning experience, fostering students' engagement and comprehension of informational and literary texts. By embedding cultural narratives and traditions, the module enhances literacy skills while strengthening cultural identity. This study demonstrates that a Luwu Culture-Based Digital Teaching Module is a valid and practical tool for literacy development. Its adaptability to diverse cultural contexts suggests its potential for broader application in literacy education. Future research should explore its long-term impact on student learning outcomes.
Artificial Intelligence dalam Prediksi dan Manajemen Bencana: Tinjauan Literatur Komprehensif Andi Wafda
Journal Artificial: Informatika dan Sistem Informasi Vol. 2 No. 1 (2024): April 2024
Publisher : Pustaka Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54065/artificial.542

Abstract

Peningkatan frekuensi dan intensitas bencana alam seperti banjir, gempa bumi, dan tsunami menuntut pengembangan sistem yang lebih baik untuk prediksi dan manajemen bencana. Artificial Intelligence (AI) menawarkan potensi besar dalam meningkatkan akurasi dan efektivitas prediksi bencana dengan menganalisis data besar dan kompleks. Namun, penerapannya menghadapi tantangan seperti keterbatasan data dan kebutuhan sumber daya komputasi. Penelitian ini bertujuan untuk mengeksplorasi aplikasi AI dalam prediksi dan manajemen bencana dengan menilai algoritma Machine Learning (ML) dan Deep Learning (DL), serta mengidentifikasi kekuatan dan kelemahan metode yang digunakan. Penelitian ini menggunakan metode tinjauan literatur sistematis dengan beberapa langkah utama. Pencarian literatur dilakukan melalui basis data akademis seperti IEEE Xplore, ScienceDirect, dan ArXiv, dengan kata kunci terkait penerapan AI dalam manajemen bencana. Artikel yang memenuhi kriteria inklusi dan eksklusi dipilih, kemudian melalui seleksi awal berdasarkan judul dan abstrak serta tinjauan teks penuh. Data dari artikel yang relevan diekstraksi, dikategorikan, dan disintesis untuk mengidentifikasi pola dan tren. Penelitian ini menemukan bahwa penerapan AI dalam prediksi bencana alam, seperti banjir, gempa bumi, dan tsunami, telah meningkatkan akurasi dan efektivitas sistem peringatan dini. Teknik deep learning seperti LSTM dan model hibrida efektif dalam prediksi banjir. Untuk gempa bumi, model seperti ELM dan 3D CNN meningkatkan akurasi prediksi. Teknik seperti CNN dan autoencoder menunjukkan hasil menjanjikan dalam memprediksi dan merekonstruksi tsunami. Meskipun demikian, tantangan terkait keterbatasan data dan kebutuhan sumber daya komputasi masih ada, mempengaruhi penerapan praktis AI dalam sistem manajemen bencana. Penerapan AI menunjukkan kemajuan signifikan dalam prediksi bencana, namun beberapa tantangan harus diatasi untuk meningkatkan efektivitasnya. Penelitian lebih lanjut dianjurkan untuk mengeksplorasi integrasi AI dengan sistem respons bencana yang ada dan memperbaiki teknik AI untuk menangani data yang tidak lengkap dan tidak seimbang.
Transfer Learning Advancements: A Comprehensive Literature Review on Text Analysis, Image Processing, and Health Data Analytics Andi Wafda
Journal Artificial: Informatika dan Sistem Informasi Vol. 3 No. 1 (2025): April 2025
Publisher : Pustaka Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54065/artificial.544

Abstract

This literature review delves into the recent advancements in transfer learning, examining its applications and enhancements across diverse domains. Focused on the conclusions drawn from various studies, the review highlights the evolution of transfer learning in three key areas: text analysis, image processing, and health data analytics. In text analysis, innovations such as BERT, CNN-BiLSTM, AdapterFusion, and T-BERT Framework showcase the ongoing efforts to improve efficiency and adaptability in understanding complex natural language tasks. Similarly, in image processing, the review emphasizes the varied use of pre-trained models, feature extraction techniques, and diversified datasets, leading to enhanced performance in tasks like image classification, object detection, and facial recognition. Furthermore, the application of transfer learning in health data analytics, particularly with CNN models like AlexNet, ResNet, GoogLeNet, and EfficientNet, reflects significant progress in tasks such as MRI brain image classification, skin lesion analysis, brain tumor detection, and other medical image analyses. The advantages of transfer learning, including consistent performance improvement and computational efficiency through the use of pre-trained models, are discussed. However, challenges such as overfitting in specific contexts and the need for careful adaptation in medical data analytics are acknowledged. The review concludes with recommendations for future research directions, urging a focus on improving domain-specific adaptation, exploring multi-modal model integration, enhancing transfer learning for limited health datasets, and investigating its potential for multi-task applications in text, image, and health data analytics. The comprehensive insights provided in this review contribute to the understanding and advancement of transfer learning in the current landscape of artificial intelligence research.
Co-Authors Dhomas Hatta Fudholi Fitriani A. Jaka Nugraha Muhammad Rusli Baharuddin