Novi Eka Rahmawati
Universitas Negeri Surabaya

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Analisis Sentimen Opini Warga X terhadap Banjir di Sumatera Menggunakan Naive Bayes Novi Eka Rahmawati; Rahmatul Ummah; Harun Al Rosyid
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.462

Abstract

This study aims to analyze public sentiment regarding flooding in Sumatra based on data from social media platform X. Flooding is a frequent natural disaster in Sumatra and elicits a variety of public responses, many of which are expressed through social media. Social media platform X was chosen as the data source because it is open and real-time, allowing it to broadly represent public opinion. The research data consists of 1,030 Indonesian-language tweets collected through a crawling process using the official API for X, using keywords related to flooding in Sumatra. After data cleaning, 873 tweets were obtained, which were then processed through text mining stages, including text preprocessing, manual sentiment labeling, and dividing the data into training and test data. The training data consisted of 650 tweets, while the test data consisted of 223 tweets. Sentiment classification was performed using the Naive Bayes algorithm with the assistance of RapidMiner software. Model evaluation was performed using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results showed that the Naive Bayes algorithm performed quite well in sentiment classification. Furthermore, the analysis shows that public opinion regarding the flooding in Sumatra is dominated by negative sentiment. This research is expected to provide insight into public perceptions and inform disaster management policymaking.
Rancang Bangun “Alsycode” Dengan Pendekatan Problem Based Leraning Untuk Pembelajaran Algoritma Pemrograman Di SMKS Antartika Surabaya Novi Eka Rahmawati; Ramadhan Cakra Wibawa
IT-Edu : Jurnal Information Technology and Education Vol. 11 No. 03 (2026): Volume 11 No. 03 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/it-edu.v11i03.80325

Abstract

Abstrak: Pembelajaran Informatika di SMKS Antartika Surabaya masih didominasi pendekatan teacher-centered learning, ditambah dengan keterbatasan fasilitas komputer di laboratorium sekolah, sehingga siswa kurang memperoleh kesempatan belajar mandiri dan praktik dalam memahami konsep algoritma pemrograman yang bersifat abstrak. Kondisi tersebut menunjukan perlunya media pembelajaran yang dapat digunakan secara fleksibel, mencakup aspek pembelajaran sekaligus praktik. Penelitian ini bertujuan mengembangkan media pembelajaran berbasis web Alsycode (Algoritma Learning System) pada materi Algoritma Pemrograman serta menguji kelayakan dan pengaruhnya terhadap hasil belajar siswa. Media dikembangkan menggunakan model ADDIE dengan pendekatan Problem Based Learning (PBL) dan teknologi Laravel 11, PHP 8.2, MySQL, Tailwind CSS 4, serta Skulpt. Penelitian menggunakan desain one-group pretest-posttest dengan sampel 24 siswa kelas X TKJ 2. Hasil validasi memperoleh persentase 83% dari ahli media dan 84% dari ahli materi dengan kategori sangat layak. Respon siswa berdasarkan Technology Acceptance Model (TAM) mencapai 87% (sangat positif). Nilai rata-rata pretest meningkat dari 43,96 menjadi 85,42, dengan N-Gain 0,73 (tinggi). Uji paired sample t-test menunjukkan Sig. (2-tailed) = 0,000 < 0,05, sehingga Alsycode layak dan efektif meningkatkan hasil belajar algoritma pemrograman. Abstract: Informatics learning at SMKS Antartika Surabaya is still dominated by a teacher-centered learning approach, coupled with limited computer laboratory facilities, resulting in limited opportunities for students to engage in independent learning and practice in understanding the abstract concepts of programming algorithms. These conditions indicate the need for a flexible learning medium that supports both learning activities and practical implementation. This study aimed to develop a web-based learning medium called Alsycode (Algorithm Learning System) for the Programming Algorithm topic and to evaluate its feasibility and effect on students' learning outcomes. The media was developed using the ADDIE model with a Problem-Based Learning (PBL) approach and implemented using Laravel 11, PHP 8.2, MySQL, Tailwind CSS 4, and Skulpt. The study employed a one-group pretest-posttest design involving 24 tenth-grade students of the TKJ 2 class. The validation results showed scores of 83% from the media expert and 84% from the subject matter expert, both categorized as highly feasible. Student responses based on the Technology Acceptance Model (TAM) reached 87%, indicating a very positive level of acceptance. The average pretest score increased from 43.96 to 85.42, with an N-Gain score of 0.73 (high category). The paired-sample t-test yielded a significance value of 0.000 (p < 0.05), indicating that Alsycode is feasible and effective in improving students' learning outcomes in programming algorithms.
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