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THE FUNCTION OF ASSERTIVE SPEECH ACTS IN THE NOVEL CALABAI BY PEPI AL-BAYQUNIE Isnaeni Isnaeni; Mantasiah R.; Hasmawati Hasmawati
KLASIKAL : JOURNAL OF EDUCATION, LANGUAGE TEACHING AND SCIENCE Vol 7 No 1 (2025): Klasikal: Journal of Education, Language Teaching and Science
Publisher : Fakultas Keguruan dan Ilmu Pendidikan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52208/klasikal.v7i1.1305

Abstract

This research aims to analyze the function of assertive speech acts contained in the novel Calabai by Pepi Al-Bayqunie. Assertive speech act is a type of speech act that states something in accordance with reality, such as stating, informing, recommend, refuting, emphasizing, demanding, reporting, denying, disprove, hinting, boasting, complaining, and claiming. This study examines how the characters in the novel use assertive speech acts to express opinions, explain facts, and form social relations. The research is conducted by using pragmatic approach based on Searle's theory then the functions are mapped based on Leech's theory. The results show that the functions of assertive speech acts in this novel include cooperative functions, competitive functions, conflicting functions, and fun functions. These functions illustrate the social values and Bugis culture. Thus, the assertive speech acts in the novel Calabai not only function as a communication tool, but also as a means of conveying social criticism and strengthening identity.
Penerapan Model Problem Based Learning untuk Meningkatkan Hasil Belajar Matematika Siswa pada Materi Teorema Pythagoras Susanti Susanti; Hasmawati Hasmawati; Ariana Ariana
MUARA PENDIDIKAN : Jurnal Ilmiah Ilmu Pendidikan & Sosial Humaniora Vol. 2 No. 1 (2026): MURADIK
Publisher : CV MUARA EDUKASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64365/muradik.v2i1.188

Abstract

This study aims to determine the effect of the application of the Problem Based Learning (PBL) learning model on improving students' mathematics learning outcomes on the Pythagorean Theorem. The type of research used is a quasi-experimental design with a one-group pretest-posttest design. The subjects were 13 students of class VIIIA UPT SMPN 4 Mangarabombang who were selected through cluster random sampling techniques. The research instrument was a multiple-choice test of 10 questions that measured the understanding of the Pythagorean Theorem concept. Data analysis was carried out through the Kolmogorov-Smirnov normality test, the Paired Sample t-Test, and the N-Gain test. The results showed that the average student score increased from 30.77 in the pretest to 63.85 in the posttest, with a significance value of 0.000 (<0.05) indicating a significant difference between the pretest and posttest results. In addition, the N-Gain value of 0.49 is included in the moderate category, which means that the application of the PBL model has a positive effect on improving student learning outcomes on the Pythagorean Theorem
Analisis Sentimen Mobil Listrik Pada X (Twitter) Menggunakan Metode Long Short-Term Memory (LSTM) Muhammad Jilan Hilmi; Hani Nurrahmi; Hasmawati Hasmawati
eProceedings of Engineering Vol. 13 No. 1 (2026): Februari 2026
Publisher : eProceedings of Engineering

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

Abstract

Pertumbuhan kendaraan listrik di Indonesia menghadapi tantangan, salah satunya adalah rendahnya adopsi oleh masyarakat. Untuk memahami persepsi publik, dilakukan analisis sentimen berdasarkan opini masyarakat di media sosial X (Twitter) dengan kata kunci mobil listrik. Metode yang digunakan pada penelitian ini adalah Long Short-Term Memory (LSTM), salah satu algoritma deep learning yang efektif untuk mengklasifikasikan data berbasis teks. Data diambil dari Twitter menggunakan kata kunci “Mobil Listrik” dalam rentang Januari 2023 hingga April 2024, dengan total 10,283 tweet. Setiap tweet divalidasi oleh lima responden dan dikategorikan ke dalam sentimen negatif, netral, atau positif berdasarkan mayoritas suara. Proses pengujian dilakukan menggunakan tiga skenario split dataset (70:30, 80:20, dan 90:10) dan dievaluasi menggunakan metrik akurasi, precision, recall, dan f1- score. Hasil pengujian menunjukkan bahwa model LSTM memiliki akurasi pengujian sebesar 55,30% dan akurasi validasi sebesar 57,13%. Model hanya mampu mengenali sentimen netral dengan baik (f1-score: 71%), namun gagal mengklasifikasikan sentimen positif dan negatif. Hal ini menunjukkan adanya ketidakseimbangan kelas, sehingga dibutuhkan upaya perbaikan seperti penyeimbangan data dan optimasi model agar hasil klasifikasi lebih merata. Kata kunci: analisis sentimen, mobil listrik, LSTM, Twitter, klasifikasi.