Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Jurnal Riset Informatika Jurnal Fakultas Ilmu Keislaman UNISA Kuningan Jurnal Pembelajaran Bahasa dan Sastra
Fitria, Yesi
Unknown Affiliation
Religion Computer Science & IT Education Languange, Linguistic, Communication & Media Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
STRATEGI GURU PENDIDIKAN AGAMA ISLAM DALAM MENGHADAPI PERILAKU BULLYING VERBAL DI MTsN 5 KUNINGAN KABUPATEN KUNINGAN Fitria, Yesi; Saepudin, Aep
Jurnal Fakultas Ilmu Keislaman UNISA Kuningan Vol. 5 No. 3 (2024): Jurnal Fakultas Ilmu Keislaman UNISA Kuningan
Publisher : Jurnal Fakultas Ilmu Keislaman UNISA Kuningan

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

Abstract

Perilaku bullying adalah perilaku negatif dan kontradiktif, dari nilai moral, dan etika pendidikan. Perilaku ini bertentangan dengan visi dan misi yang ada di MTsN 05 Kuningan. Pada penelitian ini bertujuan untuk mengetahui bagaimana bentuk-bentuk perilaku bullying verbal, bagaimana strategi guru PAI dalam menghadapi perilaku bullying verbal serta mencari tahu hambatan dan solusi dalam menghadapi perilaku bullying verbal. Jenis penelitian yang digunakan penelitian kualitatif, Teknik pengumpulan data menggunakan teknik wawancara, observasi dan dokumentasi. Teknik analisis data dengan cara mereduksi data, display data, dan menarik kesimpulan. Pemeriksaan keabsahan data menggunakan triangulasi sumber dan teknik. Hasil penelitian menunjukan ditemukannya aksi bullying verbal seperti mengejek temannya dengan perkataan kasar, menghina bentuk tubuh, warna kulit dan memanggil nama dengan sebutan nama samaran orang tuanya. Adapun strategi yang digunakan oleh guru PAI dalam menghadapi perilaku bullying verbal adalah melaksanakan program Malam Bina Iman dan Taqwa (MABIT), memberikan pemahaman melalui hadits Nabi SAW dan menggunakan kartu lindung (card game). Sedangkan hambatan yang ditemui guru PAI dalam menghadapi perilaku bullying verbal adalah peserta didik sudah terbiasa memanggil temannya dengan sebutan yang tidak pantas dan menganggap hal itu hanya sebuah candaan. Untuk solusi yang diberikan guru PAI dalam menghadapi perilaku bullying verbal yaitu memberikan nasihat ketauladanan dari kisah Rasul, dan yang lebih utama kesadaran dari diri sendiri. Kata Kunci: Strategi, Guru, Pendidikan Agama Islam, Bullying Verbal
Symbolic Figures of Speech in the Lyrics of the Song Kolam Susu by Koes Plus Fitria, Yesi; Andini, Annisa Putri; Marsya, Bunga Ivanny; Robaaniyya, Karlia Marhamatur; Kusumah, Encep
Jurnal Pembelajaran Bahasa dan Sastra Vol. 4 No. 6 (2025): November 2025
Publisher : Raja Zulkarnain Education Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55909/jpbs.v4i6.782

Abstract

Koes Plus is a music band that was once established in Indonesia which was formed in 1969, this music group is widely known as one of the pioneers of pop and rock and roll genre songs in Indonesia, as well as one of the most productive and longest-standing music groups. "Kolam Susu" is a song created by Yok Koeswoyo and sung by the music group Koes Plus and first appeared on the album Volume 8 in 1973. This study aims to facilitate listeners in understanding the meaning contained in the song Kolam Susu through the analysis of symbolic figures of speech contained in the song lyrics. This study uses a descriptive method based on a qualitative approach with the main data source in the form of the song lyrics Kolam Susu. The data of this study, namely written song lyrics as a data source, were collected using observation guidelines with time triangulation techniques through a checklist. The analysis technique is based on clause language units to identify the symbolic figures of speech used. The results of the study revealed that there are six clauses in the song Kolam Susu that contain symbolic figures of speech, which play an important role in conveying meaning implicitly and enriching the song's message. This finding shows that symbolic figures of speech are effectively used as a tool to facilitate listeners in capturing deep meaning in song lyrics. This study is expected to contribute to the study of semantics and appreciation of Indonesian music.
BITCOIN PRICE VOLATILITY ANALYSIS: A DEEP LEARNING APPROACH TO X (FORMERLY TWITTER) SENTIMENT Puji Astuti; Sidiq Endrasmoyo, Rangga; Syawalluddin; Fitria, Yesi; Budiyono, Pungkas
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1039.697 KB) | DOI: 10.34288/jri.v8i1.432

Abstract

This study investigates the relationship between social media sentiment and Bitcoin price volatility using advanced natural language processing techniques. We collected X data from April 10-29, 2025, analyzing cryptocurrency-related tweets alongside Bitcoin price movements obtained through the CoinGecko API. Five sentiment analysis methodologies were comparatively evaluated: VADER, TextBlob, BERTweet, RoBERTa Base, and RoBERTa Large. Bitcoin price volatility was measured using log returns to capture market fluctuations accurately. Correlation analysis revealed significant differences in methodological effectiveness. Traditional lexicon-based approaches (VADER and TextBlob) demonstrated weak correlations with volatility (r = -0.2232 and r = -0.0710 respectively). Transformer-based models showed superior performance, with RoBERTa Large achieving the strongest correlation (r = 0.4569, p = 0.0428), representing the only statistically significant relationship. The positive correlation indicates that increased social media sentiment corresponds to higher Bitcoin price volatility rather than directional price movements. These findings demonstrate that sophisticated deep learning models can effectively capture sentiment-driven market dynamics, providing valuable insights for cryptocurrency investors, trading platforms, and market analysts seeking to understand social media influence on digital asset markets.
Co-Authors Aep Saepudin Andini, Annisa Putri Budiyono, Pungkas Encep Kusumah Marsya, Bunga Ivanny Puji Astuti Robaaniyya, Karlia Marhamatur Sidiq Endrasmoyo, Rangga Syawalluddin