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All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK)
Yelfi Vitriani
Universitas Islam Negeri Sultan Syarif Kasim
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Social Sciences

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Cryptocurrency Sentiment Classification Based on Comments On Facebook Using K-Nearest Neighbor Ramu Will Sandra; Yelfi Vitriani; Muhammad Affandes; Suwanto Sanjaya
IJISTECH (International Journal of Information System and Technology) Vol 6, No 2 (2022): August
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1168.805 KB) | DOI: 10.30645/ijistech.v6i2.237

Abstract

Cryptocurrencies continue to develop and have received world attention, price changes that occur every day are influenced by uncertain factors such as political problems and global economic problems. The author will explore the problems discussed by the public regarding positive and negative cryptocurrency comments on Facebook comments using the K-Nearest Neighbor method. This study uses 1000 data comments which are divided into 500 positive data and 500 negative data. The data was obtained manually by using the keyword "bitcoin price" on social media facebook. The results of the testing process using the confusion matrix get the highest accuracy at a comparison of 90: 10 by 62%, recall 70%, error rate 38% and precision 60,34% with k value of 11 and threshold 9.
Cryptocurrency Sentiment Classification Based on Comments On Facebook Using K-Nearest Neighbor Ramu Will Sandra; Yelfi Vitriani; Muhammad Affandes; Suwanto Sanjaya
IJISTECH (International Journal of Information System and Technology) Vol 6, No 2 (2022): August
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i2.237

Abstract

Cryptocurrencies continue to develop and have received world attention, price changes that occur every day are influenced by uncertain factors such as political problems and global economic problems. The author will explore the problems discussed by the public regarding positive and negative cryptocurrency comments on Facebook comments using the K-Nearest Neighbor method. This study uses 1000 data comments which are divided into 500 positive data and 500 negative data. The data was obtained manually by using the keyword "bitcoin price" on social media facebook. The results of the testing process using the confusion matrix get the highest accuracy at a comparison of 90: 10 by 62%, recall 70%, error rate 38% and precision 60,34% with k value of 11 and threshold 9.
Chatbot Hybrid Fatwa MUI Menggunakan Retrieval Augmented Generation dan Large Language Model Surya Hidayatullah Firdaus; Nazruddin Safaat H; Yelfi Vitriani; Novriyanto
Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK) Vol. 4 No. 1 (2026): JURNAL PENGEMBANGAN TEKNOLOGI INFORMASI DAN KOMUNIAKSI (JUPTIK)
Publisher : Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/juptik.v4i1.4318

Abstract

Aksesibilitas dokumen digital Fatwa Majelis Ulama Indonesia (MUI) yang terfragmentasi membuat pencarian informasi kurang efektif. Di sisi lain, sistem tanya jawab AI berbasis satu sumber dokumen (single-corpus) rentan menghasilkan jawaban tidak akurat (halusinasi) pada pertanyaan di luar domain. Penelitian ini mengembangkan Chatbot Hybrid Fatwa MUI menggunakan arsitektur Hybrid Retrieval bertingkat dengan dua sumber pengetahuan: dokumen Fatwa MUI sebagai korpus utama dan 12.370 hadis Bukhari-Muslim sebagai mekanisme cadangan (fallback). Sistem ini menerapkan pencarian semantik, verifikasi topik otomatis oleh model bahasa, dan pengalihan ke basis data hadis jika konteks fatwa dinilai tidak relevan. Hasil evaluasi menunjukkan peningkatan kesamaan makna jawaban sebesar 13,23% (dari 0,6664 menjadi 0,7546) dan peningkatan kesetiaan pada rujukan (faithfulness) sebesar 10,57% (dari 85,37% menjadi 94,39%), dengan tingkat penolakan (abstain rate) identik sebesar 26,83%. Pendekatan multi-korpus ini terbukti signifikan meningkatkan relevansi dan keakuratan jawaban dibandingkan RAG standar.
Co-Authors Muhammad Affandes Nazruddin Safaat H Novriyanto Ramu Will Sandra Surya Hidayatullah Firdaus Suwanto Sanjaya