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All Journal Journal of Applied Computer Science and Technology (JACOST) Jurnal Teknik Informatika
Hendi Muhammad, Alva
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Computer Science & IT

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Analisis Sentimen Berita terhadap Bitcoin dengan Metode Klasifikasi K-Nearest Neighbor Susanto, Hari; Setyanto, Arief; Hendi Muhammad, Alva
JEKIN - Jurnal Teknik Informatika Vol. 4 No. 2 (2024)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

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Abstract

Globalisasi telah mempengaruhi berbagai aspek kehidupan, terutama dalam kemajuan teknologi informasi dan cryptocurrency sebagai inovasi dalam teknologi finansial. Cryptocurrency seperti Bitcoin berfungsi sebagai media pertukaran dan penyimpan nilai, meski belum diakui sebagai alat pembayaran sah. Pasar cryptocurrency berkembang pesat, dengan lebih dari 10.000 aset crypto beredar di seluruh dunia. Jumlah pengguna meningkat signifikan dari 18 juta pada 2017 menjadi 516 juta pada 2023. Bitcoin mendominasi dengan pangsa pasar 60,14%, menegaskan posisinya sebagai pionir dan mencerminkan minat tinggi dari investor serta masyarakat. Penelitian ini juga mengkaji pergerakan harga Bitcoin melalui analisis sentimen menggunakan metode klasifikasi k-nearest neighbor (KNN). Hasil penelitian ini memberikan wawasan mendalam mengenai dinamika pasar mata uang kripto. Metode KNN mencapai rata-rata akurasi 74,40%, menunjukkan efektivitas pengklasifikasian menggunakan metode ini.
A Systematic Literature Review of Retrieval-Augmented Generation: Methods, Applications, and Future Research Directions Hajar, Muhammad Rizky; Utami, Ema; Hendi Muhammad, Alva
Journal of Applied Computer Science and Technology Vol. 6 No. 2 (2025): Desember 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i2.1170

Abstract

Retrieval-Augmented Generation (RAG) represents a growing research direction in the advancement of large language models (LLMs) by incorporating external information sources into the response generation process. As LLM-based systems are increasingly deployed in information-sensitive domains such as healthcare, education, and law, the demand for responses that are not only fluent but also verifiable and context-aware has become more pronounced. This study conducts a systematic literature review (SLR) of 100 recent publications to examine methodological approaches, application domains, technical challenges, and research contributions related to RAG. The review draws on studies indexed in major academic databases, including IEEE, ACM, and Springer, and applies structured inclusion and exclusion criteria to ensure analytical rigor. The findings reveal a strong emphasis on architectural optimization, particularly in the interaction between retrieval and generation components, alongside widespread adoption in domain-specific contexts. Persistent challenges identified across the literature include limitations in retriever effectiveness, system integration complexity, and the absence of standardized evaluation benchmarks. Overall, this review provides a structured synthesis of current RAG research and highlights directions for future investigation and practical deployment..
Co-Authors Arief Setyanto Ema Utami Hajar, Muhammad Rizky Hari Susanto