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💬Tekan Play Demo untuk lihat bagaimana Literature Review Agent mensintesis kajian.
Demo Complete — All Stages Generated
01 WhatsApp Command
02 Requirement Analysis
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03 Python Script Generation
1
📥
Kumpul kertas kerjaFetch papers from databases
2
🔍
Baca & nilaiReview and score each paper
3
🧩
Sintesis dapatanSynthesize findings across papers
4
📋
Laporan literaturGenerate literature review report
import pandas as pd
from datetime import datetime
def fetch_papers(query, db):
return db[db['title'].str.contains(query, case=False)]
def score_paper(paper):
score = 0
if paper['citations'] > 50: score += 3
if paper['year'] >= 2020: score += 2
if paper['journal_rank'] <= 2: score += 2
return min(10, score + paper['methodology_score'])
papers = fetch_papers('machine learning', db)
papers['score'] = papers.apply(score_paper, axis=1)
synthesis = papers.groupby('topic').agg({'score':'mean','title':'count'})
print(synthesis.to_json())
04 Block Diagram
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05 System Schematic
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⚠️ Literature reviews should be verified by domain experts before publication.
06 Simulasi Literature Review
Mula Review
07 Literature Review Report
Terminal Log
[SISTEM] Research Literature Review Agent sedia
System Architecture
🤖 Agent Layer
Paper fetcher
Review scorers
Synthesis engine
🎓 Education Layer
Research DB
Citation index
Review reports
Safety Notes
- Verify AI-generated reviews with domain experts.
- Ensure proper citation and attribution of sources.
- Configure plagiarism detection for synthesized content.
- Browser demo uses simulated data only.