0.5×← Perlahan · Cepat →
💬Tekan Play Demo untuk lihat bagaimana Platform Fee Analysis Agent mengoptimumkan kos komisen.
Demo Complete — All Stages Generated
01 WhatsApp Command
02 Requirement Analysis
Menunggu Sedang aktif Selesai
03 Python Script Generation
1
📥
Ambil laporan yuranAPI platform — komisen, subsidi, caj
2
🧮
Kira peratusanKomisen vs harga jualan setiap order
3
🔍
Analisis trendBandingkan yuran bulan ke bulan
4
📊
Laporan & cadanganPeluang penjimatan + notifikasi
import requests, json, pandas as pd
API_FEES = "https://api.shopee.com/v2/finance/fees"
def fetch_fee_report(month):
resp = requests.get(API_FEES, params={"month": month})
return resp.json().get("fees", [])
def analyze_fees(fees):
df = pd.DataFrame(fees)
df["pct"] = df["commission"] / df["order_amount"] * 100
avg_fee = df["pct"].mean()
high_fees = df[df["pct"] > avg_fee * 1.2]
return {
"avg_commission": round(avg_fee, 2),
"total_fees": round(df["commission"].sum(), 2),
"high_fee_orders": len(high_fees),
"savings_potential": round(high_fees["commission"].sum() * 0.15, 2)
}
report = analyze_fees(fetch_fee_report("2026-06"))
print(json.dumps(report))
04 Block Diagram
Menunggu Sedang aktif Selesai
05 System Schematic
Menunggu Sedang aktif Selesai
⚠️ Jangan share laporan yuran dengan pihak ketiga tanpa kebenaran.
06 Simulasi Analisis Yuran
Mula Analisis
ShopeeRM 450↑ 12%
LazadaRM 380→ 0%
PG MallRM 120↓ 5%
TiktokRM 290↑ 18%
TotalRM 1,240↑ 8%
07 Fee Analysis Report
Terminal Log
[SISTEM] Platform Fee Analysis Agent sedia
System Architecture
🤖 Agent Layer
Fee fetcher
Commission calc
Trend analyzer
LLM optimizer
🛒 Ecommerce Layer
Platform API
Fee reports
Order data
Savings export
Safety Notes
- Human review required before sharing fee analysis externally.
- Configure role-based access for financial dashboards.
- Verify platform commission structures regularly.
- Browser demo does not touch real platform APIs.