Update src/streamlit_app.py

src/streamlit_app.py

@@ -145,6 +145,37 @@ if method == "AHP":
 
 elif method == "None":
     st.subheader("Pilih Metode terlebih dahulu")
+elif method =="TOPSIS":
+    # TOPSIS 
+    st.subheader("📋 Input Nilai Alternatif terhadap Kriteria")
+    empty_data = pd.DataFrame(np.nan, index=alternatives, columns=criteria)
+    df = st.data_editor(empty_data, use_container_width=True, key="input_matrix")
+    if df.isnull().values.any():
+        st.warning("⚠ Harap lengkapi semua nilai pada tabel sebelum menjalankan perhitungan untuk TOPSIS/Profile Matching.")
+
+    st.subheader("⚖ Bobot Kriteria (Manual)")
+    weight_dict = {}
+    cols = st.columns(len(criteria))
+    for i, c in enumerate(criteria):
+        with cols[i]:
+            weight_dict[c] = st.number_input(f"Bobot untuk '{c}'", min_value=1.0, max_value=10.0, value=5.0, step=0.1, key=f"weight_{c}")
+
+    weights = np.array([weight_dict[c] for c in criteria])
+    weights /= weights.sum()
+    st.write("🎯 Bobot Kriteria (Ternormalisasi):")
+    st.dataframe(pd.Series(weights, index=criteria, name="Bobot"))
+
+    criteria_default_type= {
+        "Building Area" : "benefit", 
+        "Road Access": "benefit",
+        "Distance" : "benefit",
+        "Rental Price" : "cost"
+    }
+    if criteria_default_type == "benefit":
+        types = 1
+    else:
+        types = 0
+    
 else:
     # TOPSIS dan Profile Matching
     st.subheader("📋 Input Nilai Alternatif terhadap Kriteria")
@@ -231,8 +262,10 @@ if run_calc:
             weighted_data = norm_data * w
 
             # Tentukan ideal positif dan negatif (max/min)
-            ideal_pos = np.max(weighted_data, axis=0)
-            ideal_neg = np.min(weighted_data, axis=0)
+            # ideal_pos = np.max(weighted_data, axis=0)
+            # ideal_neg = np.min(weighted_data, axis=0)
+            ideal_pos = np.where(types == 1, weighted.max(), weighted.min())
+            ideal_neg = np.where(types == 1, weighted.min(), weighted.max())
 
             # Jarak ke ideal positif dan negatif
             dist_pos = np.sqrt(((weighted_data - ideal_pos) ** 2).sum(axis=1))