Update src/streamlit_app.py

src/streamlit_app.py

@@ -30,143 +30,200 @@ img_batch = os.path.join(BASE_DIR, "slice3-1-1536x830.png")
 # ==============================
 # KONFIGURASI HALAMAN & STATE NAVIGASI
 # ==============================
-st.set_page_config(page_title="SKRIPSI - BTS", page_icon="₿", layout="wide")
+st.set_page_config(page_title="SKRIPSI - Sentimen BTC", page_icon="₿", layout="wide", initial_sidebar_state="collapsed")
 
 if 'page' not in st.session_state:
     st.session_state.page = "uji_kalimat"
 
 # ==============================
-# INJEKSI CSS DINAMIS (TEMA HALAMAN)
+# GLOBAL CSS (MINIMALIST & PROFESSIONAL)
+# ==============================
+st.markdown("""
+<style>
+    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
+
+    /* Menyembunyikan elemen default Streamlit agar terlihat seperti website asli */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    header {visibility: hidden;}
+    
+    /* Mengatur padding utama halaman */
+    .block-container {
+        padding-top: 2rem !important;
+        padding-bottom: 2rem !important;
+        max-width: 1200px !important;
+    }
+
+    /* Global Font */
+    html, body, [class*="css"] {
+        font-family: 'Inter', sans-serif !important;
+    }
+
+    /* Styling Navbar Container */
+    .nav-container {
+        display: flex;
+        justify-content: space-between;
+        align-items: center;
+        padding-bottom: 1.5rem;
+        margin-bottom: 2rem;
+        border-bottom: 1px solid rgba(128, 128, 128, 0.2);
+    }
+    
+    /* Styling Tombol Standar Streamlit menjadi Minimalis */
+    div[data-testid="stButton"] > button {
+        border-radius: 30px;
+        font-weight: 600;
+        padding: 0.5rem 1.5rem;
+        transition: all 0.3s ease;
+        border: 1px solid transparent;
+    }
+    
+    /* Menghilangkan border focus merah default Streamlit */
+    div[data-testid="stButton"] > button:focus:not(:active) {
+        border-color: transparent;
+        box-shadow: none;
+        color: inherit;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# ==============================
+# TEMA DINAMIS PER HALAMAN
 # ==============================
 if st.session_state.page == "uji_kalimat":
-    # TEMA ORANYE (Mirip Vancouver Bitcoin Landing Page)
+    # TEMA DARK/ORANGE (Premium Landing Page Look)
     st.markdown("""
     <style>
-        @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
-        
         .stApp {
-            background-color: #F38220; /* Oranye */
-            color: #FFFFFF;
-            font-family: 'Inter', sans-serif;
+            background-color: #0F172A; /* Dark Slate Minimalist */
+            color: #F8FAFC;
+        }
+        h1, h2, h3, h4, h5, h6, p, label {
+            color: #F8FAFC !important;
         }
         
-        h1, h2, h3, h4, h5, h6, p, span, label {
+        /* Tombol Aksi Utama - Orange Bitcoin */
+        div[data-testid="stButton"] > button {
+            background-color: #F7931A !important;
             color: #FFFFFF !important;
+            border: none;
+            box-shadow: 0 4px 6px rgba(247, 147, 26, 0.2);
         }
-        
-        /* Styling Text Area & Inputs agar kontras di latar oranye */
-        .stTextArea textarea {
-            background-color: #FFFFFF;
-            color: #111827 !important;
-            border-radius: 8px;
+        div[data-testid="stButton"] > button:hover {
+            background-color: #E68310 !important;
+            transform: translateY(-2px);
+        }
+
+        /* Navigasi Button di Halaman 1 */
+        .nav-btn-active > button {
+            background-color: transparent !important;
+            border: 2px solid #F7931A !important;
+            color: #F7931A !important;
+        }
+        .nav-btn-inactive > button {
+            background-color: transparent !important;
+            border: 1px solid #475569 !important;
+            color: #94A3B8 !important;
         }
         
-        /* Tombol Aksi */
-        .action-btn > button {
-            background-color: #1E1E24;
-            color: #FFFFFF !important;
-            border: none;
-            border-radius: 25px;
-            padding: 0.6rem 2rem;
-            font-weight: 600;
+        /* Styling Text Area Input */
+        .stTextArea textarea {
+            background-color: #1E293B !important;
+            color: #F8FAFC !important;
+            border: 1px solid #334155 !important;
+            border-radius: 12px;
+            font-size: 1rem;
+            padding: 1rem;
         }
-        .action-btn > button:hover {
-            background-color: #33333C;
+        .stTextArea textarea:focus {
+            border-color: #F7931A !important;
+            box-shadow: 0 0 0 1px #F7931A !important;
         }
     </style>
     """, unsafe_allow_html=True)
-    
 else:
-    # TEMA PUTIH/KREM (Mirip Vancouver Bitcoin Info Page)
+    # TEMA TERANG (Dashboard Analisis Clean)
     st.markdown("""
     <style>
-        @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
-        
         .stApp {
-            background-color: #FFFDF5; /* Krem / Putih Gading */
-            color: #111827;
-            font-family: 'Inter', sans-serif;
+            background-color: #FFFFFF;
+            color: #0F172A;
         }
-        
         h1, h2, h3, h4, h5, h6, label {
-            color: #111827 !important;
+            color: #0F172A !important;
         }
-        
         p, span {
-            color: #4B5563 !important;
+            color: #475569 !important;
         }
         
-        hr { border-color: #E5E7EB; }
-        
-        /* Tombol Aksi */
-        .action-btn > button {
-            background-color: #312E81;
+        /* Tombol Aksi Utama - Dark Navy */
+        div[data-testid="stButton"] > button {
+            background-color: #0F172A !important;
             color: #FFFFFF !important;
             border: none;
-            border-radius: 25px;
-            padding: 0.6rem 2rem;
-            font-weight: 600;
         }
-        .action-btn > button:hover {
-            background-color: #4338CA;
+        div[data-testid="stButton"] > button:hover {
+            background-color: #1E293B !important;
+            transform: translateY(-2px);
+        }
+
+        /* Navigasi Button di Halaman 2 */
+        .nav-btn-inactive > button {
+            background-color: transparent !important;
+            border: 1px solid #CBD5E1 !important;
+            color: #64748B !important;
+        }
+        .nav-btn-active > button {
+            background-color: transparent !important;
+            border: 2px solid #0F172A !important;
+            color: #0F172A !important;
         }
         
-        /* Expander Header Text */
-        .streamlit-expanderHeader { color: #111827 !important; }
+        /* Styling Dataframe / Table */
+        div[data-testid="stDataFrame"] {
+            border: 1px solid #E2E8F0;
+            border-radius: 12px;
+            overflow: hidden;
+        }
     </style>
     """, unsafe_allow_html=True)
 
-# CSS Global untuk Navigasi (Tetap sama di kedua tema)
-st.markdown("""
-<style>
-    .nav-container {
-        padding: 10px 0;
-        border-bottom: 2px solid rgba(255, 255, 255, 0.2);
-        margin-bottom: 20px;
-    }
-    .nav-btn > button {
-        background-color: transparent !important;
-        border: 2px solid currentColor !important;
-        border-radius: 20px !important;
-        font-weight: 600 !important;
-        transition: all 0.3s ease;
-    }
-    .nav-btn > button:hover {
-        opacity: 0.8;
-        transform: translateY(-2px);
-    }
-</style>
-""", unsafe_allow_html=True)
-
 # ==============================
-# FUNGSI NAVIGASI
+# HEADER & NAVIGASI
 # ==============================
 def set_page(page_name):
     st.session_state.page = page_name
 
-# Header Navigasi (Sistem Berpindah Halaman)
-col_logo, col_nav1, col_nav2, col_space = st.columns([2, 1.5, 1.5, 4])
+# Menggunakan Columns untuk Navbar yang rapi
+col_logo, col_space, col_nav1, col_nav2 = st.columns([3, 4, 1.5, 1.5])
+
 with col_logo:
-    st.markdown("<h3 style='margin:0; padding:0;'>₿itcoin Sentimen</h3>", unsafe_allow_html=True)
+    # Logo text
+    color_logo = "#F8FAFC" if st.session_state.page == "uji_kalimat" else "#0F172A"
+    st.markdown(f"<h3 style='margin:0; padding:10px 0; font-weight:700; color:{color_logo} !important;'>₿itcoin Sentimen</h3>", unsafe_allow_html=True)
 
 with col_nav1:
-    st.markdown('<div class="nav-btn">', unsafe_allow_html=True)
-    if st.button("Uji Kalimat 📝", use_container_width=True):
+    btn_class = "nav-btn-active" if st.session_state.page == "uji_kalimat" else "nav-btn-inactive"
+    st.markdown(f'<div class="{btn_class}">', unsafe_allow_html=True)
+    if st.button("Uji Kalimat 📝", use_container_width=True, key="nav_uji"):
         set_page("uji_kalimat")
         st.rerun()
     st.markdown('</div>', unsafe_allow_html=True)
 
 with col_nav2:
-    st.markdown('<div class="nav-btn">', unsafe_allow_html=True)
-    if st.button("Analisis Batch 📊", use_container_width=True):
+    btn_class = "nav-btn-active" if st.session_state.page == "analisis_batch" else "nav-btn-inactive"
+    st.markdown(f'<div class="{btn_class}">', unsafe_allow_html=True)
+    if st.button("Analisis Batch 📊", use_container_width=True, key="nav_batch"):
         set_page("analisis_batch")
         st.rerun()
     st.markdown('</div>', unsafe_allow_html=True)
 
-st.markdown("<div style='margin-bottom: 40px;'></div>", unsafe_allow_html=True)
+# Divider Navbar
+st.markdown("<div style='border-bottom: 1px solid rgba(128,128,128,0.2); margin-top: 10px; margin-bottom: 40px;'></div>", unsafe_allow_html=True)
+
 
 # ==============================
-# DOWNLOAD REQUIRED RESOURCES
+# DOWNLOAD RESOURCES & LOAD MODELS (Tetap Sama)
 # ==============================
 @st.cache_resource
 def download_nltk_resources():
@@ -178,22 +235,19 @@ def download_nltk_resources():
 download_nltk_resources()
 stop_words = set(stopwords.words('english'))
 
-# ==============================
-# LOAD MODELS
-# ==============================
 @st.cache_resource
 def load_all_models():
-    with st.spinner('Memuat model NLP...'):
-        vader = SentimentIntensityAnalyzer()
-        bertweet = pipeline("sentiment-analysis", model="finiteautomata/bertweet-base-sentiment-analysis", device=-1, truncation=True, max_length=128)
-        roberta = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment", device=-1, truncation=True, max_length=512)
-        roberta_large = pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english", device=-1, truncation=True, max_length=512)
-        return vader, bertweet, roberta, roberta_large
+    vader = SentimentIntensityAnalyzer()
+    bertweet = pipeline("sentiment-analysis", model="finiteautomata/bertweet-base-sentiment-analysis", device=-1, truncation=True, max_length=128)
+    roberta = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment", device=-1, truncation=True, max_length=512)
+    roberta_large = pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english", device=-1, truncation=True, max_length=512)
+    return vader, bertweet, roberta, roberta_large
 
-vader, bertweet, roberta, roberta_large = load_all_models()
+with st.spinner('Mempersiapkan model NLP...'):
+    vader, bertweet, roberta, roberta_large = load_all_models()
 
 # ==============================
-# CLEAN TEXT & MAPPING
+# FUNGSI CLEAN TEXT & MAPPING (Tetap Sama)
 # ==============================
 def clean_text(text):
     text = str(text).lower()
@@ -221,50 +275,55 @@ def get_daily_label(score):
     elif score < -0.05: return 'Negative'
     else: return 'Neutral'
 
+
 # ==============================================================================
-# HALAMAN 1: UJI KALIMAT (BACKGROUND ORANYE)
+# HALAMAN 1: UJI KALIMAT (TEMA DARK/ORANGE)
 # ==============================================================================
 if st.session_state.page == "uji_kalimat":
-    col_text, col_img = st.columns([1.2, 1], gap="large")
+    col_text, col_img = st.columns([1.1, 1], gap="large")
     
     with col_text:
         st.markdown("""
-        <div style="padding-top: 0.5rem;">
-            <h1 style="font-size: 3rem; line-height: 1.2; margin-bottom: 1rem; color: white;">Bitcoin Volatility vs Public Sentiment</h1>
-            <p style='font-size: 1.15rem; font-weight: 500;'>Analisis Volatilitas Harga Bitcoin Terhadap Sentimen Publik Pada Platform X Berbasis Python</p>
-            <p style='font-size: 0.95rem; margin-top: 1.5rem; margin-bottom: 2rem; border-left: 4px solid white; padding-left: 10px;'>
-                <b>Peneliti:</b> Arya Galuh Saputra (H1D022022)
+        <div style="padding-top: 2rem;">
+            <h1 style="font-size: 3.5rem; line-height: 1.1; margin-bottom: 1.5rem; font-weight: 700; letter-spacing: -1px;">
+                Bitcoin Volatility <br><span style="color: #F7931A;">vs Public Sentiment</span>
+            </h1>
+            <p style='font-size: 1.15rem; font-weight: 400; color: #94A3B8 !important; margin-bottom: 2rem;'>
+                Analisis Volatilitas Harga Bitcoin Terhadap Sentimen Publik Pada Platform X Berbasis Python.
             </p>
+            <div style="background-color: rgba(247, 147, 26, 0.1); border-left: 4px solid #F7931A; padding: 15px; border-radius: 4px; margin-bottom: 2rem;">
+                <p style="margin: 0; font-size: 0.95rem; color: #E2E8F0 !important;">
+                    <b>Peneliti:</b> Arya Galuh Saputra (H1D022022)
+                </p>
+            </div>
         </div>
         """, unsafe_allow_html=True)
         
-        user_input = st.text_area("Masukkan Tweet (Bahasa Inggris):", "Great, Bitcoin just crashed another 10% today.", height=130)
+        user_input = st.text_area("Masukkan Tweet (Bahasa Inggris):", "Great, Bitcoin just crashed another 10% today.", height=120)
         
-        st.markdown('<div class="action-btn">', unsafe_allow_html=True)
-        analyze_btn = st.button("🚀 Analisis Sentimen", use_container_width=True)
-        st.markdown('</div>', unsafe_allow_html=True)
+        st.markdown("<br>", unsafe_allow_html=True)
+        analyze_btn = st.button("🚀 Analisis Sentimen Sekarang", use_container_width=True)
                 
     with col_img:
         try:
             st.image(img_hero, use_container_width=True)
-        except Exception as e:
-            st.error(f"⏳ Menunggu gambar diunggah... (Pastikan file {img_hero} tersedia)")
+        except Exception:
+            st.info(f"Visualisasi Hero akan muncul di sini. (Pastikan file {os.path.basename(img_hero)} tersedia)")
 
     if analyze_btn:
-        st.markdown("<hr>", unsafe_allow_html=True)
-        st.subheader("📋 Hasil Deteksi Sentimen")
+        st.markdown("<br><hr style='border-color: #334155;'><br>", unsafe_allow_html=True)
+        st.markdown("<h3 style='text-align: center; margin-bottom: 2rem;'>📋 Hasil Deteksi Sentimen</h3>", unsafe_allow_html=True)
+        
         try:
             if detect(user_input) != 'en':
-                st.warning("⚠️ Peringatan: Teks sepertinya bukan bahasa Inggris. Hasil mungkin bias.")
+                st.warning("⚠️ Teks sepertinya bukan bahasa Inggris. Hasil prediksi mungkin memiliki bias.")
         except:
             pass
 
         text = clean_text(user_input)
         
-        with st.spinner("Menganalisis..."):
-            try:
-                v_score = vader.polarity_scores(text)['compound']
-                v_label = "positive" if v_score > 0.05 else "negative" if v_score < -0.05 else "neutral"
+        with st.spinner("Mesin NLP sedang memproses..."):
+            try: v_label = "positive" if vader.polarity_scores(text)['compound'] > 0.05 else "negative" if vader.polarity_scores(text)['compound'] < -0.05 else "neutral"
             except: v_label = "neutral"
                 
             try: t_label = classify_tb(TextBlob(text).sentiment.polarity)
@@ -285,55 +344,57 @@ if st.session_state.page == "uji_kalimat":
                 return "⚪ Neutral"
 
             data_test = {
-                "Metode": ["VADER", "TextBlob", "BERTweet", "RoBERTa Base", "RoBERTa Large"],
-                "Sentimen": [format_label(v_label), format_label(t_label), format_label(b_label), format_label(r_label), format_label(rl_label)]
+                "Metode NLP": ["VADER", "TextBlob", "BERTweet", "RoBERTa Base", "RoBERTa Large"],
+                "Hasil Sentimen": [format_label(v_label), format_label(t_label), format_label(b_label), format_label(r_label), format_label(rl_label)]
             }
-            st.dataframe(pd.DataFrame(data_test), use_container_width=True, hide_index=True)
+            
+            col_tabel, _ = st.columns([2, 1])
+            with col_tabel:
+                st.dataframe(pd.DataFrame(data_test), use_container_width=True, hide_index=True)
 
 
 # ==============================================================================
-# HALAMAN 2: ANALISIS BATCH DATA (BACKGROUND PUTIH/KREM)
+# HALAMAN 2: ANALISIS BATCH DATA (TEMA TERANG/KREM CLEAN)
 # ==============================================================================
 elif st.session_state.page == "analisis_batch":
-    # Ubah tema grafik matplotlib agar sesuai dengan background terang
+    # Tema Matplotlib
     plt.style.use('default') 
     sns.set_theme(style="whitegrid", rc={
-        "axes.facecolor": "#FFFFFF", 
-        "figure.facecolor": "#FFFDF5", 
-        "axes.edgecolor": "#E5E7EB", 
-        "text.color": "#111827", 
-        "xtick.color": "#4B5563", 
-        "ytick.color": "#4B5563",
-        "grid.color": "#F3F4F6"
+        "axes.facecolor": "#F8FAFC", 
+        "figure.facecolor": "#FFFFFF", 
+        "axes.edgecolor": "#E2E8F0", 
+        "text.color": "#0F172A", 
+        "xtick.color": "#64748B", 
+        "ytick.color": "#64748B",
+        "grid.color": "#F1F5F9"
     })
 
-    col_img_batch, col_upload = st.columns([1, 1.5], gap="large")
-    
-    with col_img_batch:
-        try:
-            st.image(img_batch, use_container_width=True)
-        except Exception:
-            st.empty() 
+    col_upload, col_img_batch = st.columns([1.5, 1], gap="large")
             
     with col_upload:
         st.markdown("""
-        <div style="padding-top: 1.5rem;">
-            <h2 style="font-size: 2.5rem; margin-bottom: 0.5rem; color: #111827;">📂 Analisis Batch Data</h2>
-            <p style='font-size: 1.1rem; margin-bottom: 1.5rem;'>Unggah file ekstensi .txt yang berisi history tweet untuk dianalisis secara masal.</p>
+        <div style="padding-top: 1rem;">
+            <h2 style="font-size: 2.5rem; margin-bottom: 0.5rem; font-weight: 700; letter-spacing: -0.5px;">Analisis Batch Data</h2>
+            <p style='font-size: 1.1rem; margin-bottom: 2rem;'>Unggah file rekam jejak tweet (.txt) untuk diekstraksi dan dianalisis secara masal terhadap volatilitas pasar.</p>
         </div>
         """, unsafe_allow_html=True)
         
         tweet_files = st.file_uploader("Pilih file Tweet (.txt)", type=['txt'], accept_multiple_files=True)
         
-        with st.expander("📌 Lihat Format TXT yang Benar"):
+        with st.expander("📌 Format TXT yang Didukung"):
             st.code("username | 2024-03-01 14:00:00\nIsi tweet baris pertama di sini\n\nusername2 | 2024-03-01 15:30:00\nIsi tweet baris kedua di sini", language="text")
 
-        st.markdown('<div class="action-btn">', unsafe_allow_html=True)
-        analyze_batch_btn = st.button("⚙️ Eksekusi Analisis", key="batch_btn")
-        st.markdown('</div>', unsafe_allow_html=True)
+        st.markdown("<br>", unsafe_allow_html=True)
+        analyze_batch_btn = st.button("⚙️ Mulai Eksekusi Analisis", key="batch_btn")
+        
+    with col_img_batch:
+        try:
+            st.image(img_batch, use_container_width=True)
+        except Exception:
+            st.empty() 
 
     if tweet_files and analyze_batch_btn:
-        st.markdown("---")
+        st.markdown("<hr style='border-color: #E2E8F0; margin: 3rem 0;'>", unsafe_allow_html=True)
         tweet_files = sorted(tweet_files, key=lambda x: x.name)
         
         data = []
@@ -367,9 +428,7 @@ elif st.session_state.page == "analisis_batch":
                 short_date = date_val[:10]
                 text = clean_text(text_raw)
                 
-                try:
-                    v_score = vader.polarity_scores(text)['compound']
-                    vader_label = "positive" if v_score > 0.05 else "negative" if v_score < -0.05 else "neutral"
+                try: v_score = vader.polarity_scores(text)['compound']; vader_label = "positive" if v_score > 0.05 else "negative" if v_score < -0.05 else "neutral"
                 except: vader_label = "neutral"
 
                 try: tb_label = classify_tb(TextBlob(text).sentiment.polarity)
@@ -401,11 +460,14 @@ elif st.session_state.page == "analisis_batch":
         df = pd.DataFrame(data)
         
         if df.empty:
-            st.error("❌ DataFrame kosong. Pastikan format TXT benar dan menggunakan bahasa Inggris.")
+            st.error("❌ Data kosong. Pastikan format penulisan TXT benar dan tweet berbahasa Inggris.")
         else:
-            col_metric1, col_metric2 = st.columns(2)
-            col_metric1.metric("Total Tweet Berhasil Diproses", f"{total_tweets_uploaded} Tweets")
-            col_metric2.metric("Total Tweet Dihapus (Non-EN/Invalid)", f"{total_tweets_skipped} Tweets")
+            # Metrics Dashboard
+            st.markdown("### 📊 Ringkasan Pemrosesan")
+            col_metric1, col_metric2, col_metric3 = st.columns(3)
+            col_metric1.metric("Tweet Diproses", f"{total_tweets_uploaded}", border=True)
+            col_metric2.metric("Tweet Diabaikan (Non-EN)", f"{total_tweets_skipped}", border=True)
+            col_metric3.metric("Total Model", "5 NLP Models", border=True)
             
             target_dates = sorted(df['date'].unique())
             start_unix = int(datetime.strptime(target_dates[0], "%Y-%m-%d").replace(tzinfo=timezone.utc).timestamp()) - 86400
@@ -440,14 +502,12 @@ elif st.session_state.page == "analisis_batch":
                         df_price = df_price[df_price["date"].isin(pd.to_datetime(target_dates).date)]
                         
                         if df_price.empty:
-                            st.warning("⚠️ Data Harga API kosong setelah di-filter. Pastikan format penulisan tanggal di .txt sesuai (yyyy-mm-dd).")
+                            st.warning("⚠️ Data Harga API kosong. Pastikan rentang tanggal di .txt sesuai (yyyy-mm-dd).")
                         else:
-                            st.markdown("---")
-                            st.header("📋 Ringkasan Data")
+                            st.markdown("<hr style='border-color: #E2E8F0; margin: 3rem 0;'>", unsafe_allow_html=True)
                             
                             st.markdown("#### 🗣️ Data Sentimen Mentah")
-                            st.caption("Tweet asli, hasil preprocessing, dan label prediksi dari 5 model sebelum konversi ke numerik.")
-                            raw_display_cols = ["date", "raw_tweet", "cleaned_tweet", "vader", "textblob", "bertweet", "roberta", "roberta_large"]
+                            raw_display_cols = ["date", "raw_tweet", "vader", "textblob", "bertweet", "roberta", "roberta_large"]
                             st.dataframe(df[raw_display_cols], use_container_width=True, hide_index=True)
 
                             sentiment_map = {"positive": 1, "neutral": 0, "negative": -1}
@@ -466,40 +526,30 @@ elif st.session_state.page == "analisis_batch":
                             for col in models:
                                 daily_display_cols.extend([col, f"{col}_label"])
 
-                            st.markdown("<br>", unsafe_allow_html=True)
-                            st.markdown("#### 🔢 Skor Sentimen Harian")
-                            st.caption("Rata-rata skor sentimen harian yang dikonversi ke representasi metrik.")
-                            st.dataframe(df_sentiment_daily[daily_display_cols], use_container_width=True, hide_index=True)
-
-                            st.markdown("<br>", unsafe_allow_html=True)
-                            st.markdown("#### ₿ Historis Harga & Volatilitas Bitcoin")
-                            st.caption("Data pergerakan rata-rata harga, persentase perubahan, dan Log Return (CoinGecko API).")
+                            st.markdown("<br>#### ₿ Historis Harga & Volatilitas Bitcoin", unsafe_allow_html=True)
                             st.dataframe(df_price[["date", "price", "pct_change", "log_return"]], use_container_width=True, hide_index=True)
 
                             df_merged = pd.merge(df_price, df_sentiment_daily, on="date", how="inner")
 
-                            st.markdown("---")
-                            st.markdown("### 🗂️ Dataset Final (Tabel Terintegrasi Siap Uji)")
+                            st.markdown("<br>### 🗂️ Dataset Final (Terintegrasi)", unsafe_allow_html=True)
                             final_display_cols = ["date", "price", "pct_change", "log_return"] + [c for c in daily_display_cols if c != "date"]
                             st.dataframe(df_merged[final_display_cols], use_container_width=True, hide_index=True)
 
-                            col_dl1, col_dl2, _ = st.columns([1, 1, 2])
+                            # Tombol Download
+                            col_dl1, col_dl2, _ = st.columns([1, 1, 3])
                             csv_data = df_merged.to_csv(index=False).encode('utf-8')
-                            col_dl1.download_button("📥 Download CSV", data=csv_data, file_name="sentiment_volatility.csv", mime="text/csv")
+                            col_dl1.download_button("📥 Unduh CSV", data=csv_data, file_name="sentiment_volatility.csv", mime="text/csv", use_container_width=True)
                             
                             buffer = io.BytesIO()
                             with pd.ExcelWriter(buffer, engine='xlsxwriter') as writer:
                                 df_merged.to_excel(writer, index=False)
-                            col_dl2.download_button("📥 Download Excel", data=buffer.getvalue(), file_name="sentiment_volatility.xlsx", mime="application/vnd.ms-excel")
+                            col_dl2.download_button("📥 Unduh Excel", data=buffer.getvalue(), file_name="sentiment_volatility.xlsx", mime="application/vnd.ms-excel", use_container_width=True)
 
-                            st.markdown("---")
+                            st.markdown("<hr style='border-color: #E2E8F0; margin: 3rem 0;'>", unsafe_allow_html=True)
 
                             # UJI KORELASI PEARSON
-                            st.subheader("🔬 Uji Korelasi Pearson (Sentiment vs Log Return)")
-                            
-                            with st.expander("💡 Dasar Pengambilan Keputusan"):
-                                st.write("- Jika `p-value < 0.05` maka korelasi dianggap **Signifikan** (Terdapat hubungan).")
-                                st.write("- Jika `p-value >= 0.05` maka korelasi dianggap **Tidak Signifikan**.")
+                            st.subheader("🔬 Uji Korelasi Pearson")
+                            st.caption("Menganalisis hubungan statistik antara skor sentimen harian dan volatilitas log-return BTC.")
                             
                             corr_data = []
                             raw_corr_results = []
@@ -516,33 +566,31 @@ elif st.session_state.page == "analisis_batch":
                                     "p-value": f"{pval:.4f}",
                                     "Status": signifikansi
                                 })
-                                
                                 raw_corr_results.append({"metode": method.upper(), "r": corr, "p": pval})
                                 
                             st.table(pd.DataFrame(corr_data))
 
                             # LINE CHART 
-                            st.markdown("---")
+                            st.markdown("<br>", unsafe_allow_html=True)
                             st.subheader("📈 Trend Analisis: Sentiment vs BTC Volatility")
                             
                             fig_line, ax_line = plt.subplots(figsize=(14, 6))
                             
                             ax_line.plot(df_merged["date"], df_merged["log_return"], label="BTC Log Return", color="#F7931A", linewidth=3, linestyle="-")
                             
-                            colors = ["#4F46E5", "#10B981", "#F472B6", "#14B8A6", "#6366F1"]
+                            colors = ["#3B82F6", "#10B981", "#EC4899", "#14B8A6", "#6366F1"]
                             for idx, method in enumerate(["vader", "textblob", "roberta", "roberta_large", "bertweet"]):
                                 ax_line.plot(df_merged["date"], df_merged[method], label=f"Sentiment: {method.upper()}", color=colors[idx], linewidth=1.5, linestyle="--", alpha=0.8)
                             
-                            ax_line.set_title("Pergerakan Rata-Rata Sentimen Harian Terhadap Volatilitas Harga Bitcoin", color="#111827", fontsize=14, pad=15, fontweight='bold')
-                            ax_line.set_xlabel("Tanggal", color="#4B5563", fontsize=11)
-                            ax_line.set_ylabel("Nilai (Value)", color="#4B5563", fontsize=11)
-                            ax_line.legend(loc='upper left', bbox_to_anchor=(1, 1), frameon=True, facecolor='#FFFFFF', edgecolor='#E5E7EB')
+                            ax_line.set_title("Pergerakan Sentimen vs Log Return Bitcoin", fontsize=14, pad=15, fontweight='bold')
+                            ax_line.set_xlabel("Tanggal", fontsize=11)
+                            ax_line.set_ylabel("Nilai Metrik", fontsize=11)
+                            ax_line.legend(loc='upper left', bbox_to_anchor=(1, 1), frameon=True)
                             plt.tight_layout()
                             st.pyplot(fig_line)
 
                             # SCATTER PLOT
-                            st.markdown("---")
-                            st.subheader("🔵 Pola Distribusi (Scatter Plot & Trendline)")
+                            st.markdown("<br>### 🔵 Pola Distribusi Scatter", unsafe_allow_html=True)
                             
                             cols = st.columns(3) 
                             models_list = ["vader", "textblob", "bertweet", "roberta", "roberta_large"]
@@ -551,21 +599,17 @@ elif st.session_state.page == "analisis_batch":
                                 with cols[idx % 3]:
                                     fig_scatter, ax_scatter = plt.subplots(figsize=(5, 4))
                                     sns.regplot(data=df_merged, x=method, y="log_return", ax=ax_scatter, 
-                                                scatter_kws={"s": 50, "color": "#4F46E5", "alpha": 0.6}, 
-                                                line_kws={"color": "#F7931A", "linewidth": 2.5})
-                                    ax_scatter.set_title(f"{method.upper()} vs Log Return", color="#111827", fontweight='bold')
-                                    ax_scatter.set_xlabel("Skor Sentimen", color="#4B5563")
-                                    ax_scatter.set_ylabel("Log Return", color="#4B5563")
+                                                scatter_kws={"s": 40, "color": "#0F172A", "alpha": 0.5}, 
+                                                line_kws={"color": "#F7931A", "linewidth": 2})
+                                    ax_scatter.set_title(f"{method.upper()}", fontweight='bold')
+                                    ax_scatter.set_xlabel("Sentimen Score")
+                                    ax_scatter.set_ylabel("Log Return")
                                     plt.tight_layout()
                                     st.pyplot(fig_scatter)
                             
-                            with st.expander("📖 Panduan Membaca Grafik"):
-                                st.write("- **Garis Orange (Trendline):** Menunjukkan arah korelasi (Naik = Positif, Turun = Negatif).")
-                                st.write("- **Titik Ungu:** Sebaran data, semakin merapat ke garis orange berarti korelasi semakin kuat.")
-
                             # KESIMPULAN
-                            st.markdown("---")
-                            st.header("📝 Ekstraksi Kesimpulan")
+                            st.markdown("<hr style='border-color: #E2E8F0; margin: 3rem 0;'>", unsafe_allow_html=True)
+                            st.subheader("📝 Kesimpulan Otomatis")
                             
                             max_volatility_idx = df_merged["log_return"].idxmax()
                             min_volatility_idx = df_merged["log_return"].idxmin()
@@ -576,17 +620,17 @@ elif st.session_state.page == "analisis_batch":
                             strongest_model = max(raw_corr_results, key=lambda x: abs(x["r"]))
                             arah_text = "berbanding lurus (positif)" if strongest_model["r"] > 0 else "berbanding terbalik (negatif)"
 
-                            st.markdown(f"Puncak lonjakan positif (*maximum log return*) terjadi pada **{date_max}**, sedangkan penurunan tajam paling ekstrem (*minimum log return*) terjadi pada **{date_min}**.")
+                            st.write(f"Puncak lonjakan positif (*max log return*) terjadi pada **{date_max}**, sedangkan penurunan ekstrem terjadi pada **{date_min}**.")
                             
                             if len(sig_models) > 0:
                                 st.success(f"""
-                                ✅ **Hipotesis Diterima (H1):** Ditemukan korelasi linier yang signifikan pada metode **{', '.join(sig_models)}** (*p-value* < 0.05). 
+                                **Hipotesis Diterima (H1):** Ditemukan korelasi linier yang signifikan pada metode **{', '.join(sig_models)}** (*p-value* < 0.05). 
                                 Metode dengan pemetaan respons pasar terkuat adalah **{strongest_model['metode']}**, dengan sifat hubungan **{arah_text}**.
                                 """)
                             else:
                                 st.warning("""
-                                ⚠️ **Hipotesis Ditolak (H0 Diterima):** Tidak ditemukan bukti empiris korelasi linier yang signifikan (seluruh *p-value* >= 0.05). Volatilitas harga Bitcoin pada rentang ini cenderung dipengaruhi oleh metrik atau faktor fundamental lain di luar sentimen platform X.
+                                **Hipotesis Ditolak (H0 Diterima):** Tidak ditemukan bukti empiris korelasi linier yang signifikan (seluruh *p-value* >= 0.05). Volatilitas harga cenderung dipengaruhi oleh faktor teknikal/fundamental di luar sentimen X.
                                 """)
 
             except Exception as e:
-                st.error(f"❌ Terjadi kesalahan saat memproses data/API: {e}")
+                st.error(f"❌ Terjadi kesalahan sistem: {e}")