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Di sampingmu, ada pelatih yang terus kasih arahan: "Luruskan tanganmu!", "Jangan lupa tendang kakinya!", dan "Kepalamu jangan terlalu masuk ke air!" Pelatih itu memastikan Kamu belajar dengan benar dari tiap gerakan yang Kamu lakukan. Nah, kalau mesin belajar kayak gini, itu disebut <strong>Supervised Learning</strong>. </p><p>Dalam <strong><a href="https://en.wikipedia.org/wiki/Supervised_learning">supervised learning</a></strong>, kita memberikan data yang sudah diberi label ke mesin, kayak pelatih renang yang kasih contoh gerakan. Mesin ini akan belajar menemukan pola dari data tersebut, lalu menerapkannya ke data baru. Penasaran gimana cara kerjanya atau ingin tahu <strong>contoh supervised learning</strong> di dunia nyata? Yuk, kita eksplor lebih dalam sambil tetap santai, oke? </p><h2>Contoh Supervised Learning</h2><p><img src="https://img.mesinpintar.com/apa-itu-supervised-learning-email-spam.jpg" alt="Contoh Supervised Learning"><br><em>Contoh supervised learning dalam analisis data.</em> </p><p>Untuk memahami <strong>apa itu supervised learning</strong>, mari kita lihat <strong>contoh supervised learning</strong> yang nyata. Misalnya, ada aplikasi email yang bisa mendeteksi mana email yang spam dan mana yang bukan. Awalnya, mesin dilatih dengan data yang sudah diberi label, seperti “ini spam” atau “ini bukan spam.” </p><p>Contoh lainnya adalah prediksi harga rumah. Algoritma dilatih menggunakan data historis: ukuran rumah, lokasi, jumlah kamar tidur, dan harga jual sebelumnya. Dengan data ini, mesin bisa memprediksi harga rumah baru berdasarkan pola dari data yang ada. </p><p>Dan jangan lupa, ada juga aplikasi pengenalan wajah yang bisa mengidentifikasi wajah Kamu di foto. Itu semua berkat <strong>supervised learning</strong> yang belajar dari ribuan foto berlabel. </p><h2>Algoritma Supervised Learning</h2><p>Di balik semua contoh keren tadi, ada beberapa algoritma yang sering digunakan dalam <strong>supervised learning</strong>. Ini beberapa yang paling populer: </p><ol><li><strong>Regresi Linear</strong>: Digunakan untuk memprediksi nilai kontinu, seperti harga rumah atau suhu udara. </li><li><strong>K-Nearest Neighbors (KNN)</strong>: Algoritma yang bekerja dengan cara membandingkan data baru dengan data terdekatnya. </li><li><strong>Random Forest</strong>: Metode yang menggunakan banyak pohon keputusan untuk meningkatkan akurasi prediksi.</li></ol><p>Setiap algoritma ini punya cara kerja dan keunggulan masing-masing. Kalau Kamu tertarik, kita bisa bahas lebih dalam di artikel berikutnya! </p><h2>Kesimpulan</h2><p>Bayangkan dunia tanpa email filter atau prediksi harga rumah—rumit banget, kan? Dengan <strong>supervised learning</strong>, kita bisa membuat mesin lebih cerdas dan hidup lebih mudah. Tapi tunggu, supervised learning hanya satu sisi dari cerita besar. Kalau Kamu ingin tahu lebih banyak tentang sisi lainnya, yaitu <strong>unsupervised learning</strong>, di sini <a href="https://www.mesinpintar.com/blog/apa-itu-unsupervised-learning">Apa itu Unsupervised learning</a>. 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