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Kecerdasan Buatan & Jaringan Syaraf Buatan

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Buku ini terbagi menjadi 2 bagian. Bagian pertama akan membahas tentang Artificial Intelligence, sedangkan bagian kedua akan membahas tentang Jaringan Syaraf tiruan. Buku inijuga dilengkapi dengan latihan soal agar mempermudah pembaca dalam penguasaan materi.Dalam buku ini juga akan diterangkan machine Learning dan cara untuk membangunnya. Data Mining atau yang sering kita sebut penambangan data adalah metode dalam ilmu komputer yang biasa digunakan dalam proses pencarian knowledge. Metode-metode dan tahapan datamining akan diuraikan secara detail dalam buku ini. Perkembangan dan pengembanganJaringan syaraf tiruan akan dijelaskan dan diuji dalam buku ini.\r\nDAFTAR ISI\r\n\r\nHALAMAN JUDUL ...................................................................................................... iii\r\nKATA PENGANTAR...................................................................................................... iv\r\nDAFTAR ISI ................................................................................................................ v\r\nBAGIAN 1\r\nBAB 1 ARTIFICIAL INTELLIGENCE ................................................................................. 1\r\n1.1. Apakah Artificial Intelligence (Kecerdasan Buatan) itu? .................................... 1\r\n1.2. Ilmu Otak dan Pemecahan Masalah ................................................................. 2\r\n1.3. Tes Turing dan Chatterbots ................................................................................ 4\r\n1.4. Sejarah AI ........................................................................................................... 5\r\n1.5. Logika Memecahkan (Hampir) Semua Masalah ................................................ 7\r\n1.6. Koneksionisme Baru ........................................................................................... 8\r\n1.7. Penalaran di Bawah Ketidakpastian ................................................................... 8\r\n1.8. Agen Terdistribusi, Otonom dan Pembelajaran ................................................ 9\r\n1.9. AI Tumbuh Dewasa ............................................................................................ 9\r\n1.10. Revolusi AI .......................................................................................................... 10\r\n1.11. AI dan Masyarakat ............................................................................................. 10\r\n1.12. Apakah AI Menghancurkan Pekerjaan? ............................................................. 10\r\n1.13. AI dan Transportasi ............................................................................................ 13\r\n1.14. Layanan Robotika ............................................................................................... 14\r\n1.15. Agen ................................................................................................................... 15\r\n1.16. Sistem Berbasis Pengetahuan ............................................................................ 17\r\n1.17. Latihan ................................................................................................................ 19\r\nBAB 2 LOGIKA PROPORSISI ........................................................................................ 20\r\n2.1 Sintaks ................................................................................................................ 20\r\n2.2 Semantik ............................................................................................................. 20\r\n2.3 Sistem Bukti ........................................................................................................ 22\r\n2.4 Klausa Tanduk .................................................................................................... 28\r\n2.5 Komputasi dan Kompleksitas ............................................................................. 29\r\n2.6 Aplikasi dan Batasan .......................................................................................... 30\r\n2.7 Latihan ................................................................................................................ 30\r\nBAB 3 LOGIKA PREDIKAT ORDE PERTAMA .................................................................. 33\r\n3.1 Sintaks ................................................................................................................ 33\r\n3.2 Semantik ............................................................................................................. 35\r\n3.3 Ekualitas ............................................................................................................. 36\r\n3.4 Kuantifier dan Bentuk Normal ........................................................................... 37\r\n3.5 Kalkulus Bukti ..................................................................................................... 40\r\n3.6 Resolusi .............................................................................................................. 41\r\n3.7 Strategi Resolusi ................................................................................................. 43\r\n3.8 Ekualitas ............................................................................................................. 44\r\n3.9 Pembukti Teorema Otomatis ............................................................................. 44\r\n3.10 Contoh Matematika ........................................................................................... 46\r\n3.11 Aplikasi ............................................................................................................... 48\r\n3.12 Ringkasan ........................................................................................................... 50\r\n3.13 Latihan ................................................................................................................ 50\r\nBAB 4 KETERBATASAN LOGIKA ................................................................................... 52\r\n4.1 Masalah Ruang Pencarian .................................................................................. 52\r\n4.2 Decidability dan Ketidaklengkapan .................................................................... 54\r\n4.3 Penguin Terbang ................................................................................................ 55\r\n4.4 Ketidakpastian Pemodelan ................................................................................ 57\r\n4.5 Latihan................................................................................................................. 58\r\nBAB 5 PEMROGRAMAN LOGIKA DENGAN PROLOG .................................................... 59\r\n5.1 Sistem dan Implementasi PROLOG .................................................................... 59\r\n5.2 Kontrol Eksekusi dan Elemen Prosedural .......................................................... 62\r\n5.3 List ...................................................................................................................... 63\r\n5.4 Program modifikasi sendiri ................................................................................ 65\r\n5.5 Constraint Logic Programming(CLP) .................................................................. 66\r\n5.6 Ringkasan ........................................................................................................... 69\r\n5.7 Latihan ................................................................................................................ 69\r\nBAB 6 SEARCH, GAME, DAN PROBLEM SOLVING ........................................................ 72\r\n6.1 Pendahuluan ...................................................................................................... 72\r\n6.2 Pencarian Luas-Pertama .................................................................................... 78\r\n6.3 Analisis Pencarian .............................................................................................. 79\r\n6.4 Pendalaman berulang ........................................................................................ 80\r\n6.5 Analisis ............................................................................................................... 80\r\n6.6 Perbandingan ..................................................................................................... 82\r\n6.7 Pemeriksaan Siklus ............................................................................................. 82\r\n6.8 Pencarian Heuristik ............................................................................................ 83\r\n6.9 Greedy Search .................................................................................................... 85\r\n6.10 Perencanaan Rute dengan Algoritma Pencarian A? .............................................. 88\r\n6.11 Perbandingan Empiris dari Algoritma Pencarian ............................................... 90\r\n6.12 Ringkasan ........................................................................................................... 92\r\n6.13 Game dengan Lawan .......................................................................................... 92\r\n6.14 Pencarian Minimax ............................................................................................ 93\r\n6.15 Pemangkasan Alfa-Beta ..................................................................................... 94\r\n6.16 Fungsi Evaluasi Heuristik .................................................................................... 96\r\n6.17 Belajar Heuristik ................................................................................................. 97\r\n6.18 Negara Seni ........................................................................................................ 98\r\n6.19 Latihan ................................................................................................................ 100\r\nBAB 7 PEMIKIRAN KETIDAKPASTIAN .......................................................................... 103\r\n7.1 Komputasi dengan Probabilitas ......................................................................... 105\r\n7.2 Probabilitas Bersyarat ........................................................................................ 107\r\n7.3 Aturan Rantai ..................................................................................................... 108\r\n7.4 Marginalisasi ...................................................................................................... 108\r\n7.5 Prinsip Entropi Maksimum ................................................................................. 111\r\n7.6 Entropi Maksimum Tanpa Batasan Eksplisit ...................................................... 115\r\n7.7 Probabilitas Bersyarat Versus Implikasi Material .............................................. 116\r\n7.8 MaxEnt-System .................................................................................................. 117\r\n7.9 Diagnosis Apendisitis dengan Metode Formal .................................................. 119\r\n7.10 Basis Pengetahuan Probabilistik Hibrida ........................................................... 119\r\n7.11 Penerapan LEXMED ............................................................................................ 121\r\n7.12 Menentukan Grafik Ketergantungan ................................................................. 123\r\n7.13 Manajemen Risiko Menggunakan Matriks Biaya ............................................... 126\r\n7.14 Penalaran dengan Bayesian Networks ............................................................... 130\r\n7.15 Variabel independen .......................................................................................... 130\r\n7.16 Representasi Grafis Pengetahuan sebagai Jaringan Bayesian ........................... 131\r\n7.17 Independensi Kondisional .................................................................................. 131\r\n7.18 Perangkat Lunak untuk Jaringan Bayesian ......................................................... 134\r\n7.19 Pengembangan Jaringan Bayesian ..................................................................... 135\r\n7.20 Semantik dari Bayesian Networks ...................................................................... 138\r\n7.21 Ringkasan ........................................................................................................... 140\r\n7.22 Latihan ................................................................................................................ 141\r\nBAB 8 MACHINE LEARNING DAN DATA MINING ......................................................... 145\r\n8.1 Apa itu learning? ................................................................................................ 146\r\n8.2 Apa Itu Data Mining? ......................................................................................... 149\r\n8.3 Perceptron, Pengklasifikasi Linier ....................................................................... 152\r\n8.4 Pengoptimalan dan Outlook .............................................................................. 157\r\n8.5 Metode Tetangga Terdekat ............................................................................... 158\r\n8.6 Jarak Adalah Relevan ......................................................................................... 162\r\n8.7 Penalaran Berbasis Kasus ................................................................................... 165\r\n8.8 Pembelajaran Pohon Keputusan ........................................................................ 166\r\n8.9 Entropi Sebagai Metrik Untuk Konten Informasi ............................................... 168\r\n8.10 Penerapan C4.5 .................................................................................................. 171\r\n8.11 Pembelajaran Diagnosis Apendisitis .................................................................. 174\r\n8.12 Memangkas—Memotong Pohon ....................................................................... 177\r\n8.13 Cross-Validasi dan Overfitting ............................................................................ 180\r\n8.14 Mempelajari Jaringan Bayesian ......................................................................... 181\r\n8.15 One-Class Learning ............................................................................................. 188\r\n8.16 Deskripsi Data Tetangga Terdekat ..................................................................... 188\r\n8.17 Clustering ........................................................................................................... 189\r\n8.18 Metrik Jarak ........................................................................................................ 190\r\n8.19 k-Means dan Algoritma EM ............................................................................... 191\r\n8.20 Pengelompokan Hirarkis .................................................................................... 192\r\n8.21 Data Mining Dalam Prakteknya ......................................................................... 197\r\n8.22 Ringkasan ........................................................................................................... 200\r\n8.23 Latihan ................................................................................................................ 202\r\nDAFTAR PUSTAKA ...................................................................................................... 617

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

006.3 JOS k

Penerbit

Semarang : Yayasan Prina Agus Teknik., 2021

Deskripsi Fisik

639 hal

Bahasa

Indonesia

ISBN/ISSN

9786235734217

Klasifikasi

006.3

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

-

Subjek

Kecerdasan Buatan

Info Detail Spesifik

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Pernyataan Tanggungjawab

Muhammad Sholikam, M. Kom

Versi lain/terkait

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