{"id":311,"date":"2026-02-03T04:40:11","date_gmt":"2026-02-03T04:40:11","guid":{"rendered":"https:\/\/binus.ac.id\/humanitarian-ai\/?p=311"},"modified":"2026-02-03T05:13:37","modified_gmt":"2026-02-03T05:13:37","slug":"computer-vision-and-image-processing","status":"publish","type":"post","link":"https:\/\/binus.ac.id\/humanitarian-ai\/blog\/2026\/02\/03\/computer-vision-and-image-processing\/","title":{"rendered":"Computer Vision and Image Processing"},"content":{"rendered":"<h1 style=\"text-align: center\" data-start=\"326\" data-end=\"342\"><span style=\"font-size: 18pt\">Training Syllabus<\/span><\/h1>\n<h4 data-start=\"1783\" data-end=\"1799\">\ud83d\udd58 Duration: <strong>4\u00a0full days<\/strong> (08:00 AM \u2013 05:00 PM GMT+7)<\/h4>\n<h4 data-start=\"1845\" data-end=\"1873\">\ud83d\udc65 Minimum Participants: <strong data-start=\"1874\" data-end=\"1886\">5 people<\/strong><\/h4>\n<h4 data-start=\"1888\" data-end=\"1899\">\ud83d\udcbc Fee: <strong data-start=\"1900\" data-end=\"1932\">Based on agreement and venue<\/strong><\/h4>\n<hr class=\"\" data-start=\"1934\" data-end=\"1937\" \/>\n<h3 class=\"\" data-start=\"1939\" data-end=\"1967\">\ud83d\udccb <strong data-start=\"1946\" data-end=\"1967\">Training Overview<\/strong><\/h3>\n<pre id=\"tw-target-text\" class=\"tw-data-text tw-text-large tw-ta\" dir=\"ltr\" role=\"text\" data-placeholder=\"Translation\" data-ved=\"2ahUKEwjrz66qzNyQAxVgTGwGHbaxAKwQ3ewLegQIChAV\" aria-label=\"Translated text: This Python Programming for Deep Learning training is designed for participants who already understand the basics of Python and want to delve deeper into the world of artificial intelligence (AI), specifically deep learning. Participants will learn how Python is used to build, train, and evaluate neural network models using modern libraries like TensorFlow and Keras. This training focuses on hands-on coding and real-world case studies, enabling participants to understand the deep learning workflow, from data preprocessing and model architecture to training and evaluation.\">This training aims to equip participants with conceptual understanding and fundamental to intermediate skills in digital image processing and computer vision, enabling them to analyze, process, and develop image-based solutions for various industrial and research applications. It is a basic step for mastering robot vision.<\/pre>\n<hr class=\"\" data-start=\"2415\" data-end=\"2418\" \/>\n<h3 class=\"\" data-start=\"2420\" data-end=\"2445\">\ud83d\udcda <strong data-start=\"2427\" data-end=\"2445\">Topics Covered<\/strong><\/h3>\n<p><strong>Session 1 \u2013 Introduction to Image Processing and Computer Vision<\/strong><\/p>\n<ul>\n<li data-start=\"2108\" data-end=\"2183\">\n<p data-start=\"2110\" data-end=\"2183\">Definition and differences between image processing and computer vision<\/p>\n<\/li>\n<li data-start=\"2184\" data-end=\"2219\">\n<p data-start=\"2186\" data-end=\"2219\">Technology evolution and trends<\/p>\n<\/li>\n<li data-start=\"2220\" data-end=\"2292\">\n<p data-start=\"2222\" data-end=\"2292\">Industrial applications (medical, manufacturing, security, automotive)<\/p>\n<\/li>\n<\/ul>\n<p><strong>Session 2 \u2013 Introduction to Digital Images and OpenCV<\/strong><\/p>\n<ul>\n<li data-start=\"2445\" data-end=\"2477\">\n<p data-start=\"2447\" data-end=\"2477\">Digital image representation<\/p>\n<\/li>\n<li data-start=\"2478\" data-end=\"2515\">\n<p data-start=\"2480\" data-end=\"2515\">Pixels, resolution, and bit depth<\/p>\n<\/li>\n<li data-start=\"2516\" data-end=\"2545\">\n<p data-start=\"2518\" data-end=\"2545\">Grayscale vs color images<\/p>\n<\/li>\n<li data-start=\"2516\" data-end=\"2545\">Computer vision basic with OpenCV<\/li>\n<\/ul>\n<p><strong>Session 3 \u2013 Feature Extraction<\/strong><\/p>\n<ul>\n<li data-start=\"4074\" data-end=\"4095\">\n<p data-start=\"4076\" data-end=\"4095\">Template matching<\/p>\n<\/li>\n<li data-start=\"4096\" data-end=\"4123\">\n<p data-start=\"4098\" data-end=\"4123\">Object detection and Haar cascade (overview)<\/p>\n<\/li>\n<li data-start=\"4124\" data-end=\"4163\">\n<p data-start=\"4126\" data-end=\"4163\">Limitations of classical approaches<\/p>\n<\/li>\n<li data-start=\"4124\" data-end=\"4163\">Advanced image programming\u00a0 with OpenCV<\/li>\n<\/ul>\n<p><strong>Session 4 \u2013 Machine Learning for Computer Vision<\/strong><\/p>\n<ul>\n<li data-start=\"2108\" data-end=\"2183\">Role of machine learning in computer vision<\/li>\n<li data-start=\"4502\" data-end=\"4541\">\n<p data-start=\"4504\" data-end=\"4541\">Supervised vs unsupervised learning<\/p>\n<\/li>\n<li data-start=\"4542\" data-end=\"4572\">\n<p data-start=\"4544\" data-end=\"4572\">Datasets and data labeling<\/p>\n<\/li>\n<li data-start=\"4542\" data-end=\"4572\">Machine Learning methods<\/li>\n<li data-start=\"4542\" data-end=\"4572\">Case study<\/li>\n<\/ul>\n<p><strong>Session 5 \u2013 Advanced Image Processing<\/strong><\/p>\n<ul>\n<li data-start=\"5056\" data-end=\"5092\">\n<p data-start=\"5058\" data-end=\"5092\">Artificial Neural Networks (ANN) and Deep Learning<\/p>\n<\/li>\n<li data-start=\"5093\" data-end=\"5132\">\n<p data-start=\"5095\" data-end=\"5132\">Convolutional Neural Networks (CNN)<\/p>\n<\/li>\n<li data-start=\"5133\" data-end=\"5160\">\n<p data-start=\"5135\" data-end=\"5160\">Basic CNN architectures<\/p>\n<\/li>\n<li data-start=\"5310\" data-end=\"5339\">\n<p data-start=\"5312\" data-end=\"5339\">Transfer learning concept<\/p>\n<\/li>\n<li data-start=\"5340\" data-end=\"5362\">\n<p data-start=\"5342\" data-end=\"5362\">Pre-trained models<\/p>\n<\/li>\n<li data-start=\"5133\" data-end=\"5160\">\n<p data-start=\"5135\" data-end=\"5160\">Project Presentation<\/p>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<hr class=\"\" data-start=\"2710\" data-end=\"2713\" \/>\n<h3 class=\"\" data-start=\"2715\" data-end=\"2743\">\ud83c\udfaf <strong data-start=\"2722\" data-end=\"2743\">Learning Outcomes<\/strong><\/h3>\n<p class=\"\" data-start=\"2744\" data-end=\"2791\">By the end of this training, participants will be able to:<\/p>\n<ul data-start=\"2792\" data-end=\"3002\">\n<li class=\"\" data-start=\"2792\" data-end=\"2861\">\u00a0Understand image processing and computer vision concepts<\/li>\n<li class=\"\" data-start=\"2792\" data-end=\"2861\">Process and analyze digital images<\/li>\n<li class=\"\" data-start=\"2792\" data-end=\"2861\">Apply fundamental to intermediate computer vision methods<\/li>\n<li class=\"\" data-start=\"2792\" data-end=\"2861\">Understand the development direction of AI-based vision solution.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3 class=\"\" data-start=\"1518\" data-end=\"1531\">\ud83d\udcde Contact:<\/h3>\n<ul data-start=\"1532\" data-end=\"1679\">\n<li class=\"\" data-start=\"1532\" data-end=\"1597\">\n<p class=\"\" data-start=\"1534\" data-end=\"1597\">\ud83d\udce7 Email: <a class=\"cursor-pointer\" rel=\"noopener\" data-start=\"1544\" data-end=\"1595\">wbudiharto@binus.edu<\/a><\/p>\n<\/li>\n<li class=\"\" data-start=\"1598\" data-end=\"1639\">\n<p class=\"\" data-start=\"1600\" data-end=\"1639\">\ud83d\udcf1 Bpk. Prof. Widodo (WA): +62 856 9887 384<\/p>\n<\/li>\n<li class=\"\" data-start=\"1640\" data-end=\"1679\">\n<p class=\"\" data-start=\"1642\" data-end=\"1679\">\ud83d\udcf1 Ibu Dr. Emny (WA): +62 813 8741 3863<\/p>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<hr \/>\n<h2 class=\"\" style=\"text-align: center\" data-start=\"162\" data-end=\"191\"><strong data-start=\"168\" data-end=\"189\">Silabus Pelatihan<\/strong><\/h2>\n<h3 class=\"\" style=\"text-align: center\" data-start=\"192\" data-end=\"237\">Python Programming for Deep Learning<\/h3>\n<h4 data-start=\"266\" data-end=\"280\">\ud83d\udd58 Durasi: <strong>4\u00a0hari fullday<\/strong> (08:00 &#8211; 17:00 WIB)<\/h4>\n<h4 data-start=\"321\" data-end=\"351\">\ud83d\udc65 Jumlah Peserta Minimum: <strong data-start=\"352\" data-end=\"363\">5 orang<\/strong><\/h4>\n<h4 data-start=\"365\" data-end=\"378\">\ud83d\udcbc Biaya: <strong data-start=\"379\" data-end=\"429\">Sesuai dengan kesepakatan dan lokasi pelatihan<\/strong><\/h4>\n<hr class=\"\" data-start=\"431\" data-end=\"434\" \/>\n<h3 class=\"\" data-start=\"436\" data-end=\"466\">\ud83d\udccb <strong data-start=\"443\" data-end=\"466\">Deskripsi Pelatihan<\/strong><\/h3>\n<p>Pelatihan <strong>Python Programming for Deep Learning<\/strong> ini dirancang untuk peserta yang telah memahami dasar-dasar Python dan ingin melangkah lebih jauh ke dunia <strong>kecerdasan buatan (AI)<\/strong>, khususnya <strong>deep learning<\/strong>. Peserta akan belajar bagaimana Python digunakan untuk membangun, melatih, dan mengevaluasi model neural network menggunakan pustaka modern seperti <strong>TensorFlow<\/strong> dan <strong>Keras<\/strong>.<\/p>\n<p>Pelatihan ini berfokus pada <strong>praktik langsung (hands-on coding)<\/strong> dan <strong>studi kasus nyata<\/strong>, agar peserta dapat memahami alur kerja deep learning dari preprocessing data, arsitektur model, pelatihan (training) hingga evaluasi.<\/p>\n<hr class=\"\" data-start=\"906\" data-end=\"909\" \/>\n<h3 class=\"\" data-start=\"911\" data-end=\"949\">\ud83d\udcda <strong data-start=\"918\" data-end=\"949\">Materi yang Akan Dipelajari<\/strong><\/h3>\n<p><strong>Sesi 1 \u2013 Pengenalan Deep Learning dan Environment<\/strong><\/p>\n<ul>\n<li>Review singkat Python dan NumPy<\/li>\n<li>Konsep Machine Learning vs Deep Learning<\/li>\n<li>Arsitektur Neural Network<\/li>\n<li>Instalasi dan setup TensorFlow\/Keras<\/li>\n<li>Hands-on<\/li>\n<\/ul>\n<p><strong>Sesi 2 \u2013 Persiapan dan Manipulasi Data untuk Deep Learning<\/strong><\/p>\n<ul>\n<li>Data loading dan preprocessing (normalisasi, one-hot encoding)<\/li>\n<li>Split data: training, validation, test<\/li>\n<li>Menggunakan Pandas dan scikit-learn untuk pipeline data<\/li>\n<li>Visualisasi distribusi data dengan Matplotlib\/Seaborn<\/li>\n<li>Hands-on: Dataset MNIST dan analisis awal<strong style=\"font-family: inherit\">\u00a0<\/strong><\/li>\n<\/ul>\n<p><strong>Sesi 3 \u2013 Membangun dan Melatih Neural Network<\/strong><\/p>\n<ul>\n<li>Konsep layer, neuron, activation function, loss, dan optimizer<\/li>\n<li>Backpropagation dan gradient descent (konsep intuitif)<\/li>\n<li>Implementasi model DNN (Fully Connected Network)<\/li>\n<li>Evaluasi model: accuracy, loss curve<\/li>\n<li>Hands-on: Membangun model klasifikasi digit (MNIST)<\/li>\n<\/ul>\n<p><strong>Sesi 4 \u2013 Convolutional Neural Network (CNN) untuk Data Gambar<\/strong><\/p>\n<ul>\n<li>Konsep convolution, pooling, dan filter<\/li>\n<li>Arsitektur CNN populer<\/li>\n<li>Implementasi CNN dengan Keras<\/li>\n<li>Augmentasi data untuk meningkatkan performa<\/li>\n<li>Hands-on: Klasifikasi gambar<\/li>\n<\/ul>\n<p><strong>Sesi 5 \u2013 Recurrent Neural Network (RNN) dan LSTM untuk Data Sequential<\/strong><\/p>\n<ul>\n<li>Konsep sequence data dan temporal dependencies<\/li>\n<li>Arsitektur RNN dan LSTM<\/li>\n<li>Implementasi RNN\/LSTM dengan Keras<\/li>\n<li>Studi kasus: Analisis sentimen teks sederhana<\/li>\n<li>Hands-on: Prediksi teks<\/li>\n<\/ul>\n<p><strong>Sesi 6 \u2013 Optimisasi dan Evaluasi Model<\/strong><\/p>\n<ul>\n<li>Overfitting vs underfitting<\/li>\n<li>Regularisasi (Dropout, Early Stopping)<\/li>\n<li>Hyperparameter tuning<\/li>\n<li>Model evaluation dan confusion matrix<\/li>\n<li>Hands-on: Meningkatkan performa model CNN<\/li>\n<\/ul>\n<p><strong>Sesi 7 \u2013 Transfer Learning dan Fine-Tuning<\/strong><\/p>\n<ul>\n<li>Konsep transfer learning<\/li>\n<li>Menggunakan model pre-trained (MobileNet, ResNet, EfficientNet)<\/li>\n<li>Fine-tuning untuk dataset kustom<\/li>\n<li>Hands-on: Klasifikasi gambar kustom dengan transfer learning<\/li>\n<\/ul>\n<p><strong>Sesi 8 \u2013 Mini Project: Deep Learning Case Study<\/strong><\/p>\n<ul>\n<li>Pemilihan dataset (gambar, teks, atau suara)<\/li>\n<li>Preprocessing dan eksplorasi data<\/li>\n<li>Membangun model, pelatihan, dan evaluasi<\/li>\n<li>Interpretasi hasil (visualisasi prediksi, confusion matrix)<\/li>\n<li>Presentasi hasil mini project<\/li>\n<\/ul>\n<hr class=\"\" data-start=\"1207\" data-end=\"1210\" \/>\n<h3 class=\"\" data-start=\"1212\" data-end=\"1239\">\ud83c\udfaf <strong data-start=\"1219\" data-end=\"1239\">Tujuan Pelatihan<\/strong><\/h3>\n<p>Setelah mengikuti pelatihan ini, peserta diharapkan mampu:<\/p>\n<ol>\n<li>Memahami konsep dasar <strong>machine learning<\/strong> dan <strong>deep learning.<\/strong><\/li>\n<li>Menggunakan <strong>Python dan library populer (NumPy, Pandas, Matplotlib)<\/strong> untuk menyiapkan data.<\/li>\n<li>Memahami struktur dasar <strong>neural network<\/strong> dan proses pelatihannya.\n<ol>\n<li style=\"list-style-type: none\"><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<hr class=\"\" data-start=\"1513\" data-end=\"1516\" \/>\n<h3 class=\"\" data-start=\"1518\" data-end=\"1531\">\ud83d\udcde Kontak<\/h3>\n<ul data-start=\"1532\" data-end=\"1679\">\n<li class=\"\" data-start=\"1532\" data-end=\"1597\">\n<p class=\"\" data-start=\"1534\" data-end=\"1597\">\ud83d\udce7 Email: <a class=\"cursor-pointer\" rel=\"noopener\" data-start=\"1544\" data-end=\"1595\">wbudiharto@binus.edu<\/a><\/p>\n<\/li>\n<li class=\"\" data-start=\"1598\" data-end=\"1639\">\n<p class=\"\" data-start=\"1600\" data-end=\"1639\">\ud83d\udcf1 Bpk. Prof. Widodo (WA): +62 856 9887 384<\/p>\n<\/li>\n<li class=\"\" data-start=\"1640\" data-end=\"1679\">\n<p class=\"\" data-start=\"1642\" data-end=\"1679\">\ud83d\udcf1 Ibu Dr. Emny (WA): +62 813 8741 3863<\/p>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Training Syllabus \ud83d\udd58 Duration: 4\u00a0full days (08:00 AM \u2013 05:00 PM GMT+7) \ud83d\udc65 Minimum Participants: 5 people \ud83d\udcbc Fee: Based on agreement and venue \ud83d\udccb Training Overview This training aims to equip participants with conceptual understanding and fundamental to intermediate skills in digital image processing and computer vision, enabling them to analyze, process, and develop [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-311","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/posts\/311","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/comments?post=311"}],"version-history":[{"count":3,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/posts\/311\/revisions"}],"predecessor-version":[{"id":315,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/posts\/311\/revisions\/315"}],"wp:attachment":[{"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/media?parent=311"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/categories?post=311"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/tags?post=311"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}