{"id":20153156,"url":"https://github.com/hendrowunga/ML-Learning-Path","last_synced_at":"2025-05-06T22:31:11.134Z","repository":{"id":237576187,"uuid":"794794584","full_name":"hendrowunga/ML-Learning-Path","owner":"hendrowunga","description":"Repository documenting a learning journey in Machine Learning and Classical Algorithms, including code, datasets, and implementations. 🧠💻","archived":false,"fork":false,"pushed_at":"2025-04-23T11:44:34.000Z","size":41643,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-23T12:35:16.562Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hendrowunga.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-05-02T01:02:59.000Z","updated_at":"2025-04-23T11:48:49.000Z","dependencies_parsed_at":"2025-04-23T12:50:02.166Z","dependency_job_id":null,"html_url":"https://github.com/hendrowunga/ML-Learning-Path","commit_stats":null,"previous_names":["hendrowunga/pembelajaran-python","hendrowunga/python","hendrowunga/pembelajaran_mesin-python","hendrowunga/mesin_learning-and-algoritma-optimisasi","hendrowunga/ml-learning-path"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hendrowunga%2FML-Learning-Path","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hendrowunga%2FML-Learning-Path/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hendrowunga%2FML-Learning-Path/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hendrowunga%2FML-Learning-Path/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hendrowunga","download_url":"https://codeload.github.com/hendrowunga/ML-Learning-Path/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252778953,"owners_count":21802858,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-13T23:17:25.272Z","updated_at":"2025-05-06T22:31:06.122Z","avatar_url":"https://github.com/hendrowunga.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning Semester Project\n\nIni adalah repositori untuk proyek pembelajaran mesin selama satu semester, mencakup berbagai topik dan materi yang telah dipelajari dan dikerjakan selama pembelajaran ini.\n\n## Daftar Isi\n\n1. **Laporan**\n2. **Tugas**\n3. **UTS (Ujian Tengah Semester)**\n4. **UAS (Ujian Akhir Semester)**\n\n## Materi Pembelajaran\n\n### 1. Pre-Processing\n- Deskripsi: Langkah-langkah pre-processing data sebelum masuk ke proses analisis.\n\n### 2. Clustering\n- Deskripsi: Metode clustering untuk mengelompokkan data berdasarkan kesamaan.\n\n### 3. Hierarchical Clustering\n- Deskripsi: Penerapan metode hierarchical clustering untuk analisis struktur data.\n\n### 4. Feature Selection dan Feature Extraction\n- Deskripsi: Teknik untuk memilih dan mengekstraksi fitur yang paling relevan dari data.\n\n### 5. Klasifikasi dengan Random Forest\n- Deskripsi: Implementasi algoritma Random Forest untuk klasifikasi data.\n\n### 6. Klasifikasi dengan Support Vector Machine (SVM)\n- Deskripsi: Penggunaan Support Vector Machine untuk klasifikasi data.\n\n### 7. Neural Network (Multilayer Perceptron Backpropagation)\n- Deskripsi: Jaringan saraf tiruan dengan algoritma backpropagation untuk pembelajaran.\n\n### 8. Neural Network #2 (Deep Learning - CNN)\n- Deskripsi: Jaringan saraf konvolusional (CNN) untuk pembelajaran mendalam pada data gambar.\n\n## Catatan Tambahan\n- Silakan jelajahi setiap folder untuk melihat implementasi, hasil, dan analisis yang telah dilakukan selama semester ini.\n- Kode dan laporan dapat diakses secara terbuka untuk referensi dan pembelajaran lebih lanjut.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhendrowunga%2FML-Learning-Path","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhendrowunga%2FML-Learning-Path","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhendrowunga%2FML-Learning-Path/lists"}