{"id":22061899,"url":"https://github.com/sermonzagoto/uji_akurasi_model","last_synced_at":"2026-04-15T23:31:13.005Z","repository":{"id":207131668,"uuid":"283979394","full_name":"sermonzagoto/uji_akurasi_model","owner":"sermonzagoto","description":"Uji coba model menggunakan 3 algorima machine learning untuk klasifikasi","archived":false,"fork":false,"pushed_at":"2020-07-31T08:32:17.000Z","size":189,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-07-29T19:14:01.688Z","etag":null,"topics":["desiciontree","logistic-regression-classifier","machine-learning-algorithms","naive-bayes-classifier","python3"],"latest_commit_sha":null,"homepage":"https://github.com/sermonzagoto/uji_akurasi_model","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sermonzagoto.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2020-07-31T08:06:34.000Z","updated_at":"2020-07-31T08:33:17.000Z","dependencies_parsed_at":"2023-11-14T09:38:03.661Z","dependency_job_id":null,"html_url":"https://github.com/sermonzagoto/uji_akurasi_model","commit_stats":null,"previous_names":["sermonzagoto/uji_akurasi_model"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sermonzagoto/uji_akurasi_model","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2Fuji_akurasi_model","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2Fuji_akurasi_model/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2Fuji_akurasi_model/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2Fuji_akurasi_model/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sermonzagoto","download_url":"https://codeload.github.com/sermonzagoto/uji_akurasi_model/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sermonzagoto%2Fuji_akurasi_model/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31864919,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-15T15:24:51.572Z","status":"ssl_error","status_checked_at":"2026-04-15T15:24:39.138Z","response_time":63,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["desiciontree","logistic-regression-classifier","machine-learning-algorithms","naive-bayes-classifier","python3"],"created_at":"2024-11-30T18:16:09.385Z","updated_at":"2026-04-15T23:31:12.964Z","avatar_url":"https://github.com/sermonzagoto.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Uji Akurari Model\nKali ini saya membagikan hasil pengerjaan saya terkait dengan module Customer Churn Prediction using Machine Learning dari DQLab dengan mentor Mas Anton Suhartono sebagai Data Scientist Telkom Indonesia.\n\nJika pada module DQLab menggunakan Algoritma Logistic Regression, Random Forest Classifier, dan Gradient Boosting Classifier, maka kali ini saya tetap menggunakan algoritma Logistic Regression namun dengan tambahan algoritma lainnya yaitu Decision Tree Classifier dan Naive Bayes Classifier.\n\nRingkasan Akurasi Model\n1. Logistic Regression (akurasi training 80%, akurasi testing 79%)\n2. Decision Tree Classifier (akurasi training 100%, akurasi testing 73%)\n3. Naive Bayes Classifier (akurasi training 74%, akurasi testing 73%)\n\nDapat dilihat bahwa model Logistic Regression dan Naive Bayes mampu memprediksi sama baiknya di fase training maupun testing. Namun pada akhirnya model terbaik dimiliki oleh model Logistic Regression karena memiliki angka akurasi yang tinggi di banding model Naive Bayes.\n\nUntuk mengunduh hasil project saya di folder ini, Anda dapat menjalankan perintah berikut di terminal Anda:\ngit clone https://github.com/sermonzagoto/uji_akurasi_model.git\nAtau meng-klik tombol hijau bertuliskan \"Code\" dan \"Download ZIP\" di bagian kanan atas halaman ini.\n\n# ⚙️ Instalasi\nUntuk menggunakan ekstensi **.ipynb** project saya, pastikan perangkat anda baik itu Laptop atau PC sudah terinstall jupyter notebook. Kebetulan saya menggunakan Google Collaboratory untuk mengerjakan project ini\n\n# ⚖️ Lisensi\nKarya ini sudah memiliki lisensi MIT License https://opensource.org/licenses/MIT. Untuk mengetahui lebih lanjut, silahkan kunjungi https://opensource.org/licenses/MIT\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsermonzagoto%2Fuji_akurasi_model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsermonzagoto%2Fuji_akurasi_model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsermonzagoto%2Fuji_akurasi_model/lists"}