{"id":15159903,"url":"https://github.com/hanifheinrich/thesis-topic-recomendation","last_synced_at":"2026-02-06T17:06:00.845Z","repository":{"id":254537753,"uuid":"846839671","full_name":"hanifheinrich/thesis-topic-recomendation","owner":"hanifheinrich","description":"Implementasi Algoritma Naive Bayes untuk Sistem Rekomendasi Topik Tugas Akhir Mahasiswa DSI Unand","archived":false,"fork":false,"pushed_at":"2024-08-24T08:16:14.000Z","size":177,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-06T05:42:49.111Z","etag":null,"topics":["kmeans-clustering","naive-bayes-classifier","python","streamlit"],"latest_commit_sha":null,"homepage":"https://rekomendasi-tugas-akhir.streamlit.app/","language":"Python","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/hanifheinrich.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}},"created_at":"2024-08-24T05:12:18.000Z","updated_at":"2024-08-24T08:16:55.000Z","dependencies_parsed_at":"2025-02-09T19:41:37.689Z","dependency_job_id":null,"html_url":"https://github.com/hanifheinrich/thesis-topic-recomendation","commit_stats":null,"previous_names":["hanifheinrich/thesis-topic-recomendation"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hanifheinrich/thesis-topic-recomendation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hanifheinrich%2Fthesis-topic-recomendation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hanifheinrich%2Fthesis-topic-recomendation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hanifheinrich%2Fthesis-topic-recomendation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hanifheinrich%2Fthesis-topic-recomendation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hanifheinrich","download_url":"https://codeload.github.com/hanifheinrich/thesis-topic-recomendation/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hanifheinrich%2Fthesis-topic-recomendation/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29169384,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-06T16:33:35.550Z","status":"ssl_error","status_checked_at":"2026-02-06T16:33:30.716Z","response_time":59,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: 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":["kmeans-clustering","naive-bayes-classifier","python","streamlit"],"created_at":"2024-09-26T22:01:50.408Z","updated_at":"2026-02-06T17:06:00.828Z","avatar_url":"https://github.com/hanifheinrich.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Web Rekomendasi Topik Tugas Akhir Mahasiswa Departemen Sistem Informasi Unand\n\nTugas akhir merupakan tahap akhir dari program studi yang menuntut mahasiswa untuk mengaplikasikan pengetahuan yang mereka peroleh selama kuliah dalam suatu proyek. Namun, seringkali mahasiswa kesulitan dalam memilih topik yang sesuai dengan minat dan kemampuan mereka, terutama dalam lingkup yang luas seperti sistem informasi.\n\n## Table of Contents:\n\n- Dataset\n- K-Means Clustering\n- Naive Bayes Classification\n- Streamlit Deployment\n- Preview\n  \n## Dataset\nDataset yang digunakan kali ini adalah data dummy yang dibuat menggunakan fungsi excel.Jumlah dataset yang digunakan adalah sebanyak 150 row data.\nMenggunakan Twitter API, saya mengumpulkan 999 tweet dengan kata kunci \"Karen's Dinner\" untuk analisis sentimen dan tren percakapan.\nScore = `INDEX($O$4:$O$10,RANDBETWEEN(1,COUNTA[$O$4:$O$10)},1)`\n\n## K-Means Clustering\nKlaster yang dibentuk adalah empat yang merepresentasikan profil lulusan DSI Unand yang antara lain; ERP, EA, GIS, dan BI. Proses klasterisasi kali ini menggunakan algortima K-Means. Kluster ini nantinya akan digunakan sebagai atribut target dalam proses klasifikasi.\n```python\nscaler_fit = MinMaxScaler()\nscaled_x = scaler_fit.fit_transform(array_x)\nkmeans = KMeans(n_clusters = 4, random_state=123)\nkmeans.fit(scaled_x)\nprint(kmeans.cluster_centers_)\ndataset[\"kluster\"] = kmeans.labels_\nprint(dataset.kluster)\n```\n\n## Naive Bayes Classification\nPenerapan metode Naive Bayes pada dataset yang terdiri dari 150 data dengan pembagian data testing sebesar 20% dan data training sebesar 80% menghasilkan akurasi sebesar 83% berdasarkan confusion matrix.\n```python\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\nclf=GaussianNB()\nclf.fit(X_train,y_train)\ny_pred=clf.predict(X_test)\nprint(classification_report(y_test,y_pred))\n```\n![image](https://github.com/user-attachments/assets/42540f5c-25fc-42ba-9178-411bd1f54520)\n\n\n## Streamlit Deployment\nPengimplementasian model dilakukan dengan cara menerapkan model yang telah dibuat sebelumnya ke dalam Streamlit. Streamlit adalah sebuah framework open-source yang digunakan untuk membangun antarmuka pengguna interaktif untuk aplikasi data dan machine learning\n```python\nimport streamlit as st\nimport pandas as pd\nimport webbrowser as wb\nfrom sklearn.naive_bayes import GaussianNB\nfrom PIL import Image\n\nst.header('Referensi Judul Tugas Akhir Sebelumnya')\n\ndataea=pd.read_csv('dataset/ea.csv')\ndatabi=pd.read_csv('dataset/bi.csv')\ndataerp=pd.read_csv('dataset/erp.csv')\ndatasig=pd.read_csv('dataset/sig.csv')\n\n\nst.subheader('Business Intelligence')\ndatabi\nst.subheader('Sistem Informasi Geografis')\ndatasig\nst.subheader('Enterprise Architecture')\ndataea\nst.subheader('Enterprise Resource Planning')\ndataerp\n\n#referensi\nurl = 'http://scholar.unand.ac.id/'\nst.subheader('Referensi terkait Rekomedasi Tugas Akhir lainnya dapat dilihat di')\n\nif st.button('Klik Disini'):\n    wb.open_new_tab(url)\n```\n\n## Preview\n![image](https://github.com/user-attachments/assets/b39c8dc0-751d-4640-9a37-05c1fe17c7bd)\n![image](https://github.com/user-attachments/assets/ad15a71b-0b05-490d-8239-ca3ed16485e4)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhanifheinrich%2Fthesis-topic-recomendation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhanifheinrich%2Fthesis-topic-recomendation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhanifheinrich%2Fthesis-topic-recomendation/lists"}