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Penelitian ini meningkatkan manajemen keluhan melalui analisis sentimen menggunakan algoritma Naive Bayes dengan metode CRISP-DM. Tahapannya meliputi pemahaman bisnis dan data, persiapan data (stop words removal dan tokenisasi), pemodelan, evaluasi, serta implementasi. Model diklasifikasikan ke kategori keluhan ringan dan berat dengan akurasi 89,0%, serta dievaluasi memakai metrik akurasi, presisi, recall, dan F1-score untuk membantu penanganan cepat dan tepat.","Analisis Sentimen Keluhan Pegawai dengan Menggunakan Machine Learning  \nAnita Alfi Syahra, Mohamad Nurkamal Fauzan, Cahyo Prianto  \nJurusan Informatika, Vokasi, Universitas Logistik Bisnis Internasional  \nBandung, Jawa Barat  \n[anitasyahra17@gmail.com](anitasyahra17@gmail.com)*, [m.nurkamal.f@ulbi.ac.id](m.nurkamal.f@ulbi.ac.id), [cahyo@ulbi.ac.id](cahyo@ulbi.ac.id)  \nDiterima: 4 Desember 2024. Disetujui: 30 November 2024. Dipublikasikan: 4 Februari 2025  \nAbstract-PT Dirgantara Indonesia (PTDI) faces challenges in managing employee complaints. This research aims to improve PTDI employee complaint management through sentiment analysis using the Naive Bayes algorithm with the CRISP-DM method. The stages applied include business understanding, data understanding, data preparation, modeling, evaluation and implementation. Employee complaint data is collected and processed using stop words removal and tokenization techniques. The Naive Bayes model is trained and evaluated using accuracy, precision, recall and F1-score metrics. The research results show that the Naive Bayes model is effective in grouping employee complaints into mild and severe categories. The model has an accuracy of 89.0%. The implementation of this sentiment analysis system is expected to help PTDI management handle employee complaints more quickly and precisely, increasing satisfaction and productivity. This research also contributes to the development of the science of sentiment analysis and machine learning, as well as its application in complaint management in companies. With this system, PTDI management can identify and prioritize complaints that require immediate handling, increasing operational efficiency and service quality to employees. This research provides practical solutions for PTDI and adds insight into the application of machine learning in managing employee complaints.  \nKeywords: sentiment analysis, naive bayes, employee complaint management, PT Dirgantara Indonesia, CRISP-DM.  \nAbstrak--PT Dirgantara Indonesia (PTDI) menghadapi tantangan dalam mengelola keluhan karyawan. Penelitian ini bertujuan untuk meningkatkan manajemen keluhan karyawan PTDI melalui analisis sentimen menggunakanalgoritma Naive Bayes dengan metode CRISP-DM. Tahapan yang diterapkan mencakup pemahaman konteks bisnis, pemahaman data, persiapan data, pemodelan, evaluasi, dan implementasi. Data keluhan karyawan dikumpulkan dan diproses menggunakan teknik penghapusan stop words dan tokenisasi. Model Naive Bayes dilatih dan dinilai menggunakan metrik seperti akurasi, presisi, recall, dan skor F1. Hasil penelitian menunjukkan bahwa model Naive Bayes secara efektif mengkategorikan keluhan karyawan menjadi kelompok ringan dan berat. Model ini memilikiakurasi 89.0%. Implementasi sistem analisis sentimen ini diharapkan dapat membantu manajemen PTDI menanganikeluhan karyawan dengan lebih cepat dan tepat, meningkatkan kepuasan dan produktivitas. Penelitian ini jugamemajukan bidang analisis sentimen dan pembelajaran mesin, serta penerapannya dalam manajemen keluhan perusahaan. Dengan model ini, manajemen PTDI dapat secara otomatis mengklasifikasikan keluhan karyawan berdasarkan tingkat keparahannya, sehingga dapat lebih cepat dalam menangani keluhan yang memerlukan penanganan segera. Model ini memberikan solusi praktis bagi PTDI dan menambah wawasan tentang penerapanalgoritma Naive Bayes dalam mengelola keluhan karyawan.  \nKata Kunci : Analisis Sentimen, Naive Bayes, Manajemen Keluhan Pegawai, PT Dirgantara Indonesia, CRISP-DM.  \nI. PENDAHULUAN  \nPT. Dirgantara Indonesia (PTDI) merupakan industri pesawat terbang satu-satunyadi Indonesia dan di wilayah Asia Tenggara. PTDItelah menerapkan manajemen layanan keluhan pegawai yang masih kurang dalam pelayanannya. Layanan ini membantu pegawai dalam menangani masalah yang muncul pada aplikasi. Beberapa masalah yang biasanya dapat diatasi meliputi perbaikan data, mengubah permintaan daripegawai, dan memberikan panduan penggunaanaplikasi di area pr","cbCaikrWNZZs66Y7","https://ap.wps.com/l/cbCaikrWNZZs66Y7","pdf",384500,4,1,8,"Indonesian","id",113,"# Pendahuluan","[{\"question\":\"Penelitian ini bertujuan apa dalam manajemen keluhan karyawan PTDI?\",\"answer\":\"Penelitian bertujuan meningkatkan manajemen keluhan karyawan PTDI melalui analisis sentimen agar penanganan lebih cepat dan tepat.\"},{\"question\":\"Metode apa yang digunakan untuk melakukan analisis sentimen pada keluhan pegawai?\",\"answer\":\"Penelitian menggunakan algoritma Naive Bayes dengan metode CRISP-DM, mencakup pemahaman bisnis dan data, persiapan data, pemodelan, evaluasi, dan implementasi.\"},{\"question\":\"Bagaimana pemrosesan data keluhan dilakukan sebelum pelatihan model?\",\"answer\":\"Data keluhan dikumpulkan dan diproses menggunakan teknik penghapusan stop words dan tokenisasi, lalu digunakan untuk melatih dan menilai model.\"}]","Analisis Sentimen Keluhan Pegawai dengan Menggunakan Machine Learning - Naive Bayes - CRISP-DM | PDF",1785820957,12,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"sentiment-analysis-of-employee-complaints-using-machine-learning-naive-bayes-crisp-dm","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/id/document/sentiment-analysis-of-employee-complaints-using-machine-learning-naive-bayes-crisp-dm/124193/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Penelitian ini bertujuan apa dalam manajemen keluhan karyawan PTDI?","Question",{"text":76,"@type":77},"Penelitian bertujuan meningkatkan manajemen keluhan karyawan PTDI melalui analisis sentimen agar penanganan lebih cepat dan tepat.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Metode apa yang digunakan untuk melakukan analisis sentimen pada keluhan pegawai?",{"text":81,"@type":77},"Penelitian menggunakan algoritma Naive Bayes dengan metode CRISP-DM, mencakup pemahaman bisnis dan data, persiapan data, pemodelan, evaluasi, dan implementasi.",{"name":83,"@type":74,"acceptedAnswer":84},"Bagaimana pemrosesan data keluhan dilakukan sebelum pelatihan model?",{"text":85,"@type":77},"Data keluhan dikumpulkan dan diproses menggunakan teknik penghapusan stop words dan tokenisasi, lalu digunakan untuk melatih dan menilai model.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]