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PT KAI merespons kebutuhan pelayanan digital dengan inovasi melalui aplikasi KAI Access. Penelitian ini menganalisis kepuasan pelanggan menggunakan machine learning dari data survei pengguna, melalui tahapan cleansing yang menghasilkan 1117 data dari 1561 data awal. Mayoritas pengguna menunjukkan sentiment positif dan memberikan rating puas. Model Support Vector Machine mencapai akurasi tertinggi dibanding K-NN dan Decision Tree, sehingga mampu memprediksi kepuasan pelanggan terkait penggunaan KAI Access.",{"@graph":63,"@context":120},[64,81,103],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/implementation-of-machine-learning-for-customer-satisfaction-analysis-on-kai-access-app-use-customer-satisfaction-analysis/126762/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":96,"encodingFormat":94,"isAccessibleForFree":97,"interactionStatistic":98},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/implementation-of-machine-learning-for-customer-satisfaction-analysis-on-kai-access-app-use-customer-satisfaction-analysis/126762.png","ImageObject",300,407,{"name":89,"@type":90},"Aurora","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-19","2026-08-05",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",9,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Bagaimana penelitian mengukur kepuasan pelanggan terhadap KAI Access?","Question",{"text":110,"@type":111},"Kepuasan dianalisis dari penilaian pengguna aplikasi KAI Access, kemudian diproses melalui pendekatan data mining dan pemodelan machine learning untuk memprediksi kepuasan berdasarkan data yang tersedia.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Berapa jumlah data yang digunakan setelah cleansing?",{"text":115,"@type":111},"Penelitian menggunakan 1561 data awal, lalu dilakukan cleansing hingga diperoleh 1117 data yang siap digunakan untuk analisis.",{"name":117,"@type":108,"acceptedAnswer":118},"Model machine learning mana yang memiliki akurasi tertinggi dan apa perbandingannya?",{"text":119,"@type":111},"Support Vector Machine (SVM) memiliki akurasi tertinggi, dibandingkan dua pembanding yaitu K-NN dan Decision Tree.","https://schema.org",{"og:url":79,"og:type":122,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":124,"canonical":79},"index,follow",{"doc_id":126,"site_id":56},126762,1785934651,{"code":4,"msg":5,"data":129},{"doc_id":126,"user_id":130,"nickname":89,"user_avatar":131,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":132,"file_id":133,"file_url":134,"file_type":135,"file_size":136,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":137,"language":138,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":61,"update_tm":127,"read_time":142},962084926284,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","IMPLEMENTASI MACHINE LEARNING SEBAGAI ANALISIS KEPUASAN PELANGGAN TERHADAP PENGGUNAANAPLIKASI KAI ACCESS  \nFebrina Tesalonika Nugraha1), Hendry2),  \n1. Universitas Kristen Satya Wacana, Indonesia  \n2. Universitas Kristen Satya Wacana, Indonesia  \nArticle Info  \nKata Kunci: KAI Access; Machine Learning, Orange  \nKeywords: KAI Access; Machine Learning, Orange  \nArticle history:  \nReceived 15 May 2023  \nRevised 29 May 2023  \nAccepted 12 June 2023  \nAvailable online 1 December 2023  \nDOI :  \n[https://doi.org/10.29100/jipi.v8i4.4185](https://doi.org/10.29100/jipi.v8i4.4185)  \n* Corresponding author. Febrina Tesalonika Nugraha E-mail address:  \n[672019309@student.uksw.edu](672019309@student.uksw.edu)  \nABSTRAK  \nRevolusi Industri 4.0 adalah sebuah disruption era sebagaimana hal iniditandai dari cara kerja yang berpindah atau berubah baik salah satunya adalah pada bidang transportasi. Kereta api adalah transportasi darat yang berjalan dengan rangkaian kendaraan lainnya dan bergerak diatas rel. Dengan mengikuti perkembangan zaman seperti yang sudah di kemukakan maka KAI meningkatkan pelayanan dengan berinovasi untuk memmbuat aplikasi KAI Access. Dari jumlah data awal sebanyak 1561 lalu dilakukan cleansing data hingga didapatkan 1117 data. Hasil dari penelitian ini adalah pengguna KAI Access dominan memiliki sentiment positif yang dinilai mayoritas memberikan rating puas terhadap penggunaan aplikasi KAI Access. Dalam hal performa metode Support Vector Machine memiliki tingkat akurasi tertinggi dibandingkan duapembanding lainnya yaitu K-NN dan Decision Tree. Penelitian ini memberikan wawasan tentang pengaplikasian machine learning sebagai model prediksi kepuasan pelanggan terhadap penggunaan KAI Access.  \nABSTRACT  \nThe Industrial Revolution 4.0 is a disruption era as this is marked by the way of work that moves or changes, one of which is in the field of transportation. Train is land transportation that runs with a series of other vehicles and moves on rails. By following the development of the times as stated above, KAI is improving its services by innovating to create the KAI Access application. From the initial data of 1561, data cleansing was carried out to obtain 1117 data. The results of this study are that dominant KAI Access users have positive sentiments, which are considered by the majority to give satisfaction ratings to the use of the KAI Access application. In terms of performance, the Support Vector Machine method has the highest level of accuracy compared to the other two comparisons, namely K-NN and Decision Tree. This research provides insight into the application of machine learning as a predictive model of customer satisfaction with the use of KAI Access.  \nI. PENDAHULUAN  \nEVOLUSI Industri 4.0 adalah sebuah disruption era sebagaimana hal ini ditandai dari cara kerja yang berpindah atau berubah. Perubahan ini adalah perubahan cara kerja dari konvensional menjadi modern yang  \nRdilaluaskukbaagnimeladunial,utiipdendekatanak terkecua-pliendInedkatanonesiadigInitaldon. Eesria Revoa yanglusi Industri 4merupakan ne.0gini memberikanara berkembang dammenpdaakpyataknagn  \ndampak dari Revolusi Industri 4.0 dengan dilihat dari perkembangan baik dalam ekonomi, sosial, hingga transportasi. Pertumbuhan atau perkembangan dalam era digital semacam ini dibarengi dengan kemunculan perusahaan-perusahaan teknologi yang menunjang peningkatan perubahan secara massif. Peningkatan ini berorientasipada inovasi serta efisiensi yang semakin mempermudah masyarakat sebagai konsumen. Berkaitan dengan perkembangan perusahan-perusahaan yang berbasis digital ini memberikan dampak maraknya penggunaan berbagaimacam aplikasi daring. Aplikasi daring ini secara massif berkembang dalam bidang sektor jasa transportasi jika dilihat dari perpektif penyedia layanan. Salah satu transportasi yang juga berkembang pesat saat ini adalah kereta api.. Kereta api adalah transportasi darat yang berjalan dengan rangkaian kendaraan lainnya dan bergerak diatas rel. Keret","cbCaiaJCmQ1B4Itm","https://ap.wps.com/l/cbCaiaJCmQ1B4Itm","pdf",670262,8,"Indonesian","# Pendahuluan\n## Latar belakang Revolusi Industri 4.0 dan transformasi layanan transportasi\n## Gambaran aplikasi KAI Access\n## Tujuan penelitian dan pendekatan data mining\n# Metodologi dan Analisis Machine Learning\n## Pengumpulan data dan proses cleansing\n## Model klasifikasi: Support Vector Machine, K-NN, dan Decision Tree\n# Hasil dan Pembahasan\n## Distribusi sentimen pengguna\n## Perbandingan akurasi model\n# Kesimpulan","[{\"question\":\"Bagaimana penelitian mengukur kepuasan pelanggan terhadap KAI Access?\",\"answer\":\"Kepuasan dianalisis dari penilaian pengguna aplikasi KAI Access, kemudian diproses melalui pendekatan data mining dan pemodelan machine learning untuk memprediksi kepuasan berdasarkan data yang tersedia.\"},{\"question\":\"Berapa jumlah data yang digunakan setelah cleansing?\",\"answer\":\"Penelitian menggunakan 1561 data awal, lalu dilakukan cleansing hingga diperoleh 1117 data yang siap digunakan untuk analisis.\"},{\"question\":\"Model machine learning mana yang memiliki akurasi tertinggi dan apa perbandingannya?\",\"answer\":\"Support Vector Machine (SVM) memiliki akurasi tertinggi, dibandingkan dua pembanding yaitu K-NN dan Decision Tree.\"}]","IMPLEMENTASI MACHINE LEARNING SEBAGAI ANALISIS KEPUASAN PELANGGAN TERHADAP PENGGUNAAN APLIKASI KAI ACCESS - Analisis kepuasan pelanggan | PDF",12]