[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119948-id":3,"doc-seo-119948-113":31,"detail-sidebar-cat-0-id-113":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},119948,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",54,"Penelitian & Laporan","Perbandingan Analisis Sentimen PLN Mobile - Machine Learning vs. Deep Learning","Analisis sentimen berbasis ulasan aplikasi menilai kualitas layanan melalui rating dan komentar pengguna. Penelitian ini memfokuskan PLN Mobile yang sejak diluncurkan mendapatkan opini beragam dan menimbulkan tantangan bagi pengguna maupun pengembang dalam memahami komentar. Data 3.000 ulasan (1.965 positif, 1.035 negatif) diuji menggunakan algoritma machine learning: logistic regression, decision tree, dan random forest, serta deep learning: MLP dan LSTM. Logistic regression mencapai 84,47% akurasi, decision tree 79,30%, random forest 83,64%. Model deep learning memperoleh 84,47% untuk MLP dan 78,83% untuk LSTM. Model ML berbasis logistic regression dan DL berbasis MLP menunjukkan kinerja akurasi terbaik dibanding metode lain dalam klasifikasi sentimen positif-negatif.","Terakreditasi SINTA Peringkat 3  \nSurat Keputusan Direktur Jenderal Pendidikan Tinggi, Riset, dan Teknologi Nomor 225/E/KPT/2022 masa berlaku mulai Vol.7 No. 1 tahun 2022 s.d Vol. 11 No. 2 tahun 2026  \nTerbit online pada laman web jurnal:  \n[http://publishing-widyagama.ac.id/ejournal-v2/index.php/jointecs](http://publishing-widyagama.ac.id/ejournal-v2/index.php/jointecs)  \n\n|  |  | JOINTECS\u003Cbr>(Journal of Information Technology and Computer Science) |\n| --- | --- | --- |\n| Vol. 8 No. 1 (2024) 01-10 | e-ISSN:2541-6448 p-ISSN:2541-3619 |  |\n\nPerbandingan Analisis Sentimen PLN Mobile: Machine Learning vs. Deep  \nLearning  \nIsmail Akbar 1, Muhammad Faisal2  \nMagister Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Islam Negeri Maulana Malik Ibrahim  \nMalang  \n[1](1ismaelakbar12@gmail.com)[ismaelakbar12@gmail.com](1ismaelakbar12@gmail.com), [2](2mfaisal@ti.uin-malang.ac.id)[mfaisal@ti.uin-malang.ac.id](2mfaisal@ti.uin-malang.ac.id)  \nAbstract  \nPlay Store app ratings hold significant value as they offer critical insights for app developers to enhance digital service quality. The research centers on the PLN Mobile app, which has garnered mixed user opinions since its launch. These reviews come with challenges for users and developers when interpreting user comments. This study conducts tests, comparing several machine learning algorithms: logistic regression, decision trees, random forests, and specific deep learning algorithms, including neural network multi-layer perceptron (MLP) and long short-term memory (LSTM) for sentiment classification, i.e., positive or negative. The study collected 3,000 PLN Mobile user reviews, comprising 1,965 positive and 1,035 negative reviews. Logistic regression achieved an 84.47% accuracy rate, decision trees scored 79.30%, and random forests reached 83.64%. In contrast, deep learning models, particularly the Neural Network Multilayer Perceptron (MLP), reached an accuracy rate of 84.47%, while the LSTM achieved an accuracy rate of 78.83%. In the context of sentiment analysis of PLN Mobile user reviews, machine learning models using the logistic regression algorithm and deep learning models employing the multi-layer perceptron (MLP) neural network algorithm demonstrated higher accuracy compared to other methods.  \nKeywords: sentiment analysis; machine learning; deep learning; PLN Mobile.  \nAbstrak  \nRating ulasan aplikasi play store memiliki nilai strategis karena merupakan informasi penting bagi pengembang aplikasiuntuk meningkatkan kualitas layanan di dunia digital. Salah satu aplikasi yang dijadikan subjek penelitian ini adalah PLN Mobile. Sejak diluncurkannya aplikasi PLN Mobile, terbukti masih banyak opini masyarakat yang tidak puas dengan penggunaan aplikasi PLN Mobile. Oleh karena itu, masih memiliki kelemahan bagi pengguna aplikasi dan pengembangaplikasi saat menganalisis komentar penulis pengguna. Dalam penelitian ini, dilakukan pengujian dengan membandingkan beberapa algoritma machine learning terdiri dari logistic regression, decision tree, random forest serta algoritma deep learning terdiri neural network multi-layer perceptron (MLP) dan long short-term memory (LSTM) untuk mengklasifikasikan senitmen positif atau negatif. Penelitian ini menghasilkan 3.000 ulasan pengguna aplikasi PLN Mobile, yang terdiri dari 1.965 ulasan positif dan 1.035 ulasan negatif. Data tersebut kemudian diuji dengan menggunakan model logistic regression yang memiliki akurasi sebesar 84,47%, decision tree yang memiliki akurasi sebesar 79,30%, dan random forest yang memiliki akurasi sebesar 83,64% . Sedangkan model algoritma deep learning khususnya Neural Network Multilayer Perceptron (MLP) memiliki akurasi sebesar 84,47%, sedangkan pengujian dengan Long Short Term Memory (LSTM) memberikan akurasi sebesar 78,83% . Berdasarkan penelitian analisis sentimen dalam ulasan pengguna aplikasi PLN Mobile, model machine learning yang menggunakan algoritma logistic regression dan model deep learning yang mengguna","cbCaikBOscYIRWdW","https://ap.wps.com/l/cbCaikBOscYIRWdW","pdf",580802,7,1,10,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang kebutuhan informasi dan layanan digital\n## PLN dan aplikasi PLN Mobile sebagai objek penelitian\n## Ulasan pengguna serta kebutuhan analisis sentimen","[{\"question\":\"Apa tujuan penelitian tentang PLN Mobile dalam analisis sentimen ini?\",\"answer\":\"Menentukan perbandingan kinerja algoritma machine learning dan deep learning untuk mengklasifikasikan sentimen ulasan PLN Mobile menjadi positif atau negatif.\"},{\"question\":\"Algoritma apa saja yang dibandingkan pada penelitian ini?\",\"answer\":\"Machine learning: logistic regression, decision tree, dan random forest. Deep learning: neural network multi-layer perceptron (MLP) dan long short-term memory (LSTM).\"},{\"question\":\"Bagaimana hasil akurasi terbaik pada klasifikasi sentimen?\",\"answer\":\"Logistic regression dan MLP sama-sama mencapai akurasi 84,47%, sedangkan decision tree 79,30% dan random forest 83,64% serta LSTM 78,83%.\"}]","Perbandingan Analisis Sentimen PLN Mobile - Machine Learning vs. Deep Learning | PDF",1785727145,15,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"sentiment-analysis-comparison-of-pln-mobile-machine-learning-vs-deep-learning","",{"@graph":37,"@context":87},[38,55,70],{"@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":54},"https://docshare.wps.com/id/document/sentiment-analysis-comparison-of-pln-mobile-machine-learning-vs-deep-learning/119948/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-17","2026-08-03",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Apa tujuan penelitian tentang PLN Mobile dalam analisis sentimen ini?","Question",{"text":77,"@type":78},"Menentukan perbandingan kinerja algoritma machine learning dan deep learning untuk mengklasifikasikan sentimen ulasan PLN Mobile menjadi positif atau negatif.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Algoritma apa saja yang dibandingkan pada penelitian ini?",{"text":82,"@type":78},"Machine learning: logistic regression, decision tree, dan random forest. Deep learning: neural network multi-layer perceptron (MLP) dan long short-term memory (LSTM).",{"name":84,"@type":75,"acceptedAnswer":85},"Bagaimana hasil akurasi terbaik pada klasifikasi sentimen?",{"text":86,"@type":78},"Logistic regression dan MLP sama-sama mencapai akurasi 84,47%, sedangkan decision tree 79,30% dan random forest 83,64% serta LSTM 78,83%.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,100,104,108,112,116,118,122,126,130,134],{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":98,"slug":99},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":98,"slug":103},48,"Cerita & Novel","story-novel",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":98,"slug":107},56,"Gaya Hidup","lifestyle",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":98,"slug":111},51,"Komik","comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":98,"slug":115},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":98,"slug":117},"research-report",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":98,"slug":121},49,"Sastra","literature",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":98,"slug":125},52,"Teknologi","technology",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":98,"slug":129},50,"Ujian","exam",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":98,"slug":133},57,"Umum","general",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":4,"slug":137},181,"Formulir","formulir"]