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Latar masalah mencakup tingginya prevalensi depresi dan gangguan kejiwaan yang meningkat pada masa pandemi Covid-19, serta kaitan penggunaan media sosial dengan gangguan emosional pada remaja. Hasil menunjukkan Naive Bayes dan Decision Tree efektif, dengan akurasi 91% dan 89%, serta dominasi sentimen positif (78,7%) dibanding negatif (21,3%).","Article history  \nReceived July 22, 2024 Accepted Nov 30, 2024 Published Nov 30, 2024  \nANALISIS SENTIMEN MASYARAKAT TERHADAP KESEHATAN MENTAL PADA MEDIA SOSIAL TWITTER DENGAN MENGGUNAKAN  \nMACHINE LEARNING  \nHudatul Aulia1), Muhammad Zulfadhilah1), Septyan Eka Prastya1), Muhammad Syahid Pebriadi2)  \n1 Program Studi Sarjana Teknologi Informasi, Fakultas Sains dan Teknologi, Universitas Sari Mulia  \n2 Program Studi Komputerisasi Akuntansi, Jurusan Akuntansi, Politeknik Negri Banjarmasin  \nemail: [auliahuda96@gmail.com](auliahuda96@gmail.com)  \nAbstract  \nMental health affects lives globally, with around 300 million people experiencing depression in 2019, including 15.6 million in Indonesia. The Covid-19 pandemic increased cases of anxiety and depression, and by 2022, WHO reported 23 million people suffering from psychiatric disorders. In Indonesia, adolescent mental health issues are also high, with excessive social media use linked to an increase in emotional disorders. Twitter, with its real-time data, is becoming an important tool for analyzing public sentiment and understanding opinions through analytics and machine learning techniques. This study aims to determine public sentiment towards mental health in Indonesia through Twitter social media and test the effectiveness of using machine learning in sentiment analysis. The results show that the Naive Bayes and Decision Tree methods are effective in analyzing sentiment, with an accuracy of 91% and 89% respectively. The average result of cross validation shows a value of 73.21% for Naive Bayes and 67.02% for Decision Tree. In this study, positive sentiment is more dominant with a percentage value of 78. 7%, while negative sentiment is only 21.3%. The findings indicate that Indonesians'awareness of the importance of mental health is increasing, and they increasingly understand the importance of maintaining mental health.  \nKeywords: decision tree, mental health, machine learning, naive bayes.  \nAbstrak  \nKesehatan mental sangat mempengaruhi kehidupan global, dengan sekitar 300 juta orang mengalamidepresi pada 2019, termasuk 15,6 juta di Indonesia. Pandemi Covid-19 meningkatkan kasus kecemasan dan depresi, dan pada 2022, WHO melaporkan 23 juta orang menderita gangguan kejiwaan. Di Indonesia, masalah kesehatan mental remaja juga tinggi, dengan penggunaan media sosial berlebihanterkait peningkatan gangguan emosional. Twitter, dengan data real-time, menjadi alat penting untuk menganalisis sentimen publik dan memahami opini melalui teknik analisis dan machine learning. Penelitian ini bertujuan untuk mengetahui sentimen masyarakat terhadap kesehatan mental di Indonesia melalui media sosial Twitter serta menguji keefektifan penggunaan machine learning dalam analisis sentimen. Hasil penelitian menunjukkan bahwa metode Naive Bayes dan Decision Tree efektif dalam menganalisis sentimen, dengan akurasi sebesar 91% dan 89% masing-masing. Rerata hasil dari cross validation menunjukkan nilai 73,21% untuk Naive Bayes dan 67,02% untuk Decision Tree. Padapenelitian ini sentimen positif lebih dominan dengan persentase nilai 78,7%, sementara sentimen negatif hanya 21,3%. Temuan ini mengindikasikan bahwa kesadaran masyarakat Indonesia tentang pentingnya kesehatan mental semakin meningkat, dan mereka semakin memahami pentingnya menjaga kesehatan mental.  \nKata Kunci: decision tree, kesehatan mental, machine learning, naive bayes.  \n1. INTRODUCTION  \nKeadaan mental yang sehat sangat penting dan berpengaruh besar pada kehidupan setiapindividu [1] . Menurut data Organisasi Kesehatan Dunia (WHO) pada tahun 2019, sekitar 300 jutaorang di seluruh dunia mengalami depresi, termasuk 15,6 juta penduduk Indonesia [2] . Padatahun 2020, gangguan kecemasan meningkat sebesar 26% dan depresi sebesar 28% akibat pandemi Covid-19 [3] . Pada tahun 2022, WHOmenyatakan ada sekitar 23 juta orang yang menderita gangguan kejiwaan seperti skizofreniaatau psikosis. Dari jumlah tersebut, hanya 31,3% yang menerima layanan dari","cbCairpYm5Zuc9LI","https://ap.wps.com/l/cbCairpYm5Zuc9LI","pdf",423023,4,1,7,"Indonesian","id",113,"# INTRODUCTION\n## Latar belakang kesehatan mental dan dampak media sosial\n## Alasan menggunakan Twitter sebagai sumber data\n## Tujuan penelitian dan konsep analisis sentimen","[{\"question\":\"Apa tujuan penelitian ini?\",\"answer\":\"Penelitian bertujuan untuk mengetahui sentimen masyarakat terhadap kesehatan mental di Indonesia melalui Twitter dan menguji keefektifan machine learning dalam analisis sentimen.\"},{\"question\":\"Metode apa yang digunakan untuk menganalisis sentimen?\",\"answer\":\"Penelitian menggunakan metode Naive Bayes dan Decision Tree untuk menganalisis sentimen berdasarkan data Twitter.\"},{\"question\":\"Bagaimana hasil akurasi dan perbandingan sentimen positif vs negatif?\",\"answer\":\"Naive Bayes dan Decision Tree masing-masing memiliki akurasi 91% dan 89%. Sentimen positif lebih dominan dengan persentase 78,7%, sedangkan sentimen negatif 21,3%. \"}]","Analisis Sentimen Masyarakat Terhadap Kesehatan Mental Pada Media Sosial Twitter Dengan Menggunakan Machine Learning | PDF",1785809146,11,{"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-public-attitudes-toward-mental-health-on-twitter-using-machine-learning","",{"@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-public-attitudes-toward-mental-health-on-twitter-using-machine-learning/122165/",{"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","2026-08-18","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},"Apa tujuan penelitian ini?","Question",{"text":76,"@type":77},"Penelitian bertujuan untuk mengetahui sentimen masyarakat terhadap kesehatan mental di Indonesia melalui Twitter dan menguji keefektifan machine learning dalam analisis sentimen.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Metode apa yang digunakan untuk menganalisis sentimen?",{"text":81,"@type":77},"Penelitian menggunakan metode Naive Bayes dan Decision Tree untuk menganalisis sentimen berdasarkan data Twitter.",{"name":83,"@type":74,"acceptedAnswer":84},"Bagaimana hasil akurasi dan perbandingan sentimen positif vs negatif?",{"text":85,"@type":77},"Naive Bayes dan Decision Tree masing-masing memiliki akurasi 91% dan 89%. 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