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Dampak negatifnya mencakup produktivitas, relasi sosial, serta kualitas hidup, sehingga diperlukan prediksi yang akurat untuk intervensi dini. Penelitian ini mengukur potensi machine learning dengan memanfaatkan dataset komprehensif serta algoritma K-Nearest Neighbor dan Support Vector Machine. Model terbaik untuk klasifikasi depresi, kecemasan, dan stres adalah SVM dengan akurasi 99%, sementara penerapan Exploratory Data Analysis pada variabel tambahan memengaruhi tingkat akurasi. Hasil menegaskan variabel demografis berpengaruh pada klasifikasi.",{"@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/measuring-psychological-demographic-factors-predicting-depression-anxiety-and-stress-using-machine-learning/127946/",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/measuring-psychological-demographic-factors-predicting-depression-anxiety-and-stress-using-machine-learning/127946.png","ImageObject",300,407,{"name":89,"@type":90},"Oliver","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",6,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Alat ukur apa yang digunakan untuk menilai depresi, kecemasan, dan stres?","Question",{"text":110,"@type":111},"Penelitian menggunakan DASS-42 (Depression Anxiety Stress Scales) sebagai alat ukur untuk menilai tingkat depresi, kecemasan, dan stres.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Faktor apa saja yang dianalisis selain hasil DASS-42?",{"text":115,"@type":111},"Penelitian menganalisis faktor demografis seperti usia, jenis kelamin, tingkat pendidikan, dan status sosial untuk memperkuat analisis.",{"name":117,"@type":108,"acceptedAnswer":118},"Algoritma machine learning apa yang memberikan performa terbaik dan berapa akurasinya?",{"text":119,"@type":111},"Algoritma terbaik untuk klasifikasi depresi, kecemasan, dan stres adalah Support Vector Machine (SVM) dengan akurasi 99%.","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},127946,1785943160,{"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},687207024643,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Komputika: Jurnal Sistem Komputer  \nVolume 13, Nomor 2, Oktober 2024, hlm. 157-164 DOI: 10.34010/komputika.v13i2 .11793  \np-ISSN: 2252-9039  \ne-ISSN: 2655-3198  \nMengukur Faktor Demografi Psikologis: Memprediksi Depresi, Kecemasan, dan Stres dengan menggunakan Machine Learning  \nSiti Juwariyah 1*, Alfajri Hulvi 2, Nor Riduan3, Kusrini4  \n1,2,3) Informatika Program Magister PJJ, Universitas Amikom Yogyakarta  \n4) Magister Teknik Informatika, Universitas Amikom Yogyakarta  \nJl. Ring Road Utara, Ngringin, Condongcatur, Kec. Depok, Yogyakarta 55281 Indonesia  \nemail: [siti.juwariyah27@students.amikom.ac.id](siti.juwariyah27@students.amikom.ac.id)  \n(Naskah masuk: 26 Desember 2023; direvisi: 07 September 2024; diterima untuk diterbitkan: 27 September 2024)  \nABSTRAK – Kesehatan mental merupakan aspek penting dalam kehidupan manusia. Depresi, kecemasan, dan stres adalah beberapa gangguan kesehatan mental yang paling umum terjadi. Gangguan-gangguan ini berdampak negatifpada kehidupan sehari-hari, termasuk produktivitas, hubungan sosial, dan kualitas hidup sehingga membutuhkan prediksi yang akurat untuk intervensi dini. Salah satu alat ukur psikologis yang digunakan untuk menilai tingkat depresi, kecemasan, dan stres seseorang adalah DASS-42 (Depression Anxiety Stress Scales) . Selain hasil DASS-42, faktor demografis seperti usia, jenis kelamin, tingkat pendidikan, dan status sosial penting untuk dianalisis untuk memperkuat analisa. Machine learning merupakan alat yang kuat untuk menganalisis data yang kompleks sepertimemprediksi faktor demografis psikologis yang terkait dengan kondisi kesehatan mental. Penelitian ini menggali potensi ML menggunakan dataset yang komprehensif, algoritma K-Nearest Neighbor dan Support Vector Machine untuk menilai performa prediksi. Temuan ini menggarisbawahi efektivitas model ML dalam memprediksi depresi, kecemasan, dan stres dengan akurasi yang cukup tinggi. Algoritma terbaik pada penelitian kali ini untuk klasifikasidepresi, kecemasan dan stress adalah SVM dengan akurasi 99% namun penggunaan teknik Exploratory Data Analysis (EDA) untuk mengolah variabel tambahan berpengaruh pada tingkat akurasi model sehingga dapat disimpulkan bahwa variabel demografis mempunyai pengaruh terhadap klasifikasi depresi, kecemasan dan stress.  \nKata Kunci – depresi; faktor demografi psikologis; kecemasan; KNN; stres; SVM.  \nMeasuring Psychological Demographic Factors : Predicting Depression, Anxiety, and Stress using Machine Learning  \nABSTRACT – Mental health is an important aspect of human life. Depression, anxiety and stress are some of the most common mental health disorders. These disorders negatively impact daily life, including productivity, social relationships, and quality of life, requiring accurate prediction for early intervention. One of the psychological measurement tools used to assess a person's level of depression, anxiety, and stress is the DASS-42 (Depression Anxiety Stress Scales). In addition to the DASS-42 results, demographic factors such as age, gender, education level, and social status are important to analyze to strengthen the analysis. Machine learning is a powerful tool for analyzing complex data such as predicting psychological demographic factors associated with mental health conditions. This research explores the potential of ML using a comprehensive dataset, K-Nearest Neighbor algorithm and Support Vector Machine to assess prediction performance. The findings underscore the effectiveness of ML models in predicting depression, anxiety and stress with high accuracy. The best algorithm in this study for the classification of depression, anxiety and stress is SVM with an accuracy of 99% but the use of Exploratory Data Analysis (EDA) techniques to process additional variables affects the accuracy of the model so it can be concluded that demographic variables have an influence on the classification of depression, anxiety and stress.  \nKeywords – depression; psychological demographic factors; anx","cbCaiemXmcHMDZAZ","https://ap.wps.com/l/cbCaiemXmcHMDZAZ","pdf",829805,8,"Indonesian","# Pendahuluan\n## Kesehatan mental: depresi, kecemasan, stres\n## Faktor risiko demografi\n## Alat ukur DASS-42 dan tujuan pengukuran\n## Konsep gejala dan pembobotan item\n## Pemanfaatan machine learning (ML)","[{\"question\":\"Alat ukur apa yang digunakan untuk menilai depresi, kecemasan, dan stres?\",\"answer\":\"Penelitian menggunakan DASS-42 (Depression Anxiety Stress Scales) sebagai alat ukur untuk menilai tingkat depresi, kecemasan, dan stres.\"},{\"question\":\"Faktor apa saja yang dianalisis selain hasil DASS-42?\",\"answer\":\"Penelitian menganalisis faktor demografis seperti usia, jenis kelamin, tingkat pendidikan, dan status sosial untuk memperkuat analisis.\"},{\"question\":\"Algoritma machine learning apa yang memberikan performa terbaik dan berapa akurasinya?\",\"answer\":\"Algoritma terbaik untuk klasifikasi depresi, kecemasan, dan stres adalah Support Vector Machine (SVM) dengan akurasi 99%.\"}]","Mengukur Faktor Demografi Psikologis - Memprediksi Depresi, Kecemasan, dan Stres dengan menggunakan Machine Learning | PDF",12]