DETEKSI DEPRESI DAN CEMAS MELALUI METODE CLOSED-LOOP MENGGUNAKAN DASS-21

SIHOMBING, HARRY CANDRA (2018) DETEKSI DEPRESI DAN CEMAS MELALUI METODE CLOSED-LOOP MENGGUNAKAN DASS-21. S2 thesis, Universitas Mercu Buana Jakarta-Menteng.

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Abstract

The rapid pace of change in information and communication technology has brought many changes in daily life. The way humans interacting is changing, through technology it is possible to express each form of communication directly and instantly. The continued use of social media has contributed data in size, diversity and capacity and quality known as Big Data. Based on this, then the idea was to see and measure the tendency of depression and anxiety through social media using the Closed-Loop method using Text Mining posts on Facebook. Through the stages of pre-processing, including the removal of short text, deletion of punctuation and numbers and text extraction using the Naïve Bayes machine learning model for text classification, the early signs of depression and anxiety are measured using DASS-21 as a marker parameter. In total, 22,934 Facebook posts were generated which were used as training and learning data during testing proccess. Results analysis and mapping of social demographics of social media users can show things that are usually the source of depression, and anxiety, such as grief, illness, household affairs, school children and others. Statements through text on social media in relation to depression and anxiety have met the conditions required by DASS-21. Keywords: Facebook, Big Data, Closed-Loop, Text Mining, Naïve Bayes, DASS- 21, Machine Learning and Depression & Anxiety. Perkembangan teknologi informasi dan komunikasi telah membawa banyak perubahan dalam kehidupan sehari-hari. Cara manusia berinteraksi dengan sesamanya menjadi berubah, melalui teknologi sangat dimungkinkan untuk mengekspresikan setiap bentuk-bentuk komunikasi secara langsung dan seketika. Penggunaan media sosial secara terus menerus telah menyumbang data dalam ukuran, keragaman dan kapasitas dan kualitas yang dikenal sebagai Big Data. Berdasarkan hal tersebut, kemudian didapatkan gagasan untuk melihat dan mengukur kecenderungan depresi dan cemas melalui media sosial menggunakan pendekatan metode Closed-Loop menggunakan Text Mining posting pada Facebook. Melalui tahapan pra-pemrosesan, termasuk penghapusan teks pendek, penghapusan tanda baca dan angka dan ekstraksi teks dengan mamanfaatkan model pembelajaran mesin Naïve Bayes untuk klasifikasi teks, dapat dilihat tanda- tanda awal depresi dan cemas menggunakan DASS-21 sebagai parameter penanda. Secara total, sebanyak 22.934 posting Facebook digunakan sebagai data training dan learning pada saat proses pengujian. Hasil Analisa dan pemetaan demografi sosial para pengguna media sosial dapat menunjukkan hal-hal yang biasanya menjadi sumber penyebab depresi, dan cemas yaitu seperti kedukaan, sakit-penyakit, urusan rumah tangga, anak sekolah dan lain-lain. Pernyataan melalui teks pada media sosial serta kaitannya dengan depresi dan cemas telah memenuhi kondisi yang disyaratkan pada DASS-21. Keywords: Facebook, Big Data, Closed-Loop, Text Mining, Naïve Bayes, DASS- 21, Machine Learning and Depression & Anxiety.

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