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All Journal CCIT (Creative Communication and Innovative Technology) Journal Jurnal Medika Hutama
Atika Rahmawati
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Computer Science & IT Medicine & Pharmacology Nursing Public Health Veterinary

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ANALISIS SENTIMEN PUBLIK PADA MEDIA SOSIAL TWITTER TERHADAP PELAKSANAAN PILKADA SERENTAK MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE Atika Rahmawati; Aris Marjuni; Junta Zeniarja
CCIT Journal Vol 10 No 2 (2017): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (167.708 KB) | DOI: 10.33050/ccit.v10i2.539

Abstract

Pilkada Serentak is a very important event for the future viability regions and countries. Through this election people can cast their vote and elect representatives of the people according to their choice. Public respond can be expressed through twitter social media. Using twitter social media sentiment analysis can then be made about the public response to the implementation of the election simultaneously. The classification process can be detected via text tweeted by twitter users. In this study, the classification of responses detected by text because it is easily obtained and applied. This study determined the classification of the response to the Indonesian language text and increase accuracy by using SVM.Tweet classification method used by the categorical approach is divided into two classes tweet basic level: positive and negative. Data collected from Indonesian twitter tweet as much as 3000. The labeling is not done manually but using clustering method that divides the 3000 data into two groups. Cluster 1 as a group of positive tweets and Cluster 2 as a negative group tweet.2700 for training data and 300 for the test data. The stage of pre-processing the data includetokenization, casenormalization, stop word detection, and stemming. The process of classification using Support Vector Machine (SVM). Accuracy of SVM showed the highest yield that is 91% compared to the k-means clustering with the results of 82%.
Risk Factor of Low Back Pain Atika Rahmawati
Jurnal Medika Hutama Vol. 3 No. 01 Oktober (2021): Jurnal Medika Hutama
Publisher : Yayasan Pendidikan Medika Indonesia

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Abstract

Nyeri punggung bawah atau low back pain (LBP) merupakan gangguan muskuloskeletal akibat dari ergonomi yang salah. Nyeri punggung bawah didefinisikan sebagai nyeri yang lokasinya antara batas costae dan lipatan gluteaus inferior yang berlangsung lebih dari satu hari. Klasifikasi nyeri punggung bawah antara lain akut dan kronis. Nyeri punggung bawah akut terjadi dalam waktu kurang dari 12 minggu ditandai dengan rasa nyeri yang menyerang secara tiba-tiba. Rasa nyeri ini dapat hilang atau sembuh. Sedangkan nyeri punggung bawah kronis terjadi dalam waktu lebih dari 3 bulan. Rasa nyeri dapat berulang atau kambuh kembali. Nyeri punggung bawah bisa terjadi kepada siapa saja, dari mulai anak-anak hingga dewasa. Terdapat beberapa faktor yang dapat mempengaruhi kejadian nyeri punggung bawah, antara lain usia, jenis kelamin, Indeks massa tubuh, ergonomi, beban, masa kerja, kebiasaan merokok, dan faktor aktivitas atau kebiasaan olahraga.
Co-Authors Junta Zeniarja Luh Putu Ratna Sundari