Dataset Indonesia untuk Analisis Sentimen

Ferdiana, Ridi; Jatmiko, Fahim; Purwanti, Desi Dwi; Ayu, Artmita Sekar Tri; Dicka, Wiliam Fajar

doi:10.22146/jnteti.v8i4.533

Cited by 32 publications

(26 citation statements)

References 4 publications

(5 reference statements)

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“…Pada proses ini data yang sudah melewati proses pre-processing dengan baik akan dilanjutkan dengan pembobotan [9]dan akan diberikan algoritma Naive Bayes. Proses pembelajaran akan dilakukan dengan data yaitu data latih.…”

Section: Gambar 2 Tahapan Analisisunclassified

Sentiment Analysis of the Community in the Twitter to the 2020 Election in Pandemic Covid-19 by Method Naive Bayes Classifier

Muzaki

Witanti

2021

J. Tek. Inform. (JUTIF)

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The 2020 regional elections in the midst of the COVID-19 pandemic are starting to get crowded starting from the real world and in cyberspace, especially on Twitter social media. Twitter's existence has been widely used by various communities in recent years. Twitter is one of the media that represents the public response regarding public issu. Ahead of the general election (PEMILU), there are usually some parties who want to know the results of public sentiment or response to the issue, namely academics, intellectuals or even political opponents. Nevertheless, the implementation of local elections is very polemic in the community, therefore this study tries to analyze tweets that talk about issue public, namely the 2020 elections in the wake of the COVID-19 Pandemic. The analysis usually uses the classification of tweets containing public sentiment about the issue. The classification method used in this research is Naive Bayes Classifier (NBC) And Support Vector Machine (SVM). Naive Bayes Classifier is combined with features that can detect weighting using probability. The classification of tweets in this study was obtained based on a combination of two classes namely sentiment class and category class. The classification of sentiment consists of positive and negative. Test results on built-in applications show that accuracy with Naive Bayes delivers better results than Support Vector Machine. However, overall the use of the Naive Bayes method has a good performance to classify tweets with an accuracy rate of 92.2%

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Section: Gambar 2 Tahapan Analisisunclassified

Sentiment Analysis of the Community in the Twitter to the 2020 Election in Pandemic Covid-19 by Method Naive Bayes Classifier

Muzaki

Witanti

2021

J. Tek. Inform. (JUTIF)

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“…Beberapa penelitian terdahulu juga melakukan analisis sentimen dengan mengulas hal-hal sebagai berikt : komparasi fitur seleksi [111]; kinerja KPU Pemilu [112]; kepuasan pengguna penyedia layanan komunikasi seluler [113]; kepuasan dalam proses pendidikan [114]; kehadiran Mass Rapid Transit (MRT) [115]; kebijakan pemindahan Ibukota Republik Indonesia [1], [116], [117]; kebijakan ganjil genap [118]- [120]; jasa ojek online [121], [122]; jasa ekspedisi barang [123]; impor beras [124]; hotel [125]; ujaran kebencian [6]; evaluasi kinerja dosen [126], [127]; Dewan Perwakilan Rakyat [128]; debat calon presiden [129]; debat calon gubernur [130]; dataset Indonesia [131]; cyberbullying [132]; calon gubernur [133]- [136]; berita kebakaran hutan [137]; asuransi [138]; acara televisi [139], [140]. Berdasarkan kuantitas riset, dapat diketahui bahwa ulasan sentimen analisis dengan topik kebijakan pemindahan ibukota Republik Indonesia, kebijakan ganjil genap, jasa ojek online, evaluasi kinerja dosen, calon gubernur dan acara televisi.…”

Section: Gambar 2 Hasil Klasifikasi Studi Kasus Kajian Analisis Sentimen Sumber : Hasil Penyandian Axial Menggunakan Nvivo 12 Plusunclassified

Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review

Singgalen

2021

Journal-ISI

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This article use Systematic Literature Review (SLR) to classify sentiment analysis based on case studies, methods, social media, and platforms. The coding stage is divided into three stages, namely the open, selective and axial coding. The literature study on sentiment analysis is divided into two parts: identifying gaps based on case studies and data sources and identifying gaps based on the methods or algorithms used. The gap identification results based on case studies and data sources show that popular review topics are synonymous with entertainment, economic and political content. Therefore, the quantity of research with review topics related to the implementation of education, the dynamics of the bureaucracy, health facilities and services, and non-governmental organization’s activities need to be increased. Meanwhile, the most dominant platforms used as data sources are website and mobile-based applications. The results of the gap identification based on the method and algorithm show that the quantity of research with the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) method or algorithm is more dominant than the k-Nearest Neighbor (k-NN) and Lexicon-based. Thus, it is necessary to increase the number of other classification methods such as Particle Swarm Optimization, BM25, Decision Tree, K-Means, and Neural Networks.

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“…Pada tahap pre-processing, dilakukan beberapa bentuk text processing guna meningkatkan kualitas data saat digunakan untuk analisis (Ferdiana, 2019). Tahap text processing yang dilakukan antara lain: menghapus simbol dan tanda baca; tokenizing, case folding, stemming dan stopword removal.…”

Section: Metodeunclassified

Analisis Media Sosial Twitter Tentang Pendidikan Daring Pada Masa Pandemi Covid-19 Di Indonesia

Permatasari¹,

Yosral²,

Annisa³

2021

semnasoffstat

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PSBB yang diberlakukan oleh pemerintah pusat maupun pemerintah daerah menyebabkan perubahan aktivitas sosial yang cukup besar, salah satunya di bidang pendidikan. Hampir semua kegiatan yang semula dilakukan secara luring (luar jaringan) berubah menjadi daring (dalam jaringan). Berbagai perubahan aktivitas tersebut menuai berbagai perbincangan di media sosial, salah satunya adalah Twitter. Penelitian ini bertujuan untuk menangkap fenomena perbincangan kegiatan pendidikan yang berlangsung selama pandemic Covid-19 di Indonesia. Data yang digunakan pada penelitian ini merupakan data Twitter berbahasa Indonesia yang dikumpulkan pada tanggal 16 Maret 2020 - 31 Juli 2020. Analisis yang digunakan pada penelitian ini antara lain: analisis sentimen, social network analysis, analisis bot/spam, analisis emosi, dan analisis geografis. Data cuitan yang berhasil dikumpulkan dihasilkan berjumlah 153.204 cuitan dengan total akun sebanyak 91.306 user. Hasil sentimen menunjukkan perbincangan didominasi oleh sentimen netral, dengan tidak terdapat perbedaan signifikan dari sentimen positif dan negatif. Hasil SNA menunjukkan bahwa akun “collegemenfess” adalah aktor yang berperan sebagai influencer dan mempunyai pengaruh terbesar pada setiap user yang mengunggah cuitan terkait pendidikan daring. Hasil analisis bot/spam membagi akun menjadi lima kelas, dimana semakin besar nilai kelas, maka semakin kuat dugaan bahwa akun terindikasi bot atau pelaku spam. Proporsi akun pada lima kelas tersebut yaitu: 90.98% (kelas 0-1), 7.19% (kelas 1-2), 0.97% (kelas 2-3), 0.7% (kelas 3-4), 0.16% (kelas 4-5). Hasil dari analisis geografis menunjukkan lima provinsi yang memiliki sebaran cuitan isu tertinggi yaitu Jawa Barat, Jawa Tengah, Jawa Timur, DKI Jakarta, dan DI Yogyakarta, serta lima provinsi yang memiliki sebaran cuitan isu terendah yaitu Papua Barat, Maluku Utara, Gorontalo, Sulawesi Tenggara, dan Sulawesi Barat. Tiga emosi yang mendominasi cuitan mengenai pendidikan daring adalah ‘Trust’, ‘Anticipation’ dan ‘Fear’, dan peristiwa tertentu di bidang pendidikan mempengaruhi pergolakan jumlah emosi yang terkandung pada cuitan.

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Dataset Indonesia untuk Analisis Sentimen

Cited by 32 publications

References 4 publications

Sentiment Analysis of the Community in the Twitter to the 2020 Election in Pandemic Covid-19 by Method Naive Bayes Classifier

Sentiment Analysis of the Community in the Twitter to the 2020 Election in Pandemic Covid-19 by Method Naive Bayes Classifier

Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review

Analisis Media Sosial Twitter Tentang Pendidikan Daring Pada Masa Pandemi Covid-19 Di Indonesia

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