Online transportation in Indonesia is a new trend of transportation that is currently used among the lower to upper society. The change in behavior began in 2011 and is growing to this day, The comments that are growing on social media are very important for the online transportation company the negative comments lower the level of users while the good comments increase the users' level. Thus, the comments influence the overall trust of their customers. Among social media, Twitter is a place where many people convey feelings of pleasure and displeasure timely, especially at a time when the COVID-19 pandemic is becoming a serious outbreak. Through these "tweets," many customers express their experience with the service. In this paper, we aim to analyze the experience of online transportation consumers using Support Vector Machine. The data were taken in two periods, i.e. April 2019 ("the normal era") and June 2020 ("the COVID-19 pandemic outbreak. Class logging is done based on 3 categories namely positive, negative and normal, while in mining the data we labeled with the keywords @grabID and @gojekindonesia, 1618 data were obtained with a ratio of 1183 is normal era data and 435 data in the era of COVID-19 pandemic, The highest accuracy results occurred in the normal era with a ratio of 10% as test data and 90% as its training data on linear and sigmoid kernels of 0.8060 while the COVID-19 era only got the highest accuracy of 0.59 in linear kernels with a ratio of 60:40. This is a sign that the COVID-19 pandemic does not contribute to decreasing trust in the service.
ABSTRAK
Pemerintahan kabupaten karawang untuk mengetahui kedisiplinan pegawainya secara dini yaitu dengan system kontrol kedisiplinan pegawai. Sistem kontrol tersebut yaitu dengan mengontrol absensi, walaupun absensi belum mewakili semua bahwa dengan absensi yang bagus berarti pegawai tersebut etos kerjanya juga bagus, namun setidaknya untuk memulai dari sisi kedisiplinan masuk kerja tepat waktu dan pulang tepat pada waktunya. Program kedisiplinan pegawai dengan menerapkan setiap pegawai masuk kerja harus absensi dengan fingerprint saat datang di pagi hari jam 08:00 dan pulang di sore hari jam 17:00 wib, dan hasil sistem absensi tersebut direport dan dipantau langsung oleh badan kepegawaian kabupaten karawang dengan nama presensi pegawai kabupaten karawang.
Kata Kunci: fingerprint, absensi
Secara tradisional, brosur banyak digunakan dalam penyampaian informasi tentang suatu produk. Seiring dengan perkembangan teknologi, brosur ini dapat diubah menjadi brosur digital yang bersifat interaktif dalam bentuk aplikasi Android. Salah satunya adalah dengan mengimplementasikan augmented reality agar konten aplikasi dapat berinteraksi dengan lingkungan ketika aplikasi ini digunakan. Metode augmented reality yang digunakan disini adalah markerless augmented reality. Hasil pengujian menggunakan rating scale menunjukkan bahwa aplikasi brosur digital yang dikembangkan tergolong baik dan dapat bermanfaat baik bagi tenaga pemasaran produk mobil Suzuki Ertiga maupun bagi pelanggan
The Covid-19 pandemic that has occurred in Indonesia and even in the world has not yet ended. Various efforts have been made by the Indonesian government to minimize the spread of this virus, such as the implementation of a lockdown, Large-Scale Social Restrictions (PSBB), a ban on going home during the Eid al-Fitr holiday, and so on. One of the new policies issued by the government is the vaccination program, where the government has started implementing the program since early 2021 for the people of Indonesia, which aims to increase antibodies to avoid exposure to the Covid-19 virus. To find out opinions, comments, or feedback given by the public on this new policy, sentiment analysis can be done. The process of this sentiment analysis includes data collection, namely the crawled tweet data originating from the Twitter social media. The data is then selected for further pre-processing stage so that the data is clean and ready for classification. Furthermore, sentiment weighting is carried out for data labeling using a lexicon dictionary and negative words. Then after that, the terms or words are weighted with tf-idf and followed by the feature selection process using Information Gain. Furthermore, the classification process is carried out using the Naive Bayes Classifier algorithm to classify the data into 3 classes, namely positive, negative, and neutral sentiments. The results of this study are to produce a model accuracy rate of 78%, recall 80%, and an AUC score of 0.904.
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