Kepatuhan wajib pajak dalam melaporkan Surat Pemberitahuan (SPT) merupakan syarat utama bagi penerimaan pajak yang optimal. Sejak tanggal 1 Februari 2012, Direktorat Jenderal Pajak (DJP) telah menyediakan layanan yang dapat memudahkan wajib pajak dalam melaporkan SPT, yaitu melalui layanan e-Filing. Tingkat penerimaan user terhadap suatu teknologi baru merupakan hal yang perlu diperhatikan Teknologi yang memiliki kegunaan (perceived of usefulness) dan kemudahan pengoperasian (perceived ease of use) akan mempengaruhi sikap (attitude toward us) user yang kemudian akan mempengaruhi minat (intention to use) user dalam menggunakan teknologi tersebut. Penelitian ini bertujuan untuk mengetahui bagaimana perceived of uselness (PU) dan perceived ease of use (PEOU) dapat mempengaruhi attitude towards use (ATU) yang kemudian akan mempengaruhi intention to use (ITU)layanan e-Filing oleh wajib pajak di KPP Pratama Purwakarta. Penelitian ini menggunakan kuesionar sebagai alat pengumpulan data. Teknik analisis data yang digunakan adalah analisis statistik deksriptif, melibatkan 20 indikator yang akan dianalisis dengan teknik analisis Partial Least Squares (PLS) dengan menggunakan bantuan software SmartPLS 2.0. Sampel yang digunakan yaitu sebanyak 123 responden dengan teknik purposive sampling. Hasil penelitian menunjukkan bahwa persentase tanggapan responden mengenai PU dari e-Filing yaitu sebesar 83.42% yang berarti sangat setuju, PEOU sebesar 78.05% yang berarti sangat setuju, ATU sebesar 80.13% yang berarti sangat setuju, dan ITU sebesar 79.32% yang berarti sangat setuju. Selain itu, hasil penelitian juga menunjukkan bahwa PU dan PEOU memiliki pengaruh positif ATU serta ATU memiliki pengaruh positif terhadap ITU. Pemeliharaan (maintenance) secara teratur dan berkala perlu dilakukan untuk mempertahankan kualitas layanan dari aspek kegunaan layanan e-Filing. Selain itu, penambahan fitur-fitur interaktif pada e-Filling akan sangat bermanfaat dalam meningkatkan minat penggunaan e-Filing.

Technological developments continue to encourage the creation of various innovations in almost all aspects of human life. One of the innovations that is becoming a worldwide phenomenon today is the presence of cryptocurrency as a digital currency that is able to replace the role of conventional currency as a means of payment. Currently, the number of cryptocurrency investors in Indonesia has reached 4.45 million people as of March 2021, an increase of 78% compared to the end of the previous year. Very volatile price movements make cryptocurrency investments considered speculative so the risks faced are also very high. The purpose of this study is to build a predictive model that is able to forecast prices on the cryptocurrency market. The algorithm used to build the prediction model is Long Short Term Memory (LSTM). LSTM is the development of the Recurrent Neural Network (RNN) algorithm to overcome problems in the RNN in managing data for a long period. LSTM is considered superior to other algorithms in managing time series data. The data in this study were taken from the Yahoo Finance website using the Pandas Datareader library through Google Collaboratory. The entire prediction model development process is carried out through Google Collaboratory tools. To improve the accuracy of the model, the Nadam optimization algorithm was used and three testing sessions were carried out with the number of Epochs of 1, 10, and 20 in each session. The final test results show that the best prediction performance occurs when testing the DOGE coin type with the number of Epoch 20 which gets an RMSE value of 0.0630.

Human behavior quantification is an essential part of psychological science. One of the cases is measuring human personality. Social media provide rich text, which can be beneficial as a data source to get valuable insight. Previous researches show that social media offered favorable circumstances for psychological researchers by tracking, analyzing, and predicting human character. In this research, we propose a personality measurement design to help to assess human character through linguistic usage from human digital traces. We construct our model by classifying social media text to the predetermined personality facet from Big Five personality traits, mapping the knowledge to the ontology model, and implementing the model as a platform dictionary. Our model is based on the Indonesian language, which to the best of our knowledge is the first in the subject area. The platform is running effectively by using a well-established sorting algorithm, called the radix tree. Our objective is to support psychological science in adapting to a new technological era.

Akhir tahun 2019 dunia digemparkan dengan kemunculan virus baru yaituCorona Virus yang diakibatkan dari pathogen SARS Cov-2 atau dikenal dengan COVID-19. Usaha preventif yang dilakukan pemerintah untuk menanggulangi penyebaran virus ini adalah dengan menerapkan protokol kesehatan yang ketat. Strategi lain yang juga dilakukan pemerintaha adalah melakukan vaksinasi agar terbentuk herd-immunity (kekebalan kelompok) secara cepat. Kendala yang dihadapi dalam program vaksinasi adalah munculnya kalangan yang menolak vaksin. Kejadian tersebut menimbulkan keresahan dimasyarakat. Respon kekhawatiran masyarakat diekspresikan dalam media sosial, salah satu media sosial yang digunakan sebagai pilihan untuk menyampaikan respon dan opini tersebut adalah twitter. Tujuan dari penelitian ini adalah melakukan analisis sentimen terhadap opini terkait vaksin yang beredar di twitter serta melakukan analisis jaringan sosial (SNA) yang terbentuk untuk mengetahui aktor yang paling berperan dalam penyebaran informasi mengenai vaksin COVID-19. Penelitian menggunakan metode klasifikasi dengan algoritma naïve bayes dan metode Social Network Analysis (SNA). Hasil dari penelitian ini menunjukan bahwa 92% sentimen pengguna twitter adalah positif terhadap vaksin COVID-19 dan aktor yang paling berperan dalam penyebaran informasi adalah akun @jokowi.Keywords: COVID-19, vaksinasi, analisis sentimen, SNA, twitter

Tourism is one of the sectors that have contributed to a wide variety, not just economically, but also socially political, culturally, regionally and environmentally. In Indonesia, the tourism sector has an important role in contributing to economic growth, especially in foreign exchange earnings. Therefore, it is very important to maintain and encourage the growth of tourism in Indonesia with the need for a model of forecasting the arrival of foreign tourists to Indonesia to assist the government in developing a tourism plan strategy. There are factors in demand forecasting which affect a demand in the tourism sector. In this study data in the form of fuel prices, exchange rates, per capita GDP, and the volume of bilateral trade from five countries over the span of January 2012 to December 2019 are used as variables that affect the arrival of foreign tourists. The method used to create a forecasting model is one of recurrent neural network architectures namely long short-term memory (LSTM). Three models are put in test and each model uses four types of parameters that are look-back value, hidden layer, number of epochs, and batch size. The first prediction model gives 97.21% of the highest accuracy. The second predictive model provides 99.17% of the highest accuracy. Lastly, the third prediction model provides 99.21% of the highest accuracy.