Claim Missing Document
Check
Articles

Found 3 Documents
Search

Analisis Kalman filter berbasis Google Trends untuk Prediksi Kedatangan Wisatawan Mancanegara di Indonesia Pasca Pandemi Evita Purnaningrum; Hanief Khoyyir Nafah
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 14 No 2 (2021): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (336.156 KB) | DOI: 10.36456/jstat.vol14.no2.a4956

Abstract

Pada tahun 2019 kunjungan wisatawan mancanegara (wisman) ke Indonesia mengalami peningkatan yang cukup signifikan. Sehingga, pariwisata diprediksi menjadi salah satu penopang terbesar dari penerimaan negara. Namun, saat wabah Coronavirus terjadi di akhir tahun 2019, sektor ini menjadi sektor industri yang paling terdampak dengan penurunan yang sangat tajam dan perkirakan akan membaik sekitar tahun 2035 hingga 2045. Kejadian tersebut mendorong penelitian untuk merumuskan model proyeksi terbaik bagi wisatawan asing pasca pandemi dengan menggunakan metode Kalman filter. Kalman filter merupakan model state space yang dapat diulang untuk menghasilkan nilai akurasi estimasi yang tinggi. Model ini didukung oleh analisis google trends yang mampu menangkap minat negara lain terhadap pariwisata Indonesia, terutama di masa pandemi. Hasil penelitian menunjukkan bahwa meskipun pandemi, beberapa negara masih memiliki minat terhadap objek wisata di Indonesia. Selain itu, Kalmanfilter memiliki akurasi yang tinggi dalam peramalan wisatawan asing
Modified Multi-Kernel Support Vector Machine for Mask Detection Muhammad Athoillah; Evita Purnaningrum; Rani Kurnia Putri
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.7873

Abstract

Indonesia is one of the countries most affected by the Coronavirus pandemic with millions confirm cases. Hence, the government has increased strict procedures for using face masks in public areas. For this reason, the detection of people wearing face masks in public areas is needed. Face mask detection is a part of the classification problem. Thus Support Vector Machine (SVM) can be implemented. SVM is still known as one of the most powerful and efficient classification algorithms. The research aims to build an automatic face mask detector using SVM. However, it needs to modify it first because it only can classify linear data. The modification is made by adding kernel functions, and a Multi-kernel approach is chosen. The proposed method is applied by combining various kernels into one kernel equation. The dataset used in the research is a face mask image obtained from Github. The data are public datasets consisting of faces with and without masks. The results present that the proposed method provides good performance. It is proven by the average value. The values are 83.67% for sensitivity, 82.40% for specificity, 82.00% for precision, 82.93% for accuracy, and 82.77% for F1-score. These values are better than other experiments using single kernel SVM with the same process and dataset.
KNOWLEDGE AS THE INTERVENING VARIABLE ON RELIGIOSITY VALUE OF ACCOUNTANT STUDENTS' SAVING INTEREST FOR ISLAMIC BANKING Wahyuning Murniati; Evita Purnaningrum
International Journal of Accounting and Management Research Vol. 1 No. 2 (2020): September
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/ijamr.v1i2.645

Abstract

The development of Islamic economics in banking practices in society develops innovatively. Various sharia banking products have become the people's choice in supporting their daily economic activities. The same thing also happened to students. The purpose of study is to analyze the religiosity effect of students' interest in saving for Islamic banking with knowledge as intervening variable. Multiple linear regression is an analytical technique used here, with hypothesis testing and path analysis to support its conclusions. The result of this study is that the level of religiosity significantly effects on students' interest in saving for Islamic banking with positive relationship. Likewise, knowledge is able mediate the relationship between the religiosity variable and students' interest in saving.