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All Journal Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Information System, Applied, Management, Accounting and Research
Rahmawati, Nidya Sari
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Computer Science & IT Decision Sciences, Operations Research & Management

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APPLICATION OF MACHINE LEARNING FOR BITCOIN EXCHANGE RATE PREDICTION AGAINST US DOLLAR Wiliani, Ninuk; Hesananda, Rizki; Rahmawati, Nidya Sari; Prianggara, Erdham Hestiadhi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 7 No 2 (2022): JITK Issue February 2022
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1590.378 KB) | DOI: 10.33480/jitk.v7i2.2880

Abstract

Predicting a currency Exchange rate and performing analysis is an action to try to determine the price valuation of a currency or other financial instrument traded on an exchange platform. Bitcoin is a consensus network that enables new payment systems and fully digital money. Bitcoin is the first decentralized peer to peer payment network that is fully controlled by its users without any central authority or intermediary. From the user's point of view, Bitcoin is like cash in the internet world. Bitcoin can also be viewed as the most prominent triple bookkeeping system in existence today. The change in Bitcoin's behavior against the US dollar is influenced by many factors. Basic or economic factors that may be affected include inflation rates and money supply. In this study, data was collected by obtaining all data through the API provided by binance.com and labeled with the specified attribute. The modeling is done by using the rapidminer application. The process begins by taking training data that has been provided previously. The next stage is the data testing process, all operators that have been previously determined are connected and tested using the Linear Regression operator. The purpose of testing this data is to predict stock prices from the testing data that has been made by the Split Data operator, which is 19% of the total data that has been prepared.
TREND ANALYSIS AND CORRELATION OF TOURIST, RESTAURANT AND HOTEL VISITS IN KUNINGAN REGENCY Hesananda, Rizki; Trihandoyo, Agus; Wiliani, Ninuk; Rahmawati, Nidya Sari
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.4618

Abstract

This study conducts an in-depth analysis of the tourism sector in Kuningan Regency, focusing specifically on hotel stays, tourist arrivals, and restaurant visits. Utilizing forecasting models and correlation analyses, the research aims to uncover trends and interdependencies within the sector. The primary objective is to identify actionable insights that can inform data-driven decision-making. The study employs the FBProphet algorithm for forecasting future trends and conducts Kendall correlation analysis to examine relationships among key variables. Data collected spans a time series of 84 months, from January 2016 to December 2022. FBProphet accurately predicts trends in hotel stays, while variations exist in predictions for tourist arrivals and restaurant visits. Mean values for hotel stays, tourist arrivals, and restaurant visits are 21,098.67, 135,647.33, and 130,660.83, respectively. Kendall correlation analysis reveals a moderate positive correlation (0.214, p-value = 0.004) between tourist arrivals and restaurant visits, a strong positive correlation (0.324, p-value = 1.291e-05) between tourist arrivals and hotel stays, and a weaker positive correlation (0.176, p-value = 0.019) between restaurant visits and hotel stays. These findings underscore the intricate dynamics of Kuningan Regency's tourism sector, providing stakeholders with critical insights for strategic planning. The research contributes significantly to sustainable growth initiatives by guiding stakeholders in leveraging the interconnected elements of tourism and making well-informed decisions.
PENGEMBANGAN SISTEM QR WATERMARKING UNTUK PERLINDUNGAN DAN MONETISASI KARYA VISUAL DIGITAL Yuyan, Achmad; Rahmawati, Nidya Sari; Laurencius, Andreas Octaviando; Yusuf, Muhammad
Journal of Information System, Applied, Management, Accounting and Research Vol 9 No 4 (2025): JISAMAR (Journal of Information System, Applied, Management, Accounting and Resea
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v9i4.2122

Abstract

Perlindungan karya visual digital dan proses monetisasi langsung menjadi dua tantangan utama bagi kreator konten di era distribusi terbuka. Penelitian ini bertujuan untuk mengembangkan sistem QR Watermarking yang mampu menyematkan tanda air (watermark) secara otomatis dan mendukung transaksi pembelian karya digital melalui integrasi kode pembayaran berbasis Quick Response Indonesian Standard (QRIS). Metode penelitian menggunakan pendekatan rekayasa sistem dengan tahapan analisis kebutuhan, perancangan arsitektur, implementasi, dan pengujian fungsional menggunakan metode black-box testing. Hasil pengujian menunjukkan bahwa sistem mampu menjalankan proses unggah gambar, penyematan watermark semi-transparan, serta validasi pembayaran hingga pemberian akses file asli tanpa watermark. Platform ini berpotensi menjadi solusi technopreneurship yang efektif bagi kreator visual dalam mendistribusikan karya secara aman, efisien, dan bernilai ekonomi.
Co-Authors Agus Trihandoyo Hesananda, Rizki Laurencius, Andreas Octaviando Muhammad Yusuf Prianggara, Erdham Hestiadhi Wiliani, Ninuk Yuyan, Achmad