Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Jurnal Pengabdian Magister Pendidikan IPA Stationer: Journal of Statistical Innovations and Applications
Nur Asmita Purnamasari
Unknown Affiliation
Education

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Pengenalan Data Science Untuk Mempersiapkan Era Digital Pada Siswa Di SMAN 1 Gunung Sari Dina Eka Putri; Baskara, Zulhan; Lisa Harsyiah; Agus Kurnia; Nur Asmita Purnamasari; Mustika Hadijati; Lilik Hidayati; Helmina Andriani; Jihadil Qudsi; Hafizah Ilma; Adis Tia Juli Agil Asri; Yuliana Lestari; Jihan Melani; Rifdah Fadhilah; M. Syahrul; M. Naoval Husni
Jurnal Pengabdian Magister Pendidikan IPA Vol 7 No 4 (2024): Oktober-Desember 2024
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpmpi.v7i4.9022

Abstract

The Fourth Industrial Revolution and Society 5.0 have created a demand for technology-based skills, including Data Science. This community service program aimed to introduce Data Science concepts to students at SMAN 1 Gunung Sari, preparing them for the digital era. Through interactive training sessions covering Data Science basics, data analysis simulations, and career discussions, both students and teachers gained essential foundational knowledge. The results showed an increase in students' knowledge and motivation towards STEM fields, as well as new skills for teachers in integrating data-driven learning. This program also strengthened the school's profile as an institution proactive in preparing students for future technological challenges.
Penguatan Data Kepariwisataan di Desa Lembar Selatan untuk Mengungkap Potensi Desa menuju Dewi Cantik: Penguatan Data Kepariwisataan di Desa Lembar Selatan untuk Mengungkap Potensi Desa menuju Dewi Cantik Hidayati, Lilik; Mustika Hadijati; Desy Komalasari; Nur Asmita Purnamasari; Adis Tia Juli Agil Asri; Kamal Faisal Hikam
Jurnal Pengabdian Magister Pendidikan IPA Vol 8 No 3 (2025): Juli-September 2025
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpmpi.v8i3.12876

Abstract

South Lembar Village has very promising tourism potential, both in terms of natural beauty, cultural richness, and unique ecotourism such as Cemare Beach, the Sacred Tomb, and the Mangrove Forest area. However, the utilization of this potential has not been optimal due to low awareness among the community and village officials regarding the importance of data in tourism management and development. Additionally, limited technical capabilities in data collection and analysis also pose an obstacle to creating effective and sustainable tourism promotion and development strategies. Based on this urgency, this service activity aims to increase community and village government awareness and capacity in building a simple, participatory, and sustainable tourism data collection system, thru the DEWI CANTIK or Desa Wisata Cinta Data Statistik concept approach. The methods used include socializing the importance of data, providing technical training for village officials and tourism awareness groups, assisting with the use of simple software for data management, forming village data working groups, and developing standard operating procedures for tourism data management. All stages are carried out collaboratively and adapted to local conditions. The targeted outcomes of this activity include increased data literacy among the community, the formation of a Village Data Working Group as tourism information managers, the development of standard operating procedures for the village tourism data collection system, and the availability of data that can be used for more targeted and evidence-based tourism planning and promotion. With this program, it is hoped that Lembar Selatan Village can become a model for independent, innovative, and sustainable data-based tourism villages.
Comparison of Apple Inc Stock Forecasting Accuracy Using Hybrid TSR Linear-ARIMA Model and ARIMA Model Aulia Padhila; Muhammad Rijal Alfian; Nur Asmita Purnamasari
Stationer: Journal of Statistical Innovations and Applications Vol. 1 No. 1 (2026): Stationer: Journal of Statistical Innovations and Applications
Publisher : Stationer: Journal of Statistical Innovations and Applications

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to compare the accuracy of Apple Inc. stock price forcasting using two time series models, namely the hybrid TSR Linear-ARIMA model and the ARIMA model. The background of this research is the need for more accurate forcasting methods in a dynamic stock market, especially for technology stocks such as Apple which have high volatility. The research methodology uses the quantitative approach with daily Apple stock price time series data for the period 2023. The hybrid TSR Linear-ARIMA model incorporates trend and residual components, while the ARIMA model uses the Box-Jenkins approach. Both models were implemented using statistical software R Studio and Minitab. The results that the ARIMA model provided better forcasting accuracy compared to the hybrid TSR Linear-ARIMA model. Comparative analysis using the MAPE shows the ARIMA model has a lowwer error rate. Specifically, the ARIMA model produces a MAPE of 2.909%, while the hybrid TSR Linear-ARIMA model produces a MAPE of 3.780%. in conclusion, the ARIMA model proved to be more effective in forecasting the stock price of Apple Inc. compared to the hybrid TSR Linear-ARIMA model. This research contributes to the development of forecasting techniques in finance and investment, especially for technology stock.
Co-Authors Adis Tia Juli Agil Asri Agus Kurnia Aulia Padhila Baskara, Zulhan Desy Komalasari Dina Eka Putri Hafizah Ilma Harsyiah, Lisa Helmina Andriani Jihan Melani Kamal Faisal Hikam Lilik Hidayati, Lilik M. Naoval Husni M. Syahrul Muhammad Rijal Alfian Mustika Hadijati Qudsi, Jihadil Rifdah Fadhilah Yuliana Lestari