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All Journal Jurnal Pengabdian Magister Pendidikan IPA Stationer: Journal of Statistical Innovations and Applications Timuris : Journal of Computational and Information Research
Nur Asmita Purnamasari
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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

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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.
Sentiment Analysis of TikTok Comments on the Weakening of the Rupiah Exchange Rate as an Indicator of Public Perception of Financial Risk Baiq Wira Hartati; Ahda Sabila Wulandari; Risma Anggraeni; Nur Asmita Purnamasari
Timuris: Journal of Computational and Information Research Vol. 1 No. 1 (2026): Timuris: Journal of Computational and Information Research
Publisher : Kiswah Institute

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Abstract

The weakening of the rupiah against the U.S. dollar is an economic issue that can influence the public’s perception of financial risk. The social media platform TikTok has become one of the channels the public uses to express their opinions on current economic conditions. This study aims to analyze the sentiment of TikTok users’ comments regarding the weakening of the rupiah as an indicator of the public’s perception of financial risk. The method used is text mining with a Lexicon-Based Sentiment Analysis approach. The research data consists of 163 TikTok comments discussing the weakening of the rupiah exchange rate. The analysis stages include data preprocessing, word cloud visualization, and sentiment classification into positive, neutral, and negative categories. The results show that negative sentiment dominates at 56.44%, followed by neutral sentiment at 38.65%, and positive sentiment at 4.91%. The most frequently occurring words include “Prabowo,” “MBG,” “rupiah,” “president,” “rise,” and “dollar.” The dominance of negative sentiment indicates public concern regarding the impact of the weakening rupiah, such as rising prices, declining purchasing power, and economic uncertainty. The research results suggest that social media sentiment analysis can serve as an early indicator for understanding the public’s perception of financial risks in real time.
Comparison of Historical Simulation and Variance-Covariance Methods for Value at Risk Estimation of BBRI Stock Jihan Afifah; Nanda Aulia Sudiasmini; Nur Aminingsih; Nur Asmita Purnamasari
Timuris: Journal of Computational and Information Research Vol. 1 No. 1 (2026): Timuris: Journal of Computational and Information Research
Publisher : Kiswah Institute

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Abstract

Stock investment is exposed to market risk arising from fluctuations in stock prices. Therefore, accurate risk measurement is essential for investors and risk managers. Among the various tools available for quantifying investment risk, Value at Risk (VaR) has gained widespread adoption as a method for determining the worst expected loss under a given probability threshold. This study compares the Historical Simulation and Variance-Covariance methods in estimating the Value at Risk of PT Bank Rakyat Indonesia (Persero) Tbk (BBRI) stock using daily closing price data from January 2, 2024, to December 31, 2025. The Jarque-Bera normality test indicated that the return data were not normally distributed, suggesting the presence of non-normal characteristics in the return distribution. Based on an assumed investment value of IDR 10,000,000, the VaR estimates at the 95% confidence level were IDR 334,739 and IDR 356,444 using Historical Simulation and Variance-Covariance, respectively. At the 99% confidence level, the estimated VaR values were IDR 534,695 and IDR 500,158, respectively. Kupiec Proportion of Failures (POF) backtesting showed that both methods produced statistically valid VaR estimates. However, Historical Simulation generated a more conservative risk estimate at the 99% confidence level, indicating a greater ability to capture extreme losses under non-normal return distributions. Therefore, Historical Simulation is recommended as the preferred method for measuring the market risk of BBRI stock.
Co-Authors Adis Tia Juli Agil Asri Agus Kurnia Ahda Sabila Wulandari Aulia Padhila Baiq Wira Hartati Baskara, Zulhan Desy Komalasari Dina Eka Putri Hafizah Ilma Harsyiah, Lisa Helmina Andriani Jihan Afifah Jihan Melani Kamal Faisal Hikam Lilik Hidayati, Lilik M. Naoval Husni M. Syahrul Muhammad Rijal Alfian Mustika Hadijati Nanda Aulia Sudiasmini Nur Aminingsih Qudsi, Jihadil Rifdah Fadhilah Risma Anggraeni Yuliana Lestari