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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan PROJECT (Professional Journal of English Education) Jurnal Abdimas Sang Buana
Faladiba, Muthia Nadhira
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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PELATIHAN PENGGUNAAN GOOGLE FORM DALAM PENGUMPULAN DATA STUNTING DI DESA DAYEUHKOLOT KABUPATEN SUBANG Marliana, Reny Rian; Roshafara, Fauziah; Suliadi, Suliadi; Faladiba, Muthia Nadhira
Jurnal Abdimas Sang Buana Vol 5 No 2 (2024): Jurnal Abdimas Sang Buana - November
Publisher : LPPM Universitas Sangga Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32897/abdimasusb.v5i2.3686

Abstract

Stunting is a significant threat to the development of future generations' quality in Indonesia, as it can hinder children's growth, reduce learning abilities, and increase the risk of chronic diseases. Therefore, stunting prevention must be carried out through monitoring, starting from the village as the frontline. Dayeuhkolot Village in Subang Regency is one of the villages in West Java Province with a relatively low stunting rate. This success is closely linked to the monitoring efforts carried out by the Human Development Cadres (KPM) in the village. One of the primary responsibilities of KPM is to report data on monitoring the prevention and reduction of stunting. However, the data reporting process still relies on conventional methods, such as paper-based records, which create difficulties in archiving, data recapitulation, and report access. To address these challenges, the utilization of information technology, in accordance with Law No. 23 of 2006, is essential. Google Forms, a user-friendly information technology tool, offers an effective solution. By using Google Forms, stunting data can be collected in real-time and accessed online by relevant stakeholders. Therefore, this community service project was conducted using a science and technology diffusion method, aiming to provide outreach and training on the use of Google Forms for collecting monitoring data on stunting prevention and reduction in Dayeuhkolot Village. The outcomes of this activity show an improvement in the participants' ability to utilize information technology, and the use of Google Forms has proven to overcome the challenges faced by conventional data collection methods.
Technology-Mediated TBLT on Student's ‎Confidence and Motivation Rosyidah, Ummu; Faladiba, Muthia Nadhira
PROJECT (Professional Journal of English Education) Vol. 8 No. 1 (2025): VOLUME 8 NUMBER 1, JANUARY 2025
Publisher : IKIP Siliwangi

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Abstract

The abrupt transition to online instruction due to the COVID-19 pandemic has prompted educators to implement a wide range of innovative teaching strategies. This scenario becomes even more complex when English is taught as a supplementary subject in the first year of pharmacy programs. A primary concern in online English language instruction is how to maintain student engagement and encourage continued use of the target language. However, understanding students' learning motivation is a crucial factor in establishing a conducive environment that will motivate them to continually study English and achieve proficiency in the four language skills. This study aimed to determine whether interactive, technology-mediated task-based language teaching can enhance students' motivation and confidence and explore their perceptions of the tasks. Employing a qualitative approach, the study relied on observations and questionnaires as the primary data collection methods. The findings demonstrate that interactive, technology-mediated tasks significantly increase students' motivation and confidence for learning English. Additionally, the paper provides examples of how technology-mediated tasks can be designed to promote learners' productive use of English. Finally, the study discusses some anticipated challenges in implementing technology-mediated task-based language teaching in online instructional contexts.
BAYES ESTIMATION OF EXPONENTIALLY DISTRIBUTED SURVIVAL DATA UNDER SYMMETRIC AND ASYMMETRIC LOSS FUNCTIONS Faladiba, Muthia Nadhira; Ahdika, Atina
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp0973-0986

Abstract

In a research study, population data are often not available, so the population parameter is unknown. Meanwhile, knowledge about the population parameter is needed to know the characteristics of the studied population. Therefore, it is needed to estimate the parameter of the population which can be estimated by sample data. There are several methods of parameter estimation which are generally classified into classical and Bayesian method. This research studied the Bayesian parameter estimation method to determine the parameters of the exponentially distributed survival data associated with the reliability measure of the estimates under symmetric and asymmetric loss functions for complete sample data in a closed form. The symmetric loss functions used in this research are Squared Error Loss Function (SELF) and Minimum Expected Loss Function (MELF). The asymmetric loss functions used are the General Entropy Loss Function (GELF) and Linex Loss Function (LLF). Performance of some loss functions used in this research are then compared through numerical simulation to select the best loss function in determining the parameter estimation of the exponentially distributed survival data. We also studied which loss function is best for underestimation and overestimation modeling. Based on simulation results, the Bayes estimates using MELF is the best method to estimate population parameters of the exponentially distributed survival data for the overestimation modeling, while LLF is the best for the underestimation modeling. We provided direct application in a case study of fluorescence lamp survival data. The results show that the best method to estimate the parameter of the standard fluorescence life data is using LLF for underestimation with and MELF for overestimation with .
Co-Authors Reny Rian Marliana Roshafara, Fauziah Rosyidah, Ummu Suliadi Suliadi