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All Journal Engagement: Jurnal Pengabdian Kepada Masyarakat International Journal of Applied Sciences and Smart Technologies
Putri Nilam Cayo
Universitas Sriwijaya
Religion Arts Humanities Education Social Sciences

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The Facilitated Support for Strengthening Statistical Literacy among Fishermen in Burai Village within the Context of Local Fishing Culture Dian Cahyawati; Eka Susanti; Muji Gunarto; Putri Nilam Cayo; Husnul Khotimah; Andi Tenri Ajeng Nur
Engagement: Jurnal Pengabdian Kepada Masyarakat Vol. 10 No. 2 (2026): May 2026
Publisher : Asosiasi Dosen Pengembang Masyarajat (ADPEMAS) Forum Komunikasi Dosen Peneliti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29062/engagement.v10i2.2277

Abstract

Background: Burai Village is a riverside fishing community with not high enough levels of formal education and limited use of data in daily decision making. Fishermen typically rely on intuition when planning fishing activities, managing income, and interpreting river conditions. Purpose of the Study: This community service program addressed the need to strengthen statistical literacy as a foundation for data-driven decision making. The program aimed to improve fishermen’s ability to record, organize, and interpret catch data, while increasing awareness of the value of information and simple technology in supporting economic decisions. Methods: A community-based mentoring model was applied, consisting of dialogue sessions, participatory workshops, and hands-on training in data recording. A culturally contextual learning approach linked statistical concepts with local fishing practices. Results: The results demonstrated a measurable improvement in participants’ statistical understanding. The mean score increased from 65 (SD = 21.21) in the pretest to 75 (SD = 10.00) in the posttest, indicating both improved performance and more consistent comprehension among participants. Participants also showed improved ability to interpret tables and graphs and increased interest in using simple digital tools for data documentation.
Grouping Weekly Weather Based on Weather Elements in Pagaralam by Using K-Means Clustering Analysis Sri Indra Maiyanti; Irmeilyana; Putri Nilam Cayo; Dinny Indah Angelia; Angelina
International Journal of Applied Sciences and Smart Technologies Vol. 8 No. 1 (2026): Volume 08, Issue 1, June 2026
Publisher : Faculty of Science and Technology, Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/w2m4fy05

Abstract

Weather is the result of the interaction and combination of various atmospheric elements that occurs in a relatively small place or region over a short period of time. This study aims to analyze weekly weather characteristics in Pagaralam using the K-Means Clustering method. The data used was weekly weather in 2022 and 2023 with 15 variables, namely Maximum temperature, Minimum temperature, Temperature, Dew, Humidity, Precipitation, Precipitation cover, Wind gust, Wind speed, Wind direction, Sea pressure, Cloud cover, Solar radiation, UV Index, and Moon phase. The analysis process began with data standardization, followed by clustering using K-Means Clustering with several K values to observe variations in cluster structure. The optimal number of clusters was determined using the elbow and silhouette methods. The best optimal K value ​​for each of the 2022 and 2023 data was K=3. A small number of weeks in both years had high temperatures and solar radiation and accompanied by lower dew, humidity, precipitation, and cloud cover than other weeks. A small number of weeks in 2022 had low minimum temperature and were also accompanied by lower cloud cover, dew, and humidity, but they had higher wind gusts and wind speeds than other weeks. Meanwhile, a small number of weeks in 2023 had lower temperatures and accompanied by higher cloud cover, wind gusts, and wind speeds than other weeks.
Co-Authors Andi Tenri Ajeng Nur Angelina Dian Cahyawati Dinny Indah Angelia Eka Susanti Husnul Khotimah Irmeilyana Muji Gunarto Sri Indra Maiyanti