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Improving Scientific Article Writing Skills for Science Teachers in Banjarnegara Regency through Project-Based Learning Budi Astuti; Langlang Handayani; Ani Rusilowati; Sutikno Sutikno; Budi Naini Mindiyarto; Agus Yulianto; Ellianawati Ellianawati; Sugiyanto Sugiyanto; Nur’ainiyyah Hasni; Shelly Salista Rahmayanti; Candra Fanleon
BERDAYA Indonesian Journal of Community Empowerment Vol. 3 No. 2 (2024)
Publisher : Universitas Negeri Semarang

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

Abstract

There are three main elements of activities assessed in assigning credit points to teachers. First is education, second is learning and third is continuous professional development. Education and learning are activities that are usually carried out by a teacher. However, not many teachers are still doing sustainable professional development. One of them is in scientific publications. For this reason, a service was carried out regarding training in writing scientific papers to increase the acquisition of credit scores for Banjarnegara Regency MGMP Science teachers. The training methods applied include planning, implementing training and mentoring and monitoring. Based on the service carried out, it was found that the length of time teaching did not show a positive relationship with writing ability and quantity of publications. Through this service, teachers already have the motivation to start writing even though there have been no published results from this service activity.
Integration of Ethnoscience in Natural Science learning: Literacy Study Sudirman Sudirman; Sutikno Sutikno; Dyah Rini Indriyanti; Woro Sumarni; Margareta Rahayuningsi
Jurnal Penelitian Pendidikan IPA Vol 11 No 6 (2025): June
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i6.9980

Abstract

Science education is essential for developing scientific literacy and critical thinking. However, traditional approaches often lack contextual relevance, especially in areas rich in local wisdom. This study aims to examine how ethnoscience—science learning that integrates local cultural values—can enhance meaningful learning while preserving cultural identity. The objective is to analyze its application, identify potential local cultural topics, and evaluate effective teaching methods. This qualitative research is conducted through a literature review following the PRISMA framework. From 98 articles identified via the Scopus database, 27 were selected for in-depth analysis. Data were analyzed using thematic content analysis. Results show that 55.56% of the studies used interdisciplinary approaches, while Biology topics were underrepresented (7.40%). Physics and Chemistry showed high contextual relevance, particularly in traditional technologies and natural materials. Common local cultural themes include crafts, martial arts, measurement tools, games, traditional medicine, and eco-friendly pesticides. Media used include modules, digital worksheets, LMS, and interactive technology. Effective pedagogical models include Ethno-STEM, EthCBL, RE-STEM, E-PjBL, and SIL, all of which promote scientific literacy, 21st-century competencies, and cultural appreciation. The findings highlight the potential of ethnoscience to support holistic and culturally responsive science education.
Feature Selection Method to Improve the Accuracy of Diabetes Mellitus Detection Instrument Sari Ayu Wulandari; Sutikno Madnasri; Ratih Pramitasari; Susilo Susilo
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09203

Abstract

The need for aroma recognition devices or often known as enose (electronic nose), is increasing. In the health field, enose can detect early diabetes mellitus (DM) type 2 from the aroma of urine. Enose is an aroma recognition tool that uses a pattern recognition algorithm to recognize the urine aroma of diabetics based on input signals from an array of gas sensors. The need for portable enose devices is increasing due to the increasing need for real-time needs. Enose devices have an enormous impact on the choice of the gas sensor Array in the enose. This article discusses the effect of the number of sensor arrays used on the recognition results. Enose uses a maximum of 4 sensors, with a maximum feature matrix. After that, the feature matrix enters the PCA (Principal Component Analysis) feature extraction and clustering using the FCM (Fuzzy C Means) method. The number of sensors indicates the number of features. Enose using method for feature selection, it’s a variation from 4 sensors, where experiment 1 uses 4 sensors, experiment 2 uses a variation of 3 sensors and experiment 3 uses a variation of 2 sensors. Especially for sensors 3 and 4 using feature extraction method, PCA (Principal Component Analysis), to reduce features to only 2 best features. As for the variation of 2 sensors use primer feature matrix. After feature selection, the number of features is 2 out of 11 variations. Next, do the grouping using the FCM (Fuzzy C Means) method. The results show that using two sensors has a high accuracy rate of 92.5%.