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All Journal Jurnal Pendidikan IPA Indonesia Journal of the Indonesian Tropical Animal Agriculture Journal of Robotics and Control (JRC)
Ahmed, M. A.
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Mechanical Engineering

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Ecological evaluation of microelements in Astrakhan region and the dynamics of microelements in organs and tissues of Soviet Merino sheep Ahmed, M. A.; Vorobyov, V. I.; Vorobyov, D. V.
Journal of the Indonesian Tropical Animal Agriculture Vol 46, No 1 (2021): March
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jitaa.46.1.40-47

Abstract

Microelements are important for stabilizing cell structures, but in deficient conditions they can stimulate alternative pathways and cause disease. This study was aimed to presents the monitoring data on the biogeochemical situation of pasture ecosystems in the Astrakhan region, southern Russia. Microelements in the collected samples from the pasture ecosystem, as well as the organs and tissues of Soviet Merino sheep, were determined by atomic absorption method. It was found a low level of microelements in soil, plants and forages of the ecosystem in the Astrakhan region. In addition, it was found a low level of microelements (selenium, iodine, and cobalt) in the organs and tissues of Merino sheep. Hypomicroelementosis in sheep leads to oxidative stress in animals, lower productivity and decrease the immunity of animals, which can be a predisposing for other diseases.
Human Activity Recognition Using Accelerometer & Gyroscope Smartphone Sensor by Extract Statistical Features Abdullah, Muthana Hmod; Ahmed, M. A.
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22443

Abstract

Understanding behavioral patterns and forecasting the bodily motions of persons heavily relies on detecting human activities. This has profound ramifications in several domains, including healthcare, sports, and security. This study sought to identify and classify 18 human actions recorded by 90 people using smartphone sensors using the KU-HAR dataset. The primary aim of this study is to examine statistical features such as (mean, mod, entropy, max, median …etc.) derived from time-domain sensory data collected using accelerometers and gyroscopes. Activity detection utilizes many machine learning methods such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LG), Naïve Bayes (NB), and AdaBoost. The RF model achieves the highest overall accuracy of 99%. While the DT model gets 95%, SVM receives 93%, and the KNN gets 82%. At the same time, the other model didn’t get good results. The research is evaluated using accuracy, recall, precision, and f1-scor. The research contribution is to extract the statistical feature from the raw file of the sensor to create a new dataset. This research recommends employing statistical features in time series. Future research is recommended to solve misclassification in certain activities, which could be achieved using feature selection to reduce the number of features.
The Influence of WE-ARe (Warm-Up, Exploring, Argumentation, Resume) Model Integrated with 21st-Century Skills on Prospective Biology Teachers' Communication Skills Amin, A. M.; Adiansyah, R.; Mustami, M. K.; Yani, A.; Hujjatusnaini, N.; Ahmed, M. A.
Jurnal Pendidikan IPA Indonesia Vol 13, No 1 (2024): March 2024
Publisher : Program Studi Pendidikan IPA Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpii.v13i1.47911

Abstract

The research aims to identify the influence of the WE-ARe (Warm-up, Exploring, Argumentation, Resume) model integrated with 21st-century skills on prospective biology teachers’ communication skills. The research type was quantitative, using a quasi-experiment research design. The research sample consisted of 60 biology education students in three classes. The research instruments used were valid and reliable observation sheets and questionnaires to measure communication skills. Data were analyzed using Covariate Analysis (ANCOVA) with a significant level of 5%. The research shows that the average communication in the experimental group after receiving treatment is 90.63, while in the positive control group, it is 73.54, and in the negative control group, it is 39.14. The test results to find out the difference in communication skills between the experimental and control groups obtained a calculated F value of 2846.491 with a significance value of 0.000. The significance value is smaller than the real level alpha 5% or (0.05). The research concludes that there is an influence of the WE-ARe model integrated with 21st-century skills on prospective biology teachers’ communication skills. The learning phases of the WE-ARe model integrated with 21st-century skills are proven to improve verbal and non-verbal communication skills. WE-ARe is a new learning model developed by researchers to accommodate the learning needs of students in the Industry 4.0 era towards the Society 5.0 era by using the principles of constructivism, collaborative, and participatory elaboration. The research is expected to contribute to increasing the global competence of university graduates, especially teachers.
Human Activity Recognition Using Accelerometer & Gyroscope Smartphone Sensor by Extract Statistical Features Abdullah, Muthana Hmod; Ahmed, M. A.
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22443

Abstract

Understanding behavioral patterns and forecasting the bodily motions of persons heavily relies on detecting human activities. This has profound ramifications in several domains, including healthcare, sports, and security. This study sought to identify and classify 18 human actions recorded by 90 people using smartphone sensors using the KU-HAR dataset. The primary aim of this study is to examine statistical features such as (mean, mod, entropy, max, median …etc.) derived from time-domain sensory data collected using accelerometers and gyroscopes. Activity detection utilizes many machine learning methods such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LG), Naïve Bayes (NB), and AdaBoost. The RF model achieves the highest overall accuracy of 99%. While the DT model gets 95%, SVM receives 93%, and the KNN gets 82%. At the same time, the other model didn’t get good results. The research is evaluated using accuracy, recall, precision, and f1-scor. The research contribution is to extract the statistical feature from the raw file of the sensor to create a new dataset. This research recommends employing statistical features in time series. Future research is recommended to solve misclassification in certain activities, which could be achieved using feature selection to reduce the number of features.
Co-Authors A. Yani Abdullah, Muthana Hmod Adiansyah, R. Amin, A. M. Hujjatusnaini, N. Mustami, M. K. Vorobyov, D. V. Vorobyov, V. I.