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Studi Deskriptif Gaya Belajar Siswa Sebagai Penentu Model Pembelajaran Terdiferensiasi Sudarmanto, Agus
Proceedings Series on Social Sciences & Humanities Vol. 19 (2024): Proceedings of Webinar International Globalizing Local Wisdom: Integrating Cultural
Publisher : UM Purwokerto Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/pssh.v19i.1326

This study aims to determine the learning style to determine the learning model of class X students at SMA Negeri 1 Karangkobar. The type of research carried out is descriptive research. The research steps are initiated by giving a questionnaire to the respondents. The number of respondents in class X amounted to 360 students. After the questionnaire was given, 258 students were returned in full and all the statements in the questionnaire were answered. The questionnaire used was tested with validity and reliability tests. Student learning style is calculated by percentage. Based on the percentage of students' answers to the questionnaire, it was concluded that class X students had an auditory learning style or 42.6%. Students have a kinesthetic learning style or 17.1%. Students who have a visual learning style are 40.3%.

EEG Classification while Listening to Murottal Al-Quran and Classical Music using Random Forest Method Sumarti, Heni; Septiani, Fahira; Sudarmanto, Agus; Caesarendra, Wahyu; Edison, Rizki Edmi
Knowledge Engineering and Data Science Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p157-169

This study is aimed to classify the brain activity of adolescents associated with audio stimuli; murottal Al-Quran and classical music. The raw data were filtered using Independent Component Analisys (ICA) and followed by band-pass filter in Python on the Google Colab Extraction was processed with Power Spectral Density (PSD) and the Random Forest Method in Weka Machine Learning was used for classification. The research results showed the same results between the two types of stimulation, namely the order of brain waves from highest to lowest were delta, alpha, theta and beta. The average brain waves of teenagers when given murottal al-Quran stimulation were 45.32% delta, 31.60% alpha, 17.02 theta and 6.05% beta. Meanwhile, the average brain waves of teenagers when given classical music stimulation were 46.54% delta, 28.64% alpha, 19.21% theta and 5.50% beta. Classification is obtained with the best value that frequently appears (mode) from the prediction results for each sample using random forest methods. The accuracy, precision, and recall of classifying adolescent brain waves when given murottal and classical music stimuli using the Random Forest method with cross-validation technique (optimum at k-fold=5) were 65.38%, 76.92%, and 70.00%, respectively. The results of this study show that stimulation using murottal al-Quran and classical music effectively improves adolescent relaxation conditions.

Tilt Building (TB) Gun-an Arduino Nano Based Device for Detecting Building Inclination Sudarmanto, Agus; Poernomo, Joko Budi; Nurjanah, Resa Ahliana
Physics Education Research Journal Vol. 6 No. 2 (2024)
Publisher : Faculty of Science and Education, UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/perj.2024.6.2.20351

The inclination of a building can be a serious issue with potentially fatal consequences if not addressed correctly from the beginning, as it may lead to the building collapsing. Therefore, a tool is needed to detect the tilt of a building to determine the level of inclination. This research aims to design and build the Tilt Building Gun (TB Gun) as a portable device for detecting building inclination that can be easily used anywhere. The research methodology includes the design of hardware, development of software, and overall testing of the device. A miniature building in the form of a laptop-sized box was also constructed. The components used in this research include Arduino Nano, 2 HC-SR04 ultrasonic sensors, and an OLED display. The test results of ultrasonic sensor 1 for horizontal direction yielded an accuracy value of 99.06% and a relative error of 0.94%. Meanwhile, the testing results of ultrasonic sensor 2, with an additional distance of 13.1 cm (distance between ultrasonic sensor 1 and ultrasonic sensor 2 to the ground surface) for vertical direction, resulted in an accuracy of 96.29% and a relative error of 3.71%. Subsequently, testing the building inclination angle with a horizontal distance of 25 cm for ultrasonic sensor 1 and a vertical distance of 20 cm for ultrasonic sensor 2 yielded an accuracy of 98.86% and a relative error of 1.14%. From these accuracy values, it can be concluded that the prototype has excellent accuracy. The building inclination angle data is then displayed on the OLED.

Enhancing Physics Learning in Sekolah Indonesia Kuala Lumpur (SIKL) by Utilizing Artificial Intelligence Khalif, Muhammad Ardhi; Saputri, Affa Ardhi; Juwanda, Kuntoro Adi; Sudarmanto, Agus; Fatimatuzzahro, Voni
Dimas: Jurnal Pemikiran Agama untuk Pemberdayaan Vol. 24 No. 1 (2024)
Publisher : LP2M of Institute for Research and Community Services - UIN Walisongo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/dms.2024.241.19315

The surge in digital learning technology, particularly Artificial Intelligence (AI), has reshaped education. This community service initiative at Sekolah Indonesia Kuala Lumpur in Malaysia targets AI training to innovate physics education. The multifaceted approach encompasses preparation, implementation, and evaluation stages, employing methods like Focus Group Discussions, training sessions, and mentoring. AI plays a pivotal role in assisting students with assignments, analyzing physics problems, and creating presentation media for school tasks, utilizing applications such as Canva+ChatGPT and Google Slides+ChatGPT. Training outcomes at Sekolah Indonesia Kuala Lumpur showcase participants' proficiency in AI-driven presentation media, achieving an impressive average score of 84.67. Among 27 participants, only one fell short of the 75-point passing criteria. To maximize AI's role in physics learning, ongoing implementation and support are crucial in subsequent activities.

Tilt Building (TB) Gun-an Arduino Nano Based Device for Detecting Building Inclination Sudarmanto, Agus; Poernomo, Joko Budi; Nurjanah, Resa Ahliana
Physics Education Research Journal Vol. 6 No. 2 (2024)
Publisher : Faculty of Science and Education, UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/perj.2024.6.2.20351

The inclination of a building can be a serious issue with potentially fatal consequences if not addressed correctly from the beginning, as it may lead to the building collapsing. Therefore, a tool is needed to detect the tilt of a building to determine the level of inclination. This research aims to design and build the Tilt Building Gun (TB Gun) as a portable device for detecting building inclination that can be easily used anywhere. The research methodology includes the design of hardware, development of software, and overall testing of the device. A miniature building in the form of a laptop-sized box was also constructed. The components used in this research include Arduino Nano, 2 HC-SR04 ultrasonic sensors, and an OLED display. The test results of ultrasonic sensor 1 for horizontal direction yielded an accuracy value of 99.06% and a relative error of 0.94%. Meanwhile, the testing results of ultrasonic sensor 2, with an additional distance of 13.1 cm (distance between ultrasonic sensor 1 and ultrasonic sensor 2 to the ground surface) for vertical direction, resulted in an accuracy of 96.29% and a relative error of 3.71%. Subsequently, testing the building inclination angle with a horizontal distance of 25 cm for ultrasonic sensor 1 and a vertical distance of 20 cm for ultrasonic sensor 2 yielded an accuracy of 98.86% and a relative error of 1.14%. From these accuracy values, it can be concluded that the prototype has excellent accuracy. The building inclination angle data is then displayed on the OLED.

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