Garuda - Garba Rujukan Digital

Effectiveness of E-Learning-Based Learning in the Era of Digital Transformation: A Meta-Analysis Setyawan, Herlin; Sukardi; Risfendra; Jalinus, Nizwardi; Mardizal, Jonni; Ananda, Gheri Febri
Indonesian Journal of Educational Research and Review Vol. 7 No. 2 (2024): July 2024
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/ijerr.v7i2.76166

Amid digital transformation in the 21st century, the use of digital technology, such as e-learning, in the learning process has become urgent to develop vocational school students' knowledge and skills. However, some studies show that online learning is ineffective in improving vocational school students' knowledge and skills. Online learning cannot improve students' vocational skills because the online learning process cannot conduct actual practicum. Therefore, this study aims to analyze the effectiveness of online learning to improve vocational students' knowledge and skills. The research method used was random effect model meta-analysis with Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) article analysis technique. A total of 25 articles from the Scopus, Google Scholar, and Wiley databases were obtained and analyzed in this study. The articles analyzed were free from publication bias, as indicated by the funnel plot, Egger's, and Fail-safe N test results. Based on the meta-analysis results, it is known that the effect size value obtained is 0.703 (medium category). Thus, implementing online learning in vocational schools has a moderate impact on improving vocational students' knowledge and skills. The findings of this study indicate that the implementation of online learning in vocational schools can support the effectiveness of learning in vocational schools and encourage students to develop the knowledge and skills they will master. This research is expected to be the basis for teachers and vocational schools to integrate online learning into the learning process.

PENGARUH KURIKULUM MERDEKA BELAJAR TERHADAP PENINGKATAN AKADEMIK SISWA KELAS X DI SMKN 5 DUMAI Ananda, Gheri Febri; Khaira, Rizka; Yannuar, Yannuar
Tunjuk Ajar: Jurnal Penelitian Ilmu Pendidikan Vol 7, No 1 (2024)
Publisher : Jurusan Ilmu Pendidikan, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jta.v7i1.142-151

This research aims to determine the impact of the Merdeka Belajar curriculum on improving the academic achievement of level X students. This research uses a quantitative approach with descriptive and inferential statistical analysis. A sample of 55 students was randomly selected from the entire class X population at SMKN 5 Dumai, consisting of 5 students from each department. The collected data was then analyzed using a one-sample t-test by comparing the average score of the population in the semester before implementing the Merdeka Belajar curriculum with the scores in several samples after the Merdeka Belajar curriculum was implemented. The analysis results show a statistical t-test value of 14.05, which exceeds the critical t value (critical value) with a significance level of α = 0.05 (1.675), or in other words, the p-value is smaller than α. Therefore, the conclusion that can be drawn is that there is sufficient strong evidence to reject the null hypothesis (H0) and conclude that the average student score in the 2022/2023 academic year after implementing the Merdeka Belajar curriculum is higher than the average in 2021/ 2022 before the implementation of the Merdeka Belajar curriculum, at a significance level of 0.05.

Design of An Automatic Follower Shopping Trolley Based on Image Processing Ananda, Gheri Febri; Wahyuni, Cici; Sania, Ellsa; Sukardi, Sukardi
Teknomekanik Vol. 2 No. 2 (2019): Regular Issue
Publisher : Universitas Negeri Padang

In the era of industrial revolution 4.0, all human activities are replaced by machines. Humans are required to optimize brain function than muscle function. All human work is facilitated by robots or tools work automatically. For example, shopping activities in the modern market. Consumers prefer to shop in the modern market than the traditional market, because the items are complete, neatly arranged, and they have facilities like trolleys as transporters. Trolleys can be pushed and pulled easily. But when shopping, parents have a problem with pushing or pulling a trolley when they carrying their children. Therefore, we created a design for automatic follower shopping trolley based on image processing which can follow the consumers when shopping. This trolley has a webcam camera as a sensor to take pictures of the objects or special accessories that used by consumers. The result of data are processed in such a way and then it will be obtained the x and y coordinates positions of the consumer and the next data will be sent to the Raspberry Pi 3 which will automatically determine the movement of the trolley. The test results showed that the distance range detected between trolleys and consumers ranged from 0.5 meters to 2 meters, and trolleys can move according to the point where the object was detected. Thus, the trolley can follow the user well.

Analysis and Design of a Rooftop Photovoltaic (PV) System in Bulaksumur, Yogyakarta Using Archelios Pro Ananda, Gheri Febri; Bintoro, Kukuh; Khair, Asdaqul
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 4 No. 2 (2024): September 2024
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v4i2.18342

This research focuses on the design and analysis of a rooftop photovoltaic (PV) system in Bulaksumur, Yogyakarta, using the Archelios Pro software. The objective of the study is to evaluate the efficiency and effectiveness of the PV system in terms of energy production and cost savings. The methods used include simulation and analysis with Archelios Pro, which allows for accurate modeling and estimation of energy production. The load profile at the research site was analyzed to determine the annual energy consumption, which reached 8,859 kWh. The designed PV system consists of 15 modules with a total capacity of 6.60 kWp. The results show that the PV system, without battery storage, produces an annual energy output of 10,185 kWh, meeting 115% of the household's annual energy needs. However, without battery storage, only 25.8% of the generated energy is directly usable. In the scenario with battery storage, self-consumption increases from 25.8% to 45.8%, and reliance on the electricity grid decreases from 5,510 hours to 4,179 hours per year. Economic analysis reveals annual cost savings of Rp 3,847,926, although the payback period exceeds 20 years. The use of the PV system also reduces annual carbon emissions by 3,965.25 kg of CO‚‚, contributing to efforts to achieve net-zero emissions in Indonesia.

Enhanced Fashion-MNIST Classification Using a Hybrid VGG-16-DenseNet121 Architecture Ananda, Gheri Febri; Risfendra, Risfendra; Wahyudi, Eko
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.32225

This study aims to explore the effectiveness of a hybrid model combining the VGG16 and DenseNet121 architectures for image classification tasks on the Fashion MNIST dataset. This model is designed to leverage the advantages of both architectures to produce richer feature representations. In this study, the performance of the hybrid model is compared with several other architectures, including LeNet-5, VGG-16, ResNet-20, ResNet-50, EfficientNet-B0, and DenseNet-121, using various optimizers such as Adam, RMSProp, AdaDelta, AdaGrad and SGD. The test results indicate that the Adam and SGD optimizers deliver excellent results. The VGG16 + DenseNet121 hybrid model achieved perfect training accuracy 100%, the highest validation accuracy 94.65%, and excellent test accuracy 94.16%. Confusion matrix analysis confirms that this model is capable of correctly classifying the majority of images, although there is some confusion between classes with visual similarities. These findings affirm that a hybrid approach and the appropriate selection of optimizers can significantly enhance model performance in image classification tasks.

Deep Learning-Based Waste Classification with Transfer Learning Using EfficientNet-B0 Model Risfendra, Risfendra; Ananda, Gheri Febri; Setyawan, Herlin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5875

Recycling of waste is a significant challenge in modern waste management. Conventional techniques that use inductive and capacitive proximity sensors exhibit limitations in accuracy and flexibility for the detection of various types of waste. Indonesia generates approximately 175,000 tons of waste per day, highlighting the urgent need for efficient waste management solutions. This study develops a waste classification system based on deep learning, leveraging the powerful EfficientNet-B0 model through transfer learning. EfficientNet-B0 is designed with a compound scaling method, which uniformly scales network depth, width, and resolution, providing an optimal balance between accuracy and computational efficiency. The model was trained on a dataset containing six classes of waste—glass, cardboard, paper, metal, plastic, and residue—totalling 7014 images. The model was trained using data augmentation and fine-tuning techniques. The training results show a test accuracy of 91.94%, a precision of 92.10%, and a recall of 91.94%, resulting in an F1-score of 91.96%. Visualization of predictions demonstrates that the model effectively classifies waste in new test data. Implementing this model in the industry can automate the waste sorting process more efficiently and accurately than methods based on inductive and capacitive proximity sensors. This study underscores the significant potential of deep learning models, particularly EfficientNet-B0, in industrial waste classification applications and opens opportunities for further integration with sensor and robotic systems for more advanced waste management solutions.

PENGARUH KURIKULUM MERDEKA BELAJAR TERHADAP PENINGKATAN AKADEMIK SISWA KELAS X DI SMKN 5 DUMAI Ananda, Gheri Febri; Khaira, Rizka; Yannuar, Yannuar
Tunjuk Ajar : Jurnal Penelitian Ilmu Pendidikan Vol. 7 No. 1 (2024): February 2024
Publisher : Jurusan Ilmu Pendidikan Fakultas Keguruan dan Ilmu Pendidikan Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33578/

This research aims to determine the impact of the Merdeka Belajar curriculum on improving the academic achievement of level X students. This research uses a quantitative approach with descriptive and inferential statistical analysis. A sample of 55 students was randomly selected from the entire class X population at SMKN 5 Dumai, consisting of 5 students from each department. The collected data was then analyzed using a one-sample t-test by comparing the average score of the population in the semester before implementing the Merdeka Belajar curriculum with the scores in several samples after the Merdeka Belajar curriculum was implemented. The analysis results show a statistical t-test value of 14.05, which exceeds the critical t value (critical value) with a significance level of α = 0.05 (1.675), or in other words, the p-value is smaller than α. Therefore, the conclusion that can be drawn is that there is sufficient strong evidence to reject the null hypothesis (H0) and conclude that the average student score in the 2022/2023 academic year after implementing the Merdeka Belajar curriculum is higher than the average in 2021/ 2022 before the implementation of the Merdeka Belajar curriculum, at a significance level of 0.05.

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