Garuda - Garba Rujukan Digital

Pelatihan Kecerdasan Artifisial (KA) kepada Guru SD di Kabupaten Blora Jawa Tengah untuk Peningkatan Kemampuan di Bidang Digital Muhammad Munsarif; Samsudi Raharjo; Muhammad Sam'an
Jurnal Surya Masyarakat Vol 5, No 1 (2022): November 2022
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.5.1.2022.101-105

In this era of digitalization, the Government is trying from an early age to introduce programming concepts and Artificial Intelligence, which in the future can be used to support the learning process of elementary school education. This training aims to provide new knowledge about how coding programs are included in the subject matter of elementary school children. The teachers are given lessons on basic things suitable to be applied in class and immediately put into practice their projects. With this method, teachers learn to adapt to the new model, where the material will be taught to students in class. Activities are carried out offline at the Arra Cepu Hotel. Stages of Training through Presentations, Videos, and Quizzes. In this activity, the teachers gain knowledge and skills in coding programs for beginners; pre-test and post-test will be used as a measure to assess it. In the future, teachers will experiment with coding lessons with students. An early introduction to programming and artificial intelligence for elementary school teachers is expected to positively impact students, mainly in their ease of receiving and understanding a lesson.

Pelatihan Kecerdasan Artifisial (KA) kepada Guru SD di Kabupaten Blora Jawa Tengah untuk Peningkatan Kemampuan di Bidang Digital Muhammad Munsarif; Samsudi Raharjo; Muhammad Sam'an
Jurnal Surya Masyarakat Vol 5, No 1 (2022): November 2022
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.5.1.2022.101-105

In this era of digitalization, the Government is trying from an early age to introduce programming concepts and Artificial Intelligence, which in the future can be used to support the learning process of elementary school education. This training aims to provide new knowledge about how coding programs are included in the subject matter of elementary school children. The teachers are given lessons on basic things suitable to be applied in class and immediately put into practice their projects. With this method, teachers learn to adapt to the new model, where the material will be taught to students in class. Activities are carried out offline at the Arra Cepu Hotel. Stages of Training through Presentations, Videos, and Quizzes. In this activity, the teachers gain knowledge and skills in coding programs for beginners; pre-test and post-test will be used as a measure to assess it. In the future, teachers will experiment with coding lessons with students. An early introduction to programming and artificial intelligence for elementary school teachers is expected to positively impact students, mainly in their ease of receiving and understanding a lesson.

Optimization of compressor work, power and heat on efficiency refrigerant r290 on split ac Samsudi Raharjo; RM Bagus Irawan; M. Edi Pujianto
SINTEK JURNAL: Jurnal Ilmiah Teknik Mesin Vol 16, No 2 (2022): SINTEK JURNAL
Publisher : University of Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/sintek.16.2.77-81

The vapor compression cycle refrigeration machine is the type of refrigeration machine that is most widely used today, generally the medium used as a working fluid that transfers heat from the product being cooled to its environment is a synthetic refrigerant. The working fluid is called refrigerant. Refrigerant is the main working fluid used in air conditioning systems to absorb heat and convert it in the refrigeration system. This then encourages countries in the world including Indonesia to then jointly carry out prevention and improvement by making agreements. Then this agreement was known as the Vienna Convention in 1985 followed by the Montreal agreement in 1987. In order to reduce the impact of ODS (Ozone Depleting Substances), in 1992 Indonesia signed the Montreal protocol. At that time, Indonesia also launched the "Indonesian Country for the phase Out of Ozone Depleting Substances (ODS) under the Montreal Protocol". This study aims to compare the work of compression, heat absorbed, and the electric power used by the split type refrigeration machine between refrigerant R-22 and refrigerant R-290. These results and analysis show that R-290 has a higher value than R-22 in compression performance. R22 power consumption is 0.30 kw and R290 is 0.26. R290 this is influenced by the light work of the compressor so that it will have an impact on electric power consumption. The power consumption of R290 is 42% less than R22. The heat absorbed by R 22 is smaller than that using hydrocarbons. The results of the optimization of compressor work, power and heat variables show an effect on efficiency.

In Vitro Degradation and Mechanical Performance of Mg AZ31B for Biodegradable Bone Implant Applications Slamet Saefudin; Purnomo; Muhammad Subri; M. Edi Pujianto; Ilham Yustar Afif; Samsudi Raharjo
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2652

Magnesium AZ31B is a promising biodegradable implant material due to its mechanical properties comparable to natural bone and its ability to degrade in physiological environments, potentially eliminating the need for secondary surgery. However, its rapid degradation can cause a significant loss of mechanical integrity, limiting its use in load-bearing applications. This study investigates the evolution of mechanical properties and surface characteristics of AZ31B during in-vitro immersion in Simulated Body Fluid (SBF). Tensile tests were conducted on triplicate specimens after immersion for 3, 6, and 9 days, while surface morphology and corrosion products were analyzed using SEM–EDX. In addition, pH variation and mass loss were monitored to evaluate corrosion behavior. The results show a progressive decrease in tensile strength from 279.77 ± 5.30 MPa (0 days) to 167.64 ± 2.31 MPa after 9 days of immersion, representing an overall reduction of approximately 40%. This degradation was accompanied by increased surface corrosion, mass loss, and solution alkalization. These findings provide quantitative insight into the relationship between corrosion progression and mechanical degradation of AZ31B, highlighting its time-dependent performance limitations and the need for surface modification strategies in biodegradable implant applications.

High Precision Cascaded Spatio Temporal Deep Inference for Real Time Histamine Risk Prediction: A Health Informatics Approach Hanityo A Nugroho; Dorojatun AN; Rubijanto Juni Pribadi; Samsudi Raharjo
Journal of Intelligent Computing & Health Informatics Vol 7, No 1 (2026): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v7i1.19802

Rapid histamine accumulation in tropical fisheries constitutes a substantial public health hazard, particularly via scombroid poisoning, and underscores the need for rigorous, data-driven cold-chain surveillance. Artisanal vessels (≤ 30 GT), however, predominantly depend on ice-based cooling strategies that are thermally unstable and lack real-time diagnostic functionality, thereby failing to sufficiently suppress microbial growth kinetics under ambient conditions that frequently exceed 30°C. To address this gap, we propose a Cascaded Spatio-Temporal Deep Inference Architecture that couples a Convolutional Neural Network (CNN) for spatial feature denoising with a Long Short-Term Memory (LSTM) network for temporal kinetic modeling. This hybrid architecture assimilates high-frequency thermal measurements from an optimized R404A vapor-compression refrigeration system and predicts histamine risk indices under Arrhenius-based kinetic constraints. Field deployment on a 10 GT vessel demonstrated that the system maintained a highly stable storage temperature of -20.1 ± 0.5°C. The proposed model exhibited high predictive accuracy with an R2 of 0.97 and an RMSE of 0.45°C, significantly outperforming a Linear Regression baseline (RMSE = 1.85°C, p < 0.01). Importantly, the system extended the prime-quality shelf life by more than 52 hours while keeping histamine concentrations well below the U.S. FDA limit of 50 mg/kg. Collectively, these findings support a scalable health informatics framework and indicate that AI-driven predictive certification can substantially reduce food safety risks in resource-limited maritime supply chains.

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