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PELATIHAN APLIKASI AVOGADRO UNTUK MENINGKATKAN PEHAMAMAN DAN MINAT SISWA DALAM BIDANG KIMIA DI SMAN 10 MALANG Ahmad Atif Fikri
Jurnal Pengabdian Pendidikan dan Teknologi (JP2T) Vol 2, No 2 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um080v2i22021p95-100

Pelatihan aplikasi Avogadro dalam bentuk pengabdian ini bertujuan untuk meningkatkan pemahaman siswa dan menambah minat siswa SMAN 10 Malang dalam bidang kimia. Hasil dari kegiatan pelatihan ini yaitu guru dan siswa dapat menerapkan pembelajaran kimia berbasis teknologi dengan menggunakan aplikasi Avogadro. Guru-guru maupun pihak sekolah terkait dapat memaksimalkan pembelajaran berbasis teknologi ini dengan cara tatap muka secara langsung di sekolah atau lebih spesifiknya di laboratorium komputer sekolah, karena kita sadari bahwa tidak semua siswa memiliki fasilitas yang memadai di rumah. Namun, selama masa pandemi ini pelatihan kepada siswa dilakukan secara daring. Hanya pelatihan kepada guru kimia yang dilakukan di laboratorium sekolah. Evaluasi kegiatan ini dilakukan dengan memberikan kuesioner setelah pelatihan dilakukan. Kegiatan ini mendapat respon positif dari peserta maupun guru bidang kimia. Kata Kunci : Pelatihan, Avogadro, SMAN 10 Malang

Quantum Mechanics Approach for Metal-Organic Frameworks Deformation Effect on Carbon Capture Performance: A Density Functional Theory Study Muhdi, Krisna Dwipa; Fikri, Ahmad Atif
Journal of Mechanical Engineering, Science, and Innovation Vol 5, No 1 (2025): (April)
Publisher : Mechanical Engineering Department - Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.jmesi.2025.v5i1.7415

Increasing carbon dioxide (CO₂) emissions from fossil fuel combustion demand the development of effective and efficient carbon capture technologies. Metal-Organic Frameworks (MOFs) are excellent candidates as adsorbent materials because they have uniform pores, specific surface area, and can modified according to purpose. However, performance of MOFs may decrease due to structural deformation during adsorption-desorption process, especially under extreme conditions. This study uses a quantum mechanical approach, namely Density Functional Theory (DFT), to analyze effect of deformation, specifically hMOF-13, on its performance in CO₂ adsorption. Through modeling the atomic structure of hMOF-13, an understanding of the quantum interactions between atoms, changes in position of atoms and cells due to deformation is obtained. Simulation results show that mechanical deformation of hMOF-13 decreases CO₂ adsorption performance through pore narrowing and electrostatic charge redistribution. In addition, excessive deformation can trigger structural failures that reduce regeneration cycles and lower carbon capture efficiency. Insights from this study can guide the subsequent development of MOFs with enhanced mechanical resistance, contributing to the optimization of industrial-scale carbon capture processes. By improving the structural stability of MOFs, industries can achieve higher adsorption efficiency, longer material life, and reduced operational costs, making carbon capture technology more feasible and sustainable.

Sensor Fusion of Laser and Inertial Units with Kalman-KMeans-Fuzzy Framework for Real-Time Railway Geometry Monitoring Fikri, Ahmad Atif; Subhan, Muhammad Ferindin Nuha; Suryanto, Heru; Muhdi, Krisna Dwipa; Pratama, Daniel Febrian; Iqbal, Ahmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13780

Maintaining railway track geometry integrity is essential to ensuring transportation safety and predictive maintenance. Conventional manual inspection methods are limited by low sampling frequency, subjective interpretation, and delayed anomaly detection. This study introduces a real-time, embedded monitoring system using VL53L0X infrared laser sensors and an MPU6050 IMU to measure gauge, cross-level height, and inclination. Sensors are mounted on a lightweight aluminum trolley and sampled every 0.5 seconds using an Arduino-based platform. A Kalman Filter reduces measurement noise, with tuned covariance matrices based on field calibration. Filtered outputs are clustered via K-Means (K = 2), validated by the Elbow Method and Silhouette Score (>0.6). Maintenance categories are assigned through a fuzzy logic system, with a ±1 mm sensitivity analysis confirming >85% decision stability. Field results demonstrate a measurement noise, achieving RMSE and MAE values of 0.8165 mm and 0.3175 mm for gauge and height, and 0.3086° and 0.0952° for inclination, respectively and a SNR gain from 0.5 dB to 21.7 dB. The low-cost, modular setup supports scalable, condition-based maintenance and demonstrates robustness in noisy environments. This approach offers a practical foundation for future integration with predictive analytics and digital twin technologies in smart rail infrastructure.

Advancing Green Urea Recovery Flow: Enhancing Efficiency Through Flow Conditioning to Control Turbulence and Pressure Drop Saeful, Albarrobi Nabila; Saiful, Jusef; Fikri, Ahmad Atif
TURBO [Tulisan Riset Berbasis Online] Vol 14, No 2 (2025): TURBO: Jurnal Program Studi Teknik Mesin
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/trb.v14i2.4086

Green urea production requires high efficiency, in addition to reducing carbon emissions, high efficiency is needed to prevent waste from being wasted too much. High turbulence results in the creation of biuret that can interfere with the efficiency of urea production, one of the solutions that can be used to overcome this problem using flow conditioners. The analytical method is used to analyze and select the flow conditioner design, with the aim of having the most optimal turbulence and pressure drop values. The numerical method of Computational Fluid Dynamics (CFD) is also used in this study, the purpose of which is to validate the results and visualize the flow. The results show that the difference between the results of the analytical and numerical methods is not more than 5%, and the turbulence value can be reduced by about 5x by only losing about 0.3 bar of pressure from the system without the use of flow conditioners. This shows that the green urea production process can be more efficient and reduce the waste produced.

Adaptive Maintenance Strategy in Natural Stone Processing Industry: Integrating OEE-FMEA-Based Priority Matrix in the i-TPM-Lean+ Ahmed Jabari; Ahmad Atif Fikri; Muhammad Alfian Mizar
Jurnal Teknik Mesin (Sinta 3) Vol. 22 No. 2 (2025): OCTOBER 2025 (SINTA 3)
Publisher : Institute of Research and Community Outreach, Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jtm.22.2.94-107

This study proposes a hybrid model integrating total productive maintenance (TPM) and Lean Manufacturing tools to improve machine effectiveness in a marble processing company in Indonesia. High downtime, performance inefficiencies, and quality losses were identified as key contributors to low overall equipment effectiveness (OEE). A structured framework, called i-TPM-Lean+, was implemented by combining OEE measurement with failure mode and effect analysis (FMEA) to prioritize improvement strategies. TPM pillars such as Autonomous Maintenance, Planned Maintenance, and Focused Improvement were applied alongside Lean tools including 5S and single minute exchange of dies (SMED). Real operational data was collected for 30 days before and after implementation to evaluate changes in machine performance. The results revealed a substantial improvement in OEE from 59% to 70%, with notable reductions in downtime (–67%) and increases in availability (from 79% to 88%), performance (from 81% to 84%), and quality rate (from 92% to 94%). Benchmarking with other relevant studies confirmed that this integrated approach leads to enhanced machine reliability and production efficiency. The proposed model offers a practical reference for manufacturing companies seeking to improve operational performance through a combined TPM-Lean approach, supported by data-driven prioritization and systematic evaluation.

Advancing Green Urea Recovery Flow: Enhancing Efficiency Through Flow Conditioning to Control Turbulence and Pressure Drop Albarrobi Nabila Saeful; Jusef Saiful; Ahmad Atif Fikri
TURBO [Tulisan Riset Berbasis Online] Vol 14 No 2 (2025): TURBO: Jurnal Program Studi Teknik Mesin
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/trb.v14i2.4086

Green urea production requires high efficiency, in addition to reducing carbon emissions, high efficiency is needed to prevent waste from being wasted too much. High turbulence results in the creation of biuret that can interfere with the efficiency of urea production, one of the solutions that can be used to overcome this problem using flow conditioners. The analytical method is used to analyze and select the flow conditioner design, with the aim of having the most optimal turbulence and pressure drop values. The numerical method of Computational Fluid Dynamics (CFD) is also used in this study, the purpose of which is to validate the results and visualize the flow. The results show that the difference between the results of the analytical and numerical methods is not more than 5%, and the turbulence value can be reduced by about 5x by only losing about 0.3 bar of pressure from the system without the use of flow conditioners. This shows that the green urea production process can be more efficient and reduce the waste produced.

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