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All Journal Mechatronics, Electrical Power, and Vehicular Technology Sinergi VOX EDUKASI: Jurnal Ilmiah Ilmu Pendidikan Journal of Renewable Energy, Electrical, and Computer Engineering Heca Journal of Applied Sciences Leuser Journal of Environmental Studies Journal of Educational Management and Learning Grimsa Journal of Science Engineering and Technology
Yandri, Erkata
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Other

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Cultivating Energy Conscious Communities: The Path to Increased Efficiency Lodewijk, Dewi Putriani Yogosara; Yandri, Erkata; Murdiyansah, Novan; Ariati, Ratna
Heca Journal of Applied Sciences Vol. 2 No. 1 (2024): March 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i1.157

Abstract

This research addresses the critical need for increased energy efficiency in communities, emphasizing the pivotal role of community involvement and awareness. With the growing concern for sustainable energy practices, empowering communities to contribute to energy efficiency initiatives is imperative. Thus, the research aims to investigate and understand the role of community empowerment in increasing energy efficiency through community role and awareness. The theory applied to the research is the theory of planned behavior. A descriptive quantitative approach is employed, utilizing a structured questionnaire based on the Likert scale. Then, after the questionnaires were collected, statistical data processing was carried out using the T-test, F-test, and validity and reliability tests. The questionnaire gauges participants' perceptions and behaviors about energy efficiency, enabling a comprehensive analysis of the community's role and awareness in promoting sustainable energy practices. Preliminary findings indicate a positive correlation between community empowerment, heightened awareness, and increased energy efficiency. The Likert scale responses provide valuable insights into the areas where communities excel and areas that require targeted interventions. The data also reveal notable patterns in community behaviors and perceptions of energy consumption and conservation. In conclusion, the research underscores the significance of community empowerment as a catalyst for enhancing energy efficiency. The findings suggest that fostering community awareness and active involvement can lead to tangible improvements in sustainable energy practices. This study contributes valuable insights for policymakers, community leaders, and energy advocates seeking effective strategies to address the global energy challenge through localized, community-driven initiatives.
Predictive Maintenance with Machine Learning: A Comparative Analysis of Wind Turbines and PV Power Plants Uhanto, Uhanto; Yandri, Erkata; Hilmi, Erik; Saiful, Rifki; Hamja, Nasrullah
Heca Journal of Applied Sciences Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i2.219

Abstract

The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-based predictive maintenance is a promising solution for enhancing the reliability and efficiency of renewable energy systems.
Potential for Electrical Energy Savings in AC Systems by Utilizing Exhaust Heat from Outdoor Unit Hamja, Nasrullah; Yandri, Erkata; Hilmi, Erik; Uhanto, Uhanto; Saiful, Rifki
Heca Journal of Applied Sciences Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i2.223

Abstract

This study explores the potential of utilizing waste heat from air conditioning systems, one of the largest consumers of electrical energy. Currently, most of the waste heat generated by outdoor units is typically released into the environment without being utilized, leading to missed energy-saving opportunities. This study analyzes the potential for improving electrical energy efficiency in air conditioning (AC) systems by harnessing this waste heat. Two primary approaches are evaluated: the first is the use of waste heat for domestic water heating, and the second is the conversion of heat into electrical energy using thermoelectric generators (TEG). The results of this research indicate that both methods have the potential to improve overall energy efficiency significantly. However, challenges related to conversion efficiency and integration of these technologies with AC systems require further, more specific studies. These findings are expected to contribute to more efficient and environmentally friendly cooling systems by optimizing technology and overcoming barriers to wider implementation.
STRATEGI RANCANGAN DAN PRODUK PEMBELAJARAN MIKRO BUKU DIGITAL (MICROLEARNING EBOOK) UNTUK PENDIDIKAN MAGISTER ENERGI TERBARUKAN Viendyasari, Mila; Nur, Syukri M; Yandri, Erkata; Uyun, Aep Saepul
VOX EDUKASI: Jurnal Ilmiah Ilmu Pendidikan Vol 14, No 2 (2023): NOPEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/ve.v14i2.2423

Abstract

ABSTRAKMicrolearning merupakan metode pembelajaran mikro yang berupaya menyampaikan pengetahuan atau bidang ilmu secara parsial kepada mahasiswa dan publik. Metode ini dikaji dan diadaptasikan pada proses pendidikan mahasiswa pascasarjana teknik energi terbarukan. Tujuan riset adalah perancangan alur kerja dan penyusunan materi kuliah dengan metode microlearning. Metode riset dilaksanakan melalui tinjauan pustaka dan penerapan rencana pembelajaran semester yang menjadi panduan belajar di perguruan tinggi. Alur kerja penyusunan microlearning telah diterapkan untuk mata kuliah konversi energi biomassa dan angin, kemudian menghasilkan tiga produk ebook dan memilik legalitas berupa ISBN dan hak cipta. Pengembangan alur kerja dan produk microlearning perlu dilakukan untuk mata kuliah lainnya, termasuk evaluasi kinerja metode ini berdasarkan respon penggunanya yaitu mahasiswa.Kata Kunci: Microlearning, Covid-19, renewable energy, distance learning, mobile learningABSTRACTMicrolearning is a micro learning method that seeks to partially convey knowledge or fields of knowledge to students and the general public. This method is studied and adapted to the educational process of students who apply renewable energy techniques. The aim of the research is to design workflows and prepare course materials using the microlearning method. The research method is carried out through literature reflection and the implementation of semester learning plans which become learning guides in tertiary institutions. The microlearning workflow has been implemented for the biomass and wind energy conversion courses, then produces three ebook products and has legality in the form of ISBN and copyright. Developing workflows and microlearning products needs to be done for other courses, including evaluating the performance of this method based on user responses, namely students.Keywords; Microlearning, Covid-19, renewable energy, distance learning, mobile learning 
Design Concept of Information Control Systems for Green Manufacturing Industries with IoT-Based Energy Efficiency and Productivity Yandri, Erkata; Idroes, Rinaldi; Maulana, Aga; Zahriah, Zahriah
Leuser Journal of Environmental Studies Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v1i1.36

Abstract

In today's and future industrial competition, IoT and the Fourth Industrial Revolution are unavoidable. Indonesia must be prepared to compete globally in an increasingly efficient and integrated industry, including efficient energy use and renewable energy. This issue has received little strategic and scientific thought, particularly in Indonesia. This study purposes to create a conceptual model of an information control system in the industry, which will include operational performance. The method involves four steps. Firstly, the process flow within the industry is comprehensively analyzed, including the input, process, and output (IPO) aspects. Secondly, all information pertaining to each production process is integrated into the information system. Thirdly, a management control system (MCS) is proposed, incorporating key performance indicators (KPIs), allowing real-time monitoring by management. Lastly, real-time information data on resource sharing is submitted to the information sharing control system within similar industrial clusters. This enables related business parties to optimize their resource utilization based on the provided information. The results show that green manufacturing can be initiated by controlling energy-saving and productivity-related KPIs. The concept of IoT green manufacturing depends on active involvement from the government, industry and the public. A crucial aspect of this system is how the industry effectively manages production performance through shop floor control (SFC).
Utilization of Drone with Thermal Camera in Mapping Digital Elevation Model for Ie Seu'um Geothermal Manifestation Exploration Security Bahri, Ridzky Aulia; Noviandy, Teuku Rizky; Suhendra, Rivansyah; Idroes, Ghazi Mauer; Yanis, Muhammad; Yandri, Erkata; Nizamuddin, Nizamuddin; Irvanizam, Irvanizam
Leuser Journal of Environmental Studies Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v1i1.40

Abstract

Geothermal energy is a viable alternative energy source, particularly in Indonesia. This study was conducted at Ie Seu’um, Mount Seulawah Agam, which is a potential site for a geothermal power plant with an estimated electrical output of 150 megawatts. The objective of this study was to analyze and construct a digital elevation model (DEM) map of the geothermal manifestations. We analyzed water temperature, FLIR (Forward Looking Infrared) temperature, and temperature data from Landsat 8 satellite imagery. To map the heat signature of geothermal features, we utilized the DJI Phantom 4 Standard equipped with the FLIR One Gen 2 sensor. Additionally, we used the Milwaukee Mi306 to calculate the water temperature. Each test was conducted three times to obtain an optimal average level of accuracy. The DEM map was created to assess the level of safety in geothermal manifestation exploration. Elevation and slope values were analyzed to generate a 3D map display, providing a clearer image of the research site. In conclusion, drones prove to be an excellent method for ensuring the safety of exploration in geothermal manifestation areas.
Leading Light: The Impact of Advanced Lighting Technologies on Indonesia's Office Industry Murdiyansyah, Novan; Yandri, Erkata; Lodewijk, Dewi Putriani Yogosara; Ariati, Ratna
Leuser Journal of Environmental Studies Vol. 2 No. 1 (2024): April 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v2i1.140

Abstract

Addressing concerns over resource scarcity and environmental sustainability necessitates a global shift towards sustainable energy, notably facilitated by adopting Light-Emitting Diode (LED) lamps. This transition is pivotal for ensuring global energy security and aligning with sustainability goals. This study endeavors to comprehensively analyze potential energy savings achievable through the transition from Fluorescent (FL) lamps to LED lamps within industrial offices. Emphasis is placed on highlighting the central role of energy efficiency. Utilizing false color rendering as a visual guide, the study systematically identifies areas where FL lamps inadequately illuminate. The findings prompt recalculations for determining optimal room illumination achievable through implementing LED lamps. Lux calculations are then employed to showcase the superior illumination offered by LED lamps, revealing consistent monthly cost savings of 35%, particularly when harmonized with Building Management System (BMS) control in industrial office buildings. The study's results indicate that LED lamps provide superior illumination, yielding a noteworthy 35% monthly cost savings, especially when integrated with BMS control. Lamps contribute modestly (21-30%) to overall energy consumption, while air conditioning commands a substantial 60%, underscoring the critical need for advanced lighting technology. This need is emphasized, particularly with Solar PV as a sustainable energy source. Understanding technological developments, especially in BMS, is crucial to optimize energy efficiency in industrial offices. The imperative implementation of LED lighting technology is a critical solution to address resource scarcity and environmental concerns in industrial offices. The efficacy of LED lamps in achieving significant energy savings, especially when coupled with advanced systems like BMS and complemented by renewable energy sources such as Solar PV. The conclusion stresses the significance of staying abreast of technological advancements to foster sustained progress towards energy-efficient and environmentally conscious practices within industrial environments.
Optimizing University Admissions: A Machine Learning Perspective Maulana, Aga; Noviandy, Teuku Rizky; Sasmita, Novi Reandy; Paristiowati, Maria; Suhendra, Rivansyah; Yandri, Erkata; Satrio, Justinus; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.46

Abstract

The university admission process plays a pivotal role in shaping the future of aspiring students. However, traditional methods of admission decisions often fall short in capturing the holistic capabilities of individuals and may introduce bias. This study aims to improve the admission process by developing and evaluating machine learning approach to predict the likelihood of university admission. Using a dataset of previous applicants' information, advanced algorithms such as K-Nearest Neighbors, Random Forest, Support Vector Regression, and XGBoost are employed. These algorithms are applied, and their performance is compared to determine the best model to predict university admission. Among the models evaluated, the Random Forest algorithm emerged as the most reliable and effective in predicting admission outcomes. Through comprehensive analysis and evaluation, the Random Forest model demonstrated its superior performance, consistency, and dependability. The results show the importance of variables such as academic performance and provide insights into the accuracy and reliability of the model. This research has the potential to empower aspiring applicants and bring positive changes to the university admission process.
Machine Learning for Early Detection of Dropout Risks and Academic Excellence: A Stacked Classifier Approach Noviandy, Teuku Rizky; Zahriah, Zahriah; Yandri, Erkata; Jalil, Zulkarnain; Yusuf, Muhammad; Mohamed Yusof, Nur Intan Saidaah; Lala, Andi; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v2i1.191

Abstract

Education is important for societal advancement and individual empowerment, providing opportunities, developing essential skills, and breaking cycles of poverty. Nonetheless, the path to educational success is marred by challenges such as achieving academic excellence and preventing student dropouts. Early identification of students at risk of dropping out or those likely to excel academically can significantly enhance educational outcomes through tailored interventions. Traditional methods often fall short in precision and foresight for effective early detection. While previous studies have utilized machine learning to predict student performance, the potential for more sophisticated ensemble methods, such as stacked classifiers, remains largely untapped in educational contexts. This study develops a stacked classifier integrating the predictive strengths of LightGBM, Random Forest, and logistic regression. The model achieved an accuracy of 80.23%, with precision, recall, and F1-score of 79.09%, 80.23%, and 79.20%, respectively, surpassing the performance of the individual models tested. These results underscore the stacked classifier's enhanced predictive capability and transformative potential in educational settings. By accurately identifying students at risk and those likely to achieve academic excellence early, educational institutions can better allocate resources and design targeted interventions. This approach optimizes educational outcomes and supports informed policymaking, fostering environments conducive to student success.
Optimizing Compressed Air Operations for Electrical Energy Savings: A Case Study in Pharmaceutical Packaging Manufacturing Candra, Arief; Yandri, Erkata; Saiful, Rifki; Uhanto, Uhanto; Hilmi, Erik; Hamja, Nasrullah; Ariati, Ratna
Grimsa Journal of Science Engineering and Technology Vol. 2 No. 2 (2024): October 2024
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v2i2.58

Abstract

This study in pharmaceutical packaging manufacturing focuses on improving compressed air efficiency through targeted strategies at both the source and user levels by establishing a baseline to analyze energy consumption patterns. Key measures, including minimizing air leaks, adjusting pressure, and optimizing compressor performance, aim to achieve a 20-50% increase in efficiency, thereby supporting environmental sustainability. The User Point and Source Point approaches are expected to lower Specific Power Consumption (SPC), with data collected from December 2020 to May 2022 providing insights into potential energy savings. Establishing this baseline, based on machine runtime and productivity, offers a solid foundation for evaluation. Results show a 23% reduction in compressor electricity usage and a 7-8% decrease in compressed air consumption. A structured improvement process and strong collaboration between engineering and management are essential for enhancing productivity and achieving sustainable energy efficiency in the industrial sector.
Co-Authors Amaral, Clizardo Ariati, Ratna Aryati, Ratna Bahri, Ridzky Aulia Candra, Arief Fitriani Fitriani Ghazi Mauer Idroes Hamja, Nasrullah Hilmi, Erik Ibrahim, Riki Firmandha Irvanizam, Irvanizam Isandono, Hafit Lala, Andi Lodewijk, Dewi Putriani Yogosara Ludji, Omrie Maria Paristiowati Maulana, Aga Mohamed Yusof, Nur Intan Saidaah Muhammad Yanis Muhammad Yusuf Murdiyansah, Novan Murdiyansyah, Novan Nizamuddin Nizamuddin Nur, Syukri M Nur, Syukri Muhammad Pramono, Kukuh Priyo Rinaldi Idroes Saiful, Rifki Sasmita, Novi Reandy Satrio, Justinus Sidharta, Rendy Sihombing, Very Suhendra, Rivansyah Teuku Rizky Noviandy Timba, Ayub Tirta, Andy Uhanto, Uhanto Uyun, Aep Saepul Viendyasari, Mila Zahriah, Zahriah Zulkarnain Jalil