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All Journal ELKHA : Jurnal Teknik Elektro Jurnal EECCIS JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING International Journal of Community Service Learning JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JE-Unisla Journal of Electrical Engineering and Computer (JEECOM) Jurnal Hurriah: Jurnal Evaluasi Pendidikan dan Penelitian Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Smart Society: Community Service and Empowerment Journal Blend Sains Jurnal Teknik Fuse-teknik Elektro Jurnal Elektro Jurnal Quancom: Jurnal Quantum Komputer Jurnal Informatika dan Teknik Elektro Jurnal Teknik Elektro: Ilmu dan Teknologi Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Yandhika Surya Akbar Gumilang
Universitas Negeri Surabaya, Indonesia
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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Energy Density Prediction of Metal-Organic Frameworks (MOFs) From Synthesis Conditions Using Deep Neural Network (DNN): Hydrogen Storage Application Wahyu Sasongko Putro; Yandhika Surya Akbar Gumilang; Farid Baskoro
JURNAL HURRIAH: Jurnal Evaluasi Pendidikan dan Penelitian Vol. 7 No. 1 (2026): Jurnal Hurriah: Journal of Educational Evaluation and Research
Publisher : Yayasan Pendidikan dan Kemanusiaan Hurriah Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56806/jh.v7i1.414

Abstract

The global transition toward sustainable energy systems necessitates efficient and scalable hydrogen storage technologies. Metal–organic frameworks (MOFs) have emerged as promising candidates for hydrogen storage due to their high surface area, tunable pore structures, and favorable surface chemistry that enhance adsorption performance. However, real-time experimental measurement of hydrogen uptake using physical sensing systems is costly, computationally intensive, and operationally complex. To address these limitations, this study proposes a data-driven soft-sensor framework based on machine learning to predict energy density for hydrogen storage applications from synthesis parameters. High-fidelity secondary data sourced from an open-access Kaggle dataset were utilized, focusing on synthesis descriptors including metal type, oxidation state, temperature, and reaction time. Recognizing the intrinsic influence of transition metals on structural stability and adsorption behavior, a per-metal modeling strategy was implemented to capture material-specific relationships. A Deep Neural Network (DNN) employing a Multi-Layer Perceptron (MLP) architecture trained via backpropagation was developed to model nonlinear interactions between structural variables and energy density. To enhance interpretability, complementary linear regression models were also constructed, yielding explicit predictive equations. Model performance was rigorously evaluated using statistical error metrics, achieving a Mean Squared Error (MSE) of 0.0821 and a Root Mean Squared Error (RMSE) of 0.2852, demonstrating strong predictive capability and generalization across different metallic linkers. The low error values confirm that artificial neural network–based soft sensors provide a reliable, low-latency alternative to physical sensing systems for monitoring hydrogen storage performance. This approach significantly reduces experimental burden, accelerates materials screening, and supports intelligent optimization of hydrogen-based fuel cell technologies, contributing to the advancement of scalable clean energy infrastructure
Internet Of Things (IoT) Based Soil Leveling and Compaction Prototype Muhammad Fahrul Amin; Wahyu Dirgantara; Yandhika Surya Akbar Gemilang
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK Vol. 14 No. 1 (2024): ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/

Abstract

The purpose of this research is to design a prototype of a soil leveling and compaction tool based on the Internet of Things (IoT). Currently, many projects still rely on manual tools for soil leveling and compaction, which can result in reduced effectiveness, lack of safety for workers, and high levels of fatigue. Therefore, this tool is equipped with ESP32-Cam, DC Motor, and BTS 7960 Motor Driver, and will be operated remotely via a smartphone's Android remote control, utilizing IoT technology to enhance efficiency and accuracy in soil leveling and compaction. By three trials result on sandy soil showed a more significant reduction, with an average of 3.6 mm in 4 seconds, compared to rocky soil, which had an average reduction of 3.3 mm in 5.3 seconds over three trials.
Goods Movement Arm Robot Based On Color Image Processing Karitas Darson; Ir. Abd. Rabi; Yandhika Surya Akbar Gumilang
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK Vol. 13 No. 1 (2023): ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v13i1.p44-49

Abstract

The process of sending goods by humans can consider several factors, namely the weight of the goods, the  amount  of  goods,  the  length  of  the  trip  and  the  required  human  labor.  The  heavier  and  more  goods  or  the farther the distance that must be traveled, the more human power is needed to move the goods. In addition, the impossibility of human work for 24 hours is also a factor that affects the efficiency and quality of the operation. From these problems,  we need a robotic arm for moving goods based on image processing. Where in the early stages it starts with collecting color data on objects using a Raspberry Pi camera which will be processed by the Raspberry Pi 3b for data to be sent to Esp32 and will be forwarded to the servo control as a tool that drives the servo motor as the main mover on the robot arm.
Co-Authors Abd Rabi Abd. Rabi’ Abdul Rozaq Abdur Rabi' Aditama Yudha Atmanegara Ahing, Yayan Mutu Aji Setiawan Amin, Muchammad Fahrul Amin, Muchammad Fahrul Amin Aziz Alfayid Benyamin, Sam Binti Afifah Dholifun Nafsi, Muhamad Zidan Dyah Lestari Elta Sonalitha Fahreza, Moch. Aditya Rizky Fahreza, Mochamad Aditya Rizky Farid Baskoro Haitsam Haitsam Haitsam, Haitsam Ir. Abd. Rabi Karitas Darson Karitas Darson Karitas Darson Kartika Sari, Resi Dwi Jayanti Krisdianto Krisdianto Krisdianto Krisdianto Sugiyanto Mahendra, Sri Aji Eka Moch. Aditya Rizky Fahreza Mokh Sholihul Hadi Muhammad Fahrul Amin Nachrowie Nachrowie Nachrowie Nachrowie, Nachrowie Permatasari, Delila Cahya Prasetya, Ananda Yoga Rabi, Abd Rabi, Abd. Resi Dwi Jayanti Kartika Sari Riadi, Muhammad Prakarsa Saeed Abioye Bello Subairi Subairi Subairi Subairi Sumarahinsih, Andrijani Wahyu Dirgantara Wahyu Dirgantara Wahyu Dirgantara Wahyu Sasongko Putro Wilibrodus Wanggur Mboi Yuki Novaliandra Zaeni, Ilham Ari Elbaith Zahroya J.M.F. , Isvine Zidane, Fajar Maulid