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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 Energy : Jurnal Ilmiah Ilmu-Ilmu Teknik 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
Yandhika Surya Akbar Gumilang
Universitas Merdeka Malang
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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Deteksi penyakit padi menggunakan YOLO Krisdianto, Krisdianto; Elta Sonalitha; Yandhika Surya Akbar Gumilang
Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Vol. 2 No. 3 (2024): September: Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/uranus.v2i3.259

Abstract

. Padi (Oryza sativa) merupakan salah satu tanaman pangan utama di dunia, menempati urutan ketiga setelah jagung dan gandum. Di Asia Tenggara, khususnya Indonesia, sekitar 80% penduduknya menjadikan nasi sebagai makanan pokok. Namun, setiap tahunnya petani mengalami kegagalan panen hingga 37% akibat serangan hama dan penyakit, menurut International Rice Research Institute (IRRI). Penelitian ini bertujuan untuk membantu petani mengatasi penyakit pada tanaman padi dengan mengembangkan sistem klasifikasi otomatis menggunakan algoritma YOLO (You Only Look Once). Penelitian ini mengklasifikasikan empat jenis kondisi daun padi: Bacterial leaf blight, leaf smut, brown spot, dan daun padi sehat. Dataset yang digunakan berjumlah 661 gambar, dibagi menjadi 70% untuk data pelatihan, 10% untuk data validasi, dan 20% untuk data pengujian. Hasil penelitian menunjukkan bahwa akurasi terbaik pada pelatihan dicapai pada epoch ke-300 dengan akurasi sebesar 77%. Pengujian menggunakan confusion matrix juga menunjukkan akurasi rata-rata sebesar 77%. Algoritma YOLO terbukti efektif dalam mengklasifikasikan penyakit pada daun padi, memberikan solusi yang akurat dan efisien bagi petani dalam mengelola tanaman mereka.
Energy Density Prediction of Metal-Organic Frameworks (MOFs) From Synthesis Conditions Using Deep Neural Network (DNN): Hydrogen Storage Application Putro, Wahyu Sasongko; Gumilang, Yandhika Surya Akbar; Baskoro, Farid
JURNAL HURRIAH: Jurnal Evaluasi Pendidikan dan Penelitian Vol. 7 No. 1 (2026): Jurnal Hurriah: Journal of Educational Evaluation and Research (In Progres)
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
From Line to Logic: STEM Learning Based on Line Follower Robot Program for Vocational Students' Logical Thinking Development Gumilang, Yandhika Surya Akbar; Subairi, Subairi; Rabi', Abdur; Bello, Saeed Abioye; Putro, Wahyu Sasongko; Afifah, Binti
Smart Society Vol. 6 No. 1 (2026): Smart Society
Publisher : FOUNDAE (Foundation of Advanced Education)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/smartsociety.v6i1.1036

Abstract

In the current digital era, logical thinking skills and technological understanding are increasingly essential for vocational high school (SMK) students preparing to enter a technology-driven workforce. This community service program aimed to strengthen students’ logical thinking skills through STEM-based learning activities focused on the configuration and programming of line follower robots. The program was conducted over one month and comprised three stages: focus group discussion with school partners, development of instructional materials, and training sessions. The training involved 17 students. Participants were introduced to fundamental concepts of line follower robots, including basic logic, sensors, and programming principles, and then applied this knowledge through practical tasks. Evaluation results showed that 76% of students expressed increased interest in further learning robotics, while all participants (100%) successfully completed the assigned task of programming the robot to navigate from the starting point to the finish line. These findings indicate that robotics-based learning effectively supports the program’s objective of enhancing logical thinking while simultaneously increasing students’ engagement with STEM concepts. By integrating theoretical explanations with direct practice, the line follower robot served as an accessible and meaningful medium for translating abstract logical reasoning into concrete technological applications. The main contribution of this community service activity lies in offering an applied STEM learning model for vocational high schools, particularly in contexts with limited prior exposure to robotics. This program provides a practical reference for integrating educational robotics into SMK learning environments to strengthen logical reasoning, technical competence, and students’ motivation to pursue STEM-related fields.
Co-Authors Abd Rabi Abd. Rabi’ Abdul Rozaq Aditama Yudha Atmanegara Afifah, Binti Ahing, Yayan Mutu Aji Setiawan Amin, Muchammad Fahrul Amin, Muchammad Fahrul Amin Aziz Alfayid Bello, Saeed Abioye Benyamin, Sam Dholifun Nafsi, Muhamad Zidan Dyah Lestari Elta Sonalitha Fahreza, Moch. Aditya Rizky Fahreza, Mochamad Aditya Rizky Farid Baskoro Haitsam Haitsam Haitsam, Haitsam 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 Nachrowie Nachrowie Nachrowie Nachrowie, Nachrowie Permatasari, Delila Cahya Prasetya, Ananda Yoga Putro, Wahyu Sasongko Rabi', Abdur Rabi, Abd Rabi, Abd. Resi Dwi Jayanti Kartika Sari Riadi, Muhammad Prakarsa Subairi Subairi Sumarahinsih, Andrijani Wahyu Dirgantara Wahyu Dirgantara Wilibrodus Wanggur Mboi Yuki Novaliandra Zaeni, Ilham Ari Elbaith Zahroya J.M.F. , Isvine Zidane, Fajar Maulid