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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Simetris ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JURNAL NASIONAL TEKNIK ELEKTRO Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Techne : Jurnal Ilmiah Elektroteknika Jurnal Pendidikan Fisika dan Teknologi Journal of Appropriate Technology for Community Services Journal of Biomedical Science and Bioengineering Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII
Elvira Sukma Wahyuni
Universitas Islam Indonesia
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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Economic Dispatch Unit Pembangkit Termal Memperhitungkan Kekangan Emisi Lingkungan Menggunakan Metode Differential Evolutionary Algorithm Yogi Agus Priatna; Firmansyah Nur Budiman; Elvira Sukma Wahyuni
Retii Prosiding Seminar Nasional ReTII ke-13 2018
Publisher : Institut Teknologi Nasional Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Economic Dispatch (ED) adalah permasalahan untuk menentukan alokasi daya optimum diantara unit-unit pembangkit untuk melayani beban total, sehingga didapat total biaya operasi minimum dengan tetap memperhitungkan kekangan-kekangan sistem. Untuk sistem unit pembangkit termal, penggunaan bahan bakar menjadi salah satu pertimbangan dalam melakukan optimasi ED karena timbulnya emisi gas buang dari bahan bakar tersebut. Diantara emisi gas yang dihasilkan unit termal, CO2 merupakan emisi gas paling besar. Tujuan dari penelitian ini adalah untuk menerapkan optimasi ED pada sistem unit pembangkit termal dengan emisi CO2 sebagai salah satu fungsi pengekangnya. Proses optimasi diselesaikan menggunakan algoritma Differential Evolution Algorithm (DEA) dan sistem yang menjadi objek penelitian adalah sistem IEEE 24 bus dengan 26 unit termal. ED diterapkan selama 24 jam dengan total energi yang disuplai sebesar 54910 MWh. Tanpa memperhitungkan kekangan emisi CO2, didapatkan biaya total pembangkitan sebesar $861714,5. Dengan memperhitungkan emisi CO2, dimana nilai emisi maksimum yang diijinkan adalah 37503,53 ton, didapatkan biaya total pembangkitan sebesar $902895,79. Kuantitas emisi CO2 yang dihasilkan adalah 37473,01 ton. Kenaikan biaya total pembangkitan ini karena pembatasan penggunaan unit-unit berbahan bakar batu bara, yang berbiaya rendah namun menghasilkan emisi CO2 yang tinggi. Konsekuensinya, penggunaan unit-unit berbahan bakar minyak, yang emisi CO2-nya rendah namun berbiaya mahal, dinaikkan. Akibatnya, biaya operasi total naik secara signifikan.
Smoke and Fire Detection Base on Convolutional Neural Network WAHYUNI, ELVIRA SUKMA; HENDRI, MUHAMMAD
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 7, No 3: Published September 2019
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v7i3.455

Abstract

ABSTRAKDeteksi api dan asap adalah langkah pertama sebagai deteksi dini kebakaran. Deteksi dini kebakaran berdasarkan pemrosesan gambar dianggap mampu memberikan hasil yang efektif. Pilihan metode deteksi adalah kunci penting. Metode ekstraksi fitur berdasarkan analisis statistik dan analisis dinamis kadang-kadang memberikan akurasi kurang akurat dalam mendeteksi asap dan api, terutama pada deteksi asap, hal ini disebabkan oleh karakteristik objek asap yang transparan dan bergerak. Dalam penelitian ini, metode Convolutional Neural Network (CNN) diterapkan untuk deteksi asap dan api. Dari penelitian ini, diketahui bahwa CNN memberikan kinerja yang baik dalam deteksi kebakaran dan asap. Akurasi deteksi tertinggi diperoleh dengan menggunakan 144 data pelatihan, 20.000 iterasi dengan dropout.Kata kunci: Deteksi asap, deteksi kebakaran, Jaringan Syaraf Konvolusional ABSTRACTFire and smoke detection is the first step as early detection of fires. Early detection of fire based on image processing is considered capable of providing effective results. The choice of detection method is an important key. Feature extraction methods based on statistical analysis and dynamic analysis sometimes provide less accurate accuracy in detecting smoke and fire, especially on smoke detection, this is due to the characteristics of transparent and moving smoke objects. In this study, the Convolutional Neural Network (CNN) method was applied for smoke and fire detection. From this study, it is known that CNN provides good performance in fire and smoke detection. The highest detection accuracy is obtained by using 144 training data, 20,000 iterations and dropout is true.Keywords: Smoke detection, Fire detection, Convolutional Neural Network
Bahasa Inggris Wahyuni, Elvira Sukma; Alvita Widya Kustiawan Putri; Nisa Agustin Pratiwi Pelu; Firdaus; Idha Arfianti Wiraagni
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 1: March 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n1.1148.2024

Abstract

Wounds result from physical violence that damages the continuity of body tissues and are frequently observed in forensic medicine and medicolegal science. In forensic medicine and medicolegal science, wounds play a significant role in creating a medicolegal examination and report (VeR) for deceased individuals and living victims. However, research findings indicate that the quality of clinical forensic descriptive results in VeR needs to improve in several hospitals in Indonesia. Meanwhile, high-quality VeR results are crucial in determining penalties for perpetrators in court, and poor VeR results can hinder the legal process. The application of information technology in medicine has yielded numerous tools that can assist experts in carrying out their duties. Likewise, clinical forensics, a generally conservative forensic pathology practice, can be enhanced through image-processing techniques and machine learning. Digital technology support for forensic cases has been available previously, such as in forensic photography; however, its application still needs improvement, and further development is required. This study applied a Yolo V4-based machine learning and image processing algorithm to classify and detect types of wounds. This algorithm was chosen for its high speed and accuracy in classification and detection tasks. The research results showed that the learning model's performance, measured in accuracy, precision, recall, and average F1 score, reached 92%. Usability testing showed that the system performed well and could be helpful with minor improvements.
Peningkatan Efisiensi Produksi Pakan dan Keselamatan Kerja di Kelompok Ternak 99 Farm Melalui Implementasi Mesin Pencacah Rumput Hemat Energi Worldailmi, Elanjati; Annisa, Putri Dwi; Wahyuni, Elvira Sukma; Masalik, Hasan; Fauziyah, Nada Putri; Ningtyas, Anggun Galuh Puspita
Journal of Appropriate Technology for Community Services Vol. 6 No. 1 (2025)
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jattec.vol6.iss1.art8

Abstract

The 99 Farm Livestock Group faces challenges in increasing feed production efficiency and occupational safety and health (OHS). These challenges are caused by the use of inefficient and unergonomic grass chopping machines, which pose risks to livestock farmers' safety and increase operational costs. This community service program aims to address these issues by introducing energy-efficient grass chopping machines designed with ergonomic and OHS aspects in mind. This study examines the impact of implementing this technology on livestock farmers' operational efficiency and welfare. The results show a significant increase in the efficiency of the grass chopping process, reduced operational costs, and improved working conditions for livestock farmers. The implementation of this technology is also expected to be an example for other livestock farmers to implement more sustainable and safe practices.
Electric Vehicle Technology Course for Generation Z: Discussion-Based, Project-Based, and Laboratory Activities Imawati, Iftitah; Mubarok, Husein; Wahyuni, Elvira Sukma
Jurnal Pendidikan Fisika dan Teknologi (JPFT) Vol 10 No 1 (2024): January-June
Publisher : Department of Physics Education, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpft.v10i1.6861

Abstract

Generation Z, growing up in an era of constantly evolving technology and information, presents new challenges for the world of education. They are known as a digitally connected generation, quick to adapt to technological advancements, and in search of interactive experiences in learning. One excellent step in responding to these needs is using suitable learning methods. For the new mandatory course, Electric Vehicle Technology with a weight of 3 credits, in the Electrical Engineering Bachelor's Program at Universitas Islam Indonesia, a learning approach that matches the characteristics and preferences of Generation Z was used. In the odd semester of 2023/2024, two classes were opened for Electric Vehicle Technology, class A with 56 students and class B with 50 students. The combination of discussion-based learning methods, project-based learning, and laboratory activities was used in the learning process to meet the needs of Generation Z students in the Electric Vehicle Technology course. The assessment used consisted of summative and formative assessments. The scoring rubric was key in helping to categorize the value of the work done by students. The integration of assessment weights considers the contribution from each learning model. Overall, the application of discussion-based learning models, project-based learning, and laboratory activities together have created a stimulating learning environment and aroused student enthusiasm in the Electric Vehicle Technology course. With combined methods of learning, the course passing rate for each of the course's learning outcomes was higher than 80%. For class A, the overall course passing rate was 88%, while for class B, it was 84%.
Penguatan Pembelajaran STEM melalui Gamifikasi Digital di Sanggar Bimbingan Sentul, Kuala Lumpur, Malaysia Wahyuni, Elvira Sukma; Nur Budiman, Firmansyah; Nurul Huda, Sheila; Ramdhan Yusuf, Muhammad; Mukafasyadiah, Diena
Journal of Appropriate Technology for Community Services Vol. 7 No. 1 (2026)
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jattec.vol7.iss1.art8

Abstract

Program pengabdian masyarakat ini dilaksanakan di Sanggar Bimbingan Sentul (SB Sentul), Kuala Lumpur, yang menampung anak-anak migran Indonesia dengan keterbatasan akses pendidikan formal. Kegiatan bertujuan untuk meningkatkan motivasi dan pemahaman siswa terhadap STEM (Science, Technology, Engineering, and Mathematics) melalui pendekatan gamifikasi digital yang mencakup level, poin, dan challenge dalam bentuk platformer game. Workshop dilaksanakan selama dua hari dengan topik Math & Science berbasis gim edukatif pada hari pertama dan Teknologi & Engineering melalui praktik pembuatan gerbang logika sederhana pada hari kedua. Metode pelaksanaan mencakup penyusunan modul interaktif, penggunaan gim edukatif Little Thinker, praktik langsung, serta evaluasi melalui pre-test dan post-test. Sebanyak 45 siswa sekolah dasar mengikuti kegiatan ini dengan antusias. Hasil menunjukkan adanya peningkatan signifikan pemahaman siswa. Rata-rata nilai pre-test sebesar 64,8 meningkat menjadi 89,16 pada post-test. Siswa terlihat lebih aktif dan berani dalam proses pembelajaran, sementara guru dan relawan memperoleh keterampilan baru dalam pemanfaatan media digital. Program ini berhasil menghadirkan pengalaman belajar yang menyenangkan sekaligus meningkatkan literasi STEM siswa. Selain itu, kegiatan ini memberikan alternatif metode pembelajaran berbasis teknologi yang relevan untuk pendidikan nonformal anak-anak migran. Ke depan, diperlukan pengembangan modul digital yang lebih komprehensif serta pelatihan intensif bagi guru untuk menjamin keberlanjutan program.
Wound Depth Measurement System in Forensic Cases using Image Processing and Machine Learning Wahyuni, Elvira Sukma; Ahnaf, Kern Cesarean; Firdaus, Firdaus; Abdul-Kadir, Nurul Ashikin; Zakaria, Nor Aini; Wiraagni, Idha Arfianti; Kadarmo, Diwangkoro Aji
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1636

Abstract

Accurate evaluation of wound depth is crucial in forensic investigations, as it significantly affects case assessments and outcomes. This study introduces a method for classifying wound depth using a Support Vector Machine (SVM) model and compares its performance with Decision Tree and Logistic Regression models. The classification was based on color features extracted from HSV and LAB color spaces. The da-taset consisted of 76 images categorized into three stages: stage 2 (36 images), stage 3 (12 images), and stage 4 (28 images). Model performance was evaluated using confusion matrices, precision, recall, and F1-score. The SVM model achieved an overall accuracy of 85%, demonstrating higher precision and re-call across all stages compared to the Decision Tree and Logistic Regression models, which achieved 50% and 70%, respectively. The results indicate that the SVM model performed particularly well in distinguish-ing stage 2 wounds, although differentiating between stages 3 and 4 remained challenging. Overall, the proposed system shows potential to enhance the accuracy and efficiency of forensic wound evaluation by providing a rapid and objective classification tool. However, as the system was tested on a limited dataset under controlled conditions, further research should expand the dataset, incorporate additional features, and explore other machine learning algorithms to improve robustness and applicability in real forensic contexts.
Co-Authors Abdul Manan Abdul-Kadir, Nurul Ashikin Ahnaf, Kern Cesarean Almira Budiyanto Alvita Widya Kustiawan Putri Annisa, Putri Dwi Dzata Farahiya Elanjati Worldailmi Farahiya, Dzata Fauziyah, Nada Putri Firdaus Firdaus Firdaus Firmansyah Nur Budiman, Firmansyah Nur Hendra Setiawan Imawati, Iftitah Iqbal, Zulfika Kadarmo, Diwangkoro Aji Masalik, Hasan Mohammad Brado Frasetyo Mubarok, Husein Muhammad Hendri Mukafasyadiah, Diena Ningtyas, Anggun Galuh Puspita Nisa Agustin Pratiwi Pelu Nur Budiman, Firmansyah Nurul Huda, Sheila Ramdhan Yusuf, Muhammad Wiraagni, Idha Arfianti Yogi Agus Priatna Zakaria, Nor Aini Zulfika Iqbal