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Hybrid Fuzzy-PID Design Based on Flower Pollination Algorithm for Frequency Control of Micro-Hydro Power Plant Hakim, Ermanu Azizul; Norazizah; Zulfatman; Setyawan, Novendra
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1755

Abstract

Micro-Hydro Power (MHP) Plant System is the renewable energy resource that utilizes water potential energy. In MHP, the energy flows depend on the rotation speed of the generator which cause instability and nonlinearity in the frequency of electrical power. It is also supported by the fluctuation on the electricity load. Therefore, this study used Fuzzy Logic Controller combined with FPA-tuned PID to control the power frequency of the load. This test consisted of 4 stages, namely testing the system without a controller, testing the system using PID, testing the MHP system with a PID controller tuned to the Flower Pollination Algorithm, and testing the system using a Fuzzy PID tuned by the Flower Pollination Algorithm. Based on these tests, the Micro-Hydro Power Plant system response using a Fuzzy PID-tuned FPA controller performed best, especially in accelerating the time to a steady state, reducing overshoot and undershoot with the fastest rise time. As for the output signal from the controller used in the MHP, optimizing the Flower Pollination Algorithm for the Kp, Ki, and Kd parameters is effective and smooth in improving all elements in the Micro-Hydro Power Plant frequency stabilization. Meanwhile, the role of the fuzzy logic controller (FLC) is not very significant, and there is relatively a lot of noise in the output signal of the Fuzzy PID controller itself in terms of stabilizing the load frequency on the Micro-Hydro Power Plant.
Ensembled Machine Learning Methods and Feature Extraction Approaches for Suicide-Related Social Media Merinda Lestandy; Abdurrahim Abdurrahim; Amrul Faruq; M. Irfan; Novendra Setyawan
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.70016

Abstract

Suicide is a pressing public health concern that affects both young people and adults. The widespread use of mobile devices and social networking has facilitated the gathering of data, allowing academics to assess patterns, concepts, emotions, and opinions expressed on these platforms. This study is to detect suicidal inclinations using Reddit online dataset. It allows for the identification of people who express thoughts of suicide by analyzing their postings. The method addresses and evaluates different machine learning classification models, namely linear SVC, random forest, and ensemble learning, along with feature extraction approaches such as TF-IDF, Bag of Words, and VADER.   This study utilised a voting classifier in our ensemble model, where the projected class output is selected by the class with the highest probability. This approach, typically known as a "voting classifier," employs voting to forecast results. The results collected suggest that employing ensemble learning with the TF-IDF 2-grams approach yields the highest F1-score, specifically 0.9315. The efficacy of TF-IDF 2-grams can be determined to their capacity to capture a greater amount of contextual information and maintain the order of words.
Deteksi Kepadatan Penumpang di Stasiun Kereta Api Berbasis ViT-Base pada Jetson Orin Nano Nurul Achmadiah, Mas; Setyawan, Novendra; Risdhayanti, Anindya Dwi
Jurnal Elektronika dan Otomasi Industri Vol. 12 No. 1 (2025): Vol 12 No 1 (Mei 2025): Jurnal Elkolind Vol 12 No 1 (Mei 2025)
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v12i1.7495

Abstract

Pemantauan kepadatan penumpang di stasiun kereta api secara akurat dan real-time merupakan aspek krusial dalam mendukung peningkatan kenyamanan, efisiensi, dan keselamatan pada sistem transportasi publik. Penelitian ini mengusulkan implementasi arsitektur Vision Transformer (ViT-Base) yang telah melalui tahap pelatihan awal (pre-trained) pada dataset ImageNet-21K, untuk melakukan deteksi dan estimasi kepadatan penumpang berbasis visual. Model tersebut dioptimalkan agar dapat dijalankan secara efisien pada perangkat komputasi edge Jetson Orin Nano, sehingga memungkinkan pemrosesan data secara lokal dengan konsumsi sumber daya yang rendah. Evaluasi kinerja dilakukan berdasarkan empat parameter utama, yakni tingkat akurasi, latensi, konsumsi energi, dan efisiensi komputasi. Hasil eksperimen menunjukkan bahwa ViT-Base mampu mencapai akurasi deteksi sebesar 91,17%, dengan latensi rata-rata sebesar 46,59 ms, konsumsi energi 0,1332 joule, dan efisiensi komputasi sebesar 0,171 %/msW. Temuan ini mengindikasikan bahwa ViT-Base merupakan solusi yang menjanjikan untuk sistem pemantauan kepadatan penumpang berbasis edge computing, khususnya dalam konteks penerapan pada lingkungan transportasi publik yang menuntut efisiensi dan kecepatan tinggi.