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All Journal IAES International Journal of Robotics and Automation (IJRA) Indonesian Journal of Electronics and Instrumentation Systems Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Elektronika dan Telekomunikasi CYCLOTRON Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Multitek Indonesia : Jurnal Ilmiah Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri Jurnal Nasional Teknik Elektro dan Teknologi Informasi POLIGRID SinarFe7
Novendra Setyawan
Universitas Muhammadiyah Malang
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering

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MPPT Menggunakan Algoritme Particle Swarm Optimization dan Artificial Bee Colony Ermanu Azizul Hakim; Tamadar Al Ghufran; Machmud Effendy; Novendra Setyawan
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1172.478 KB) | DOI: 10.22146/jnteti.v9i2.81

Abstract

Solar power plant is a renewable electricity generator that utilizes heat from sunlight. However, because the intensity of light received by the solar cell and the temperature in the solar cell is always changing, the power generated is not optimal. To optimize the output power of the solar cell, a Maxi-mum Power Point Tracking (MPPT) system is needed. Solar cells can be optimized by looking for MPPT and also by using a DC-DC converter. In this study, boost converter is optimized using Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. The results show that the highest efficiency obtained from boost converter is 78.25%,using duty cycle of 20%. For the overall system testing conducted at 09:00 WIB until 11:10 WIB, the average power obtained without using MPPT is 12.55 W, the average power of MPPT system using boost converter with PSO algorithm is 16.79 W, and average power of MPPT system using boost converter with ABC algorithm is 14.52 W. From the results, it was concluded that the output power of MPPT system using boost converter with PSO algorithm is more optimal than the MPPT system using boost converter with ABC algorithm.
Hybrid Frequency and Period Based for Angular Speed Measurement of DC Motor Using Kalman Filter Novendra Setyawan; Basri Noor Cahyadi; Ermanu Azizul Hakim; Mas Nurul Achmadiah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 2, May 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i2.1420

Abstract

The Incremental Rotary Encoder have been widely used to measure the angular speed of electrical drive such as Permanent Magnet Direct Current Motor (PMDCM). Nevertheless, speed measurement of PMDCM from the encoder signals can be subject to errors in some special condition such as in low resolution encoder. There are two main methods to measure the angular speed of PMDCM through encoder signal such as frequency-based and period-based wich has its own properties. Hence in this reseach aimed to improve the angular speed measurement with hybridization of frequency and period-based measurement. The Hybrid method is defined as paralleling the period and frequency then estimated the angular speed using sensor fusion with Kalman Filter. The experiment is doing by comparing of all method to get the best way in measuring. From the experimental showed that the Kalman filter parameter was fine tuned that resulting the sensor fusion or the mixed measurement between the frequency-based and the period based measure the angular speed accurately.
Klasterisasi Kualitas Kesehatan Gardu Distribusi Pada PT. PLN Unit Layanan Pelanggan Kota Malang Dengan Kohonen Neural Network (KNN) Syachbani Amin Hidayat; Nur Alif Mardiyah; Novendra Setyawan
PoliGrid Vol 3, No 1 (2022): Juni
Publisher : Politeknik Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46964/poligrid.v3i1.1487

Abstract

Abstrak- Gardu distribusi adalah suatu bangunan gardu listrik yang setiap komponennya harus bekerja secara kontinu dalam suatu sistem penyaluran energi listrik. Manajemen perbaikan atau pemeliharaan ribuan gardu distribusi harus terus dilakukan untuk memperpanjang umur operasi, mengantisipasi kerusakan yang tidak diinginkan, dan untuk memastikan gardu distribusi dapat terus bekerja dalam kondisi yang prima. Pada penelitian ini klasterisasi gardu distribusi dapat memaksimalkan manajemen gardu distribusi. Metode klasterisasi yang digunakan adalah Kohonen Neural Network (KNN) dengan hasil pengukuran Load Reading and Profilling sebagai variabel klasterisasinya (Persentase Pembebanan Arus, Keseimbangan Arus antar fasa, Persentase Arus Netral, dan Persentase Pembebanan Trafo). Hasil terbaik yang paling mendekati hasil klasterisasi secara konvensional adalah hasil pengujian kedua dengan besar kecocokan data 50,94%, dimana ada 72 unit gardu dalam klaster “Baik”, 12 unit gardu dalam klaster “Cukup”, 116 unit gardu dalam klaster “Kurang”, dan 65 unit gardu dalam klaster “Buruk”.
DETEKSI DAN PREDIKSI TRAJEKTORI OBJEK BERGERAK DENGAN OMNI-VISION MENGGUNAKAN PSO-NN DAN INTERPOLASI POLYNOMIAL Novendra Setyawan; Nur Alif Mardiyah; Khusnul Hidayat
MULTITEK INDONESIA Vol 13, No 1 (2019): Juli
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1157.755 KB) | DOI: 10.24269/mtkind.v13i1.1691

Abstract

In the Indonesian wheeled soccer robot competition in one team consists of three robots, where one robot is a goalkeeper. In the competition the movement of robots and balls is very dynamic. So that a method is needed to predict the movement of the ball so that the goalkeeper can anticipate the movement of the ball. In this research the ball detected by digital image processing and Particle Swarm Optimization-Neural Network (PSO-NN) is used as a calibration model for object distance through omnidirectional cameras. The interpolation approach of the polynomial curve is used to obtain estimates of the model from two-dimensional data from detected objects. The results showed that the distance conversion in object detection with the PSO-NN model obtained 0.13% in percentage of average squared error (PMSE) measurement and  20% in an average prediction error.
SIGNATURE PSO: MODIFIED PARTICLE SWARM OPTIMIZATION DENGAN FUZZY SIGNATURE DAN IMPLEMENTASI PADA OPTIMALISASI KENDALI LQR Novendra Setyawan; Ermanu Azizul Hakim; Zulfatman Zulfatman
MULTITEK INDONESIA Vol 13, No 2 (2019): Desember
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1094.723 KB) | DOI: 10.24269/mtkind.v13i2.2227

Abstract

Particle Swarm Optimization (PSO) is an optimization that is simple and reliable to complete optimization. In this method, the distribution of particles through global search and local search is the key obtained through searching with PSO through the inertia parameter. This paper describes the method of changing the weights on PSO using fuzzy signatures. In this paper, the method used to solve the problem of optimizing the LQR control parameters on the stabilization of a double inverted pendulum. Performance evaluation is done by another weight change algorithm. Integral Time Absolute Error (ITAE) 7% compared to other algorithms. PSO signatures have resilience and are optimal in fulfilling these interests.
Convolutional Neural Network (CNN) sebagai Metode Pendeteksi Penderita covid-19 pada x-ray Paru-Paru Manusia Mas Nurul Achmadiah; Julianto Muchtadirul Hasan; Novendra Setyawan
CYCLOTRON Vol 5, No 2 (2022): CYCLOTRON
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (721.624 KB) | DOI: 10.30651/cl.v5i2.12549

Abstract

Pandemi COVID-19 adalah pandemi dengan penyebaran yang cepat hingga ke seluruh dunia. Dampak dari pandemi COVID-19 menyebabkan penurunan hampirdisemua sektor terutama di sektor kesehatan. Sejauh ini, deteksi pasien terpapar covid atau tidak berdasar pada PCR (polymerase chain reaction) dan swab. Hal ini dinilai kurang efektif dikarenakan penderita COVID-19 makin bertambah dan berbanding terbalik dengan tenaga medis masih terbatas. Pengecekan dengan metode tersebut membutuhkan waktu lebih serta diagnosis yang akurat. Pada penelitian ini penulis mengembangakan metode deep learning Convolutional NeuralNetworks (CNN) untuk suatu sistem pendeteksian COVID-19. Dengan memanfaatkan algoritma pembelajaran Convolutional Neural Networks (CNN) sistem dapat mendeteksi paru-paru berdasarkan gambar X-Ray paru-paru. Hasil klasifikasi yang didapatkan dengan menggunakan CNN  memiliki accuarcysebesar 98%.
Optimasi Biaya Pembangkitan Pada Sistem Standar IEEE 30 Bus Menggunakan Adaptive Particle Swarm Optimization Ali Mukti; Ermanu Azizul; Novendra Setyawan
SinarFe7 Vol. 2 No. 1 (2019): Sinarfe7-2 2019
Publisher : FORTEI Regional VII Jawa Timur

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

Abstract

Dalam sistem pengembangan tenaga listrik, pemakaian bahan bakar adalah hal yang perlu diperhatikan, karena sekitar 60% dari total operasi adalah bahan bakar. Untuk dapat mengatur operasi pembangkit, sistem penjadwalan yang tepat dan akurat sangat diperlukan, yaitu dengan mengatur setiap unit pembangkit untuk beroperasi secara optimal dan ekonomis serta rugi-rugi dari transmisi dapat direduksi yang tentunya muncul dalam sistem pembangkit listrik. Analisis untuk menyelesaikan biaya pembangkitan biasa disebut Economic Dispatch (ED). Hal ini bertujuan untuk menekan biaya operasional pembangkitan dan juga mengurangi rugi-rugi daya. Penelitian ini menggunakan tiga metode, yaitu metode konvensional Newton Rapshon (N-R), metode Particle Swarm Optimization (PSO), dan metode Adaptive Particle Swarm Optimization (APSO). Hasil simulasi menunjukkan bahwa metode APSO dapat mereduksi daya sebesar 9.86 % dari metode konvensional N-R dan 2.65 % dari metode PSO, sedangkan hasil biaya pembangkitan didapatkan sebesar 8.04 % dari metode konvensional N-R dan 0.01 % pada metode PSO. Dari hasil perhitungan, APSO lebih unngul untuk optimalisasi daya daripada metode konvensional N-R dan metode PSO. Sedangkan untuk opimasi biaya pembangkitan terbaik didapatkan dengan metode PSO dan APSO.
PID Trajectory Tracking Control 4 Omni-Wheel Robot Ghufron Wahyu Kurniawan; Novendra Setyawan; Ermanu Azizul Hakim
SinarFe7 Vol. 2 No. 1 (2019): Sinarfe7-2 2019
Publisher : FORTEI Regional VII Jawa Timur

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

Abstract

Penelitian ini membahas tentang trajectory tracking pada 4 omniwheel robot menggunakan kontrol PID (Proporsional, Integral, Derivative), penggunaan kontrol PID ini dimaksutkan untuk meminimalisir error pergerakan robot, sehingga saat terjadi error pergerakan diharapkan robot dapat kembali ke lintasannya.Cara kerja dari sistem ini didapatkan dengan cara membandingkan hasil dari tracking trajectory terhadap trajectory planing sehingga didapatkan nilai beda (error) yang digunakan sebagai massukan dari kontrol PID, keluaran dari PID ini berupa sinyal kontrol yang diubah kedalam besaran nilai PWM (pulse widh modulation) dan digunakan untuk mengendalikan kecepatan dari masing motor yang terhubung ke roda omnidrive. Hasil pengujian menunjukkan nilai rata rata error sebesar 6,48% pada tracking target nilai X=-185 cm dan target Y =260 cm, dan 6.83% pada tracking target nilai X=185 cm dan target Y =260, sedangkan pada gambar 10 dan 11 menunjukkan grafik dimana garis warna biru(path/trajectory planning) berhimpit dengan garis bewarna biru(path/trajectory tracking) hal tersebut menunjukkan bahwa pola gerak robot dapat mengikuti pola planning yang telah ditentukan dengan baik.
Buck-boost Converter using GA-based MPPT for Solar Energy Optimization Syafaah, Lailis; Faruq, Amrul; Noor Cahyadi, Basri; Hidayat, Khusnul; Setyawan, Novendra; Lestandy, Merinda; Zulfatman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 3, August 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i3.1658

Abstract

Energy optimization in the Solar Power Plant system needs to have more attention. Indonesia is a tropical country that has two seasons, where the weather and cloud movements are frequently unpredictable, especially in the southern region of Java Island. To overcome this problem, an inverter equipped with maximum power point tracking (MPPT) was used. However, the current MPPT switching system was still not optimal with an efficiency of around 90%. In this study, the installation of MPPT was carried out in order to optimize the power in solar photovoltaic (PV) system due to the fluctuations of solar irradiation at PT. Jatinom Indah Agri, Blitar City. The maximum power generated by solar photovoltaic could be achieved by using the combination of DC - DC converter and artificial intelligence. In this study, the modeling of solar PV system was made using MATLAB software, where the design of the solar PV system consisted of a PV module with capacity 240W, DC to DC converter, battery and MPPT. Genetic Algorithm (GA)-based MPPT had been tested and compared to Particle Swarm Optimization (PSO)-based MPPT and conventional MPPT, where the GA-based MPPT worked well in finding the maximum power point in the solar photovoltaic system. It was found that GA-based MPPT produced a maximum power point close to PV power with an efficiency of 92%, while the effciciency of PSO-based MPPT and conventional MPPT were 85% and 79% respectively. In selecting the method for designing MPPT, a method with a wide range of sample data is required. This is due to the fluctuation of solar irradiance received by the solar PV.
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.
Co-Authors Ali Mukti Ali Mukti Amrul Faruq Amrul Faruq Amrul Faruq Anggar Kusuma Basri Noor Cahyadi Bella Retno Chatarina Umbul Wahyuni Diding Suhardi Dwi Nur Fajar Erdiaz Rakha Praditya Ermanu Azizul Ermanu Azizul Ermanu Azizul Hakim Ermanu Azizul Hakim Fadhila Milenasari Fajar, Dwi Nur Fitri Yakub Ghufron Wahyu Kurniawan Ghufron Wahyu Kurniawan Haris Rahmana Putra Ilyas Hamzah Alkatiri Indah Sulistiyowati Julianto Muchtadirul Hasan Khusnul Hidayat Komarudin Achmad Leonardo Kamajaya M. Irfan Machmud Effendy Mardiyah, Nur Alif Mardiyah, Nuralif Mas Nurul Achmadiah Mas Nurul Achmadiah Merinda Lestandy Mohammad Chasrun Hasani Muhammad Faris Pradana Muhammad Ikhwanul Khair Muhammad Irfan Muhammad Nasar Nasihol Fattah Noor Cahyadi, Basri Norazizah Nur Alif Mardiyah Nur Kasan, Nur Nuralif Mardiyah Nuralif Mardiyah Nurhadi Nurhadi Nurhadi, Nurhadi Nurul Achmadiah, Mas Rahayu Pandunengsih Retno, Bella Risdhayanti, Anindya Dwi Syachbani Amin Hidayat Syafaah, Lailis Syafaah, Lailis Tamadar Al Ghufran Yudawan Hidayat Zulfatman Zulfatman