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Anang Budikarso
Politeknik Elektronika Negeri Surabaya
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RANCANG BANGUN SPREAD SPECTRUM DENGAN METODE SINKRONISASI SERIAL CORRELATOR BERBASIS FPGA Noer Soedjarwanto; Anang Budikarso; Kukuh Setyadjit
Jurnal Teknik Ilmu dan Aplikasi Vol. 3 No. 2 (2022): Jurnal Teknik Ilmu dan Aplikasi
Publisher : Politeknik Negeri Malang

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

Abstract

Makalah ini menitik-beratkan pada pembuatan sistem sinkronisasi pada teknik pentransmisian spread spectrum asinkron. Dalam merealisasikan modul digunakan Field Programmable Gate Array ( FPGA ) Spartan II XC2S100-5 tq 144 yang terintegrasi pada Board XSA 100, implementasinya digunakan board XSA 100 dan software Xilinx ISE 6.1i. Modul terhubung secara wireline dan dirancang seperti terhubung secara wireless dimana pengaruh delay transmisi sangat besar pada proses tersebut. Dalam perancangan dan implementasi modul ini digunakan sistem Direct Sequence Spread Spectrum dan kode acak semu (pseudorandom code) maxlength dengan taping [5,2]. Pada proses transmisi antara pemancar dan penerima ditambahkan delay sebesar 1 periode chip atau sekitar 0.2 microsecond, sehingga menjadi sistem yang asinkron. Proses sinkronisasi pada penerima digunakan Serial Korelator yang terintegrasi dengan Digital Control Oscillator ( DCO ). Rangkaian ini bekerja pada proses akuisisi untuk mendapatkan timing sinyal yang benar pada proses despreading. Hasil pengujian dilakukan dan divisualisasi dengan Logic Analyser.
Pendeteksian Harmonisa Arus Berbasis Feed Forward Neural Network Secara Real Time Endro Wahjono; Dimas Okky Anggriawan; Achmad Luki Satriawan; Aji Akbar Firdaus; Eka Prasetyono; Indhana Sudiharto; Anang Tjahjono; Anang Budikarso
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (869.076 KB) | DOI: 10.17529/jre.v16i1.15093

Abstract

The development of power electronics converters has been widespread in the industrial, commercial, and home applications. The device is considered to produce harmonics in non-linear loads. Harmonics cause a decrease in power quality in the electric power system. To prevent a decrease in power quality caused by harmonics in the power system, the detection of harmonics has an important role. Therefore, this paper proposed feed forward neural network (FFNN) for harmonic detection. The design of harmonic detection device is designed with a feed forward neural network method that it has two stages of information processing, namely the training stage and the testing stage. FFNN has input harmonics and THDi as output. To detect harmonics, frst training is conducted to recognize waveform patterns and calculate the fast fourier transform (FFT) process offline. Prototype using the AMC1100DUB current sensor, microcontroller and display. To validate the proposed algorithm, compared by standard measurement tool and FFT. The results show the proposed algorithm has good performance with the average percentage error compared by standard measurement tool and FFT of 5.33 %.
Identification of Power Quality Disturbances Based on Fast Fourier Transform and Artificial Neural Network Dimas Okky Anggriawan; Endro Wahjono; Indhana Sudiharto; Anang Budikarso
Jurnal Rekayasa Elektrika Vol 19, No 1 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (911.405 KB) | DOI: 10.17529/jre.v19i1.27120

Abstract

This paper presents the proposed algorithms for the identification of Short Duration RMS Variations and Long Duration RMS Variations combined with harmonic. The proposed algorithms are Fast Fourier Transform (FFT) and Artificial Neural Network (ANN). The Algorithms identify nine types of Power Quality (PQ) disturbances such as normal signal, voltage sag, voltage swell, under voltage, over voltage, voltage sag combined harmonic, voltage swell combined harmonic, undervoltage combined harmonic, and over voltage combined harmonic. FFT is used to obtain the frequency spectrum of each PQ disturbance with frequency sampling of 1000 Hz, data length of 200. Output FFT is used to input data for ANN. Output ANN is a type of nine PQ disturbances. The result shows that proposed algorithms (FFT combined ANN) are effective for identification, which ANN with 20 neurons in the hidden layer has an accuracy of approximately 99.95 %
Identification of Power Quality Disturbances Based on Fast Fourier Transform and Artificial Neural Network Dimas Okky Anggriawan; Endro Wahjono; Indhana Sudiharto; Anang Budikarso
Jurnal Rekayasa Elektrika Vol 19, No 1 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i1.27120

Abstract

This paper presents the proposed algorithms for the identification of Short Duration RMS Variations and Long Duration RMS Variations combined with harmonic. The proposed algorithms are Fast Fourier Transform (FFT) and Artificial Neural Network (ANN). The Algorithms identify nine types of Power Quality (PQ) disturbances such as normal signal, voltage sag, voltage swell, under voltage, over voltage, voltage sag combined harmonic, voltage swell combined harmonic, undervoltage combined harmonic, and over voltage combined harmonic. FFT is used to obtain the frequency spectrum of each PQ disturbance with frequency sampling of 1000 Hz, data length of 200. Output FFT is used to input data for ANN. Output ANN is a type of nine PQ disturbances. The result shows that proposed algorithms (FFT combined ANN) are effective for identification, which ANN with 20 neurons in the hidden layer has an accuracy of approximately 99.95 %
Pendeteksian Harmonisa Arus Berbasis Feed Forward Neural Network Secara Real Time Endro Wahjono; Dimas Okky Anggriawan; Achmad Luki Satriawan; Aji Akbar Firdaus; Eka Prasetyono; Indhana Sudiharto; Anang Tjahjono; Anang Budikarso
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i1.15093

Abstract

The development of power electronics converters has been widespread in the industrial, commercial, and home applications. The device is considered to produce harmonics in non-linear loads. Harmonics cause a decrease in power quality in the electric power system. To prevent a decrease in power quality caused by harmonics in the power system, the detection of harmonics has an important role. Therefore, this paper proposed feed forward neural network (FFNN) for harmonic detection. The design of harmonic detection device is designed with a feed forward neural network method that it has two stages of information processing, namely the training stage and the testing stage. FFNN has input harmonics and THDi as output. To detect harmonics, frst training is conducted to recognize waveform patterns and calculate the fast fourier transform (FFT) process offline. Prototype using the AMC1100DUB current sensor, microcontroller and display. To validate the proposed algorithm, compared by standard measurement tool and FFT. The results show the proposed algorithm has good performance with the average percentage error compared by standard measurement tool and FFT of 5.33 %.
IMPLEMENTASI SISTEM MONITORING PENAMPUNG AIR BERBASIS TELEGRAM DAN NODEMCU DI SEKTOR INDUSTRI UMKM Mohamad Ridwan; Ari Wijayanti; Djoko Santoso; Rahardhita Widyatra Sudibyo; Arifin Arifin; Hari Wahjuningrat Suparno; Nur Adi Siswandari; Anang Budikarso; Okkie Puspitorini; Yoedy Moegiharto; Rini Satiti; Moga Kurniajaya; Karimatun Nisa; Paramita Eka Wahyu Lestari; Via Alviana; Achmad Hildan Syahputra; Rahmadani Najwa Alfriza; Muhammad Luqmanul Chakim
BUDIMAS : JURNAL PENGABDIAN MASYARAKAT Vol 5, No 1 (2023): BUDIMAS : VOL. 5, NO.1, 2023
Publisher : LPPM ITB AAS Indonesia Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/budimas.v5i1.6583

Abstract

Pada zaman serba digital seperti saat ini, manusia mulai mengembangkan berbagai teknologi untuk memudahkan pekerjaan di segala bidang. Salah satu bidang yang membutuhkan inovasi akan perkembangan teknologi adalah bidang industri. Sektor industri yang masih belum menerapkan kemajuan teknologi terdapat pada industri UMKM (Usaha Mikro, Kecil, dan Menengah). Penerapan teknologi pada UMKM dapat digunakan untuk melakukan monitoring proses produksi agar dapat berjalan dengan baik. Pada pengabdian masyarakat ini akan dibuat alat untuk Implementasi Sistem Kontrol Penampung Air Berbasis Telegram dan NodeMCU. Alat tersebut menggunakan mikrokontroler NodeMCU ESP8266 yang di program menggunakan Bahasa C pada Arduino IDE dengan monitoring melalui Platform Thinger.io dan aplikasi Telegram. Sensor suhu, sensor ultrasonic, dan sensor turbidity akan mendeteksi air yang berasal dari sumber air secara real time dan akan mengirimkan data ke Thinger.io Cloud, lalu data akan dikirimkan ke Telegram. Terdapat 3 buah parameter yang akan dimonitoring yakni level air, suhu dan kekeruhan. Output dari alat tersebut juga akan ditampilkan pada LCD (Liquid Crystal Display) yang terpasang pada bak penampungan air.
Co-Authors Achmad Hildan Syahputra Achmad Luki Satriawan Akuwan Saleh Anang Tjahjono, Anang Ari Wijayanti Arifin Arifin Aripin Budi Aswoyo Dimas Okky Anggriawan Djoko Santoso Eka Prasetyono, Eka Endro Wahjono Firdaus, Aji Akbar Hari Wahjuningrat Suparno Indhana Sudiharto Karimatun Nisa Kukuh Setyadjit M. Milchan Moga Kurniajaya Mohamad Ridwan Muhammad Luqmanul Chakim Noer Soedjarwanto Nur Adi Siswandari Okkie Puspitorini Paramita Eka Wahyu Lestari Rahardhita Widyatra Sudibyo Rahmadani Najwa Alfriza Rini Satiti Via Alviana Yoedy Moegiharto