Article Per Year (5 Year)
p-Index From 2020 - 2025
6.638
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Majalah Ilmiah Teknologi Elektro Jurnal INKOM Jurnal Simetris Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Teknologi JURNAL ELEKTRO International Journal of Advances in Intelligent Informatics Majalah Ilmiah MOMENTUM ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIV : International Journal on Informatics Visualization Jurnal Elementer (Elektro dan Mesin Terapan) JMM (Jurnal Masyarakat Mandiri) KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) MIND (Multimedia Artificial Intelligent Networking Database) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Journal of Electronics, Electromedical Engineering, and Medical Informatics JTIM : Jurnal Teknologi Informasi dan Multimedia JMECS (Journal of Measurements, Electronics, Communications, and Systems) Indonesian Journal of electronics, electromedical engineering, and medical informatics Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Aiti: Jurnal Teknologi Informasi Prosiding Konferensi Nasional PKM-CSR INTECH (Informatika dan Teknologi) Jurnal Nasional Teknik Elektro dan Teknologi Informasi eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Achmad Rizal
Telkom University
Humanities 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 Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Nursing Public Health Social Sciences Other

Published : 99 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 99 Documents
Search

 2   3   4   5   6 
RANCANG BANGUN SISTEM PENGERING GAPLEK TIPE HIBRIDA ANTARA EFEK RUMAH KACA (ERK) DAN TUNGKU BIOMASSA Brahmantya Aji Pramudita; Bandiyah Sri Aprillia; Achmad Rizal
Jurnal Elektro dan Mesin Terapan Vol. 6 No. 2 (2020): Jurnal Elektro dan Mesin Terapan (ELEMENTER)
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (338.773 KB) | DOI: 10.35143/elementer.v6i2.4423

Abstract

The greenhouse system works by utilizing solar thermal energy. It has a deficiency because it depends on weather to get maximum solar thermal energy. This deficiency can interfere with the drying process of plantation products, especially gaplek. Therefore, biomass furnace is added to overcome the dependence of solar thermal energy. The result of the proposed hybrid dryer system can control the temperature below 60℃. Moreover, the humidity can reach a minimum point of 60% while the temperature is 57℃. Those results show the system can give nomerous dry air to the dryer room because the greenhouse system is equipped with an exhaust fan. Thus, gaplek can appropriately dry according to the desired grade. Then, the cost of operating the proposed hybrid drying system is inexpensive, which is Rp 158.194,65 per year.
Pemanfaatan marketplace tokopedia untuk pengembangan usaha dalam meningkatkan perekonomian di tengah dampak pandemi covid-19 Brahmantya Aji Pramudita; Muhammad Hablul Barri; Wahmisari Priharti; Achmad Rizal; Novi Prihatiningrum; Iswahyudi Hidayat
Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Vol 5, No 1 (2022): Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS)
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jipemas.v5i1.11694

Abstract

Perbandingan Skema Dekomposisi Paket Wavelet untuk Pengenalan Sinyal EKG Achmad Rizal
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 4 No 2: Mei 2015
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (531.854 KB)

Abstract

One indicator of a person's health is signal pattern of electrocardiogram (ECG). ECG signals are generated by the heart's electrical activity. ECG signal pattern is recognized by physicians to determine a patient's heart health. Some of the techniques were developed by researchers to automatically recognize the ECG signal. One of the most popular techniques is wavelet transform. In this study, two wavelet packet decomposition schemes for ECG signal recognition are compared to find the best one. The first scheme generates 32 features while the second scheme generates 15 features. Accuracy testing shows that the first scheme produce the best average accuracy of 94.67%, better than the second scheme. Using features selection on the first scheme, four dominant features that produce higher accuracy than using 32 features are obtained. These results indicate that the first scheme is better than the second scheme for ECG signal recognition using wavelet packet decomposition.
Simulasi Deteksi Tonsilitis Mengunakan Pengolahan Citra Digital Berdasarkan Warna dan Luasan pada Tonsil Sang Made Lanang Prasetya; Achmad Rizal; I Nyoman Apraz Ramatryana
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 4 No 1: Februari 2015
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (181.138 KB)

Abstract

Tonsillitis or known as tonsils is a medical condition characterized by inflammation of the tonsils, causing sore throat, difficulty swallowing, fever, and in certain cases can lead to heart attack or pneumonia. Doctors diagnose tonsillitis in a visual way, see tonsil inflammation and assess subjectively. This study designed a tool to calculate the area of inflamed areas that can be used to help doctors diagnose tonsillitis. Tonsils image processed on the red layer to quantify the extent of tonsils. Furthermore, the red area was calculated as area ofinflammation. In next stage, find the feature extraction using histogram analysis to find the distribution of image intensity levels. The results were classified using k-Nearest Neighbor (k-NN). From 64 datas which consists of 32 normal and 32 tonsillitis, a system can reach 90,625% accuracy rate. This value is achieved at the cityblock distance measurement and k = 1.
Sistem Otentikasi Biometrik Berbasis Sinyal EKG Menggunakan Convolutional Neural Network 1 Dimensi FAUZI FRAHMA TALININGSIH; YUNENDAH NUR FU’ADAH; SYAMSUL RIZAL; ACHMAD RIZAL; MUHAMMAD ADNAN PRAMUDITO
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 7, No 1 (2022): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v7i1.1-10

Abstract

ABSTRAKBiometrik merupakan salah satu analisis karakteristik individu yang saat ini banyak digunakan, seperti sidik jari, pengenalan suara, dan pengenalan wajah. Metode biometrik tersebut masih memiliki kelemahan seperti mudah untuk dimanipulasi. Oleh karena itu, penelitian ini akan menggunakan sinyal Elektrokardiogram (EKG) sebagai salah satu metode biometrik. Sinyal EKG memiliki keunikan pada setiap individu sehingga sulit untuk dimanipulasi. Penelitian ini mengembangkan sistem otentikasi biometrik berbasis sinyal EKG. Data yang digunakan berasal dari ECG-ID database dengan jumlah 90 subjek. Sinyal EKG yang digunakan hanya menggunakan gelombang PQRST sebagai input model Convolutional Neural Network 1 Dimensi (CNN). Hasil akurasi yang diperoleh menunjukkan 92.2%. Dengan demikian, sistem yang dikembangkan memungkinkan digunakan sebagai otentikasi biometrik.Kata kunci: Biometrik, Sinyal EKG, Convolutional Neural NetworkABSTRACTBiometrics is analyses individual characteristics that are currently widely used, such as fingerprints, voice recognition, and face recognition. The biometric method still has weaknesses, such as being easy to manipulate. Therefore, this study will use an Electrocardiogram (ECG) signal as a biometric method. The ECG signal is unique to each individual, so it is not easy to manipulate. This study develops a biometric authentication system based on ECG signals. The data used comes from the ECG-ID database with a total of 90 subjects. The ECG signal used only PQRST waves as input for the 1-Dimensional Convolutional Neural Network (CNN) model. The accuracy results obtained show 92.2%. Thus, the developed system allows it to be used as biometric authentication.Keywords: Biometric, ECG Signal, Convolutional Neural Network
Identifikasi Sinyal Congestive Heart Failure dengan Metode Convolutional Neural Network 1D MUHAMMAD ADNAN PRAMUDITO; YUNENDAH NUR FU’ADAH; RITA MAGDALENA; ACHMAD RIZAL; FAUZI FRAHMA TALININGSIH
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 7, No 1 (2022): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v7i1.11-20

Abstract

ABSTRAKPenyakit jantung merupakan salah satu penyebab utama kematian di dunia. Salah satu penyakit jantung yang perlu diperhatikan adalah congestive heart failure (CHF). CHF adalah suatu kondisi di mana jantung tidak mampu memompa darah ke seluruh tubuh. Penyakit ini dapat didiagnosis dengan EKG. Oleh karena itu, pada penelitian ini dibuat sebuah sistem yang dapat mengidentifikasi penyakit CHF secara otomatis menggunakan metode convolutional neural network (CNN) dengan 4 hidden layer dan 16 output channel, fully connected layer, dan aktivasi Softmax. Data yang digunakan dalam penelitian ini diambil dari MITBIH dan BIDMC. Penlitian ini memberikan akurasi 100%, sehingga deteksi penyakit CHF otomatis membantu staf medis mendiagnosis pasien untuk menerima perawatan yang tepat.Kata kunci: Elektrokardiogram (EKG), Convolutional Neural Network (CNN), Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF)ABSTRACTHeart disease is one of the leading causes of death in the world. One of the heart diseases that need to be considered is congestive heart failure (CHF). CHF is a condition in which the heart is unable to pump blood throughout the body. ECG can diagnose this disease. Therefore, this study created a system that can automatically identify CHF disease using the convolutional neural network (CNN) method with four hidden layers and 16 output channels, a fully connected layer, and Softmax activation. The data used in this study were taken from MIT-BIH and BIDMC. In this study provides 100% accuracy. Automated CHF disease detection helps medical staff diagnose patients to receive appropriate treatment.Keywords: Electrocardiogram (ECG), Convolutional Neural Network (CNN), Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF) 
Improved Heart Rate Measurement Accuracy by Reducing Artifact Noise from Finger Sensors Using Digital Filters Anita Miftahul Maghfiroh; Liliek Soetjiatie; Bambang Guruh Irianto; Triwiyanto Triwiyanto; Achmad Rizal; Nuril Hidayanti
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 4 No 2 (2022): May
Publisher : Department of electromedical engineering, Health Polytechnic of Surabaya, Ministry of Health Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v4i2.4

Abstract

Heart rate is an important indicator in the health sector that can be used as an effective and rapid evaluation to determine the health status of the body. Motion or noise artifacts, power line interference, low amplitude PPG, and signal noise are all issues that might arise when measuring heart rate. This study aims to develop a digital filter that reduces noise artifacts on the finger sensor to improve heart rate measurement accuracy. Adaptive LMS and Butterworth are the two types of digital filters used in this research. In this study, data were collected from the patient while he or she was calm and moving around. In this research, the Nellcor finger sensor was employed to assess the blood flow in the fingers. The heart rate sensor will detect any changes in heart rate, and the measurement results will be presented on a personal computer (PC) as signals and heart rate values. The results of this investigation showed that utilizing an adaptive LMS filter and a Butterworth low pass filter with a cut-off frequency of 6Hz, order 4, and a sampling frequency of 1000Hz, with the Butterworth filter producing the least error value of 7.57 and adaptive LMS maximum error value of 27.65 as predicted by the researcher to eliminate noise artifacts. This research could be applied to other healthcare equipment systems that are being monitored to increase patient measurement accuracy.
Wrapper Feature Subset Selection for Feature Extraction of Bonang Barung Single Tone Convertion Into Numeric Notation Inung Wijayanto; Nurina Listya Hakim; Achmad Rizal
JMECS (Journal of Measurements, Electronics, Communications, and Systems) Vol 1 No 1 (2015): JMECS
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v1i1.1477

Abstract

Several researches have been done to study the characteristics of the bonang barung, one of Javanese Gamelan music instrument. One of them is convertion of bonang barung single tone to numeric notation using Harmonic FFT as feature extraction and Backpropagation Artificial Neural Network (ANN) for classification. The tone detection accuracy result from previous research is 70,74%. In this research we try to improve the detection result by searching the dominant features using Wrapper Feature Subset Selection (WFSS). Sequential forward selection (SFS) and sequential backward selection (SBS) are used as searching algorithm. The input of the system is a song recorded from a bonang barung then the detected tone is converted into numeric notation. From the experiment, WFSS-SFS produced 6 features with 86,4% accuracy while WFSS-SBS give a better result, it produced 13 features with 92,9% accuracy of tone detection.
Comparison of Multiscale Entropy for Lung Sound Classification Achmad Rizal; Risanuri Hidayat; Hanung Adi Nugroho
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp984-994

Abstract

Lung sound is a biological signal that can be used to determine the health level of the respiratory tract. Various digital signal processing techniques have been developed for automatic classification of lung sounds. Entropy is one of the parameters used to measure the biomedical signal complexity. Multiscale entropy is introduced to measure the entropy of a signal at a particular scale range. Over time, various multiscale entropy techniques have been proposed to measure the complexity of biological signals and other physical signals. In this paper, some multiscale entropy techniques for lung sound classification are compared. The result of the comparison indicates that the Multiscale Permutation Entropy (MPE) produces the highest accuracy of 97.98% for five lung sound datasets. The result was achieved for the scale 1-10 producing ten features for each lung sound data. This result is better than other seven entropies. Multiscale entropy analysis can improve the accuracy of lung sound classification without requiring any features other than entropy.
Lung Sounds Classification Based on Time Domain Features Achmad Rizal; Istiqomah Istiqomah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 2 (2022): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i2.24007

Abstract

Signal complexity in lung sounds is assumed to be able to differentiate and classify characteristic lung sound between normal and abnormal in most cases. Previous research has employed a variety of modification approaches to obtain lung sound features. In contrast to earlier research, time-domain features were used to extract features in lung sound classification. Electromyogram (EMG) signal analysis frequently employs this time-domain characteristic. Time-domain features are MAV, SSI, Var, RMS, LOG, WL, AAC, DASDV, and AFB. The benefit of this method is that it allows for direct feature extraction without the requirement for transformation. Several classifiers were used to examine five different types of lung sound data. The highest accuracy was 93.9 percent, obtained Using the decision tree with 9 types of time-domain features. The proposed method could extract features from lung sounds as an alternative.
Co-Authors Abdillah Nur Isnaini Abdillah Nur Isnaini Achmad Ibnu Abas Aditya, Muhammad Billy Agung Muliawan Agung Surya Wibowo Agustina Trifena Dame.S Akhmad Alfaruq Alfian Akbar Gozali Alvin Oktarianto Alvy Suhandi Nataprawira Amalia, Qoriina Dwi Andi Arini Hidayanti Andi Farmadi Andi Wahyu Adi Arryansyah Andjar Pudji Andro Harjanto Anggit Syorgaffi Anita Miftahul Maghfiroh Anna Yoneta Ulu Arif Abdul Aziz Arif Abdul Aziz Arifah Putri Caesaria Aura Awaliani Puteri Aurick Daffa Muhammad Ayu, Devina Dara Aziz, Burhanuddin Azriansyah Azriansyah Azriansyah Azriansyah Bambang Guruh Irianto Bambang Hidayat Bandiyah Sri Aprillia Bella Fatonah Nur Anisya Beu, Donny Setiawan Bhagas Nugroho Brahmantya Aji Pramudita Burhanuddin Aziz Chandra Purna Darmawan Chandraditya Aridela Deni Saepudin Deny Sugiarto Wiradikusuma Desri Kristina Silalahi Devi Anggraini Dien Rahmawati Djoko Kurnia Putra Dyah Ayu Pratiwi Egidius Pai Laka Eka Nuryanto Budi Susila Elfrida Ratnawati Ellia Nurazizah Endro Yulianto Enzel D. S. Situmorang Estananto Fachrul Nazif Fadhlul Amar Fadlillah Muharam Saeful Fahira Deviana Putri Pasaribu Fajra Octrina Faqih Alam FARDAN FARDAN Fathul Fajar Fatma Indriani FAUZI FRAHMA TALININGSIH Fiky Y. Suratman Fively Darmadi Freyssenita Kanditami P Hanan, Hafizh Khoirul Hanung Adi Nugroho Hanung Tyas Saksono Hasbian Fauzi Perdana Hezron Eka Lattang Hilman Fauzi, Hilman I Nyoman Apraz Ramatryana Ig. Prasetya Dwi Wibawa Ilham Edwian Berliandhy Ilham Rabbani Des Chandra Aziz Inung Wijayanto Istiqomah Istiqomah Istiqomah Istiqomah Istiqomah Istiqomah Iswahyudi Hidayat Jafar Hifdzullisan Jatmiko Kuntoro Nugroho Jidan Sandika Hidayat Jondri Jondri Junartho Halomoan Khilda Afifah Khoirunnisa Azizah Koredianto Usman La Bamba Puang P T S Kami Lestari, Rahma Dania Aleem Liliek Soetjiatie M. Ary Murti Mayco Ikhsan Hanafi Mazaya 'Aqila Meidiana Ajeng Lestari Mohamad Ramdhani Mohamad Sofie Mohamad Sofie Mohamad Sofie, Mohamad MUHAMMAD ADNAN PRAMUDITO Muhammad Afif Ridwansyah Muhammad Al Makky Muhammad Ary Murti Muhammad Fahriza Bahrudin Muhammad Fahriza Bahrudin Muhammad Fahriza Bahrudin Muhammad Hablul Barri Muhammad Hasbi Ashshiddieqy MUHAMMAD JULIAN, MUHAMMAD Muhammad Nadim Mubaarok Muhammad Nashih Rabbani Muhammad Rafiqy Zulfahmi Muhammad Ridha Makruf Muhammad Satya Annas Muhammad Thariq Machaz Muhammad Yusuf Salman Muliadi Naufal Widad Sundawa Naufal Widad Sundawa Ni Wayan Ratna Juami Novi Prihatiningrum Nur Afifah Nuril Hidayanti Nurina Listya Hakim Nursanto Nursanto NURSANTO NURSANTO, NURSANTO Nurul Fathanah Muntasir Patih Muhammad Philip Tobianto Daely Purba Daru Kusuma Putri Famela Azhari R. Yunendah Nur Fu’adah Radian Sigit Raditiana Patmasari Rama Ihya Ulumuddin Ramdhan Nugraha Ratri Dwi Atmaja Raudhatul Jannah Reza Budiawan, Reza Rheza Faurizki Rahayu Rifka Aulia Natasya Risanuri Hidayat Rita Magdalena Rizkia Dwi Auliannisa Ruri Octari Dinata Saepulloh Saepulloh Sang Made Lanang Prasetya Sania Marcellina Bryan Saragih, Triando Hamonangan Sari Luthfiyah Sigit, Radian Siti Nur Azizah Sugiharto Soediponegoro Soediponegoro Soediponegoro Soediponegoro Sofia Naning Hertiana Sony Sumaryo Sugondo Hadiyoso Suryani Alifah Suryo Wibowo Syamsul Rizal Tedy Gumilang Sejati Teguh Patriananda Teguh Satria Triwiyanto Triwiyanto Unang Sunarya Vania Rei Syifa Vera Suryani Viko Adi Rahmawan Vincentius Adisurya Fransisco Antu Wahmisari Priharti Wahyu Kurniawan Widiawan, Babel Willy Anugrah Cahyadi Wisudantyo Wahyu Priambodo, Wisudantyo Wahyu YULI SUN HARIYANI Ziani Said Ziani, Said