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Deteksi Sinyal : Overview Model Parametrik menggunakan Kriteria Neyman-Pearson SURATMAN, FIKY YOSEF; PRAMUDITA, ALOYSIUS ADYA; ARSENO, DHARU
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 7, No 1 (2019): ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v7i1.14

ABSTRAKDeteksi sinyal banyak diimplementasikan dalam sistem pengolahan sinyal yang sangat kompleks. Sebagai contoh digunakan pada sub sistem pengolahan sinyal radar pengintai yang berfungsi untuk deteksi dan pelacakan target. Salah satu implementasi terbaru dari deteksi sinyal adalah untuk fungsi spectrum sensing pada Cognitive Radio. Deteksi sinyal dapat didefinisikan sebagai binary hypothesis testing, yaitu memutuskan satu dari dua keadaan: hanya derau atau tidak ada sinyal (null hypothesis), dan ada sinyal (alternative hypothesis). Teori deteksi sinyal merupakan bidang yang cukup luas, sehingga paper ini fokus pada pendekatan parametrik dengan Teorema Neyman-Pearson. Kedua hypothesis dimodelkan dengan variabel acak dengan distribusi rapat kemungkinan yang sama tetapi mempunyai parameter yang berbeda. Ditunjukkan penurunan test statistic untuk dua skenario, yaitu distribusi dengan diketahui sebagian dan diketahui penuh. Bagian simulasi menunjukkan kinerja detektor sinyal secara analitis mempunyai hasil yang serupa dengan simulasi Monte Carlo.Kata kunci: deteksi sinyal, Neyman-Pearson, hypothesis testing, spectrum sensing, radar. ABSTRACTSignal detection has been used in many sophisticated signal processing systems, such as for signal processing in surveillance radar which is to detect and to track a radar target. Recently, signal detection is widely used for spectrum sensing in Cognitive Radio. Signal detection is a binary hypothesis testing problem which is to choose one out of two conditions, i.e., noise only or signal absence (null hypothesis), and signal presence (alternative hypothesis). Since signal detection theory is a wide area, this paper only focuses on parametric approach using Neyman-Pearson theorem. The two hypotheses are modeled by random variables having the same distribution but different parameters. The derivations of test statistics (detectors) are shown for two scenarios, i.e., partially known and perfectly known distributions. Analytical results and Monte Carlo simulations of the derived detectors show similar performances.Keywords: signal detection, Neyman-Pearson, hypothesis testing, spectrum sensing, radar.

Deteksi Sinyal : Overview Model Parametrik menggunakan Kriteria Neyman-Pearson FIKY YOSEF SURATMAN; ALOYSIUS ADYA PRAMUDITA; DHARU ARSENO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 7, No 1 (2019): ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v7i1.14

ABSTRAKDeteksi sinyal banyak diimplementasikan dalam sistem pengolahan sinyal yang sangat kompleks. Sebagai contoh digunakan pada sub sistem pengolahan sinyal radar pengintai yang berfungsi untuk deteksi dan pelacakan target. Salah satu implementasi terbaru dari deteksi sinyal adalah untuk fungsi spectrum sensing pada Cognitive Radio. Deteksi sinyal dapat didefinisikan sebagai binary hypothesis testing, yaitu memutuskan satu dari dua keadaan: hanya derau atau tidak ada sinyal (null hypothesis), dan ada sinyal (alternative hypothesis). Teori deteksi sinyal merupakan bidang yang cukup luas, sehingga paper ini fokus pada pendekatan parametrik dengan Teorema Neyman-Pearson. Kedua hypothesis dimodelkan dengan variabel acak dengan distribusi rapat kemungkinan yang sama tetapi mempunyai parameter yang berbeda. Ditunjukkan penurunan test statistic untuk dua skenario, yaitu distribusi dengan diketahui sebagian dan diketahui penuh. Bagian simulasi menunjukkan kinerja detektor sinyal secara analitis mempunyai hasil yang serupa dengan simulasi Monte Carlo.Kata kunci: deteksi sinyal, Neyman-Pearson, hypothesis testing, spectrum sensing, radar. ABSTRACTSignal detection has been used in many sophisticated signal processing systems, such as for signal processing in surveillance radar which is to detect and to track a radar target. Recently, signal detection is widely used for spectrum sensing in Cognitive Radio. Signal detection is a binary hypothesis testing problem which is to choose one out of two conditions, i.e., noise only or signal absence (null hypothesis), and signal presence (alternative hypothesis). Since signal detection theory is a wide area, this paper only focuses on parametric approach using Neyman-Pearson theorem. The two hypotheses are modeled by random variables having the same distribution but different parameters. The derivations of test statistics (detectors) are shown for two scenarios, i.e., partially known and perfectly known distributions. Analytical results and Monte Carlo simulations of the derived detectors show similar performances.Keywords: signal detection, Neyman-Pearson, hypothesis testing, spectrum sensing, radar.

EVALUASI MULTI-ANTENNA BERBASIS PENDEKATAN GLRT PADA COGNITIVE RADIO Mochammad Haldi Widianto; Fiky Yosef Suratman; Bambang Hidayat
Jurnal Elektro dan Telekomunikasi Terapan (e-Journal) Vol 5 No 1: JETT Juli 2018
Publisher : Direktorat Penelitian dan Pengabdian Masyarakat, Universitas Telkom

Kebutuhan terhadap teknologi nirkabel semakin meningkat, Sementara itu ketersediaan spektrum frekuensi mendekati batasnya. Masalah ini dapat diatasi dengan pemanfaatan spektrum yang masimal. Salah satu teknologi yang dapat memaksimalkan ketersediaan spektrum adalah cognitive radio. Spektrum sensing adalah salah satu komponen yang ada di cognitive radio (CR). Algoritma sensing yang biasanya digunakan adalah deteksi energi. Karena ada beberapa kelemahan pada deteksi energi, yang mana sangat sensitif terhadap daya noise yang tidak menentu. Sehingga dibentuk metode baru berdasarkan pendekatan Generalized likelihood ratio tests (GLRT). Di paper ini analisis spektrum sensing pada cognitive radio berbasis pendekatan GLRT dan deteksi energi. Primary User (PU) menggunakan space-time block coding (STBC) dan kanal menggunakan Geometrically-Based Single Bounce (GBSB). Hasil evaluasi menunjukan beberapa masalah yang mempengaruhi kinerja pendekatan GLRT seperti; jumlah antenna penerima (nR), Skema MIMO STBC, bentuk kanal GBSB, Terakhir algoritma pendekatan GLRT dapat menyelesaikan masalah deteksi energi..

Pengembangan Sistem Pengendali Kursor Menggunakan Sinyal Elektrookulogram (EOG) Hasbian Fauzi Perdana; Fiky Yosef Suratman; Achmad Rizal
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 1 No 2 (2019): August
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v1i2.19

Amyotrophic Lateral Sclerosis (ALS) is an illness due to lack of nourishment in human motoric nerves. This illness causes the sufferer a loss in motoric movement. Thankfully there is still an organ that move well regardless the illness, and that is eye movement. Eye tracking method have been applied to controlling computer. Majority of eye tracking methods are divided into two methods, video-oculography (VOG), and electrooculography (EOG). VOG used camera as video recorder and processed using image processing to track eye movement. EOG used skin electrode that were usually used in electrocardiography, which could detect electrical activity on the back of eye. The principle in this research is to design a cursor controlling system using EOG sensor and classified the signal using simple thresholding method. The result would be cursor movement based on eye movement. Purpose of this final assignment is to design a cursor controlling system based EOG sensor. The experimental results yield an accuracy of 98% for the cursor movement controlling. The system can control the direction of the cursor's movement but cannot control other activities of the cursor.

ANALISA PENGARUH RANGSANGAN AROMATERAPI LAVENDER DAN KAYU CENDANA TERHADAP KUALITAS TIDUR BERBASISKAN GELOMBANG EEG Alyani Durrah Fauzan; Nushrotul Lailiyya; Dwi Esti Kusumandari; Fiky Yosef Suratman
TEKTRIKA Vol 4 No 1 (2019): TEKTRIKA Vol.4 No.1 2019
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v4i1.1608

Abstrak Tidur merupakan aktivitas yang penting bagi tubuh. Aktivitas tidur membantu tubuh untuk menyembuhkan sel-sel yang rusak dan meningkatkan sistem kekebalan tubuh. Tetapi banyak dari kita yang tidak mendapatkan kualitas tidur yang baik untuk menerima manfaat tersebut. Demi meningkatkan kualitas tidur, sebagian besar masyarakat percaya bahwa penggunaan aromaterapi dapat membuat tubuh lebih rileks dan membantu penggunanya tidur lebih lelap. Paper ini melakukan studi mengenai ada tidaknya pengaruh aromaterapi terhadap kualitas tidur dengan memanfaatkan sinyal biopotensial pada otak, yaitu electroencephalogram (Sinyal EEG). Sinyal EEG didapatkan dari proses akuisisi menggunakan Mitsar-EEG-202 dan Software WinEEG. Selanjutnya, sinyal EEG akan dibaca secara visual berdasarkan bentuk, frekuensi, amplitudo, dan lokasi. Proses pembacaan sinyal akan menghasilkan nilai latensi tidur, durasi fase tidur (NREM dan REM), dan WASO. Data-data tersebut akan diuji secara manual (menghitung efisiensi tidur) per individu dan statistik (uji kesamaan dua rata-rata dan uji kesamaan dua varians). Hasil analisis secara statistik menunjukkan bahwa tidak adanya pengaruh yang signifikan antara subjek yang diberi stimulus aromaterapi terhadap subjek tanpa stimulus. Sedangkan pada analisis per individu, kualitas tidur dengan stimulus aromaterapi lebih baik dibandingkan tanpa stimulus pada beberapa subjek. Jika dihitung secara rata-rata, stimulus aromaterapi lavender dan kayu cendana dapat menaikkan efisiensi tidur, namun tidak signifikan.

ANALISIS PENGARUH MUSIK KLASIK DAN MUSIK ALAM TERHADAP KUALITAS TIDUR BERDASARKAN SINYAL ELECTROENCEPHALOGRAM Adriani Rizka Amalia; Fiky Yosef Suratman; Dwi Esti Kusumandari; Nusharatul Lailiyya
TEKTRIKA Vol 3 No 1 (2018): TEKTRIKA Vol.3 No.1 2018
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v3i1.2204

Tidur merupakan kebutuhan dasar bagi setiap individu. Kondisi seseorang bisa dipengaruhi oleh kualitas tidurnya. Menjaga kualitas tidur penting untuk dilakukan karena dapat membantu menurunkan stress, meningkatkan mood dan fokus. Sebagian besar masyarakat meyakini bahwa pemberian stimulus musik dapat menambah kenyamanan tidur. Rangsangan suara merupakan salah satu variabel yang dapat mempengaruhi kehadiran gelombang listrik di otak, serta dapat membantu seseorang untuk merasa lebih rileks. Penelitian ini mempelajari pengaruh musik klasik dan musik alam terhadap kualitas tidur dari sinyal electroencephalogram (EEG). Sinyal EEG adalah salah satu cara untuk dapat mengetahui kualitas tidur seseorang. Kualitas tidur dipelajari melalui sinyal EEG, dengan memberikan rangsangan musik yang secara bertahap kepada individu, berdasarkan total waktu di setiap tahapan tidur, sleep latency dan efisiensi tidur. Masukan sistem merupakan sinyal yang didapat dari perekaman sinyal menggunakan sensor Mitsar EEG-202, yang pada penerapannya akan diletakkan pada 19 titik (multi channel) sesuai dengan sistem internasional 10-20. Tahap awal penelitian pengaruh stimulus musik berdasarkan sinyal EEG ini adalah akuisisi data, kemudian pembacaan data dilakukan secara visual dan telah diverifikasi oleh dokter spesialis syaraf. Setelah itu penentuan kualitas tidur ditentukan dengan melihat adanya pengaruh musik dengan metode statistik uji kesamaan dua rata-rata dan F-test. Hasil analisis dari 9 subjek dengan menggunakan uji kesamaan dua rata-rata menunjukkan bahwa adanya pengaruh pada Non Rapid Eye Movement (NREM). tahap 3 dengan musik klasik. Analisis F-test menunjukkan adanya perbedaan yang signifikan pada NREM tahap 1 dengan musik klasik maupun musik alam.

Automatic Warning System for Weather Station Power Supply Mumtazanisa Fairuzen; Angga Rusdinar; Fiky Yosef Suratman; Denny Darlis
Ultima Computing : Jurnal Sistem Komputer Vol 13 No 2 (2021): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v13i2.2261

Weather observation is one of the important factors in agriculture. Data from weather observations can be used for various things, including to predict future risks due to these weather conditions. An Automatic Weather Station (AWS) is needed to read weather conditions continuously. Some of the devices that will be built for the AWS system are data communication, sensors, and power supply. AWS is usually installed in certain areas where there is no power source. Hence, it takes a power supply system that can stand alone and has a security system that can monitor the components connected to the system in real-time. This research successfully designed a power supply system for a weather station that is equipped with current and voltage measurement features for its load as well as a warning system feature in case of interference on GSM SIM900-based Weather Station. Based on the results of the study the system using solar cell modules has an efficiency of 14,1% and is supported with the help of batteries that can be recharged through solar energy. Using the INA219 sensor to measure the voltage and load current connected to devices that have an error percentage value of less than 1%, the data is then uploaded to Thingspeak. Testing of warning systems at the Weather Station is conducted using Magnetic reed sensors capable of detecting changes when the separation distance between the sensor and other magnets is more than 3cm.

Electrocardiogram feature selection and performance improvement of sleep stages classification using grid search Lyra Vega Ugi; Fiky Yosef Suratman; Unang Sunarya
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3529

Sleep analysis is often used to identify sleep-related human health. In many cases, sleep disorders could cause a particular disease. One of the approaches to detect sleep disorders is by investigating human sleep stages. However, the selection of the proper electrocardiogram (ECG) features is still considered challenging and becomes an issue to achieve the performance of the algorithm used. Therefore, it is necessary to investigate which ECG features are very significant to the performance of the algorithm. In this study, the support vector machine (SVM) method has been utilized to classify sleep stages into two classes namely awake and sleep. In order to improve the classification performances, an optimization method of grid search was used to find the best parameters of the SVM. Feature selection of information gain was then used to find the most significant ECG features. To validate the performance results, one leave-subject out cross-validation has been conducted during the implementation. There were ten subjects involved in this implementation. The ECG signals from those ten subjects were used to differentiate awake from sleep state. Based on the results, our method obtained an average accuracy of 85.46% a precision of 84.05% and a recall of 85.44% respectively.

Ultra Wideband Radar for Respiratory Monitoring on Sleep Position Nurul Qashri Mahardika T; Erfansyah Ali; Fiky Yosef Suratman
JMECS (Journal of Measurements, Electronics, Communications, and Systems) Vol 8 No 1 (2021): JMECS
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v8i1.2873

Sleep apnea is a sleep disorder that has a relation with respiratory system during sleep.One of the sleep apnea characteristic is suddenly stop breathing during sleep. Peoplehave the dierent of respiratory rate (RR) which is affected by sleep positions andbody mass index (BMI).There are four sleep positions aecting the respiratory rate(RR). Polysomnography (PSG) is conventionally used to analysis the sleep apnea. Thistechnique requires body contact that might be uncomfortable for the patient. In thisstudy, the Xethru X4M200 radar sensor is proposed as non-contact tool to detect the RRby implementing the Doppler effect. Furthermore, the relation between RR with the sleepposition and the BMI are discused. For that purpose, 20 participants (10 males and 10females) with dierent BMIs and sleep positions are examined by monitoring their chestmovement. This method is able to detect the indication of bradypnoea or tachypnoea.Futher systematic study and more participants are required to confirm our results andprovide better non-contact technique for RR measurement.

Deteksi Radar Terhadap Multi-Object Bergerak Dengan Pemrosesan Doppler Reyhan Fahmirakhman Abdullah; Dharu Arseno; Fiky Yosef Suratman
Proceedings Series on Physical & Formal Sciences Vol. 1 (2021): Proceedings of Smart Advancement on Engineering and Applied Science
Publisher : UM Purwokerto Press

In general, Radar or Radio Detection and Ranging is an electromagnetic wave system that is useful to measure distance and answer and make maps of surrounding objects. Radar has an advantage compared to other navigation tools, which is that radar does not require a transmitter station as a transmitter. Radar has an electronic wave emission principle that emits short radio wave pulses emitted in a narrow beam by a directional antenna. In this study, a multi-object radar detection simulation was carried out using Dopler processing both MTI and PDP, which later on the radar will detect related objects. Multi-object here is a condition that is achieved when a navigation radar detects more than one object. The result of this research is a multi-object detection process using the MTI and PDP methods and the matched-filter obtained from the predetermined data. So Doppler processing aims to mitigate the clutter signal to improve the detection performance of moving targets even though there is a dominance of signals originating from stationary clutter.

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