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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer

Rizal Maulana

Teknik Komputer, Fakultas Ilmu Komputer, Universitas Brawijaya

Author-ID : 2376499

Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 157 Documents Claim Missing Document

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Fast Obstacle Distance Estimation using Laser Line Imaging Technique for Smart Wheelchair Fitri Utaminingrum; Hurriyatul Fitriyah; Randy Cahya Wihandika; M Ali Fauzi; Dahnial Syauqy; Rizal Maulana
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 4: August 2016
Publisher : Institute of Advanced Engineering and Science

This paper presents an approach of obstacle distance estimation for smart wheelchair. A smart wheelchair was equipped with a camera and a laser line. The camera was used to capture an image from the environment in order to sense the pathway condition. The laser line was used in combination with camera to recognize an obstacle in the pathway based on the shape of laser line image in certain angle. A blob method detection was then applied on the laser line image to separate and recognize the pattern of the detected obstacles. The laser line projector and camera which was mounted in fixed-certain position ensured a fixed relation between blobs-gap and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to respresent this relation as the estimated obstacle distance. As a result, the average error between the estimation and the actual distance was 1.25 cm from 7 data testing experiments. Therefore, the experiment results show that the proposed method was able to estimate the distance between wheelchair and the obstacle.

Distributed rule execution mechanism in smart home system Agung Setia Budi; Hurriyatul Fitriyah; Eko Setiawan; Rakhmadhany Primananda; Rizal Maulana
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4439-4448

Smart home systems become an interesting topics in the last few years. Many researchers have been studied some features. Most of smart home system use a centralized architecture know as centralized smart home system (CSHS). The centralizedmechanism is easy to manage and to configure. However, in fault-tolerant systemparadigm it produces a problem. The entire system will fail, if the master station fails.Another problem of CSHS is centralized mechanism gives more data-flow. This condition makes the system has a greater delay time. To solve the problem, we proposea distributed rule execution mechanism (DREM). Compared to the centralized mechanism, the DREM allows a device to provide its service without any commands fromthe master station. In this mechanism, since the information does not need to go tothe master station, the data-flow and the delay-time can be decreased. The experimentresults show that the DREM is able to mask the failure in the master station by directlytransmit the data from trigger device to service device. This mechanism makes the services provision without master station possible. The mathematical analysis also shows that the delay time of the service provision of the DREM is less than the delay time ofCSHS.

Classification of lung condition for early diagnosis of pneumonia and tuberculosis based on embedded system Rizal Maulana; Alfatehan Arsya Baharin; Hurriyatul Fitriyah
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.3033

The lungs are the main organs in the respiratory system that have a function as a place for exchange of oxygen and carbon dioxide. Due to the importance of lung function, indications of lung disorders must be detected and diagnosed early. Research on the classification of lung conditions generally uses chest x-ray image data. Where a time-consuming procedure is needed to obtain the data. In this research, an embedded system to diagnose lung conditions was designed. The system was made to be easy to use independently and provides real-time examination results. This system uses parameters of body temperature, oxygen saturation, fingernail color and lung volume in classifying lung conditions. There are three conditions that can be classified by the system, that is healthy lungs, pneumonia and tuberculosis. The k-nearest neighbor method was used in the classification process in the designed system. The dataset used was 51 data obtained from the hospital. Each data already has a label in the form of lung condition based on the doctor’s diagnosis. The proposed system has an accuracy of 88.24% in classifying lung conditions.

Pengkondisian Sinyal Electromyography sebagai Identifikasi Jenis Gerakan Lengan Manusia Rizal Maulana; Rekyan Regasari Mardi Putri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5, No 3: Juni 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Sinyal biomedis merupakan sinyal yang diperoleh dari dalam tubuh manusia yang membawa informasi mengenai gambaran kondisi jaringan atau organ terkait dengan sinyal biomedis tersebut. Electromyography (EMG) merupakan salah satu teknik yang bisa digunakan untuk merekam sinyal biomedis dalam mengetahui informasi dari pergerakan otot lengan. Sinyal-sinyal biomedis tidak hanya dipergunakan untuk mendeteksi adanya gangguan pada jaringan atau organ tubuh manusia, namun bisa juga digunakan untuk memberikan gambaran dari sebuah organ dalam melakukan suatu mekanisme kerja. Sinyal EMG menghasilkan sinyal sesuai dengan gerakan yang dilakukan oleh lengan, namun sinyal yang dihasilkan dari elektroda masih tercampur oleh sinyal noise yang dihasilkan oleh beberapa sumber. Akibatnya pendeteksian sinyal EMG menjadi kurang optimal. Untuk mengatasi hal tersebut diperlukan sebuah sistem yang dapat melakukan pembacaan sinyal EMG dan menghilangkan noise yang ada pada sinyal. Pada penelitian ini dirancang sebuah rangkaian pengkondisi sinyal yang terdiri dari penguat instrumentasi, high pass filter (HPF) dan low pass filter (LPF). Rangkaian ini digunakan untuk memproses sinyal yang dihasilkan oleh elektroda yang ditempatkan pada lengan manusia. Sinyal yang telah diproses akan dianalisis besar amplitudonya untuk dapat ditentukan jenis gerakan lengan yang sedang dilakukan. Dari hasil pengujian didapatkan nilai amplitudo rata-rata sebesar 0.252 V untuk gerakan lengan lurus, 1.138 V untuk gerakan lengan membentuk sudut 90° dan 1.774 V untuk gerakan lengan membentuk sudut 180°. Abstract Biomedical signal is signals obtained from the human body that carry information about the condition of tissues or related organs. Electromyography (EMG) is one method in biomedical field that can be used to record biomedical signal to gain the information about arm muscle activity. The EMG signal generates signals according to the movement performed by the arm, however the signals generated from the electrode are still contaminated by noise signals generated by multiple sources, as a result the EMG signal detection becomes less accurate. To resolve these problems, it is necessary a system that can perform EMG signals detection and remove the noise from EMG signals. In this research, we have designed a signal conditioning circuit consist of instrumentation amplifier, high pass filter and low pass filter. This circuit was used to process signals generated by the electrode placed on the arm. The results of the signal conditioning circuit are reprocessed using an exponential filter to obtain a more accurate signal. The signals that have been processed will be analyzed the amplitude to determine the type of arm movement performed. From the test results obtained average amplitude of 0.166 V for straight arm movement, 0.588 V for the 45° arm movement, 1.049 V for the 90° arm movement, 1.367 V for the 135° arm movement and 1.647 V for the 180° arm movement. In addition, the system has an accuracy of 86.67% in determining the type of arm movement.

Deteksi Gulma Berdasarkan Warna HSV dan Fitur Bentuk Menggunakan Jaringan Syaraf Tiruan Hurriyatul Fitriyah; Rizal Maulana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8, No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021854719

Gulma merupakan tanaman pengganggu dalam lahan pertanian. Herbisida merupakan obat yang efektif membunuh gulma tersebut. Penyemprotan herbisida harus tepat sasaran kepada gulma saja dan tidak mengenai tanaman. Penelitian ini membuat sistem yang dapat mendeteksi gulma secara otomatis di antara tanaman pada lahan pertanian riil. Sistem ini menggunakan gambar lahan pertanian riil dimana tanaman tampak utuh (daun dapat lebih dari satu) yang diambil menggunakan kamera dengan posisi vertikal menghadap ke bawah. Algoritma yang dibuat menggunakan segmentasi berdasarkan warna hijau dalam ruang warna HSV untuk mendeteksi daun, baik gulma maupun tanaman pada beragam pencahayaan. Sebanyak tiga fitur bentuk domain spasial digunakan untuk membedakan gulma dengan tanaman yang memiliki karakteristik bentuk daun yang berbeda. Fitur bentuk yang digunakan adalah Rectangularity, Edge-to-Center distances function, dan Distance Transform function. Klasifikasi gulma dan tanaman menggunakan metode Jaringan syaraf tiruan (JST) yang dapat dilatih secara offline. Dari 149 tanaman yang terdeteksi dimana 70% sebagai data training, 15% data validasi dan 15% data uji, didapati akurasi pengujian sebesar 95.46%.AbstractWeed is a major challenge in a crop plantation. A herbicide is the most effective substance to kill this unwanted vegetation. Spraying the herbicide must be done carefully to target the weeds only. Here in this research, we develop an algorithm that detects weeds among the plants based on the shape of their leaves. The detection is based on images that were acquired using a camera. The leaves of weeds and plants were detected based on their green color using segmentation in HSV color-space as it is more effective to detect objects in various illumination. Three shape features were extracted, which are Rectangularity that is based on Rectangularity, Edge-to-Center distance function, and Distance Transform function. Those features were fed into a learning algorithm, Artificial Neural Network (ANN), to classify whether it is the plant or the weed. The testing on the weed classification in a real outdoor environment showed 95.46% accuracy using a total of 149 detected plants (70% as training data, 15% as validation data, and 15% as testing data).

Klasifikasi Tingkat Dehidrasi Berdasarkan Kondisi Urine, Denyut Jantung dan Laju Pernapasan Rizal Maulana; Muhammad Rheza Caesardi; Eko Setiawan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8, No 2: April 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021824379

Dehidrasi merupakan suatu kondisi ketika tubuh kekurangan cairan. Secara umum terdapat tiga tingkatan dehidrasi, yaitu dehidrasi ringan, sedang dan berat. Tingkatan dehidrasi berat sangat berbahaya bagi penderitanya, bahkan dapat mengakibatkan kematian. Untuk mencegah terjadinya tingkatan dehidrasi yang berbahaya, maka diperlukan pendeteksian secara dini agar penderita dehidrasi segera mendapatkan penanganan yang cepat dan tepat. Terdapat beberapa parameter yang dapat digunakan untuk mendeteksi dehidrasi, diantaranya warna dan kadar ammonia pada urine, denyut jantung dan laju pernapasan. Pada penelitian ini, dirancang sebuah sistem klasifikasi tingkatan dehidrasi berdasarkan empat parameter tersebut dengan menggunakan metode klasifikasi k-nearest neighbor. Sistem yang dirancang mampu memberikan kemudahan untuk melakukan pemeriksaan secara mandiri dan mendapatkan hasil klasifikasi tingkat dehidrasi secara real-time. Dataset yang digunakan dalam penelitian ini berjumlah 75 data yang didapatkan dari pasien diare yang menjalani perawatan di Rumah Sakit. Data tersebut sudah memiliki tingkatan dehidrasi berdasarkan diagnosis dari dokter. Dari hasil pengujian yang telah dilakukan, metode k-nearest neighbor memiliki tingkat akurasi terbaik pada penggunaan nilai k=5 dan k=7 dengan nilai akurasi sebesar 96%. Abstract Dehydration is a condition when the body lacks of fluids, caused by the amount of fluid released by the body is greater than the fluids that enters the body. Dehydration is divided into three levels, mild, moderate and severe. Severe dehydration is very dangerous and can even lead to death in patients. To prevent dangerous levels of dehydration, early detection is needed to provide fast and precise treatment to patients. There are several parameters that can be used to detect dehydration, such as color and ammonia levels in urine, heart rate and respiratory rate. This paper designed a system to classify dehydration levels based on these four parameters using k-nearest neighbor classification method. The system is designed to be easy to use independently and provides real-time classification results. There are 75 datasets used in this paper, obtained from diarrhea patients in a hospital in Malang. Each data already has a label in the form of dehydration level based on the doctor’s diagnosis. From the test result, k-nearest neighbor has the best classification accuracy at k=5 and k=7 with the accuracy of 96%.

Controlling the Nutrition Water Level in the Non-Circulating Hydroponics based on the Top Projected Canopy Area Hurriyatul Fitriyah; Agung Setia Budi; Rizal Maulana; Eko Setiawan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 2 (2022): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.70556

Deep Water Culture Hydroponics is suitable for a large-scale plantation as it does not require turn-on the electric pump constantly. Nevertheless, this method needs an electric aerator to give Oxygen to the roots. Kratky’s and Dry Hydroponics are the two methods that suggest an air gap between the raft and the nutrient water level. The gap gives Oxygen to the roots without an aeration pump. Controlling the nutrient water level is required to give a good distance of air gap for Precision Agriculture. The root length estimation used to be done manually by opening the raft, but this research promotes automatic and non-contact estimation using the camera. The images are used to predict the root length based on the Top Projected Canopy Area (TPCA) using various Regression Methods. The test shows that the TPCA gives a high correlation toward the Root Length (>0.9). To control the nutrient water level, this research compares If-Else and the Linear Regression. The error between the actual level that is measured using an Ultrasonic sensor and the setpoint is fed to an Arduino Uno to control the duration of an inlet pump and the outlet pump. The If-Else and the Linear Regression method show good results.

UDP Pervasive Protocol Design and Implementation on Multi Devices using MyRIO Mochammad Hannats Hanafi Ichsan; Rizal Maulana; Octavian Metta Wisnu Wardhana
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.v8i2.23835

Pervasive Computing is one of the practical computing applications to facilitate computer operations by minimizing human interaction with computers. Pervasive Computing has been developed using UDP protocol to recognize the other devices without manual configuration. NI MyRIO device is one of the most reliable devices for the prototyping process. However, there are still not many implementations of data transmission using specific protocols. And the direction of use for smart homes or smart environments is still not widely done. This research contribution implemented Pervasive UDP protocols on PC devices and two NI MyRIO using LabVIEW programming language. UDP protocols are used because they do not require a handshake to recognize another device to reduce delays and have smaller data sizes due to the absence of recognition fields and sequence fields. Each device uses a dual-state machine system design that has a function to detect other devices automatically and act as an application to use the address of another device. PC represents the host, and MyRIO represents the client. Using the same state machine to detect all devices can recognize more than one device on the same network. The obtained test results show that all functional testing scenarios succeeded 100%. The discovery time is averaged at 0.202754 seconds for First MyRIO as First Client and 0.303201 seconds for Second MyRIO as Second Client. The delay in sending data from the host to the client is no more than 2 seconds. Based on this research, MyRIO has the ability to pervasive Computing with other devices. And can be used for prototyping models with good capabilities.

Sistem Pendeteksi Central Sleep Apnea Menggunakan Metode Neural Network dengan Fitur RR Interval dan Durasi QRS Dittha Ratanasari; Edita Rosana Widasari; Rizal Maulana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9, No 7: Spesial Issue Seminar Nasional Teknologi dan Rekayasa Informasi (SENTRIN) 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022976758

Penyakit Central Sleep Apnea (CSA) adalah gangguan tidur akibat otak gagal memberi tahu otot-otot untuk bernapas sehingga terjadi periode henti napas ketika tidur. Kondisi ini menganggu jumlah, kualitas atau durasi tidur seseorang dan memicu sumber masalah kelelahan di siang hari, masalah jantung, tekanan darah tinggi dsb. Standar diagnosis pemeriksaan kondisi CSA adalah polisomnografi yang terkenal terbatas. Sebab tingginya prevalensi Sleep Apnea dan kurangnya ketersediaan diagnosis pemeriksaan, juga dibutuhkan biaya yang relatif tinggi. Penelitian ini dilakukan untuk mengembangkan sistem portable dalam membantu mendeteksi penyakit CSA. Sinyal ECG jantung dimanfaatkan karena irama jantung berdetak secara berbeda saat periode henti napas tiba-tiba waktu tidur, yang telah dinilai membantu proses diagnosis. Sistem dirancang dengan mikrokontroller Arduino Uno, sensor AD8232 dan modul Bluetooth HC-05. Sensor sebagai pendeteksi aktifitas listrik jantung, dengan 3 buah elektroda menempel pada dada untuk merekam lalu diekstraksi fitur RR interval dan durasi QRS. Kedua fitur pada 18 set data uji diklasifikasi dengan metode Neural network, keluarannya berupa kelas Normal atau Apnea ditampilkan pada smartphone dengan konektivitas Bluetooth. Pengujian kinerja sistem untuk deteksi sensor memperoleh nilai 96.18%, dan presentase akurasi klasifikasi Neural Network menghasilkan 83.3% dengan waktu komputasi 46.44 ms. AbstractCentral sleep apnea (CSA) is a sleep disease in which the brain fails to send signals to the muscles to breathe, resulting in periods of no breathing during sleep. This disorder interferes with a person's sleep quantity, quality, or duration, which can lead to daytime weariness, heart difficulties, high blood pressure, and other issues. Polysomnography is the primary diagnostic technique for Central Sleep Apnea, yet it is notoriously restricted. The expenditures are relatively expensive due to the high incidence of sleep apnea and the paucity of diagnostic methods. The goal of this study was to create a portable device for detecting CSA illness. It has been evaluated to help in the diagnosing process and uses cardiac ECG data since the heart rhythms alter during periods of abrupt stoppage during sleep. The Arduino Uno microcontroller, AD8232 sensor, and HC-05 Bluetooth module are used in the system. With three electrodes attached to the chest to record and then extract the RR interval and QRS duration properties, the sensor is used to monitor the electrical activity of the heart. The Neural network technique classifies the two properties in the 18 test data sets, and the output in the form of Normal or Apnea classes is shown on a smartphone with Bluetooth connectivity. The sensor detection system performance test yielded a result of 96.18%, and the percentage accuracy of Neural Network classification was 83.3% with a processing time of 46.44ms.

Perancangan dan Implementasi Real Segway Pada Skateboard Roda Satu Menggunakan Gyroscope dan Accelerometer Muhammad Kholis Fikri; Barlian Henryanu Prasetio; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 1 (2017): Januari 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1151.225 KB)

Perkembangan teknologi membuat kualitas kehidupan manusia semakin berkembang, banyak dari perusahaan yang saling berlomba dalam membuat teknologi yang dapat memudahkan kehidupan manusia, salah satu teknologinya adalah alat transportasi. Alat transportasi sangat bermacam-macam dari yang di darat, laut ataupun udara semua menggunakan teknologi. Transportasi darat sekarang ini tidak hanya difungsikan sebagai alat transportasi pada umumnya tetapi dibuat gaya hidup. Alat transportasi yang sedang banyak dikembangkan adalah alat transportsi satu roda atau sering disebut Segway, teknologi ini dapat digunakan jarak pendek, karena bebannya yang cukup berat sehingga sulit untuk dibawa kemana. Dari masalah tersebut pada penelitian ini dibuat alat transportasi yang cukup ringan dan memenuhi daya hidup yaitu skateboard beroda satu. Pada umumnya skatebord beroda empat, untuk memenuhi gaya hidup dibuat skatebord dengan sentuhan teknologi yaitu sakteboard beroda satu. Skateboard ini memanfaatkan gyroscope dan accelerometer sebagai sensor untuk menyeimbangkan badan dari skateboard. Roda memanfaatkan motor BLDC dan driver motor IBT2, arduino nano sebagai mikrokontrollernya dan PID sebagai metode kontroller. Dengan menggunakan metode proporsional integratif derivatif yang ditanamkan pada kontroller arduino uno dengan nilai Kp=6.98, Ki=4.61 dan Kd=1.15 sistem dapat berjalan sesuai yang diinginkan yaitu skateboard dapat setimbang

Co-Authors Abdullah Asy Syakur Abdurrahman Arif Kasim Addin Miftachul Firdaus Adhly Hasbi Fadhlillah Adinugroho, Sigit Adit Ilham Nugroho Aditya Rafly Syahdana Agung Setia Budi Ahmad Fahmi AdamSyah Ahmad Rizqi Pratama Alfatehan Arsya Baharin Alfatehan Arsya Baharin Alfaviega Septian Pravangasta Ali Ilham Ainur Rahman Allif Maulana Althaf Banafsaj Yudhistira Amelio Eric Fransisco Amri Yahya Ananda Ribelta Anata Tumonglo Andre Ananda Pratama Anggi Fajar Andana Aras Nizamul Aryo Anwar Ariq Monetra Aufa Nizar Faiz Axel Elcana Duncan Bagas Nur Rahman Bambang Gunawan Tanjung Barlian Henryanu Prasetio Barlian Henryranu Prasetio Boris Wiyan Pradana Bramantyo Ardi Cahyanita Qolby Rahmarta Rizaputri Chandra Gusti Nanda Putra Chikam Muhammad Dadang Kurniawan Dahnial Syauqy Dian Bagus Setyo Budi Didik Wahyu Saputra Dien Nurul Fahmi Dipatya Sakasana Dittha Ratanasari Dony Satrio Wibowo Dwi Firmansyah Dwi Fitriani Dwiki Nuridhuha Edita Rosana Widasari Edita Rosana Widasari Eko Setiawan Eko Setiawan Eko Setiawan Eko Setiawan Ezra Maherian Fachrur Febriansyah Manangkalangi Fajar Miftakhul Ula Falachudin Akbar Farah Amira Mumtaz Farid Aziz Shafari Fauzan Rivaldi Fauzi Awal Ramadhan Fikri Fauzan Fikriza Ilham Prasetyo Fitrahadi Surya Dharma Fitriyah, Hurriyatul Galang Eiga Prambudi Gembong Edhi Setiawan Gembong Edhi Setyawan Govinda Dwi Kurnia Sandi Gusti Arief Gilang Habib Muhammad Al-Jabbar Habib Zainal Sarif Hafid Ilmanu Romadhoni Hafiz Nul Hakim Hafizhuddin Zul Fahmi Hamdan Zuhdi Dewanul Arifin Handoko Ramadhan Hani Firdhausyah Hanif Yudha Prayoga Hanifa Nur Halimah Hendriawan Dwi Saputro Hurriyatul Fitriyah Ichwanul Muchlis Ihsanurrahim Ihsanurrahim Imam Syafi'i Al Ghozaly Iqbal Koza Irham Manthiqo Noor Issa Arwani Istiqlal Farozi Izza Febria Nurhayati Jodie Putra Kahir Kezia Amelia Putri Kiki M. Rizki Lamidi Lamidi Leina Alimi Zain Lia Safitri M. Ali Fauzi M. Sandy Anshori M. Sifa'un Ni'am Mahesha Bayu Paksi Mario Kitsda M Rumlawang Marrisaeka Mawarni Mhd. Idham Khalif Misran Misran Moch Zamroni Mochamad Hannats Hanafi Ichsan Mochammad Hannats Hanafi Ichsan Mochammad Hannats Hanafi Ichsan Mohamad Abyan Naufal Fachly Mohamad Muhlason Nur Aziz Mohammad Ali Muhsin Muhajir Ikhsanushabri Muhamad Ichwan Sudibyo Muhamad Irfanul Hadi Muhamad Taufiq Firmansyah Muhammad Bilal Muhammad Eko Lutfianto Muhammad Fatikh Hidayat Muhammad Jibriel Bachtiar Muhammad Kholis Fikri Muhammad Prabu Mutawakkil Muhammad Raihan Al Hakim Muhammad Rheza Caesardi Muhammad Rheza Caesardi Muhammad Yaqub Muhammad Yusuf Hidayat Nadi Rahmat Endrawan Nobel Edgar Nugraha Pangestu Octavian Metta Wisnu Wardhana Octavian Metta Wisnu Wardhana Oktaviany Setyowati Pabela Purwa Wiyoga Pinandhita Yudhaprakosa Priyo Prasetyo Putri Laras Rinjani Rachmat Eko Prasetyo Rahadian Sayogo Rahmat Yusuf Afandi Rakhmadhany Primananda Rakhmadhany Primananda Randy Cahya Wihandika Refsi Ilham Cahya Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Renita Leluxy Sofiana Ricky Zefani Aria Zurendra Ridzhal Hachim Wahyunanto Rifqi Alvaro Rifqi Anshari Riko Andianto Rimas Oktama Rint Zata Amani Rioadam Sayyid Abidin Riski Kurniawan Rizki Septiansyah Rizky Widya Mahendra Romario Siregar Rosyana Lencie Mampioper Sabitha Wildani Hadi Sabriansyah Rizqika Akbar Sabriansyah Rizqika Akbar Salsabiil Hasanah Satyaki Kusumayudha Shafa Sabilla Zuain Sulthan Ghiffari Awdihansyah Sutrisno Sutrisno Syahriel Diovanni Yolanda Tatit Kisyaprakasa Tedy Kurniawan Tezza Rangga Putra Tibyani Tibyani Tio Haryanto Adi Putra Tri Putra Anggara Upik Jamil Shobrina Utaminingrum, Fitri Vatikan Aulia Makkah Wijaya Kurniawan Willy Andika Putra Yanuar Enfika Rafani Yohana Angelina Sitorus Yohana Kristinawati Yurliansyah Hirma Fajar

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