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All Journal International Journal of Electrical and Computer Engineering International Journal of Reconfigurable and Embedded Systems (IJRES) ComEngApp : Computer Engineering and Applications Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Dahnial Syauqy
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Sistem Pendeteksi Kesegaran Ikan Bandeng Berdasarkan Bau Dan Warna Daging Berbasis Sensor MQ135 Dan TCS3200 Dengan Metode Naive Bayes Govinda Dwi Kurnia Sandi; Dahnial Syauqy; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The freshness of milkfish is influenced by several factors, one of which is the time of storage of fish. In the process of storage and processing at traders and households, it is still done manually and causes errors in determining the freshness of milkfish. To overcome these problems, a tool that can determine the freshness of milk fish will be designed quickly and automatically. In making this tool an arduino microcontroller and MQ135 gas sensor will be used to detect ammonia, and TCS3200 sensor to detect the RGB color of milkfish. The results of the two sensors in the form of 4 parameters or features will be used to determine the freshness of milkfish with the Naive Bayes method ... The Naive Bayes method was chosen because this method is very flexible if there are changes to the training data, and requires little training data to can do Naive Bayes calculations, and finally the results of the classification method are also quite accurate. From the testing carried out starting from the sensor testing method and computational time the result is the TCS3200 error percentage when detecting RGB meat is 2.2%. In testing the sensor MQ135 sensor correlation value obtained with an output voltage of 99.22%. For testing methods using 100 training data and 18 test data, classification using Naive Bayes obtained an accuracy of 94.4% with an average computing time of 2.7 seconds.
Sistem Tertanam Pendeteksi Kondisi Ideal Fermentasi Susu Kefir berdasarkan Kadar Alkohol dan pH menggunakan Metode Naive Bayes Izza Febria Nurhayati; Dahnial Syauqy; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Kefir is a fermented milk product that contains probiotics which is very useful for body health. Kefir is fermented milk that contains alcohol and has a low pH than milk. At this time in the process of kefir milk fermentation is done manually so as to allow failure and a decrease in the quality of kefir milk. From these problems, a study was conducted called the Embedded Detection System for Ideal Fermentation of Kefir Milk based on Alcohol Levels and pH using the Naive Bayes Method, so kefir milk producers can improve the quality of kefir milk and reduce the potential for failure during the kefir manufacturing process. In this study the parameters used in determining the condition of kefir milk are pH and alcohol content. PH and alcohol parameters play a role in determining how long the fermentation takes place so that the condition of the kefir milk is finally known. The pH was detected using a SKU SEN pH sensor and the alcohol content in kefir milk was detected using an MQ-3 sensor and processed by the Arduino Uno microcontroller using the Naive Bayes method. The use of the Naive Bayes method was chosen for the classification of kefir milk conditions, because this method is one classification method that is quite effective and fast in its calculations. From the results of several tests conducted it is known that the error percentage of the SKU-SEN pH color sensor reading is 10.087% and the error percentage value of the MQ-3 gas sensor is 12.65%. In testing the accuracy of Naive Bayes classification obtained 70% with 10 test data from 60 training data with a system computing time of 3,0781 seconds..
Sistem Klasifikasi Ikan Tongkol yang mengandung Formalin dengan Sensor HCHO dan Sensor pH menggunakan Metode K-Nearest Neighbor berbasis Arduino Dedi Siswanto; Dahnial Syauqy; Agung Setia Budi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Tuna has a high protein content that can meet the nutritional needs of the human body. One of the sea fish that contains omega 3, vitamins, protein, and minerals is tuna. Tuna meat is a type of meat that is easily damaged (rot). Usually to avoid damage of fish, fishermen and sellers usually preserve fish using ice cubes. However, preserving fish using ice cubes requires large amounts of ice cubes, thereby reducing the amount of profit gained and also impractical. To replace ice cubes, usually cheat seller use dangerous chemicals such as formalin. To overcome the cheating, it is needed a classification system for tuna that contains formalin. This system uses several components, such as: arduino mega 2560 microcontroller to process data and calculate k-nearest neighbor, HCHO sensor which is used for detecting HCHO gas released by formalin, and pH sensor used to measure of the pH value in tuna. The system to distinguishing tuna that contains formalin with tuna that does not contain formalin using the k-nearest neighbor method gets 90% accuracy. Test the K value by using odd numbers 3, 5, 7, 9. After that, the results of changes in value K are compared to see which K values are more accurate on the k-nearest neighbor method applied to the system.
Sistem Pembeda Daging Sapi dan Daging Babi berdasarkan Warna dan Kadar Amonia menggunakan Metode Jaringan Syaraf Tiruan Berbasis Android Muchamad Rafi Dharmawan; Dahnial Syauqy; Gembong Edhi Setyawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Protein is a substance that is needed by humans to be able to carry out daily activities. Aside from being a source of energy, a lot of other benefits provided by the protein for the body of human. Beef is one of source by protein that's much favoured by the peoples. But, with the high interest making naughty seller take advantage of this situation by mixing pork with beef. These conditions occur because the lack of public knowledge about the difference between both of them coupled with the prices of beef relatively higher. This made the majority people of Indonesians are scared, especially Muslims. So we need a study of systems that can distinguish between beef and pork. With this research it's expected to be able to solve existing problems and minimize cheating by sellers and losses by consumers. This system is designed using TCS3200 color sensor and MQ135 gas sensor because in this study, parameters to distinguish two types of meat are color and ammonia levels. The results of the sensors are used as input for processing by Arduino Nano using Artificial Neural Network (ANN) method to get result of classification by system. The architecture of ANN used in this study consisted of 4 input neurons, 5 hidden layers, and 1 output layer. The main system will be only used for prediction process based on readings value of the sensor. The training process for getting weight values is running on other devices using MATLAB. The results of tests on 30 meat samples as test data produce an accuracy rate up to 90% with an average of computation time 45.3 millisecond.
Implementasi Alat Bantu Komunikasi Menggunakan Pergerakan Bola Mata berbasis Electrooculography dengan Metode Derivative Function Hafiz Nul Hakim; Rizal Maulana; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 3 (2020): Maret 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that causes humans to not be able to move parts of their body and can even cause conditions of speech difficulties. The cause of this disease is genetic and environmental factors. In the early stages, ALS sufferers's muscle will weaken in one of the legs or hands. Muscle weakness slowly spreads such as the neck muscles, back muscles, muscles needed to talk, breathe, swallow food and other muscles that will progressively suffer from paralysis. But the bladder muscles, eye and sphinfer muscles are declared free of this disease (Palmowski, et al., 1995). Electrodes, signal conditioning circuits, arduino uno microcontrollers with bluetooth module, and smartphones can detect eye movements and implement them to sufferers to be able to communicate with people around them.The signal conditioning circuit includes the instrumentation amplifier circuit and the low pass filter. Eye movements can be classified into five movement there are front, right, left, top and bottom. From eye movements, it can be used as a command to communicate the needs of sufferers with those around them via a smartphone, so bluetooth communication is needed to send data in microcontroller to the smartphone. The system has been tested 10 times and has an accuracy of 80%.
Klasifikasi Lokasi Indoor atau Outdoor berdasarkan Pengaruh Cahaya Terhadap Alat Tracking GPS pada Hewan menggunakan Metode K-Nearest Neighbor Samuel Lamhot Ladd Palmer Simarmata; Dahnial Syauqy; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 5 (2020): Mei 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Pets, animals maintained by humans, are exceedingly required to look after and take care by the owners. The development of science and technology has been sustaining to apply these purposes by the existence of the tracking devices which can be inserted to the pets. The classification of indoor and outdoor location based on the effect of the lights is conducted in this research to control the data sending from the location of the pets. K-Nearest Neighbor algorithm is required to support the system in determining the location classification of pets. The implementation of the systems is based on the two prototypes; master and slave. Slave system is to record the value of the lights and the coordinates of the pets. Besides, the master system is to receive the data, process the K-Nearest Neighbor algorithm, and connect to the laptop in order to show the output data. The accuracy obtained from the indoor and outdoor classification in this study was 98%. The test of K-value in this method was applied with the K-value of K=3, K=7 and K=9.
Sistem Pengukur Kesegaran Daging Sapi menggunakan Metode K-Nearest Neighbor (K-NN) dengan Fitur Penambahan Data Latih berbasis EEPROM Jeffry Atur Firdaus; Eko Setiawan; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 5 (2020): Mei 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Beef is one of the food products that is in demand by the people of Indonesia because it has high nutritional value. 70% air, 20% protein, 9% fat and 1% ash. The freshness of beef affects the quality of beef. Simple characteristics of beef that is still fresh is the color of fresh red meat, soft flesh fiber and yellow fat. The quality loss in beef can be marked by changes in color, taste and smell. This is caused by the development of microorganisms in beef. Eating beef contaminated by microorganisms can cause food poisoning and other health problems. In this research, a system can classify beef quality using the K-Nearest Neighbor algorithm and Arduino nano EEPROM. This system uses beef RGB color input using TCS3200 sensor, gas quality to measure the intensity of NH3/ammonia gas using the MQ135 sensor and push-button as a medium of user interaction with the system. Further sample data is classified using K-Nearest Neighbor on Arduino Nano using training data stored on EEPROM. The results of the classification of "fresh" "medium" or "rotten" grade beef will be shown on the LCD. In addition, data on the EEPROM can also be added and removed for system development through a menu on the LCD. The average computation time obtained in the system to classify beef quality is 117ms and the classification system with 81 training data on 27 test data obtains an accuracy of up to 85%.
Pengoptimalan Lampu Lalu Lintas menggunakan Metode Naive Bayes Classifier Ivan Kasogi; Eko Setiawan; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 6 (2020): Juni 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Congestion is one of the main problems for big cities in Indonesia. This is influenced by the growth number of vehicles in Indonesia that are not matched by road growth. So, congestion cannot be avoided. In addition to these factors, the static traffic system in Indonesia contributes as a cause of congestion. at a traffic junction, sometimes a segment must wait for waiting time, while in other segments that intersect have no queue. So in this research of traffic light optimization system was designed using the naive bayes classifier method which was simulated using the SUMO Simulator Software and will communicated with Arduino through serial communication. In this research the density or length of the queue and each section will be used as input system that will be processed with training datas. The output system is the duration of traffic light that adjust by density in each sections. From the tests carried out, the suitability of naive bayes classifier with training datas reaches 100%. The Arduino and SUMO Simulator suitability tests reached 100% and the traffic light system optimization test using the naive bayes classifier produces a percentage of 86,66% better than the traffic system without naive bayes classifier of 15 times the side carried out.
Object Following Robot berbasis Pembacaan Jarak menggunakan Metode PID Controller Dyas Restu Palupi; Dahnial Syauqy; Agung Setia Budi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 7 (2020): Juli 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Most human activities are carried out by hand. These activities can be well carried out if the items we carry are sufficient in the capacity of our hands, both in quantity and weight. But if the items we carry are too many or large, the activity will be difficult. To solve this problem, we need a system that is able to make these activities easier. In this research, a robot is designed to follow a human. This robot is called object following robot. Using this robot, humans can carry things hands-free. The working principle of the robot is to detect the object by using a proximity sensor and to keep the robot's distance from the object using the PID controller. The robot uses three ultrasonic sensors HC-SR04 as its proximity sensors. These sensors are placed in front, right front, and left front of the robot. Data from the sensor is processed using a PID controller embedded in arduino uno. The output generated from the PID controller is a PWM that will be connected to a DC motor. Meanwhile, to determine the direction of movement of the robot used condition selection from three sensors. Based on the test results, the robot can keep a distance from the object and follow the movement of the object. The settling time (ts) of this robot with a set point of 35 cm is about 1 second. The test result shows that the robot works well, with a relatively fast response time.
Rancang Bangun Sistem Klasifikasi Rasa Permen Karet Berdasarkan Warna Dengan Metode K-Nearest Neighbor (KNN) Wisnu Mahendra; Dahnial Syauqy; Agung Setia Budi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 7 (2020): Juli 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Gum candy is a pretty much food forth by the Indonesian society because rawish gum can increase concentration and can also remove stress. Many types of taste contained in gum is and every person have a kind of favorite taste in different gum. In one container of rubber candy that has been issued by the factory or which is contained in the store has been mixed with various sense of gum. And make people hard to choose the type of sense of rubber gum according to the preferred. Therefore, the design prototype of the taste classification process in gum candy using K-Nearest Neighbor method. In this study using TCS3200 color sensor connected with arduino nano microcontroller. This sensor will later read every color on gum. The method used in this study is K-Narest Neighbor for calculation of classification on gum. From the test results that have been made there is a percentage of error of the TCS3200 color sensor reading of 0.23%. The result of testing on the casification of the rawish casification class by using the K-Narest Neighbor method with 10 times tested obtained 90% accuracy and average computing time of 3.1494 seconds.
Co-Authors A. Ashar Ashari A. Baihaqi Mubarok Abdul Aziz, Muhammad Rafi Abdul Rahman Halim Abdussalam, Ghifarie Sa'id Achmad Basuki Achmad Fanani Kurniawan Saputra Achmad Rizal Zakaria Addin Miftachul Firdaus Adharul Muttaqin Adhisuwignjo, Supriatna Adhitya Bhawiyuga, Adhitya Adi Setiyawan Adinugroho, Sigit Adit Ilmawan Adryan Chiko Pratama Afdy Clinton Afflatuslloh Adi Salung Agastya Bramanta Sanjaya Aghnadiin, Radifan Muhammad Agi Putra Kharisma Agra Firmansyah Agung Bachtiar Sukmaarta Agung Leona Suparlin Agung Prasetyo Agung Setia Budi Agung Setia Budi, Agung Setia Agung Widya Gumelar Agung Wismawan Rochmatullah Ahmad Mustafidul Ibad Ahmad Rizqi Pratama Ahmad Wildan Ahmad Yazid Bastomi AJI, IBRAHIM Akbar, Muhammad Daffa Pradipta Akbar, Muhammad Faithur Adel Patria Alfian Reza Pahlevi Alrynto Alrynto Althaf Banafsaj Yudhistira Andhika Nino Pratama Anggi Diatma Styandi Angsar, Mohamad Rinaldi Anisa Awalia Rizky Anjasmoro, Reza Ardiansyah Ardiansyah Arief Kurniawan Arief Wahyu Wicaksono Aulady, Fadhli Aulia Zhafran Barlian Henryranu Prasetio Bayu Rahayudi Bayu Santoso Belsazar Elgiborado Giovani Djoedir Billy Gusparentaqi Bima Muridianto, Muhammad Bimo Dimas Nugraraga Buce Trias Hanggara Bukhori Darmawan Bunga Boru Hasian Siahaan Cahyanita Qolby Rahmarta Rizaputri Cipto Bagus Jati Kusumo Constantius Leonardo Pratama Dading Firwandhi Sukma Daffa, Ali Zhafran Dedi Siswanto Defri Alif Raihan Denis Reza Ramdani Devo Harwan Pradiansyah Dimas Rizqi Firmansyah Dini Eka Ristanti Dini Ismawati Duwi Hariyanto Dwi Arini, Talitha Dwi Firmansyah Dwiki Ilham Bagaskara Dyas Restu Palupi Edita Edita Rosana Widasari Edita Rosana Widasari, Edita Rosana Eka Nanda Sugianto Eko Ardiansyah Eko Hilmi Firmansyah Eko Setiawan Eko Setiawan Elisabeth Agustina Era Imanningtyas Ezra Maherian Fachry Ananta Fahmi Gymnastiar Gozali, Muhammad Faizal Ardiansyah FAQIH, ABDULLAH Farras Nabil Fatur Rahman, Mohammad Fauzi Ali Farhi Fauzi Rivani Fikri Fauzan Firdy Yantama Firmanda, Dwi Ady Firza Zamzani, Muhammad Fitriyah, Hurriyatul Fungki Pandu Fantara Ganda Wibawa Putra Gembong Edhi Setyawan Ghazy Timor Prihanda Govinda Dwi Kurnia Sandi Graciella Fiona Br. Panjaitan Grafidi, Alif Akbar Gunawan Wahyu Andreanto Hafidz Abdillah Masruri Hafiz Nul Hakim Hamdan Bagus Firmansyah Hamzah Attamimi Hanggara, Buce Trias Hannats Hanafi Ichsan Haqiqi, Farih Akmal Harahap, Syazwandy Hazal Kurniawan Putra Hazbiy Shaffan, Nur Henryranu Prasetio, Barlian Herenda Madi, Matius Herwin Yurianda Hurriyatul Fitriyah Hurriyatul Fitriyah Hurriyatul Fitriyah, Hurriyatul Idang Wahyuddin Septiawan Ihsanurrahim Ihsanurrahim Ikhwan Zulfy Imam Cholissodin Irfan Pratomo Putra Irvan Ramadan Issa Arwani Ivan Kasogi Izaaz Waskito Widyarto Izza Febria Nurhayati Jeffry Atur Firdaus Jevandika Jezriel Lukas Lumbantobing Johannes Archika Waysaka Khairul Anwar Khairul Anwar Kresna Wiska Kafila Kurnia, Yudisthira Dwi Kurniawan, Rizaldy Ariobimo Kurwniawan, Wijaya La Ode Muh. Fadlun Akbar Lase, Nicolash Jeremy Onoma Latief Nurrohman Alfansuri Lavanna Indanus Ramadhan Lb Novendita Ariadana Lutfi Anang Makruf M Nuzulul Marofi M. Adib Fauzi Rahmana M. Ali Fauzi Mahendra, I Gusti Putu Krisna Suaba Malik, Hifdzul Megananda, Muhammad Rifqi Mela Tri Audina Merry Hassani, Fadila Muqtadaro Mhd. Idham Khalif Moch. Alfian Zainullah Moch. Alvin Yasyfa Salsabil Mochamad Iswandaru Mochammad Hannats Hanafi Mochammad Hannats Hanafi Ichsan Moh. Saifud Daulah Moh. Zainur Rodhi Mohammad Ali Muhsin Mohammad Faizal Ajizi Muchamad Rafi Dharmawan Muchammad Cholilulloh Muh. Syifau Mubarok Muhajir Ikhsanushabri Muhammad Alif Alfajra, Andi Muhammad Aminul Akbar Muhammad Daffa Bintang Nugroho Muhammad Eraz Zarkasih Muhammad Fadhil Sadeli Muhammad Fajaruddin Akbar Muhammad Habib Jufah Alhamdani Muhammad Hanif Haikal Muhammad Hannats Hanafi Ichsan Muhammad Irvine Fidellio Maiza Muhammad Jibriel Bachtiar Muhammad Kholash Fadhilah Muhammad Naufal Muhammad Nazrenda Ramadhan Muhammad Rizqi Zamzami Muhammad Wingga Woggiasworo Muhammad Yusuf Ramadan Mukhamad Angga Setiawan Mukhamad Roni Mukmin Mukmin Munif Cleveriandy, Ahmad Musharrif, Mohammad Faiz Mustajib Furqon Haqiqi Mutiara Pramesti Utami Muzayyin, Asep Nabila Eka Putri, Alisya Nadhifa, Nadaa Nanda Epriliana Asmara Putri Navayo, Bagja Nicho Ferdiansyah Kusna Nikmatus Soleha Niko Aji Nugroho Noveriko Noveriko Nur Aini Afifah Isbindra Nur Fuady, Muhammad Sholahuddin Nurul Ikhsan Nyoman Wira Prasetya Oggy Setiawan Parja, Mujianto Anda Perkasa, Septiyo Budi Prakoso, Aldo Hani Pramandha Saputra Prasetya, Nyoman Wira Prasetyo, Budi Eko Prasojo, Satya Haryo Pricillia, Lidya Ruth Purnomo, Welly Putra Pandu Adikara Putra Pandu Adikara Putra, Brylliano Maza Raga Jiwanda Raharja, Kahfi May Rahayu, Vina Trisnawati Rahman, Edy Raka Bagas Perdana Rakhamadhany Primananda Rakhmadhany Primananda Rakhmadhany Primananda, Rakhmadhany Randy Cahya Wihandika Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Renal Prahardis Reza Budi Pratikto Rezak Andri Purnomo Rifqi Anshari Ringga Aulia Primahayu Rint Zata Amani Rioadam Sayyid Abidin Riza Irfan, Muhammad Rizal Maulana Rizal Maulana, Rizal Rizal Setya Perdana Rizka Ayudya Pratiwi Rizky Putra Wijaya Rizqi Muh. Muqoffi Ashshidiqi Ronilaya, Ferdian Rudy Agus Santoso Sabrian Rizqika Akbar Sabriansyah Rizkiqa Akbar Sabriansyah Rizqika Akbar Sabriansyah Rizqika Akbar Sabriansyah Rizqika Akbar Safirurrasul Santoso, Mush'ab Safrudin Bendang, Dehleezto Lawanangkara Salman Farizy Nur Samuel Lamhot Ladd Palmer Simarmata Santoso, Bayu Saputro, Mauna Mohammad Wahyu Sari, Sylvia Sentosa, Azy Dwi Putra Septino, Fernando Setiawan , Eko Shaffan, Nur Hazbiy Shelsa Faiqotul Himmah Sigi Syah Wibowo Siradjuddin, Indrazno Sulaiman, Ihsan Susilo, Faizal Andy Sutikno Sutikno Syarief Taufik Hidayatullah Syauqi, Mohd Alfitra Syazwana, Selvia Tibyani Tibyani Tio Haryanto Adi Putra Toar, Mikhael Ryan Tobias Sion Julian Utaminingrum, Fitri Utomo, Satria Wahyudi Vira Muda Tantriburhan Mubarak Virza Audy Ervanda Wahyu Adi Prayitno Welly Purnomo Widasari, Edita Rosana Widhy Hayuhardhika Nugraha Putra Wijaya Kurniawan Wijaya Kurniawan Wijaya Kurniawan Wijaya, Jason Wildo Satrio Wirafadil Nugraha Wisik Dewa Maulana Wisnu Mahendra Xavierro Lawrenza Yanottama Oktabrian Yudhistira, Gevan Putra Yuita Arum Sari Yunan Alamsyah Nasution Yunus, Ahmad Haykal Yurliansyah Hirma Fajar Yusriansyah Shohibul Hamzah Zahra, Inez Bedwina Zakaria, Akhmad Nizar