Article Per Year (5 Year)
p-Index From 2021 - 2026
15.331
P-Index
This Author published in this journals
All Journal Jurnal Bisnis dan Manajemen Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal POINTER Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Seminar Nasional Teknologi Informasi Komunikasi dan Industri DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi (JUST-SI) Bulletin of Social Informatics Theory and Application
Bayu Rahayudi
Fakultas Ilmu Komputer - Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Public Health Social Sciences

Published : 208 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 208 Documents
Search

 4   5   6   7   8 
Prediksi Kebangkrutan Menggunakan Metode Backpropagation (Studi Kasus: Perseroan Terbatas Terdaftar Pada Bursa Efek Indonesia) Nanda Alifiya Santoso Putri; Dian Eka Ratnawati; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (971.897 KB)

Abstract

Bankruptcy is a condition while a company fails either economic failure or even financial failure. Bankruptcy causes a general seizure of all the assets of a bankrupt Debitor (company) that settled and managed by the Curator (supervisor of Debitor's asset). Because it can causes a severe consequences, several attemps were done as an alternative for bankruptcy prevention. One of those attemps is by predicting the bankruptcy itself. Backpropagation is a method of artificial neural network that widely used in the context of classification or regression datasets, one of the regression problem is prediction, because backpropagation is one of the supervised learning algorithm which the output or input values already known. In this study, backpropagation works for predicting the bankruptcy with Altman's five variabels as inputs and the results of Z-Score calculation as output target. The entire test that has been done produces the best MAPE value with average at 0,062% using learning rate parameter value at 0,2, 1000 iterations and 6 neurons in the hidden layer. This MAPE value is under 10% and close to 0% which included in the criteria of prediction with very good accuracy.
Optimasi Fuzzy Time Series Menggunakan Algoritme Particle Swarm Optimization Untuk Peramalan Produk Domestik Bruto (PDB) Indonesia Dloifur Rohman Alghifari; Bayu Rahayudi; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (785.752 KB)

Abstract

As one of the input indicators for development programs. This Gross Domestic Product (GDP) forecasting is expected to provide information about economic growth and performance in Indonesia. Data sources of GDP usually come from survey results or from administrative records from various institutions. Sometimes the source data is incomplete or not available when calculating GDP values, it must be determined how to calculate the GDP value so that it can be used to estimate GDP forecasting using fuzzy time series. To improve forecasting accuracy, we use fuzzy time series optimization intervals using particle swarm optimization (PSO). Based on the parameters obtained with a dimension length of 40, many particles of 40, 450 for maximum iteration, the value of c1 and c2 is equal to 1.5 and for inertial weight of 0.3, the forecasting error rate generated using MAPE is 2.48% of the 10 test data. These results indicate good forecasting ability with a low error rate. The comparison of forecasting results for the proposed method is slightly better than the fuzzy time series method with the determination of the average interval based on MAPE 2.66%. But it is no better than the linear regression method with MAPE 1.52%
Sistem Rekomendasi Pemilihan Prioritas Surat Masuk Menggunakan Metode AHP-SAW (Study Kasus: DJBC KANWIL JATIM I) Heryadi Mochamad Ramdani; Edy Santoso; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1144.408 KB)

Abstract

DJBC KANWIL JATIM 1 is a government agency that serves the public in the field of customs and excise. Every day DJBC KANWIL JATIM1 receives letters from all sectors of the region waiting to receive orders to take action. Sometimes the regional head of office is confused about choosing which letter to take action first. Because the head of the regional office must give quick action to the letter that has the highest priority in order to get quick action. Then a letter priority recommendation system is needed that is more efficient in determining which letter is first carried out. In this study, the Analytical Hierarchy Process (AHP) and SAW (Simple Additive Weighted) methods were combined to determine the priority of incoming letters at DJAN KANWIL JATIM I. The combination of AHP and SAW methods is very well used in decision making. The AHP method is used to identify the weights for each criterion taken from the paired comparison matrix, while the SAW method is used to determine the highest priority entry letter for action. Based on the testing using the Spearman Rank Correlation Coefficient calculation produces a value of 0.86015. The results show that the system is running well.
Prediksi Rating Pada Review Produk Kecantikan Menggunakan Metode Semantic Orientation Calculator dan Regresi Linier Bastian Dolly Sapuhtra; M. Ali Fauzi; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (694.06 KB)

Abstract

Crowded producers of beauty product produce good and varied products. This has attracted consumers to use these beauty products. More and more consumers are using these beauty products, making producers try various innovations on their products. Innovation can be obtained from many comments, advices, or reviews made by consumers on variety of products. Benefits of product reviews for consumers are also useful to obtain information before buy a product. Many results of the review are not accompanied by rating. This makes it difficult for producers to classify reviews into certain sentiments. In this research aims to classify review into certain sentiments automatically into rating. In this research built a system using Semantic Orientation Calculator and Linear Regression methods. Breaking sentences in a review into n-gram (bigram and trigram) and one sentence aims to improve the results of predictions. Results of testing on this system are 23%, 71%, 67% on accuracy of bigram, 24%, 71%, 67% on accuracy of trigram, and lowest 24%, 67%, 64% on accuracy of one sentence with tolerance 0, tolerance 1, and sentiment reviews. The best result of testing on breaking sentence using n-gram (bigram and trigram) was good enough to solve problem in this research.
Relevance Feedback Pada Sistem Temu Kembali Informasi Dokumen E-Book Berbahasa Indonesia Menggunakan Metode BM25 Tasya Agiyola; Indriati Indriati; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1360.436 KB)

Abstract

The use of e-books on the development of digital technology makes it easy for users to get more practical information than having to use printed books. The number of e-books spread on the internet is very numerous and varied, therefore a system for retrieving information on e-book documents in Indonesian is needed. To improve the relevance of the results of the returned documents, relevance feedback techniques can be applied. Relevance feedback is a technique where users can provide feedback on previous document search results. Sorting the number of documents returned based on queries is calculated using the BM25 method. This study aims to determine the results of the application and the results of testing of relevance feedback on the retrieval system of Indonesian e-book document information using the BM25 method. Based on the test results, the AVP value after relevance feedback has decreased. In testing based on K values, the AVP value before RF is 0.592, after RF(20) is 0,558, and after RF(50) is 0.573. While in testing based on the expansion terms, the AVP value before RF is 0.593, after RF(20) is 0,587 and after RF(50) is 0.570.
Prediksi Penjualan Roti Menggunakan Metode Exponential Smoothing (Studi Kasus : Harum Bakery) Reyhan Dzickrillah Laksmana; Edy Santoso; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1406.315 KB)

Abstract

Bread is a very widely known in the community and the invention in its technology was also very rapidly growing. Bread has now become part of everyday life and become staple human food. The bakery industry is constantly evolving to make bread companies do a product innovation and the right sales strategy. That is becomes a challenge for Harum Bakery to get maximum profit and suffered no losses. The predictions in this research use Exponential Smoothing method. Exponential Smoothing is a method that continuously performs forecasting improvements by taking the average value of smoothing past values from time expanding data in exponential way. In this research, prediction were performed using three Exponential Smoothing method which is, Single Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing. This method was evaluated by calculating average error rate using Mean Absolute Percentage Error (MAPE) method. The smallest MAPE for Single Exponential Smoothing method when value of α parameter's is 0,1 with MAPE value 27,4039%, for Double Exponential Smoothing method when value of α parameter's is 0,1 with MAPE value 25,124% , and for Triple Exponential Smoothing method when value of α parameter's is 0,1, β parameters is 0,1 and γ parameters is 0,4 with MAPE value 25,303%. So the concluded is the Double Exponential Smoothing has better accuracy than Single Exponential Smoothing and Triple Exponential Smoothing on Bread Sales Prediction Case Study of Harum Bakery.
Implementasi Extreme Learning Machine dan Fast Independent Component Analysis untuk Klasifikasi Aritmia Berdasarkan Rekaman Elektrokardiogram Aditya Septadaya; Candra Dewi; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (846.887 KB)

Abstract

The type of arrhythmia can indicate the location of the disorder and its causes. The way to identify the arrhythmia is to use an electrocardiogram (ECG) strip. Machine learning can be used as an approach to assist identification of arrhythmias through an ECG. Extreme Learning Machine (ELM) is one single-hidden layer feedforward neural networks (SLFNs) that can be used for the classification of arrhythmias in order to assist medical diagnosis. To optimize ELM performance, Fast Independent Component Analysis (FastICA) algorithm is used for preprocessing and extracting ECG signals. In this study, several parameter tests were conducted to determine the impact on the performance of the classification model. ECG data obtained from the arrhythmia database managed by the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH). Each data is a 3 seconds ECG snippet with total of 210 data divided into 6 arrhythmia classes and normal rhythms. The results showed that the classification model was able to achieve perfect performance with accuracy, precision-recall, and F-1 score of 100% at the training stage. However, the classification model was experiencing overfitting at the testing stage with the mean of matthew correlation coefficient is approximately 0. Overfitting occured because the feature representation is too complex and not proportional to the amount of available data. This resulted in poor performance in the ELM-FastICA test for data that was not yet recognized.
Prediksi Kinerja Akademik Mahasiswa Pada Mata Kuliah Pemrograman Dasar dengan Algoritme Backpropagation Aldous Elpizochari; Ahmad Afif Supianto; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (597.451 KB)

Abstract

Basic programming course is one of the courses taken by new students and usually some of those students have difficulties understanding the basic concepts of programming. This study aims to identify students who are struggling on the course at the earliest possible time by using factors that can be collected before any test or evaluation is taken so that the lecturer can provide additional assistance for students who encounter such difficulties. The classification method proposed in this study is Neural Network Backpropagation. Tests will be done to find out whether the proposed method can solve the problem of this study and to find out the best value for parameters such as the number of hidden neurons and hidden layer and learning rate for this study. Some test scenarios are also used in this study such as using all of the data features, using PCA with 85%, 90%, 95% variance, and using only significant features based on Pearson correlation. The test results of this study revealed that the proposed method can be used to solve the problem in this study, with the highest average accuracy of 0.74 in two scenarios, the PCA with 95% variance and using only significant features scenario. Test results also show that the parameters which produce the best result is 7 hidden neurons in one hidden layer and learning rate value of 0.7.
Prediksi Jumlah Kunjungan Wisatawan Mancanegara Ke Indonesia Menggunakan Metode Average-Based Fuzzy Time Series Models Teri Kincowati; Muhammad Tanzil Furqon; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1129.48 KB)

Abstract

Indonesia is a country that has a great diversity of cultures and natural wealth. One of Indonesia's natural wealth that is the attraction of the world is its tourist attractions. The beauty of tourist attractions in Indonesia makes Indonesia becomes a country that is often visited by foreign tourists on vacation. Tourism is one business that can increase economic growth. Tourists visit is increased sigficantly due to many factors, including competitive travel costs, promotion strategies that continue to be intensified, and many adequate travel routes. This must be balanced with adequate facilities and guaranteed security. The number of visitors that cannot be ascertained must be predictable in anticipation of sudden increases or deterioration, so that the state can determine policies towards changes in the number of visitors in the future. The method that will be used to predict in this study is the average-based fuzzy time series models and the using 216 data obtained from the official website of the Statistic Indonesia, it is data of the number of foreign tourist visits to Indonesia in the period January 1999 to December 2016. Based on the result of the study obtained MAPE value is 10,140%, that MAPE value is good to predict, because it is under 20%. So can be concluded that average-based fuzzy time series is good enough to predict the number of foreign tourists visit to Indonesia.
Prediksi Harga Bitcoin Menggunakan Metode Extreme Learning Machine (ELM) dengan Optimasi Artificial Bee Colony (ABC) Arjun Nurdiansyah; Muhammad Tanzil Furqon; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (919.787 KB)

Abstract

Bitcoin is the most popular cryptocurrency currently being favored as a means of investment like stocks. Its nature is not centralized or decentralized which causes the price of Bitcoin can experience inflation at any time. So we need a method to predict the price of Bitcoin accurately to make decisions in Bitcoin buying and selling transactions. The ELM method has better learning speed than other methods and a simple structure, but it has disadvantages in choosing input weights and biases randomly. To overcome these shortcomings, the ABC method is used because it also has a very simple and flexible structure. Therefore, the price of Bitcoin will be predicted using the ELM-ABC method. This research uses Bitcoin price time series data from the Indodax cryptocurrency exchange from 01 December 2017 to 31 August 2018. ABC functions to produce the most optimal input weights and biases for the ELM training stage. Furthermore, input weights, biases, and output weights will be used for ELM testing stages to obtain the prediction result prices. Then, error evaluation value calculated from the results of the Bitcoin price prediction using MAPE. The ELM-ABC parameter test results get the best combination of 12 features, 20 hidden neurons, 20 bee populations, and 5 iterations. The combination produces an average MAPE value of 1,96983% and an accuracy of 98,03017%, while ELM amounted to 2,70401% and 97,29599%.
Co-Authors Abdullah Harits Abdurrahim, Ahmad Azmi Abhiram, Muhammad Tegar Achmad Choirur Roziqin Achmad Ridok Adam Hendra Brata Ade Wahyu Muntizar Adi Mashabbi Maksun Adi Maulana Rifa'i Adi, Tri Adinda Putri, Lintang Gladyza Adinugroho, Sigit Aditya Septadaya Aditya, Nathanael Chandra Afif Ridhwan Ageng Wibowo Agus Wahyu Widodo Agus Wahyu Widodo, Agus Wahyu Ahmad Afif Supianto Ahmada Bastomi Wijaya Aldi Bagus Sasmita Aldous Elpizochari Alfarisi, Raihan Alfian Reza Pahlevi Alip Setiawan Allifira Andara Hasna Alvian Akmal Nabhan Amaliah Gusfadilah Andhi Surya Wicaksana Andro Subagio Angga Wahyudi Kurniawan Pratama Anggi Novita Sari Anne Diane Rachmadani Arif Indra Kurnia Arina Rufaida Aristides, Joy Vianoktya Arjun Nurdiansyah Arsan, Danish Alif Arsti Syadzwina Fauziah Audia Refanda Permatasari Ayezha Halidar Putri Irwanda Ayuda Dhira Pramadhari Bachtiar, Harsya Bafagih, Novel Bagas Laksono Bastian Dolly Sapuhtra Basuki, Akbar Lucky Bisma Anassuka Brillian Aristyo Rahadian Buce Trias Hanggara Budi Darma Setiawan Cahyo Gusti Indrayanto Candra Dewi Candra Dewi Chandra, Ardhya Khrisna Christina Sri Ratnaningsih Cindy Cynthia Nurkholis Dahnial Syauqy Daniel Agara Siregar Dany Primanita Kartika Sari Dany Primanita Kartikasari Davia Werdiastu Dedy Surya Pradana Dese Narfa Firmansyah Devi Nazhifa Nur Husnina Dhaifa Farah Zhafira Dhimas Wida Syahputra Dhiva Mustikananda Diamanta, Ananda Dian Eka Ratnawati Dian Ratnawati Dian Sisinggih Dimas Adi Syahbani Achmad Putra Djoko Pramono Djoko Pramono Dloifur Rohman Alghifari Dwi H Sulistyarini Dwija Wisnu Brata Dwija Wisnu Brata Dwija Wisnu Brata Dzulkarnain, Tsania Dzulkarnain, Tsania - Edgar Maulana Thoriq Edy Santoso Eko Wahyu Hidayat Ellita Nuryandhani Ananti Ema Rosalina Eni Hartika Harahap Fadilah Islamawan, Adam Faiz Abiyandani Faizatul Amalia Fajar Pangestu Faradila Puspa Wardani Faris Febrianto Farizky Novanda Pramuditya Fauzia, Sri Febrina Sarito Sinaga Ferina Kusuma Anjani Ferry Jiwandhono Fitria Yesisca Gagas Budi Waluyo Gani Kharisma Wardana Gilang Pratama Gusti Reza Maulana Haidar Azmi Rabbani Hanggara , Buce Trias Hardyan Zalfi Harris Imam Fathoni Haryuni Siahaan Hayunanda, alanela ganagisarama Heryadi Mochamad Ramdani Hidayati, Chofifa Hilmy Ramadhan, Achmad Zhafran Huda Minhajur Rosyidin Husalie, Levin Vinnu Imam Cholisoddin Imam Cholissodin Imam Cholissodin Immanuel Tri Putra Sihaloho Indriati Indriati Indriati Indriati Indriati, Indriati - Intan Sartika Eris Maghfiroh Irany Windhyastiti Irwan Shofwan Issa Arwani Issa Arwani Ivan Agustinus Jasico Da Comoro Aruan Jefri Hendra Prasetyo Jonemaro, Eriq Muhammad Adams Jumerlyanti Mase K., Anggraeni Dwi Kautsar, Ahmad Izzan Kevin Nastatur Chatriavandi Khairul Rizal Krishna Febianda Ksatria, Willyan Eka Kurnianingtyas, Diva Laila Diana Khulyati Lailil Muflikhah Liwenki Jus'ma Olivia M. Ali Fauzi M. Ali Fauzi M. Attala Reza Syahputra Made Tri Ganesha Madjid, Marchenda Fayza Marji Marji Marpaung, Veronika Oktafia Marwa Mudrikatussalamah Maulana Syahril Ramadhan Hardiono Maulana, M. Ighfar Maulidhia, Abrilian Meriza Nadhira Atika Surya Michael Eggi Bastian Mochammad Ilman Asnada Mohammad Aditya Noviansyah Mohammad Setya Adi Fauzi Mohammad Zahrul Muttaqin Muh. Arif Rahman Muhammad Ferian Rizky Akbari Muhammad Hidayat Muhammad Ikhsan Nur Muhammad Jibril Alqarni Muhammad Kevin Sandryan Muhammad Nadzir Muhammad Nurhuda Rusardi Muhammad Razan Nadhif Muhammad Reza Utama Pulungan Muhammad Shidqi Fadlilah Muhammad Syahputra Muhammad Tanzil Furqon Mukhtar Darma Hidayat, Alif Ahmad Muthia Maharani Muzayyani, Muhammad Farid Nadiah Nur Fadillah Ramadhani Najihah, Siti Waheeda Nanang Yudi Setiawan Nanang Yudi Setiawan Nanda Alifiya Santoso Putri Nashihul Ibad Al Amin Niken Hendrakusuma Wardani, Niken Hendrakusuma Nilna Fadhila Ganies Novanto Yudistira Nur M. F. Dinia Nurfadhilah, Rakhmad Giffari Nuril Haq, Muhammad Nurizal Dwi Priandani Nurul Hidayat Nurul Ihsani Fadilah Obed Manuel Silalahi Panjaitan, RE. Miracle Pascad Wijanata, Ida Bagus Prakosa, Wira Zeta Pramudita, Julina Larasati Primayuda, Averil Priscillia Vinda Gunawan Purnomo, Welly Putra Pandu Adikara Putranto, Rezky Donny Putri Ratna Sari Putri, Firda Qhafari, Abi Al Qoid A Fadhlurrahman Rafli, Mohammad Ali Rahinda, Muhammad Abiyyi Ramadhani, T. Zalfa Randy Cahya Wihandika Rani Metivianis Rasif Nidaan Khofia Ahmadah RE. Miracle Panjaitan Reinaldi Guista Pradana Ismail Reiza Adi Cahya Renaldi Muhammad Revan Yosua Cornelius Sianturi Reyhan Dzickrillah Laksmana Reza Aprilliana Fauzi Rheza Raditya Andrianto Rifwan Hamidi Riswan Septriayadi Sianturi Riza Rizqiana Perdana Putri Rizky Ardiawan Rizky Nuansa Nanda Permana Rohimatus Sholihah Roisul Setiawan Roma Akbar Iswara Rudianto Raharjo Safa S Istafada Saifurrijaal, Muchammad Salsabila, Dhea Rani Sandi Dewo Rahmadianto Satrio Agung Wicaksono Sekeon, Yerobal Gustaf Setiana, Maya Setiawan, Roisul Shafira Eka Aulia Putri Slamet Thohari Sofi Hidyah Anggraini Sugeng Santoso Sugiarto S Sugiono Sugiono Sukmawati, Annisa Sultan Saladdin Sultan, Muhammad Attharsyah Firdaus Supraptoa Supraptoa Tanica Rakasiwi Tasya Agiyola Teri Kincowati Tri Astoto Kurniawan Trias Hanggara, Buce Trio Pamujo Wicaksono Ulva Febriana Umar Basher, Nizar Umu Khouroh Vivilia Putri Agustin Wahyu Bimantara Wayan Firdaus Mahmudy Welly Purnomo Welly Purnomo Weni Agustina Wenny Ramadha Putri Wibowo, Dhimas Bagus Bimasena Wicaksono, Satrio A. Widhy Hayuhardhika Nugraha Putra Widhy Hayuhardika Nugraha Putra Widodo, Ibnu Sam Widyadhana, Fawwaz Kumudani Wiku Galindra Wardhana Wisnu Brata, Dwija Yahya, Faiz Yesaya Sergio Vito Putranta Yudi Setiawan, Nanang Yuita Arum Sari Yusuf Afandi Zhafira, Dhaifa Farah