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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika Jurnal Fisika FLUX KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal IMPACT: Implementation and Action Journal of Electronics, Electromedical Engineering, and Medical Informatics Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Kepada Masyarakat (Mediteg) Jurnal Abdimas Madani dan Lestari (JAMALI) Bubungan Tinggi: Jurnal Pengabdian Masyarakat Computer Science and Information Technologies Madaniya Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Dodon Turianto Nugrahadi
Lambung Mangkurat University
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Implementasi Algoritma Enkripsi AES-128 bit pada Data RFID pada Jarngan 802.11n dan 802.11ac dengan Monitoring Jarak Jauh Berbasis MQTT M. Apriannur; Dodon Turianto Nugrahadi; Andi Farmadi; Muhammad Itqan Mazdadi; Fatma Indriani
Jurnal Informatika Polinema Vol. 10 No. 1 (2023): Vol 10 No 1 (2023)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i1.1494

Abstract

Message Queue Telemetry Transport atau yang biasa disebut MQTT yaitu protokol untuk komunikasi yang bersifat machine to machine atau M2M dan bekerja pada layer ketujuh atau aplikasi dan bersifat lightweight message. Protokol MQTT secara default hanya memiliki mekanisme autentikasi saja secara default masih belum terenkripsi. Maka dilakukan penelitian implementasi algoritma enkripsi AES-128 bit dengan koneksi 802.11n berbasis mqtt pada sistem kunci RFID pintu otomatis dengan monitoring jarak jauh. Dalam penelitian ini pembacaan sensor rfid dilakukan menggunakan skenario dengan enskripsi dan tanpa enkripsi dengan pengiriman data sebanyak 50 kali. Hardware yang digunakan pada penelitian yaitu mikrokontroler NodeMCU ESP8266, adaptor 5V, dan sensor MFRC522 yang diimplementasikan pada Tp-link Archer C54 dan Tp-link TL-MR100. Tujuan pada penelitian ini adalah untuk mengetahui pengaruh proses enkripsi AES 128-bit data RFID pada mikrokontroler NodeMCU melalui protokol MQTT menggunakan jaringan 802.11n Archer dan jaringan 802.11n MR100 terhadap penggunaan RAM, delay dan throughput. Pada parameter penggunaan RAM, tipe router 802.11n Archer, skenario enkripsi menghasilkan sisa RAM rata – rata sebesar 18,829 KB. Sedangkan skenario tanpa enkripsi sebesar 23,225 KB. Tipe router 802.11n MR100, skenario enkripsi menghasilkan sisa RAM rata – rata sebesar 18,828 KB. Sedangkan skenario tanpa enkripsi sebesar 23,287 KB. Terjadi peningkatan penggunaan RAM 32 KB dari penggunaan tanpa enkripsi dan enkripsi yaitu pada router 802.11n Archer 23%, sedangkan pada router 802.11n MR100 24%. Pada parameter delay, tipe router 802.11n Archer, Transfer data enkripsi memiliki delay rata – rata sebesar 137,79 ms. Sedangkan Transfer data tanpa enkripsi sebesar 128,08 ms. Tipe router 802.11n MR100, Transfer data enkripsi memiliki delay rata – rata sebesar 145,71 ms. Sedangkan transfer data tanpa enkripsi sebesar 126,45 ms. Terjadi peningkatan delay dari penggunaan tanpa enkripsi dan enkripsi yaitu pada router 802.11n Archer 8%, sedangkan pada router 802.11 MR100 15%. Pada parameter ukuran throughput, tipe router 802.11n Archer, skenario enkripsi memiliki throughput sebesar 1,659 KB/s sedangkan tanpa enkripsi sebesar 0,491 KB/s. Tipe 802.11n MR100, skenario enkripsi memiliki throughput sebesar 1,586 KB/s sedangkan tanpa enkripsi sebesar 0,513 KB/s. Terjadi peningkatan throughput dari penggunaan tanpa enkripsi dan enkripsi.
Pengelompokan PMKS menggunakan Self Organizing Maps dengan perbaikan missing value Naïve Bayes Imputation Hidayah, Noor; -, Muliadi; Budiman, Irwan; Nugrahadi, Dodon Turianto; Herteno, Rudy
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 4, Year 2022 (October 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14424

Abstract

Penyandang Masalah Kesejahteraan Sosial (PMKS) merupakan permasalahan pada kelompok masyarakat yang memiliki kesulitan dalam menjalankan fungsi sosial. Penelitian dilakukan untuk mengetahui karakteristik permasalahan di wilayah Kalimantan Selatan dengan menggunakan klasterisasi. Metode klasterisasi yang digunakan adalah SOM dan pengisian data kosong menggunakan NBI yang dibandingkan dengan Metode Statistik (Mean, Median, dan Modus). Proses dimulai dari mengisian data kosong dengan NBI dan Metode Statistik, dilanjutkan dengan klaster SOM dan hasil klaster dievaluasi menggunakan DBI. Hasil yang didapatkan adalah perbaikan NBI menempati hasil klasterisasi terbaik dengan nilai 0,032 pada pembagian 2 klaster. Klaster pertama berjumlah 8 wilayah yaitu Tanah Laut, Kota Baru, Tapin, Hulu Sungai Selatan, Hulu Sungai Tengah, Hulu Sungai Utara, Tabalong, dan Tanah Bumbu. Klaster kedua berjumlah 5 wilayah yaitu Banjar, Barito Kuala, Balangan, Banjarmasin, dan Banjarbaru. Tingkat prioritas yang diperoleh dari rata-rata klaster didapatkan bahwa klaster kedua sebagai prioritas pertama.
TEKNOLOGI MEMBRAN FILTRASI AIR RAWA/GAMBUT BERBASIS PANEL SURYA UKM PENGOLAH IKAN ASIN DESA MUNING BARU dodon turianto nugrahadi; Totok Wiyanto; Sri Cahyo Wahyono; Ahmad Rusadi Arrahimi; sholih Afif
Jurnal Pengabdian Kepada Masyarakat (MEDITEG) Vol. 7 No. 1 (2022): Jurnal Pengabdian Kepada Masyarakat (MEDITEG)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat (P3M) Politeknik Negeri Tanah Laut (Politala)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/mediteg.v7i1.100

Abstract

River water and well water are the main sources for the daily water needs for the people of South Kalimantan. However, the peatland water has a muddy and smelly that has an effect on health. This cloudy and smelly condition is due to the condition of the peatlands in the South Kalimantan region. The use of peat water by the community in Muning Baru Village, South Daha, HSU has been carried out for a long time, especially UKM salted fish processing. The quality and quantity of fish production are affected by the quality of clean water. Implementation begins with the design of the filtration membrane, assembly of solar panels, pumps and filtration tubes. It is hoped that this application will support the society towards society 5.0. The results of the implementation are giving the needs of clean water up to 80%, either else NTU 30 become 3.44 NTU, TSS 522 mg/l become 352 mg/l, COD 31.9 mgO2 /l become 6.09 mg02/l.
An Electrocardiogram Signal Preprocessing Strategy in the LSTM Algorithm for Biometric Recognition Rahayu, Fenny Winda; Faisal, Mohammad Reza; Nugrahadi, Dodon Turianto; Nugroho, Radityo Adi; Muliadi, Muliadi; Redjeki, Sri
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.93895

Abstract

Electrocardiogram (ECG) signals are a very important tool for clinical diagnosis and can be used as a new biometric modality. The aim of this research is to determine the results of ECG signal processing using RNN methods such as the Long Short Term Memory (LSTM) algorithm by utilizing several preprocessing techniques. In this study, the ECG signal itself was previously tested by carrying out the LSTM classification process without preprocessing, and the results obtained were 0% accurate, so preprocessing was needed. The preprocessing methods tested with the LSTM classification method are Adjacent Segmentation and R Peak Segmentation to find out which preprocessing techniques greatly influence LSTM classification accuracy. The experimental results were that LSTM classification with R Peak Segmentation preprocessing obtained the highest accuracy on the two data used, namely filtered and raw data, with 80.7% and 78.95%, respectively. Meanwhile, the accuracy obtained from LSTM classification when using Adjacent Segmentation preprocessing is not good. This research compares LSTM accuracy from each preprocessing stage to determine which combination has the best results in the ECG data classification process. This research also offers new insights into the preprocessing stages that can be carried out on ECG data.
PENGOLAHAN AIR GAMBUT MENJADI AIR BERSIH BAGI SANTRI DI PESANTREN NURUL HIJRAH JORONG KALIMANTAN SELATAN Nugrahadi, Dodon Turianto; Wianto, Totok; Wahyono, Sri Cahyo; Gunawan, Gunawan; Azwari, Ayu RianaSari; Arrahimi, Ahmad Rusadi; Apriana, Susi; Utomo, Edy Setyo
Kumawula: Jurnal Pengabdian Kepada Masyarakat Vol 7, No 1 (2024): Kumawula: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/kumawula.v7i1.51325

Abstract

Pada saat ini kebutuhan akan pengolahan air untuk mendukung perkembangan ekonomi dan kesehatan dialami oleh pondok pesantren. Pondok Pesantren Nurul Hijrah Jorong Kalimantan Selatan menggunakan air dari sumur bor air gambut untuk memenuhi kebutuhan. Air tersebut merupakan air gambut, hal ini disebabkan oleh kondisi daratan di Kalimantan Selatan yang merupakan lahan gambut. Berbagai masalah penggunaan air saat ini diantaranya kebersihan dan kesehatan, seperti meninggalkan noda coklat hasil endapan serta kondisi gatal-gatal kulit yang dialami santri, dengan jumlah lebih 300 santri dan jamaah di Pondok Pesantren Nurul Hijrah Jorong. Hal ini masih merupakan masalah yang harusnya tersolusikan, maka tujuan pengabdian masyarakat ini yaitu upaya untuk meningkatkan kualitas air sumur bor air gambut tersebut sesuai baku mutu air bersih. Metode yang dilakukan yaitu proses pengolahan air yang menggabungkan proses filtrasi, absorpsi dan ultrafiltrasi dengan sistem single flow ultrafiltrasi. Hasil evaluasi berdasarkan laboratorium menunjukkan bahwa terjadi penurunan yaitu nilai jumlah zat terlarut (total dissolved solid/TDS) 0,2%, kekeruhan 25,8%, warna air 63,6%, nitrat 95%, coliform 49,8% serta peningkatan nilai keasaman 2%. Hasil produksi air bersih memiliki kapasitas besar hingga 2400 lt. 80% perwakilan santri dan ustad pengelola mendapatkan pengetahuan dan keterampilan tentang penggunaan dan perawatan teknologi pengolahan air ini. At this time, Islamic boarding schools experience the need for water treatment to support economic development and health. The Nurul Hijrah Islamic Boarding School in Jorong, South Kalimantan, uses water from drilled peat wells to meet its needs. This water is peat water caused by the condition of the land in South Kalimantan, which is peat land. Various problems with water use today include cleanliness and health, such as leaving brown stains from sediment and itchy skin conditions experienced by students, with more than 300 students and congregations at the Nurul Hijrah Jorong Islamic Boarding School. So, this community service aims to improve the water quality of drilled peat wells according to clean water quality standards. The method used is a water treatment process that combines filtration, absorption, and ultrafiltration processes with a single-flow ultrafiltration system. The results of the evaluation based on the laboratory showed that there was a decrease in the value of the total dissolved solids  (TDS) 0.2%, turbidity 25.8%, watercolor 63.6%, nitrate 95%, coliform 49.8% and increased acidity value 2%. Besides, clean water production has a large capacity of up to 2400 lt, and the management ustad has knowledge and skills of up to.
Implementation of Monarch Butterfly Optimization for Feature Selection in Coronary Artery Disease Classification Using Gradient Boosting Decision Tree Siti Napi'ah; Triando Hamonangan Saragih; Dodon Turianto Nugrahadi; Dwi Kartini; Friska Abadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.331

Abstract

Coronary artery disease, a prevalent type of cardiovascular disease, is a significant contributor to premature mortality globally. Employing the classification of coronary artery disease as an early detection measure can have a substantial impact on reducing death rates caused by this ailment. To investigate this, the Z-Alizadeh dataset, consisting of clinical data from patients afflicted with coronary artery disease, was utilized, encompassing a total of 303 data points that comprise 55 predictive attribute features and 1 target attribute feature. For the purpose of classification, the Gradient Boosting Decision Tree (GBDT) algorithm was chosen, and in addition, a metaheuristic algorithm called monarch butterfly optimization (MBO) was implemented to diminish the number of features. The objective of this study is to compare the performance of GBDT before and after the application of MBO for feature selection. The evaluation of the study's findings involved the utilization of a confusion matrix and the calculation of the area under the curve (AUC). The outcomes demonstrated that GBDT initially attained an accuracy rate of 87.46%, a precision of 83.85%, a recall of 70.37%, and an AUC of 82.09%. Subsequent to the implementation of MBO, the performance of GBDT improved to an accuracy of 90.26%, a precision of 86.82%, a recall of 80.79%, and an AUC of 87.33% with the selection of 31 features. This improvement in performance leads to the conclusion that MBO effectively addresses the feature selection issue within this particular context.
A Comparative Study of Machine Learning Methods for Baby Cry Detection Using MFCC Features Riadi, Putri Agustina; Faisal, Mohammad Reza; Kartini, Dwi; Nugroho, Radityo Adi; Nugrahadi, Dodon Turianto; Magfira, Dike Bayu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.350

Abstract

The vocalization of infants, commonly known as baby crying, represents one of the primary means by which infants effectively communicate their needs and emotional states to adults. While the act of crying can yield crucial insights into the well-being and comfort of a baby, there exists a dearth of research specifically investigating the influence of the audio range within a baby cry on research outcomes. The core problem of research is the lack of research on the influence of audio range on baby cry classification on machine learning. The purpose of this study is to ascertain the impact of the duration of an infant’s cry on the outcomes of machine learning classification and to gain knowledge regarding the accuracy of results F1 score obtained through the utilization of the machine learning method. The contribution is to enrich an understanding of the application of classification and feature selection in audio datasets, particulary in the context of baby cry audio. The utilized dataset, known as donate-a-cry-corpus, encompasses five distinct data classes and possesses a duration of seven seconds. The employed methodology consists of the spectrogram technique, cross-validation for data partitioning, MFCC feature extraction with 10, 20, and 30 coefficients, as well as machine learning models including Support Vector Machine, Random Forest, and Naïve Bayes. The findings of this study reveal that the Random Forest model achieved an accuracy of 0.844 and an F1 score of 0.773 when 10 MFCC coefficients were utilized and the optimal audio range was set at six seconds. Furthermore, the Support Vector Machine model with an RBF kernel yielded an accuracy of 0.836 and an F1 score of 0.761, while the Naïve Bayes model achieved an accuracy 0.538 and F1 score of 0.539. Notably, no discernible differences were observed when evaluating the Support Vector Machine and Naïve Bayes methods across the 1-7 second time trial. The implication of this research is to establish a foundation for the advancement of premature illness identification techniques grounded in the vocalizations of infants, thereby facilitating swifter diagnostic processes for pediatric practitioners.
Comparison of CatBoost and Random Forest Methods for Lung Cancer Classification using Hyperparameter Tuning Bayesian Optimization-based Zamzam, Yra Fatria; Saragih, Triando Hamonangan; Herteno, Rudy; Muliadi; Nugrahadi, Dodon Turianto; Huynh, Phuoc-Hai
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.382

Abstract

Lung Cancer is a disease that has a high mortality rate and is often difficult to detect until it reaches a very severe stage. Data indicates that lung cancer cases are typically diagnosed late, posing significant challenges to effective treatment. Early detection efforts offer potential for better recovery chances. Therefore, this research aims to develop methods for the identification and classification of lung cancer in the hope of providing further knowledge on effective ways to detect this condition at an early stage. One approach under scrutiny involves employing machine learning classification techniques, anticipated to serve as a pivotal tool in early disease detection and enhancing patient survival rates. This study involves five stages: data collection, data preprocessing, data partitioning for training and testing using 10-fold cross validation, model training, and analysis of evaluation results. In this research, four experiments consist of applying two classification methods, CatBoost and Random Forest, each tested using default hyperparameter and hyperparameter tuning using Bayesian Optimization. It was found that the Random Forest model using hyperparameter tuning Bayesian Optimization outperformed the other models with accuracy (0.97106), precision (0.97339), recall (0.97185), f-measure (0.97011), and AUC (0.99974) for lung cancer data. These findings highlight Bayesian Optimization for hyperparameter tuning in classification models can improve clinical prediction of lung cancer from patient medical records. The integration of Bayesian Optimization in hyperparameter tuning represents a significant step forward in refining the accuracy and effectiveness of classification models, thus contributing to the ongoing enhancement of medical diagnostics and healthcare strategies.
Baby Cry Sound Detection: A Comparison of Mel Spectrogram Image on Convolutional Neural Network Models Junaidi, Ridha Fahmi; Faisal, Mohammad Reza; Farmadi, Andi; Herteno, Rudy; Nugrahadi, Dodon Turianto; Ngo, Luu Duc; Abapihi, Bahriddin
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.465

Abstract

Baby cries contain patterns that indicate their needs, such as pain, hunger, discomfort, colic, or fatigue. This study explores the use of Convolutional Neural Network (CNN) architectures for classifying baby cries using Mel Spectrogram images. The primary objective of this research is to compare the effectiveness of various CNN architectures such as VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152 in detecting baby needs based on their cries. The datasets used include the Donate-a-Cry Corpus and Dunstan Baby Language. The results show that AlexNet achieved the best performance with an accuracy of 84.78% on the Donate-a-Cry Corpus dataset and 72.73% on the Dunstan Baby Language dataset. Other models like ResNet-50 and LeNet-5 also demonstrated good performance although their computational efficiency varied, while VGG-16 and VGG-19 exhibited lower performance. This research provides significant contributions to the understanding and application of CNN models for baby cry classification. Practical implications include the development of baby cry detection applications that can assist parents and healthcare provide.
The Comparison of Extreme Machine Learning and Hidden Markov Model Algorithm in Predicting The Recurrence Of Differentiated Thyroid Cancer Using SMOTE Aida, Nor; Saragih, Triando Hamonangan; Kartini, Dwi; Nugroho, Radityo Adi; Nugrahadi, Dodon Turianto
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.467

Abstract

Differentiated thyroid cancer is the most common type of thyroid cancer; the types in this category are papillary, follicular, and hurthel cell carcinoma. Up to 20% of DTCs will experience recurrence, although this figure reduces to 5% in low-risk patients. There is still little research on thyroid cancer prediction using a machine learning approach, especially the prediction recurrence of DTCs. This research aims to compare the performance of the Extreme Learning Machine and the Hidden Markov Model using SMOTE in predicting the recurrence of DTCs. The dataset used in this research is differentiated thyroid cancer recurrence from Kaggle. This research methodology comprises preprocessing, data sharing, SMOTE, ELM and HMM modeling algorithms, and evaluation. ELM with SMOTE gets the best results at a ratio of 90:10 with 35 hidden neurons that get an accuracy value of 1.00, precision 1.00, recall 1.00, and AUC 1.00. ELM modeling gets the best results at a ratio of 90:10 with 45 hidden neurons that get an accuracy value of 1.00, precision 1.00, recall 1.00, and AUC 1.00. HMM modeling gets the best value at a ratio of 70:30 with two hidden states and two iterations, which get an accuracy value of 0.8696, precision 0.8696, recall 0.7944, and AUC 0.9575. Last, HMM modeling with SMOTE gets the best results at a ratio of 60:40 with two hidden states and two iterations, with an accuracy value of 0.8696, precision of 0.8832, recall of 0.7848, and AUC of 0.9174. Based on the results of this study, it can be concluded that ELM with SMOTE gets the best performance, followed by ELM without SMOTE, HMM without SMOTE, and finally, HMM with SMOTE. The implication is that ELM with SMOTE can produce high accuracy in predicting the recurrence of DTCs.
Co-Authors Abadi, Friska Abdul Gafur Adi Mu'Ammar, Rifqi Adi, Puput Dani Prasetyo Adi, Puput Dani Prasetyo Ahmad Rusadi Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Aida, Nor Aji Triwerdaya Alfando, Muhammad Alvin Andi Farmadi Andi Farmadi Andi Farmadi Andi Farmadi Ando Hamonangan Saragih Apriana, Susi Ardiansyah Sukma Wijaya Arfan Eko Fahrudin Arifin Hidayat Azwari, Ayu Riana Sari Azwari, Ayu RianaSari Bachtiar, Adam Mukharil Badali, Rahmat Amin Bahriddin Abapihi Bedy Purnama Cahyadi, Rinova Firman Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Emy Iryanie, Emy Faisal Murtadho Faisal, Mohammad Reza Fajrin Azwary Fatma Indriani Fhadilla Muhammad Fitra Ahya Mubarok Fitria Agustina fitria Fitriani, Karlina Elreine Fitrinadi Friska Abadi Gunawan Gunawan Gunawan Gunawan Halim, Kevin Yudhaprawira Hariyady, Hariyady Herteno, Rudy Herteno, Rudy Heru Kartika Candra, Heru Kartika Huynh, Phuoc-Hai Ichsan Ridwan Indah Ayu Septriyaningrum Irwan Budiman Irwan Budiman Irwan Budiman Ismail Didit Samudro Julius Tunggono Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Kartika, Najla Putri Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Liling Triyasmono M Kevin Warendra M. Apriannur Martalisa, Asri Maulidha, Khusnul Rahmi Mera Kartika Delimayanti Miftahul Muhaemen Muhamad Ihsanul Qamil Muhammad Alkaff Muhammad Anshari Muhammad Haekal Muhammad Hasan Muhammad Irfan Saputra Muhammad Itqan Masdadi Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairin Nahwan Muhammad Mirza Hafiz Yudianto Muhammad Nazar Gunawan Muhammad Reza Faisal, Muhammad Reza Muhammad Rofiq Muhammad Sholih Afif Muhammad Solih Afif Muliadi Muliadi Muliadi MULIADI -, MULIADI Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi, M Musyaffa, Muhammad Hafizh Nafis Satul Khasanah Nahdhatuzzahra Nahdhatuzzahra Ngo, Luu Duc Noor Hidayah Nursyifa Azizah Ori Minarto Padhilah, Muhammad Pirjatullah Pirjatullah Pirjatullah Prastya, Septyan Eka Priyatama, Muhammad Abdhi Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani, Rahmat Ramadhan, Muhammad Rizky Aulia Riadi, Putri Agustina Rifki Izdihar Oktvian Abas Pullah Rifki Riza Susanto Banner Rizal, Muhammad Nur Rizki Amelia Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Saman Abdurrahman Saputro, Setyo Wahyu Saputro, Setyo Wahyu Saputro, Setyo Wahyu Saragih, Triando Hamonangan Satou, Kenji Selvia Indah Liany Abdie Setyo Wahyu Saputro sholih Afif Siti Napi'ah Soesanto, Oni Sri Cahyo Wahyono Sri Rahayu Sri Redjeki Sri Redjeki Totok Wianto Totok Wiyanto Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Utomo, Edy Setyo Wahyu Dwi Styadi Wahyu Saputro, Setyo Wardana, Muhammad Difha Winda Agustina Yabani, Midfai Yanche Kurniawan Mangalik YILDIZ, Oktay Yudha Sulistiyo Wibowo Zamzam, Yra Fatria