Article Per Year (5 Year)
p-Index From 2021 - 2026
8.651
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika JUITA : Jurnal Informatika Jurnal Fisika FLUX KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik International Journal of Advances in Data and Information Systems Jurnal Pengabdian UNDIKMA Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Muhammad Itqan Mazdadi
Universitas Lambung Mangkurat
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

Published : 63 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 63 Documents
Search

 1   2   3   4   5 
Comparison Algorithm for Diabetes Classification with Consideration of Mutual Information and Information Feature Rahmat Ramadhani; Triando Hamonangan Saragih; Muhammad Itqan Mazdadi; Muliadi Muliadi
Jurnal Komputasi Vol 11, No 1 (2023): Jurnal Komputasi
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v11i1.6649

Abstract

Diabetes is a prevalent disease in humans that is caused by excessive sugar levels in the body. If left untreated, it can lead to severe consequences such as paralysis, decay in certain parts of the body, and even death. Unfortunately, early detection of diabetes is difficult, and many cases go untreated until it is too late. However, the development of technology has opened up new possibilities for early detection and treatment of diabetes. One such approach is classification, a commonly used method in the field of Computer Science. Classification is used in various fields, including health, agriculture, and animal diseases, to draw conclusions based on input data using cause-and-effect relationships. Many different learning concepts and methods can be used in classification, with the Decision Tree concept being one of the most popular examples. This study compares several classification methods, including Decision Tree, Random Forest, AdaBoost, and Stochastic Gradient Boost, with feature selections carried out using MI and IF. The study aims to evaluate the effectiveness of these methods and the influence of feature selection on improving their performance. Based on the results of the study, it can be concluded that feature selection using Mutual Information and Importance Feature can improve the classification accuracy in some methods, particularly in Random Forest, AdaBoost, and Stochastic Gradient Boost. However, the Decision Tree algorithm did not show any improvement in accuracy after feature selection. The best classification accuracy was achieved with the Stochastic Gradient Boost method using the original dataset without feature selection, while the Random Forest method showed the highest accuracy after using all the features. Overall, the results suggest that feature selection can be a useful technique for improving the performance of classification algorithms in diabetes prediction. The study suggests that future research could investigate other classification methods, such as Neural Network or Deep Learning, and use optimization algorithms like Genetic Algorithm or Particle Swarm Optimization to improve feature selection results.
Application of SMOTE to Handle Imbalance Class in Deposit Classification Using the Extreme Gradient Boosting Algorithm Dina Arifah; Triando Hamonangan Saragih; Dwi Kartini; Muliadi Muliadi; Muhammad Itqan Mazdadi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26155

Abstract

Deposits became one of the main products and funding sources for banks and increasing deposit marketing is very important. However, telemarketing as a form of deposit marketing is less effective and efficient as it requires calling every customer for deposit offers. Therefore, the identification of potential deposit customers was necessary so that telemarketing became more effective and efficient by targeting the right customers, thus improving bank marketing performance with the ultimate goal of increasing sources of funding for banks. To identify customers, data mining is used with the UCI Bank Marketing Dataset from a Portuguese banking institution. This dataset consists of 45,211 records with 17 attributes. The classification algorithm used is Extreme Gradient Boosting (XGBoost) which is suitable for large data. The data used has a high-class imbalance, with "yes" and "no" percentages of 11.7% and 88.3%, respectively. Therefore, the proposed solution in the research, which focused on addressing the Imbalance Class in the Bank marketing dataset, was to use Synthetic Minority Over-sampling (SMOTE) and the XGBoost method. The result of the XGBoost study was an accuracy of 0.91016, precision of 0.79476, recall of 0.72928, F1-Score of 0.56198, ROC Area of 0.93831, and AUCPR of 0.63886. After SMOTE was applied, the accuracy was 0.91072, the precision was 0.78883, the recall was 0.75588, F1-Score was 0.59153, ROC Area was 0.93723, and AUCPR was 0.63733. The results showed that XGBoost and SMOTE could outperform other algorithms such as K-Nearest Neighbor, Random Forest, Logistic Regression, Artificial Neural Network, Naïve Bayes, and Support Vector Machine in terms of accuracy. This study contributes to the development of effective machine learning models that can be used as a support system for information technology experts in the finance and banking industries to identify potential customers interested in subscribing to deposits and increasing bank funding sources.
Application of Extreme Learning Machine Method With Particle Swarm Optimization to Classify of Heart Disease Adela Putri Ariyanti; Muhammad Itqan Mazdadi; Andi - Farmadi; Muliadi Muliadi; Rudy Herteno
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 3 (2023): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.86291

Abstract

Penyakit jantung koroner adalah tersumbatnya suplai darah jantung. Penyakit jantung adalah penyebab utama kematian di seluruh dunia. Berbagai faktor risiko berkontribusi terhadap penyakit jantung, termasuk merokok, gaya hidup tidak sehat, kolesterol tinggi, dan hipertensi. Dengan demikian, prediksi penyakit dapat dilakukan untuk mengidentifikasi individu yang berisiko guna mencegah peningkatan kematian akibat penyakit jantung. Penambangan data, khususnya metode Extreme Machine Learning (ELM), biasanya digunakan untuk tujuan ini. ELM adalah metode jaringan saraf dalam kecepatan pelatihan dan tidak memerlukan propagasi balik, dan menentukan jumlah node tersembunyi yang optimal dan mencapai hasil yang akurat tetap menjadi tantangan. Pada penelitian ini, ELM dengan Particle Swarm Optimization (PSO) diusulkan untuk mengoptimalkan klasifikasi penyakit jantung, yang bertujuan untuk mencapai hasil optimal dengan pembelajaran cepat. Penelitian ini mengikuti proses yang sistematis, termasuk pengumpulan data, preprocessing, pemodelan, dan evaluasi menggunakan analisis matriks konfusi. Hasil dan pembahasan menyajikan efektivitas metode yang diusulkan dengan mengevaluasi akurasi klasifikasi berdasarkan berbagai parameter, seperti ukuran populasi, jumlah node tersembunyi, dan iterasi. Temuan menunjukkan bahwa ELM dengan optimasi PSO dapat memberikan hasil klasifikasi yang akurat untuk diagnosis penyakit jantung, dengan tingkat akurasi yang menjanjikan.
Implementation of Particle Swarm Optimization Feature Selection on Naïve Bayes for Thoracic Surgery Classification Shalehah; Muhammad Itqan Mazdadi; Andi Farmadi; Dwi Kartini; Muliadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.305

Abstract

Thoracic surgery is among the operations that are most often performed on patients with lung cancer. Naive Bayes is one of the data mining classification techniques that may be used to handle thoracic surgery data. Therefore, the goal of this study is to assess the precision of all research models using Naive Bayes with and without Particle Swarm Optimization. This study's methodology includes the dataset used, the Naive Bayes algorithm theory, the particle swarm optimization algorithm, test validation using split validation, and performance assessment using the confusion matrix and AUC evaluation approaches. In this inquiry, secondary data are retrieved via the UCI Repository website. Thoracic surgery weight optimization accuracy is increased using particle swarm optimization. The test results of the Naive Bayes technique utilizing the thoracic surgery dataset showed the highest accuracy of 81.91% at a ratio of 80:20 and an AUC value of 0.620. The highest accuracy score is 93.62% with an AUC value of 0.773 at a ratio of 90:10, with three characteristics, namely PRE6, PRE14, and PRE17, having zero weight. This accuracy score was achieved when Particle Swarm Optimization was used to refine feature selection for attribute weighting. As a consequence, Naïve Bayes accuracy in thoracic surgery has increased as a result of attribute weighting on feature selection utilizing Particle Swarm Optimization. In turn, this research contributes to increasing the precision and efficiency with which thoracic surgical data are processed, which benefits lung cancer diagnosis in both speed and accuracy.
Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine Muhamad Fawwaz Akbar; Muhammad Itqan Mazdadi; Muliadi; Triando Hamonangan Saragih; 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.v5i4.328

Abstract

In the current digital era, sentiment analysis has become an effective method for identifying and interpreting public opinions on various topics, including public health issues such as COVID-19 vaccination. Vaccination is a crucial measure in tackling this pandemic, but there are still a number of people who are skeptical and reluctant to receive the COVID-19 vaccine. This public perception is largely influenced by, including information received from social media and online platforms. Therefore, sentiment analysis of the COVID-19 vaccine is one way to understand the public's perception of the COVID-19 vaccine. This research has the purpose to enhance the classification performance in sentiment analysis of COVID-19 vaccines by implementing Information Gain Ratio (IGR) and Particle Swarm Optimization (PSO) on the Support Vector Machine (SVM). With a dataset of 2000 entries consisting of 1000 positive labels and 1000 negative labels, validation was performed through a combination of data splitting with an 80:20 ratio and stratified 10-Fold cross-validation. Applying the basic SVM, an accuracy of 0.794 and an AUC value of 0.890 were obtained. Integration with Information Gain Ratio (IGR) feature selection improved the accuracy to 0.814 and an AUC of 0.907. Furthermore, through the combination of SVM based on PSO and IGR, the accuracy significantly improved to 0.837 with an AUC of 0.913. These results demonstrate that the combination of feature selection techniques and parameter optimization can enhance the performance of sentiment classification towards COVID-19 vaccines. The conclusions drawn from this research indicate that the integration of IGR and PSO positively contributes to the effectiveness and predictive capability of the SVM model in sentiment classification tasks.
Analisis Seleksi Fitur Binary PSO Pada Klasifikasi Kanker Berdasarkan Data Microarray Menggunakan DWKNN Yanche Kurniawan Mangalik; Triando Hamonangan Saragih; Dodon Turianto Nugrahadi; Muliadi Muliadi; Muhammad Itqan Mazdadi
Jurnal Informatika Polinema Vol. 9 No. 2 (2023): Vol 9 No 2 (2023)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v9i2.1128

Abstract

Salah satu penyakit mematikan penyebab kematian terbesar secara global adalah kanker. Kematian akibat kanker dapat diredam melalui deteksi dini terhadap kanker dengan memanfaatkan teknologi microarray. Namun teknologi ini memiliki kekurangan, yaitu jumlah gen (fitur) yang terlalu banyak. Kekurangan tersebut dapat diatasi dengan melakukan seleksi fitur terhadap data microarray. Salah satu algoritma seleksi fitur yang dapat digunakan adalah Binary Particle Swarm Optimizationi (BPSO). Pada penelitian ini, dilakukan seleksi fitur dengan BPSO pada data microarray dan klasifikasi menggunakan Distance Weighted KNN (DWKNN). Kemudian akan dilihat perbandingan hasil akurasi, presisi, recall, dan f1-score antara DWKNN dan BPSO-DWKNN. Seleksi fitur dan klasifikasi (BPSO-DWKNN) pada dataset Leukemia menghasilkan akurasi, presisi, recall, dan f1-score tertinggi beturut-turut sebesar 93,12%, 94,39%, 95,92%, dan 94,8%. Pada dataset Lung Cancer diperoleh akurasi, presisi, recall, dan f1-score tertinggi beturut-turut sebesar 98,36%, 98,77%, 99,35%, dan 99,03%. Pada dataset Prostate Cancer diperoleh akurasi, presisi, recall, dan f1-score tertinggi beturut-turut sebesar 86,81%, 89,13%, 88,04%, dan 88,07%. Pada dataset Diffuse Large B-Cell Lymphome diperoleh akurasi, presisi, recall, dan f1-score tertinggi beturut-turut sebesar 85,8%, 93,21%, 88,1%, dan 89,76%. Hasil perbandingan menunjukkan peningkatan akurasi, presisi, recall, dan f1-score pada algoritma DWKNN dengan seleksi fitur BPSO dibandingkan dengan algoritma DWKNN tanpa seleksi fitur BPSO.
Implementation of Particle Swarm Optimization on Sentiment Analysis of Cyberbullying using Random Forest Helma Herlinda; Muhammad Itqan Mazdadi; Muliadi Muliadi; Dwi Kartini; Irwan Budiman
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17920

Abstract

Social media has exerted a significant influence on the lives of the majority of individuals in the contemporary era. It not only enables communication among people within specific environments but also facilitates user connectivity in the virtual realm. Instagram is a social media platform that plays a pivotal role in the sharing of information and fostering communication among its users through the medium of photos and videos, which can be commented on by other users. The utilization of Instagram is consistently growing each year, thereby potentially yielding both positive and negative consequences. One prevalent negative consequence that frequently arises is cyberbullying. Conducting sentiment analysis on cyberbullying data can provide insights into the effectiveness of the employed methodology. This research was conducted as an experimental research, aiming to compare the performance of Random Forest and Random Forest after applying the Particle Swarm Optimization feature selection technique on three distinct data split compositions, namely 70:30, 80:20, and 90:10. The evaluation results indicate that the highest accuracy scores were achieved in the 90:10 data split configuration. Specifically, the Random Forest model yielded an accuracy of 87.50%, while the Random Forest model, after undergoing feature selection using the Particle Swarm Optimization algorithm, achieved an accuracy of 92.19%. Therefore, the implementation of Particle Swarm Optimization as a feature selection technique demonstrates the potential to enhance the accuracy of the Random Forest method.
PENERAPAN MWMOTE UNTUK MENGATASI KETIDAKSEIMBANGAN KELAS PADA KLASIFIKASI RISIKO KREDIT Maria Ulfah; Triando Hamonangan Saragih; Dwi Kartini; Muhammad Itqan Mazdadi; Friska Abadi
Jurnal Informatika Polinema Vol. 9 No. 4 (2023): Vol. 9 No. 4 (2023)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v9i4.1331

Abstract

Salah satu bentuk usaha yang dijalankan oleh perbankan adalah pemberian kredit terhadap nasabaah. Bank akan selalu berusaha mengoptimalkan penyaluran kredit terhadap nasabah, akan tetapi tidak menutup kemungkinan bahwa kredit yang diberikan tersebut memiliki risiko. Guna menekan dan meminimalisir risiko kredit pihak bank perlu melakukan analisis terhadap data yang dimiliki nasabah agar dapat mengambil keputusan apakah nasabah atau calon debitur layak diberikan pinjaman dalam bentuk kredit. Salah satu cara untuk menyelesaikan masalah analisa risiko kredit adalah dengan melakukan klasifikasi dengan menggunakan machine learning. Pada penelitian ini dilakukan klasifikasi dengan menggunakan algoritma Support Vector Machine (SVM) serta oversampling data dengan menggunakan MWMOTE dan Improve MWMOTE. Data yang digunakan pada penelitian ini adalah data german credit risk yang memiliki Kelas bad credit yang terdiri atas 300 data dan kelas good credit terdiri atas 700 data. Penelitian dilakukan dengan membandingkan klasifikasi SVM dengan dan tanpa oversampling. Hasilnya didapatkan bahwa nilai akurasi dari klasifikasi Improve MWMOTE SVM memiliki nilai tertinggi jika dibandingan dengan SVM MWMOTE, dan SVM yaitu sebesar 77,95%.
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.
Identification of Social Media Posts Containing Self-reported COVID-19 Symptoms using Triple Word Embeddings and Long Short-Term Memory Amalia, Raisa; Faisal, Mohammad Reza; Indriani, Fatma; Budiman, Irwan; Mazdadi, Muhammad Itqan; Abadi, Friska; Mafazy, Muhammad Meftah
Telematika Vol 17, No 1: February (2024)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v17i1.2774

Abstract

The COVID-19 pandemic has permeated the global sphere and influenced nearly all nations and regions. Common symptoms of this pandemic include fever, cough, fatigue, and loss of sense of smell. The impact of COVID-19 on public health and the economy has made it a significant global concern. It has caused economic contraction in Indonesia, particularly in face-to-face interaction and mobility sectors, such as transportation, warehousing, construction, and food and beverages. Since the pandemic began, Twitter users have shared symptoms in their tweets. However, they couldn't confirm their concerns due to testing limitations, reporting delays, and pre-registration requirements in healthcare. The classification of text from Twitter data about COVID-19 topics has predominantly focused on sentiment analysis regarding the pandemic or vaccination. Research on identifying COVID-19 symptoms through social media messages is limited in the literature. The main objective of this study is to identify symptoms using word embedding techniques and the LSTM algorithm. Various techniques such as Word2Vec, GloVe, FastText, and a composite approach are used. LSTM is used for classification, improving upon the RNN technique. Evaluation criteria include accuracy, precision, and recall. The model with an input dimension of 147x100 achieves the highest accuracy at 89%. This study aims to find the best LSTM model for detecting COVID-19 symptoms in social media tweets. It evaluates LSTM models with different word embedding techniques and input dimensions, providing insights into the optimal text-based method for COVID-19 detection through social media texts.
Co-Authors AA Sudharmawan, AA Abdilah, Muhammad Fariz Fata Abdullayev, Vugar Ade Agung Harnawan, Ade Agung Adela Putri Ariyanti Afifa, Ridha Ahdyani, Annisa Salsabila Ahmad Rusadi Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Shofi Khairian Ahmad Tajali Aidil Akbar Al Ghifari, Muhammad Akmal Alamudin, Muhammad Faiq Amalia, Raisa Andi - Farmadi Andi Farmadi Andi Farmadi Anna Khumaira Sari Anshory, Muhammad Naufal Ansyari, Muhammad Ridho Antoh, Soterio Ardiansyah Sukma Wijaya Athavale, Vijay Anant Athavale, Vijay Annant budiman, irwan Buih, Putri Helena Junjung Deni Sutaji Dina Arifah Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Dzira Naufia Jawza Erdi, Muhammad Faisal, Mohammad Reza Fathmah, Siti Fatma Indriani Fayyadh, Muhammad Naufaldi Fitriani, Karlina Elreine Fitrinadi Friska Abadi Haekal, Muhammad Hafizah, Rini Helma Herlinda Herteno, Rudi Herteno, Rudy Indriani, Fatma Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman M. Apriannur M. Khairul Rezki Mafazy, Muhammad Meftah Maulana, Muhammad Rafly Alfarizqy Muflih Ihza Rifatama Muhamad Fawwaz Akbar Muhamad Ihsanul Qamil Muhammad Adika Riswanda Muhammad Khairin Nahwan Muhammad Mada Muhammad Mirza Hafiz Yudianto Muhammad Mursyidan Amini Muhammad Reza Faisal, Muhammad Reza Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Nabella, Putri Noorhafizi, Muhammad Normaidah, Normaidah Nugraha, Muhammad Amir Nursyifa Azizah P., Chandrasekaran Patrick Ringkuangan Prastya, Septyan Eka Putri Nabella Radityo Adi Nugroho Rahmah, Indah Noor Rahmat Hidayat Rahmat Ramadhani Rahmat Ramadhani Rahmawati, Nanda Hesti Rahmawati, Nanda Putri Ramadhan, Mita Azzahra Ramadhani, Muhammad Irfan Ramadhani, Rahmat Ratnapuri, Prima Happy Riadi, Agus Teguh Rifki Izdihar Oktvian Abas Pullah Rifki Rinaldi Rizky, Muhammad Miftahur Rozaq, Hasri Akbar Awal Rozaq, Hasri Awal Akbar Rudy Herteno Saputra, Adryan Maulana Saputro, Setyo Wahyu Saragih, Triando Hamonangan Satrio Yudho Prakoso Setyo Wahyu Saputro Shalehah Syahputra, Muhammad Reza Tajali, Ahmad Totok Wianto Wahyu Dwi Styadi Wijaya Kusuma, Arizha Yanche Kurniawan Mangalik YILDIZ, Oktay Yoga Pambudi Yudha Sulistiyo Wibowo Zaini Abdan