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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) International Journal of Electronics and Communications Systems Journal of Computing Theories and Applications Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Setyo Wahyu Saputro
Lambung Mangkurat University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Materials Science & Nanotechnology

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Perancangan Arsitektur Sistem Pengelolaan Kegiatan Penelitian dan Pengabdian Masyarakat pada STMIK Banjarbaru Menggunakan Kerangka Kerja TOGAF Setyo Wahyu Saputro - STMIK Banjarbaru
Bianglala Informatika Vol 4, No 1 (2016): Bianglala Informatika 2016
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (416.389 KB) | DOI: 10.31294/bi.v4i1.660

Abstract

Abstract - Sekolah Tinggi Manajemen Informatika dan Komputer (STMIK) is one higher education institution in Indoenesia which runs Tri Darma Higher Education as the cornerstone of the implementation of educational activities that must be done for each of the lecturers, especially lecturers in STMIK Banjarbaru. STMIK Banjarbaru has a special unit that manages the activities of Tri Dharma College, especially in research activities and community service (P3M), namely the Pusat Penelitian Pengembangan dan Pengabdian pada Masyarakat (Puslitbang dan P2M) in charge of managing the activities P3M in STMIK Banjarbaru. The results of the research faculty and students will be published in journals: the Journal of Information Technology and Information Systems (Jutisi) and Progressive Journal. A total of 5 for the second edition of the journal, published annually, and the Research Center for P2M STMIK Banjarbaru need to have a system that is used to manage P3M activities including managing the publication of research results. Actually Puslitbang dan P2M STMIK Banjarbaru already using Open Journal Systems (OJS) as an application for managing and publication of research results, but since OJS merely manage the publication of research results lecturer in STMIK Banjarbaru. Puslitbang and P2M STMIK in performing its duties and functions do not use information systems, particularly in managing P3M, this causes difficulties in registration activities, management and reporting activities P3M. So in this research will be the design of Research and Development System on STMIK Banjarbaru use patterns Enterprise Architecture by utilizing a framework TOGAF to conduct a needs analysis internally using Value Chain as a component of architectural design vision in the framework TOGAF for achieving the vision Puslitbang and P2M STMIK Banjarbaru manage activities P3M in STMIK Banjarbaru, information systems design and architecture in accordance with the needs and become a basis for making a blue print that can be used as the basis for system development. Keywords: Enterprise Architecture, framework, TOGAF, Value Chain   Abstrak – Sekolah Tinggi Manajemen Informatika dan Komputer (STMIK) Banjarbaru merupakan salah satu institusi pendidikan tinggi di Indoenesia yang menjalankan Tri Darma Perguruan Tinggi sebagai landasan penyelenggaraan kegiatan pendidikan yang wajib dilakukan bagi setiap dosen khususnya dosen di STMIK Banjarbaru. STMIK Banjarbaru memiliki unit kerja khusus yang mengelola kegiatan Tri Darma Perguruan Tinggi khususnya pada kegiatan penelitian dan pengabdian pada masyarakat (P3M) yaitu Pusat Penelitian Pengembangan dan Pengabdian pada Masyarakat (Puslitbang dan P2M) yang bertugas mengelola kegiatan P3M di STMIK Banjarbaru. Hasil penelitian dosen dan mahasiswa akan dipublikasi dalam bentuk jurnal yaitu Jurnal Teknik Informatika dan Sistem Informasi (Jutisi) dan Jurnal Progresif. Sebanyak 5 edisi untuk 2 jurnal yang diterbitkan per tahunnya, Puslitbang dan P2M STMIK Banjarbaru perlu memiliki sebuah sistem yang digunakan untuk mengelola kegiatan P3M termasuk di dalamnya mengelola publikasi hasil penelitian. Sebenarnya Puslitbang dan P2M STMIK Banjarbaru sudah menggunakan Open Journal System (OJS) sebagai aplikasi untuk mengelola dan publikasi hasil penelitian, namun sejak OJS hanya sebatas mengelola publikasi hasil penelitian dosen di STMIK Banjarbaru. Puslitbang dan P2M STMIK dalam melakukan tugas dan fungsinya belum menggunakan sistem informasi, khususnya dalam mengelola kegiatan P3M, ini menyebabkan adanya kesulitan dalam kegiatan pencatatan, pengelolaan dan pembuatan laporan kegiatan P3M. Sehingga peneliti akan melakukan perancangan Sistem Pengelolaan Kegiatan Penelitian dan Pengabdian  STMIK Banjarbaru menggunakan pola Enterprise Architecture dengan memanfaatkan sebuah kerangka kerja TOGAF dengan melakukan analisis kebutuhan internal  menggunakan Value Chain sebagai komponen perancangan arsitektur visi pada kerangka kerja TOGAF untuk mencapai visi Puslitbang dan P2M STMIK Banjarbaru dalam mengelola kegiatan P3M di STMIK Banjarbaru, merancang sistem informasi dan arsitekturnya sesuai dengan kebutuhan dan menjadi dasar pembuatan blue print yang dapat digunakan sebagai dasar pengembangan sistem. Kata kunci:  Enterprise Architecture, kerangka kerja, TOGAF, Value Chain
Aplikasi Kelayakan Kerja Karyawan pada P.T. Borneo Alam Semesta Setyo Wahyu Saputro; Ujang Ruyandi; Yulia Yudihartanti
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 3, No 2: Agustus 2014
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (222.848 KB) | DOI: 10.35889/jutisi.v3i2.28

Abstract

Sitem keputusan layak kerja pada karyawan berdampak besar dalam kinerja perusahanan dalam perencanaan pembangunan sistem agar lebih baik lagi, khususnya dalam pemilihan karyawan layak bekerja. Pada penelitian ini dibangun aplikasi penentuan kelayakan kerja terhadap karyawan PT.BAS di kecamatan Jorong desa Banjarsari Jorong, berdasarkan kriteria kriteria yang dipergunakan pada penelitian ini. Hasil dari penelitian ini sebanyak 85% dari 10 orang responden kuisioner user acceptance menyetujui hasil dari Aplikasi Layak Kerja Pada Karyawan PT.Borneo Alam Semesta ini, sudah menentukan faktor-faktor yang mempengaruhi dan saling berkaitan dengan keputusan layak kerja pada karyawan PT.BAS .Kata kunci: Aplikasi, Keputusan Layak kerja, PT. BAS Kecamatan Jorong
Model Aplikasi Edukasi Mengenal Hewan Berbantuan Augmented Reality Akhmad Zakirin; Setyo Wahyu Saputro; Wahyudi Ariannor
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 11, No 3: Desember 2022
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v11i3.989

Abstract

In the process of learning to recognize animals, many animal objects are difficult to find in the Early Childhood environment, so that learning to recognize animal objects experiences obstacles. There are many students who find it difficult to distinguish animal objects, especially those that have similarities. Augmented Reality-based application models for animal recognition education will be applied to the teaching and learning process. The Multimedia Development Life Cycle method is used to develop the system. System design uses the Unified Modeling Language (UML) modeling tools, while system development uses the Vuforia library, and functional testing uses the Blackbox test. The Augmented Reality-based application model for animal recognition education helps teachers in children's cognitive learning to provide children with an understanding of natural concepts around animal recognition, and can attract interest in learning and increase children's natural intelligence.Keyword: Recognition; Virtual; Cognitive; Natural Intelligence AbstrakPada proses belajar mengenali hewan, banyak objek hewan yang sulit ditemui di lingkungan Anak Usia Dini, sehingga pembelajaran mengenali objek hewan mengalami kendala. Terdapat banyak peserta didik yang merasa kesulitan dalam membedakan objek hewan, terutama yang memiliki kemiripan. Model aplikasi berbasis Augmented Reality untuk edukasi pengenalan hewan, akan diterapkan untuk proses belajar mengajar. Metode Multimedia Development Life Cycle digunakan untuk mengembangkan sistem. Desain sistem menggunakan tools pemodelan Unified Modelling Language (UML), sedangkan pembangunan sistem dengn library Vuforia, serta pengujian fungsional dengan uji Blackbox. Model aplikasi berbasis Augmented Reality untuk edukasi pengenalan hewan membantu guru dalam pembelajaran kognitif anak untuk memberi pemahaman anak terhadap konsep alam sekitar pengenalan hewan, serta dapat menarik minat belajar dan meningkatkan kecerdasan natural anak-anak.
Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction Suryadi, Mulia Kevin; Herteno, Rudy; Saputro, Setyo Wahyu; Faisal, Mohammad Reza; Nugroho, Radityo Adi
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.v6i2.375

Abstract

Software defect prediction is necessary for desktop and mobile applications. Random Forest defect prediction performance can be significantly increased with the parameter optimization process compared to the default parameter. However, the parameter tuning step is commonly neglected. Random Forest has numerous parameters that can be tuned, as a result manually adjusting parameters would diminish the efficiency of Random Forest, yield suboptimal results and it will take a lot of time. This research aims to improve the performance of Random Forest classification by using SMOTE to balance the data, Genetic Algorithm as selection feature, and using hyperparameter tuning to optimize the performance. Apart from that, it is also to find out which hyperparameter tuning method produces the best improvement on the Random Forest classification method. The dataset used in this study is NASA MDP which included 13 datasets. The method used contains SMOTE to handle imbalance data, Genetic Algorithm feature selection, Random Forest classification, and hyperparameter tuning methods including Grid Search, Random Search, Optuna, Bayesian (with Hyperopt), Hyperband, TPE and Nevergrad. The results of this research were carried out by evaluating performance using accuracy and AUC values. In terms of accuracy improvement, the three best methods are Nevergrad, TPE, and Hyperband. In terms of AUC improvement, the three best methods are Hyperband, Optuna, and Random Search. Nevergrad on average improves accuracy by about 3.9% and Hyperband on average improves AUC by about 3.51%. This study indicates that the use of hyperparameter tuning improves Random Forest performance and among all the hyperparameter tuning methods used, Hyperband has the best hyperparameter tuning performance with the highest average increase in both accuracy and AUC. The implication of this research is to increase the use of hyperparameter tuning in software defect prediction and improve software defect prediction performance.
Optimizing Software Defect Prediction Models: Integrating Hybrid Grey Wolf and Particle Swarm Optimization for Enhanced Feature Selection with Popular Gradient Boosting Algorithm Angga Maulana Akbar; Herteno, Rudy; Saputro, Setyo Wahyu; Faisal, Mohammad Reza; Nugroho, Radityo Adi
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.v6i2.388

Abstract

Software defects, also referred to as software bugs, are anomalies or flaws in computer program that cause software to behave unexpectedly or produce incorrect results. These defects can manifest in various forms, including coding errors, design flaws, and logic mistakes, this defect have the potential to emerge at any stage of the software development lifecycle. Traditional prediction models usually have lower prediction performance. To address this issue, this paper proposes a novel prediction model using Hybrid Grey Wolf Optimizer and Particle Swarm Optimization (HGWOPSO). This research aims to determine whether the Hybrid Grey Wolf and Particle Swarm Optimization model could potentially improve the effectiveness of software defect prediction compared to base PSO and GWO algorithms without hybridization. Furthermore, this study aims to determine the effectiveness of different Gradient Boosting Algorithm classification algorithms when combined with HGWOPSO feature selection in predicting software defects. The study utilizes 13 NASA MDP dataset. These dataset are divided into testing and training data using 10-fold cross-validation. After data is divided, SMOTE technique is employed in training data. This technique generates synthetic samples to balance the dataset, ensuring better performance of the predictive model. Subsequently feature selection is conducted using HGWOPSO Algorithm. Each subset of the NASA MDP dataset will be processed by three boosting classification algorithms namely XGBoost, LightGBM, and CatBoost. Performance evaluation is based on the Area under the ROC Curve (AUC) value. Average AUC values yielded by HGWOPSO XGBoost, HGWOPSO LightGBM, and HGWOPSO CatBoost are 0.891, 0.881, and 0.894, respectively. Results of this study indicated that utilizing the HGWOPSO algorithm improved AUC performance compared to the base GWO and PSO algorithms. Specifically, HGWOPSO CatBoost achieved the highest AUC of 0.894. This represents a 6.5% increase in AUC with a significance value of 0.00552 compared to PSO CatBoost, and a 6.3% AUC increase with a significance value of 0.00148 compared to GWO CatBoost. This study demonstrated that HGWOPSO significantly improves the performance of software defect prediction. The implication of this research is to enhance software defect prediction models by incorporating hybrid optimization techniques and combining them with gradient boosting algorithms, which can potentially identify and address defects more accurately
Impact of a Synthetic Data Vault for Imbalanced Class in Cross-Project Defect Prediction Putri Nabella; Rudy Herteno; Setyo Wahyu Saputro; Mohammad Reza Faisal; Friska Abadi
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.v6i2.409

Abstract

Software Defect Prediction (SDP) is crucial for ensuring software quality. However, class imbalance (CI) poses a significant challenge in predictive modeling. This study delves into the effectiveness of the Synthetic Data Vault (SDV) in mitigating CI within Cross-Project Defect Prediction (CPDP). Methodologically, the study addresses CI across ReLink, MDP, and PROMISE datasets by leveraging SDV to augment minority classes. Classification utilizing Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF), also model performance is evaluated using AUC and t-Test. The results consistently show that SDV performs better than SMOTE and other techniques in various projects. This superiority is evident through statistically significant improvements. KNN dominance in average AUC results, with values 0.695, 0.704, and 0.750. On ReLink, KNN show 16.06% improvement over the imbalanced and 12.84% over SMOTE. Similarly, on MDP, KNN 20.71% improvement over the imbalanced and a 10.16% over SMOTE. Moreover, on PROMISE, KNN 13.55% improvement over the imbalanced and 7.01% over SMOTE. RF displays moderate performance, closely followed by LR and DT, while NB lags behind. The statistical significance of these findings is confirmed by t-Test, all below the 0.05 threshold. These findings underscore SDV's potential in enhancing CPDP outcomes and tackling CI challenges in SDV. With KNN as the best classification algorithm. Adoption of SDV could prove to be a promising tool for enhancing defect detection and CI mitigation
A Comparative Analysis of Polynomial-fit-SMOTE Variations with Tree-Based Classifiers on Software Defect Prediction Nur Hidayatullah, Wildan; Herteno, Rudy; Reza Faisal, Mohammad; Adi Nugroho, Radityo; Wahyu Saputro, Setyo; Akhtar, Zarif Bin
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.455

Abstract

Software defects present a significant challenge to the reliability of software systems, often resulting in substantial economic losses. This study examines the efficacy of polynomial-fit SMOTE (pf-SMOTE) variants in combination with tree-based classifiers for software defect prediction, utilising the NASA Metrics Data Program (MDP) dataset. The research methodology involves partitioning the dataset into training and test subsets, applying pf-SMOTE oversampling, and evaluating classification performance using Decision Trees, Random Forests, and Extra Trees. Findings indicate that the combination of pf-SMOTE-star oversampling with Extra Tree classification achieves the highest average accuracy (90.91%) and AUC (95.67%) across 12 NASA MDP datasets. This demonstrates the potential of pf-SMOTE variants to enhance classification effectiveness. However, it is important to note that caution is warranted regarding potential biases introduced by synthetic data. These findings represent a significant advancement over previous research endeavors, underscoring the critical role of meticulous algorithm selection and dataset characteristics in optimizing classification outcomes. Noteworthy implications include advancements in software reliability and decision support for software project management. Future research may delve into synergies between pf-SMOTE variants and alternative classification methods, as well as explore the integration of hyperparameter tuning to further refine classification performance.
Optimization of Backward Elimination for Software Defect Prediction with Correlation Coefficient Filter Method Muhammad Noor; Radityo Adi Nugroho; Setyo Wahyu Saputro; Rudy Herteno; Friska Abadi
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.v6i4.466

Abstract

Detecting software defects is a crucial step for software development not only to reduce cost and save time, but also to mitigate more costly losses. Backward Elimination is one method for detecting software defects. Notably Backward Elimination may remove features that may later become significant to the outcome affecting the performance of Backward Elimination. The aim of this study is to improve Backward Elimination performance. In this study, several features were selected based on their correlation coefficient, with the selected feature applied to improve Backward Elimination final model performance. The final model was validated using cross validation with Naïve Bayes as the classification method on the NASA MDP dataset to determine the accuracy and Area Under the Curve (AUC) of the final model. Using top 10 correlation feature and Backward Elimination achieve an average result of 86.6% accuracy and 0.797 AUC, while using top 20 correlation feature and Backward Elimination achieved an average result of 84% accuracy and 0.812 AUC. Compare to using Backward Elimination and Naïve Bayes respectively the improvement using top 10 correlation feature as follows: AUC:1.52%, 13.53% and Accuracy: 13%, 12.4% while the improvement using top 20 correlation feature as follows: AUC:3.43%, 15.66% and Accuracy: 10.4%, 9.8%. Results showed that selecting the top 10 and top 20 feature based on its correlation before using Backward Elimination have better result than only using Backward Elimination. This result shows that combining Backward Elimination with correlation coefficient feature selection does improve Backward Elimination’s final model and yielding good results for detecting software defects.
Comparative Analysis of Distance Metrics in KNN and SMOTE Algorithms for Software Defect Prediction Maulidha, Khusnul Rahmi; Faisal, Mohammad Reza; Saputro, Setyo Wahyu; Abadi, Friska; Nugrahadi, Dodon Turianto; Adi, Puput Dani Prasetyo; Hariyady, Hariyady
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.3008

Abstract

As the complexity and scale of projects increase, new challenges arise related to handling software defects. One solution uses machine learning-based software defect prediction techniques, such as the K-Nearest Neighbors (KNN) algorithm. However, KNN’s performance can be hindered by the majority vote mechanism and the distance/similarity metric choice, especially when applied to imbalanced datasets. This research compares the effectiveness of Euclidean, Hamming, Cosine, and Canberra distance metrics on KNN performance, both before and after the application of SMOTE (Synthetic Minority Over-sampling Technique). Results show significant improvements in the AUC and F-1 measure values across various datasets after the SMOTE application. Following the SMOTE application, Euclidean distance produced an AUC of 0.7752 and an F1 of 0.7311 for the EQ dataset. With Canberra distance and SMOTE, the JDT dataset produced an AUC of 0.7707 and an F-1 of 0.6342. The LC dataset improved to 0.6752 and 0.3733 in tandem with the ML dataset, which climbed to 0.6845 and 0.4261 with Canberra distance. Lastly, after using SMOTE, the PDE dataset improved to 0.6580 and 0.3957 with Canberra distance. The findings confirm that SMOTE, combined with suitable distance metrics, significantly boosts KNN’s prediction accuracy, with a P-value of 0.0001.
Pengembangan Sistem Manajemen Sarana Dan Prasarana, IT, Serta Laboratorium Di SMK Telekomunikasi Putri Nabella; Rudy Herteno; Setyo Wahyu Saputro; Friska Abadi; Muhammad Itqan Mazdadi; Nabella, Putri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 1: Februari 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025128649

Abstract

Bidang Sarana dan Prasarana, IT, serta Laboratorium di SMK Telekomunikasi menghadapi tantangan dalam pengelolaan data yang tersebar di berbagai file Microsoft Excel, menyebabkan kesulitan dalam pengumpulan laporan untuk audit dan sertifikasi. Penelitian ini bertujuan mengembangkan sistem manajemen terpadu menggunakan framework CodeIgniter 4, PHP, dan MySQL dengan metode Rational Unified Process (RUP) dan desain Unified Modelling Language (UML). Sistem ini dirancang untuk menyelaraskan pengelolaan data dan memfasilitasi penyajian informasi yang efisien. Hasil pengujian black box menunjukkan tingkat keberhasilan 100%, sementara user acceptance testing memperoleh skor 92% dengan predikat sangat baik. Implementasi sistem ini diharapkan meningkatkan efisiensi dan efektivitas manajemen sarana, prasarana, IT, dan laboratorium di SMK Telekomunikasi, memberikan kontribusi signifikan terhadap peningkatan kualitas pengelolaan dan kepuasan pengguna.   Abstract. The Facilities and Infrastructure, IT, and Laboratory Department at SMK Telekomunikasi faces challenges in managing data scattered across various Microsoft Excel files, resulting in difficulties in compiling reports for audits and certifications. This research aims to develop an integrated management system using the CodeIgniter 4 framework, PHP, and MySQL, employing the Rational Unified Process (RUP) methodology and Unified Modelling Language (UML) design. This system is designed to streamline data management and facilitate efficient information presentation. The results of the black box testing showed a success rate of 100%, while the user acceptance testing scored 92% with an excellent rating. The implementation of this system is expected to enhance the efficiency and effectiveness of managing facilities, infrastructure, IT, and laboratories at SMK Telekomunikasi, significantly contributing to improved management quality and user satisfaction.
Co-Authors Abadi, Friska Adi, Puput Dani Prasetyo Adini, Muhammad Hifdzi Aflaha, Rahmina Ulfah Akhmad Zakirin Akhtar, Zarif Bin Andi Farmadi Andi Farmadi Angga Maulana Akbar Anshory, Muhammad Naufal As’ary Ramadhan Badali, Rahmat Amin Deni Sutaji Dodon Turianto Nugrahadi Dwi Kartini, Dwi Erick Kurniawan Fadhillah, Muhammad Alif Faisal, Mohammad Reza Fatma Indriani Febrian, Muhamad Michael Friska Abadi Hana, Elvina Nur Hariyady, Hariyady Herteno, Rudy Herteno, Rudy Hertono, Rudy Irwan Budiman Irwan Budiman Kamil, Hawariul Martalisa, Asri Maulana Abdul Rahman Maulidha, Khusnul Rahmi Muhammad Fikri Muhammad Ikhwanul Hakim Muhammad Itqan Mazdadi Muhammad Khalid Maulana Muhammad Nazar Gunawan Muhammad Noor Muhammad Reza Faisal, Muhammad Reza Muliadi Nabella, Putri Nur Hidayatullah, Wildan Putri Nabella Radityo Adi Nugroho Rahmat Budianoor Rahmayanti Rahmayanti Raidra Zeniananto Ramadhan, Muhammad Rizky Aulia Reza Faisal, Mohammad Rezeki, Abdillah Rozaq, Hasri Akbar Awal Rudy Herteno Saragih, Triando Hamonangan Satou, Kenji Siti Aisyah Solechah Suryadi, Mulia Kevin Syahputra, Muhammad Reza Ujang Ruyandi Umiatin, Umiatin Wahyudi Ariannor Wardana, Muhammad Difha YILDIZ, Oktay Yulia Yudihartanti