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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Sains dan Teknologi TELKOMNIKA (Telecommunication Computing Electronics and Control) CESS (Journal of Computer Engineering, System and Science) Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika SISFOTENIKA JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Jurnal Mantik Penusa JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) JSiI (Jurnal Sistem Informasi) Jurnal Pengembangan Riset dan Observasi Teknik Informatika JURIKOM (Jurnal Riset Komputer) Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Building of Informatics, Technology and Science Jurnal Mantik Aisyah Journal of Informatics and Electrical Engineering INTI Nusa Mandiri Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI JPM: JURNAL PENGABDIAN MASYARAKAT Journal La Multiapp KLIK: Kajian Ilmiah Informatika dan Komputer International Journal of Basic and Applied Science JUSTIN (Jurnal Sistem dan Teknologi Informasi) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Equivalent: Jurnal Ilmiah Sosial Teknik SAGA: Journal of Technology and Information Systems
Agung Triayudi
Universita Nasional
Aerospace Engineering Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Analisis Perbandingan Metode Dempster Shafer dan Certainty Factor pada Sistem Pakar Untuk Mendeteksi Penyakit Jantung Koroner Muhammad Rafi Fadhilah; Agung Triayudi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1624

Abstract

This research aims to develop an expert system for detecting coronary heart disease by comparing the Demster-Shafer and Certainty Factor methods in providing accurate solutions. Coronary heart disease (CHD) is a disease that often threatens human health. To overcome this problem, the development of expert systems has become an important approach in diagnosing CHD accurately and efficiently. The problems faced include the level of complexity in diagnosing CHD and the need for solutions that can provide a high level of confidence. The method used involves collecting data from various sources and analysis using both methods to determine a diagnosis. The research results show that both methods are able to provide satisfactory results, however, a comparison between the two provides additional insight in understanding the reliability and accuracy of the expert system being developed. A thorough analysis shows that the Demster-Shafer method provides a higher degree of accuracy in some cases, while Certainty Factor tends to provide faster results. However, this research also reveals that optimal results can be achieved by combining the two methods. Thus, this research makes an important contribution to the development of an expert system for coronary heart disease detection and provides a foundation for further development in this domain. In conclusion, the integration of the Demster-Shafer and Certainty Factor methods shows the potential to improve the performance and reliability of expert systems in supporting CHD diagnosis effectively. The calculation results of both methods show that the Dempster-Shafer Method produces a certainty level of 99.8%, while the Certainty Factor Method provides a confidence level of 92%.
Penerapan Algoritma K-Means Data Mining untuk Clustering Kinerja Karyawan Koperasi Jhiro Faran; Agung Triayudi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1728

Abstract

Employees are people who are the main element in every organization/company. An employee is someone who can carry out work and provide the results of their work to the employer or agency where the employee works, where the results of their work are in accordance with the profession or work based on their expertise. The role of employees in cooperatives is the same as the role of employees in general in every other organization/company. Giving rewards to employees is a form of company appreciation for its employees. Reward or recognition is a form of gratitude from the company for the dedication and performance of employees, namely those who have good quality work and have met the criteria for employees with good performance. The problem faced is that currently there is no process that has been carried out to group employee performance. Grouping employee performance is a fairly important problem and must be resolved immediately by the company. The solution to this problem can be solved by paying attention to patterns based on processes or data that occurred in the past. Data mining is the right way to solve this problem. Data mining is a process of processing data and extracting data to get information back from a collection of data. Clustering is a process of grouping data contained in a dataset. Grouping data in a dataset using clustering is done based on the similarity values or characteristics of each data. The K-Means algorithm is part of clustering data mining, where the K-Means algorithm can be used to form new groups of data. The results obtained from the research are that the formation of new groups/clusters is based on a total of 15 data, so there are 2 (two) clusters where in cluster 1 there is 7 data and cluster 2 there is 8 data
Penerapan Metode Dempster Shafer dalam Mendiagnosa Penyakit Pneumonia Muhammad Rafi Fadhilah; Agung Triayudi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1734

Abstract

The aim of this research is to apply the Dempster Shafer Method in diagnosing pneumonia. This research aims to apply the Dempster Shafer Method in diagnosing pneumonia. The main problem faced in the diagnosis of this disease is the complexity and uncertainty in the interpretation of symptoms and medical test results. Dempster Shafer's method, a method in belief theory that allows combining information from multiple sources with different levels of certainty, was proposed as a solution to overcome this uncertainty. In this study, symptom data and medical test results from patients suspected of suffering from pneumonia were collected. Then, the Dempster Shafer Method is applied to combine information from various sources, such as blood test results, lung X-rays, and the patient's medical history. This method makes it possible to establish the level of confidence in the resulting diagnosis. The research results show that the application of the Dempster Shafer Method in diagnosing pneumonia provides more accurate results compared to traditional approaches. By considering the uncertainty and complexity in diagnosis, the Dempster Shafer Method is able to provide more reliable estimates and help doctors make more appropriate decisions in treating pneumonia cases. Application of the Dempster Shafer Method also produces a framework that can be adapted to diagnose other diseases that require managing uncertainty. Additionally, this approach can help increase efficiency in the diagnosis process, leading to a reduction in diagnostic errors and an improvement in the overall quality of patient care. Thus, this research makes an important contribution to the development of more sophisticated and reliable diagnostic methods in the medical field. The results of analysis using the Dempster-Shafer method show that the maximum value for each combination of symptoms that is important in diagnosing pneumonia is 0.9811, which is equivalent to 98.11%. Based on this interpretation, it is estimated that the patient has a high chance of suffering from severe pneumonia.
Penerapan Data Mining Dengan Menggunakan Algoritma Clustering K-Means Untuk Pembagian Jurusan Pada Sekolah Menengah Atas Setiawan, Ikbal Danu; Triayudi, Agung
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4970

Abstract

Senior High School is the last level that must be taken before continuing education at a higher level such as a Diploma or Bachelor's degree. Where in general high schools have class majors for students who will move up to class XI from class Improving the quality of education carried out in the class majoring process means that students will be more focused in accordance with the field of interest of the major that the student/I should take. The process that occurs in determining majors is only based on the wishes expressed by the students without taking into account the academic grades of the subjects that the students have passed or completed in class X. This problem is not a small problem that should be ignored, it This is an important problem that must be resolved immediately because if the problem is not resolved immediately it will have lasting impacts later. The process of determining the division of majors for students can be seen based on the patterns or values of previous students. Data mining is a process used to complete processing of large data. The data that is processed is a collection of data that becomes Big Data from past data that is stored in a storage container and can then be reused by processing it. Clustering is an appropriate way to solve problems. Where in clustering grouping is carried out based on the distance to each data object. The K-Means algorithm is part of Clustering Data Mining, where this algorithm can be used to carry out new groupings based on how clusters are formed. From the results obtained, there are 2 (two) new formation clusters. In cluster 1 there are 9 (nine) students and in cluster 2 there are 6 (six) students.
Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa Setiawan, Ikbal Danu; Triayudi, Agung
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4971

Abstract

Scholarships are a program intended to help students with economic problems. For universities, especially private universities, scholarships are an attraction or a campus promotional event to attract prospective students to register at the campus. The scholarships provided by the campus are independent scholarships which are based on funding from the university's foundation. This is very important to pay attention to, where apart from the achievements of prospective students, they must also consider their readiness or ability to participate in the learning process that takes place at the university. Therefore, paying attention to the grades obtained from prospective students is very important to pay attention to. Another problem is that the quota given by the foundation for scholarships is also limited, which is not covered by all prospective students who register or submit scholarship applications. In terms of determining or awarding scholarships, there is not yet a reference standard that is used for determination in the decision-making process, so scholarship awards are often misdirected. Mistakes in awarding scholarships are of course very detrimental to the campus. Therefore, this problem should require special attention and treatment. This problem can be easily resolved by finding a pattern of rules for accepting scholarships. Data mining is a process method that is widely used today, this is because data mining is very helpful in the decision making process. The process carried out by data mining is divided into several techniques such as Clustering. Clustering is a way to group new data. The K-Means algorithm carries out a solution process based on grouping, therefore the K-Means algorithm is classified as a clustering part of data mining. The aim of the research to be carried out is to assist in the process of grouping prospective students who will be prioritized in receiving scholarships. Based on the results of this research, it can later help to find students who are truly worthy of receiving the scholarship. The results obtained from the research are that there are 2 (clusters) obtained from the K-Means algorithm process. Where in cluster 1 there are 10 grouping data and in cluster 2 there are 5 grouping data.
ANALYSIS OF THE EFFECTIVENESS OF POLYNOMIAL FIT SMOTE MESH ON IMBALANCE DATASET FOR BANK CUSTOMER CHURN PREDICTION WITH XGBOOST AND BAYESIAN OPTIMIZATION Faran, Jhiro; Triayudi, Agung
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.1284

Abstract

The case of churn in the banking industry, namely customers who leave or no longer use bank services, is a serious problem that requires an appropriate solution. The aim of this research is to predict churn and take appropriate preventive actions using machine learning. The dataset contains 10,000 bank customer data with 14 relevant features. Only about 20% of customers experience churn, creating a data imbalance problem in classification. To overcome data imbalances, the SMOTE oversampling technique was applied. Also introduced was the development of the SMOTE technique, namely, Polynomial Fit SMOTE Mesh (PFSM). PFSM works by combining each point in the data with a linear function and producing synthetic data at each connected distance. Experimental results show that the model developed using PFSM and optimized with Bayesian Optimization for the XGBoost algorithm achieved 86.1% accuracy, 70.87% precision, 53.81% recall, and 61.17% F-score. This indicates that the approach is successful in improving predictive capabilities and identifying potential customers for churn earlier. This research has significant relevance in the banking industry, helping banks to safeguard their customers and improve banking business performance..
Sistem Pendukung Keputusan Penilaian Kinerja Dosen Menggunakan Metode (COPRAS) Wulandari, Faras Tira; Triayudi, Agung; Mesran, Mesran; Sussolaikah, Kelik
Journal of Information System Research (JOSH) Vol 5 No 2 (2024): Januari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i2.4805

Abstract

The lecturer performance evaluation decision support system aids the Quality Control Bureau (BPM) in establishing the lecturer performance index (IKD). It employs the Complex Proportional Assessment (COPRAS) method for this assessment. COPRAS facilitates the analysis of diverse alternatives, estimating their utility levels by considering attribute values within intervals, enhancing precision and efficiency in decision-making. The regular evaluation of lecturer performance at the university is pivotal for the continual enhancement of their quality. Thus, a decision support system is imperative to streamline processes and minimize errors in rapidly and accurately computing system data, employing the Complex Proposal Assessment (COPRAS) method. The examination outcomes consistently recognize alternative A4, as the optimal alternative for exemplary lecturer performance, achieving a perfect score of 100.
Sistem Pendukung Keputusan Seleksi Pertukaran Mahasiswa Dalam Mendukung Kampus Merdeka Menerapkan Metode ROC dan TOPSIS Assrani, Dwika; Triayudi, Agung; Simanjuntak, Handayani; Panjaitan, Fricia Oktaviani; Mesran, Mesran
Journal of Information System Research (JOSH) Vol 5 No 2 (2024): Januari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i2.4806

Abstract

The Indonesian Minister of Education, Nadiem Anwar Makarim, B.A., M.B.A. make a policy, namely an independent campus that strongly supports students in doing learning outside of campus in order to get new views of creative and innovative mindsets so that university graduates are expected to be able to compete in the world of work. In conducting the selection of student exchanges in supporting the independent campus, Budi Darma University is less effective, so to facilitate the student selection process in supporting the independent campus, a decision support system is needed in decision making, namely by using the Rank Order Centroid (ROC) method and the Technique For Orders Reference by Similarity to Ideal Solution (TOPSIS). The results of this study are the TOPSIS method, the best alternative is A4 for Desi Novria Siregar with a value of 1 and the lowest alternative is A6 for Fabyen Sabillah Chan with a value of 0.0645
Design of an Employee Recruitment System Based on the AHP-MOORA Algorithm Case Study of PT XYZ Ripin, Muhamad; Triayudi, Agung; Ningsih, Sari
JURNAL FASILKOM Vol. 14 No. 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6829

Abstract

The development of internet-based information technology has had a positive impact on human survival. One of the benefits of the Internet is support in building an employee recruitment system that is oriented towards good corporate governance. PT XYZ is a company that has been recruiting manually. Therefore, it is necessary to build an internet-based system for the employee recruitment process. This research uses the Analytical Hierarchy Process - Multi-Objective Optimization based on Ratio Analysis (AHP-MOORA) algorithm to determine the best applicants based on several criteria. The criteria used in this research are age, academic certificate, highest level of education, and work experience. The results of this study show that age and academic certification are priority criteria based on weight assessment using the AHP approach. Furthermore, five applicants became samples in application testing. The MOORA test results showed that three applicants had the highest scores. This research concludes that the application of the AHP-MOORA algorithm can provide objective results in determining applicants who meet the specified criteria.
Implementasi Klasifikasi Data Mining Untuk Penentuan Kelayakan Pemberian Kredit dengan Menggunakan Algoritma Naïve Bayes Agung Triayudi; Sumiati Sumiati
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4653

Abstract

Credit today is very widely used in the transaction process. At first, lending was only done by banks, but with the development of time and also the increasing needs and purchases from the public, lending is not only done by banks. The granting of credit for financing goods by the company to the buyer is not done haphazardly, but must go through several selection processes. The process of granting credit must be carried out through detailed and strict stages. This causes the process to be lengthy and also lengthens the work of the selection team. Data mining is a data processing technique that is useful for obtaining important patterns from data sets. The Naïve Bayes algorithm is part of the data mining classification process. The process of the Naïve Bayes algorithm is based on the concept of the Bayes theorem. The result of the research is that the new alternative data is ACCEPTABLE for credit applications, it can be seen that the probability value of ACCEPTED is greater than the probability value of REJECTED, which is 0.011108
Co-Authors ., Hervian AAN SETIAWAN Abdul Azis Abdul Aziz Hasibuan Abdulah, Muhamad Biyan Aceng Supriyadi Achmad Syaifudin Rodhi Achmad Syirod Ade Muhammad Nur Fauzi Adi Firman Ari Saputra Adi Yulianto Adian Fatchur Rochim Aditya lutfi Irawan Afid Rozaqi Afiyan Nur Chafidin Afrasim Yusta Afriany, Joli Agus Iskandar Agus Iskandar Agus Iskandar Ahmad Arief Fadila Ahmad Avivanto Ahmad Rizki Firdaus Aji Juliana Akhmad Primulyana Albaar Rubhasy Albaar Rubhasy Aldi Andres Ardiansah Aldya Bagas Prahastyo Alfian Muhharam Ali Rahman Alisa Fitriyani Alvian Nur Efendi Ananda Sustantiara Andarweni, Dhea Andreas Gerhard Simorangkir Andrianingsih Andrianingsih Andriansyah Utomo Anggita Putri Maharani Anhar Hawari Anharudin Anharudin apiek maniek Ardinsah Ardinsah Ardiyanto Wantudi Arie Gunawan Ariel Cahyono Arika Zuraidah Aris Gunaryati Arya Dimas Setiadi Arya Sastranegara Astri Pertiwi Atikah Suhaimah Ben Rahman Benrahman Bernardito Jordan Cahya, Nilam Candra Kurniawan Chafidin, Afiyan Nur Chuy Mandala Putra Cintya Damayanti Dandi Putra Daud Iswandii Della Diniyati Deny Hidayatullah Dewi Janetta Az Zahra Dhea Andarweni Dhieka Avrilia Lantana Dian Yunita Sihombing Diaz Samba Prayogi Dicke Rifki Fajrin Dimas Aryanto Wijaya Dini Nofrisa Diniyati, Della Diranisha, Virly Djamaludin, Muhammad Ariel Dwi Auditira Dwi Ifan Ramadhan Dwi Juliastuti Dwika Assrani Dwina Pri Indini Dwiyatno, Saleh Dzahabi Yunas, Rio Al E, Endah Tri Efendi, Alvian Nur Eka Febriyanto Riski Eka Permana Putra Endah Tri Esti Handayani Eri Mardiani Eri Mardiani Fachid, Syakirah Fadhil Muhammad Supriyanto Fadjar, Agung Rahmad Faiq Husain Pratama Faizal Kurniawan Fajar Setiawan Hidayat Fajrin, Dicke Rifki Faran, Jhiro Fardila Inastiana Fatha Alsidqi Husaini Fathiya Zahra, Hawra Ferina Gunawan Fifto Nugroho Fikar Wahyu Tyas Tono Fikri Fajar Asshiddiqi Fikrianzi Nindyo Kusumo Fildzah Fildzah Firzatullah, Raden Muhamad Flipo Hariski Frankly Sept Genius Zendrato Gatot Soepriyono Genius Zendrato, Frankly Sept Ghulam Prasetyo Utomo Hadi Ansyah Hakam, Muhammad Aulia Haris Triono Sigit Hasibuan, Abdul Aziz Hervian . Heryanto, Yayan Hidayat, Fajar Setiawan Hoga Saragih Ibnu Nur Khawarizmi Ikbal Danu Setiawan Iksal Iksal Iksal Iksal Imam Rizqi Imanuel Sinuraya Inastiana, Fardila indrawan indrawan ingsih, Andrian Ira Diana Sholihati Ira Diana Sholihati Ira Diana Solihati Ira Diana Solihati Iskandar Fitri Ismi Naili Qurrotul Aini Ismia Iwandini Jhiro Faran Juliana, Aji Jumpa Dorisman Rajagukguk Junior, Reza Phahlevi Kabeleke Melanesia L Kartika Salma Nadhiva Karyaningsih, Dentik Kiai Agus Priyaharto Mulia I Kodim Suparman Kusumaningtyas, Grasiella Yustika Rezka Talita Latif Arif Anggoro lia kamelia Lili Dwi Yulianto Listrina Turnip Ma'arif, Ridwan Ahmad Made Yoga Mahardika Mardiani, Eri Mauludani Muhammad Melati Indah Petiwi Melisa Theresia Mesran, Mesran Moh Dani Ariawan Muhamad Biyan Abdulah, Muhammad Andhika Maulana Muhammad Ariel Djamaludin Muhammad Aulia Hakam Muhammad Faisal Abdillah Muhammad Faizal Muhammad Farhan Adistyra Muhammad Ilyas Sahputra Muhammad Jordy Muhammad Prabowo Chaniago Muhammad Rafi Fadhilah Muhammad Rizki Wardhana Muhammad Rizki Zidan Muhammad Rizky Hamdan Mutiara Mala Khairunnisa Nabilah Ananda Pratiwi Nanda Fathi Rizky Nesha Putri Pratama Nifea Kusumawardhani Nova Saragih Novi Dian Nathasia Novi Dian Nathasia Nur Hayati Nur Hayati Nur Hayati Nur Iskandar Zulkarnaen Nurfatanah Nurfatanah Nurfazriah Attamami Nurhadiyan, Thoha Oktaviani Oktaviani Oktaviani oktaviani Oky Triadi Sampurno Panjaitan, Fricia Oktaviani Penny Hendriyati Putra Dama Ramadhan Raffi Dima Sampurno Rafi Syahputra Rahmat Aji Santoso Raihan Abdi Negoro Rais Rabtsani, Muhamad Raka Alvianda Rama Setiawan Ramadhan, Duta Pramudya Ratih Mardianti Ratih Titi Komalasari Repi, Viktor Vekky Ronald Resha Anjariansyah Reynaldo, Yohanes Reza Phahlevi Junior Riad Sahara Rian Aditia Rian Rasyidhi Rian Tineges Ricky Andri Widayat Rifki Nur Apriyono Rima Tamara Aldisa Rima Tamara Aldisa Rima Tamara Aldisa Rio Al Dzahabi Yunas Ripin, Muhamad Riska Setiawati Riska Susilawati Rivaldi Okta Pratama Rizal Bagus Pambudi Rizal Toha, Muhammad Rizkah Fadillah Rizki Kurnia Rizky Setiawan Rodhi, Achmad Syaifudin Rosaima Situmorang Rosalina, Vidila Rudi Adityawan Sahputra, Muhammad Ilyas Sampurno, Raffi Dima Sari Ningsih Sawindri, Sawindri sawindri Seanand Sonia Shabrilianti Seno Hardijanto Purnomo Setiawan, Ikbal Danu Setiawati, Riska Setiono, Aji Shafira Shalehanny Shintia Mutiarani Sholihati, Ira Diana Simanjuntak, Handayani Singgih Yulianto Bastian Siti Nurhalizah Soepriyono, Gatot Solihati, Ira Diana Suginam Sugitha, I Kadek Agga Suhaimah, Atikah Suherman, Suherman Sultana Namira Sumiati Sumiati Sumiati Sumiati Sumiati, Sumiati Suparman, Kodim Susilawati . Susilawati, Riska Sussolaikah, Kelik Syabana, Ulwi Syafrida Hafni Sahir Syavira Cahyaningsih Syirod, Achmad Thoha Nurhadiyan Titih Aji Kurniawan Titik Abdul Rahman Tiyas Asih Qurnia Putri Tobby Wiratama Putra Tyas Tono, Fikar Wahyu Untoroseto, Dedi Utami, Yulianti Pratiwi Vendy Blessing Gulo Vidila Rosalina Vivimaryati Vivimaryati Vivimaryati, Vivimaryati Wahid Al Jufri Wahyu Oktri Widyarto Wardhana, Muhammad Rizki Wibowo, Adhitya Eka Winarsih Winarsih Winarsih Winarsih Winarsih Winda Antika Putri Wiratama Putra, Tobby Wulan Kartika Murti Wulan Widhari Wulandari, Faras Tira Yana Tania Haryanto Yandi Makmur Yani Sugiyani Yanto Murnihati Waruwu Yohanes Reynaldo Yulianti Pratiwi Utami Yunan Fauzi Wijaya Zahrach Artamevia Zuraidah, Arika