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All Journal Jurnal Ilmu Ternak JURNAL ILMU DAN TEKNOLOGI PETERNAKAN Ilmu Administrasi Publik ELT Worldwide: Journal of English Language Teaching Jurnal Sosialisasi: Jurnal Hasil Pemikiran, Penelitian dan Pengembangan Keilmuan Sosiologi Pendidikan Jurnal Ilmu Pangan dan Hasil Pertanian Fashion and Fashion Education Journal Sinkron : Jurnal dan Penelitian Teknik Informatika GIZI INDONESIA Chimica et Natura Acta Jurnal Pendidikan Matematika dan Sains Jurnal SOLMA Jurnal Analisis Bisnis Ekonomi SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Fish Protech Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan SEIKO : Journal of Management & Business DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) The International Journal of Business Review (The Jobs Review) Nursing Inside Community Math Educa Journal Jurnal Pendidikan Teknologi Pertanian Bahasa: Jurnal Keilmuan Pendidikan Bahasa dan Sastra Indonesia Building of Informatics, Technology and Science Jurnal Ilmiah Kebidanan Indonesia (Indonesian Midwifery Scientific Journal) Informatika Jurnal Agrium JURNAL PEMBELAJARAN DAN MATEMATIKA SIGMA (JPMS) Dinasti International Journal of Education Management and Social Science PREPOTIF : Jurnal Kesehatan Masyarakat Jurnal Abdi Insani Jurnal Fisika dan Terapannya Jurnal Peduli Masyarakat Green Medical Journal Jurnal Kesehatan dan Keselamatan Kerja Universitas Halu Oleo JURNAL APARATUR Indonesian Journal of Business and Management Buletin Peternakan Tropis (Bulletin of Tropical Animal Science) MAHESA : Malahayati Health Student Journal Indonesian Journal of Intellectual Publication (IJI Publication) Jurnal Pengabdian Masyarakat (ABDIRA) Jurnal Agrisistem Jurnal EduHealth INDONESIAN JOURNAL OF HEALTH Proceedings of International Conference on Multidiciplinary Research Jurnal Pendidikan dan Profesi Keguruan Indo-MathEdu Intellectuals Journal Phydagogic : Jurnal Fisika dan Pembelajarannya Healthy Tadulako Journal (Jurnal Kesehatan Tadulako) Jurnal Manajemen Perbankan Keuangan Nitro Jurnal Iman dan Spiritualitas Journal of Computer Science and Information Systems (JCoInS) Seminar Nasional Pengabdian Kepada Masyarakat Dulang: Jurnal Pengabdian Kepada Masyarakat Makara Journal of Health Research Jurnal Ekobismen TAUJIHAT: Jurnal Bimbingan Konseling Islam Review of Accounting and Business Sahabat Sosial: Jurnal Pengabdian Masyarakat Pengabdian Jurnal Abdimas Prohealth Journal Jurnal Pengabdian Masyarakat Jurnal Riset Multidisiplin dan Inovasi Teknologi Cendikia Pendidikan Tut Wuri Handayani : Jurnal Keguruan dan Ilmu Pendidikan PEDAMAS (Pengabdian Kepada Masyarakat) Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Panrita AGROTEKBIS: Jurnal Ilmu Pertanian Journal Of Sport Education, Coaching, And Health Jurnal Pembangunan Agribisnis (Journal of Agribusiness Development) Hexagon: Jurnal Ilmu dan Pendidikan Matematika Paramacitra : Jurnal Pengabdian Masyarakat Jurnal Medika Veterinaria English Edu: Journal of English Teaching and Learning Jurnal Agroterpadu Pinisi Journal Pendidikan Guru Sekolah Dasar Jurnal Ekonomi Manajemen dan Kewirausahaan Kajian Ilmiah Mahasiswa Administrasi Publik (KIMAP) Pharmacia PENDIMAS Health Science & Biomedical Journal (HSBJ)
Irmayanti Irmayanti, Irmayanti
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Implementation of the Naïve Bayes Algorithm to Predict New Student Admissions Salsabila, Aulia; Nasution, Marnis; Irmayanti, Irmayanti
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5363

Abstract

New student admissions are critical to the success of an educational institution because they determine the existence and financial sustainability of that institution. The number of prospective students who register changes every year. The school cannot anticipate the number of students who will come. Additionally, data on prospective students who enroll is collected annually without being analyzed to extract valuable information. The school must make predictions to estimate the number of new students in the next school year. Predictions are essential for effective planning, both in the long and short term. This research aims to apply the Naïve Bayes algorithm with Gaussian type to predict new student admissions. To find out whether the Naïve Bayes algorithm works well, an evaluation matrix is used. The methods applied include the dataset collection process, data preprocessing, split data training and testing, feature engineering, the implementation of Naïve Bayes, and results evaluation. The dataset is divided into 70% training data and 30% testing data. The research results show an accuracy score of 86.11% during training and an accuracy score of 90.62% during model testing, with an increase of 4.51%. These results show that there is no indication of overfitting in the machine learning algorithm used. The evaluation matrix produces an accuracy score of 90.62%, precision of 100%, recall of 90.62%, and f1-score of 95.08%. From the results of the evaluation matrix scores, it can be concluded that the naive Bayes algorithm with Gaussian type succeeded in predicting new student admissions well.
Analisis Sistem Antrian Cafe Ipong Kane Menggunakan Metode Single Channel Agustin, Sinta Fortuna; Risky, Wiranti Cahya; Ritonga, Eva Indriani; Ritonga, Risa Agustika; Irmayanti, Irmayanti
Indo-MathEdu Intellectuals Journal Vol. 5 No. 1 (2024): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v5i1.825

Abstract

The purpose of this research is to find out how long it takes customers when experiencing a queue for service at Cafe Ipong Kane using the single channel method. The research methodology used is descriptive observational research by directly observing the queuing system that occurs at the Ipong Kane Café. The results and discussion of this research are the arrival of data and services, and queue calculations. The conclusion of the research that has been carried out is: Single channel model queuing process, from the calculation that the average number of customers waiting in line is around 10 customers within 25 minutes in busy conditions. The average time spent by customers waiting in line is around 9.6 minutes at the longest and 3 minutes at the fastest
Perubahan Tekanan Darah dengan Konsumsi Kalsium pada Ibu Hamil Riwayat Preeklampsi Irmayanti, Irmayanti; Tandiallo, Devianti; Ibrahim, Fitriana
Jurnal Ilmiah Kebidanan Indonesia Vol 11 No 01 (2021): Jurnal Ilmiah Kebidanan Indonesia (Indonesian Midwifery Scientific Journal) Sek
Publisher : Q PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33221/jiki.v11i01.938

Abstract

Preeklampsia adalah suatu sindrom spesifik pada kehamilan dengan gejala klinis berupa penurunan perfusi organ akibat vasospasme dan aktivasi endotel. Tujuan penelitian ini adalah untuk mengetahui tekanan darah pada ibu hamil dengan riwayat preeklampsi sebelum dan setelah pemberian kalsium selama 8 minggu dengan dosis 3x500 mg/hari. Penelitian ini menggunakan metode desain quasi eksperimental dengan rancangan pre-post test. Tehnik pengambilan sampel adalah Purposive sampel yaitu 30 ibu hamil yang terbagi atas 13 dengan hipertensi dan 17 dengan tekanan darah normal. Analisis data menggunakan analisis univariat dan bivariat dengan uji statistik Paired T test. Hasil penelitian menunjukkan bahwa pada ibu hamil hipertensi didapatkan TD sistole dengan pValue .000 < 0,05 yang artinya signifikan, dan pada sampel tekanan darah normal didapatkan TD sistole dengan pValue .046 < 0,05 yang artinya signifikan.
FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN KEPATUHAN PENGGUNAAN APD PADA PEKERJA PT. WIJAYA KARYA BANGUNAN GEDUNG TBK TAHUN 2022 Irmayanti, Irmayanti; Pratiwi, Arum Dian; Yusran, Sartiah
Jurnal Kesehatan dan Keselamatan Kerja Universitas Halu Oleo Vol 4, No 3 (2023):
Publisher : FKM Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37887/jk3-uho.v4i3.46413

Abstract

Kecelakaan akibat kerja di Indonesia semakin meningkat setiap tahunnya yang menyebabkan 80-85% kecelakaan industri disebabkan oleh kelalaian manusia, yaitu bekerja tanpa menggunakan alat pelindung diri (APD). Penelitian ini bertujuan untuk mengetahui hubungan antara persepsi, motivasi, dan supervisi dengan kepatuhan penggunaan APD. Penelitian ini merupakan penelitian kuantitatif dengan mengaplikasikan desain observasional analitik dan pendekatan cross sectional. Populasi dalam penelitian ini adalah seluruh staf lapangan PT. Wijaya Karya Gedung Tbk yang berpartisipasi dalam proyek The Park Mall Kendari yang berjumlah 64 orang, Sampel dalam penelitian ini dipilih menggunakan teknik total sampling, dimana jumlah sampel yang diambil sebanyak 64 orang. Instrumen yang digunakan dalam pengumpulan data yaitu kuesioner, lembar observasi dan dokumentasi. Analisis data dilakukan dengan menggunakan uji chi-square. Hasil penelitian ini menunjukkan tidak ada hubungan antara persepsi dan kepatuhan penggunaan APD (p-value 0,240). Ada hubungan antara motivasi dan kepatuhan penggunaan APD (p-value 0,008) serta tidak terdapat hubungan antara pengawasan dan kepatuhan penggunaan APD (p-value 0,740). Adapun kesimpulan dari penelitian ini adalah terdapat hubungan antara motivasi dengan kepatuhan penggunaan APD pada pekerja PT. Wijaya Karya Bangunan Gedung Tbk serta tidak terdapat hubungan antara persepsi dan pengawasan dengan kepatuhan penggunaan APD pada pekerja PT. Wijaya Karya Bangunan Gedung Tbk. Kata kunci: kepatuhan APD, motivasi, pengawasan, persepsi
Chatbot Design for Interview Questions Using Neural Network Models on the CarTech Website Sihotang, Diko Pradana; Harahap, Syaiful Zuhri; Irmayanti, Irmayanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13603

Abstract

Abstract: This research focuses on analyzing interview questions using a neural network model, implemented on the CarTech website. With the main aim of optimizing the interaction between users and the system through the questions asked, this research takes an innovative step by utilizing Google Collab as a development platform. For this research, several paragraphs were carried out, namely problem scoping, data acquisition, data exploration, modeling, evaluation, and deployment. These stages were carried out so that this research could get good results, plus the integration between Google Collab and chatbot which made it possible for this research to get good results. Google Collab makes it easy to use neural network models and integrate with chatbots, enabling efficient and effective testing and deployment of models. The results of this study are quite impressive, with an accuracy of 92%, demonstrating the model's ability to process and understand interview questions with high precision. The aim of this research is not only to explore the potential of neural network models in automatically understanding questions and providing accurate responses, but also to show how this technology can be integrated into web applications to improve the quality of user interactions, making AI-based chatbots a viable solution and effective in improving user experience on the CarTech website. In conclusion, by utilizing AI you will also get good results. As in this research, AI can help analyze interview questions with neural network models.
Application of Data Mining using the K-Means Method for Visitor Grouping Syah, Rahmayuni; Nasution, Marnis; Irmayanti, Irmayanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13624

Abstract

Grouping amusement ride visitor data is an important process that aims to identify certain patterns of visitors, enabling management to adjust marketing strategies and improve their services more effectively. This process begins with a data selection stage where relevant visitor data is collected and prepared for analysis. The next stage is data pre-processing, which involves cleaning the data from noise or irrelevant data, as well as ensuring the data is in a format suitable for analysis. After that, the data mining model design is carried out by selecting the most appropriate method for grouping visitor data. The next stage is testing and evaluating the model to verify its accuracy and effectiveness. The results of model testing show that visitor data can be categorized into three groups: C1 with 50 data, C2 with 20 data, and C3 with 48 data. The results of the model evaluation confirm that the designed model succeeded in classifying data with perfect accuracy, namely 100%. This success shows that the model is highly effective in identifying and segmenting visitor patterns, providing valuable insights for strategic decision making in service improvement and marketing. This success also opens up opportunities for the application of similar methods to other datasets in an effort to improve visitor experience and operational efficiency.
Performance Analysis of Random Forest Algorithm for Network Anomaly Detection using Feature Selection Agustina, Triya; Masrizal, Masrizal; Irmayanti, Irmayanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13625

Abstract

As the volume and complexity of computer network traffic continue to increase, network administrators face a growing challenge in monitoring and discovering unusual activity. To keep the network safe and functioning, detecting anomalies is essential. Machine learning-based anomaly detection techniques have become increasingly popular in recent years. This is due to the fact that conventional anomaly detection methods make it difficult to detect unknown and complex attacks. This research aims to conduct a performance analysis of two feature selection methods using the random forest algorithm using the UNSW-NB15 dataset to determine which model is most effective in detecting network traffic anomalies. The models evaluated were random forest with the filter method and random forest with the wrapper method. A number of metrics used for model performance assessment are accuracy, F1-score, receiver operating characteristic curve, and precision-recall. Dataset collection, data pre-processing, feature selection, model construction, and evaluation are the main components of the research methodology. The research results show that the Random Forest approach with the Filter method has an accuracy of 0.8950, F1-score of 0.8333, ROC score of 0.8928, and a precision-recall value of 0.8347. Meanwhile, the approach using the Wrapper method obtained an accuracy of 0.9151, F1-score of 0.8510, ROC score of 0.9136, and a precision-recall value of 0.8637. This shows that the performance of Random Forest with the Wrapper method is superior in all assessment metrics. Random Forest with the Wrapper Method is the right choice of model for detecting network traffic anomalies because of its stable performance and ability to handle complex patterns
Analysis of Student Excellence Classes in Data Mining Using the KNN Method Ritonga, Arvida; Masrizal, Masrizal; Irmayanti, Irmayanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13627

Abstract

Excellent classes are programs designed to maximize the academic and non-academic potential of students and girls, with the aim of improving their overall achievement. This program aims to provide more intensive learning and a curriculum tailored to students' needs and abilities, so that they can develop their talents and competencies optimally. In order to evaluate the effectiveness of the superior class program and to identify students who are most suitable for the program, this research was conducted using the K-Nearest Neighbors (KNN) method in data mining. The research process includes several critical stages, namely determining relevant data, designing a machine learning model, testing the model to ensure its effectiveness, and evaluating the model to assess the accuracy and reliability of the results. This research used sample data consisting of 92 male and female students, where the results of the analysis showed that 42 of them met the criteria to enter the superior class, while 50 other students did not. These criteria are determined based on various factors, including academic achievement, participation in extracurricular activities, and other individual characteristics assessed through the KNN method. The accuracy results obtained from the model evaluation show excellent performance, confirming that the approach used is effective in classifying students based on their potential to excel in superior class programs. The conclusion of this research shows that the use of the KNN method in data mining can accurately identify students who will benefit most from superior class programs. Thus, this approach offers a valuable tool for educational institutions to optimize student potential and raise overall standards of achievement.
Prediction of Stunting in Toddlers Combining the Naive Bayes Method and the C4.5 Algorithm Melyani, Sri; Harahap, Syaiful Zuhri; Irmayanti, Irmayanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13651

Abstract

Research conducted to predict the incidence of stunting in toddlers, using data mining methods such as Naive Bayes and the C4.5 algorithm has been applied to analyze health data. The main aim of this research is to develop a predictive model that can identify toddlers who are at high risk of stunting, based on variables that have been collected from medical records and health surveys. The use of the Naive Bayes and C4.5 methods in this research aims to compare the effectiveness of the two methods in dealing with complex and unbalanced classification problems. This research involves a series of crucial stages starting from data selection, data pre-processing, data mining model design, data mining model testing, to method evaluation. In this study, the sample used consisted of 200 toddlers, of which 159 were diagnosed as having stunting and 41 others were not. The classification results show significant effectiveness in both methods used. The accuracy results of both methods are very encouraging, with both methods showing success rates of more than 90%. This shows that both Naive Bayes and C4.5 are very effective in identifying patterns related to the risk of stunting among toddlers. These highly accurate results not only demonstrate the power of data mining techniques in the field of public health but also provide insights that health practitioners can use to intervene earlier in at-risk populations.
Application of Neural Network Method to Determine Public Satisfaction Level on Pertalite Fuel Rahmadani, Fitri; Masrizal, Masrizal; Irmayanti, Irmayanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13869

Abstract

This research aims to analyze public interest in Pertalite fuel using the Data Mining method, specifically using the Neural Network method. The stages in this research include Data Analysis, Data Preprocessing, Designing Classification Models in Data Mining, Classification Results in Data Mining, Designing Evaluation Models in Data Mining, and Evaluation Results on Data Mining. The classification results show that of the total of 105 community data analyzed, 97 community data showed interest in Pertalite fuel, while only 8 community data showed no interest. The accuracy results obtained were 100%, indicating that the Neural Network method is very suitable and effective in classifying people's interest in Pertalite fuel. The Data Analysis process was carried out to understand and analyze the characteristics of data regarding public interest in Pertalite fuel. Data preprocessing is carried out to clean, transform and integrate data so that it is ready for the classification process. Next, the Designing Classification Models in Data Mining process is carried out to design a classification model using the Neural Network method. Classification Results in Data Mining produces information that the majority of people have an interest in Pertalite fuel. Designing Evaluation Models in Data Mining is carried out to design classification evaluation models, which then produce Evaluation Results on Data Mining which show an accuracy level of 100%. Thus, this research shows that the Neural Network method is very effective in classifying people's interest in Pertalite fuel.
Co-Authors A, Nurmala Sinta Abd. Rasyid Syamsuri Abdul Halik Abdul Rahim Abidin, Saenal Adi, Yusram Afni, Fauza Agustin, Sinta Fortuna Agustina, Triya Ahmad Fadli Ahyar, Zakya Aisyah, St. Ajeng Restu Wahyuni Aliasra, A Fany Alwi, Muh. Alyas, Alyas Amanda S, Maria Catharine Ambalele , Elisabeth Andriani, Nur Putri Angraini, Nurvadillah Apriani, Laelati Dwina AR, Chairuni Arfah, Dinda Julia Arham, Muhammad Arta Farmawati Arum Dian Pratiwi, Arum Dian Aslan Abidin, Aslan Asmeri Lamona, Asmeri Asriani Ilyas Astuti - Bahtiar, Rizal Bangun, Budianto Basri, Muhammad Ridhwan Berry Erida Hasbi Besse Mahbuba We Tenri Gading Bulan, Yunita Embong Bunyamin Bunyamin Chairil Anwar Chairil Anwar Chairul Amni, Chairul CUT ITALINA Daud, Nur Ramahdani Dewi, Anita Candra Didik Santoso Dwi Irawati Dwi Ratna Sari, Dwi Ratna Eliyah Acantha Manapa Sampetoding Elva Nuraina Erliza Noor Etty Nurwati Fadiyah, Faiza Fahmitasari, Furi Faradilah, Rahma Farida Styaningrum Fattah, Nurfachanti Fitri Andriani, Fitri Fitri Rahmadani, Fitri Fitriani Fitriani Fuadi, Anis Gumilang, Randi Muhammad HA, Umar Hada, Hadayani Hamidah Suryani Hamka Hamsar, Israwati Harahap, Muhammad Wirawan Hasan, Muh Said Hasibuan, Lily Rohanita Hasruddin Herdiansyah, Roydido Heriyanti, Anggy Hidayat, Amat HIKMAD HAKIM Hikmawaty, Hikmawaty Hotimah, Hotimah Husain Syam Hutagalung, Devi lestari I Gusti Wayan Murjana Yasa I Wayan Darmadi Ibrahim, Fitriana Ika Rezvani Aprita Ilham Ilham Inar, Inar Indah Lestari Daeng Kanang Indayanti, Nova Indriani Indrianto Kadeko Irhami, Irhami Irna Diyana Kartika Irna Diyana Kartika Kamaluddin Isamu, Kobajashi Togo Ismet Ismet, Ismet Jamaluddin P, Jamaluddin P Juanti, Putri Judrah, Muh Juliani Julyanti, Eva Jumriati, Jumriati Junedi, Beni Kandatong, Hasanuddin Kartika Kartika Kemalawaty, Mulla Kusmiah, Nurhaya Laksmayani, Made Krina Laksono Trisnantoro Lewangka, Oesman Linda Fauziah Ariyani Lita, Wang Lukmanul Hakim Lusianawati, Hayu M Daud AK M Farhan Anwar M, Nelvi Nurrizqi Mabrukah, Ratu Makmur, Teuku Manuntungi, Andi Ernawati Martalena Br Purba, Martalena Br Martinawati Martinawati Masrijal, Masrijal Masruroh MASRUROH Maulinda, Maulinda Melyani, Sri Minanga, Yunus Tandi Moeljadi Moeljadi, Moeljadi Muhammad Halmi Dar Mulia, Ayu Munawaroh, Roudatul Munir, Wahida Musi, Muhammad Akil Mutmainnah Mutmainnah Nabil Makarim Nasution, Marnis Nasution, Maya Khairani Natsir, Pratiwi NFH, Alifya Nirwan, Siti Fauziyyah Novianty, Iin Nurelly Noro Waspodo, Nurelly Noro Nurhijrah, Nurhijrah Nurjannah Nurjannah Nurmadilla, Nesyana Nurmalasari Nurmalasari Nursakinah Annisa Lutfin Palayukan, Jisril Pannyiwi, Rahmat Prasodjo, Hendris Agung pratiwi, arezky Prema Hapsari Pribadi, Wikan Purwanto, Sri Putra, Fasdiansyah Raehan, Raehan Rahim, Samsir Rahmad Husein Rahmaniah, Rahmaniah Rahmawati, Laely Rahmi Rahmi Raisa, Nur Ratna Ratna Ratnasari, Jayanti Refiati, Refiati Rein, Rein Risky, Wiranti Cahya Rissa Megavitry Ristiana, Evi Ritonga, Arvida Ritonga, Eva Indriani Ritonga, Risa Agustika Rizki, Paqrul Rohaendi, Nendi Rosalia, Maya Rosmiaty Rosmiaty RR. Ella Evrita Hestiandari Rukmaini, Rukmaini Rusmayadi Rusmiati, Tati Ruth Dameria Haloho Safitri, Asrini Safitri, Asrini Salam, Salam Salfauqi Nurman, Salfauqi Salsabila, Aulia Sarman, Aco Parawansa Sesilia Tandondo, Hana Sherliana, Sherliana Sihotang, Diko Pradana Simatupang, Isnani Nuraminah Siregar, Ade Elvi Rizki Siti Nuraliah, Siti Sitti Husaebah Pattah Sofiani, Anggi Solly Aryza Sri Endah Wahyuningsih Sri Julyani Sri Vitayani Suandana, Nana Suardi, Suardi Suci Ramadhani, Suci Suhria Suhria Sukmawati Mardjuni Sukraini, Sukraini Supandi, Achmad Supriyatman, Supriyatman Suriansyah, Suriansyah Suriyati, Suriyati Suryadi, Sudi Suryana, Syarifah Sutihat, Kaifa Suyono Suyono Syah, Rahmayuni Syahri, Misba Wana Syahril, Erlin Syaiful Zuhri Harahap Tamrin Tamrin Tandiallo, Devianti Taufik, Muhammad Akram Taufiq, Raihan Teuku Isnaini Titi Candra Sunarti Topik Hidayat Touku Umar Urfani, Yulinda Usman, Al Zaima Utami, Yuri Pratiwi Wahyudi, Gusti Weny Dwi Ningtiyas Wury Damayantie Yeheskiel, Yeheskiel Yosua Yosua YUDI NUR SUPRIADI Yulia, Ruka Yulianti Yulianti Yulita Sirinti Pongtambing Yuni Tri Lestari, Yuni Tri Yusran, Sartiah Yusuf, Halmiah Zakirah Raihani Ya’la