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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Agromet IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Veteriner Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi Lingua Jurnal Bahasa dan Sastra PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmu Komputer dan Agri-Informatika Journal of the Indonesian Mathematical Society Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Seminar Nasional Informatika (SEMNASIF) Widyariset Indonesian Journal of Science and Technology Jurnal Sains Matematika dan Statistika Al-Jabar : Jurnal Pendidikan Matematika JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Matematika: MANTIK MAJALAH ILMIAH GLOBE Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Teorema: Teori dan Riset Matematika Jambura Journal of Mathematics Jambura Geoscience Review SALINGKA Jurnal Matematika UNAND Building of Informatics, Technology and Science Sains, Aplikasi, Komputasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science InPrime: Indonesian Journal Of Pure And Applied Mathematics Widyariset Jambura Journal of Biomathematics (JJBM) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Pijar MIPA Jurnal Sains Terapan : Wahana Informasi dan Alih Teknologi Pertanian Journal of Applied Agricultural Science and Technology Milang Journal of Mathematics and Its Applications Jurnal Sintak Jurnal Matematika Integratif Indonesian Journal of Mathematics and Applications Jurnal Pendidikan Progresif Indonesian Journal of Mathematics and Natural Sciences MILANG Journal of Mathematics and Its Applications Majalah Ilmiah Bahasa dan Sastra
Sri Nurdiati
Department Of Mathematics, Faculty Of Science And Mathematics, IPB University, Jl. Meranti, Kampus IPB Dramaga, Bogor 16680
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Perbandingan Metode Tree Based Classification untuk Masalah Klasifikasi Data Body Mass Index Alifah, Rifdah Nur; Najib, Mohamad Khoirun; Nurdiati, Sri; Sari, Annisa Permata; Herlambang, Karen; Noval; Ginting, Dini Tri Putri Br; Sya’adah, Syifa Noer
Indonesian Journal of Mathematics and Natural Sciences Vol. 47 No. 1 (2024): Volume 47 Nomor 1 Tahun 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/m2k97436

Abstract

Body mass index (BMI) atau indeks massa tubuh merupakan salah satu indikator yang dapat mengawasi dan menjelaskan status gizi seseorang. Penelitian ini bertujuan untuk mengklasifikasikan BMI berdasarkan gender, tinggi badan, dan berat badan dengan menggunakan metode Tree Based Classification yang terdiri atas model Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, dan XGBoost menggunakan bahasa pemrograman python. Model Tree Based classification tersebut akan mengklasifikasikan BMI kedalam 6 kelas indeks. Hasil penelitian menunjukkan model klasifikasi XGBoost memiliki akurasi terbaik setelah dilakukan tuning hyperparameter dengan nilai akurasi data test 83.7%. Performa model terbaik sebelum tuning hyperparameter dihasilkan model Random Forest dengan nilai F1-score (macro) untuk data test sebesar 88%. Sementara itu, performa model terbaik setelah tuning hyperparameter dihasilkan model XGBoost dengan nilai F1-score (macro) untuk data test dan data train masing-masing sebesar 79% dan 85%. Berdasarkan model XGBoost, variabel prediktor yang paling berkontribusi terhadap BMI adalah berat badan dengan nilai permutation importance 68.1%.
From Serial to Parallel: Enhancing Needleman-Wunsch Performance through GPU-Based Computing Suharini, Yustina Sri; Kusuma, Wisnu Ananta; Nurdiati, Sri; Batubara, Irmanida
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6620

Abstract

The increasing demand for faster bioinformatics analysis calls for more efficient approaches for sequence alignment. In this study, we demonstrate that a GPU-based implementation of the Needleman-Wunsch algorithm can achieve up to 14.8× speedup compared to its traditional CPU-based serial counterpart, without compromising alignment accuracy. By leveraging the parallel processing capabilities and shared memory of an NVIDIA GeForce RTX 3060 Laptop GPU, we significantly accelerated global sequence alignment tasks. Using clinically relevant genes such as NRAS, BRCA1, BRCA2, and Saccharomyces cerevisiae from NCBI ensures realistic alignment challenges and biological significance. Performance evaluation across a wide range of sequence lengths demonstrates the scalability and efficiency of the parallel approach. More importantly, this study provides a unique contribution by showing that a commodity GPU, such as the NVIDIA GeForce RTX 3060 Laptop, can serve as a practical alternative when high-performance computing clusters are unavailable or prohibitively expensive, thereby offering an accessible and cost-effective pathway to high-throughput bioinformatics workflows.
Probabilistic Prediction Model Using Bayesian Inference in Climate Field: A Systematic Literature Ardiyani, Evi; Nurdiati, Sri; Sopaheluwakan, Ardhasena; Najib, Mohamad Khoirun; Rohimahastuti, Fadillah
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 3 (2023): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i3.13651

Abstract

Wildfires occur repeatedly every year and have a negative impact on natural ecosystems. Anticipation of wildfires is very necessary, therefore a prediction model is needed that can produce predictions with a good level of accuracy. One approach to develop probabilistic prediction models is Bayesian inference. The purpose of this research is to review the methods that can be used in developing probabilistic prediction models using the Bayesian approach. The methodology used is Systematic Literature Review (SLR) which can be used to provide a comprehensive review of Bayesian inference research in developing probabilistic prediction models. The research strategy used was the Boolean Technique applied to database sources including Scopus, IEEE Xplore, and ArXiv. The articles used have novelty and ease of explanation of Bayesian methods, especially predictions in the field of climate so that articles are selected based on inclusion and exclusion criteria. The results show that probabilistic models can provide more accurate results than deterministic models. The Bayesian Model Averaging (BMA) method is a widely used method because it is easy to implement and develop so that the prediction results can be more accurate. The development of probabilistic prediction models with a Bayesian approach has great potential to grow as seen from the development of the number of research publications over the past 5 years. The research position of probabilistic prediction models with Bayesian approaches in the field of climate is dominated by the research community in China with the main problems related to hydrology.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian //  TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//
Pengembangan Sistem Manajemen Pengetahuan di Organisasi Asosiasi Alumni Program Beasiswa Amerika - Indonesia (ALPHA-I) Nurwegiono, Muhammad; Nurdiati, Sri; Wijaya, Sony Hartono
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 3: Juni 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020712249

Abstract

Organisasi ALPHA-I (Asosiasi Alumni Program Beasiswa Amerika – Indonesia) memiliki anggota lebih dari 400 orang yang tersebar di sepuluh daerah di Indonesia. Jumlah alumni penerima beasiswa pendidikan dari United States Agency for International Development (USAID) akan bertambah setiap tahun dan akan tergabung di organisasi ini. Hasil observasi menunjukkan bahwa organisasi ALPHA-I memiliki dua masalah utama. Permasalahan pertama adalah ALPHA-I belum menyediakan sarana berbagi pengetahuan tacit pada lima fokus bidang beasiswa USAID. Permasalahan kedua adalah pengetahuan explicit karyawan seperti Standar Operasional Prosedur (SOP), laporan kegiatan, laporan hasil rapat, daftar mitra dan dokumen penting lainnya yang masih dibukukan. Permasalahan tersebut dapat diselesaikan dengan membuat sistem manajemen pengetahuan. Tujuan penelitian ini adalah mengembangkan sistem manajemen pengetahuan yang dapat memudahkan proses menangkap, mengembangkan, membagikan, dan memanfaatkan pengetahuan tacit alumni dan pengetahuan explicit karyawan di organisasi ini. Penelitian ini dilakukan dengan menggunakan metode Knowledge Management System Life Cycle (KMSLC). Hasil dari penelitian ini adalah sistem manajemen pengetahuan yang dibangun dengan framework PHP dan MySQL sebagai Relational Database Management System (RDBMS) berbasis website. Hasil pengujian Black box dari 36 kasus uji yang telah dilakukan menyatakan bahwa semua fungsi pada sistem berjalan sesuai dengan perintah yang diberikan. AbstractThe ALPHA-I Organization (Alumni Association of US - Indonesia Scholarship Programs) has more than 400 members that have spread in ten regions (chapters) in Indonesia. The number of alumni who receive educational scholarships from United States Agency for International Development (USAID) will increase every year and will join this organization. The result of observation to ALPHA-I organization showed that there are two main problems. The first problem is ALPHA-I organization did not provide equipment for the alumni to share their tacit knowledge on five focused areas of USAID scholarships. The second problem is the explicit knowledge of employees to record the Standard Operational Procedure (SOP), activity reports, meeting report, partner list, and other relevant documents were written by books. These problems can be solved by creating a knowledge management system. The purpose of this study is to develop a knowledge management system that can facilitate the process of creation, development, share, and utilize tacit knowledge of alumni and explicit knowledge of employees at ALPHA-I. This research was conducted using the Knowledge Management System Life Cycle (KMSLC) method. The result of this study was a knowledge management system that was built with PHP framework and MySQL-as a Relational Database Management System (RDBMS) based on website. The result of black box testing from 36 case studies demonstrated that all functions in the system run according to the commands given.
Blockchain dan Kecerdasan Buatan dalam Pertanian : Studi Literatur Wihartiko, Fajar Delli; Nurdiati, Sri; Buono, Agus; Santosa, Edi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0814059

Abstract

Dewasa ini teknologi blockchain dan kecerdasan buatan (artificial intelligence/AI) telah diimplementasikan dalam bidang pertanian. Teknologi blockchain menjanjikan keamanan dan peningkatan kepercayaan untuk pengguna. Teknologi kecerdasan buatan menjanjikan berbagai kemudahan bagi pengguna. Perpaduan kedua teknologi tersebut dapat meningkatan kepercayaan terhadap sistem kecerdasan buatan (blockchain for AI) atau dapat juga digunakan untuk meningkatkan kinerja sistem blockchain (AI for blockchain). Tujuan penelitian ini mengulas kedua teknologi tersebut dalam studi literatur serta memberikan tantangan riset ke depan terkait implementasinya di bidang pertanian.  Metodologi yang digunakan adalah Systematic Literature Review (SLR) dan text mining. Text mining digunakan untuk memberikan deskripsi riset yang ada berdasarkan kata-kata di setiap artikel terpilih. SLR digunakan untuk memberikan ulasan yang komprehensif terkait riset Blockchain dan kecerdasan Buatan dalam pertanian. Hasil penelitian menunjukan bahwa terdapat 10 % penelitian terkait penerapan blockchain dan AI dalam pertanian. Riset tersebut memiliki potensi besar untuk berkembang terlihat dari peningkatan jumlah publikasi dalam 2 tahun terakhir. Kontribusi penelitian ini meliputi posisi riset terkini dan usulan riset ke depan dengan mempertimbangkan kondisi pertanian Indonesia. Posisi riset tersebut didominasi komunitas peneliti dari negara-negara di Asia seperti India (33%), Pakistan (33%), China (14%) dan Korea (14%). Originalitas penelitian ini terletak pada studi literatur dari integrasi teknologi blockchain dan kecerdasan buatan dalam bidang pertanian menggunakan SLR dan text mining. AbstractArtificial intelligence and blockchain technology are being developed and implemented in Agriculture. Blockchain technology promises security and trust for users. Moreover, artificial intelligence technology promises convenience for users. The combination of these two technologies will increase trust in artificial intelligence systems. Besides, this combination can also increase security on the blockchain system through the application of artificial intelligence. This paper summarizes the application of both technologies and reviews them in a systematic literature review, presents a description of articles based on text mining, and provides future research challenges related to the implementation of blockchain and artificial intelligence in agriculture. The methodologies used are Systematic Literature Review (SLR) and text mining. Text mining is used to describe a description of existing research based on the words in each selected article. SLR is used to provide a comprehensive review of Blockchain research and Artificial intelligence in agriculture. The results showed that there were 10% of research related to the application of blockchain and AI in agriculture. This research has great potential for growth as seen from the increase in the number of publications in the last 2 years. The contribution of this research includes the latest research positions and future research proposals taking into account the conditions of Indonesian agriculture. The research position is dominated by the research community from countries in Asia such as India (33%), Pakistan (33%), China (14%) and Korea (14%). The originality of this research is a literature study on the integration of blockchain and artificial intelligence in agriculture using SLR and text mining.
Prediksi Angka Harapan Hidup Menggunakan Regresi Linear Berganda, Lasso, Ridge, Elastic Net, dan Kuantil Lasso Fauzan, Muhammad Daryl; Najib, Mohamad Khoirun; Nurdiati, Sri; Khoerunnisa, Nazwa; Maulia, Syammira Dhifa; Triwulandari, Raden Roro Carissa; Aziz, Muhammad Farhan
Jurnal Sains Matematika dan Statistika Vol 10, No 2 (2024): JSMS Juli 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v10i2.27916

Abstract

Angka harapan hidup mejadi salah satu indikator penting dalam mengevaluasi kesejahteraan dan kualitas hidup suatu populasi atau negara. Metode yang biasa digunakan untuk memprediksi adalah regresi linear berganda. Terdapat banyak perkembangan model regresi linear berganda, seperti regresi lasso, ridge, elastic net, kuantil, serta kuantil lasso. Untuk melihat kontribusi setiap variabel independen pada model, digunakan metode Mean Absolute Shapley Values (MASV). Oleh karena itu, tujuan dari penelitian ini adalah membandingkan model regresi linear berganda, lasso, ridge, elastic net, kuantil, serta kuantil lasso dalam memprediksi nilai angka harapan hidup. Penelitian diawali dengan melakukan eksplorasi data. Selanjutnya, model-model regresi tersebut dilatih. Pelatihan model tersebut juga dilakukan berulang kali dengan mengacak data pada pembagian data latih dan data uji. Terakhir, kontribusi setiap variabel independen diukur. Performa model regresi linear berganda pada iterasi pertama cukup baik dengan nilai r-square lebih besar dari 85% baik pada data latih dan data uji. Namun, Performa model lasso, ridge, elastic net, kuantil, dan kuantil lasso tidak jauh berbeda dengan performa model regresi linear berganda. Ketika dilakukan pengacakan data latih dan data uji.  Model regresi kuantil lasso memiliki performa yang lebih konsisten dalam memprediksi nilai angka harapan hidup dibandingkan model lainnya. Pada setiap model regresi, tingkat kelahiran dan tingkat kematian bayi merupakan variabel yang memiliki kontribusi terbesar dalam memprediksi nilai angka harapan hidup, sedangkan persentase orang yang mengikuti sekolah formal dan persentase populasi yang tinggal di perkotaan bukan variabel independen yang cukup baik untuk memprediksi angka harapan hidup. Kata Kunci:  angka harapan hidup, model regresi, data latih, data uji.
Student Readiness Scores a Rasch Model’s for Facing E-Learning Using Decision Tree and Ensemble Methods Antika, Ester; Nurdiati, Sri; Junus, Kasiyah; Najib, Mohamad Khoirun
Jurnal Pendidikan Progresif Vol 14, No 1 (2024): Jurnal Pendidikan Progresif
Publisher : FKIP Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstract: Prediction of Rasch Model’s Student Readiness Scores for Facing E-Learning Using Decision Tree and Ensemble Methods. Objective: This research aims to predict student readiness score in facing e-learning using Rasch models and machine learning. Methods: This research is a quantitative research using a non test instrument ini the form of a questionnaire using a Likert scale. The sample used were IPB University students. Analysis techniques use Rasch model, decision tree, and ensemble. Finding: Item reliability value is 0,93, person reliability value is 0,97, and cronbachalpha is 0,99. The standard deviation value is 2,34 and the average logit of respondents is 1,9. 34% of students have high readiness with a person measure value >2,34. 4% of students have moderate readiness with a score of 1,9 < person measure < 2,34. 62% of students have low readiness with a person measure value < 1,9. The accuracy of the decision tree model reached 75,97%. Conclusion: Based on person measure from the Rasch model, it can be concluded that the majority of respondents (62%) have low ability to carry out e-learning. Male students and those who have experience in dealing with e-learning have a higher percentage of having high ability in dealing with e-learning at the university level. Moreover, machine learning models are able to predict students' abilities in dealing with e-learning based on the measure score from the Rasch model. Furthermore, ensemble models are able to increase the accuracy of decision tree models. We found that the ensemble model with the LogitBoost (adaptive logistic regression) method provides best model in term of its accuracy (82.17%) and execution time. Keywords: decision tree, e-learning, ensemble, machine learning, rasch model.DOI: http://dx.doi.org/10.23960/jpp.v14.i1.202437
Komentar untuk artikel Savitri et al.: Implementasi algoritma genetika dalam mengestimasi kepadatan populasi jackrabbit dan coyote Najib, Mohamad Khoirun; Nurdiati, Sri
Jambura Journal of Biomathematics (JJBM) Volume 3, Issue 2: December 2022
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjbm.v3i2.16857

Abstract

This article is a commentary on research conducted by Savitri et al which was published in Jambura Journal of Biomathematics volume 3 number 1 in 2022. It was found that there was an error in the MAPE calculation for the approximation of population density of coyote. The MAPE obtained for coyotes was 66.05% so there was a significant difference from what had been given before. With these results, there is an opportunity to estimate parameters with better accuracy.
Bias Correction of Lake Toba Rainfall Data Using Quantile Delta Mapping Rafhida, Syukri Arif; Nurdiati, Sri; Budiarti, Retno; Najib, Mohamad Khoirun
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 9, No 2 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v9i2.29124

Abstract

Lake Toba, located in North Sumatra, is the largest tectonic and volcanic lake in Indonesia. Lake Toba has an equatorial climate characterized by abundant rainfall throughout the year. High rainfall, coupled with annual increases due to climate change, results in a vulnerability to the unpredictable extreme weather, causing harm to the surrounding communities. Consequently, a rainfall prediction model is needed to anticipate the impacts of such extreme rainfall. One of the rainfall prediction models used is ERA5-Land. However, this prediction model has biases that can be avoided. A method that can be used is the statistical bias correction using the quantile delta mappings (QDM) by correcting ERA5-Land model data against BMKG observation data. The QDM method used in this study employs two types of methods: monthly and full distribution. The results shows that both methods can improve biases at Silaen, Laguboti, and Doloksanggul stations, as well as improve the model during the equatorial dry seasons in May, June, July, and August. However, the first method improves the model distribution more in Silaen and Laguboti, while the second method improves the model distribution more in Doloksanggul.
Copula in Wildfire Analysis: A Systematic Literature Review Najib, Mohamad Khoirun; Nurdiati, Sri; Sopaheluwakan, Ardhasena
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 3 No. 2 (2021)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v3i2.22131

Abstract

AbstractCopula model is a method that can be implemented in various study fields, including analyzing wildfires. The copula distribution function gives a simple way to define joint distribution between two or more random variables. This study aims to review the application of copula in the analysis of wildfires using a Systematic Literature Review (SLR) and provide insight into research opportunities related to the application in Indonesia. The results show there are very few articles using the copula model in the analysis of wildfires. However, the increasing number of article citations each year shows the importance of such article research and has contributed to wildfire analysis development. In that article, 50% of studies applied the copula model to direct wildfire analysis (using fire data) in Canada, Portugal, and the US. Meanwhile, the other 50% use the copula model for indirect wildfire analysis (not using fire data) in Canada and the European region. The outcome of the presented review will provide the latest research positions and future research opportunities on the application of copula in the analysis of wildfires in Indonesia.Keywords: copula; wildfire; systematic literature review. AbstrakModel copula merupakan metode yang dapat diimplementasikan pada berbagai bidang penelitian, salah satunya pada analisis kebakaran hutan. Fungsi sebaran copula memberikan cara yang mudah untuk mendefinisikan sebaran peluang bersama antara dua peubah acak atau lebih. Tujuan penelitian ini mengulas penerapan model copula tersebut pada analisis kebakaran hutan dalam studi literatur menggunakan Systematic Literature Review (SLR) serta memberikan peluang riset ke depan terkait implementasinya pada analisis kebakaran hutan di Indonesia. Hasil penelitian menunjukkan bahwa model copula pada analisis kebakaran hutan masih sangat sedikit. Namun, peningkatan jumlah sitasi artikel tiap tahun menunjukkan pentingnya penelitian tersebut dan memiliki kontribusi pada perkembangan analisis kebakaran hutan. Pada artikel tersebut, sebanyak 50% penelitian menerapkan model copula pada analisis kebakaran secara langsung (menggunakan data kebakaran) di Kanada, Portugal, dan Amerika. Sementara, sebanyak 50% lainnya menerapkan model copula pada analisis kebakaran secara tak langsung (tidak menggunakan data kebakaran), yaitu di Kanada dan kawasan Eropa. Hasil tinjauan memberikan posisi riset terkini serta usulan riset ke depan mengenai penerapan model copula untuk analisis kebakaran hutan dan lahan di Indonesia.Kata kunci: copula; kebakaran hutan; studi literatur sistematik. 
Co-Authors AA Gede Rai Gunawan Abisha, Nicholas Ade Irawan Ade Irawan Agah D. Garnadi Agung Widyo Utomo Agus Buono Aldri Frinaldi Alifah, Nayla Nur Alifah, Rifdah Nur Amalia, Rizki Nurul Amanah, Ayu Anak Agung Gede Rai Gunawan Andriani, Rizka D. Annisa Permata Sari, Annisa Permata Antika, Ester Ardhana, Muhammad Reza Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardiyani, Evi Ayu Amanah Aziz, Muhammad Farhan Bib Paruhum Silalahi Blante, Trianty Putri Budiarti, Retno Cece Sumantri Chairunisa, Ghevira Deni Suwardhi DEWI RAHMAWATI Edi Santosa Ekaputri, Dhea Elis Khatizah Endar Hasafah Nugrahani Eragilang Muhammad Hastapatria Ester Antika Fahren Bukhari Fahren Bukhari Fahren Bukhari Faiqul Fikri Fajar Delli Wihartiko Fatmawati, Linda Leni Fauzan, Muhammad Daryl Ginting, Dini Tri Putri Br Hanief, Hafzal Hany Savitry Hasafah Nugrahani, Endar Heliza Rahmania Hatta, Heliza Rahmania Henny Nuraini Henriyansah Herlambang, Karen Hilmi, Kautsar I Wayan Mangku Imni, Salsabila F. Indra Jaya Irman Hermadi Irmanida Batubara Jauhari, Muhammad Fakhri Junus, Kasiyah Karlisa Priandana Kasiyah Junus Kautsar Hilmi Khatizah, Elis Khoerunnisa, Nazwa Komariah . Lana Syakina Linda Leni Fatmawati M. Syamsul Maarif Maman Turjaman Marimin Marimin Mas’oed, Teduh W. Maulia, Syammira Dhifa Mochamad Tito Julianto Mohamad Khoirun Najib Muhamad Syukur Muhammad Adam Tripranoto Muhammad Fikri Isnaini Muhammad Ilyas Muhammad Reza Ardhana Muhammad Tito Julianto Muhammad Zidane Bayu Mukhlis Mukhlis Muliawan Sebastian, Denny Nadiyah, Fadilah Karamun Nisaa Najib, Mohamad K. Najib, Mohamad Khoirun Najib, Mohamad Khoirun Nandika Safiqri NGAKAN KOMANG KUTHA ARDHANA Niswati, Za'imatun Noval Nur Fallahi, Putri Afia Nurwegiono, Muhammad Nuzhatun Nazria Pandu Septiawan Pratama, Yoga Abdi Prihasuti Harsani Putri, Renda S. P. Rachma Fauziah Krismayanti Rafhida, Syukri Arif Rafhida, Syukri Arif Redytadevi, Tita Putri REFI REVINA Retno Budiarti Rika Kusumawati Rohimahastuti, Fadillah Ruben Harry Valentdio Salsabila, Fitra Nuvus Salsabilla Rahmah Salsabilla, Fitra Nuvus Sanjaya, Wardah Septian Dhimas Setyawati, Suci Nur Shelvie Nidya Neyman Sony Hartono Wijaya Sopaheluwakan, Ardhasena Sri Hartati Sri Mulatsih Srihadi Agungpriyono Sriwahyuni, Lilis SUHARINI, YUSTINA SRI Sukmana, Ihwan SYAHID AHMAD MUKRIM Sya’adah, Syifa Noer Trianty Putri Blante Triwulandari, Raden Roro Carissa Valentdio, Ruben Harry Verry Riyanto Vicky Zilvan Wisnu Ananta Kusuma Yandra Arkeman Yasin Yusuf Yoga Abdi Pratama