Article Per Year (5 Year)
p-Index From 2021 - 2026
11.345
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Public Health Science (IJPHS) Hemera Zoa IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Natural Science: Journal of Science and Technology Journal of English Language Teaching E-JUPEKhu Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUTI: Jurnal Ilmiah Teknologi Informasi Lingua Cultura IJOG : Indonesian Journal on Geoscience Jurnal Ilmu dan Industri Peternakan Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi Biotropika JURNAL PENELITIAN SAINTIKA Jurnal Riset Kimia International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Journal on English as a Foreign Language (JEFL) Jurnal Pendidikan Kimia FKIP Universitas Halu Oleo Economic Education Analysis Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Farmasi Galenika (Galenika Journal of Pharmacy) JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL MANAJEMEN DAN PELAYANAN FARMASI (Journal of Management and Pharmacy Practice) SRIWIJAYA INTERNATIONAL JOURNAL OF DYNAMIC ECONOMICS AND BUSINESS Jurnal Pendidikan Kimia (JPKim) JITK (Jurnal Ilmu Pengetahuan dan Komputer) METASASTRA: Jurnal Penelitian Sastra Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan Komunikasi Mesin FENOMENA Jurnal Informasi Kesehatan Indonesia (JIKI) Jurnal Agrotek Tropika Beta: Jurnal Tadris Matematika SEIKO : Journal of Management & Business English Language and Literature International Conference (ELLiC) Proceedings NUSANTARA : Jurnal Ilmu Pengetahuan Sosial Fisitek : Jurnal Ilmu Fisika dan Teknologi Surya Abdimas Logista: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Ilmiah Ecosystem Jurnal Hilirisasi IPTEKS (JHI) Jurnal Riset Manajemen Bisnis dan Publik JTP - Jurnal Teknologi Pendidikan Jurnal Literasiologi Techno-Fish Seminar Nasional Lahan Suboptimal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of English Language Teaching and Linguistics INTEK: Jurnal Penelitian PEDULI: Jurnal Imiah Pengabdian Pada Masyarakat Journal of Community Based Environmental Engineering and Management Menara Ilmu DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pengabdian Dharma Laksana Hydrogen: Jurnal Kependidikan Kimia Atrium Pendidikan Biologi Building of Informatics, Technology and Science KREATIF : Jurnal Ilmiah Prodi Manajemen Universitas Pamulang Jurnal GeoEkonomi English Language Teaching Educational Journal Indonesian Journal of Clinical Pathology and Medical Laboratory (IJCPML) PERSPEKTIF Jurnal Perspektif: Jurnal Kajian Sosiologi dan Pendidikan Jurnal Paradigma Jurnal Ilmiah Pendidikan Matematika Al Qalasadi Jurnal Riset Teknologi Industri JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) JIIP (Jurnal Ilmiah Ilmu Pendidikan) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Hukum Adigama Journal of Data Science and Its Applications Jurnal Ecogen Indonesian Journal on Geoscience International Journal of Southeast Asia Indonesian Journal on Geoscience Indian Journal of Forensic Medicine & Toxicology Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Charity : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Wahana Pendidikan Jurnal Pengabdian Polinema Kepada Masyarakat Jurnal Pengabdian kepada Masyarakat Kreator MATHunesa: Jurnal Ilmiah Matematika Indonesian Community Journal Jurnal Kesehatan dan Olahraga Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabmas Masyarakat Sehat Jurnal MathEducation Nusantara Kronologi The Indonesian Journal of Gastroenterology, Hepatology and Digestive Endoscopy International Journal of Educational Research Excellence (IJERE) eProceedings of Engineering Jurnal INFOTEL
ZK Abdurahman Baizal
Universitas Telkom
Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

Published : 197 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 197 Documents
Search

 15   16   17   18   19 
Book Recommender System Using Matrix Factorization with Alternating Least Square Method Hafid Ahmad Adyatma; Z. K. A. Baizal
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3816

Abstract

In this digital age, we are faced with countless choices of books. Finding books that match our interests and desires becomes a complex challenge. However, the existence of a book recommender system is useful to help provide the best decision-making experience that users can have. This research develops a book recommender system using Collaborative Filtering (CF) Matrix Factorization with Alternating Least Squares method which is compared with Singular Value Decomposition method to see an accurate recommender system. This research uses datasets from Goodreads in the form of book data and rating data. This research uses several evaluation metrics, namely RMSE and MAE for regression metrics and F1-Score and Precision for classification metrics. Based on the research that has been done, SVD gets a better accuracy value with an RMSE value of around 0.86822, for MAE values around 0.6903, for F1-Score values around 0.827923 and for Precision values around 0.568347. Meanwhile, the ALS algorithm gets an RMSE value of around 1.09320, for MAE value of around 0.86479, for F1-Score value of around 0.000304 and for Precision value of around 0.000596.
Chatbot-Based Book Recommender System Using Singular Value Decomposition Muhammad Attalariq; Z. K. A. Baizal
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3817

Abstract

In the era of information overload, finding the right book that matches one's preferences and interests has become a challenging task for users as many online book provider service websites such as Amazon, Goodreads, and Gramedia provide books of various types and choices. Recommender systems can be used in addressing such issues, it works by filtering information that provides predictions and suggests the best product or service to the user. Currently, various book recommender systems have been developed, but the systems do not provide interaction between the user and the system. Therefore, we propose a recommender system built with a conversational approach so that it can interact with natural language. Recommender system built using matrix factorization method with Singular Value Decomposition (SVD) algorithm, SVD is proven to have advantages for handling large datasets, extracting features, reducing noise and dimensionality so as to speed up computation. We performed two types of evaluation on the system. First, we tested the prediction accuracy using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. Second, we use questionnaires to measure user satisfaction levels. The evaluation of the system shows that the results of the prediction accuracy obtain an MAE value of 0.6481 and an RMSE value of 0.8287. Then, the accuracy performance of the system found that 83.2% of users get recommendations according to their interests. The user satisfaction with the whole system is 87.9%. The system built can provide a fairly good recommendation performance, and the chatbot can interact well with users based on the evaluation results obtained.
Ontology-Based Physical Exercise Recommender System for Underweight Using Ontology and Semantic Web Rule Language Christhofer Laurent Juliant; Z. K. A. Baizal; Ramanti Dharayani
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3823

Abstract

Inactive lifestyles and unhealthy diets are often the result of people's busy lives. because of these bad habits, many people are underweight. diet and lack of physical activity are factors that cause underweight. Due to lack of information, people prefer to live lazily and not exercise. To solve this problem, we propose a physical exercise recommendation system that is explicitly designed for Indonesian people who are struggling with underweight. Despite the existence of various research studies advocating for physical activities tailored to individual preferences, there is currently no recommendation system available within a chatbot framework that includes a comprehensive session to be completed, along with specific sets and repetitions for each activity. This research proposes the utilization of ontology and Semantic Rule Web Language (SWRL) to represent and process the knowledge presented, enabling the development of rules for generating physical activity recommendations based on user preferences. By integrating the user profile, ontology, and the rules created, our system recommends physical exercise based on gender, weight, height, activity level, difficulty of movements, and the type of muscle to be trained. From the sample user data obtained, 408 physical exercises menu are recommended. The performance of the system is quite good, together with the validation results from personal trainers, obtained a precision value of 0.8, recall of 1, and f-score of 0.888. Concluding that the system we designed can provide physical activity recommendations in accordance with user preferences.
Effectiveness of Using Autoencoder Method on Recommender System in E-Commerce Domains Jayana Citra Agung Pramu Putra; Z K A Baizal
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6346

Abstract

The growth of large data in the online market can cause problems for users, one of which is in finding products that are to their liking. Recommender systems can overcome this problem by providing specific product recommendations to be promoted and offered to buyers, for example with Collaborative Filtering. The Collaborative Filtering Paradigm consists of Memory-based and Model-based techniques. Model-based techniques are considered to be able to complement the shortcomings of memory-based because of their high scalability, accuracy, and reduced dimensions. The type of model-based that is best known for having good results is Singular Value Decomposition (SVD) and what has been frequently used recently is deep learning, especially Autoencoder. The both models are very popular for use in dimension reduction so they are suitable for making recommendations. The advantage of Deep Learning is this method can be done without preprocessing so it can minimize the process that must be done. The evaluation results show that the errors produced by SVD and Autoencoder are lower compared to other studies. RMSE is 0.7 and MAE is 0.5. Even though the RMSE and MAE on the Autoencoder are greater than the SVD, the results of the T-Test show that there is no significant difference in the two error results. Autoencoders have been shown to have good results without preprocessing and are more effective with shorter processes and there are no significant differences with SVDs. Thus, Autoencoder can be said to be worthy of use and more effective in giving recommendations.
LONG SHORT TERM MEMORY APPROACH FOR SHORELINE CHANGE PREDICTION ON ERETAN BEACH Iryanto Iryanto; Ari Satrio; Ahmad Lubis Ghozali; Eka Ismantohadi; ZK Abdurahman Baizal; Putu Harry Gunawan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.4139

Abstract

Eretan Beach is one of the beaches in Indramayu and has a reasonably severe abrasion rate from year to year. The Eretan coastline always experiences significant changes due to erosion every year. Therefore, it is necessary to study changes in the coastline at Eretan beach. This study obtained coastline data from the Google Earth engine using CoastSat, a python-based open-source toolkit, from 1992 – 2022. The open-source geographic information system software used to process the data is the Quantum Geographic Information System. This study aims to analyze the Long Short-term Memory (LSTM) algorithm in predicting shoreline changes at Eretan Beach. The eight optimizer functions in the LSTM are used with nine different scenarios to analyze the algorithm's performance. The results of this study show that RMSProp has the best performance compared to other optimizers. The RMSE and MAPE values on the RMSProp are 35.06258 and 2.2923 on the training data and 9.2457 and 1.06786 on the test data. In addition, from the predictions for the next ten years at transect point 251, it was found that there would be an increase in the coastline.
Diet and Physical Exercise Recommendation System Using a Combination of K-Means and Random Forest Muhammad Ilham Hafizha; Z. K. A. Baizal
Indonesia Journal on Computing (Indo-JC) Vol. 9 No. 2 (2024): August, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.2.959

Abstract

Public health has become a significant focus in this modern era due to the increasing number of people suffering from various diseases. Unhealthy diets and lack of physical activity are often associated with multiple health problems, one of which is obesity. Several studies have been conducted to develop food recommendation systems for individuals with obesity, using K-Means and Random Forest algorithms to provide food recommendations based on user-specific aspects. However, these studies do not provide supporting information, such as physical activity recommendations to address fitness issues or lack of physical activity. This study develops a diet and physical exercise recommendation system for individuals with obesity using a combination of K-Means and Random Forest. The system categorizes and classifies foods and physical exercises and provides customized recommendations based on user data analysis. The accuracy of the system was evaluated using the MAPE metric, with the highest accuracy for dietary food recommendations being 99.03% for the non-vegan lunch diet meal recommendation and the lowest being 70.74% for the vegan morning meal diet recommendation. The MAPE for physical exercise recommendations was consistently at 26.35%, indicating a stable accuracy of 73.65%. The test results show that the system recommends diet and physical exercise accurately.
NEWS RECOMMENDER SYSTEM USING HYBRID CONTENT-BASED FILTERING AND COLLABORATIVE FILTERING Nurjayanto, Bagus Wicaksono; Baizal, Z. K. A.
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 1 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i1.4256

Abstract

The development of online news services has offered users numerous choices, resulting in information overload. This makes it challenging for users to locate desired news within a spesific timeframe. to adress this, recommender systems have developed to help users discover and select news article.
Improved Collaborative Filtering Recommender System Based on Missing Values Imputation on E-Commerce P, Kadek Abi Satria A V; Baizal, Z K A
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): March 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (416.756 KB) | DOI: 10.47065/bits.v3i4.1214

Abstract

One of the important aspects in e-commerce is how to recommend a product to users accurately. To achieve this goal, many e-commerce starts to build and research about recommender system. Many methods can be used to build a recommender system, one of them is using the collaborative filtering technique. This technique often experiences data sparsity problem that can impact to the recommender system prediction accuracy. To solve this problem, we apply improved collaborative filtering. This method predicts the missing values in the user item rating matrix. First, we do an initial selection to determine potential users who have the same characteristics with the active user. After that, we calculate the average distance between the active user and the other selected user. Next, we calculate missing values prediction. Missing values predictions is only done for items that have never been rated by other’s selected user but has been rated by the active user. We used Amazon electronic product with high sparsity level in this research to simulate the actual condition of e-commerce. We used MAE and RMSE to measure prediction accuracy. The methods we apply succeeds to improve the prediction accuracy compare to the conventional collaborative filtering method. The average MAE for method that we apply is 0.78 and RMSE 1.07
Movie Recommendation System Using Knowledge-Based Filtering and K-Means Clustering Wibowo, Kurnia Drajat; Baizal, Z K A
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): March 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (400.826 KB) | DOI: 10.47065/bits.v3i4.1236

Abstract

The movie recommender system has an important role in providing movie recommendations for users, but new users have difficulty choosing movies that are given by the recommender system because of the cold start problem. This study aims to overcome the cold start problem using a knowledge-based recommender system, i.e association rule mining using an apriori algorithm. The apriori algorithm aims to extract correlations between product itemsets, but the problem in the apriori algorithm is the large number of association rules that make the complex computation. To overcome this problem, we combine the apriori algorithm and k-means to produce more accurate recommendations, because the items are grouped before the recommendation process using the k-means algorithm. In this study, we use a dataset of movies and ratings from the Kaggle website. This study uses a minimum value of 0.5 confidence, and a minimum value of 4 lifts. To produce the best itemset in the form of antecedents and consequents of the Beauty and the Beast item with The Passion of Joan of Arc which has a value of 0.107981 support, 0.779661 confidence, 4.151695 lift
Conversational Recommender System based on Functional Requirement using Knowledge Graph for Building Personal Computer Aryanta, Rafi Rizkya; Baizal, Z. K. A
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2978

Abstract

When a person wants to build a personal computer, this person needs to browse many kinds of computer components. Besides that, this person needs to consider the compatibility between hardware and an affordable price. This will be a problem for people who are still unfamiliar with the computer, due to their lack of understanding of how compatibility between computer components works and the time-consuming nature of market research. To deal with this problem, the recommender system will assist in finding and matching compatibility efficiently based on the functional requirements of the user. The recommender system will issue various products based on the preferences and interests of the user, but some recommendations still need to be checked for compatibility. With the help of developing a Conversational Recommender System by utilizing the Knowledge Graph, it will be easier to construct the relationship between component compatibility. We propose this research by using Knowledge Graph as alternative from ontology to build Conversational Recommender system in Building Personal Computer. This research will involve the user to prove whether the recommendations from this system meet the needs and accuracy of the recommendations requested. The main results of this study will issue a recommendation for the development of personal computers by considering compatibility using the Conversational Recommender System using the Knowledge Graph approach.
Co-Authors Abdul Muqit Abdullah Helmy Ade Kosasih Ade Romadhon Ade Sukma Adisti Rastosari Aditya, Naufal Adri Nur Fajari Afriani Sandra Agung Toto Wibowo Agus Alim Abdullah Ahmad Lubis Ghozali Akbar, Yoan Amri Alam Rahmatulloh Albi Fitransyah Ali, Muhammad Haidir Allismawita Allismawita amnah amnah An Fauzia Rozani Syafei Ana Fitriana Poerana Andiety, Rich Andini, Andini Andjioe, Oscar Rynandi Angelina Sagita Sastrawan Anindya, Widya Dara Aniq A Rahmawati Aniq A. Rahmawati Anisa Herdiani Annisa Cahya Anggraeni Annisa Cahya Anggraeni Antonius Randy Arjun Ardi Ardi Ari Satrio Arie Lasaprima Arifa Nur Hasanah Aryadi Pramarta Aryanta, Rafi Rizkya Ayunda Farah Istiqamah Budiarti, L Endang Burhanuddin Bahar Cahya, Anindya Cahyani, Hilda Canda Ayu Arum Pertiwi Christhofer Laurent Juliant Cut Sri Maulina D. Novia Daffa Barin Tizard Riyadi Damayanti, Elok Dana Sulistyo Kusumo Danang Triantoro Murdiansyah Darmawan, Faiha Adzra Dede Tarwidi Dedi Romli Triputra Dendy Andra Deni Novia Dessy Abdullah Devi Pratami Dhiva Rezzy Pratama Diah Mahmuda Diah Pudi Langgeni Didik Tri Setiyoko Didit Adytia Djoko Wahyono Donni Richasdy Dreyfus, Shoshana Dwi H Widyantoro Dwi Maya Sari Dwinda Tamara Edy Tandililing Eka Ismantohadi Elly Roza Elsa Rachel Dementieva Erbina Selvia Br Perangin-Angin Erliansyah Nasution Erni Masdupi Erwin B. Setiawan Erwin Budi Setiawan Esa Alfitrassalam Evitayani Evitayani Fadillah, Ichsan Alam Fatimah Nurhayani Fatimatus Zahroh Favian Dewanta Ferawati Ferawati Fernandy Marbun Ferry Lismanto Syaiful Firmansyah Firmansyah Fitriani Mangerangi Gentra Aditya Putra Ruswanda Gesit Tabrani Ghazi Ahmad Fadhlullah Gholib Gholib Grace Yohana Grace Yohana Gusti Ayu Marheni Gustina Lubis Hafid Ahmad Adyatma Hakim, Lukman Nur Hary Yuswadi Hasanuddin Hasanuddin Hasanusi, Mohammad Helmi Arifin Hendra Naldi Humaizi, Humaizi Humaizi, Humaizi Ichwanul Muslim Karo Karo Ida Ayu Putu Sri Widnyani Igga Febrian Virgiani Ika Arum Puspita Ilham Mujaddid Al Masyriq Imam Sunarno, Imam Ina Rofi’atun Nasihati Indira Adnani Indri Juliyarsi Inggrid Resmi Benita Intan Dwi Novieta, Intan Dwi Irfan Darmawan Irhas Jaya Iryanto Iryanto Iut Tri Utami Izzatul Ummah Jaka E. Sembodo Jamhari Jamhari Jamsari Jamsari Jaya, Irhas Jayana Citra Agung Pramu Putra Joni Dwi Pribadi Kalsum Kalsum Kemas M Lhaksmana Kemas M. Lhaksmana Kemas Muslim Lhaksmana Khaidarmansyah Khairiah, Khairiah Khamim, Khamim Khasrad . Khatimah, Ummu Husnul Khoirunnisaa’ Khoirunnisaa’ Khusnul Diana Kun Mustain Kusnadi, Kusnadi Lie Othman Lilis Suryani Lisa Rahmi Litasari Widyastuti, Litasari Liviandra, Monica Loiz, Andhika Lubis, Putri Handayani Lutfi Ambarwati M. Duskri M. Naufal Mu'afa M. Qadrian M. Rayhan Hakim Mahmud Dwi Sulistiyo Mahmud Imrona Mala Nurilmala Mansyur Arif Marayasa, I Nyoman Marendra Septianta Mayasari Mayasari Mella Ismelina Farma Rahayu Miranti Andhita Scantya Mirna Fitrani Misna Ariani Mizanul Kirom Moch Arif Bijaksana Moh Naufal Mizan Saputro Moh Z Mubarok Moh. Mahsus Mudayatiningsih, Sri Muhamad Faishal Irawan Muhamad Hafidh Nofal Muhammad Adlim Muhammad Agus Muljanto Muhammad Alwi Nugraha Muhammad Attalariq Muhammad Bilal Rafif Azaki Muhammad Ilham Hafizha Muhammad Ilham Hafizha Muhammad Ridha Anshari Muhammad Zaid Dzulfikar Muhammad, Widi Sayyd Fadhil Mustakim, ' Musthafa Zaki Bahar Mustofa, Mutmainnah Nabila Wardah Zamani Najla Nur Adila Naufal Akbar Hartono Ni Nyoman Sumiasih Ni Wayan Armini Niken Titi Pratitis Ningsih Purba Ningsih, Ayu Oktavia Nirmala Ayu Aryanti Nisa, Intan Khairu Nofal, Muhamad Hafidh Nora. AN, Desri Nungki Selviandro Nur Azlina Nur Jamilah Nur Rahmawati Nur Ulfa Maulidevi Nuraini Lubis Nurfadhlina Mohd Sharef Nurjayanto, Bagus Wicaksono Nurul Ikhsan Okky Brillian Hibrianto Okky Brillian Hibrianto P, Kadek Abi Satria A V Pahrurrobi Pahrurrobi Prasetia, Reza Putra, A. D. A. Putu Harry Gunawan Qisti R Arvianti Rachmi Helfianur Radhiva Hibatullah, Muhammad Rafiuddin, Rafiuddin Rahmat Firdaus Rahmi Wati Raihani Mohamed Rais Rais Ramadhan, Sageri Fikri Ramadhani, Nur Laili Ramanti Dharayani Randika Dwi Maulana Rasyid Ranestari Sastriani Rasbawati, Rasbawati Rayhan M Auliarahman Reinaldo Kenneth Darmawan Rena Feri Wijayanti Restu Aditya Rachman Reza Rendian Septiawan Rezano, Tomi Richo Fedhia Saldhi Rika Afriani Rina Dahlyanti Rinaldi Jasmi Rinita Amelia Risa Tiuria Risfaheri - Riska Padilah Riski Hernando Rita Rismala Rizaldy Arigi Rizky Andrian Rizqi Bayu Aji Robi Amizar Roby Dwi Hartanto Rohmat Gunawan Romy Adzani Adiputra Roseno, Rizky Haffiyan Rr Amanda Pasca Rini, Rr Amanda Pasca Ruh Devita Widhiana Prabowo Rusli, Ridho Kurniawan S. Syamsurizal Sa'diatul Fuadiyah Sahlya Handayati Salam N. Aritonang Sanusi Ibrahim Sarini Vita Dewi Sedyo Mukti, Putri Ayu Sepri Reski Shaufiah . Sigit Budisantoso Silvia Atika Anggrayni Simon He Siti Rohani Sitorus, Angela Tiara Maharani Sri Andayani Sri Melia Suci Aprianti Sukanta Sumaryati Syukur Suyitman Suyitman Syaifuddin Ahrom Syaifuddin Ahrom Syamsul Hadi Tebay, Selvi Teguh Surya Apri Handoyo Theriana Ayu Waskitaning Tyas Thoriq Akhdan, Muh Titi Sumanti Tongku Nizwan Siregar Ufra Neshia Umar Ali Ahmad Urnemi - Urnemi Urnemi Utomo, Muhajir Veritia, Veritia Vici E.H.S. Susilowati Wibowo, Kurnia Drajat Winardhi, Sonny Winardhi, Sonny Wiratama, Arga Kusuma Wiwik Handayani Wizna (Wizna) Wulandari, Dinda Atikah Yani Riyani Yanuar Firdaus Yanuar Firdaus A Yanuar Firdaus A.W. Yesi Chwenta Sari Yoan Amri Akbar Yolani Utami Yudha E. Pratama Yudha Endra Pratama Yuherman Yuherman Yulia Murni Yulia Yellita Yuliant Sibaroni Yulianti Fitri Kurnia Yuliawati Yuliawati Yulisna Gita Hapsari Yundari, Yundari Yusran Khery, Yusran Yusri Dianne Jurnalis Yusza Reditya Murti Zidni Mubarok Zoni Hidayat