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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal POINTER Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Multimedia JAST : Jurnal Aplikasi Sains dan Teknologi TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi (JUST-SI) DEDIKASI PKM Jurnal Informatika dan Multimedia Enrichment: Journal of Multidisciplinary Research and Development Majority Science Journal
Dian Eka Ratnawati
Brawijaya University, Malang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Analisis Sentimen Pengguna Twitter Terhadap Opini Non Fungible Token di Indonesia Menggunakan Algoritma Random Forest Classifier Oceandra Audrey; Dian Eka Ratnawati; Issa Arwani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Non-fungible tokens are a way to record, verify, and track the ownership of an asset, both physical and digital, in the form of artwork, music, in-game items, domain names for websites, sports highlight videos to digital products. NFT's popularity in Indonesia started to rise when an account owner on the OpenSea NFT marketplace platform named Ghozali Everyday sold a collection of selfies of himself with sales of more than 1.5 billion rupiah. The popularity of the NFT trend has also triggered people in Indonesia to participate in selling resident identity cards in the form of KTPs which are then used as NFTs. The misuse of this trend has led to many positive, negative and neutral opinions from the public regarding the development of NFTs in Indonesia, one of which is through social media Twitter. A company engaged in the software house sector, Technobit Indonesia, has a similar product in the form of an NFT marketplace application. Technobit Indonesia is re-development taking into account the public's view of the current NFT, thereby delaying the release of their marketplace application to the public. Based on these problems, a classification method is needed that can classify tweet data that discusses NFT in Indonesia. This study uses the Random Forest Classifier method as a classification method for sentiment analysis. This research has several stages, including data collection, preprocessing, weighting of words with TF-IDF, Random Forest classification, testing and analysis. The test results using the best result parameters get an average of 93% for precision, recall, f-1 score, and accuracy with a total of 500 trees and a tree depth of 100 trees.
Analisis Sentimen Wisata Alun-Alun Kota Batu menggunakan Algoritma Support Vector Machine Ghani Fikri Baihaqi; Dian Eka Ratnawati; Buce Trias Hanggara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Batu town square is one of the tourist destinations in Batu city which is located in the center of Batu city and is an icon of the city. Batu City is also the number one tourist destination center in East Java with more than three million tourists every year. Batu City Square is a cheap tourist attraction which is the main destination for tourists visiting Batu City before heading to other tourist objects in this city. The tourism manager of Batu City Square, namely the Tourism Goverment, needs to know the perspective of visitors as evaluation material in building better infrastructure and services. Acquisition of visitor review data for the Batu City Square tour was obtained from the Tripadvisor website with data collection techniques using web scraping. Reviews from Tripadvisor will be categorized into two classes, positive and negative. Before classifying, text preprocessing is carried out to process the data into more structured data for research needs and to weight words using the Term Frequency - Inverse Document method. Classification is done using the Support Vector Machine algorithm. Classification uses data of 240 positive and negative data for classification in each Kernel. The best results in testing using the Support Vector Machine algorithm are testing on the rbf Kernel and a C value of 50. The Cross Validation used in this test is as many as 8 folds and produces an average value on the parameters and the number of folds, namely accuracy of 89.58%, precision of 90.73%, recall of 89.48%, f-measure of 89.45%, and specificity of 91.27%.
Analisis Sentimen Terhadap Kenaikan Cukai Rokok pada Media Sosial Twitter menggunakan Algoritma Naive Bayes Classifier Denny Manuel Yeremia Sinurat; Dian Eka Ratnawati; Dwija Wisnu Brata
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 1 (2023): Januari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Social media is a place for people to convey opinions, and also criticism. The social media that is often used by Indonesian people to express their opinions is Twitter. Twitter can be a forum for Indonesian people to express their opinions and aspirations on certain matters, such as policies made by the government. One of the policies that has sparked public discussion on social media Twitter is increasing cigarettes tax by an average of 10% starting in 2023. This policy has drawn pros and cons from the public, therefore an analysis of public sentiment regarding the cigarette tax increase policy can be carried out. This study will analyze public sentiment contained in the tweets of Twitter users about the increase cigarette tax into the classification of positive and negative sentiments. The execution process is implemented in Google Colaboratory with the Naive Bayes Classifier algorithm. Processes in the sentiment analysis which include data collection, manual labeling, text preprocessing, TF-IDF weighting, data balancing with the Synthetic Minority Oversampling Technique (SMOTE), data validation using k-fold cross validation, and test the classification results with the confusion matrix. The best accuracy results of 74% were obtained by using the data balance SMOTE results with a comparison of training data: test data of 90%:10%, text preprocessing, TF-IDF weighting, and use Multinomial Naive Bayes. The highest cross-validation score obtained was 78% with an average of 73%. Based on these results, the Naive Bayes Classifier is quite good as an alternative for sentiment analysis.
Perbandingan Naive Bayes dan K-Nearest Neighbor untuk Analisis Sentimen Aplikasi Gapura UB Berdasarkan Ulasan Pengguna pada Playstore Robiata Tsania Salsabila Aditya Putri; Dian Eka Ratnawati; Dwija Wisnu Brata
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 1 (2023): Januari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The Gapura UB application is a mobile apps-based application that makes it easy for students to access services and information related to Universitas Brawijaya such as online presence, viewing class schedules, and accessing KRS and KHS. Behind the convenience offered by the application, it was found that the performance of the application was not optimal, such as the emergence of bugs or errors in the application and the discovery of reviews on the Google Playstore that indicated user dissatisfaction with the performance of the application by containing complaints regarding the application. This research tries to classify Gapura UB application user reviews through Google Playstore by comparing two classification algorithms, namely Naive Bayes and K-Nearest Neighbor which aims to find out which algorithm is superior in Gapura UB application user review data. The review data used is 300 data divided into two sentiment classes, namely positive sentiment and negative sentiment. The results showed that the Naive Bayes algorithm has superior performance than the K-Nearest Neighbor with 88.5% accuracy, 88.7% precision, 88.2% recall, and 88.2% f-measure while the K-Nearest Neighbor produces 84.8% accuracy, 85.4% precision, recall 84.6% and 84.1% f-measure obtained from the value of k = 5.
Analisis Sentimen Pengguna Aplikasi RedBus berdasarkan Ulasan di Google Play Store menggunakan Metode Naive Bayes Devi Nazhifa Nur Husnina; Dian Eka Ratnawati; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The digitization of the transportation system has encouraged online ticket booking platforms such as the RedBus application. RedBus helps the public to order tickets from various bus operators in Indonesia. Users can download and view reviews of the RedBus application through the Google Play Store. Reviews are important for both users and developers. However, there are users who give high ratings but have negative reviews. Therefore, it is necessary to analyze user reviews to classify these reviews into positive and negative sentiments class. The total data used is 500 reviews with details of 250 reviews in each positive and negative class. The stages for classifying are manual data labeling, text preprocessing to change the data to be more structured, weighting by the TF-RF and TF-IDF methods, classification by the Naive Bayes algorithm, and testing by the confusion matrix and k-fold cross validation. This study compares 2 word weighting methods, namely TF-RF and TF-IDF to obtain optimal accuracy results. The best accuracy results obtained were using the ratio of training data and test data of 90%:10% with TF-IDF weighting and cross validation testing with a value of k = 10. The average accuracy obtained is 93.56% accuracy, 93.97% precision, 93.57% recall, 94.68% specificity, 93.53% f-measure.
Analisis Sentimen Data Ulasan Pengguna Aplikasi MyPertamina di Indonesia pada Google Play Store menggunakan Metode Random Forest Cahyo Gusti Indrayanto; Dian Eka Ratnawati; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 3 (2023): Maret 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Advances in technology make people tend to choose online transactions compared to cash. With the rapid development of technology, PT Pertamina launched their newest program, the MyPertamina application. The MyPertamina application is a combination program between loyalty and e-payment. The MyPertamina application has many users, so there are often positive and negative reviews that are irrelevant to the rating given on the Google Play Store. MyPertamina application review data will be obtained using the Web Scraping technique using the Google-Play-Scraper API. The scraped data will then be subjected to text preprocessing to clean the text so that the data can be executed. Sentiment analysis can detect whether a text contains positive or negative opinions from user reviews that are not in accordance with the rating. Random Forest is a method in analysis consisting of several decision trees as a classifier. In this study, the random forest method was used by dividing two sentiment classes, namely positive and negative, taking evaluation indicators, namely accuracy, recall, precision and f1-score. Tests were carried out based on the number of trees and tree depth on 800 data by dividing the data by 250 data per class. Based on the results of the tests and analyzes that have been carried out, with a comparison of training data and test data 80%:20% obtained values of 90% accuracy, 90% precision, 91% recall, and 89% F1-Score with a depth of 80 trees and 50 tree depths.
Pembuatan Sistem Informasi E-Commerce Untuk Unit Aktivitas Kerohanian Buddhis Universitas Brawijaya (UAKB UB) Kevin Renjiro; Dian Eka Ratnawati; Issa Arwani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 3 (2023): Maret 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Universitas Brawijaya Buddhist Spiritual Activity Unit (UAKB UB) is a Student Creativity Unit (UKM) located at Universitas Brawijaya which is a place for Buddhist students to do organization activity. There are various divisions in UAKB UB, one of which is the Logistics and Entrepreneurship division. The related division is having problems with sales due to the pandemic and entrepreneurship is only done offline. The division wanted to develop its entrepreneurship but was very limited by circumstances, moreover because the pandemic at that time greatly hampered the division's sales process. Based on these problems, researchers created an e-commerce information system which on the admin side has features for adding goods, making changes to goods, confirming transactions and verifying ktm. Meanwhile, on the side of buyers who have registered, they have the feature to transact to buy the desired item remotely. The development of this system uses the waterfall method, because all requirements have been defined at the needs analysis stage which produces 2 actors, 11 functional requirements and 1 non- functional. This system was developed using Laravel. Based on functional testing, both sides get a success rate of 100%.
Predict GO-JEK Driver Income Level in the Bali Region using Decision Tree I Dewa Gede Ngurah Bramasta Darmawan; Dian Eka Ratnawati
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 4 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i4.25

Abstract

The work in the transportation industry is one of several that is impacted by information technology, which is now developing quickly. The emergence of information technology in the transportation industry led to the creation of GO-JEK. GO-JEK is growing in popularity with the general public because, in addition to offering a wide range of services, it also creates new jobs with more flexible working hours for the community. Of course, each driver's income from working as a GO-JEK driver is unique. However, a number of things can have an impact on this income. With a focus on the Bali region, this study tries to forecast income levels for drivers of GO-JEK. J48 algorithm is used to process the data, producing a decision tree with 19 rule models. An accuracy value of 77.61%, a precision value of 78.95%, a value recall of 81.08%, and an AUC value of 0.808 are obtained from the rule model test utilizing the confusion matrix and ROC/AUC curve. These results demonstrate how effective the decision tree and rule model are.
Root Cause Analysis (RCA) berbasis Sentimen menggunakan Metode K-Nearest Neighbor (K-NN) (Studi Kasus: Pengunjung Kolam Renang Brawijaya) Nanda Petty Wahyuningtyas; Dian Eka Ratnawati; Nanang Yudi Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 5 (2023): Mei 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Ulasan merupakan salah satu media yang dapat digunakan untuk melakukan analisis untuk meningkatkan pelayanan dan kebutuhan bisnis. Ulasan pengunjung kolam renang Brawijaya dapat dilakukan analisis sentimen dengan menggunakan metode K-Nearest Neighbor (K-NN). Ulasan pengunjung dapat diperoleh melalui media sosial Google Review. Ulasan pada Google Review diperoleh dengan scraping menggunakan extension Instant Data Scraper yang disediakan oleh Google Chrome. Data ulasan pengunjung kolam renang Brawijaya yang diperoleh melalui proses scraping untuk analisis sentiment metode K-NN berjumlah 326 data positif dan 301 data negatif. Data ulasan akan dilakukan preprocessing dengan empat tahap tahap seperti, case folding, tokenizing, stopword removal, dan stemming. Preprocessing tahap stop word removal dan stemming menggunakan kamus bahasa yang dibuat manual sesuai dengan kebutuhan dokumen agar hasil preprocessing lebih baik. Hasil pengujian kinerja sistem terhadap metode K-NN mendapat nilai tertinggi pada k = 3 dan k =4. Nilai k = 3 menghasilkan nilai accuracy sebesar 0,76, precision sebesar 0,77, dan recall sebesar 0,75. Nilai k = 4 menghasilkan nilai accuracy sebesar 0,76, precision sebesar 0,76, dan recall sebesar 0,76. Hasil pengujian K-NN akan dilanjutkan untuk menganalisis akar permasalahan dengan menggunakan metode Root Cause Analysis (RCA). Analisis RCA dapat dilakukan dengan menggunakan diagram Fishbone agar lebih mudah dalam menganalisis akar permasalahan.
Sentiment Analysis on App Reviews Using Support Vector Machine and Naïve Bayes Classification Madjid, Marchenda Fayza; Ratnawati, Dian Eka; Rahayudi, Bayu
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.12161

Abstract

A review is an assessment given by someone based on certain aspects, such as the delivery of stories, pictures, effects, or visuals. Users can provide reviews which help the company know the quality of the application. However, reviews cannot be used as a reference for rating, because there are still users who provide reviews that are irrelevant to the rating given. This study aims to carry out sentiment analysis in order to classify the application user review data. The sentiment classification process begins with collecting and labeling 700 data. The data then goes through a text preprocessing, word weighting with TF-IDF, and classification using the Support Vector Machine and Naïve Bayes Classifier. The results produce the highest accuracy in the comparison of training and test data of 90%:10%. Support Vector Machine algorithm is capable of providing high accuracy with RBF kernel, γ=1, and C=10. The results obtained using 10-fold cross validation give an accuracy value of 92.86%, a precision value of 92.88%, a recall value of 92.88%, a specificity value of 94.73%, and f-measure of 92.76%. Naïve Bayes Classifier method is able to provide high accuracy by using Multinomial Naïve Bayes Classifier. The results obtained using 10-fold cross validation give an accuracy value of 92.54%, a precision value of 92.55%, a recall value of 92.51%, a specificity value of 93.9%, and f-measure of 92.44%. Based from the result, it can be concluded that the classification using the Support Vector Machine is superior to the Naive Bayes Classification.  
Co-Authors Abdurrahman Airlangga, Aria Abhiram, Muhammad Tegar Achmad Arwan Achmad Ridok Achmad, Riza Putra Adhitya, I Made Yoga Adrian Firmansah, Dani Afif Ridhwan Afrida Djulya Ika Pratiwi Agus Wahyu Widodo Agustin Kartikasari Ahmad Afif Supianto Akbar, Rozaq Aldy Satria Alfa Fadlilah Alifah, Syafira Almira Syawli, Almira Alvian Akmal Nabhan Amonito, Kurnia Ana Mariyam Puspitasari Anak Agung Bagus Arisetiawan Anam, Syaiful Ardhiansyah, Muhammad Hanif Arief Andy Soebroto Arif Pratama Asmoro, Priandhita Sukowidyanti Asroru Maula Romadlon Audia Refanda Permatasari Ayu Dwi Lestari, Cynthia Ayulianita A. Boestari Azizul Hanifah Hadi Bayu Rahayudi Bayu Satriawan, Eka Bayu Septyo Adi Bella Krisanda Easterita Bening Herwijayanti Berton, Freddy Toranggi Buce Trias Hanggara Buce Trias Hanggara Buchori Anantya Firdaus Budi Darma Setiawan Cahyo Gusti Indrayanto Candra Dewi Dany Primanita Kartikasari Darma Setiawan, Budi Darmawan, Riski Davia Werdiastu Denny Manuel Yeremia Sinurat Deny Tisna Amijaya, Fidia Devi Nazhifa Nur Husnina Dewi Yanti Liliana Dhiva Mustikananda Dimas Diandra Audiansyah Dimas Fachrurrozi Azam diniyah, zubaidah Diva, Zahra Djoko Pramono Dwi Ari Suryaningrum Dwi Febry Indarwati Dwi Purwono, Prayoga Dwija Wisnu Brata Dyva Pandhu Adwandha Dzulkarnain, Tsania Dzulkarnain, Tsania - Easterita, Bella Krisanda Edgar Maulana Thoriq Edy Santoso Elfa Fatimah Ema Agasta Entra Betlin Ladauw Eva Agustina Ompusunggu Fadhil, Muhammad Farrasseka Fadila, Putri Nur Faiz Anggiananta Winantoro Fanka Angelina Larasati Fathin Al Ghifari Fatthul Iman Fauzan Dwi Kurniawan, Fauzan Dwi Fauzidan Iqbal Ghiffari Figgy Rosaliana Firdaus, Muhammad Fariz Fitra Abdurrachman Bachtiar Fitri Dwi Astuti Fitria Yesisca Fitria, Tharessa Ghani Fikri Baihaqi glenando Gusti Ngurah Wisnu Paramartha Hadi Wijoyo, Satrio Hamas, radityo Hana Chyntia Morama Hanggara, Buce Trias Hanifa Maulani Ramadhan Haris Haris, Haris Harris Imam Fathoni Hasibuan, Herida Hafni Hasibuan, Raka Ardiansyah Heru Nurwasito Hilal, Khaliffman Rahmat Hilmy Ramadhan, Achmad Zhafran Huda Minhajur Rosyidin I Dewa Gede Ngurah Bramasta Darmawan Ibnu Aqli Ibnu Aqli, Ibnu Ibrahim Kusuma Ilyas, Muhaimin Imam Cholissodin Imam Cholissodin Imam Cholissodin Immanuel Tri Putra Sihaloho Indriati ., Indriati Indriati Indriati Ismiarta Aknuranda Issa Arwani Issa Arwani Isti Marlisa Fitriani Izza, Aisyah Nurul Jesika Silviana Situmorang Jibril Averroes, Muhammad Juan Michel Hesekiel Kartika, Annisa Wuri Kelvin Anggatanata Kevin Renjiro Khairi Ubaidah Khoba, Ahmad Faiz Khofifatunnabilah, Khofifatunnabilah Kirana, Urdha Egha Krishna Febianda Kusuma, Salsabila Azzahra' Zulfa Lailil Muflikhah Leonardo, Ryan Luqman Rizky Dharmawan M. Ali Fauzi Madjid, Marchenda Fayza Maghfiroh, Sofita Hidayatul Mahendra Data Mahendra Data Mala Nurhidayati Maliha Athiya Rahmani Marji . Marji Marji Marji Marji Marji Marji Maulana Syahril Ramadhan Hardiono Michael Eggi Bastian Mochammad Iskandar Ardiyansyah Rochman Moh Fadel Asikin Muh. Arif Rahman MUHAJIR Muhammad Iqbal Mustofa Muhammad Kevin Sandryan Muhammad Reza Utama Pulungan Muhammad Tanzil Furqon Muhyidin Ubaiddillah Muslimah, Fakhriyyatum Muthia Maharani Nabilah Iftah Nella Naily Zakiyatil Ilahiyah Nanang Yudi Setiawan Nanang Yudi Setiawan Nanda Alifiya Santoso Putri Nanda Petty Wahyuningtyas Nilna Fadhila Ganies Norma Desitasari Novirra Dwi Asri Nugraha Perdana, Aditya Nugraheni, Miftakhul Fitria Nur Adli Ari Darmawand Nur Khilmiyatul Ilmiyah Nuraini Anitasari Nuralam, Inggang Perwangsa Nurul Hidayat Nyimas Ayu Widi Indriana Oceandra Audrey Pandu Adikara, Putra Pangestu Ari Wijaya Panjaitan, RE. Miracle Prahesti, Suherni Prakoso, Ricky Pratomo Adinegoro Priyono, Mochammad Fajri Rahmatullah Rendra Puji Indah Lestari Purnomo, Welly Putra Pandu Adikara Putra, Alland Rifqy Putri, Nindy Alya Rachmad, Zikfikri Yulfiandi Raden Rizky Widdie Tigusti Rahma, Dzakiyyah Afifah Rahmah, Yusriyah Raisha, Serefika Raja Farhan Ramadha Pohan Rama Humam Syarokha Randy Cahya Wihandika Rani Metivianis Ratih Diah Puspitasari RE. Miracle Panjaitan Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Retno Indah Rokhmawati, Retno Indah Revi Anistia Masykuroh Rifqi Irfansyah, Nandana Rizal Setya Perdana Rizal Setya Perdana Robiata Tsania Salsabila Aditya Putri Rodiah Rodiah Ryan Leonardo Salsabillah, Dinar Fairus Saparila Worokinasih Saputro, Dimas Sarie, Riza Athaya Rania Satriawan, Eka Bayu Satrio Agung Wicaksono Satrio Hadi Wijoyo Sema Yuni Fraticasari Setiawan, Alexander Christo Setya Perdana, Rizal Setyowati, Andri Shafira Margaretta Sherly Witanto Sherryl Sugiono Sindarto Sigit Pangestu Silvia Ikmalia Fernanda Siregar, Fauziah Syifa R. Siti Fatimah Al Uswah Sobakhul Munir Siroj Sormin, Hartati Penta Angelina Sri Indrayani, Sri Suhhy Ramzini Sukmawati, A'inun Sutrisno Sutrisno Sutrisno, Sutrisno Syaiful Anam Syifa Namira Neztigaty Thifal Fadiyah Basar Titis Sari Kusuma Ulfa Lina Wulandari Utomo, Yoga Cahyo Vina Adelina Welly Purnomo Wibowo, Shinta Dewi Putri Widhy Hayuhardhika Nugraha Putra Wijanarko, Rizqi Winda Fitri Astiti Winurputra, Raihan Wiratama Paramasatya Yahya, Faiz Yolanda Nailil Ula Yudi Setiawan, Nanang Yuita Arum Sari Yunita Dwi Alfiyanti Yure Firdaus Arifin Zahra, Wardah