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All Journal International Journal of Electrical and Computer Engineering JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmiah Informatika Komputer Jurnal Teknik ITS IPTEK Journal of Proceedings Series Jurnal Simetris TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Jurnal Buana Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Teknosains: Media Informasi Sains dan Teknologi Jurnal Sosioteknologi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Sistem dan Informatika Scientific Journal of Informatics JUSIFO : Jurnal Sistem Informasi POSITIF Journal of Information Systems Engineering and Business Intelligence Jurnal Ilmu Komunikasi UHO : Jurnal Penelitian Kajian Ilmu Komunikasi dan Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Inspiration Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Informatika Upgris JOIN (Jurnal Online Informatika) Fountain of Informatics Journal ETTISAL Journal of Communication Jurnal Bisnis, Manajemen, dan Informatika Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Cakrawala : Jurnal Humaniora Bina Sarana Informatika JUSTITIA JURNAL HUKUM INTEGER: Journal of Information Technology JIEET (Journal of Information Engineering and Educational Technology) Information Management For Educators And Professionals (IMBI) CogITo Smart Journal BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks Jurnal Sains dan Informatika Jurnal Informatika Universitas Pamulang Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL TEKNOLOGI DAN OPEN SOURCE Data Science: Journal of Computing and Applied Informatics Majalah Ilmiah UNIKOM Jurnal Riset Bisnis dan Investasi Jurnal Komunikasi Profesional JISICOM (Journal of Information System, Infomatics and Computing) Jurnal SITECH : Sistem Informasi dan Teknologi Jurnal Teknologi Informasi dan Pendidikan JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Informatika dan Rekayasa Elektronik Indonesian Journal of Business Intelligence (IJUBI) INFORMASI (Jurnal Informatika dan Sistem Informasi) KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer JIKA (Jurnal Informatika) International Journal of Advances in Data and Information Systems JOURNAL OF BUSINESS AND ECONOMICS RESEARCH (JBE) Journal of Information Technology and Its Utilization Al-Ittishol : Jurnal Komunikasi dan Penyiaran Islam Halal Research Journal Marketing Management Studies Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer J-Intech (Journal of Information and Technology) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Indonesia Sosial Teknologi Jurnal Sintak Sewagati SATIN - Sains dan Teknologi Informasi Jurnal Sistem Informasi Jurnal Sistem Informasi dan Manajemen Jurnal PUBLIQUE Majalah Bisnis & IPTEK Jurnal Sosial dan Teknologi Terapan AMATA JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Sistem Informasi dan Aplikasi KOMPUTASI
Nur Aini Rakhmawati
Departemen Sistem Informasi Institut Teknologi Sepuluh Nopember Surabaya
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Analisis Sentimen Netizen Terhadap Personal Branding Elon Musk Pada Platform X Dengan Pendekatan Analisis Support Vector Machine Armadianti, Wanda; Brilliant Lastono, Avicenna Syeh; Putra, Fahrul Ramadhan; Al Ghozi, Ihsan Kamil; Rakhmawati, Nur Aini
Fountain of Informatics Journal Vol. 9 No. 1 (2024): Mei 2024
Publisher : Universitas Darussalam Gontor

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

Abstract

Abstrak Dalam era digital yang berkembang, personal branding menjadi kunci dalam memengaruhi opini publik. Tokoh terkenal seperti Elon Musk menggunakan media sosial, seperti Platform X, untuk mengekspresikan pandangan dan perasaan serta mengundang pujian dan kritik. Penelitian ini bertujuan untuk menganalisis sentimen netizen terhadap personal branding Elon Musk di Platform X dengan menggunakan metode Support Vector Machine (SVM) untuk pengklasifikasiannya. Beberapa proses yang dilakukan dalam penelitian ini adalah proses pengumpulan data, pelabelan data, praproses data, pembangunan model, evaluasi model, hingga visualisasi data. Data mentah berasal dari tweet netizen pada akun @elonmusk di Platform X. Tweet diklasifikasikan menjadi 3 jenis sentimen, yaitu positif, negatif, dan netral. Dari 245 data yang dikumpulkan, didapatkan data dengan sentimen positif berjumlah 82 data, negatif berjumlah 51 data, dan netral berjumlah 56 data. Model SVM menunjukkan kinerja terbaik pada klasifikasi "positif" dengan presisi tinggi (0,5135) dan recall tinggi (0,826), serta skor f1 yang baik (0,633). Untuk sentimen negatif, presisi tinggi (0,7142) tetapi recall lebih rendah (0,454). Model kurang baik dalam mengenali sentimen netral dengan presisi (0,25), recall (0,071), dan skor f1 (0,111) yang rendah. Setelah model dibangun dan dilakukan pengklasifikasian, data menunjukkan dominasi sentimen positif dalam personal branding Elon Musk. Kata kunci: Elon Musk, Personal Branding, Sentimen, SVM   Abstract [Analysis of Netizen Sentiment Towards Elon Musk's Personal Branding on Platform X Using a Support Vector Machine (SVM) Analysis Approach] In the growing digital era, personal branding is the key to influencing public opinion. Famous figures such as Elon Musk use social media, such as Platform X, to express views and feelings and invite praise and criticism. This research aims to analyze netizen sentiment toward Elon Musk's personal branding on Platform X using the Support Vector Machine (SVM) method for classification. Several processes carried out in this research are data collection, data labeling, data preprocessing, model building, model evaluation, and data visualization. The raw data comes from netizen tweets on the @elonmusk account on Platform X. Tweets are classified into 3 types of sentiment, namely positive, negative, and neutral. Of the 245 data collected, 82 data were obtained with positive sentiment, 51 negative data, and 56 neutral data. The SVM model showed the best performance on “positive” classification with high precision (0.5135) and high recall (0.826), as well as a good f1 score (0.633). For negative sentiment, precision is high (0.7142) but recall is lower (0.454). The model is not good at recognizing neutral sentiment with low precision (0.25), recall (0.071), and f1 score (0.111). After the model was built and classified, the data showed the dominance of positive sentiment in Elon Musk's personal branding. Keywords: Elon Musk, Personal Branding, Sentiment, SVM
Food Ingredients Similarity Based on Conceptual and Textual Similarity Rakhmawati, Nur Aini; Jannah, Miftahul
Halal Research Vol 1 No 2 (2021): July
Publisher : Halal Center ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (399.177 KB) | DOI: 10.12962/j22759970.v1i2.107

Abstract

Open Food Facts provides a database of food products such as product names, compositions, and additives, where everyone can contribute to add the data or reuse the existing data. The open food facts data are dirty and needs to be processed before storing the data to our system. To reduce redundancy in food ingredients data, we measure the similarity of ingredient food using two similarities: the conceptual similarity and textual similarity. The conceptual similarity measures the similarity between the two datasets by its word meaning (synonym), while the textual similarity is based on fuzzy string matching, namely Levenshtein distance, Jaro-Winkler distance, and Jaccard distance. Based on our evaluation, the combination of similarity measurements using textual and Wordnet similarity (conceptual) was the most optimal similarity method in food ingredients.
Analisis Kesadaran Mahasiswa ITS Terhadap Privasi Data Pada Media Sosial: Studi Kasus Departemen Informasi Ariadi, Evanriza Safiq; Maharani, Dewi; Falih, Laode Shaldan; Rakhmawati, Nur Aini
Al-Ittishol: Jurnal Komunikasi dan Penyiaran Islam Vol. 5 No. 2 (2024): Al-Ittishol: Jurnal Komunikasi dan Penyiaran Islam
Publisher : Prodi Komunikasi dan Penyiaran Islam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51339/ittishol.v5i2.2207

Abstract

Privasi data telah menjadi isu yang semakin penting dalam era digital ini. Penelitian ini bertujuan untuk menganalisis kesadaran mahasiswa Departemen Sistem Informasi Institut Teknologi Sepuluh Nopember (ITS) terhadap privasi data. Kebocoran data pribadi sering terjadi akibat tindakan tidak bertanggung jawab atau kelalaian individu saat bermain internet. Penelitian menggunakan metode kuantitatif dengan kuesioner yang diadaptasi dari penelitian sebelumnya untuk mengukur kesadaran keamanan informasi dan privasi. Didapatkan hasil sebanyak 34 responden mahasiswa Departemen Sistem Informasi. Dari hasil kuesioner menunjukkan bahwa mahasiswa yang telah mengambil mata kuliah Etika TI memiliki kesadaran akan pentingnya keamanan informasi dan privasi yang lebih tinggi dibandingkan yang belum mengambil mata kuliah tersebut. Oleh karena itu, pendidikan mengenai privasi data dan keamanan informasi sangat penting untuk meningkatkan kesadaran mahasiswa dalam menghadapi tantangan teknologi di masa depan.
Analisis Sentimen Pengguna Twitter Terhadap Pemilu 2024 Berbasis Model XLM-T Ghufron, Mochamad Rafli; Mahabbataka Arsyada, Muhammad Farrih; Lukman, Muhammad Rizano; Haryono Putra, Yudhistira Azhar; Rakhmawati, Nur Aini
J-INTECH (Journal of Information and Technology) Vol 11 No 2 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i2.1013

Abstract

In the current digital era, social media, especially Twitter, has become an important platform for people to share opinions, especially regarding political issues such as the 2024 Presidential Election (Pemilu) in Indonesia. This research aims to analyze public sentiment regarding the 2024 Election based on collected tweet data. By using an XLM-T based machine learning model, this research succeeded in classifying tweets into three sentiment categories: positive, negative and neutral with a model accuracy rate of 68%. The results show that tweets with positive and negative sentiments receive more interaction from the public compared to tweets with neutral sentiments, indicating the public's tendency to more actively interact with opinions that have a certain position or stance on an issue. In conclusion, sentiment analysis can provide deep insight into the public's views on the 2024 Election, which political stakeholders can utilize in designing their campaign strategies.
Halal Certification of Food and BeverageMSME’s Products in the Pacarkeling Area, Surabaya Rakhmawati, Nur Aini; Indraswari, Rarasmaya; Ulfin, Ita; Harmami, Harmami; Rahadiantino, Lienggar; Qadariyah, Lailatul
Berdikari: Jurnal Inovasi dan Penerapan Ipteks Vol. 13 No. 1: February 2025
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/berdikari.v13i1.23581

Abstract

This article is based on the Community Service (Kuliah Kerja Nyata / KKN) program, which was held to help solve the problems faced by MSMEs (micro, small, and medium enterprises) from the aspect of halal products certification, especially in the Pacarkeling area, Surabaya. In this program, we assist MSMEs in carrying out certification and com pleting documents so that MSMEs can apply for halal certification. Activities in this pro gram begin with assistant training, follow-up on MSMEs, training of prospective supervi sors, creating SJPH (Sistem Jaminan Produk Halal) documents, inputting data to SIHALAL, verification and validation, and finally, creating reports and documentation. In this pa per, the case study of the MSME assisted in this program is Canda, which runs in the food and beverage sector and sells ready-to-consume beverage products. The KKN team in this program intensively assists the MSMEs from the preparation stage until the announcement of halal certification. This KKN activity is documented in the form of news, digital documentation, articles, and videos of community community service. The result of this KKN activity is the Halal certification of the MSMEs in the Pacarkeling area, Surabaya
Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning Ni Luh Putu Chandra Savitri; Radya Amirur Rahman; Reyhan Venyutzky; Nur Aini Rakhmawati
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3216

Abstract

Covid-19 pandemic urges countries to limit interaction of their people to reduce transmission. Indonesia requires people to do activities at home, one of which is online school. Many people share their thoughts through social media Twitter. Therefore, authors conducted sentiment analysis using supervised machine learning algorithm to determine distribution of words used in commenting on online schools, relationship between sentence, length and sentiment, and best algorithms that can be used to get most accurate results. In this study, authors used the method of crawling with RapidMiner to get data from Twitter. Then authors do data cleansing, data processing with classification methods using Random Forest Classifier , Logistic Regression , BernoulliNB and SVC algorithm. After that authors evaluate using confusion matrix, accuracy rate and classification report. In this research, authors found there are positive, negative, and neutral sentiments expressed on the online school implementation through comments. Authors ranked top three most used words used to express positive sentiments which includes bahagia, rajin and senang. On negative sentiments, top three words are capek, muak and bosen. On neutral sentiments, top three words are tidur, capek, and buka. Lengthy Tweets are usually imbued with negative remarks. On the other hand, the tweet tends to be positive and neutral tweet is usually stable. Authors conclude that the weakness of online school is the amount of workload that makes students tired alongside ineffective teaching method which makes it hard for students to understand the material given by school. However, on the positive side, some people agree with policies that are implemented and they feel like they gained some benefits from the implementation. From the four supervised machine learning algorithms that have been tested, Logistic Regression shows the highest accuracy, 0,87. The analysis shows that society tends to be neutral to the implementation of online school.
Sentiment Analysis on the Ratification of Penghapusan Kekerasan Seksual Bill on Twitter Gary Dimitri Hamidi; Farida Afira Bestari; Alexandra Situmorang; Nur Aini Rakhmawati
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 3 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i3.4051

Abstract

Rancangan Undang-Undang Penghapusan Kekerasan Seksual (RUU PKS), or the bill of the Republic of Indonesia on the Elimination of Sexual Violence, is a bill that discusses sexual violence, victim protection and its scope concern the matters related to sexual violence. Elimination of sexual violence according to the PKS Bill aims to prevent all forms of violence. These discussions and conversations also occur on social media, especially on Twitter. Taking public sentiment is significant in choosing the proper messages, interference, and policy. Sentiment analysis is a field of study that analyses opinions, sentiments, judgments, evaluations of a person attitudes and emotions regarding a particular topic, service, product, individual, organization or activity. This study used the method of crawling to get data from Twitter. Then data cleansing, data processing is carried out using Bernoulli, Logistic Regression, and Support Vector Classification (SVC) algorithm. The data is then evaluated using three methods: accuracy, classification report, and confusion matrix. Based on the three algorithms used, it is found that all methods are equally accurate with 0.65. This study found positive, negative, and neutral sentiments expressed to the bill of Elimination of Sexual Violence through comments. It is shown that most people using the keyword “RUU PKS” are positive to the bill of Elimination of Sexual Violence (RUU PKS) while most people’s sentiments using #RUUPKSBukanSolusi are negative to the bill.
Analisis Klasifikasi Sentimen Terhadap Isu Kebocoran Data Kartu Identitas Ponsel di Twitter Muh Ichlasul Amal; Elsa Syafira Rahmasita; Edward Suryaputra; Nur Aini Rakhmawati
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5483

Abstract

Technology developments bring great threats related to privacy and security of personal data. In September 2022, a data leak incident of 1.3 billion SIM card registration data containing user's personal data was uploaded on dark web. Indonesian people voice their opinion regarding this issue on Twitter. This study aims to find out the word distribution and sentiment classification analysis of public opinion on Twitter related to the issue. Sentiment classification analysis was carried out using a machine learning approach with four methods, namely Random Forest, Logistic Regression, Support-Vector Machine, and IndoBERT model. The four methods will be compared to see which model produces the best performance. From the crawling process, 957 tweets were obtained, of which 609 were labeled and trained using the four methods. From the data obtained, there is an imbalance between classes, where positive sentiment has a much smaller number than the rest. Some words that are often used in the tweet are SIM card, data SIM, bocor data, miliar data, and kominfo. The results of the model show that the Support-Vector Machine has the best performance with an f1-score of 0.81, followed by Random Forest of 0.78, IndoBERT of 0.76, and Logistic Regression of 0.74. Class imbalance and lack of training data make IndoBERT's performance lower when compared to other algorithms. The results of this study can be used by the authorities to evaluate policies in dealing with data security issues by listening to opinions from the Indonesian people.
Analisis Komparatif Pengukuran Kemiripan Artikel Ilmiah menggunakan Jaccard dan Levenshtein serta Blocking Muhammad Rizqi Nur; Gandhi Surya Buana; Nur Aini Rakhmawati
Jurnal Teknik Informatika dan Sistem Informasi Vol 9 No 2 (2023): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v9i2.6414

Abstract

Paper search engines have made it easier for academics to conduct literature reviews. However, easy doesn't mean accurate. For certain niche topics, search results often aren’t quite good. Snowballing can be done to overcome this, but it is limited to the initial articles owned, especially the author's access when the article was written. As an alternative, paper databases provide recommendations for relevant articles of an article, but it’s limited to that database. A tool to search for similar articles without relying on a specific database would be very helpful, but before that, the appropriate method for measuring article similarity needs to be determined. This research aims to measure article similarity based on title, author, and keywords using Weighted Jaccard Measure and Levenshtein distance and evaluate it. This study also compares performance by adding blocking with overlap blocking and stop word removal. The Jaccard evaluation results are quite poor, but the Levenshtein + Jaccard evaluation results are decent. In addition, it was found that emphasizing weighting on the title produces the best results. Overlap blocking and stop words removal increases processing time instead. Overlap blocking can reduce the number of measurements by almost half with an overlap of 1, but overlaps above 1 will discard many pairs that should be similar. Removing stop words improves Jaccard and Levenshtein performance but requires threshold adjustment.
Analisis Etika dalam Penggunaan Media Sosial Instagram Oleh Mahasiswa ITS Mengenai Pelanggaran Privasi Rakhmawati, Nur Aini; Rendiga, Naifa Mumtazah; Aini, Sarah Auliannisa; Tertiabudi, Vania Aileen
JIEET (Journal of Information Engineering and Educational Technology) Vol. 8 No. 2 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v8n2.p90-95

Abstract

Dewasa ini tren penggunaaan media sosial semakin marak, bahkan hampir semua orang yang memiliki smartphone dan akses internet pasti memiliki media sosial. Dari sekian banyak aplikasi media sosial, salah satu yang paling banyak penggunanya yaitu Instagram. Aplikasi dimana para user nya dapat mengunggah konten seperti foto,video, ataupun teks. Dengan berbagai fitur seperti reels, story, feeds, dan live memungkinkan pengguna untuk dapat membagikan berbagai informasi sesuai keinginan mereka. Seringkali, informasi yang dibagikan merupakan informasi yang sifatnya pribadi dan dapat berbahaya jika disebarkan. Sebagian pengguna Instgram belum sadar akan pentingnya menjaga informasi-informasi yang sifatnya pribadi ini khusunya di kalangan mahasiswa. Oleh karena itu, analisis ini dilakukan untuk mengetahui bagaimana sikap seorang mahasiswa terkait pentingnya menjaga informasi pribadi agar tidak terjadi pelanggaran privasi. Teknik pengumpulan data yang digunakan berupa pengisian kuesioner melalui google form oleh mahasiswa Institut Teknologi Sepuluh Nopember, Surabaya. Dari hasil yang diperoleh menujukkan bahwa mayoritas mahasiswa ITS menyadari akan pentingnya privasi dalam bermedia sosial. Namun, mereka tidak sepenuhnya percaya terkait keamanan informasi oleh Instagram. Untuk itu, mereka melakukan tindakan seperti mengunci akun mereka dan tidak sembarangan mengunggah infomasi yang sifatnya pribadi. Berdasarkan hasil analisis ini, tingkat edukasi yang lebih lanjut diperlukan oleh pengguna Instgram khususnya di kalangan mahasiswa dalam penjagaan informasi pribadi untuk menghindari adanya pelanggaran privasi.
Co-Authors -, Hanny A Pahmi Abdul Azizun Nafi Abi Nubli Abadi Achmad Thoriq Aminullah Adam Achmad Rayhan Adam Akbar Adam Febriansyah Adellya Rizqy D Adi, Sang Intan Risqi Adinda Ayudyah Rachmawati Aditya Muhammad, Yagi Afifah Nurul Izzati Afisina, Annisa Ahmad Choirun Najib Ahmad Choirun Najib Ahmad Hilal Mubarok Ahmad Ikhsan Ahmad Laroy Bafi Ahmad Muklason Ahmad Naufal Ahmad Naufal Muzakki Ahmad Naufal Rofiif Ahmad Rifqy S Ahmadimaldeh, Ashkan Aina Divany Aini, Sarah Auliannisa Aisy, Rihhadata Aisyah Zahrah Akbar, Adam Akdeas Oktanae Widodo Akhdan Arifuddin Akhmad Syafrie Syamsudin Akmal, Dara Nasywa Fathya Afiqah Al Ghozi, Ihsan Kamil Alexander Sidum Laka Kaki Alexander Sidum LK Alexander, Shane Giorgio Alexandra Situmorang Alfarizi, Septian I Alifa Rizki Rahmarani Alifian Sukma, Alifian Aliya, Tasya Putri Alkautsar Rivandra, Muhammad Altetiko, Faizal Johan Amal, Muh Ichlasul Amartika, Nida Aulia Amir Mu’tashim Billah Ana Mardiyah Ananto, Amadeus Terra Andaru Pratama Putra Andieka Rabbani Angelica Cintya Mannuela Wibowo Anggrarista Nusty Alivia Anisa Rahmah Anita Carolina Ansar, Muhammad Armando Nur Rizqy Ansori, Daffa Daris Mahendra Anwar Romadhon Aparamarta, Hakun Wirawasista Apsarini, Fira Areta Aqila Intan Prakerti Ardha Perwiradewa Ardha Perwiradewa Ardhi Dwi Firmansyah Ari Prabowo Ariadi, Evanriza Safiq Arief Rahman Arifansyah Wicaksono Aris Purwanti Putri Aristamy, I Gusti Ayu Agung Mas Armadianti, Wanda Arrindika Pradana Ramadhansyah Arya Akbar Rivaldi Aryanti, Dwi Cindy Asmaul Husna Astian Afif Astrid Kurnia Sherlyanita Astrid Kurnia Sherlyanita, Astrid Kurnia Aura Febriyanti Puspa Sari Aurelia, A. Talitha Rezky Avelyna Ferariya Claresta Az-Zahra, Farah Helga Azmi Pratama Bagas Farhan, Muhammad Bagus Tri Handoko Bakkara, Bintang Bayu Azra Yudhantorro Bayu Narendra Jati Bekti Cahyo Hidayanto Bernadi, Ivan Pramudhana Berry Humaidi Fuad Bestari, Farida Afira Brilliant Hartono Brilliant Hartono Brilliant Lastono, Avicenna Syeh Budi, Aditya Septa Cahyadi, Firman Candrawati, Vania Farah Chamdana Taqie Samboro Chetrina Dhea Puspita Chintia, Ervina Cisatra, Aulia Cita Engedi K D, Adellya Rizqy Danendra, Farel Dea Ayu Oktaviani Deanda Bevani Aletha Deny Hermansyah Dewi Aprilia Dewi Aprilia Dewi, Kadek Mawar Kumala Diajeng Ciptaning Ayu Dimas Arief Rahman Dimas, Yoel Dinda Dinda Dinda Meidianti Kusuma Putri Dinda, Dinda Doohan Ryan Fathony Edward Suryaputra Eko Setiyo Budi Purnomo Elsa Syafira Rahmasita Elvia Ichsazene Dina Adha Erizkika Mochammad Arsy Rofi Ervina Chintia Erwada, Baringga Aurico De Eskalalita, Eskalalita Etria Sepwardhani Purba Eva Agustine Evan Evan Fabroyir, Hadziq Fadhila, Putri Rahma Fadzilah, Lutfi Nur Fadzilah, Lutfi Nur Fairuz Ghalib Faisal, Syafrie Dwi Faishol, Olive Khoirul Lukluil Maknun Al Faiz, Achmad Fajar Ramadhani Fajriyadi, Adnan Mauludin Falah, Muhammad Dary Falih, Laode Shaldan Fanny Azhary Formen Fano, Naufal Firjatulloh Fano, Nisrina Fadhilah Farhan Aji Farhan Septiadi Farida Afira Bestari Farrel Arrizal Fathony, Doohan Ryan Fauziyah, Ananda Rahmah Faza Rashif Febriansyah, Adam Febriliyan Samopa Febrine Deva A Febriyora Surya Pratiwi Feby Artwodini Muqtadiroh Felle, Sola Graciana Ferdy Pramudya Firdaus Firqa Aqila Noor Arasyi Firwam Al Ayubi Fithrotuz Zuhroh Formen, Fanny Azhary Furqon, Muhammad Ariful Galih Rendi Setyawan Gandhi Surya Buana Gary Dimitri Hamidi Ghiffari Assamar Qandi Ghufron, Mochamad Rafli Goldio Ihza Perwira Nirvana Habibullah, Bayu Liano Leader Haedar, Zulfikar Fahmi Hakun Wirawasista Aparamarta Hamidi, Gary Dimitri Hamzah Muhammad Hanissa Rizki Kurnia Hapsari Wulandari Harmami Harmami Haryono Putra, Yudhistira Azhar Hasan Ikhwani Hasna Dhiya Nafitra Helmi Muharram Hendro Nurhadi Hendro Nurhadi Hendry Naufal Marbella Herdy Ardiansyah Hindrayani, Kartika Maulida Hosiana Arga Putri Humaira Nur Pradani Humayyun Nabila Ramadhani Ibadurrahman Ziaulhaq Ikhwan Jauhar Imam Mansyur Solichin Imam Mansyur Solichin``` Imam Teguh Islamy Immanuella, Grace Michelle Indira Salsabila Ardan Indraswari, Rarasmaya Indrianingtyas, Puspa Nur Intania Chantika Alina Intania Chantika Alina Irhamah Irhamah - Irmasari Hafidz Irsyad, Akhmad Ita Ulfin Izdihar, Awliya Hanun Izzat Aulia Akbar Izzati, Afifah Nurul Izzatul Isma Jawakory, Malvin Reynara Jessica Aurelia Nadine Jessica Patricia Halim Juan Jan Juan Septian Veron Panjaitan Juwari Juwari Juwari Kanedi, Fidela Jovita Kardinata, Eunike Katili, Apridio Edward Kevin Hafizzana Untoro Wiwaha Kevin Rafi Adjie Putra Santoso Kresnawan, Hans Lacsita Devi Oktaviana Lailatul Qadariyah Laily Rahmadhani Lazuardi, Luthfi Leonardi Paris Hasugian Liefran Satrio Sim Lita, Ivana Ludia Rosema Dewi Luh Putu Gayatri Widiastuti Luh Putu Gayatri Widiastuti Lukman, Muhammad Rizano Lulu`ul Watef Luthfi Lazuardi Luthfi Lazuardi Mahabbataka Arsyada, Muhammad Farrih Maharani, Dewi Maharani, Indira Margaretha, Ribka Devina Mashuri Mashuri Maulidani, Muhammad Wildan Maulidiya Meilani Michael Christopher Miftahul Jannah Miftakhul Janah Sulastri Minokaura, Muh. Fachrul Mochammad Dwiky Andrian Mohammad Awaluddin Syarif Mokay, Hanna Gloria Mufidah, Belva Rizki Mufidah, Karima Muh Ichlasul Amal Muhammad Ainul Khakim Muhammad Ainul Khakim Muhammad Alif Noor Febriansyach Muhammad Alrifqi Muhammad Bagas Farhan Muhammad Daffa Rinaldy Yusri Muhammad Fajrul Alam Ulin Nuha Muhammad Hanif Waskito Muhammad Hilman Rafialdy Muhammad Iqbal Aditama Muhammad Irfan Muhammad Kemal Witjaksono Muhammad Muchlish Muhammad Papuandivitama Putra Muhammad Rasyid Kafif Ibrahim Muhammad Reza Pahlawan Muhammad Rifqi Hidayat Muhammad Rivza Adrian Muhammad Rizqi Nur Muhammad Wildan Maulidani Muhammad Zuhri Mursyidatun Nabilah Nabila, Nafisa Sufi Nadhif Ikbar Wibowo Nadhifa, Ufaira Khanzahasna Nanfaiq Nadia Widyawati Putri Nadiah, Rofiqoh Nadira Hanifah Nur’aini Nafi, Abdul Azizun Najib, Ahmad Choirun Najib, Ahmad Choirun Najmi, Nisa Salvia Najwa, Nina Fadilah Naseela, Qudsiyah Zahra Ilham Natania, Cecilia Melva Naufal Ihza Revandhika Naufal Tsabit Naufal, Muhammad Alifiro Naura Jasmine Azzahra Ni Luh Putu Chandra Savitri Nida Inayah Maghfirani Nida Inayah Maghfirani Nikmah, Najla Lailin Nina Fadilah Najwa Nisrina Fadhilah Fano Nody Risky Pratomo Novian Noormansyah Novita Indah Pitaloka Nur Sholekah, Nadila Nurfatikha, Rih Prajna Oktaviani, Dea Ayu Pamungkas, Adhi Yoga Muris Pande Made Risky Cahya Dinatha Permatasari, Reisa Perwira, Reynaldi Drajat Ageng Perwiradewa, Ardha Poetra, Vincentian Michael Anton Pradani, Humaira Nur Pramesta, Noverita Rizki Pramesty, Nabilla Sabta Putri Pratama, Azmi Pratomo, Nody Risky Premananda, I Gusti Agung Purnama, I Putu Adhitya Pratatama Mangku Puspa Nur Indrianingtyas Putra, Fahrul Ramadhan Putri, Aris Purwanti Putri, Malfa Liya Qonita Nailul Muna R. Aditya Rayhan Zanesty R. Aditya Rayhan Zanesty R., Echa Alfa Raden Darmawan Raden Darmawan Radityo Prasetianto Wibowo Radya Amirur Rahman Rafika Rahmawati Rahadiantino, Lienggar Rahayu, Triana Mugia Rahma Wahyu Idayani Rahman, Muhammad Daffa Alvinoer Rahman, Radya Amirur Rahmanisa Dzakiyyarani, Fathia Rahmasita, Elsa Syafira Rahmat Hidayat Rahmat Hidayat Rahmat Hidayat Rahmat Hidayat Rahmawati, Alfrida Rainal Yusril Ramadhani Galuh Candra Purtiwi Ramadhani, Humayyun Nabila Ramadhani, Slamet Ramadhani, Sulthan Alif Secca Ramadhansyah, Arrindika Pradana Ramadina, Raysa Farah Mumtaz Rasendriya, Zada Alfarras Refais Akbar Zufira Refaldi, Darrel Athaya Regita Ayu Cahyani Zulaikhah Rekyan Bayu Waskitho Renada Aulia Salsabila Rendiga, Naifa Mumtazah Rendy Ananta Resistania Anggita Putri Revandhika, Naufal Ihza Reyhan Venyutzky Reza Safira Rezky Ameron Rheindra Alfarhizi Ridho, Achmad Fahmi Ainur Rifardhi Reza Saputra Rifda Awalia Zuhroh Rihhadata Aisy Rita Sari Rivanda Putra Pratama Rivanda Putra Pratama Riyandini Devi Intan Permata Sari Rizal Maulana Hadi Rizal, Muhammad Ainur Rizqeya Irfan Pratama Rofiif, Ahmad Naufal Rofiqoh Nadiah Rosyid Abdillah Royyana Muslim Ijtihadie Rustanto, Ikhwan Sadewa, Naufal Saputra, Faris Sandra Sarah Ahya Khairunisa Saraswati, Ni Putu Septiary Devi Savitri, Ni Luh Putu Chandra Seloatmodjo, Xavier Wahyuadi Septiadi, Yogik Septian I Alfarizi Setiyo Gunawan Shade Rahmawati Shafwan Agung Shalma Rachmayani Putri Sholihah, Annisa Mufidatun Sholikah Desi Purwanti Siahaan, Inggrit Rismauli Sidum LK, Alexander Sipayung, Retha Novianty Sisca Threecya Agatha Siti Aminatus Zehroh Siti Zehroh Situmorang, Alexandra Sujiwana, Rafi Kurnia Sulthan Alif Secca Ramadhani Suryaputra, Edward Suwandi, Syifa Ilma Nabila Syahputra, Novyant Syifaul Fuada T, MHD Raihan Natigor Tantri, Ashr Hafiizh Tertiabudi, Vania Aileen Tio Arya Dewa Prakarsa Tio Arya Dewa Prakasa Tri Andika Maulana Tsabit, Naufal Tsabita Rizqiina Putri Hidayat Ufaira Khanzahasna Nanfaiq Nadhifa Utomo, Jihan Husnia Vania Rahma Dianutami Venus Oktanada Gemilang Venyutzky, Reyhan Vielita, Femi Nabila Vincentian Michael Anton Poetra Wardati, Nanda Kurnia Watef, Lulu`ul Wibowo, Nashita Kalila Wiradharma, Putu Panji Witjaksono, Muhammad Kemal Yahya Noviko Rahman Yanuandika Akbar Yoel Dimas Yoga Widhia Pradhana Yumna Cahyaning Yusri, Muhammad Daffa Rinaldy Zega, Kurniaman Andreas Zehroh, Siti Zehroh, Siti Aminatus Zeko, Chubo Zendika Dayongki S Zuhroh, Nurrida Aini Zulaikhah, Regita Ayu Cahyani Zulfikar Fahmi Haedar