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All Journal Jurnal Penelitian Saintek Teika Jurnal Buana Informatika JSI: Jurnal Sistem Informasi (E-Journal) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnas Nasional Teknologi dan Sistem Informasi Annual Research Seminar ANDHARUPA CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Information System for Educators and Professionals : Journal of Information System MBR (Management and Business Review) JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi J I M P - Jurnal Informatika Merdeka Pasuruan Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal ULTIMA InfoSys Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Ilmiah Media Sisfo JURIKOM (Jurnal Riset Komputer) JURTEKSI Jurnal Riset Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) CCIT (Creative Communication and Innovative Technology) Journal JUSIM (Jurnal Sistem Informasi Musirawas) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Building of Informatics, Technology and Science Journal of Information Systems and Informatics Jurnal Teknologi Dan Sistem Informasi Bisnis Zonasi: Jurnal Sistem Informasi Jurnal Pengabdian Masyarakat Bumi Raflesia JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Teknomatika (Jurnal Teknologi dan Informatika) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Algoritma SmartComp The Indonesian Journal of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Komtika (Komputasi dan Informatika)
Allsela Meiriza, Allsela
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Segmentasi Spasial Tingkat Kemiskinan Provinsi Sumatera Selatan Menggunakan Pendekatan Klasterisasi K-Means Jonathan Pakpahan; Septhia Charenda Putri; Ananda Khoirunnisa; Rafika Octaria Ningsih; Putri Mutiara Arinie; Arvhi Randita Setia; Ken Ditha Tania; Allsela Meiriza
Jurnal Ilmiah Komputasi Vol. 24 No. 3 (2025): Jurnal Ilmiah Komputasi : Vol. 24 No 3, September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.24.3.3820

Abstract

Kemiskinan adalah tantangan utama dalam pembangunan ekonomi yang membutuhkan analisis berbasis data. Kajian ini menerapkan metode klasterisasi K-Means untuk segmentasi spasial tingkat kemiskinan berdasarkan indikator sosial-ekonomi, seperti persentase penduduk miskin, rata-rata lama sekolah, pengeluaran per kapita, serta indeks kedalaman dan keparahan kemiskinan. Data dari BPS tahun 2024 diolah menggunakan pendekatan Knowledge Discovery in Database (KDD) melalui tahapan seleksi data, prapemrosesan, transformasi, penambangan data, dan evaluasi menggunakan RapidMiner. Hasil klasterisasi membentuk empat kelompok dengan disparitas kesejahteraan antarwilayah, di mana beberapa daerah menunjukkan tingkat kemiskinan yang lebih tinggi. Melalui pemetaan berbasis data ini, penelitian diharapkan menjadi dasar bagi pengambil kebijakan dalam merancang strategi penanggulangan kemiskinan yang efektif dan tepat sasaran guna mengurangi ketimpangan sosial serta meningkatkan kesejahteraan masyarakat di Provinsi Sumatera Selatan. Kata kunci: Kemiskinan, K-Means, Klasterisasi, Data Mining, Sumatera Selatan.
Performance Comparison of Sentiment Classification Algorithms on SIGNAL Reviews Using SMOTE Anadia, Qothrunnada Wafi; Meiriza, Allsela
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1196

Abstract

Public service apps like SIGNAL are widely used to provide public access to information and vehicle tax payments. However, diverse user reviews highlight the need to evaluate public perception through sentiment analysis. Selecting an appropriate classification algorithm is crucial to ensure accurate results, particularly when dealing with imbalanced review data. Therefore, This study examines the comparative performance of four algorithms Naïve Bayes, Random Forest, Decision Tree, and SVM in analyzing the sentiment of 36,000 user feedback obtained from Google Play Store. The dataset underwent preprocessing, feature extraction using TF-IDF, and class balancing using SMOTE. Model evaluation was conducted using accuracy, precision, recall, and F1-score. The findings indicated that Random Forest performed the best overall performance (accuracy 91.04%, F1-score 94.80%), followed by Naïve Bayes (accuracy 89.89%, F1-score 93.38%), SVM (accuracy 89.22%, F1-score 93.02%), and Decision Tree (accuracy 88.40%, F1-score 92.31%). These findings indicate that Random Forest is highly effective for balanced datasets, while SVM and Naïve Bayes offer competitive precision for applications prioritizing accuracy in positive class detection. The output of this study can be applied practically by developers and related institutions in optimizing public service applications and by applying Random Forest algorithm to gain actionable insights for optimizing features and aligning services more closely with user needs.
Comparison of Support Vector Machine and Random Forest Algorithms in Sentiment Analysis of the JMO Mobile Application Via Mariska, Inneke; Meiriza, Allsela; Lestarini, Dinda
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10764

Abstract

JMO Mobile is a digital service application that enables the public to access employment-related information and benefits. User reviews serve as a valuable resource for evaluating service quality, yet systematic sentiment analysis on this application remains limited. This study aims to classify the sentiment of user reviews and compare the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms. A total of 41,673 reviews were collected through web scraping, then preprocessed through text cleaning, tokenization, stopword removal, stemming, and feature extraction using TF-IDF. The reviews were categorized into positive, negative, and neutral sentiments, and divided into training and testing datasets with an 80:20 ratio. The choice of SVM and RF was based on their proven effectiveness in text classification tasks, with SVM excelling in handling high-dimensional data and RF recognized for its stability in producing reliable results. Model evaluation was conducted using accuracy as the primary metric. The findings indicate that Random Forest achieved an accuracy of 86.15 percent, slightly outperforming SVM at 86.06 percent. While SVM showed superior performance in identifying positive sentiment, Random Forest demonstrated greater consistency across classifications. Overall, Random Forest is considered more suitable for sentiment analysis of public service application reviews. This study contributes an automated approach to understanding user perceptions and offers a reference for selecting classification algorithms in similar cases.
Aspect-Based Sentiment Analysis of Hospital Service Reviews Using Fine-Tuned IndoBERT Maretta, Aulia; Meiriza, Allsela
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10765

Abstract

Aspect-Based Sentiment Analysis (ABSA) has become a crucial approach for extracting detailed opinions from user-generated content, especially in the healthcare domain. This study analyzes public sentiment toward hospital services in Indonesia using IndoBERT, fine-tuned on 2.448 reviews collected from Google Reviews and Instagram. Sentiment labels were automatically assigned with a pre-trained Indonesian RoBERTa classifier, while aspect extraction was performed through a lexicon-based approach covering five service dimensions: Facilities, Staff Competence, Empathy and Communication, Reliability and Responsiveness, and Cost and Affordability. To address class imbalance, the IndoBERT model was optimized using class weight adjustments. The results demonstrate strong performance, achieving an overall accuracy of 96%. In terms of sentiment classification, the model obtained F1-scores of 89% for negative, 83% for neutral, and 99% for positive sentiment, with a macro-average F1 of 90%. By aspect, Facilities (82.24%) and Empathy & Communication (91.71%) received the highest positive sentiment, while Cost & Affordability recorded the highest proportion of negative sentiment (25%). These findings underscore the effectiveness of IndoBERT-based ABSA in capturing nuanced public perceptions and highlight its potential as a decision-support tool for hospitals to enhance service quality and patient satisfaction in Indonesia.
Comparative Study of KNN and SVM Methods for Analyzing College Major Consistency Based on High School Background Rizkyllah, Anabel Fiorenza; Meiriza, Allsela; Hardiyanti, Dinna Yunika
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2521

Abstract

Selecting a college major that aligns with students’ high school background is an essential factor in supporting academic achievement and career preparation. This study focuses on a comparative analysis of the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithm ms in evaluating the consistency of college major selection. A dataset of 636 students was collected and processed for analysis. Model evaluation was performed using 5-Fold Cross Validation, in which the dataset was repeatedly partitioned into training and testing sets to ensure reliable and unbiased performance assessment. The results suggest that SVM demonstrates higher effectiveness, achieving average scores across precision, recall, F1-score, and accuracy of 85%. Meanwhile, KNN obtained average performance scores of 78%. These findings highlight that SVM provides better performance in analyzing the consistency between students’ high school majors and their chosen college majors. These findings also contribute to the development of decision support systems and counseling services to guide students in making more informed major choices.
Analisis Faktor Penerimaan TikTok Shop berdasarkan Model UTAUT2 dan SCC Sawitri, Rizky; Meiriza, Allsela
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9 No 1 (2023): April 2023
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i1.2023.33-44

Abstract

TikTok Shop merupakan salah satu social commerce untuk melakukan transaksi jual beli secara online. Fitur tersebut berhasil menjadikan aplikasi TikTok menempati peringkat pertama sebagai media sosial yang paling sering digunakan untuk berbelanja online. Meskipun demikian berdasarkan identifikasi masalah yang dilakukan oleh peneliti melalui penilaian pada Google Play Store, peneliti menemukan beberapa pengguna yang mengeluhkan fitur TikTok Shop. Keluhan tersebut diantaranya mengenai faktor kepercayaan dalam berbelanja melalui TikTok Shop yang dianggap masih lemah, keluhan mengenai proses transaksi, serta keluhan mengenai customer service yang kurang responsif. Oleh sebab itu perlu dilakukannya analisis mengenai faktor-faktor penerimaan pengguna. Tujuan dari penelitian ini adalah untuk mengetahui faktor-faktor penerimaan TikTok Shop pada masyarakat Sumatera Selatan berdasarkan model UTAUT 2 dan Social Commerce Constructs. Selain itu, penelitian ini juga menguji peran moderasi usia pelanggan terhadap niat pembelian dan perilaku penggunaan. Penelitian ini melibatkan 171 responden dari berbagai Kabupaten/Kota di Sumatera Selatan yang pernah melakukan pembelian melalui TikTok Shop. Hasil penelitian ini menunjukan bahwa kebiasaan (habit), pengaruh sosial (social influence), konstruk-konstruk social commerce (SCC), dan kepercayaan pengguna (user trust) merupakan komponen yang mempengaruhi minat masyarakat Sumatera Selatan untuk melakukan pembelian (PI) pada TikTok Shop. Selain itu, konstruk-konstruk social commerce (SCC) juga terbukti berpengaruh terhadap kepercayaan pengguna (user trust). Penelitian ini juga menunjukan bahwa terdapat pengaruh yang bervariasi antara variabel-variabel yang dimoderasi oleh usia pengguna.
UI/UX Design of Web-based Software License Management System using User-Centered Design and System Usability Scale Faizah, Ovie Nur; Oktadini, Nabila Rizky; Putra, Bayu Wijaya; Sevtiyuni, Putri Eka; Putra, Pacu; Meiriza, Allsela
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9 No 3 (2023): Desember 2023
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i3.2023.255-263

Abstract

PT Bukit Asam Tbk (PTBA), a state-owned coal mining company, must comply with government regulations regarding software licenses. They face difficulties monitoring and managing licenses that could lead to violations. To solve this problem, we try to design a website-based UI/UX for Software License Management System. This research aims to provide an intuitive interface and a comfortable user experience employing the User-Centered Design (UCD) approach, which consists of three main stages: Needs Analysis, Design and Prototyping, and Evaluation. Evaluation is carried out through usability testing using the System Usability Scale (SUS). Test results indicate that UCD is effective in designing a system responsive to user needs with a high level of usability. With an effectiveness of 99%, efficiency of 96.67%, and an SUS score of 88.25, this system design receives an 'Acceptable' rating, a (B) grade, and falls into the 'Excellent' category. The designed system is deemed suitable for further development towards the implementation phase.
USE OF TASK-CENTERED SYSTEM DESIGN IN THE INTERFACE DESIGN OF THE POPULATION DEMOGRAPHIC DATA INFORMATION SYSTEM Muhammad Azmi Zaky; Allsela Meiriza; Dinda Lestarini; Pacu Putra; Nabila Rizki Oktadini
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4186

Abstract

Abstract: The rapid development of information and communication technology has prompted the government to provide digital-based services, including in the management of demographic data. This study aims to apply the Task-Centered System Design (TCSD) method in designing the Muara Enim Regency Demographic Data Information System. The TCSD method was chosen to ensure that the prototype design process was systematic and focused on user needs and tasks. The research stages included identification, user-centered needs analysis, scenario-based design, and walkthrough evaluation. The designed prototype supports several main tasks, including viewing demographic statistics, searching for specific data, submitting data download requests, and contacting the admin. The evaluation was conducted through online usability testing using the Maze platform with the System Usability Scale (SUS) instrument involving 13 respondents. The evaluation results showed an average SUS score of 78.5, which falls into the “good” category. This confirms that the interface design has met usability standards, is user-friendly, and is capable of supporting user needs in accessing and managing demographic data. Thus, the application of the TCSD method has proven to be effective in producing an interface design that is focused on user tasks and can be the basis for further system development. Keywords: system usability scale; task centered system design; user interface
Perbandingan Kinerja SVM, Random Forest dan XGBoost pada Aplikasi Access by KAI Menggunakkan ADASYN Epriyanti, Nadia; Meiriza, Allsela; Yunika Hardiyanti, Dinna
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9139

Abstract

The rapid growth of digital applications has heightened the need to understand user perceptions more thoroughly, particularlythrough sentiment analysis of user-generated reviews. In practice, sentiment classification often faces challenges related to class imbalance, especially when neutral reviews are significantly fewer than positive or negative ones. This imbalance can limit a model’s ability to accurately detect all sentiment categories. This study examines the comparative performance of three machine learning algorithmsSupport Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) by applying the Adaptive Synthetic Sampling (ADASYN) technique to address class imbalance. This study differs from previous similar research by conducting a simultaneous comparative analysis of three algorithms using the ADASYN method in the context of Access by KAIapplication reviews, which has not been examined in prior studies. Experimental results indicate that after implementing ADASYN, model accuracies reached 75.17% for SVM, 84.06% for RF, and 83.17% for XGBoost. Although accuracy slightly decreased after oversampling, the F1-scores for the neutral class improved to 0.13 (SVM), 0.05 (RF), and 0.14 (XGBoost). Before applying ADASYN, the models achieved accuracies of 85.88% (SVM), 85.13% (RF), and 85.37% (XGBoost), but they were unable to effectivelyrecognize neutral sentiments, with F1-scores of 0.00 for SVM and RF, and 0.03 for XGBoost. These findings suggest that ADASYN enhances model sensitivity to neutral sentiment, with XGBoost demonstrating the most consistent and robust performance in sentiment classification for the Access by KAIapplication.
ANALISIS K-MEANS DENGAN RAPIDMINER UNTUK KLASIFIKASI KUALITAS PENDIDIKAN SEKOLAH DASAR DI INDONESIA Irwansyah, Muhammad Aziiz; Alinda, Yelli Nur; Nur’Aini, Risma; Alfitrah, Intan Aidita; Khairani, Annisa; Tania, Ken Dhita; Meiriza, Allsela; Rifai, Ahmad
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 3 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode September 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/a0hhsc57

Abstract

Penelitian ini menerapkan metode K-Means Clustering untuk mengelompokkan 38 provinsi di Indonesia berdasarkan kualitas pendidikan dasar. Tujuan penelitian ini adalah mengidentifikasi pola distribusi pendidikan dengan mempertimbangkan faktor tenaga pendidik, angka putus sekolah, kondisi infrastruktur sekolah, serta tingkat kesejahteraan guru. Dataset yang digunakan berasal dari Kaggle SD tahun 2023-2024 dan data Upah Minimum Provinsi (UMP) tahun 2024, kemudian dianalisis melalui tahapan Knowledge Discovery in Database (KDD) menggunakan RapidMiner. Hasil klasterisasi menghasilkan tiga kelompok provinsi dengan karakteristik berbeda: Klaster 0 dengan jumlah sekolah dan siswa tinggi serta angka putus sekolah sedang; Klaster 1 dengan tenaga pendidik dan ruang kelas terbanyak serta angka putus sekolah terendah; dan Klaster 2 dengan angka putus sekolah tertinggi meskipun UMP tertinggi. Evaluasi kualitas klasterisasi menggunakan Davies-Bouldin Index (DBI = 0,162) menunjukkan hasil yang baik. Berdasarkan analisis magnitudo vektor Euclidean, faktor dominan dalam pembentukan klaster adalah Kepala Sekolah dan Guru (1,376), Putus Sekolah (1,368), Ruang Kelas (baik) (1,324), Sekolah (1,312), Siswa (1,286), dan UMP (1,214). Penelitian ini menyimpulkan bahwa faktor tenaga pendidik dan kondisi infrastruktur memiliki dampak lebih besar terhadap kualitas pendidikan dasar dibandingkan faktor ekonomi.
Co-Authors Adhiyasa, Chandra Julian Adriansyah, Rizki Ahmad Rifai Ahmad Rifai Akbar Alzaini Akbar Alzaini Al Fachrozi, Muhammad Al-Farisy, M Hadi Alfarizi, M. Alfitrah, Intan Aidita Ali Ibrahim Ali Ibrahim Alinda, Yelli Nur Alvico, Alvico Alvines, Mahendi Alzaini, Akbar Amanda, Bella Rizkia Anadia, Qothrunnada Wafi Ananda Khoirunnisa Andini Bahri, Cheisya Andriani, Sari Ani Nidia Listianti, Ani Nidia Anindya Putri, Salsa Annisa Tri Ning Tyas Apriansyah Putra Archi Daffa Danendra, Muhammad Ari Wedhasmara Ariyani, Ishlah Putri Ariyanti, Putri Arnan, Sefian Arvhi Randita Setia Athallah Ubaid, Deni Ayu, Nabila Riska Ayuningtiyas, Pratiwi Bayu Wijaya Putra Beriadi Agung Nur Rezqe Billan, Angel Caroline Chandra Julian Adhiyasa Cynthia Sherina Fadeli Danendra, Devano Dedy Kurniawan Deni Lidianti Desty Rodiah Devano Danendra Dinda Lestarini Dinna Yunika Hardiyanti Dwi Rosa Indah Endang Lestari Ruskan Endang Lestari Ruskan Epriyanti, Nadia Ermatita - Faizah, Ovie Nur Fathoni - Fathoni - Fatimah Salsabila Fatimah, Aisyah Firda, Hiliah Gultom, Gina Destia Gumay, Naretha Kawadha Pasemah Gusti Barata Hardini Novianti Hardini Novianti Hardini Novianti Hardini Novianty Ichsan Farel Rachmad, Muhammad Idpal, Idpal Inayah, Anna Fadilla Irmawati Irmawati Irwansyah, Muhammad Aziiz Izzan Fieldi, Muhammad Jaidan Jauhari Jambak, Muhammad Ihsan Jefven Fernando Jonathan Pakpahan Karima, Dzakiah Aulia Karimsyah Lubis, Muhammad Karisa Anjani Fakhri Ken Dhita Tania, Ken Dhita Ken Ditha Tania Khairani, Annisa Khoiriyah Harahap, Dayana Larasati, Salsabila Lifiano Jamot Munthe, Gabriel Luh Sri Mulia Eni M Rifki Ali M, Nys Marliza Tiara M. Rudi Sanjaya Maharani, Wardah Shifa Maretta, Aulia Maretta, Aulia Pinkan Mariska, Inneke Via Marjusalinah, Anna Dwi Meiriza, Viola Meitiana Audya Muhamad Edric Rasyid Muhammad Aidil Fitri Syah Muhammad Ali Buchari Muhammad Azmi Zaky Muhammad Ihsan Muhammad Ihsan Muhammad Imam Riadillah Mulyadi Mulyadi Munaspin, Zahra Diva Putri Nabila Oktadini Nabila Riska Ayu Nabila Rizki Oktadini Nachwa, Syakillah Nadia Ayu Safitri Nashiroh Ramadhani, Muthia Novitia Chinoi Nurul Izmy Nur’Aini, Risma Oktadini, Nabila Oktadini, Nabila Rizky Onkky Alexander Pacu Putra Padlefi, Muhamad Riza Paulus Paskah Lino Susilo Perdani, Tharisa Antya Putri Ariyanti Putri Eka Sevtiyuni Putri Eka Sevtiyuni Putri Eka Sevtyuni Putri Mutiara Arinie Putri, Adetya Rielisa Putri, Nyayu Dwi Tarisa Rafika Octaria Ningsih Rafli Maulana, Muhammad Rahmat Izwan Heroza Ramadhan Putra Pratama, Muhammad Ramadhan, Kumara Aditya Ramadhan, Muhammad Gilang Rangga Aderiyana, Fakih Rani Mardiah Ravi Wijayanto, Muhammad Rezeki, Yunika Tri Rezqe, Beriadi Agung Nur Ricy Firnando Rido Zulfahmi Rika Septiana Riska Yunita Rizka Dhini Kurnia Rizka Rahmadhani Rizki Kurniati Rizky Herdiansyah, Muhammad Rizky Sawitri Rizkyllah, Anabel Fiorenza Rositiani, Ely Royan Dwi Saputra RR. Ella Evrita Hestiandari Sanjaya, M. Rudi Saputri, Sonia Dwi Sari Andriani Sarifah Putri Raflesia, Sarifah Putri Sasmita, Ruth Mei Sawitri, Rizky Septhia Charenda Putri Sevtiyuni, Putri Eka Silvia, Nyimas Simanullang, Eka Darmayanti Susanti, Helen Susilo, Paulus Paskah Lino Syahbani, Muhammad Husni Syarief Albani, Muhammad Tharisa Antya Perdani Theresia Pardede, Eva Titiana, Nuke Merisca Tri Zafira, Zahra Tsabitah, Laila Via Mariska, Inneke Wahyudi, Muhammad Iqbal Wulan Dari, Atikah Yadi Utama Yadi Utama Yasir Alghifari, Muhammad Yasyfi Imran, Athallah Yunika Hardiyanti, Dinna Yunita Yunita Zaki, Imam Syahputra