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All Journal Jurnal Buana Informatika JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Annual Research Seminar CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JTT (Jurnal Teknologi Terpadu) IJIE (Indonesian Journal of Informatics Education) Jurnal Manajemen STIE Muhammadiyah Palopo MBR (Management and Business Review) JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Digital Zone: Jurnal Teknologi Informasi dan Komunikasi The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) JURTEKSI JOISIE (Journal Of Information Systems And Informatics Engineering) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Darma Agung Jurnal Bisnis, Manajemen, dan Ekonomi Jurnal Generic Jurnal Pendidikan dan Teknologi Indonesia Djtechno: Jurnal Teknologi Informasi Jurnal Algoritma Jurnal Teknologi dan Manajemen Industri Terapan Indonesian Journal Computer Science (ijcs) Jurnal Indonesia Sosial Teknologi The Indonesian Journal of Computer Science Management Analysis Journal Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Buffer Informatika Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Ken Ditha Tania
Jurusan Sistem Informasi, Universitas Sriwijaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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 Materials Science & Nanotechnology Mathematics Mechanical Engineering 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.
Knowledge Discovery Through Sentiment Analysis and Topic Modeling of BCA Mobile and MyBCA Putri, Salsa Anindya; Tania, Ken Ditha; Naretha Kawadha Pasemah Gumay
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9782

Abstract

The swift adoption of mobile banking in Indonesia highlights the growing demand for secure and innovative digital financial services. PT Bank Central Asia Tbk (BCA) offers two primary applications, BCA Mobile and myBCA, catering to millions of users. Gaining insight into user perceptions is crucial for enhancing service quality and building trust. This research uses sentiment analysis and topic modeling on Google Play Store reviews for both applications to facilitate knowledge discovery. Reviews were labeled using IndoBERT, and seven classification models were assessed, including five machine learning methods and two deep learning techniques. The Gated Recurrent Unit (GRU) model demonstrated the highest performance, achieving an accuracy of 89.70%. In the realm of topic modeling, a comparison between Latent Dirichlet Allocation (LDA) and BERTopic revealed that BERTopic delivered the highest coherence score of 0.6244, identifying eight significant negative topics. The findings indicate that BCA Mobile users frequently reported issues such as login failures, unexplained balance deductions, and missing features, while myBCA users encountered problems like post-update errors, login difficulties, and challenges with face verification. This research aligns with Sustainable Development Goal (SDG) 9 by showing how knowledge discovery from user reviews can promote innovation and enhance resilient, user-centered digital banking infrastructures.
COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR COSMETIC SALES PREDICTION ON TOKOPEDIA Sahira, Mutia; Tania, Ken Ditha; Afrina, Mira
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.v11i4.4187

Abstract

Abstract: The rapid growth of the cosmetics industry on e-commerce platforms has intensified competition, creating a critical need for effective, data-driven marketing strategies. This study aims to conduct a comparative analysis of machine learning algorithms to predict the sales categories (High, Medium, Low) of cosmetic products on the Tokopedia marketplace. Four classification models; Random Forest, XGBoost, Logistic Regression, and Naive Bayes were trained and evaluated on data collected via web scraping. The methodology incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class imbalance and GridSearchCV for hyperparameter optimization to ensure a fair and robust comparison. The experimental results conclusively show that the Random Forest model achieved the best performance, yielding the highest F1-Score Macro Average of 0.75 and an accuracy of 85.3%. The superior model was subsequently implemented in a simple recommendation system to simulate optimal discount strategies, demonstrating its practical utility in providing actionable insights for business decisions. Keywords: classification; comparative analysis; machine learning; sales prediction; SMOTE Abstrak: Pertumbuhan pesat industri kosmetik pada platform e-commerce telah membuat persaingan ketat, sehingga menciptakan kebutuhan krusial akan strategi pemasaran yang efektif dan berbasis data. Penelitian ini bertujuan untuk melakukan analisis komparatif terhadap algoritma machine learning untuk memprediksi kategori penjualan (Tinggi, Sedang, Rendah) produk kosmetik di marketplace Tokopedia. Empat model klasifikasi, yaitu Random Forest, XGBoost, Regresi Logistik, dan Naive Bayes, dilatih dan dievaluasi menggunakan data yang dikumpulkan melalui web scraping. Metodologi penelitian ini menerapkan Synthetic Minority Over-sampling Technique (SMOTE) untuk mengatasi ketidakseimbangan kelas yang signifikan dan GridSearchCV untuk optimisasi hyperparameter guna memastikan perbandingan yang adil. Hasil eksperimen menunjukkan bahwa model Random Forest mencapai performa terbaik, dengan menghasilkan F1-Score Macro Average tertinggi sebesar 0,75 dan akurasi 85,3%. Model unggul ini kemudian diimplementasikan dalam sebuah sistem rekomendasi sederhana untuk menyimulasikan strategi diskon yang optimal, yang menunjukkan kegunaan praktisnya dalam memberikan wawasan yang dapat ditindaklanjuti untuk pengambilan keputusan bisnis. Kata kunci: analisis komparatif; klasifikasi; machine learning; prediksi penjualan; SMOTE
Sentiment-Based Knowledge Discovery pada Aplikasi iPusnas Menggunakan Metode Machine Learning dan Deep Learning Ayuningtiyas, Pratiwi; Tania, Ken Ditha; Sari, Winda Kurnia
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.v9i5.10258

Abstract

iPusnas is a digital library application developed by the National Library of the Republic of Indonesia since 2016, with over 1.5 million users. Despite its potential to improve literacy, the application has only received a rating of 2.0. This study conducted sentiment analysis on 7.596 reviews obatained through web scraping using the Google Play Scraper Library. The data then underwent preprocessing steps including case folding, data cleaning, tokenization, stopword removal, and stemming. Reviews were automatically labeled based on the rating score, where scores of 1-3 were categorized as negative, with 5.174 entries, and scores 4-5 as positive, with 2.422 entries. The dataset was split in an 80:20 ratio, with 80% for training, and 20% for testing. The machine learning models tested were SVM, Random Forest, CNN, LSTM, and RNN. The evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. CNN and LSTM achieved the highest accuracy (82%), Random Forest and CNN achieved the highest precision (81%), RNN the highest recall (79%) and LSTM the highest F1-score (79%). McNemar test showed a significant difference between Random Forest and CNN, Random Forest and LSTM, and between RNN and LSTM, while CNN and LSTM, as well as CNN and RNN, showed no significant difference.
Sentiment-Based Knowledge Discovery of Wondr by BNI App Reviews Using SVM, KNN, and Naive Bayes for CRM Enhancement Tri Zafira, Zahra; Ditha Tania, Ken; Kurnia Sari, Winda
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.v9i5.10323

Abstract

The rapid development of digital banking services has necessitated a deeper understanding of user perceptions and satisfaction levels. This study analyzes sentiment from user reviews of the Wondr by BNI app using a Knowledge Discovery approach and machine learning methods. Three classification algorithms were compared: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes, evaluated with accuracy, precision, recall, and f1-score. The results show that SVM and Naive Bayes achieved the best performance with F1-scores of 0.88 and 0.87, while KNN lagged behind with 0.77. An ANOVA test further confirmed that the performance differences were statistically significant (p < 0.05), with SVM and Naive Bayes consistently outperforming KNN. Word Cloud analysis revealed dominant positive terms such as "easy," "fast," and "transaction," alongside negative terms like "login," "difficult," and "verification." These findings highlight user appreciation for simplicity and speed, while pointing out functional issues that require attention. This research not only enriches the literature on Indonesian-language sentiment analysis in the financial sector but also provides practical insights for Customer Relationship Management (CRM), particularly in strengthening customer retention strategies and guiding UX redesign for digital banking services.
Knowledge Discovery on E-Commerce Customer Churn Using Interpretable Machine Learning: A Comparative Study of SHAP-Based Classifiers Amanda Ardhani, Dhita; Tania, Ken Ditha
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.v9i5.10811

Abstract

Customer churn remains one of the most pressing issues in the e-commerce sector, as it directly erodes revenue and reduces customer lifetime value. This study proposes an interpretable machine learning approach designed not only to predict churn but also to uncover practical insights that can inform retention strategies. The analysis draws on a publicly available dataset containing customer behavior and transaction records. Data preparation involved handling missing values, applying label encoding, and addressing class imbalance with SMOTE. Five classification models—Logistic Regression, Random Forest, XGBoost, Support Vector Machine, and Gradient Boosting—were trained on an 80:20 stratified split, with performance assessed through accuracy, precision, recall, F1-score, and AUC. Among these, XGBoost delivered the most consistent results, achieving 96% accuracy, 95% precision, 92% recall, and a near-perfect AUC of 0.999, followed closely by Random Forest. Logistic Regression produced the lowest AUC at 0.886. To ensure transparency in decision-making, SHAP (SHapley Additive exPlanations) was applied, revealing Tenure, Complain, and CashbackAmount as the most influential predictors. Longer customer relationships were linked to reduced churn risk, while frequent complaints and higher cashback usage indicated a greater likelihood of leaving. These findings contribute knowledge by blending robust predictive performance with interpretability, enabling e-commerce businesses to design more targeted and proactive customer retention measures.
User Review Automation: Detecting Actionable Complaints on Gojek in the Play Store using the LSTM Method Ramadhani, Indira Nailah; Sari, Winda Kurnia; Tania, Ken Ditha
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5708

Abstract

This study aims to develop an automatic complaint detector for Gojek app reviews using Long Short Term Memory (LSTM). The dataset consists of 225,002 user reviews on the Google Play Store. The purpose of this study itself is to facilitate the service team in understanding the shortcomings of the application complained by users. Automatic complaint detection will facilitate the service team to take action to resolve the problems experienced by users. Therefore, the review data provided by users is properly processed using LSTM to create an effective and efficient detection system. Processing is carried out using three different data sharing ratios, namely 90:10, 80:20, and 70:30 to ensure that the system is stable and effective. The accuracy results of the three data sharing ratios reached above 90%, thus proving that the system is able to detect complaints well. A pre-built dashboard is used to visualize the results of the system built using LSTM to facilitate monitoring the classification results. This system is expected to facilitate companies in detecting all user complaints and finding solutions to improve services to provide comfort for users.
Penerapan Metode K-Means Clustering untuk Segmentasi Performa Pembalap F1 Season 2024 Salsabila, Shofi; Sahira, Mutia; Salsabila, Adella; Najibah Putri, Aulia; Ditha Tania, Ken; Kurnia Sari, Winda
Buffer Informatika Vol. 11 No. 2 (2025): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

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

Abstract

Performa pembalap Formula 1 tidak hanya ditentukan oleh hasil akhir balapan, tetapi juga oleh konsistensi catatan waktu dan lap tercepat. Penelitian ini menerapkan algoritma K-Means clustering untuk mengelompokkan pembalap berdasarkan performa mereka. Data yang digunakan mencakup hasil balapan resmi musim 2024 yang diterbitkan oleh FIA. Proses pengolahan data mencakup pengumpulan data, preprocessing, analisis eksploratori, penerapan algoritma clustering, serta evaluasi dan interpretasi hasil. Untuk menentukan jumlah cluster yang optimal, digunakan Metode Elbow dan skor Silhouette, yang menghasilkan empat kelompok pembalap dengan karakteristik performa yang berbeda. Hasil analisis menunjukkan bahwa metode ini berhasil mengidentifikasi pola performa yang relevan, memberikan wawasan bagi tim balap dalam menyusun strategi. Evaluasi menggunakan Silhouette Score menunjukkan bahwa segmentasi yang dihasilkan cukup baik dengan nilai sebesar 0.5735.
Topic Mining-Based Knowledge Discovery of User Health Information Needs Khoiriyah Harahap, Dayana; Ditha Tania, Ken; Eka Sevtiyuni, Putri
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.270

Abstract

Understanding the user’s need for health information has become increasingly important as the use of digital health services continues to grow. However, the unstructured data of user-generated questions presents challenges in accurately capturing and analyzing these needs. This study contributes to addressing SDG 3 (Good Health and Well-being) by utilizing topic mining-based knowledge discovery to identify the primary topics emerging from user questions submitted through the “Tanya Dokter” feature on the Alodokter platform. A total of 8,550 questions were obtained through web scraping between July 2024 and June 2025. The collected data were preprocessed and subsequently analyzed using seven topic modeling approaches: Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), Latent Semantic Analysis (LSA), Non-negative Matrix Factorization (NMF), BERTopic, Top2Vec, and ProdLDA. To assess model performance, the coherence metric (c_v) was employed to identify the most effective method. Among these techniques, NMF achieved the best results, producing the highest coherence score of 0.67 with six well-defined topics. The findings show six primary areas of concern: pregnancy; menstruation and contraceptive management; general health and minor ailments; infant care; dermatological conditions; and musculoskeletal and other physical complaints. General health-related issues occurred most frequently, particularly during seasonal transitions, while menstruation and contraceptive management received the least attention, despite menstruation contributing to women’s health risks and the use of contraceptives helping to reduce maternal mortality in Indonesia. These findings offer valuable insights for digital health platforms like Alodokter to enhance information delivery and health literacy, ultimately improving online health services and supporting the achievement of SDG 3
Perbandingan Kinerja LSTM, Random Forest, dan SVR Berbasis Knowledge Discovery untuk Prediksi Harga Beras Sumatera Selatan Bahri, Cheisya Andini; Tania, Ken Ditha
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.v12i5.9140

Abstract

Rice is a primary staple food in Indonesia, particularly in South Sumatra Province. In February 2024, BBC News Indonesia reported that the price of premium rice surged to Rp18,000 per kilogram, marking the highest price in the country’s history. To anticipate and predict similar spikes in the future, this study applies a Knowledge Discovery approach and compares three machine learning models: LSTM, Random Forest, and SVR. The approach follows the stages of data selection, cleaning, transformation, modeling, and evaluation to uncover hidden patterns in historical data. The dataset, obtained from the official PIHPS Nasional website, consists of 1,412 daily rice price records from January 2020 to May 2025. Model performance was evaluated using MAPE, MAE, and RMSE metrics. The findings indicate that the SVR model outperformed LSTM and Random Forest, delivering the most accurate results. For the Super Quality II rice category, SVR achieved a MAPE of 0.00 percent, MAE of 40.93, and RMSE of 52.54. SVR also consistently produced the lowest prediction errors in other categories, such as Low Quality I (MAE 59.39) and Medium Quality I (MAE 38.92). This research is expected to serve as a foundation for developing machine learning–based food price monitoring systems to support more responsive policies and maintain rice price stability in the future.
Co-Authors Abdillah Putra, Muhafsyah Adeliani, Adeliani Adriansyah, Rizki Afdhal Nadzif, Muhammad Ahmad Rifai Ahmad Rifai Akbar Adiprama, Faris Akbar Kurniawan, Iqbal Akbar, Rifko Akhda, M. Dandi Al Fachrozi, Muhammad Al-Farisy, M Hadi Albani, Muhammad Syarief Albukhori, M Rafli Alfarizi Ramadhiyansa, Muhammad Alfarizi, M. Ali Bardadi Ali Ibrahim Ali Ibrahim (SCOPUS ID: 57203129436) Allsela Meiriza, Allsela Alsella Meiriza Alsella Meiriza Alvines, Mahendi Alzena Aisha Shakira Amanda Ardhani, Dhita Amelia Amelia Amelia Putri, Shinta Amelia, Rita Anadia, Qothrunnada Wafi Ananda Khoirunnisa Andini Bahri, Cheisya Andini, Meisya Dwi Anggun Ramadina Anindya Putri, Salsa Anisa Basulina, Nur Anissa, Cahya Rahmi Apriansyah Putra Apriansyah Putra Apriyadi Apriyadi, Apriyadi Aqil Zidane, Muhammad Aqilah Syahputra, M Fathan Archi Daffa Danendra, Muhammad Ardhillah, Onky Ari Wedhasmara Ariyani, Ishlah Putri Ariyanti, Putri Arvhi Randita Setia Ary Pratama, Muhammad Mayda Athallah Ubaid, Deni Attika Putri, Shopi Audia Faradhisa Ansori Aulia, Cantika Aurelia, Haaniyah Ayuningtiyas, Pratiwi Azmi Zaky, Muhammad Azra, Muhammad Azyumardi Bahri, Cheisya Andini Baidhawi, Alif Bimmo Fathin Tammam Cahya Aulia, Syifa Cahya Rahmi Anissa Catra, Rafa Nadira Cici Elna Sari Citra, Belia Clark Peter Wijaya, Adley Constancio, Elven Dedy Kurniawan Dian Febriansyah Dwiansyah, Octa Dzaky Agusman, Muhammad Eka Saputra Eka Sevtiyuni, Putri Elna Sari, Cici Endang Lestari Ruskan Epriyanti, Nadia Fachrozi, Muhammad Al Fahmi Aulia Hakim, Adzka faizah, haniyah Fajaria, Mutiara Fakhri Sepriansyah Fakhri Sepriansyah Farhan Daffazka Fathoni - Fatihaturrahmah, Aisyah Fatimah, Aisyah Fauzan, Muhammad Fairuz Fikri, M Fauzan Firmansyah, Zikri Gustiani, Sindy Haidar Afif Mufid, Muhammad Hanggara, Bryan Hendrawan, Deni Agus Hermanto, Muhammad Lucky Hikmahwarani, Fellycia Homausyah, Weli Ratri Ichsan Farel Rachmad, Muhammad Ikhwan Najatafani, Bintang Inayah, Anna Fadilla Indira Nailah Ramadhani Ispahan, Tarisha Izzan Fieldi, Muhammad Jackson Imanuel Manurung Jodi Pratama, Muhammad Jonathan Pakpahan Karima, Dzakiah Aulia Karimsyah Lubis, Muhammad Khoiriyah Harahap, Dayana Kurnia Sari, Winda Lakeisyah, Eka Therina Lifiano Jamot Munthe, Gabriel Lubis, Muhammad Ali M Ihsan Jambak M Luthfi Khailani, Kgs M Naufal Hisyam M. Ilham Fahlevi Mahdiyah Afifah Sari Mahdiyah Afifah Sari Maretta, Aulia Pinkan Mariska, Inneke Via Marshella, Siti Hariza Mas Ud, Khalid Al Maulana, Rahmat Maulizidan, Muammar Ramadhani Meiriza, Allsella Meiriza, Alsella Miftahul Falah Mira Afrina Mohd Rizky Putra Pratama Mufidah, Luthfiah Muhammad Adisatya Dwipansy Muhammad Dzaky Alifayoezra Muhammad Idris Muhammad Ihsan Dirgantara Muhammad Luthfi Al-Ghifari Muhammad Luthfi Al-Ghifari Munaspin, Zahra Diva Putri mutia fadhila putri, mutia fadhila Nabilatulrahmah, Raihana Nachwa, Syakillah Nadrota Acta, Muhammad Fakhri Najibah Putri, Aulia Najwa Widasari, Yesya Naretha Kawadha Pasemah Gumay Nashiroh Ramadhani, Muthia Naufaldihanif, Rihan Novrizal Eka Saputra Nugraha, Allan Nulry Izzatul Maula Nuraini Kusuma, Aisha Nurly Izzatul Maula Onkky Alexander Pacu Putra Prasetia, Dika Pratama Putra, Daffa Pratiwi, Metti Detricia Purba, Kevin Agustin Putri Ariyanti Putri Casanova, Musdalifa Putri Mutiara Arinie Putri Salsabilah Putri Silpiara Putri, Amelia Rizki Putri, Aulia Najibah Putri, Naila Raihana Putri, Salsa Anindya Rabbani, Muhammad Randy Raditya Dafa Rizki Rafika Octaria Ningsih Rafli Maulana, Muhammad Rahmah, Atika Nur Rahman, M. Fadhil Rahmat Izwan Heroza Ramadhan Putra Pratama, Muhammad Ramadhani, Indira Nailah Rangga Aderiyana, Fakih Ravi Wijayanto, Muhammad Riansyah, Muhammad Bintang Naufal Risyahputri, Aliyananda Rizka Mumtaz, Fadia Rizki Ade Ningsih Rizky Herdiansyah, Muhammad Rizkyllah, Anabel Fiorenza Robani, M Tsabita Rositiani, Ely Sabar Manahan, Nico Sabila, Amalia Sahira, Mutia Salsabila, Adella Salsabila, Shofi Sanjaya, Riska Amelia Saputra, Marco Sasmita, Ruth Mei Satria, Eka Bayu Sembiring Depari, Alrayssa Davinka Septhia Charenda Putri Sevtiyuni, Putri Eka Shelly Putri Siade, Shalya Yunia Siregar, Richi Nauli Juniarto Siswahyudianto Suci Amalia Suci Fitriani, Suci Sukamto, Ika Sumiyarsi Sukatin, Sukatin Syarief Albani, Muhammad Talitha Zafirah Theonady, Oktavio Theresia Pardede, Eva Theressa Hasioani Sianturi, Claudia Tika Octri Dieni Titiana, Nuke Merisca Tri Zafira, Zahra Triana, Ayu Triputra, Muhamad Meiko Tsabitah, Laila Ummu Farida Muthmainnah Wahyuni Cahnia Sari Wilantara, M Pandu Winda Kurnia Sari Wirnanti, Rintan Wulan Dari, Atikah Yamani, Zaqqi Yasir Alghifari, Muhammad Yasyfi Imran, Athallah Zahran Afif, Muhammad Zaqqi Yamani Zaqqi Yamani Zaqqi Yamani A Zaskia Aulia Wulandari Zidan, Umar Rahman