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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) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab 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) KACANEGARA Jurnal Pengabdian pada Masyarakat Jiko (Jurnal Informatika dan komputer) Jurnal ULTIMA InfoSys Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Ilmiah Media Sisfo The IJICS (International Journal of Informatics and Computer Science) 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 Jurnal Sistem Informasi dan Informatika (SIMIKA) Zonasi: Jurnal Sistem Informasi Jurnal Pengabdian Masyarakat Bumi Raflesia JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL Jurnal Sistem Komputer dan Informatika (JSON) Teknomatika (Jurnal Teknologi dan Informatika) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal Software Engineering and Computer Science (IJSECS) Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Algoritma Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Masyarakat Madani Indonesia SmartComp Jurnal Informatika: Jurnal Pengembangan IT 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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ANALISIS KOMPARATIF MODEL DATA MINING DALAM PREDIKSI KETEPATAN PENYELESAIAN SERVICE LEVEL AGREEMENT Aisyah Fatimah; Ken Ditha Tania; Allsela Meiriza
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol. 9 No. 1 (2025)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v9i1.4943

Abstract

Kepatuhan terhadap Service Level Agreement (SLA) sangat penting dalam manajemen layanan teknologi informasi untuk menjamin kualitas layanan dan mengatur ekspektasi penyelesaian insiden. Namun, banyak organisasi kesulitan memprediksi apakah tiket insiden akan memenuhi SLA, yang dapat menyebabkan ketidakpuasan pengguna, eskalasi masalah, dan beban kerja tinggi bagi tim IT. Tingginya volume tiket harian membuat identifikasi manual terhadap tiket berisiko tinggi menjadi tidak efektif. Penelitian ini bertujuan mengembangkan model klasifikasi berbasis machine learning untuk memprediksi kepatuhan tiket terhadap SLA. Empat algoritma dievaluasi: XGBoost, Random Forest, Decision Tree, dan Logistic Regression. Tahapan mencakup preprocessing, encoding fitur kategorikal, seleksi fitur berbasis Random Forest, penyeimbangan data menggunakan SMOTE, dan hyperparameter tuning dengan Optuna. Hasil menunjukkan XGBoost memiliki performa terbaik dengan akurasi 99,98%, precision 0,9437, recall 0,9710, dan F1-score 0,9571. Selain akurat dan efisien, model ini unggul secara interpretatif melalui SHAP, yang menjelaskan kontribusi tiap fitur. Kesimpulannya, XGBoost direkomendasikan sebagai model paling andal untuk menjadi alat bantu strategis bagi manajer layanan TI dalam mengidentifikasi insiden yang berisiko tinggi gagal memenuhi SLA.
ANALYSIS OF SEISMICITY ANOMALIES IN SULAWESI USING DBSCAN BASED ON USGS DATA (2021–2026) Muhammad Dzaky Hasyim; Muhammad Wahyu Hikmalsyah; Gabriel Sebastian Santoso; Ken Ditha Tania; Allsela Meiriza; Ahmad Rifai
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 2 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i2.11728

Abstract

Sulawesi is one of Indonesia's most tectonically complex regions, situated at the meeting point of major plates and active fault systems, which results in significant seismic activity. This study aims to analyze seismicity anomalies in Sulawesi from 2021 to 2026 using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm applied to United States Geological Survey (USGS) data. The methodology involves a hybrid approach for parameter optimization, utilizing both the K-Distance Graph (KneeLocator) and Grid Search to ensure results are geologically representative. From an initial dataset of 859 events, 839 earthquake records were processed after geographic filtering, showing an average magnitude of 4.80 and an average depth of 70.15 km. The analysis reveals that the optimal parameters (ε = 0.2897 and MinPts = 3) produced 25 distinct clusters that align with real geological structures such as the Palu-Koro Fault and the Molucca Sea subduction zone. The algorithm successfully identified 66 points (7.87%) as seismic anomalies or noise, representing sporadic tectonic activity outside primary density zones. Furthermore, Convex Hull visualization identified a significant "seismic gap" between the Palu-Koro segment and Southeast Sulawesi, indicating a high-risk zone for potential future energy release. These findings demonstrate that density-based clustering is highly effective for mapping seismic hazards in complex tectonic regions, providing vital data for sustainable disaster mitigation planning in Sulawesi.
ANALISIS SPASIO-TEMPORAL DETEKSI ANOMALI SUHU PERMUKAAN BUMI ISOLATION FOREST: STUDI KASUS INDONESIA Nicolaus Owen Marvell; Muhammad Iqbalul Khoiri; Chrisjuanito Clancy; Ken Ditha Tania; Allsela Meiriza; Ahmad Rifai
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4626

Abstract

Land surface temperature (LST) is an important indicator of climate change because increasing surface temperatures can trigger environmental degradation, drought, and extreme weather events. In Indonesia, long-term monitoring of extreme temperature anomalies remains limited, as most studies rely on conventional statistical methods that are less effective in detecting complex and non-linear anomaly patterns. Therefore, this study analyzes the spatio-temporal dynamics of LST and identifies extreme temperature anomalies across Indonesia during 1940–2024 using a machine learning approach. Monthly LST data were examined through exploratory data analysis (EDA), including temporal trend analysis and 10-year moving averages, to characterize long-term temperature variability, while the Isolation Forest algorithm was implemented as an unsupervised anomaly detection method using n_estimators = 100 and contamination = 0.05. The results identified 51 temperature anomalies, representing approximately 5% of the 1,020 monthly observations analyzed. Most anomalies occurred during periods associated with major climate disturbances and corresponded closely with documented El Niño events, particularly in 1997–1998 and 2015. Trend analysis revealed a persistent increase in Indonesia’s surface temperature, indicating an ongoing warming pattern consistent with climate change, while anomaly score distributions showed a clear separation between normal and extreme observations, confirming the effectiveness of the Isolation Forest algorithm. These findings demonstrate that integrating spatio-temporal analysis with machine learning provides a robust framework for detecting extreme temperature events and monitoring climate variability, thereby supporting climate risk assessment and strengthening BMKG’s early warning systems for climate change adaptation and mitigation.
KOMPARASI MODEL REGRESI DALAM MEMPREDIKSI GAJI PEKERJAAN ARTIFICIAL INTELLIGENCE M Rafly Ramdhani; M Luthfi Aldi Pratama; Muhammad Emirshah Yusuf; Ken Ditha Tania; Allsela Meiriza; Ahmad Rifai
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4627

Abstract

The increasing demand for Artificial Intelligence-related jobs has intensified global labor market dynamics, characterized by high salary variability and growing industry uncertainty. These conditions pose significant challenges for organizations and professionals in determining accurate, objective, and data-driven salary estimations. This study aims to develop and compare the performance of several regression models for predicting Artificial Intelligence job salaries, namely Linear Regression, Gradient Boosting, and Support Vector Regression. A large-scale global job postings dataset is employed, incorporating conventional job attributes such as location, experience level, and job type. In addition, this study integrates industry risk variables, including layoff risk and automation risk, to capture more realistic labor market dynamics. The research methodology consists of data preprocessing, model development using a machine learning pipeline to ensure consistent processing between training and testing data, and performance evaluation. The dataset is split into training and testing sets using an 80:20 ratio, and model performance is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The experimental results indicate that Gradient Boosting achieves the best performance with the lowest prediction errors and the highest explanatory power, followed by Linear Regression. In contrast, Support Vector Regression exhibits relatively poor performance on high-dimensional feature representations. These findings confirm that ensemble-based approaches are more effective in modeling the heterogeneous and non-linear salary structures of Artificial Intelligence jobs and provide valuable insights for data-driven labor market analysis.
Sentiment Analysis of Food Poisoning Incidents in Indonesia's Free Nutritious Meal Program Using TF-IDF and SVM Muhammad Lucky Hermanto; Ari Wedhasmara; Ken Ditha Tania; Allsela Meiriza
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 3 (2026): DECEMBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i3.7809

Abstract

Food poisoning incidents associated with the Free Nutritious Meal Program (MBG) have triggered significant public reactions on the social media platform X/Twitter. This study identified and classified public sentiment regarding food poisoning occurrences in the MBG program by applying a Support Vector Machine (SVM) classification framework. Data were crawled from X/Twitter via Tweet Harvest using keywords related to MBG food poisoning incidents between January 2025 and June 2026. A total of 6,929 posts were collected, and 6,902 Indonesian-language records were retained after duplicate removal. The analytical workflow comprised data selection, automated sentiment labeling with IndoBERTweet, text preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) feature weighting, and classification using the SVM algorithm. Model performance was evaluated through a confusion matrix using accuracy, precision, recall, and F1-score metrics. Based on an evaluation of 1,989 testing records, the SVM model achieved an overall accuracy of 76.42%, exhibiting its strongest performance in the negative sentiment class. However, positive sentiment recorded a low recall of 26.03%, reflecting the difficulty of detecting the minority class. Sentiment distribution analysis revealed that negative sentiment dominated public discourse at 63.78%, followed by neutral sentiment at 29.05% and positive sentiment at 7.17%. These findings indicate that public perceptions of food poisoning incidents in the MBG program were overwhelmingly negative, underscoring critical concerns regarding food safety and program oversight. Beyond contributing to machine learning-based social media analytics, this study provides actionable insights for policymakers to evaluate service delivery and reinforce food safety standards in the MBG program.
Performance Comparison of Sentiment Classification Algorithms on SIGNAL Reviews Using SMOTE Qothrunnada Wafi Anadia; Allsela Meiriza
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

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.
Sentiment Classification of TikTok Reviews on Almaz Fried Chicken Using IndoBERT and Random Oversampling Imam Syahputra Zaki; Rizka Dhini Kurnia; Allsela Meiriza
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1310

Abstract

The socio-political context surrounding the Indonesian Ulema Council's Fatwa No. 83 of 2023, which catalyzed a significant consumer shift, necessitates an accurate measure of public sentiment toward alternative local brands like Almaz Fried Chicken. Analyzing real-time consumer discourse on the challenging TikTok platform, the study utilized a final dataset of 4,374 unique comments to overcome the inherent problem of dataset imbalance and linguistic informality. The core method involved a seven-stage quantitative approach: data collection, preprocessing, sentiment labeling, data splitting (70:15:15), Random Oversampling (ROS), IndoBERT fine-tuning, and evaluation. This pipeline fine-tuned IndoBERT, a Transformer-based model, integrated with ROS applied exclusively to the training data. Evaluation demonstrated that ROS significantly reduced model bias and enhanced performance: Overall Accuracy increased by 2.0% (from 91% to 93%), and the Macro F1-Score improved by 3.4% (from 0.87 to 0.90). Most critically, the F1-Score for the minority Negative sentiment class surged from 0.78 to 0.84, confirming ROS's effectiveness in accurately detecting critical feedback. These findings provide timely, data-driven insights into brand perception amidst the boycott campaign and establish a robust, reliable IndoBERT-ROS methodology for advanced sentiment monitoring in dynamic social media environments.
Penerapan Model Recency, Frequency, Monetary untuk Segmentasi Pola Perilaku Pelanggan Indibiz: Application of the Recency, Frequency, Monetary Model for Segmentation of Indibiz Customer Behavior Patterns Pinkasari, Aulia; Putri, Meiyin Monica Amilia; Abdurahman, Gibral; Rizqi, Ahmad Fadhil; Tania, Ken Ditha; Meiriza, Allsela; Rifai, Ahmad
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2611

Abstract

PT Telkom Indonesia Witel Sumbagsel menghadapi kondisi Data Rich, Information Poor (DRIP), yaitu melimpahnya data transaksi yang belum dimanfaatkan secara optimal sebagai dasar pengambilan keputusan pada unit Payment Collection. Penelitian ini menerapkan metodologi Knowledge Discovery in Databases (KDD) menggunakan model RFM (Recency, Frequency, Monetary) dan algoritma K-Means Clustering untuk mengidentifikasi pola perilaku pembayaran pelanggan. Dataset terdiri dari 46.355 transaksi periode Januari–Desember 2025. Jumlah cluster optimal ditentukan menggunakan metode Elbow dan menghasilkan empat segmen pelanggan (k=4). Evaluasi menggunakan Silhouette Coefficient memperoleh nilai 0,3463 yang menunjukkan kualitas klaster yang dapat diterima. Hasil segmentasi mengelompokkan pelanggan ke dalam kategori loyal, potential, standard, dan churn-risk, sehingga mendukung penyusunan strategi penagihan yang lebih tepat sasaran dan berbasis data. Penelitian ini menunjukkan bahwa integrasi RFM dan K-Means efektif dalam mengubah data transaksi menjadi wawasan praktis bagi manajemen penagihan telekomunikasi.
Hybrid Machine Learning for Knowledge Discovery in E-Commerce Reviews Siahaan, Jeremiah Alwin; Syabilla, Lailla Syal; Fadli, M. Thoriqul; Natasyah, Mei Intan; Meiriza, Allsela; Tania, Ken Ditha; Rifai, Ahmad
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12635

Abstract

The rapid growth of e-commerce platforms like Tokopedia has triggered a massive accumulation of over 65,000 customer reviews, yet it is often accompanied by information pollution in the form of non-informative reviews that hinder consumer decision-making processes. This research aims to extract new knowledge regarding these review characteristics through the implementation of the Knowledge Discovery in Database (KDD) framework, integrating a hybrid K-Means Clustering and Random Forest algorithm. Diverging from conventional classification approaches, this study utilizes K-Means as an exploratory instrument to naturally map six latent topic patterns of reviews based on their textual structure. Experiments were conducted on 35,000 data samples using TF-IDF features enriched by cluster labels as structural predictors. The results indicate that the hybrid model achieves 94.41% accuracy with an F1-score of 0.90 for the non-informative class, showing high stability via 5-Fold Cross-Validation (94.56% ± 0.19%) . The most crucial knowledge discovery is evidenced through SHAP analysis, where the cluster feature ranks 7th out of 1,001 predictor features, confirming that semantic grouping provides a richer structural context than pure lexical features . Furthermore, error analysis reveals specific linguistic challenges such as sarcasm and semantic ambiguity as constraints in automated review detection . This research provides a managerial contribution to e-commerce platforms in enhancing information quality and mitigating information overload issues.
Co-Authors Abdurahman, Gibral Adhiyasa, Chandra Julian Adriansyah, Rizki Ahmad Rifai Ahmad Rifai Ahmad Rifai Ahmad Rifai Aisyah Fatimah Akbar Alzaini Akbar Alzaini Al Fachrozi, Muhammad Al-Farisy, M Hadi Alfarizi, M. Alfitrah, Intan Aidita Ali Ibrahim Alinda, Yelli Nur Alvico, Alvico Alvines, Mahendi Alya Zalfa Chairunnisa Alzaini, Akbar Amanda, Bella Rizkia Anadia, Qothrunnada Wafi Ananda Khoirunnisa Andini Bahri, Cheisya Andriani, Sari Ani Nidia Listianti, Ani Nidia Anindya Putri, Salsa Anna Dwi Marjusalinah 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 Az-Zahra, Nanda Salsabila Azera Pramesty Bayu Wijaya Putra Beriadi Agung Nur Rezqe Billan, Angel Caroline Cahyo Adi Nugraha Chandra Julian Adhiyasa Chrisjuanito Clancy Cindy Dinata Cynthia Sherina Fadeli Danendra, Devano Dedy Kurniawan Deni Lidianti Desty Rodiah Devano Danendra Dinda Lestarini Dinna Yunika Hardiyanti Dinna Yunika Hardiyanti Dinna Yunika Herdiyanti Dwi Rosa Indah Endang Lestari Ruskan Endang Lestari Ruskan Epriyanti, Nadia Ermatita - Fadli, M. Thoriqul Fahren Affandi Faizah, Ovie Nur Fathoni - Fathoni - Fatimah Salsabila Firda, Hiliah Frans Nicko Apriansyah Gabriel Sebastian Santoso Gultom, Gina Destia Gusti Barata Hardini Novianti Hardini Novianti Hardini Novianti Hardini Novianty Ichsan Farel Rachmad, Muhammad Idpal, Idpal Imam Akbar Imam Syahputra Zaki Inayah, Anna Fadilla Intan Aidita Alfitrah Irmawati Irmawati Irwansyah, Muhammad Aziiz Izzan Fieldi, Muhammad Jackson Imanuel Manurung Jaidan Jauhari Jambak, Muhammad Ihsan Jefven Fernando Jonathan Pakpahan Juliyanti, Tamara Karima, Dzakiah Aulia Karimsyah Lubis, Muhammad Karisa Anjani Fakhri Ken Dhita Tania Ken Dhita Tania Ken Dhita Tania, Ken Dhita Ken Ditha Tania Ken Ditha Tania Khairani, Annisa Khoiriyah Harahap, Dayana Khoirusy Syafaat Larasati, Salsabila Lifiano Jamot Munthe, Gabriel Luh Sri Mulia Eni M Luthfi Aldi Pratama M Naufal Hisyam M Rafly Ramdhani M Rifki Ali M, Nys Marliza Tiara M. Ilham Fahlevi M. Rudi Sanjaya Maharani, Wardah Shifa Maretta, Aulia Maretta, Aulia Pinkan Mariska, Inneke Via Marpaung, Xenia Clarissa Valencia Meiriza, Viola Meitiana Audya Merizka Azzahra Mira Afrina Mohd Rizky Putra Pratama Muhamad Edric Rasyid Muhammad Aidil Fitri Syah Muhammad Ali Buchari Muhammad Azmi Zaky Muhammad Dzaky Hasyim Muhammad Emirshah Yusuf Muhammad Husni Syahbani Muhammad Ihsan Muhammad Imam Riadillah Muhammad Iqbalul Khoiri Muhammad Lucky Hermanto Muhammad Wahyu Hikmalsyah Mulyadi Mulyadi Munaspin, Zahra Diva Putri Nabila Oktadini Nabila Riska Ayu Nabila Rizki Oktadini Nabilaputri, Silvia Nachwa, Syakillah Nadia Ayu Safitri Naretha Kawadha Pasemah Gumay Nashiroh Ramadhani, Muthia Natasyah, Mei Intan Nawirah Athqiyah Nicolaus Owen Marvell Novitia Chinoi Nurul Izmy Nur’Aini, Risma Nyimas Silvia Oktadini, Nabila Oktadini, Nabila Rizky Onkky Alexander Pacu Putra Pacu Putra Padlefi, Muhamad Riza Pakpahan, Jonathan Paulus Paskah Lino Susilo Perdani, Tharisa Antya Pinkasari, Aulia Putri Ariyanti Putri Eka Sevtiyuni Putri Eka Sevtiyuni Putri Eka Sevtyuni Putri Mutiara Arinie Putri Setiyawati Putri, Adetya Rielisa Putri, Meiyin Monica Amilia Putri, Nyayu Dwi Tarisa Qothrunnada Wafi Anadia RA Aliffyaa Ramadhani 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 Risma Nur Aini Rizka Dhini Kurnia Rizka Dhini Kurnia Rizka Rahmadhani Rizki Kurniati Rizky Herdiansyah, Muhammad Rizky Sawitri Rizkyllah, Anabel Fiorenza Rizqi, Ahmad Fadhil Rositiani, Ely Royan Dwi Saputra RR. Ella Evrita Hestiandari Salsabila, Fatimah Sanjaya, M. Rudi Saputri, Sonia Dwi Sari Andriani Sarifah Putr Raflesia Sarifah Putri Raflesia Sasmita, Ruth Mei Sawitri, Rizky Sella Juniastia Marsya Saputri Septhia Charenda Putri Sevtiyuni, Putri Eka Siahaan, Jeremiah Alwin Simanullang, Eka Darmayanti Susanti, Helen Susilo, Paulus Paskah Lino Syabilla, Lailla Syal Syahbani, Muhammad Husni Syarief Albani, Muhammad Tharisa Antya Perdani Theresia Pardede, Eva Titiana, Nuke Merisca Tri Mutiara Illahi Tri Zafira, Zahra Tsabitah, Laila Vanisa Amalia Putri Via Mariska, Inneke Wahyudi, Muhammad Iqbal Wulan Dari, Atikah Yadi Utama Yadi Utama Yasir Alghifari, Muhammad Yasyfi Imran, Athallah Yelli Nur Alinda Yoga Fradana Yunika Hardiyanti, Dinna Yunita Yunita Zaqqi Yamani Zwesty Quatra