Article Per Year (5 Year)
p-Index From 2021 - 2026
13.032
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA Syntax Jurnal Informatika Jurnal Ilmu Komputer dan Agri-Informatika SITEKIN: Jurnal Sains, Teknologi dan Industri CESS (Journal of Computer Engineering, System and Science) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Informatika Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer bit-Tech Zonasi: Jurnal Sistem Informasi Journal of Applied Engineering and Technological Science (JAETS) Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal UNITEK Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Sisfo: Jurnal Ilmiah Sistem Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia Indonesian Journal of Multidisciplinary on Social and Technology Jurnal Informatika: Jurnal Pengembangan IT Jurnal Komtika (Komputasi dan Informatika)
Fadhilah Syafria
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

Published : 117 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 9 Documents
Search
Journal : bulletin of computer science research

 1 
Implementasi Data Mining K-Means Clustering Untuk Pengelompokan Produk Keramik Berdasarkan Frekuensi, Volume, dan Jangkauan Penjualan Ferdian Arya Dinata; Alwis Nazir; Fadhilah Syafria; Teddie Darmizal; Eka Pandu Cynthia
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1195

Abstract

Ceramic inventory management at CV. Makmur Bersama has generally relied on intuition or partial sales data, without accounting for purchasing behavior patterns as a whole. This approach simultaneously creates two major risks: overstocking of slow-moving products, which burdens working capital and storage space, and stockouts of high-demand products, which can result in lost sales opportunities. This problem is further compounded by the limitation of stock data, which typically contains only a single quantitative variable such as the number of units sold and is therefore unable to comprehensively capture product demand characteristics, such as how frequently a product is purchased or how broad its customer base is. As a result, restocking decisions and promotional strategies are often poorly targeted. This research applies the K-Means algorithm to cluster ceramic products based on historical sales patterns as a solution to this limitation. Historical sales data from CV. Makmur Bersama for the 2025 period, consisting of 6,328 transactions, was processed into 417 unique products through a feature engineering approach using Frequency, Monetary, and Reach (FMR) namely transaction count, total quantity sold, and unique customer count per product. After outlier detection using the Interquartile Range (IQR) method, 381 products remained for the clustering process. The optimal number of clusters was determined using the Elbow Method, resulting in k=4 as the best cluster count. Evaluation using the Davies-Bouldin Index (DBI) produced a value of 0.8954, categorized as good, and stability testing across five iterations with different random states showed consistent results (DBI standard deviation of 0.0034). The clustering results produced Cluster 1 (190 products, 49.9%) as slow-moving products, Cluster 2 (34 products, 8.9%) as top-performing products with an average transaction frequency of 30.8 times, Cluster 3 (93 products, 24.4%) as potential products, and Cluster 4 (64 products, 16.8%) as products with limited demand. This research provides practical contributions for companies in determining restocking priorities, promotional strategies, and working capital efficiency based on actual sales patterns. This research contributes methodologically through the adaptation of the RFM framework into FMR to better suit real-world data constraints, as well as the integration of the Elbow Method, Davies-Bouldin Index, and stability testing as a comprehensive validation mechanism. Practically, the segmentation results can be directly utilized by the company as a basis for restocking priorities, promotional strategies, and working capital allocation efficiency based on actual sales patterns.
Klasifikasi Kondisi Janin Menggunakan Algoritma K-Nearest Neighbors dan Teknik SMOTE Berdasarkan Data Kardiotogram Dede Fadillah; Elin Haerani; Fitri Wulandari; Fadhilah Syafria
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.585

Abstract

Fetal health is a crucial aspect in reducing infant mortality rates, where cardiotocography (CTG) is used to monitor fetal condition through recordings of fetal heart rate and uterine contractions. However, manual interpretation of CTG data still faces challenges, particularly due to imbalanced class distribution. This study aims to develop a classification model for fetal conditions using the K-Nearest Neighbors (K-NN) algorithm combined with the Synthetic Minority Over-sampling Technique (SMOTE). The dataset used, sourced from Kaggle, consists of 2,126 CTG examinations categorized into three classes: Normal, Suspect, and Pathological. The data processing follows the Knowledge Discovery in Databases (KDD) process, including data selection, cleaning, normalization, splitting, balancing with SMOTE, and classification using K-NN. The model was evaluated using four training-testing split ratios (70:30, 80:20, 85:15, and 90:10) with accuracy and macro F1-score as metrics. The results indicate that the 85:15 split ratio achieved the highest accuracy of 89.7%, while the 90:10 ratio yielded the highest macro F1-score of 0.83. These findings suggest that the 85:15 ratio offers an optimal balance between model training and evaluation, whereas the highest F1-score at 90:10 reflects greater model sensitivity to minority classes. The combination of K-NN and SMOTE proved effective in addressing data imbalance and supports model stability in the overall classification process of fetal conditions.
Penerapan Metode ADASYN Dalam Mengatasi Imbalanced Data Untuk Klasifikasi Penyakit Stroke Menggunakan Support Vector Machine Alwaliyanto Alwaliyanto; Siska Kurnia Gusti; Iis Afrianty; Fadhilah Syafria
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.612

Abstract

Stroke is one of the leading causes of death and disability worldwide, making it essential to develop classification models that can assist in early and accurate diagnosis. This study aims to implement the Support Vector Machine (SVM) algorithm with three types of kernels linear, polynomial, and Radial Basis Function (RBF) to classify stroke disease data. The Adaptive Synthetic Sampling (ADASYN) method is employed to address the class imbalance problem, while model training and evaluation are carried out using 5-Fold Cross-Validation to ensure stable and reliable results. The findings indicate that ADASYN successfully improves the model’s sensitivity to stroke cases (the minority class), as reflected by an increase in recall and F1-score, despite a slight decrease in overall accuracy a common trade-off in handling imbalanced data. The linear kernel (after ADASYN) achieved the best performance after imbalance handling, with an average AUC-ROC of 0.8333, recall of 0.7827, and F1-score of 0.2181 for the stroke class. Although the F1-score remains relatively low, it improved compared to the pre-ADASYN results, indicating better detection of stroke cases. The implementation was conducted using Google Colab, which also contributed to efficient data processing and visualization. Overall, the results demonstrate that the combination of SVM and ADASYN is effective in enhancing the model’s sensitivity to minority classes and is well-suited for medical data classification tasks, particularly in the early diagnosis of stroke using machine learning approaches.
Penerapan Information Gain Untuk Seleksi Fitur Pada Klasifikasi Jenis Kelamin Tulang Tengkorak Menggunakan Backpropagation Nada Tsawaabul Khair; Iis Afrianty; Fadhilah Syafria; Elvia Budianita; Siska Kurnia Gusti
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.637

Abstract

Forensic anthropology and skull analysis play a crucial role in the biological identification of individuals, including sex determination. This study aims to improve the accuracy of gender classification based on skull structure by combining the Information Gain feature selection method with the Backpropagation algorithm. The dataset used is the craniometric data compiled by William W. Howells, consisting of 2,524 samples with 85 measurement features. The preprocessing stage includes data selection, data cleaning, and normalization. Feature selection was conducted using the Information Gain method with three threshold values: 0.01, 0.05, and 0.1, resulting in 79, 46, and 38 selected features, respectively. The model was evaluated using the K-Fold Cross Validation method with K=10 and K=20. The highest accuracy of 93.91% was achieved at the 0.01 threshold using the Backpropagation architecture [79:119:1], a learning rate of 0.01, and K=20. These results demonstrate that feature selection using Information Gain enhances the performance of the Backpropagation model by eliminating irrelevant features and minimizing the risk of overfitting.
Perbandingan Akurasi Arsitektur EfficientNet-B0, VGG16, dan Inception V3 Dalam Deteksi Tumor Ginjal Pada Citra CT-Scan Muhammad Fahri; Febi Yanto; Fadhilah Syafria; Rahmad Abdillah
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.670

Abstract

Kidney dysfunction can trigger the development of various diseases, including kidney tumors. Early detection of kidney tumors is very important to increase the effectiveness of treatment and the chances of patient recovery. The use of deep learning technology in medical image classification has become a promising approach, especially in detecting abnormalities in the kidney organ through CT-Scan images. This study compares the performance of three Convolutional Neural Network (CNN) architectures, namely EfficientNet-B0, Inception-V3, and VGG16, in detecting kidney tumors. The dataset used was obtained from the kaggle website, namely CT-scan images with normal and tumor classes and divided by a ratio of training  data and test data of 80:20. The hyperparameter used is Stochastic Gradient Descent (SGD) with a learning rate of 0.001 and 0.0001. The evaluation was carried out using a confusion matrix with metrics of accuracy, precision, recall, and F1-score . According to the test outcomes, the VGG16 model configured with a 0.001 learning rate achieved the highest classification performance, recording 99.46% accuracy, precision, recall, and F1-score.
Penerapan Support Vector Machine Dengan Smote Untuk Klasifikasi Sentimen Pada Data Ulasan Aplikasi Trading View Muhammad Badri; Elin Haerani; Fadhilah Syafria; Okfalisa Okfalisa; Lola Oktavia
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.793

Abstract

In the digital era, user feedback on mobile applications serves as highly valuable information for developers to evaluate app performance. One popular application in the field of finance and investment is TradingView, widely used for technical analysis by traders. User feedback on this application reflects various user sentiments, including positive, negative, and neutral. However, the large volume of reviews and the unstructured nature of text data make manual analysis inefficient and prone to high subjective bias. Therefore, the use of automatic classification methods capable of processing text data with reasonable accuracy is required. This study aims to implement the “Support Vector Machine (SVM)” technique to classify user feedback on the TradingView application. To address the issue of imbalanced sentiment class distribution, the study also employs the “Synthetic Minority Over-sampling Technique (SMOTE)”. The study utilizes 10,000 reviews obtained via web scraping from the Google Play Store. The study workflow consists of text preprocessing, feature extraction using “Term Frequency-Inverse Document Frequency (TF-IDF)”, data balancing, SVM model training, and model evaluation. The evaluation results show that the application of SVM with SMOTE achieves an accuracy of approximately ±85.56% across data splits (70:30, 80:20, 90:10). In each scenario, the highest F1-score was achieved for the positive sentiment class, while the performance of minority classes (negative and neutral) improved after data balancing with SMOTE, with an average F1-score increase of 1.67% for the negative class and 10.67% for the neutral class. Without SMOTE, the average negative F1-score was ±57%, and the neutral class was undetected (0.00%). Furthermore, validation using K-Fold Cross Validation yielded an average accuracy of 89.20%, which increased to 95.10% after applying SMOTE. This improvement was consistent across all data proportions (70:30, 80:20, 90:10), with an average increase of 5.44%. These findings confirm that integrating SVM with SMOTE not only enhances classification performance on imbalanced data but also maintains model stability. Therefore, this study contributes to the advancement of automated sentiment classification systems, particularly for financial mobile app reviews, and can serve as a reference for future research in user review analysis on similar applications.
Penerapan Metode Support Vector Machine Untuk Analisis Sentimen Pada Komentar Bitcoin Di Aplikasi X Yaskur Bearly Fernandes; Elin Haerani; Fadhilah Syafria; Muhammad Fikry; Lola Oktavia
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.928

Abstract

Social media has become a primary medium for users to express opinions, including those related to Bitcoin, whose fluctuating value often triggers diverse public responses. The large volume of unstructured comments makes manual sentiment analysis inefficient, thereby necessitating an automated approach based on machine learning. This study aims to classify positive and negative sentiments in Bitcoin-related comments on the X platform using the Support Vector Machine (SVM) algorithm with Term Frequency–Inverse Document Frequency (TF-IDF) feature weighting. The dataset consists of 1,750 Indonesian-language comments labeled by three annotators. The data were processed through several preprocessing stages, including case folding, text cleaning, tokenization, stopword removal, and stemming. Model evaluation was conducted using four data split ratios, namely 90:10, 80:20, 70:30, and 60:40. The experimental results indicate that the 90:10 ratio achieved the best performance, with an accuracy of 72.57%, precision of 0.75, recall of 0.73, and an F1-score of 0.67. The SVM model demonstrates strong performance in identifying positive sentiments; however, it is less effective in detecting negative sentiments due to class imbalance in the dataset. As an additional experiment, testing was performed using a balanced dataset obtained through an undersampling process and several SVM kernel types for comparison. The results show that using a balanced dataset leads to more evenly distributed classification performance across sentiment classes, while the linear kernel provides the most stable performance compared to other kernels. Overall, SVM with TF-IDF weighting proves to be an effective approach for sentiment analysis of Bitcoin-related comments on social media.
Klasifikasi Sentimen Bitcoin Terhadap Komentar Di Aplikasi X Menggunakan Metode Decision Tree C4.5 Habibi Putra Indrizal; Fadhilah Syafria; Elin Haerani; Yelvi Vitriani; Yusra Yusra
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.932

Abstract

Sentiment analysis is an important method for understanding user perceptions of cryptocurrency assets such as Bitcoin, whose price movements are strongly influenced by public opinion. This study aims to classify user sentiment from comments posted on the X platform into two classes, namely positive and negative, using the Decision Tree C4.5 algorithm. The dataset consists of 5,000 Indonesian-language comments collected through a web scraping process and processed through text preprocessing and TF-IDF–based feature extraction. The model was trained using a 70% training data and 30% testing data split. The evaluation results show that the C4.5 model achieved an accuracy of 78%. For the positive class, the model obtained a very high recall of 0.99 with an F1-score of 0.83, indicating strong performance in identifying positive comments. In contrast, the negative class achieved a recall of 0.51 with an F1-score of 0.67, despite having a high precision of 0.97. The disparity in performance between classes is influenced by the data distribution, which is not fully balanced, with positive comments being more dominant than negative ones, causing the model to be more sensitive to the majority class. Overall, the results indicate that the Decision Tree C4.5 algorithm is sufficiently effective for Indonesian-language Bitcoin sentiment classification, although it still has limitations in recognizing the minority class. Future research may explore the application of data imbalance handling techniques or more advanced algorithms to improve the balance of classification performance across classes.
Sistem Prediksi Produksi Kelapa Sawit Berbasis Gradio Menggunakan Algoritma Regresi Linear Berganda Irfan Jamal Matondang; Elvia Budianita; Fadhilah Syafria; Iis Afrianty
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.994

Abstract

The instability of oil palm production often leads to discrepancies between production targets and actual outputs, thereby necessitating an accurate prediction model to support operational planning. This study aims to develop an oil palm production prediction model and to identify the most influential variables affecting production outcomes as a basis for data-driven decision-making. The model was developed using the Multiple Linear Regression method based on historical data from 2020–2024, consisting of 60 monthly observations with variables including number of trees, land area, rainfall, number of fruit bunches, and plant age. The research stages included data preprocessing, variable selection through testing several feature combinations, model development, and performance evaluation using the coefficient of determination (R²), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). The results indicate that the combination of number of trees, land area, number of fruit bunches, and plant age produced the best performance, with an R² value of 0.85 on the training data and 0.81 on the testing data. The MAE values were 125,307 kg and 176,984 kg, the MSE values were 28,870,838,455 kg² and 52,809,954,662 kg², and the RMSE values were 169,914 kg and 229,804 kg, respectively. Based on the regression coefficients, the number of fruit bunches was identified as the most dominant variable, with a coefficient value of 637,720 kg. The model was subsequently implemented using the Python Gradio library in the form of an interactive interface to support production planning effectiveness and minimize the risk of inaccurate decision-making in oil palm plantation management.
Co-Authors Abdul Aziz Abdullah, Said Noor Abdussalam Al Masykur Adrian Maulana Adzhima, Fauzan Agung Syaiful Rahman Agus Buono Agustina, Auliyah Ahmad Paisal Aji Pangestu Adek Akbar, Lionita Asa Alfin Hernandes Alwaliyanto Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Amalia Hanifah Artya Aminuyati Andre Suarisman Aprima, Muhammad Dzaky Ariq At-Thariq Putra Baehaqi Bib Paruhum Silalahi Boni Iqbal Che Hussin, Ab Razak Darmila Dede Fadillah Deny Ardianto Devi Julisca Sari Dina Septiawati Dodi Efendi Eka Pandu Cynthia Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Hearani Ellin Haerani Elvia Budianita Fakhri Fakhri Faska, Ridho Mahardika Fatma Hayati Fauzan Adzim Febi Nur Salisah Febi Yanto Felian Nabila Ferdian Arya Dinata Fitra Lestari Fitri Insani Fitri Insani Fitri Wulandari Fitri Wulandari Fratiwi Rahayu Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Guswanti, Widya Habibi Al Rasyid Harpizon Habibi Putra Indrizal Hafez Almirza Hafsyah Hara Novina Putri Harni, Yulia Hertati Ibnu Afdhal Ihda Syurfi Iis Afrianty Iis Afrianty Ikhsan, Tomi Ikhsanul Hamdi Inggih Permana Irfan Jamal Matondang Irma Sanela Ismail Marzuki Ismail Marzuki Ismar Puadi Isnan Mellian Ramadhan Israldi, Tino Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Jauhari, Najwa Karina Julita Lestari Handayani Lestari Handayani Lili Rahmawati Liza Afriyanti Lola Oktavia Lola Oktavia Oktavia M Fikry M. Afif Rizky A. Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Maulana Junihardi Mawadda Warohma Mazdavilaya, T Kaisyarendika Mhd. Kadarman Mori Hovipah Mori Hovipah Morina Lisa Pura Muhammad Affandes Muhammad Alvin Muhammad Badri Muhammad Fahri Muhammad Farid Audi Rahman Simatupang Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Hanif Abdurrohman Muhammad Ichsanul Bukhari Muhammad Irsyad Muhammad Syafriandi, Muhammad Muhammad Taufiq Muhammad Yusril Haffandi Muhammad Yusuf Fadhillah Mulyono, Makmur Muslimin, Al’hadiid Nabyl Alfahrez Ramadhan Amril Nada Tsawaabul Khair Nailatul Fadhilah Nazir, Alwis Nazruddin Safaat H Negara, Benny Sukma Neni Sari Putri Juana Nesdi Evrilyan Rozanda Nining Nur Habibah Novi Yanti Novriyanto Novriyanto Nurainun Nurainun Nurika Dwi Wahyuni Okfalisa Okfalisa Okfalisa Okfalisa Permata, Rizkiya Indah Pizaini Pizaini Puspa Melani Almahmuda Putra, Fiqhri Mulianda Putri Mardatillah Putri, Widya Maulida R. Rahmadhani Rahmad Abdillah Rahmad Abdillah Rahmad Kurniawan Raja Sultan Firsky Ramadhan, Aweldri Ramadhan, Muhammad Ilham Ramadhani Herfin Ramadhani, Siti Reski Mai Candra Reski Mai Candra Reski Mai Candra Reski Mei Candra Restu Kharrisa Andini Riska Yuliana Roni Salambue Said Nanda Saputra Salmiyati Salmiyati Satria Bumartaduri Silfia Silfia Siti Ramadhani Siti Ramadhani Siti Sri Rahayu Surya Agustian Suswantia Andriani Suwanto Sanjaya Syaputra, Muhammad Dwiky Teddie Darmizal Teddie Darmizal Ummy Agustina Putri Wulandari, Fitri Yaskur Bearly Fernandes Yelvi Vitriani Yusra Yusra Yusra, Yusra Yusril Hidayat Zabihullah, Fayat Zulastri, Zulastri