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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 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) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia Jurnal Informatika: Jurnal Pengembangan IT Jurnal Komtika (Komputasi dan Informatika)
Fadhilah Syafria
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Klasifikasi Tulang Tengkorak Berdasarkan Jenis Kelamin Menggunakan Correlation-Based Feature Selection (CFS) dengan Backpropagation Neural Network (BPNN) Ma'rifah, Laila Alfi; Afrianty, Iis; Budianita, Elvia; Syafria, Fadhilah
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8616

Abstract

Abstract – In forensic anthropology, sex identification is the initial step in individual identification, with a probability level of 50%, influencing subsequent examinations such as age and height estimation. The skull is the second-best choice after the pelvis for determining sex, with an accuracy of up to 90%. Morphological and metric methods are less reliable due to the high variability of skulls, while DNA analysis is ineffective on burned or damaged bones. Therefore, this study applies Correlation-Based Feature Selection (CFS) with a Backpropagation Neural Network (BPNN) to improve classification accuracy. The dataset used originates from Dr. William Howells, consisting of 2,524 skull samples with 85 variables. CFS was applied with two thresholds, 0.1 and 0.01, and the division of training data and test data using k-fold cross validation with k=10. The BPNN parameters included learning rates of 0.01 and 0.001, along with three different architectures based on the number of input neurons. The results indicate that CFS improved accuracy from 92.06% to 93.25% under the CFS threshold of 0.01, with a learning rate of 0.001 and a BPNN architecture of [72; 95; 1]. This study confirms that combining CFS and BPNN enhances sex classification accuracy based on skull bones.Abstrak – Pada antropologi forensik, identifikasi jenis kelamin adalah langkah awal dalam mengidentifikasi individu dengan tingkat probabilitas 50%, yang berpengaruh pada pemeriksaan lain seperti perkiraan usia dan tinggi badan. Tulang tengkorak menjadi pilihan terbaik kedua setelah tulang panggul dalam menentukan jenis kelamin dengan akurasi hingga 90%. Metode morfologi dan metrik kurang dapat diandalkan karena variabilitas tengkorak yang tinggi, sementara analisis DNA tidak efektif pada tulang yang terbakar atau rusak. Oleh karena itu, penelitian ini menerapkan Correlation-Based Feature Selection (CFS) dengan Backpropagation Neural Network (BPNN) untuk meningkatkan akurasi klasifikasi. Dataset yang digunakan berasal dari Dr. William Howells, terdiri dari 2.524 sampel tengkorak dengan 85 variabel. Pada CFS digunakan dua ambang batas yaitu 0,1 dan 0,01, serta pembagian data latih dan uji data menggunakan k-fold cross validation dengan k=10. Parameter BPNN yang digunakan meliputi learning rate (0,01 dan 0,001) serta tiga arsitektur berbeda sesuai dengan jumlah neuron input. Hasil menunjukkan bahwa CFS meningkatkan akurasi dari 92,06% menjadi 93,25% pada konfigurasi ambang batas CFS 0,01 dengan learning rate 0,001 dan arsitektur BPNN [72; 95; 1]. Penelitian ini menunjukkan bahwa kombinasi CFS dan BPNN dapat meningkatkan akurasi klasifikasi jenis kelamin berdasarkan tulang tengkorak.
Clustering Data Penduduk Menggunakan Algoritma K-Means Ikhsan, Tomi; Haerani, Elin; Wulandari, Fitri; Syafria, Fadhilah
TIN: Terapan Informatika Nusantara Vol 5 No 12 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i12.7328

Abstract

Economic inequality is still a crucial factor facing Indonesia today, from big cities to remote villages, economic inequality is still a major problem. Bina Baru Village is no exception, a village inhabited by 5,760 people with a total of 1,742 families, spread across 30 neighbourhood associations (RT) and 8 community associations (RW). Various efforts are made to overcome the problem of economic inequality, one of which is by channeling assistance or providing policies that are right on target. One of the steps to overcome this problem is to group population data in Bina Baru village using the K-Means Clustering method which aims to determine the economic level of families in the region, so that local governments can more accurately make policies on the problem of economic inequality that occurs. The data used comes from a questionnaire of 1,005 family data with 64 attributes and 1,005 individual data with 84 attributes. The application of the k-means algorithm is carried out using python, also using DBI (Davies-Bouldin Index) to determine the optimum k value. In this study, the optimal k value is 3 clusters. Based on testing, it is found that Cluster 0 represents households with medium economic conditions, cluster 1 represents groups with better economic conditions and Cluster 2 is a group of households with low economic conditions. By clustering the population's economy, it is hoped that it can help stakeholders to provide targeted policies.
KLASIFIKASI PENYAKIT TANAMAN PADI MENGGUNAKAN ARSITEKTUR DENSENET-121 DAN AUGMENTASI DATA Yanto, Febi; Agustina, Auliyah; Budianita, Elvia; Iskandar, Iwan; Syafria, Fadhilah
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol 8 No 1 (2024)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v8i1.4256

Abstract

Padi (Oryza sativa) merupakan salah satu jenis tanaman pangan dimana beras sebagai hasil tanaman padi, menjadi bahan pangan utama untuk sebagian besar penduduk indonesia. Dalam proses budidaya padi, tantangan penyakit seringkali menjadi ancaman yang signifikan. Menyebarnya penyakit menyebabkan penurunan ekonomi, seperti pada tahun 2023 penurunan 0,22%. Selain itu minimnya pengetahuan dan wawasan petani dalam mengidentifikasi dan mendiagnosa jenis penyakit padi menjadi penyebab kurangnya hasil produksi padi. Oleh karena itu perlu adanya suatu klasifikasi penyakit padi menggunakan DenseNet-121 dan augmentasi data. Penelitian ini menggunakan pendekatan deep learning yakni Convolutional Neural Network (CNN) dengan arsitektur DenseNet-121 dan augmentasis data crop. DenseNet saat ini banyak digunakan untuk klasifikasi, DenseNet memanfaatkan koneksi padat antar lapisan, mengurangi jumlah parameter, memperkuat propagasi, dan mendorong pemanfaatan kembali fitur. Menggunakan dataset yang berasal dari situs Kaggle yang terdiri dari 3 jenis penyakit tanaman padi yaitu brown spot, blast, dan blihgt dengan setiap kelas terdiri dari 250 citra sehingga semua data berjumlah 750 citra. Hasil terbaik dari beberapa pengujian diperoleh akurasi terbaik sebesar 99,17% dan los 0,0355 menggunakan model DenseNEt-121, pembagian data 90;10 dengan menggunakan leraning rate 0,001 dan dropout 0,01 serta menggunakan augmentasi data, sedangkan untuk hasil akurasi tanpa augmentasi diperoleh hasil akurasi terbaik yaitu 95,00%dengan pembagian data 90;10, learning rate 0,01 dan dropuot 0,1.
KLASIFIKASI STATUS STUNTING BALITA MENGGUNAKAN METODE C4.5 BERBASIS WEB Fauzan Adzim; Budianita, Elvia; Nazir, Alwis; Syafria, Fadhilah
ZONAsi: Jurnal Sistem Informasi Vol. 5 No. 3 (2023): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode September 2023
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v5i3.15828

Abstract

Stunting pada balita merupakan permasalahan serius yang perlu diselesaikan karena berdampak negatif pada pertumbuhan dan perkembangan anak. Stunting adalah keadaan dimana balita mengalami kekurangan gizi yang kronis sehingga pertumbuhan fisik dan tinggi badannya tidak sejalan dengan usianya. Pola makan yang tidak memadai dan nutrisi yang tidak sesuai menjadi sebab terjadinya stunting pada balita. Dalam upaya pencegahan stunting dilakukan pemantauan terhadap status gizi dan tumbuh kembang balita setiap bulan di posyandu terdekat. Untuk menentukan status balita normal atau stunting masih menggunakan cara manual berdasarkan metode antropometri sehingga dapat meningkatkan risiko kesalahan dalam perhitungan atau penginputan data. Menggunakan teknik Data mining dapat menentukan klasifikasi atau prediksi pada status stunting balita dengan menganalisis pola data yang telah ada sebelumnya. C4.5 adalah algoritma klasifikasi terkenal dan familiar dan sering digunakan dengan menggunakan teknik pohon keputusan juga mempunyai keunggulan seperti mampu mengolah data numerik (kontinu) dan diskrit, merapikan nilai atribut yang tidak lengkap, menciptakan aturan yang mudah dimengerti, serta kecepatan pemprosesan yang relatif cepat dibandingkan dengan algoritma lainnya adapun dataset yang digunakan terdiri dari atribut umur, jenis kelamin, indeks menyusui dini (IMD), berat badan, dan tinggi badan. Evaluasi model dilakukan dengan mempergunakan confusion matrix dan menghasilkan tingkat akurasi terbaik sebesar 93.62%. Hasil ini diperoleh dari pemisahan data sebanyak 80% data latih sebanyak 20% data uji dengan dengan Max Depth sebesar 10 dan jumlah seluruh data sebanyak 1172.
Klasifikasi Status Stunting Balita Menggunakan Metode Naïve Bayes Gaussian Berbasis Web Mulyono, Makmur; Budianita, Elvia; Nazir, Alwis; Syafria, Fadhilah
Jurnal Informatika Universitas Pamulang Vol 8 No 3 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i3.33399

Abstract

The growth and development of toddlers must get attention from parents because toddlerhood is a golden period in shaping the growth and development and intelligence of children. Stunting is  a state of malnutrition in which stunted growth and development of children and this is included in chronic nutritional problems, the incidence of stunting  can be seen from height that is not in accordance with age. In preventing toddlers from stunting, it is necessary to anticipate early prevention by conducting examinations at the nearest posyandu which is measured using anthropometric methods. The calculation  of stunting or normal status based on anthropometric data is generally processed manually so that there is a high possibility of errors in calculating and entering data. Data mining can make classifications or predictions on the stunting status  of toddlers by studying previous data patterns. Naïve bayes is one classification method that has the advantage of high accuracy with little training data as for the attributes used in this study, namely age, gender, Early Initiation of Breastfeeding (IMD), weight, height. Based on the test results, the best average accuracy was obtained on numerical data types for age, weight, height and nominal gender attributes, Early Breastfeeding Initiation (IMD) with the highest accuracy in the 80:20 data comparison, which is 80.34% with a total of 1172 data.
EXPERT SYSTEM TO DETECT ONLINE GAME ADDICTION FOR UNIVERSITY STUDENTS USING THE BACKWARD CHAINING AND CERTAINTY FACTOR APPROACHES Muslimin, Al’hadiid; Okfalisa, Okfalisa; Pizaini, Pizaini; Syafria, Fadhilah; Che Hussin, Ab Razak
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.4.1308

Abstract

Online gaming addiction harms students' physical health, mental well-being and academic performance. The addiction to playing online games has three categories, namely high, moderate and low, which are rarely known by the general public. The significant of knowledge acquisition on the addiction symptom and preventive activities forces the emergence of new idea on expert system identification platform. Therefore, this research aims to develop an expert system using the Backward Chaining (BC) and Certainty Factor (CF) approaches to detect the initial addiction level of online games for university students. Herein, the BC is used to identify the levelling of online game addiction based on the symptoms experienced by the user. There are thirty-three symptoms (G01-G33) provided through the thorough literature reviews and interviews with psychiatrics. Meanwhile, the CF is applied to calculate the level of certainty in determining the possibility of addiction describing in six scale level interpretation. As a result, the application of these two methods has effectively succeeded and reached proper accuracy in identifying the level of addiction of students towards their behavior on playing online games. The comparison of CF testing values between the system calculation and expert judgement shows the sophisticated result. Thus, this research can be utilized by the medical and psychiatric authorities, parents, and students in assessing their symptoms of addiction as an early warning in facing the possible risks arising from online game addiction.
Feature Selection using Information Gain on the K-Nearest Neighbor (KNN) and Modified K-Nearest Neighbor (MKNN) Methods for Chronic Kidney Disease Classification Ramadhan, Aweldri; Budianita, Elvia; Syafria, Fadhilah; Ramadhani, Siti
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 2 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i2.26834

Abstract

Purpose: Kidneys has an important role in the human excretory system. Unhealthy kidneys can affect kidney function. It is important to know the symptoms of chronic kidney disease. One data mining technique that can be applied is the classification technique to determine whether a person has chronic kidney disease or not based on the symptoms (attributes) obtained from medical records. The symptoms of chronic kidney disease obtained amount to 24 symptoms or attributes,Methods/Study design/approach: In this research, the classification of chronic kidney disease is performed using the information gain feature selection method and the KNN and MKNN classification methods. The number of data used is 400 data with 2 classes, namely chronic kidney disease (CKD) and non-chronic kidney disease (non-CKD).Result/Findings: Based on the test results, it was found that the hemo (Hemoglobin) attribute has the highest information gain value, which is 0.6255. The best accuracy for the KNN classification method is 96.61%, and for the MKNN method, it is 98%. Novelty/Originality/Value: The purpose of information gain feature selection is to choose features or attributes that significantly influence chronic kidney disease. Keywords: Chronic Kidney Disease, Information Gain, KNN, MKNN
Analisis Perbandingan Algoritma C4.5 dan Modified K-Nearest Neighbor (MKNN) untuk Klasifikasi Jamur Rahmadhani, R.; Nazir, Alwis; Syafria, Fadhilah; Afriyanti, Liza
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7052

Abstract

Mushrooms are organisms that consist of several cells, contain spores, are eukaryotic (have a cell nucleus membrane), and do not have chlorophyll, so fungi depend on other organisms to get food. Mushrooms have very identical shapes, starting with size, shape, smell, and color. So it is difficult for ordinary people to differentiate between poisonous mushrooms and non-poisonous mushrooms. Mistakes in identifying mushrooms can have fatal consequences because they can cause poisoning when consuming mushrooms. Therefore, there is a need for education in classifying poisonous and non-poisonous mushrooms. By applying various classification algorithms, it can be determined which algorithm performs better. In previous research conducted by several researchers on classifying mushrooms, there were differences in the accuracy results for each algorithm. Therefore, this research will raise the question of how to measure or comparion algorithm performance in classification using the C4.5 algorithm and the Modified K-Nearest Neighbor (MKNN) algorithm. The results obtained by comparion the performance of the C4.5 algorithm and the Modified K-Nearest Neighbor (MKNN) algorithm in this research show that the C4.5 algorithm managed to obtain an accuracy level of 98.52%, precision of 98.55%, recall of 98.52%, and f1-score of 98.51%. In contrast, the Modified K-Nearest Neighbor (MKNN) algorithm using the value K=10 achieved an accuracy level of 96.62%, precision of 96.69%, recall of 96.62%, and f1-score value of 96.57%.
Klasifikasi Sentimen Presepsi Masyarakat di Instagram Terhadap Paslon Pilpres 2024 Menggunakan Naïve Bayes Classifier (NBC) Akbar, Lionita Asa; Haerani, Elin; Syafria, Fadhilah; Nazir, Alwis; Budianita, Elvia
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 1 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i1.11293

Abstract

The 2024 presidential election has attracted considerable attention as it has become a controversial issue among the public. Various positive and negative opinions generated can potentially turn into rumors. One of the means used by the public to express their opinions is the social media platform Instagram. Data on public opinions on Instagram can be processed into valuable information through sentiment classification. This research conducted sentiment classification on public perceptions towards the 2024 presidential candidates using a naïve Bayes classifier. The study utilized a dataset consisting of 1000 comments. These comments were collected from several posts on the social media platform Instagram discussing the presidential and vice-presidential candidates. The comments were manually labeled by an expert who is a lecturer in the Indonesian language. Classification was carried out after preprocessing and weighting TF-IDF stages. Based on the research findings, the naïve Bayes classifier method showed an accuracy of 82% and an F1-Score of 83.93% obtained from a 90%:10% split of training and testing data. These results indicate that the naïve Bayes classifier method is effective in classifying the sentiments of the public on Instagram towards the 2024 presidential candidates.
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.
Co-Authors Abdul Aziz Abdullah, Said Noor Abdussalam Al Masykur Adrian Maulana Adzhima, Fauzan Afriyanti, Liza Agung Syaiful Rahman Agus Buono Agustina, Auliyah Ahmad Paisal Aji Pangestu Adek Akbar, Lionita Asa Alfin Hernandes 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 Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Hearani Ellin Haerani Elvia Budianita Faska, Ridho Mahardika Fatma Hayati Fauzan Adzim Febi Nur Salisah Febi Yanto Felian Nabila Fitra Lestari Fitri Insani Fitri Insani Fitri Wulandari Fratiwi Rahayu Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Guswanti, Widya Habibi Al Rasyid Harpizon Hafez Almirza Hafsyah Hara Novina Putri Harni, Yulia Hertati Ibnu Afdhal Ihda Syurfi Iis Afrianty Iis Afrianty Ikhsan, Tomi Ikhsanul Hamdi Indrizal, Habibi Putra Inggih Permana Irma Sanela Ismail Marzuki Ismar Puadi Isnan Mellian Ramadhan Israldi, Tino Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Karina Julita Khair, Nada Tsawaabul Lestari Handayani Lestari Handayani Lili Rahmawati Liza Afriyanti Lola Oktavia Lola 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 Fahri 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 Nailatul Fadhilah Nazir, Alwis Nazruddin Safaat H Negara, Benny Sukma Neni Sari Putri Juana Nesdi Evrilyan Rozanda Nining Nur Habibah Novriyanto Novriyanto Nurainun Nurainun Okfalisa Okfalisa Permata, Rizkiya Indah Pizaini Pizaini Puspa Melani Almahmuda Putra, Fiqhri Mulianda Putri Mardatillah Putri, Widya Maulida Rahmad Abdillah Rahmad Abdillah Rahmad Kurniawan Rahmadhani, R. Raja Sultan Firsky Ramadhan, Aweldri Ramadhan, Muhammad Ilham Ramadhani, Siti Reski Mai Candra Reski Mai Candra Reski Mai Candra Reski Mei Candra Riska Yuliana Roni Salambue Said Nanda Saputra Satria Bumartaduri Silfia Silfia Siti Ramadhani Siti Sri Rahayu Suswantia Andriani Suwanto Sanjaya Syaputra, Muhammad Dwiky Teddie Darmizal Vitriani, Yelvi Wulandari, Fitri Yaskur Bearly Fernandes Yusra, Yusra Yusril Hidayat Zabihullah, Fayat Zulastri, Zulastri