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All Journal JURNAL SISTEM INFORMASI BISNIS Techno.Com: Jurnal Teknologi Informasi Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika JISTech (Journal of Islamic Science and Technology) JURNAL TEKNOLOGI DAN OPEN SOURCE JURNAL PENDIDIKAN TAMBUSAI Jurnal Nasional Komputasi dan Teknologi Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Mantik JISKa (Jurnal Informatika Sunan Kalijaga) Technologia: Jurnal Ilmiah Jurnal Ilmu Komputer dan Bisnis Health Information : Jurnal Penelitian Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Jatilima : Jurnal Multimedia Dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) JIKA (Jurnal Informatika) INFOKUM Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) El-Qist : Journal of Islamic Economics and Business (JIEB) Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Walisongo Journal of Information Technology Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Instal : Jurnal Komputer Jurnal Teknisi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mandiri IT Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer JOMLAI: Journal of Machine Learning and Artificial Intelligence Data Sciences Indonesia (DSI) Internet of Things and Artificial Intelligence Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Mhd Furqan
Universitas Islam Negeri Sumatera Utara
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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Cybersecurity Behavior as a Reflection of Ḥifẓ al-Māl in Islamic Banking: A Behavioral Model Based on Protection Motivation Theory Hutagalung, Muhammad Wandisyah R; Siregar, Saparuddin; Furqan, Mhd.; Pulungan, Ismail; Elce, Furkan
El-Qist: Journal of Islamic Economics and Business (JIEB) Vol. 15 No. 2 (2025): October (on going)
Publisher : Islamic Economics Department, Faculty of Islamic Economics and Business, Sunan Ampel State Islamic University, Surabaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15642/elqist.2025.15.2.127-153

Abstract

This study examines psychological determinants of cybersecurity protection behavior among Islamic banking customers by applying Protection Motivation Theory (PMT) within a maqāṣid al-sharīʿah framework. Using a quantitative survey (N = 384) and PLS-SEM, it tests the effects of perceived vulnerability, severity, self-efficacy, response efficacy, response cost, and social influence, as well as the moderating role of cybersecurity education. Results show that vulnerability, severity, response efficacy, and social influence significantly predict protection behavior, while self-efficacy and response cost do not. Cybersecurity education has no significant moderating effect. The model explains 69.6% of the variance, indicating strong explanatory power. The study contributes by linking PMT to Islamic economic principles, particularly ḥifẓ al-māl and amānah. It suggests that Islamic banks need community-based, values-driven cybersecurity education to foster sustainable protective behavior.
Implementasi Data Mining dengan K-Means Clustering untuk Memprediksi Pengadaan Obat Pane, Putri Pratiwi; Ramadhan Nasution, Yusuf; Furqan, Mhd.
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4920

Abstract

Community Health Center is one of the institutions that provides healthcare services. To ensure the provision of quality healthcare services, the Community Health Center management must be able to effectively manage medicine inventory to avoid the risks of shortages or excess stock. Therefore, the purpose of this research is to observe and perform clustering of medicine demands at Puskesmas Mandala using the K-Means Clustering technique. The data used includes medicine demand data from January to December 2023 at the health center. In its implementation, the RapidMiner application or software is utilized to perform clustering using the K-Means Clustering algorithm. The available medicine data will be grouped into 3 clusters: cluster 0 for high medicine demands, cluster 1 for moderate medicine demands, and cluster 2 for low medicine demands. Out of the 28 test data used, the results show the first cluster consisting of 24 items, the second cluster consisting of 3 items, and the third cluster consisting of 1 item with a Davies Bouldin Index value of 0.276. From this research, the Puskesmas can continue to procure medicine for the types classified under high-demand clusters to ensure that the medicine needs are consistently met.
Classification of Scholarships for Students in Schools Using the Naïve Bayes Method Rizki Siregar, Awal; Furqan, Mhd.
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5417

Abstract

This research addresses the challenge faced by educational institutions in selecting scholarship recipients by implementing the Naïve Bayes algorithm. The objective of this study is to simplify and improve the accuracy of the scholarship selection process at MTs As-Syarif Kuala Beringin, using data from 50 students. The background highlights the importance of scholarships in providing equal educational opportunities, particularly for students with financial challenges. The research method involves the use of Naïve Bayes to calculate the probability of eligibility based on academic performance, economic background, and student activity. The results show that seven students met the scholarship criteria, demonstrating the efficiency and objectivity of the algorithm. The practical implications include the development of a user-friendly application that facilitates data input, scholarship criteria determination, and clear evaluation results. This system enhances transparency and reliability in decision-making. In conclusion, the Naïve Bayes algorithm proves to be an effective and efficient tool for scholarship selection, enabling a more equitable opportunity for students. Further research could focus on integrating additional data points or comparing the algorithm's performance with other classification methods to enhance system reliability.
Penerapan Data Mining dalam Pengelompokan Kualitas Produk Kelapa Sawit Menggunakan Algoritma K-Means Clustering Putra, Suan Ekie Nanda; Furqan, Mhd.
CESS (Journal of Computer Engineering, System and Science) Vol. 9 No. 2 (2024): July 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i2.61682

Abstract

Minyak kelapa sawit banyak digunakan dalam berbagai produk, termasuk makanan, kosmetik, dan biodiesel. Untuk menjaga kualitas produk, diperlukan pemantauan serta analisis data secara terperinci sangat penting. Pada PT. Sri Ulina Ersada Karina, proses produksi Crude Palm Oil saat ini hanya mengikuti standar nasional tanpa analisis lebih lanjut tentang kualitas produk. Dengan analisis yang lebih mendalam, perusahaan dapat meningkatkan efisiensi dan mutu produk. Penelitian ini bertujuan untuk menerapkan teknik data mining, khususnya algoritma K-Means Clustering, untuk mengelompokkan kualitas produk kelapa sawit yang diolah menggunakan tools Jupyter Notebook. Hasil dari penelitian ini menghasilkan 3 cluster yaitu cluster 0 kategori baik dengan jumlah data sebanyak 89 sampel, Cluster 1 kategori kurang baik dengan jumlah data sebanyak 72 sampel, dan Cluster 2 kategori sangat baik dengan jumlah data sebanyak 132 sampel.
Penerapan Algoritma C4.5 Pada Klasifikasi Status Gizi Balita Ramadhan Nasution, Yusuf; Armansyah; Furqan, Mhd; Matondang, Toibatur Rahma
JURNAL FASILKOM Vol. 14 No. 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6941

Abstract

The study aims to classify the nutritional status of the child using the C4.5 algorithm. The secondary data used is derived from the assessment of the nutrition status of a child in Puskesmas Promji and Puksesmas Suka Makmur. A classification model is constructed using the C4.5 algorithm based on a number of predictor factors that have been determined. The research methodology includes data collection, data preprocessing, model development with C4.5 algorithms, model evaluation, and results analysis. Model evaluation is done using measurements such as accuracy. In addition, the significance of predictor variables in affecting the nutritional status of infants was also evaluated through data analysis. This research contributed to the development of a method of classifying the nutritional status of infants using the C4.5 algorithm approach. The implication of this study is that the classification model developed can be used as a tool to support early identification and intervention against nutritional problems in infants. Furthermore, based on testing using the confusion matrix technique with the 80:20 data division of a total of 502 datasets, consisting of 402 training data and 100 testing data, an accuracy rate of 80 percent was obtained.
PENINGKATAN KUALITAS TENAGA PENDIDIK MELALUI PUBLIKASI KARYA ILMIAH BEREPUTASI INTERNASIONAL Hasugian, Abdul Halim; Furqan, Mhd.
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 5 (2024): Vol. 5 No. 5 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i5.40408

Abstract

Penelitian ini mengkaji tantangan dan strategi dalam meningkatkan kualitas pendidik melalui publikasi ilmiah bereputasi internasional. Dengan menggunakan pendekatan kualitatif, data dikumpulkan melalui wawancara komprehensif, observasi langsung, dan analisis dokumen di beberapa perguruan tinggi terpilih. Penelitian ini mengungkapkan adanya hambatan yang signifikan termasuk kemampuan bahasa Inggris yang terbatas, keterbatasan waktu, dan kurangnya keterampilan menulis penelitian di antara para pendidik. Melalui program intervensi yang ditargetkan termasuk lokakarya khusus dan sesi pendampingan, para peserta menunjukkan peningkatan yang nyata dalam kemampuan publikasi mereka. Studi ini menunjukkan bahwa pendekatan pelatihan sistematis yang dikombinasikan dengan dukungan kelembagaan dapat secara efektif meningkatkan kapasitas pendidik untuk menghasilkan publikasi ilmiah yang diakui secara internasional. Rekomendasi yang diberikan termasuk membuat program pengembangan penulisan yang berkelanjutan, menciptakan jaringan penelitian kolaboratif, dan menerapkan sistem insentif untuk publikasi internasional.
Implementasi Gangguan Psikologi Anak Selama Belajar Daring Akibat Pandemi COVID-19 Menggunakan Metode C5.0 Nasution, Romaito; Furqan, Mhd; Santoso, Heri
Jurnal Ilmu Komputer dan Bisnis Vol. 15 No. 2 (2024): Vol. 15 No. 2 (2024)
Publisher : STMIK Dharmapala Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47927/jikb.v15i2.826

Abstract

Pada tahun 2020 terjadi wabah virus covid 19 diseluruh didunia, dimana seluruh aspek belajar mengejar dilakukan melalui daring. Dengan meluasnya penggunaan kemajuan teknologi yang semakin canggih, seperti Google Classroom, WhatsApp, Telegram, Google Meet, e-learning, dan aplikasi Zoom, pembelajaran online dapat berfungsi secara efektif. Dengan adanya wabah virus ini ada beberapa anak yang mengalami gangguan psikologi . Dalam penelitian ini, peneliti mencoba untuk menganalisis poin utama dari masalah yang ada dan tekad oleh temuan memperkuat kasus ini bahwa data dan hasil keputusan menggunakan data mining dengan metode algoritma C5.0 Pohon keputusan dapat menemukan hubungan tersembunyi antara sejumlah variabel input dengan sebuah variabel target dari data. .Penelitian ini menghasilkan pohon keputusan dari kasus yang. Akan ditampilkan daftar nilai gain dari tiap atribut dengan atribut tertinggi ialah Tidak Menderita dengan nilai entropy 0,92552578 dan atribut nilai gain terendeh ialah atribut Mood Swing Berat dengan nilai entropy 0,063067808 dengan akurasi dengan nilai 96,66%.
Sentiment Analysis Related To Covid-19 Vaccination On Social Media Using The K-Nearest Neighbor (K-NN) Method Novrianty, Amanda; Furqan, Mhd.; Sriani, Sriani
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4156

Abstract

Coronavirus 19 (COVID-19) has become a topic of great concern in the past two years. To anticipate the spread of the virus, the government has made various efforts, one of which is by procuring a COVID-19 vaccination to increase the body's immunity. In carrying out the program, the government urges the public to use social media as a means of disseminating information regarding the COVID-19 vaccination. Facebook is one of the most popular social media and is chosen by agencies as a medium of information. Information regarding the vaccination is shared by the Ministry of Health of the Republic of Indonesia through its Facebook Page and the public can provide opinions in the form of comments. Given that the comments are numerous and lengthy if you have to read the manual, it is difficult to classify which one corresponds to the positive, negative or neutral opinion class, so a system is needed to analyze them. This sentiment analysis system uses the K-Nearest Neighbor (K-NN) method to classify positive, negative and neutral opinions. This study uses 750 comments obtained from posts in November 2021 with the keywords 'vaccination' and 'vaccine', with the distribution of 700 training data and 50 test data. Furthermore, the comments are pre-processed with the stages of case folding, filtering, tokenizing, normalization, stopwords and stemming, then weighted using the TF-IDF feature. System testing is carried out using the K-Nearest Neighbor (K-NN) method with a value of k = 1, k = 3, k = 5, k = 7 and k = 9 . 1 and f-measure of 0.71428571428571. Meanwhile, the lowest accuracy value is at the value of k = 7 and k = 9 with an accuracy of 0.66 and an error rate of 0.34.
Classification of Dates Based on Texture Using Local Binary Pattern Algorithm and Support Vector Machine Mhd Furqan; Sriani Sriani; Suci Syahputri
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 4 (2025): Agustus 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i4.9358

Abstract

Abstract - Dates are a food that is widely favored by Muslims in Indonesia, especially during the month of Ramadan. The many types of dates make it difficult to distinguish the types of dates. To distinguish the types of dates can be seen from the shape, color, size or texture. In this study, dates will be distinguished based on their texture. Local Binary Pattern is one of the algorithms that can be used to extract images of dates based on their texture by comparing the center value of the pixel with the value of the surrounding pixels to facilitate the classification process. The classification used uses Support Vector Machine which works by finding the best hyperplane to determine data for each class. The combination of these two algorithms has proven to be able to classify with an accuracy level of 93%.Keywords: Classification, Local Binary Pattern, Support Vector Machine, Dates Abstrak - Kurma adalah makanan yang sangat disukai oleh umat Muslim di Indonesia, terutama selama bulan Ramadan. Beragam jenis kurma membuatnya sulit untuk membedakan jenis-jenis kurma tersebut. Untuk membedakan jenis kurma dapat dilihat dari bentuk, warna, ukuran, atau teksturnya. Dalam penelitian ini, kurma akan dibedakan berdasarkan teksturnya. Local Binary Pattern (LBP) adalah salah satu algoritma yang dapat digunakan untuk mengekstrak gambar kurma berdasarkan teksturnya dengan membandingkan nilai pusat piksel dengan nilai piksel di sekitarnya untuk memudahkan proses klasifikasi. Klasifikasi yang digunakan menggunakan Support Vector Machine (SVM) yang bekerja dengan mencari hiperplane terbaik untuk menentukan data untuk setiap kelas. Kombinasi kedua algoritma ini terbukti mampu mengklasifikasikan dengan tingkat akurasi 93%.Kata kunci: Klasifikasi, Local Binary Pattern, Support Vector Machine, Kurma
Klasifikasi Berita detik.com Terkait Teknologi Informasi Menggunakan TF-IDF dan Naive Bayes Nur Bainatun Nisa; Rivaldi Prima Nanda; Zahra Humaira Kudadiri; Bagus Ageng Alfahri; Mhd Furqan
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 3 (2025): Juni 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i3.9171

Abstract

Abstrak – Penelitian ini membahas tentang klasifikasi berita Detik.com terkait teknologi informasi dengan menerapkan metode Term Frequency-Inverse Document Frequency (TF-IDF) sebagai ekstraksi fitur dan algoritma Naive Bayes sebagai model klasifikasi. Tujuan dari penelitian ini adalah untuk mengelompokkan berita-berita yang dimuat pada situs Detik.com ke dalam beberapa kategori utama di bidang teknologi informasi, seperti kecerdasan buatan, keamanan siber, gadget, dan aplikasi. Proses penelitian diawali dengan pengumpulan 1.050 data berita dari Detik.com menggunakan search query ‘teknologi informasi’ pada rentang Maret hingga April 2025. Data kemudian diproses melalui tahapan text preprocessing, meliputi case folding, tokenizing, stopword removal, dan stemming. Selanjutnya, fitur teks diubah menjadi representasi numerik menggunakan TF-IDF, lalu dilakukan pelatihan model klasifikasi dengan algoritma Naive Bayes. Evaluasi kinerja model dilakukan menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa kombinasi TF-IDF dan Naive Bayes efektif dalam mengklasifikasikan berita teknologi informasi, dengan akurasi model mencapai 85%. Temuan ini menunjukkan bahwa pendekatan klasifikasi berbasis machine learning dapat membantu pengelompokan dan identifikasi topik utama secara otomatis dalam berita teknologi informasi di Detik.com.Kata Kunci: TF-IDF; Naive Bayes; Klasifikasi; Detik.com; Teknologi Informasi.Abstract – This study discusses the classification of Detik.com news related to information technology by applying the Term Frequency-Inverse Document Frequency (TF-IDF) method as a feature extraction and the Naive Bayes algorithm as a classification model. The purpose of this study is to group news published on the Detik.com site into several main categories in the field of information technology, such as artificial intelligence, cybersecurity, gadgets, and applications. The research process began with the collection of 1,050 news data from Detik.com using the search query 'information technology' in the range of March to April 2025. The data was then processed through the text preprocessing stage, including case folding, tokenizing, stopword removal, and stemming. Furthermore, text features were converted into numeric representations using TF-IDF, then training a classification model with the Naive Bayes algorithm. Model performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results showed that the combination of TF-IDF and Naive Bayes was effective in classifying information technology news, with a model accuracy reaching 85%. This finding suggests that a machine learning-based classification approach can help automatically cluster and identify key topics in information technology news on Detik.com.Keywords: TF-IDF; Naive Bayes; Classification; Detik.com; Information Technology.
Co-Authors Abdul Aziz Abdul Halim Hasugian Adha, Rifki Mahsyaf Agpina, Pipi Ahmad Fakhri Ab. Nasir Ahmad Fauzi Aidil Halim Lubis Aisyah Nurrahmah Siregar Akmal, Muhammad Haikal Anwar, Mufti Husain Apriansyah, Yuda Ardyanti, Tiwy Armansyah Armansyah Armansyah Armansyah Armansyah Armansyah Armansyah, A Aulia, Atiqah Aulia, Muhammad Arief Aulia, Muhammad Fathir Aulia, Rafif Risdi Badria, Lailatul Bagus Ageng Alfahri Br Rambe, Indri Gusmita Cahyadi, Bhagaskara Daulay, Ikhsan Agus Martua Elce, Furkan Fadil, Ulfi Muzayyanah Fadillah, Rini Fahrul Azis Nasution Faiza, Nayla Fakhriza, M. fandi, Fandi Ahmad FIKRI HAIKAL Gunawan, Irwan Harahap, Khaila Mukti Harahap, Raihan Rizieq Harahap, Rosa Linda Hasrul Hasibuan, Mhd Fikri Heri Santoso Himawan Hasibuan, Riswanda Ichsan HP, Kiki Iranda Hsb, Dinda Umami Hsb, Munawir Siddik Hutagalung, Muhammad Wandisyah R Ilham Fuadi Nasution Imam Zaki Husein Nst Iskandar, Rozai Ismail Pulungan Januar, Bagus juwita sari K Khairunnisa Kartikasari, Diah Putri Khairi, Nouval Khairunnisa Khairunnisa Khairunnisa, K Kurniawan, Riski Askia Lely Sahrani Lubis, Akbar Maulana M. Fakhriza Mahendra, Rifandi Matondang, Toibatur Rahma Maulana Ihsan, Maulana Mey Hendra Putra Sirait Mhd Ikhsan Rifki Mhd Reza Alfani Muhammad Akbar Ramadhan Tanjung Muhammad Farhan Muhammad Ikhsan Muhammad Luthfi Muhammad Naufal Shidqi Muhammad Ridzki Hasibuan Muhammad Rizki Munadi Munadi Nabawy, Putri Nabila, Siti Fadiyah Nasution, Afri Yunda Nasution, Irma Yunita Nasution, Romaito Nasution, Zulia Lestari Ningsih, Siti Alus Novrianty, Amanda Nugroho, Agung Nur Bainatun Nisa Nurhasanah Nurhasanah Nurhidayati Nurhidayati Nurul Hadi Muliani Hariadi Saputra Nurzannah, Laila Pane, Putri Pratiwi Pangestu, Dimas Panggabean, Alwi Andika Pratama, Haris Prayoga Elfanda Fachmi Hasibuan Prayogi, Ahmad Pulungan, Miftahul Rizky Putra, Suan Ekie Nanda Putri, Alma Irawanti Raissa Amanda Putri Rakhmat Kurniawan R Ramadani, Wily Supi Ramadhan Nasution, Yusuf Ramadhani, Fredy Kusuma Razzaq H. Nur Wijaya Reza Muhammad Rifnandy, Muhammad Fauzan Rivaldi Prima Nanda Rizka Rizki Ananda Rizki Siregar, Awal Rizqi Hidayat Tanjung RR. Ella Evrita Hestiandari Saparuddin Siregar Saputri Nasution, Intan Widya Sembiring, Yogasurya Pranantha Shafa, Dafa Ikhwanu Sinaga, Meri Siregar, Dzilhulaifa Siregar, Hervilla Amanda R. Siregar, Kalfida Eka Wati Sitepu, Anggi Jelita Siti Saniah Siti Sarah Harahap Siti Sumita Harahap Sitorus, Nur Shafwa Aulia Solly Aryza Sri Rahmadani Sri Wahyuni Sriani Sriani Sriani Sriani Sriani, S Suci Syahputri Suci Wulandari Suhardi, S Suhardi, Suhardi Susan Mayang Sari Syamia, Nanda Tambak, Tiara Ayu Triarta Tanjung, Tegar Haryahya Tria Elisa Wan Fadilla Rischa Wati, Putri Kurni Widiya Yuli Kartika Siregar Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution, Yusuf Ramadhan Zabni, Nur Hera Zahra Humaira Kudadiri Ziqra Addilah