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All Journal TEKNIK INFORMATIKA Jurnal Penelitian Saintek Teknika Jupiter Jurnal Ekonomi : Journal of Economic Jurnal Ushuluddin SITEKIN: Jurnal Sains, Teknologi dan Industri Islam Futura Jurnal Varidika Jurnal Ekonomi dan Bisnis Islam Orbith : Majalah Ilmiah Pengembangan Rekayasa dan Sosial Jurnal Manajemen & Supervisi Pendidikan Jurnal Teknologi dan Sistem Komputer Jurnal Akuntansi dan Pajak Jurnal CoreIT Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal As-Salam Sebatik Jurnal Pengabdian Masyarakat AbdiMas Jurnal Nasional Komputasi dan Teknologi Informasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Elektronik JMB : Jurnal Manajemen dan Bisnis Zonasi: Jurnal Sistem Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Computer System and Informatics (JoSYC) JUKI : Jurnal Komputer dan Informatika Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Informatika Terpadu Nusantara: Journal for Southeast Asian Islamic Studies Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Jurnal Abdimas Prakasa Dakara Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Malcom: Indonesian Journal of Machine Learning and Computer Science CONSEN: Indonesian Journal of Community Services and Engagement Sahabat Sosial: Jurnal Pengabdian Masyarakat SATIN - Sains dan Teknologi Informasi International Journal of Health Sciences Journal Of Artificial Intelligence And Software Engineering Media Kesehatan Politeknik Kesehatan Makassar Jurnal Indonesia : Manajemen Informatika dan Komunikasi Science, Technology, and Communication Journal Celebes Journal of Community Services
Nazruddin Safaat H
Fakultas Sains Dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Religion Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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SISTEM TANYA JAWAB BERBASIS CHATBOT WEBSITE MENGGUNAKAN GEMINI AI PADA DATA FIQIH KONTEMPORER Helviansyah, Try; Harahap, Nazruddin Safaat; Irsyad, Muhammad; Negara, Benny Sukma
Journal of Information System Management (JOISM) Vol. 7 No. 1 (2025): Juni
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2025v7i1.2082

Abstract

Teknologi informasi telah memberikan dampak besar pada berbagai aspek kehidupan, termasuk fiqih. Penelitian ini bertujuan mengembangkan Sistem Tanya Jawab Fiqih yang memungkinkan pengguna memperoleh jawaban cepat dan akurat terkait fiqih kontemporer. Sistem ini menggunakan LangChain dan model Gemini-2.0-flash untuk mencari jawaban dari sembilan buku fiqih kontemporer karya Syekh Al-Qardhawi. Teknologi Large Language Model (LLM) dan similarity search diterapkan guna meningkatkan akurasi dan relevansi jawaban. Penelitian ini mencakup tahap pengumpulan data, analisis, perancangan, implementasi, dan pengujian sistem. Hasil evaluasi menunjukkan sistem ini menghasilkan jawaban yang akurat dengan tingkat kepuasan responden ahli fiqih sebesar 89% dan skor evaluator LangChain rata-rata 95%, keduanya dalam kategori “Sangat Setuju”. Temuan ini menegaskan potensi teknologi informasi dalam studi fiqih serta membuka peluang pengembangan lebih lanjut guna meningkatkan akses informasi fiqih bagi umat Islam.
Implementasi Langchain dan Large Language Models Dalam Automatic Question Generation Untuk Computer Assisted Test Novri Rahman; Harahap, Nazruddin Safaat; Affandes, Muhammad; Pizaini
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.v5i4.558

Abstract

The advancement of Artificial Intelligence (AI), particularly Large Language Models (LLM), presents new opportunities in transforming educational assessment systems. This study aims to implement the LangChain framework integrated with LLM for an Automatic Question Generation (AQG) system within a Computer Assisted Test (CAT) platform, using eleventh-grade Biology subject matter as a case study. The methodology includes data collection from PDF-based instructional materials, text embedding using Facebook AI Similarity Search (FAISS) as the knowledge base, and automatic question generation through the GPT-4o model. The system is developed using a microservices architecture comprising frontend and backend services built with the Next.js, FastAPI, and Express.js frameworks. System evaluation was conducted using the User Acceptance Test (UAT) and the DeepEval framework. The evaluation results show a teacher satisfaction rate of 92.7% and a positive response from students at 67.5%. Meanwhile, the DeepEval assessment reported average scores of 3,69% for hallucination, 97,44% for contextual precision, 83,30% for contextual relevancy, 70,63% for answer relevancy, and 92,47% for prompt alignment. These findings indicate that the integration of LangChain and LLM is effective in generating contextually accurate and relevant questions, although improvements are still needed in answer relevancy. This study is expected to provide an efficient solution for digital-based educational assessment and contribute to future developments in educational AI.
Retrieval-Augmented Generation in a Web-Based Question Answering System for Fiqh Books Ahadi, Ridho; Harahap, Nazruddin Safaat; Fikry, Muhammad; Kurnia, Fitra
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.7005

Abstract

Mayoritas masyarakat Indonesia yang beragama Islam cenderung tidak mengetahui madzhab yang mereka anut, padahal pemahaman madzhab sangat penting untuk menjalankan ibadah secara benar dan sah. Salah satu madzhab yang berkembang di Indonesia, terutama melalui ulama Hadramaut, adalah madzhab Imam Asy-Syafi’i. Kurangnya akses terhadap literatur fikih otentik serta metode pembelajaran interaktif menyebabkan masyarakat kesulitan memahami fikih secara mendalam. Perkembangan teknologi kecerdasan buatan (AI), memberikan dampak signifikan dalam berbagai aspek, termasuk pendidikan dan keagamaan. Salah satu implementasi AI yang berkembang adalah chatbot, sistem interaktif berbasis percakapan yang mampu memahami dan merespons pertanyaan secara alami. Dalam konteks pembelajaran keislaman, khususnya fikih, penggunaan chatbot AI menghadirkan peluang baru untuk pembelajaran yang lebih personal dan interaktif. Fikih sebagai cabang ilmu hukum Islam menuntut pemahaman mendalam terhadap sumber klasik dan aplikasinya dalam kehidupan modern. Penelitian ini memiliki tujuan untuk mengembangkan sistem tanya jawab fikih berbasis AI dan Natural Language Processing menggunakan Large Language Model (LLM), framework LangChain, serta metode Retrieval-Augmented Generation (RAG). Sistem ini dirancang memberikan jawaban relevan berdasarkan konteks fikih. Evaluasi menggunakan metrik BERTScore menghasilkan precision 86,50%, recall 84,76%, dan F1-score 85,52%, hasil ini menunjukkan akurasi tinggi dalam menjawab pertanyaan fikih.
Hubungan Marketing Mix Dengan Loyalitas Pasien Rawat Jalan di Rumah Sakit Tk II 14.05.01 Pelamonia Makassar Imran, Ali; Hastuti, Hastuti; Rusnita, Rusnita; Windarti, Sriyani; Pratiwi, Reski Dewi; B, Muslimin; Tondok, Theresa
Media Kesehatan Politeknik Kesehatan Makassar Vol 20 No 1 (2025): Media Kesehatan
Publisher : Direktorat Politeknik Kesehatan Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32382/medkes.v20i1.1521

Abstract

The marketing mix is a key strength of hospitals, closely linked to patient behavior in utilizing services. Product mismatch with patient perceptions is a common problem in the marketing mix. Patients sometimes do not know the cost, which can be burdensome for them. Promotion does not reach the right target segment. Place refers to the location, where there are often long queues at the outpatient clinic. People rely on specific individuals or doctors for care. Process involves inefficient service delivery workflows, and physical facilities are insufficient and not fully optimized. Similarly, in Pelamonia Hospital, the marketing mix is now structured as the 10P or mega marketing mix. This theory by Dr. Samsi Jacobalis led to the decision to expand the marketing mix from 4P to 10P, which includes People, Public, Power, Pressure, and Performance. The objective of this study is to determine the relationship between the marketing mix and patient loyalty at Pelamonia Hospital TK II 14.05.01 in Makassar. The research was conducted quantitatively using a cross-sectional approach. The population consisted of 8,134 patients, with 269 samples. The sampling method used was purposive sampling. The study was conducted in April 2025 and analyzed using univariate and bivariate analysis. The results and conclusions indicate a significant relationship between the product dimension (p=0.0027), promotion (p=0.002), physical evidence (p=0.027), process (p=0.000), performance (p=0.007), public relations (p=0.026), and power (p=0.000) with patient loyalty at the outpatient department of TK II 14.05.01 Pelamonia Hospital in Makassar. There is no relationship between the dimensions of price (p=0.180), place (p=0.664), and people (p=0.713) and the loyalty of outpatients at Pelamonia Makassar General Hospital TK II 14.05.01. It is recommended that the hospital maximize its marketing mix activities to provide good service to patients and make them feel comfortable in receiving services and examinations at the hospital. Keywords: Loyalty, Marketing mix, Hospital, Outpatient
Perbandingan Performa Random Forest dan Long Short-Term Memory dalam Klasifikasi Teks Multilabel Terjemahan Hadits Bukhari: Comparison of Random Forest and Long Short-Term Memory Performance in Multilabel Text Classification of Bukhari Hadith Translation Ahmad, Rizmah Zakiah Nur; Harahap, Nazruddin Safaat; Agustian, Surya; Iskandar, Iwan; Sanjaya, Suwanto
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2046

Abstract

Hadits merupakan fondasi utama kedua dalam Islam, yang memandu umat Islam dalam menafsirkan nilai-nilai Islam dan mengimplementasikannya secara nyata dalam berbagai aspek kehidupan. Salah satu perawi hadits yang paling dihormati adalah Imam Bukhari, yang dikenal dengan ketelitian dan ketegasannya dalam memilih hadits-hadits yang otentik. Penelitian ini menggunakan data dari terjemahan hadis dari Sahih Bukhari ke dalam bahasa Indonesia yang telah diklasifikasikan ke dalam tiga kategori utama, yaitu anjuran, larangan, dan informasi. Untuk mengidentifikasi karakteristik masing-masing kategori, klasifikasi teks dilakukan dengan menggunakan dua metode populer, yaitu Random Forest (RF) dan Long Short-Term Memory (LSTM), yang dikenal efektif dalam memproses data teks berskala besar dan kompleks. Tujuan dari penelitian ini adalah untuk menguji perbedaan kinerja antara kedua metode tersebut dalam mengelompokkan hadis yang datanya telah lengkap. Hasil evaluasi menunjukkan bahwa metode RF mencapai akurasi tertinggi sebesar 89,48%, sedikit lebih unggul dari LSTM yang memperoleh 88,52%. Kedua metode mencatat nilai Hamming Loss yang sama, yaitu 0,1048 (89,52%). Temuan ini menunjukkan bahwa kelengkapan dan kualitas data hadis Bukhari berkontribusi dalam meningkatkan akurasi klasifikasi dengan memberikan konteks dan variasi yang lebih baik untuk model.
Implementasi Chatbot Tafsir Al-Qur'an Menggunakan Chainlit dengan Pendekatan Groq: Implementing a Qur'anic Tafsir Chatbot Through Chainlit with a Groq-Based Approach Maulana, Muhammad Rizky; Harahap, Nazruddin Safaat; Okfalisa, Okfalisa; Yusra, Yusra
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2082

Abstract

Pemahaman terhadap tafsir Al-Qur’an sering kali menjadi tantangan dalam dunia pendidikan, khususnya bagi siswa, mahasiswa, atau masyarakat umum yang tidak memiliki latar belakang ilmu tafsir maupun kemampuan bahasa Arab. Keterbatasan akses terhadap tafsir yang mudah dipahami, serta ketiadaan media pembelajaran yang interaktif dan mampu menjawab pertanyaan secara kontekstual, menjadi hambatan dalam proses pembelajaran keislaman. Penelitian ini bertujuan untuk mengembangkan chatbot berbasis kecerdasan buatan (AI) sebagai media pendukung pembelajaran tafsir Al-Qur’an yang responsif dan adaptif. Sistem dirancang menggunakan framework Chainlit sebagai antarmuka web, didukung oleh Groq untuk mempercepat proses inferensi, serta integrasi LangChain dan Large Language Models (LLM) untuk memahami isi tafsir, khususnya Tafsir Jalalain dalam format PDF berbahasa Indonesia. Dokumen yang digunakan berupa satu file digital utuh yang representatif. Proses sistem meliputi ekstraksi teks, pembagian teks (chunking), pembentukan embedding, dan pencarian semantik berbasis vektor. Evaluasi menggunakan BERTScore menghasilkan nilai rata-rata precision sebesar 71,84%, recall 78,11%, dan F1-score 74,80%, menunjukkan kemampuan sistem dalam memberikan jawaban yang baik secara semantik. Hasil penelitian ini berkontribusi dalam menyediakan media pembelajaran tafsir digital berbasis AI yang efisien dan kontekstual, serta menjadi solusi potensial untuk mendukung proses pendidikan Islam yang lebih interaktif dan modern.
Implementasi Sistem Pembuatan Soal Otomatisasi Pembelajaran Pendidikan Agama Islam Dengan Menggunakan Langchain Dan Llm Berbasis Gemini Fahrezy, Irgi; Harahap, Nazruddin Safaat; Wulandari, Fitri; Agustian, Surya
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2032

Abstract

This study examines the implementation of an automated question generation system in the context of Islamic Religious Education (PAI) using LangChain technology and a Gemini-based Large Language Model (LLM). The research methodology includes data collection, needs analysis, system architecture design, implementation, and validation through black-box testing and expert evaluation. The system is designed to automatically generate questions aligned with instructional content and the cognitive levels of Bloom's Taxonomy, ranging from factual knowledge to evaluation and creation. The testing process involved six expert evaluators with educational backgrounds and experience in PAI. Results indicate that the system successfully produced high-quality questions, with an average approval rate of 96.89%. Differentiated scores revealed the highest performance in theoretical interpretation questions (100%) and the lowest in critical analysis (30%), indicating varying system capabilities across the cognitive spectrum. This study demonstrates that integrating artificial intelligence into question generation is highly feasible and yields significant outcomes, although challenges remain in producing questions that stimulate higher-order and authentic thinking.
Sistem Tanya-Jawab Berbasis Chatbot Telegram Tentang Fiqih Kontemporer Menggunakan Langchain Dan LLM Mar'arif, Muhammad Mulky; Harahap, Nazruddin Safaat; Jasril, Jasril; Affandes, Muhammad
TEKNIKA Vol. 19 No. 2 (2025): Teknika Mei 2025
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15437200

Abstract

Informasi mengenai fiqih kontemporer sangat susah didapatkan, terkhusus berasal dari ustadz Syekh Al-Qardhawi. Perlunya sebuah layanan informasi yang bisa memberikan jawaban dari pertanyaan seputar fiqih kontemporer, salah satunya yaitu memanfaatkan teknologi saat ini seperti chatbot yang ada pada telegram. Dengan memanfaatkan chatbot pada telegram dapat menghasilkan sebuah sistem tanya-jawab mengenai fiqih kontemporer. Jadi, penelitian ini bertujuan untuk menerapkan sistem tanya-jawab berbasis chatbot Telegram mengenai fiqih kontemporer. Penelitian ini juga memanfaatkan metode langchain dan large language model (LLM) agar dapat menciptakan sistem tanya-jawab yang optimal. Penelitian ini berhasil menunjukkan keefektifan nya dalam memberikan jawaban mengenai fiqih kontemporer yang mana hasil pengujian User Acceptance Test (UAT) dan Black Box Testing menunjukkan bahwa sistem ini dapat memberikan informasi yang akurat dan juga berfungsi sebagaimana mestinya sebagai sistem tanya-jawab tentang fiqih kontemporer.
A classification of Quran translations using K-nearest neighbors, support vector machine and random forest method Delifah, Nur; Harahap, Nazruddin Safaat; Agustian, Surya; Irsyad, Muhammad; Iskandar, Iwan
Science, Technology, and Communication Journal Vol. 6 No. 1 (2025): SINTECHCOM Journal (October 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i1.337

Abstract

A Classification of Quranic verses based on topics is one of the efforts to facilitate understanding and searching for information in the holy book, especially for non-Arabic readers. This study aims to test and compare the performance of three text classification methods, namely K-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF), in grouping translated Quranic verses into 15 topic classes, such as Islamic arkanul, faith, the Quran, science and its branches, charity, da'wah, jihad, human and social relations, and others. The dataset used is the English translation of the Quran with full preprocessing and an 80:20 data split for training and testing. The evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results show that RF achieved the best performance with an average F1-score of 58.48% and testing accuracy of 90.81%. KNN followed with an F1-score of 54.07% and the highest testing accuracy of 92.05%, while SVM produced the lowest F1-score at 50.76% and accuracy of 88.20%. The RF demonstrates a more balanced ability in recognizing all classes, KNN excels in overall accuracy, and SVM performs less optimally in this classification task. This research is expected to serve as a foundation for developing a more intelligent and contextual topic-based verse classification system.
Implementasi Question Answering Berbasis Chatbot Telegram Pada Tafsir Al-Jalalain Menggunakan Langchain dan LLM Febrian Rizki Adi Sutiyo; Harahap, Nazruddin Safaat; Surya Agustian; Reski Mai Candra
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1784

Abstract

Technological developments are very important for efficient, accurate and fast information retrieval. Tafsir Al-Jalalain is one of the famous Tafsir Al-Qur’an, and is used as a source of life guidance for muslims. To get information about tafsir, you can go through information media such as the internet or from experts in Tafsir Al-Qur’an. However, to get information it takes a lot of time to filter the information efficiently, accurately and quickly. This problem requires a system that is able to answer human questions accurately, effectively and quickly. In this research, it is hoped that the implementation of telegram Chatbot-based Question Answering using Langchain and LLM will be a solution for providing information on Tafsir Al-Jalalain that is accurate, effective and fast. The Question Answering system will carry out learning on the Tafsir Al-Jalalain data using a language model, namely the Large Language Model, so that it is expected to be able to provide accurate, effective and fast information. The evaluation results of the research by distributing questionnaires to students majoring in Al-Qur'an and Tafsir Science at UIN SUSKA Riau, as many as seven respondents, obtained a percentage of 84.29%
Co-Authors A, Adhyatma Abd. Ghofur Abdul Aziz Abdul Aziz Abdul Ghafur Afdhalel Vickro Afdhol Rinaldi Afifah, Dhiya Dwi Ahadi, Ridho Ahmad Fauzan Ahmad Sutrio Prihatinoto Ahmad, Muhammad Iqbal Ahmad, Rizmah Zakiah Nur Aji Bayu Permadi Akbar Akbar Ali Imran Andi Nursiah , Idris , Anjar Briliannita Annis Syahzuni, Barlia Aprilia, Risma Arda, Darmi Ari Anggarani Winadi Prasetyoning Tyas Arum, Hafidza Sanshia Aryanti, Dessy Ayunda , Ayunda Fatmasari Baehaqi Bayu Dwinata Putra Yatabri Cindy , Cindy Yolanda citra ainul mardhia putri Darmizal, Teddie Delifah, Nur Deny Dewana Hastanto Desy Prastyani Dhymas Julyan Riyanto Eka Pandu Cynthia Ekawati, Nur Eko Saputra Elvia Budianita Elvina Afriani Elyani, Elyani Ernawati Ernawati Erry Yudhya Mulyani Erwan Baharudin Fachrul Hakim Fadhilah Syafria Fahmi Kasri Fahrezy, Irgi Fahroha, Fani Afrida Fajar Febriyadi Fatima Akmal Putri, Fatima Akmal Fauzi Ihsan Febi Yanto Febrian Rizki Adi Sutiyo Fina Tamima Annahwa Fitra Kurnia Fitra Kurnia Fitri Insani Fitri Wulandari Ganiyev, Avazbek Gunawan Setia Wiguna Hastuti Hastuti Helviansyah, Try Henry Arianto Heriyanti Idhafi, Zaky Iis Afrianty Irsyad , Muhammad Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jefitra Abdu Ar Razzak Julina Julina K, Hairuddin Kasmuri Kasmuri, Kasmuri Khairiah Khairiah Khamo, Khamo Waruwu kurnia, fitra Latif, Sarifudin Andi Lawami, Reza Haya Lestari Handayani Liza Afriyanti Lubis, Anggun Tri Utami BR. M. Fahrizal M. Septrio Rafinaldo M.Faisal Harahap Mar'arif, Muhammad Mulky Masduki Masduki Maulana, Muhammad Rizky Moi, Mohammed Rizki Muhammad Affandes Muhammad Fikry Muhammad Irsyad Muhammad Irsyad Muhammad Irsyad Natadisastro, Yoga Negara, Benny Sukma Niken Aisyah Maharani Herwanza Novi Yanti Novi Yanti Novri Kurniawan Kurniawan Novri Rahman Novriyanto Novriyanto Nugrahaini, Inten Nurhaedah , Nurhaedah Nurhayani Nurhayati , Nurhayati Okfalisa Okfalisa Oktariani Sari Oktavia, Lola Oppie, Oppie Meisya Tanjung Pannyiwi, Rahmat Pizaini Pizaini Pizzaini Pizzaini Poppy Gus Silviana Pratiwi, Reski Dewi Putri, Nadia Sandi Rahmad Kurniawan Rasyid, Djusmadi Reski Mai Candra Reski Mai Candra Reski Mai Candra Rina Anindita Rini Susanti Rita Musdalifah Rosidi, Imron Royhisar Martahan Simanungkalit Ruqaiyah, Ruqaiyah Rusnita, Rusnita Safiani A. Faaroek Saparuddin Siregar Saputra, Ikhsan Dwi Sari, Lydia Permata Sari, Oktariani Satriawan, Agus Serli, Serli Setiyati, Ritta Silfia Silfia Silitonga, Evalina Siregar, Siti Nurholiza Suci Rahayu Sugandi, Hatami Karsa Suharna, Jaka Suhartini Suhartini Surya Agustian Suwanto Sanjaya Syam, Rosida Teddie Darmizal Tondok, Theresa Umar, Risa Bernadip Usman Usman Vita Intan Safitri Wedi Kurniawan Wijaya, Andy Huang Windarti, Sriyani Yanuar Ramadhan Yermi, Yermi Yulis, Dian Meiliani Yusra Yusra, Yusra Zahirah, Qonitah Rifda Zuriani Rizki