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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer SMATIKA Jurnal Pendidikan Informatika dan Sains JURNAL MEDIA INFORMATIKA BUDIDARMA Tadarus: Jurnal Pendidikan Islam Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknoinfo Tibuana : Journal of Applied Industrial Engineering JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Infotek : Jurnal Informatika dan Teknologi Indonesian Journal of Law and Economics Review Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Abdimas ADPI Sains dan Teknologi Jurnal Abdimas Bina Bangsa Indonesian Journal of Education Methods Development Indonesian Journal of Innovation Studies PELS (Procedia of Engineering and Life Science) Procedia of Social Sciences and Humanities Indonesian Journal of Islamic Studies JOINCS (Journal of Informatics, Network, and Computer Science) SAGA: Journal of Technology and Information Systems Jurnal Sistem Informasi Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Innovative Technologica: Methodical Research Journal Jurnal Informatika Polinema (JIP) Indonesian Journal of Applied Technology Journal of Social Comunity Services Kanigara Journal of Technology and System Information Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) semanTIK Journal of Blockchain, Nfts and Metaverse Technology Journal of Information Technology Smatika Jurnal : STIKI Informatika Jurnal Academia Open Journal of Multidisciplinary Informatics, Artificial Intelligence, and Business Innovation
Ika Ratna Indra Astutik
Program Studi Informatika, Universitas Muhammadiyah Sidoarjo
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Implementasi Convolutional Neural Network (CNN) Untuk Mendeteksi Ujaran Kebencian Dan Emosi Di Twitter Nanda Mujahidah Andini; Yulian Findawati; Ika Ratna Indra Astutik; Ade Eviyanti
Smatika Jurnal : STIKI Informatika Jurnal Vol 14 No 02 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i02.1346

Abstract

The research aims to develop an accurate and efficient hate speech detection model on Twitter's social media platform by leveraging the power of the Convolutional Neural Network. (CNN). The focus of this research is on identifying hate speeches that are loaded with negative sentiment, especially those related to racial, religious, and sexual orientation issues in the context of the Indonesian language. The research process involved collecting relevant Twitter datasets, preprocessing text to clear and compile data, and word representation using Word2Vec to capture contextual meanings. Specifically designed CNN models are then trained on that dataset. CNN's advantages in automatically extracting semantic features from text, coupled with the use of Word2Vec, allow the model to have high accuracy, which is 87%-99% for emotional assessment and 99% for hate speech assessment. This makes the model very effective in detecting subtle patterns in language that indicate the presence of hate speech. This research has made a significant contribution to the development of a better content moderation system on social media. With its ability to detect hate speech in real time, the model can help create a safer and more inclusive online environment. However, this research still has some limitations, such as limited data set size and variations of hate speech that are not fully represented. Therefore, further research is needed to overcome these limitations and improve the performance of the model.
Klasifikasi Pola Peminjam Buku Bedarsarkan Profesi Menggunakan Algoritma Naïve Bayes Febri Rosita Dewi; Ade Eviyanti; Arif Senja Fitriani; Ika Ratna Indra Astutik
Smatika Jurnal : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1661

Abstract

As centers of literacy and learning, libraries face challenges in understanding book lending patterns to meet the needs of diverse users. The main problem faced is the lack of data-based analysis in optimizing library services and collections. This research aims to classify book borrowing patterns based on profession using the Naive Bayes algorithm, utilizing data from the Sidoarjo Library Service in 2023. The data consists of 4476 transactions with attributes such as profession, book category, and level of reading interest. This research was conducted in several phases, namely data collection preprocessing, processing using Gaussian and Multinomial Naive Bayes algorithms, and model evaluation. By testing on various data ratios (90:10, 80:20, 75:25, and 50:50), the results show that Gaussian Naive Bayes provides the highest accuracy of 97% in the random dataset scenario. The main findings show that students, university students and housewives dominate the high reading interest category, while doctors and researchers have lower reading interest. The unique value of this research is in its application of. data-based analysis to support library management. The research results provide strategic insight for developing more responsive data-based services, optimizing collections according to professional needs, and increasing the effectiveness of literacy programs. This research is anticipated to serve as the initial phase in utilizing data mining technology to overcome modern challenges in library management.
Analisis Sentimen Komentar YouTube MV K-Pop Menggunakan Naïve Bayes: Studi Kasus Jung Jaehyun ‘Horizon’ Addriana Fatma Putri Indah Sari; Ade Eviyanti; Ika Ratna Indra Astutik
Smatika Jurnal : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1691

Abstract

This research aims to analyze the sentiment of YouTube comments on the music video "Horizon" by Jung Jaehyun by applying the Naïve Bayes and Support Vector Machine (SVM). As a global phenomenon, K-pop serves as an intriguing subject for understanding interaction patterns and fan opinions on social media platforms, particularly YouTube. A total of 2,391 Indonesian-language comments were collected using the YouTube API and processed through preprocessing stages such as data cleaning, tokenization, normalization, and the removal of common stopwords. After manually labeling the comments for positive and negative sentiments, the data was analyzed using the Naïve Bayes algorithm, known for its simplicity, speed, and effectiveness with small datasets, and compared with SVM equipped with a linear kernel. The study found that while SVM with a linear kernel achieved the highest accuracy of 98% and excelled in handling imbalanced data, Naïve Bayes still delivered competitive results with an accuracy of 97%. The advantages of Naïve Bayes, including ease of implementation, computational efficiency, and performance on small datasets, make it an effective choice for similar sentiment analysis cases. Both algorithms demonstrated good performance in predicting sentiments, as shown in their confusion matrices, although challenges persisted with the negative class. This research contributes to sentiment analysis methodologies by highlighting that Naïve Bayes is an efficient and relevant algorithm for preliminary exploration, while SVM is more reliable for performance optimization on complex datasets. The findings are particularly relevant to the music industry in understanding fan sentiment as an indicator of success.
Sistem Pakar Berbasis Web untuk Diagnosis Penyakit Paru Anak dengan Forward Chaining Mochammad Raflie Lazuardi; Ika Ratna Indra Astutik; Ade Eviyanti
Smatika Jurnal : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1738

Abstract

This research aims to design an expert system using the forward chaining method to facilitate the early diagnosis of lung diseases in children, such as tuberculosis, pneumonia, and bronchitis. The system is designed to help the community, especially in areas with limited access to healthcare services, in recognizing symptoms independently. The methodology uses the stages of the Expert System Development Life Cycle (ESDLC), including problem identification, knowledge acquisition from experts, design, and testing using black box techniques. This system is capable of detecting symptoms, matching them with a rule base, and providing an initial diagnosis along with recommended actions. The implementation results show that the system can support quick and accurate medical decision-making, as well as enhance public health awareness through internet-based access.
Optimization of Stunting Prevention and Reduction through Early Detection Application, Sunting, based on Forward Chaining Inference Machine. : Optimalisasi Pencegahan dan Penurunan Stunting Melalui Aplikasi Deteksi Dini Sunting Berbasis Mesin Inferensi Forward Chaining Ika Ratna Indra Astutik; Uce Indahyanti; Evi Rinata
Academia Open Vol. 8 No. 2 (2023): December
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.8.2023.7267

Abstract

Stunting is a chronic malnutrition problem caused by a prolonged lack of sufficient nutrient intake, resulting in impaired growth and shorter stature (dwarfism) in children compared to age standards. In Indonesia, the prevalence of stunting remains high according to WHO standards, mainly due to the limited information available on stunting, particularly regarding preventive measures. In the era of Industry 4.0, rapid technological advancements, especially in the field of healthcare, have provided enhanced access to information and early disease diagnosis through expert system-based applications. The objective of this research is to design an application that assists the community in early detection of stunting in children, enabling timely intervention. The chosen approach involves forward chaining inference machine, testing input symptoms to draw conclusions based on the knowledge rules stored in the knowledge base. The outcome of this research is an application that facilitates parents and integrated service posts in preventing stunting disorders in children. Highlight: Intervensi Tepat Waktu: Aplikasi ini memungkinkan deteksi dini stunting, memungkinkan intervensi tepat waktu untuk mencegah gangguan pertumbuhan pada anak. Pendekatan Sistem Pakar: Memanfaatkan inferensi rantai maju, aplikasi menggunakan pengetahuan Integrasi Teknologi dan Layanan Kesehatan: Memanfaatkan kemajuan Industri 4.0, aplikasi ini menjembatani kesenjangan antara teknologi dan layanan kesehatan, memberdayakan masyarakat dan pos layanan terpadu untuk memerangi stunting secara efektif. Kata kunci: Stunting, Sistem pakar, Deteksi dini, Pelayanan kesehatan terpadu, Industri 4
Chatbot WhatsApp Asisten Workout dengan Integrasi Large Language Model Gemini dan spaCy M. Aris Khuzaini; Ika Ratna Indra Astutik; Arif Senja Fitrani
SemanTIK : Teknik Informasi Vol. 11 No. 1 (2025): Vol. 11 No. 1 (2025): SemanTIK Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v11i1.111

Abstract

Olahraga memiliki peran penting dalam meningkatkan kesehatan fisik dan mental serta mencegah risiko penyakit kronis. Namun, berdasarkan laporan World Health Organization (WHO) tahun 2022 menunjukkan bahwa sekitar 1,4 miliar orang dewasa di dunia masih mengalami kurangnya aktivitas fisik, yang meningkatkan risiko penyakit kronis. Penelitian ini bertujuan mengembangkan chatbot asistensi olahraga untuk membantu individu menjaga rutinitas olahraga yang konsisten. Metode pengembangan sistem menggunakan Software Development Life Cycle (SDLC) model iterative incremental, sedangkan Natural Language Processing (NLP) berbasis spaCy digunakan sebagai metode pemrosesan teks untuk memahami input pengguna. Selain itu, chatbot diintegrasikan dengan API Large Language Model (LLM) Gemini untuk menghasilkan respons berdasarkan basis pengetahuan chatbot. Hasil evaluasi menunjukkan kinerja chatbot yang baik, dengan akurasi 79%, presisi 85%, recall 79%, dan F1-score 77%. Pengujian black-box mencatat tingkat keberhasilan 91,67% dari 12 skenario. Dari hasil tersebut, dapat disimpulkan bahwa chatbot asistensi olahraga ini efektif dalam mendukung pola hidup sehat dan dapat menjadi pendekatan teknologi untuk membantu meningkatkan aktivitas fisik secara mandiri Exercise plays an important role in improving both physical and mental health while also preventing the risk of chronic diseases. However, according to the 2022 World Health Organization (WHO) report, approximately 1.4 billion adults worldwide still experience physical inactivity, which increases the risk of chronic diseases. This research aims to develop an exercise assistance chatbot to help individuals maintain a consistent workout routine. The system development process follows the Software Development Life Cycle (SDLC) iterative incremental model, while Natural Language Processing (NLP) using spaCy is employed for text processing to understand user input. Additionally, the chatbot is integrated with Gemini’s Large Language Model (LLM) API to generate responses based on its knowledge base. Evaluation results indicate good chatbot performance, with an accuracy of 79%, precision of 85%, recall of 79%, and an F1-score of 77%. Black-box testing recorded a 91.67% success rate across 12 test scenarios. These results suggest that the exercise assistance chatbot is effective in promoting a healthy lifestyle and serves as a technological solution to encourage independent physical activity.
Pengelompokkan Unit Kendaraan Sewa Pada Rental Mobil Permata Transindo Sidoarjo Menggunakan Algoritma K-Means Clustering Mahelda Asri Sudarsono; Yulian Findawati; Uce Indahyanti; Ika Ratna Indra Astutik
SemanTIK : Teknik Informasi Vol. 11 No. 1 (2025): Vol. 11 No. 1 (2025): SemanTIK Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v11i1.122

Abstract

Abstrak Latar belakang penelitian ini adalah meningkatnya kebutuhan akan layanan transportasi yang efisien, di mana rental mobil menjadi solusi praktis bagi individu dan perusahaan. Namun, perusahaan menghadapi tantangan dalam mengelola armada dan menentukan keputusan pembelian kendaraan yang tepat. Oleh karena itu, penelitian ini bertujuan untuk menganalisis pengelompokkan unit kendaraan berdasarkan data penyewaan yang dikumpulkan. Metode yang digunakan adalah K-Means Clustering, yang memungkinkan pengelompokan data berdasarkan karakteristik serupa. Hasil penelitian menunjukkan bahwa empat cluster optimal berhasil diidentifikasi, dengan Davies-Bouldin Index (DBI) terendah sebesar 0,834135. Setiap cluster mewakili kategori unit berdasarkan lama sewa dan anggaran, memberikan wawasan berharga bagi perusahaan dalam manajemen pengelolaan armada kendaraan secara efektif dalam memberikan rekomendasi unit yang sesuai dengan kebutuhan pelanggan. Penelitian ini menekankan pentingnya penerapan metode K-Means sebagai alat bantu untuk perusahaan dalam menentukan jenis mobil yang banyak tersewa sehingga memudahkan dalam pengecekkan perawatan armada. Selain itu penerapan metode K-Means juga membantu dalam memberikan rekomendasi unit kepada pelanggan sesuai dengan anggaran yang diinginkan This research is motivated by the increasing need for efficient transportation services, where car rentals have become a practical solution for individuals and companies. However, companies face challenges in managing their fleet and making appropriate vehicle purchase decisions. Therefore, this study aims to analyze the grouping of vehicle units based on collected rental data. The method used is K-Means Clustering, which allows data grouping based on similar characteristics. The research results indicate that four optimal clusters were successfully identified, with the lowest Davies-Bouldin Index (DBI) of 0.834135. Each cluster represents a unit category based on rental duration and budget, providing valuable insights for companies in effectively managing their vehicle fleet and providing unit recommendations that suit customer needs. This study emphasizes the importance of applying the K-Means method as a tool for companies in determining the types of cars that are in high demand, making it easier to check fleet maintenance. In addition, the application of the K-Means method also helps in providing unit recommendations to customers according to their desired budget
WEB SCRAPING DAN NATURAL LANGUAGE PROCESSING MENGGUNAKAN CNN UNTUK ANALISIS SENTIMEN LINTAS PLATFORM DIGITAL Arjuna Adlina Martha; Ade Eviyanti; Arif Senja Fitrani; Ika Ratna Indra Astutik
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.17128

Abstract

Pertumbuhan pesat opini publik di platform digital seperti YouTube dan Detik.com menghasilkan volume data besar yang mencerminkan persepsi masyarakat. Namun, pengolahan data tersebut sering kali menghadapi kendala efisiensi jika dilakukan secara manual dan umumnya hanya berhenti pada klasifikasi sentimen tanpa memberikan solusi konkret bagi pengambil keputusan. Penelitian ini bertujuan membangun sistem analisis sentimen lintas platform end-to-end yang tidak hanya mengklasifikasikan sentimen tetapi juga menyajikan rekomendasi kebijakan otomatis. Metode yang diterapkan meliputi web scraping dinamis berbasis kata kunci, preprocessing, dan klasifikasi menggunakan algoritma Convolutional Neural Network (CNN). Selanjutnya, sistem mengintegrasikan Large Language Model (LLM) Llama untuk mengekstraksi tren kata kunci menjadi draf solusi kebijakan. Hasil pengujian menunjukkan bahwa model CNN mampu mencapai akurasi sebesar 81% dalam mengklasifikasikan sentimen. Integrasi visualisasi real-time dan rekomendasi kebijakan berbasis LLM terbukti mampu menjembatani kesenjangan antara analisis data teknis dan kebutuhan pengambilan keputusan praktis, menjadikan sistem ini solusi yang relevan dan adaptif terhadap dinamika isu publik
IMPLEMENTASI MODEL FACEBOOK PROPHET DALAM MERAMALKAN JUMLAH PENGUNJUNG WISATA EDUKASI Ananda Firly Amelia; Ade Eviyanti; Ika Ratna Indra Astutik; Hindarto Hindarto
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 2 (2026): JATI Vol. 10 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i2.17721

Abstract

Wisata edukasi Kampung Lali Gadget (KLG) menghadapi tantangan operasional akibat fluktuasi jumlah pengunjung yang sangat ekstrem, mencapai 98% antara bulan terendah dan tertinggi. Ketidakpastian ini menjadi kendala bagi pihak pengelola dalam perencanaan kurikulum permainan, bahan ajar, serta alokasi sumber daya manusia. Penelitian ini bertujuan untuk mengimplementasikan model Facebook Prophet guna menghasilkan peramalan jumlah pengunjung yang akurat sebagai panduan operasional. Metode yang digunakan adalah analisis deret waktu menggunakan algoritma Prophet yang dioptimalkan dengan penambahan extra regressor berupa hari libur nasional, libur akademik, dan event lokal KLG. Untuk mendapatkan model dengan nilai optimum, dilakukan tahapan hyperparameter tuning menggunakan metode Grid Search pada parameter prior scale yang dilanjutkan dengan manual fine-tuning untuk menghindari overfitting. Hasil penelitian menunjukkan bahwa Model Prophet 3 memberikan akurasi terbaik dengan nilai MAPE sebesar 20,93% (kategori baik). Hasil peramalan selama enam bulan ke depan memprediksi puncak kunjungan pada Januari 2026 sebanyak 1.940 pengunjung dan titik terendah pada April 2026 sebanyak 36 pengunjung. Hasil ini memberikan dasar strategis bagi pengelola dalam pengaturan jadwal relawan dan pemeliharaan fasilitas.
ANALISIS SENTIMEN KOMENTAR YOUTUBE TERHADAP KEBIJAKAN DEDI MULYADI DENGAN METODE MACHINE LEARNING: Rizky Budi Aprianto; Ade Eviyanti; Hamzah Setiawan; Ika Ratna Indra Astutik
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 2 (2026): JATI Vol. 10 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i2.17890

Abstract

Kenakalan remaja merupakan isu sosial yang memunculkan kebijakan kontroversial berupa pengiriman anak bermasalah ke barak militer oleh Dedi Mulyadi. Kebijakan tersebut menimbulkan beragam tanggapan masyarakat di platform YouTube yang mencerminkan dinamika opini publik. Permasalahan dalam penelitian ini adalah bagaimana mengklasifikasikan sentimen komentar masyarakat secara akurat serta mengevaluasi performa metode ensemble machine learning dibandingkan algoritma tunggal. Penelitian ini bertujuan menganalisis kecenderungan opini publik dan mengukur kinerja algoritma klasifikasi dalam memetakan sentimen komentar YouTube. Metode yang digunakan meliputi pengumpulan 7.875 komentar melalui YouTube Data API, pelabelan otomatis menggunakan InSet Lexicon, preprocessing teks, serta pembobotan fitur dengan TF-IDF. Model klasifikasi yang diterapkan adalah Naïve Bayes, K-Nearest Neighbor (KNN), dan Logistic Regression, yang kemudian dikombinasikan menggunakan teknik Ensemble Majority Voting. Hasil penelitian menunjukkan bahwa sentimen publik didominasi oleh kelas positif (52,4%) dibandingkan negatif (47,6%). Logistic Regression memperoleh akurasi tertinggi sebesar 0,84, sedangkan model ensemble menunjukkan performa stabil dengan akurasi sekitar 0,82. Temuan ini menunjukkan bahwa pendekatan ensemble mampu meningkatkan stabilitas klasifikasi sentimen pada isu kebijakan publik di media sosial.
Co-Authors Addriana Fatma Putri Indah Sari Ade Eviyanti Afidah, Dewi Nur Agung Izulhaq Aisha Hanif Alfian Maulana Fajar Alfitra Oktavian Ananda Firly Amelia Ansa, Mochamad Musaddat Arif Senja Fitrani Arif Senja Fitrani Arif Senja Fitriani Arjuna Adlina Martha Ary Putranto Ayyubi, Rizky Al Aziziyah, Ismi Anisa Azmuri Wahyu Azinar Baharsyah, Muhammad Ronaldo Bahri, Ari Syamsul Bakhtiar, Muhammad Yahya Billy Al Ghazian Ramadhan Akbar Chulloh, Dafid Mizta Cindy Taurusta Danang Firmansyah David Eka Ramadhan David Eka Ramadhan Dawam Muqorrobin Dina Dwi Oktavia Rini Dwi Saka Dharmawan Dwiki Aulia Akbar Eko Fahmi Rosyada Evi Rinata Fanani, Muchammad Ichsanuddin Farrell Ega Santoso Febri Rosita Dewi Feri Tirtoni Fika Fatmala Firmansyah, Anggi Dwi Bagus Hadi, Miftakhul Hamzah Setiawan Hanafi, Rizal Handayani, Nana Nur Dwi Hidayatullah, Vika Tanjung Hindarto Ida Rindaningsih, Ida Ilham Dwi Cahyo Murti Leksono Imanudin, Giri Fajar Indah Suci Purnamasari Irwan Alnarus Kautsar Isnaini Rodiyah Istikomah Istikomah Istikomah, Istikomah Istya, Riska Adi Jefry Fernando Joko Susilo Joko Susilo Joko Susilo Khasanah, Asmaul Luluk Iffatur Rocmah M. Aris Khuzaini M. Aufa Izul Haq Mahelda Asri Sudarsono Makhfudzoh, Fury MARIA BINTANG Meis Andhiarini, Rugaya Moch Bagus Tri Cahyo Moch Eko Budi Setiawan Moch. Bahak Udin By Arifin Mochamad Alfan Rosid Mochammad Bisri Mustofa Mochammad Daffa Khasifi Nashrullah Mochammad Raflie Lazuardi Mohammad Suryawinata Mohammad Yoga Firnanda Muhammad Dwiki Ramadani Muhammad Fahruddin Mahfudhoni Muhammad Fikri Muhammad Fikri Muhammad Nur Kholis Muhammad Reza Pahlevi Mulyansyah , Muhammad Febri Muqorrobin, Dawam Nanda Mujahidah Andini Noormalita, Karina Kamza Novia Ariyanti Nugraha, Prasadhana Aditya Nurdyansyah Nuril Lutvi Azizah Pamungkas, Nicky Ibrahim Pandi Rais Pangestu, Sopia Dia Prasetyana, Dwi Gilang Ramadhan Putra F, M. Bagus Putra, Davito Rasendriya Rizqullah Putra, Ryan Hanggara Ramadhan, Arga Satria Riyan Fikri Rizaldi, Dedy Rizky Budi Aprianto Rohman Dijaya Rugaya Ruri Aditya Pratama Rusdy Yusmiawan Putra Sandi Wahyu Maulana Sandy Ismayudha Slamet Riyadi Suhendro Busono Suhendro Busono Sujadi, Rizal Iman Sukma Aji Sukma, Yudya Hastriawan Sumarno Supriyadi Supriyadi Supriyadi Syafii, Olynda Mufariihana Nur Taufiki Ma'rufan Timur, Dimas Maulana Tomi Eko Hidayat Tutut Anjarsari Uce Indahyanti Widodo, Wildan Ahmad Wignya Radhitya, Yudhistira Wildan Rifai Wiwik Sulistiyowati Yeni Kurnia Wati Yulian Findawati Yunianita Rahmawati Zaenal Zaenal Zahrotun Nisa’ , Gitsa