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All Journal Jurnal Dedikasi Jurnal Ilmu Komputer Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Simantec Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Teknologi Informasi dan Ilmu Komputer SMATIKA Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Jurnal Pilar Nusa Mandiri Network Engineering Research Operation [NERO] Jurnal Komputer Terapan Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SINTECH (Science and Information Technology) Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURTEKSI EDUMATIC: Jurnal Pendidikan Informatika Jurnal Informatika Kaputama (JIK) JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Electronics, Electromedical Engineering, and Medical Informatics Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal AbdiMas Nusa Mandiri Jurnal Perempuan & Anak Jurnal Dinamika Informatika (JDI) Makara Journal of Technology Jurnal Sistem Informasi Jurnal Informatika: Jurnal Pengembangan IT Smatika Jurnal : STIKI Informatika Jurnal Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Repositor
Yufis Azhar
Universitas Muhammadiyah Malang
Agriculture, Biological Sciences & Forestry Humanities 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 Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Batik Images Retrieval Using Pre-trained model and K-Nearest Neighbor Minarno, Agus Eko; Hasanuddin, Muhammad Yusril; Azhar, Yufis
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1299

Abstract

Batik is an Indonesian cultural heritage that should be preserved. Over time, many batik motifs have sprung up, which can lead to mutual claims between craftsmen. Therefore, it is necessary to create a system to measure the similarity of a batik motif. This research is focused on making Content-Based Image Retrieval (CBIR) on batik images. The dataset used in this research is big data Batik images. The authors used transfer learning on several pre-trained models and used Convolutional Neural Network (CNN) Autoencoder from previous studies to extract features on all images in the database. The extracted features calculate the Euclidean distance between the query and all images in the database to retrieve images. The image closest to the query will be retrieved according to the number of r, namely 3, 5, 10, or 15. Before the image is retrieved, the retrieval system is used to re-ranked with K-Nearest Neighbor (KNN), which classifies the retrieved image. The results of this study prove that MobileNetV2 + KNN is the best model in terms of Image Retrieval Batik, followed by InceptionV3 and VGG19 as the second and third ranks. Moreover, CNN Autoencoder from previous research and InceptionResNetV2 are ranked fourth and fifth. In this study, it was also found that the use of KNN re-ranking can increase the precision value by 0.00272. For further research, deploying these models, especially for MobileNetV2 is an approach for seeing a major impact on batik craftsmanship for decreasing batik motif plagiarism.
Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network Minarno, Agus Eko; Cokro Mandiri, Mochammad Hazmi; Azhar, Yufis; Bimantoro, Fitri; Nugroho, Hanung Adi; Ibrahim, Zaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.857

Abstract

Diabetic Retinopathy (DR) is a disease that causes visual impairment and blindness in patients with it. Diabetic Retinopathy disease appears characterized by a condition of swelling and leakage in the blood vessels located at the back of the retina of the eye. Early detection through the retinal fundus image of the eye could take time and requires an experienced ophthalmologist. This study proposed a deep learning method, the Efficientnet-b7 model to identify diabetic retinopathy disease automatically. This study applies three preprocessing techniques that could be implemented in the dataset "APTOS 2019 Blindness Detection". In preprocessing technique trial scenarios, Usuyama preprocessing technique obtained the best results with accuracy of 89% of train data and 84% in test data compared to Harikrishnan preprocessing technique which has 82% accuracy in test data, and Ben Graham preprocessing has 81% accuracy in test data. In this study, Hyperparameter tuning was conducted to find the best parameters for use on the EfficientNet-B7 Model. In this study, we tested the Efficientnet-B7 model with an augmentation process that can reduce the occurrence of overfitting compared to models without augmentation. Preprocessing techniques and augmentation techniques can influence the proposed EfficientNet-B7 model in terms of performance results and reduce the overfitting of models.
Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter Mujaddid Izzul Fikri; Trifebi Shina Sabrila; Yufis Azhar
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 10 No 02 (2020): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v10i02.455

Abstract

Twitter is one of the social media that is widely used by the public as a communication media and obtain information. Through this social media, users can submit various opinions or comments on an issue. The opinions and comments that users submit through the tweets they send can be used for sentiment analysis. Therefore, in this study sentiment analysis of tweets related to the University of Muhammadiyah Malang (UMM) was carried out to determine public opinion about this campus. The analysis was carried out by classifying tweets that contain people’s sentiments regarding UMM. The classification method used in this study is Naïve Bayes and Support Vector Machine (SVM) by weighting the term using TF-IDF. The result of the two methods shows that Naïve Bayes gets better accuracy than SVM with an accuracy of 73,65%
Optimasi Pengelolaan Data Anggota Melalui Sistem Informasi di Pimpinan Daerah Muhammadiyah Kota Batu Yufis Azhar; Zamah Sari; Ali Sofyan Kholimi
Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 1 (2025): Januari
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jabdimas.v8i1.7951

Abstract

Kegiatan pengabdian ini bertujuan untuk mengembangkan dan mengimplementasikan sistem informasi keanggotaan berbasis web untuk Pimpinan Daerah Muhammadiyah (PDM) Kota Batu. Sebelum implementasi, pengelolaan data keanggotaan dilakukan secara manual, yang menyebabkan ketidakkonsistenan, kesalahan, serta keterlambatan dalam pembaruan data. Sistem yang dikembangkan menawarkan solusi dengan mengintegrasikan pendaftaran, pembaruan data, manajemen anggota, serta penyajian informasi melalui dashboard. Pengujian User Acceptance Testing (UAT) menunjukkan tingkat kepuasan pengguna yang tinggi, terutama dalam aspek keandalan dan kemudahan penggunaan sistem. Pelatihan yang diberikan kepada pengurus juga berhasil meningkatkan literasi teknologi di kalangan pengguna. Hasil kegiatan ini menunjukkan bahwa sistem informasi berbasis web ini mampu meningkatkan efektivitas dan efisiensi pengelolaan data keanggotaan serta meningkatkan partisipasi anggota dalam kegiatan organisasi.
SISTEM REKOMENDASI PENYEWAAN PERLENGKAPAN PESTA MENGGUNAKAN COLLABORATIVE FILTERING DAN PENGGALIAN ATURAN ASOSIASI Gita Indah Marthasari; Yufis Azhar; Dwi Kurnia Puspitaningrum
Jurnal Simantec Vol 5, No 1 (2015)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v5i1.1008

Abstract

ABSTRAKE-commerce berbasis web gacor slot merupakan salah satu media yang efektif dalam jual beli. Banyak usaha yang telah memanfaatkan fasilitas ini. Salah satunya adalah bidang jasa persewaan alat-alat pesta. Untuk memberikan layanan yang lebih baik, e-commerce dilengkapi dengan fitur lain antara lain sistem rekomendasi. Sistem ini memudahkan konsumen menentukan barang untuk dibeli dengan cara menampilkan produk yang terkait dengan salah satu produk lain yang dibeli atau dilihat konsumen. Salah satu mekanisme untuk membangun sistem ini adalah collaborative filtering. Cara kerja collaborative filtering adalah dengan membangun sebuah basis data yang menyimpan produk-produk yang disukai konsumen. Transaksi baru yang dibuat oleh seorang konsumen akan dicocokkan dengan basis data tersebut untuk mengetahui data historis mana yang paling sesuai dengan data baru tersebut. Data historis yang paling sesuai akan ditampilkan sebagai rekomendasi bagi konsumen yang melakukan transaksi tersebut.Salah satu teknik yang dapat digunakan adalah penggalian aturan asosiasi menggunakan Algoritma Apriori. Pada penelitian ini, dibuat sebuah website persewaan alat-alat pesta dengan menerapkan sistem rekomendasi. Sistem rekomendasi dibangun menggunakan aturan-aturan yang dihasilkan oleh Algoritma Apriori. Untuk dapat menampilkan barang rekomendasi digunakan nilai support 20, sedangkan nilai confidence digunakan untuk menentukan N-teratas barang untuk gacor slot direkomendasikan.Kata kunci : sistem rekomendasi, collaborative filtering, algoritma apriori. ABSTRACTWeb-based e-commerce is an effective media for buying and selling. Many businesses have taken the advantages of this facility. One of them is the party tools rental services. To provide better service, e-commerce is equipped with other features such as a recommendation mechanism. Thismechanism allows consumers specify the goods to be purchased by displaying products that are related to another purchased product or customer visits. One mechanism for establishing this system is collaborative filtering. Collaborative filtering works by building a database that stores the products which are preferred by consumers. New transactions made by a consumer will be matched with the database to find out which data are related the most. The most appropriate historical data to be displayed as a recommendation for consumers who conduct such transactions. One technique that can be used is extracting association rules using Apriori Algorithm. In this study, a website of party tools rental service is created to implement the gacor slot recommendation system. A recommendation system built using rules generated by Apriori Algorithm. To be able to display items used on the value of the support 20, while the confidence value is used to determine the N-top items to be recommended.Keywords: recommender system, collaborative filtering, apriori algorithm.
Logistic Regression Using Hyperparameter Optimization on COVID-19 Patients’ Vital Status Vinna Rahmayanti Setyaning Nastiti; Yufis Azhar; Riska Septiana Putri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4868

Abstract

This study aims to classify COVID-19 patients based on the results of their hematology tests. Hematology test results have been shown to be useful in identifying the severity and risk of COVID-19 patients. Specifically, this study focuses on classifying COVID-19 patients based on their vital status, namely Deceased and Alive. The dataset used in this study contains four variables: white blood cells (WBC), neutrophils (NEU), lymphocytes (LYM), and Neutrophil Lymphocyte Ratio (NLR). Logistic Regression algorithm was used to solve the problem, and hyperparameter optimization was implemented to obtain the best model performance. The objective of this study was to build the best parameter in classifying the patients’ vital status. The proposed model achieved an accuracy score of 78%, which is the best performance among the tested models. The results of this study provide a key component for decision making in hospitals, as it provides a way to quickly and accurately identify the vital status of COVID-19 patients. This study has important implications for managing the COVID-19 pandemic and should be of interest to researchers and practitioners in the field.
CLASSIFICATION OF COFFE FRUIT DRYING USING VGG16 Annisa Diyan Novitasari; Yufis Azhar
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16055

Abstract

The drying process is a crucial stage in coffee post-harvest handling that directly affects the final product quality, especially in the specialty coffee segment. Assessment of the coffee fruit drying level in the field is still largely carried out visually and subjectively, which can potentially lead to inconsistent quality. This study aims to develop an automatic classification system for coffee fruit drying levels based on digital images using a deep learning method with the Convolutional Neural Network (CNN) VGG16 architecture. The dataset used consists of 561 coffee fruit images classified into three classes: Wet, Medium, and Dry. The preprocesssing stages include background removal, auto-cropping, and image standardization. Two models were developed: a baseline model without data augmentation and a model with data augmentation and selective fine-tuning on the final layers of VGG16. The evaluation results show that the baseline model achieved a validation accuracy of 83%, while the model with augmentation and fine-tuning improved the accuracy to 94%, accompanied by significant increases in precision, recall, and F1-score values. The proposed model also demonstrates a high and stable level of prediction confidence. These results prove that the VGG16 approach is effective for classifying coffee fruit drying levels and has the potential to be applied as an objective post-harvest quality control support system.
PENDEKATAN HOLISTIK VALUE PROPOSITION PENGGALIAN KEBUTUHAN SISTEM INFORMASI LEMBAGA KEMITRAAN DAN KAJIAN STRATEGIS PDM KOTA BATU Wahyu Andhyka Kusuma; Agus Eko Minarno; Yufis Azhar; Denar Regata Akbi; Yuda Munarko; Ali Sofyan Kholimi; Zamah Sari; Syaifuddin Syaifuddin; Luqman Hakim
Jurnal AbdiMas Nusa Mandiri Vol. 8 No. 3 (2026): Periode Juli 2026
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/abdimas.v8i3.6221

Abstract

The holistic value proposition approach to identifying the information system needs of the Lembaga Kemitraan dan Kajian Strategis (LKKS) of PDM Kota Batu is an initiative aimed at improving the efficiency and effectiveness of the institution's operations by developing an information system that has not yet been available within the organization. Information systems play an important role in supporting the institution's strategic activities and partnership programs. The community service method involved a series of in-depth interviews, focus group discussions, and surveys to identify users' primary needs and expectations regarding the information system. Pain and Gain analysis was employed to identify the strengths, weaknesses, opportunities, and threats associated with implementing the new information system. The results showed that the holistic value proposition approach successfully identified specific needs that were not only functionally relevant but also provided added value for all stakeholders. The implementation of the recommendations from the needs assessment is expected to improve institutional performance and collaboration. The holistic value proposition approach proved effective in identifying comprehensive information system needs and provides a strong foundation for developing a more responsive and value-added information system for LKKS PDM Kota Batu.
Penerapan Algoritma C5.0 Pada Analisis Faktor-Faktor Pengaruh Kelulusan Tepat Waktu Mahasiswa Teknik Informatika Universitas Muhammadiyah Malang Vinna Rahmayanti Setyaning Nastiti; Yufis Azhar; Andriani Eka Pramudita
Jurnal Repositor Vol. 1 No. 2 (2019): Desember 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v1i2.30377

Abstract

Kelulusan tepat waktu mahasiswa merupakan salah satu permasalahan yang sulit untuk diatasi oleh setiap pihak perguruan tinggi, begitu pula pada jurusan Teknik Informatika Universitas Muhammadiyah Malang. Permasalahan ini harus segera diatasi mengingat kualitas mahasiswa akan mempengaruhi sebuah akreditasi perguruan tinggi maupun jurusan. Oleh karena itu, perlu dilakukan analisis faktor-faktor pengaruh kelulusan tepat waktu mahasiswa Teknik Informatika UMM. Penelitian ini menggunakan algoritma C5.0 untuk melakukan seleksi fitur penting dan analisis regresi untuk melakukan estimasi peluang kelulusan tepat waktu mahasiswa. Variabel bebas yang digunakan adalah jenis kelamin, asal daerah, status masuk, SKS semester 4, SKS semester 6, IP semester 2, IP semester 4, IP semester 6, IPK semester 2, IPK semester 4, IPK semester 6, jenis SMA, status SMA, pendidikan orang tua, dan pekerjaan orang tua. Hasil implementasi algoritma C5.0 pada penelitian ini mampu melakukan seleksi fitur dengan menghasilkan 8 dari total keseluruhan 15 fitur dengan nilai akurasi yang lebih baik dibandingkan nilai akurasi yang menggunakan keseluruhan fitur. Serta, penelitian ini mampu memberikan model regresi dengan nilai akurasi sebesar 82%.
Prediksi Harga Emas Menggunakan Univariate Convolutional Neural Network Imam Halimi; Yufis Azhar; Gita Indah Marthasari
Jurnal Repositor Vol. 1 No. 2 (2019): Desember 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v1i2.30385

Abstract

Dalam berinvestasi, tak lepas dengan menebak naik turunya harga agar tidak rugi dalam berinvestasi. Hal ini diperlukan dukungan teknologi untuk dapat mengetahui informasi dalam menghadapi harga yang selalu berubah-ubah setiap hari dan bahkan setiap jamnya. Investor dalam hal ini untuk komoditi emas harus dapat memprediksi harga yang selalu serubah-ubah tersebut sebelum melakukan trasnsaksi jual maupun beli, agar investor tepat dalam melakukan aktivitas jual maupun beli saham. Dengan demikian, penulis akan membuat penelitian mengenai prediksi harga emas dunia, yang bermanfaat bagi investor maupun masyarakat yang akan melakukan jual beli emas dalam bentuk saham ataupun barang agar tepat dalam mengambil keputusan. Tujuan dari prediksi adalah memperkecil kesalahan, sehingga selisih antara perkiraan dengan kejadian yang sebenarnya diminimalkan. Suatu prediksi tidak dapat dipastikan tepat sepenuhnya, tetapi memungkinkan untuk memberikan hasil yang mendekati dengan kejadian sebenarnya. Algoritma yang digunakan dalam penelitian ini adalah Convolutional Neural Network (CNN). CNN termasuk dalam bidang Deep Learning (DL), yang termasuk dalam sub bidang dari Machine Learning (ML), yang mana menerapkan konsep dasar algoritma ANN dengan lapisan yang lebih banyak. Dalam penelitian ini, penulis menggunakan pendekatan univariate CNN. Dilakukan beberapa pengujian pada parameter model CNN. Hasil terbaik ditunjukan pada model 1 yaitu pengujian dense kondisi 5, yaitu dengan parameter model filters = 64, kernel = 2, pooling = 2, epochs = 2.000, dan dense = 50 dengan hasil RMSE yaitu 690,40.
Co-Authors A.A. Ketut Agung Cahyawan W Achmad Fauzi Saksenata Adhigana Priyatama Aditya Dwi Maryanto Aditya Dwi Maryanto Adnan Burhan Hidayat Kiat Adnan Burhan Hidayat Kiat Afdian, Riz Agus Eko Minarno Agus Zainal Arifin Ahmad Annas Al Hakim Ahmad Annas Al Hakim Ahmad Darman Huri Ahmad Hanif Nurfauzi Ahmadu Kajukaro Akbi, Denar Regata Akmal Muhammad Naim Al asqalani, Sheila Fitria Al-rizki, Muhammad Andi Alfin Yusriansyah Ali Sofyan Kholimi Amelia, Putri Juli Ananda Ayu Dianti Andhika Ade Verdiyanto Andhika Pranadipa Andhika Pranadipa Andi Shafira Dyah Kurniasari Andreawana, Andreawana Andriani Eka Pramudita Andriani Eka Pramudita Annisa Annisa Annisa Diyan Novitasari Annisa Fitria Nurjannah Aria Maulana Aripa, Laofin Aris Muhandisin arrafiq, ubay hakim Arya, Tri Fidrian Audi Bayu Yuliawan Aulia Ligar Salma Hanani Bagas Aji Aprian Basuki, Setio Bayu Yuliawan, Audi Bintang, Rahina Chandranegara, Didih Rizki Chita Nauly Harahap Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma Cokro Mandiri, Mochammad Hazmi Denny Risky Delis Putra Dewi Agfiannisa Diana Purwitasari Didih Rizki Chandranegara Doni Yulianti Doni Yulianto Dwi Anggraini Puspita Rahayu Dwi Kurnia Puspitaningrum DWI RAHMAWATI Dyah Anitia Dyah Anitia Dyah Ayu Irianti Dyah Ayu Irianti Eko Budi Cahyono Elsyah Ayuningrum Elza Norazizah Elza Norazizah Ertha Risky Pratisca Evi Febrion Rahayuningtyas Faizun Nuril Hikmah Faizun Nuril Hikmah Faldo Fajri Afrinanto Fatimah Defina Setiti Alhamdani Fenny Linsisca Putri Feny Novia Rahayu Feranandah Firdausi Ferin Reviantika Ferin Reviantika Fikri, Ulul Fiqri Azmi Fachir Fiqri Azmi Fachir Firdausi, Feranandah Firdausita, Nuris Sabila Firdausy, Aidia Khoiriyah Firdhansyah Abubekar Firdhansyah Abubekar Fitri Bimantoro Galang Aji Mahesa Galang Aji Mahesa Gita Indah Marthasari Haidar Zakki Jumali Hanung Adi Nugroho Haqim, Gilang Nuril Hardianto Wibowo Haris Diyaul Fata Haris Diyaul Fata Harmanto, Dani Hasanuddin, Muhammad Yusril Hermansyah Adi Saputra Hiu Adam Abdullah Hussin Agung Wijaya Ibrahim, Zaidah Ilham Rahmana Syihad Imam Halimi Imam Halimi Irfan, Muhammad Irham Bagus Jatiarso Ivan Dwi Nugraha Jahtra Hidayatullah Jalu Nusantoro Khoirir Rosikin Khoirir Rosikin Kiki Ratna Sari Kiki Ratna Sari Leta Anindya Riyadi Lina Dwi Yulianti Linggar Bagas Saputro Luqman Hakim Lusianti, Aaliyah M Syawaluddin Putra Jaya M. Randy Anugerah M. Syawaluddin Putra Jaya Mahar Faiqurahman Maskur Maskur Maskur Maskur Masluha, Ida Maulina Balqis Meilina Agustina Meilina Agustina Mentari Mas'ama Safitri Mentari Mas'ama Safitri Moch Shandy Tsalasa Putra Moch. Chamdani Mustaqim Mochammad Hazmi Cokro Mandiri Moh. Badris Sholeh Rahmatullah Muhammad Aji Purnama Wibowo Muhammad Al Reza Fahlopy Muhammad Andi Al-Rizki Muhammad Athaillah Muhammad Athaillah Muhammad Bima Al Fayyadl Muhammad Fadliansyah Muhammad Ferry Fernanda Muhammad Hussein Muhammad Misbahul Azis Muhammad Nuchfi Fadlurrahman Muhammad Reza Syahfahlevi Sahri Muhammad Riadi Muhammad Riadi Muhammad Rifal Alfarizy Muhammad Rivaldi Asyhari Muhammad Rizki Muhammad Rizki Muhammad Rizky Iman Permana Muhammad Rizky Iman Permana Muhammad Shalahuddin Zulva Mujaddid Izzul Fikri Mujaddid Izzul Fikri Nabillah Annisa Rahmayanti Nabillah Annisa Rahmayanti Nina Mauliana Noor Fajriah Nina Mauliana Noor Fajriah Novandha Yudyanto Novandha Yudyanto Noviani Sintia Duwi Trisna Nur Hayatin Nur Putri Hidayah Nuryasin, Ilyas Oktavia Dwi Megawati Otto Endarto Otto Endartoi Prakoso, Rahmat Pratama, Dhimas Rama Anthony Navy Pritha Aulliah Putri, Ira Ekanda Rahma Ningsih Rahma Ningsih Rangga Kurnia Putra Wiratama Ratna Sari Rifky Ahmad Saputra Rifky Ahmad Saputra Riksa Adenia Riska Septiana Putri Rista Azizah Arilya Riz Afdian Rizal Arya Suseno Rizal Rakhman Mustafa Rizal Rakhman Mustafa Rozi, Fahrur S, Vinna Rahmayanti Sabrila, Trifebi Shina Saniyya Ruzzy Marwa Saputri, Indah Sari Wahyunita Sari Wahyunita Sari, Veronica Retno Sari, Zamah Satrio Hadi Wijoyo Septiyan Andika Isanta Setiono, Fauzan Adrivano Shintya Larasabi , Auliya Tara Silcillya Ayu Astiti Siti Maghfiroh Siti Maghfiroh Sucia, Dara Suryani Rachmawati Suseno, Jody Ririt Krido Susi Ekawati Syaifuddin Syaifuddin Syaifuddin Syaifuddin Syaifudin Zuhri Syaifudin Zuhri Taufik Nurahman Taufik Nurahman Tri Fidrian Arya Ujilast, Novia Adelia Ulfah Nur Oktaviana Veronica Retno Sari Vinna Rahmayanti Vinna Utami Putri Wahyu Andhyka Kusuma Wahyu Priyo Wicaksono Wana Salam Labibah Wicaksono, Galih Wasis Widya Rizka Ulul Fadilah Wildan Suharso Wildan Suharso Wildan Suharso Yesicha Amilia Putri Yuda Munarko Yuda Munarko Yudhono Witanto Yurizal Rizqon Rifani Yusuf, Achmad Zamah Sari Zulva, Muhammad Shalahuddin