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All Journal Jurnal Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnal Informatika dan Teknik Elektro Terapan Jurnas Nasional Teknologi dan Sistem Informasi ANDHARUPA RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Informatika Jurnal Pilar Nusa Mandiri CogITo Smart Journal Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JMM (Jurnal Masyarakat Mandiri) JTAM (Jurnal Teori dan Aplikasi Matematika) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah DoubleClick : Journal of Computer and Information Technology MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Building of Informatics, Technology and Science Infotekmesin Jurnal Teknologi Informasi dan Multimedia Journal of Information Systems and Informatics Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] Scientific Journal of Informatics JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) IJIIS: International Journal of Informatics and Information Systems Indonesian Journal of Data and Science JPMB: Jurnal Pemberdayaan Masyarakat Berkarakter Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Teknika Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Journal of Technology and Informatics (JoTI) TIERS Information Technology Journal Indonesian Journal of Innovation Studies Jurnal Pengabdian Kepada Masyarakat Abdi Nusa Jurnal Minfo Polgan (JMP) Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Pengabdian Mitra Masyarakat (JPMM) JOMPA ABDI: Jurnal Pengabdian Masyarakat Digital Transformation Technology (Digitech) Journal of Multimedia Trend and Technology Journal of Artificial Intelligence and Digital Business Jurnal Krisnadana Bulletin of Social Informatics Theory and Application Jurnal Pengabdian Kepada Masyarakat Ceria Jurnal Medika: Medika Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif Prosiding Seminar Nasional Pemberdayaan Masyarakat (SENDAMAS) TECHNOVATE Edu Komputika Journal Jurnal Informatika Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer ABDINE :Jurnal Pengabdian Masyarakat
Pungkas Subarkah
Universitas Amikom Purwokerto
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Perbandingan Metode Klasifikasi Data Mining untuk Nasabah Bank Telemarketing Pungkas Subarkah; Enggar Pri Pambudi; Septi Oktaviani Nur Hidayah
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 20 No. 1 (2020)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v20i1.826

Abstract

Bank merupakan perusahaan yang memiliki data yang besar yang tersimpan di dalam database dan diolah menghasilkan sebuah informasi yang saling berkaitan tentang nasabah. Bank, harus memiliki ide dan terobosan baru guna mengetahui kendala pada nasabah telemarketing yang ingin melakukan deposito pada Bank tersebut, agar Bank terhindar dari ancaman krisis keuangan. Penelitian ini menguji keberhasilan Bank telemarketing dengan cara melakukan klasifikasi keputusan nasabah dengan menerapkan data mining. Metode yang di gunakan algoritma Classification and Regression Trees (CART) dan naive bayes menggunakan dataset diambil dari University of California Irvine (UCI) Repository Learning. Adapun metode validasi dan evaluasi yang digunakan yaitu 10-cross validation dan confusion matrix. Hasil akurasi pada algoritma CART yaitu 89.51% dengan nilai precision 87%, Recall 89% dan F-Measure 88% dan pada algoritma naive bayes mendapatkan nilai akurasi sebesar 86.88% dengan nilai precision 87%, Recall 86% dan F-Measure 87%. Dari hasil tersebut dapat disimpulkan bahwa algoritma CART lebih baik dalam memprediksi keputusan nasabah telemarketing tepat dalam penawaran deposito.
Opinion Mining on Spotify Music App Reviews Using Bidirectional LSTM and BERT Primandani Arsi; Reza Arief Firmanda; Iphang Prayoga; Pungkas Subarkah
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

The increasing number of user reviews on digital music platforms such as Spotify highlights the importance of sentiment analysis to better understand user perceptions. This study aims to develop a sentiment classification model for Spotify user reviews using a Bidirectional Long Short-Term Memory (BiLSTM) approach combined with BERT embeddings. The dataset consists of multilingual user reviews collected from the Google Play Store. Preprocessing steps include text cleaning, tokenization, and padding. BERT is utilized to generate contextual word embeddings, which are then processed by the BiLSTM model to classify sentiments as either positive or negative. The model’s performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the BiLSTM-BERT model achieves an F1-score of 0.8852, a recall of 0.9396, a precision of 0.8375, and an accuracy of 0.8374. These findings demonstrate the model’s effectiveness in handling multilingual sentiment analysis tasks, offering valuable insights for developers in enhancing user experience through data-driven decision-making.
COMPARISON OF BILSTM, SVM FOR PBB-P2 TAX POLICY SENTIMENT ANALYSIS Rofiqoh, Dayana; Subarkah, Pungkas; Isnaini, Khairunnisak Nur
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 2 (2026): Maret 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i2.4199

Abstract

Abstract: The policy to increase the Rural and Urban Land and Building Tax (PBB-P2) in Indonesia often elicits mixed reactions from the public. Some support it because they believe it can strengthen regional fiscal capacity, while others reject it because they are concerned that it will increase the economic burden on the community. Understanding public sentiment towards this policy is important for evaluating the effectiveness of the policy and formulating appropriate communication strategies. This study aims to analyze public sentiment towards the PBB-P2 increase policy using data uploaded on Platform X (Twitter). The data were collected through crawling with the keyword “building tax,” then processed through several preprocessing stages before classifying tweets into positive and negative sentiments. Two models were used: Support Vector Machine (SVM) and Bidirectional Long Short-Term Memory (BiLSTM). Results show that SVM outperformed BiLSTM, achieving training accuracy of 99.4% and testing accuracy of 85.9%, with accuracy 0.8595, precision 0.8536, recall 0.8595, and F1-score 0.8449. Meanwhile, BiLSTM achieved training accuracy of 86.9% and testing accuracy of 82.9%, with accuracy 0.8294, precision 0.8150, recall 0.8294, and F1-score 0.8080. These findings suggest SVM is more effective in classifying public sentiment and can support better evaluation of regional tax policies. Keywords: sentiment analysis; PBB-P2; BiLSTM; SVM; X platform Abstrak: Kebijakan kenaikan tarif Pajak Bumi dan Bangunan Perdesaan dan Perkotaan (PBB-P2) di In-donesia sering memunculkan beragam reaksi dari masyarakat. Sebagian mendukung karena dianggap dapat memperkuat kapasitas fiskal daerah, sementara lainnya menolak karena kha-watir menambah beban ekonomi masyarakat. Pemahaman terhadap sentimen publik atas ke-bijakan tersebut penting untuk mengevaluasi efektivitas kebijakan dan merumuskan strategi komunikasi yang tepat. Penelitian ini bertujuan menganalisis sentimen masyarakat terhadap kebijakan kenaikan PBB-P2 menggunakan data unggahan di Platform X (Twitter). Data dik-umpulkan melalui proses crawling dengan kata kunci “pajak bangunan” kemudian diproses melalui beberapa tahap preprocessing sebelum diklasifikasikan menjadi sentimen positif dan negatif. Dua model digunakan dalam penelitian ini, yaitu Support Vector Machine (SVM) dan Bidirectional Long Short-Term Memory (BiLSTM). Hasil penelitian menunjukkan bahwa SVM memiliki kinerja lebih baik dibandingkan BiLSTM, dengan akurasi pelatihan 99,4% dan akurasi pengujian 85,9%. Nilai akurasi 0,8595, precision 0,8536, recall 0,8595, dan F1-score 0,8449. Sementara itu, BiLSTM memperoleh akurasi pelatihan 86,9% dan akurasi pengujian 82,9%, dengan akurasi 0,8294, precision 0,8150; recall 0,8294; dan F1-score 0,8080. Temuan ini menunjukkan bahwa SVM lebih efektif dalam mengklasifikasikan sentimen publik serta dapat mendukung evaluasi kebijakan pajak daerah dengan lebih baik. Kata kunci: analisis sentimen; PBB-P2; BiLSTM; SVM; platform X
Rectified Linear Units and Adaptive Moment Estimation Optimizer on ANN with Saved Model Prediction to Improve The Stock Price Prediction Framework Performance Sekhudin, Sekhudin; Purwati, Yuli; Utomo, Fandy Setyo; Azmi, Mohd Sanusi; Subarkah, Pungkas
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1586.271-282

Abstract

A stock is a high-risk, high-return investment product. Prediction is one way to minimize risk by estimating future prices based on past data. There are limitations to solving the stock prediction problem from previous research: limited stock data, practical aspects of application, and less than optimal stock price prediction results. The main objective of this study is to improve the prediction performance by formulating and developing the stock price prediction framework. Furthermore, the research provides a stock price prediction framework that can produce better prediction results than the previous study with fast computation time. The proposed framework deals with data generation, pre-processing and model prediction. In further, the proposed framework includes two prediction methods for predicting stock closing prices: stored model prediction and current model prediction. This study uses an artificial neural network with Rectified Linear Units as an activation function and Adam Optimizer to predict stock prices. The model we have built for each forecasting method shows a better MAPE value than the model in previous studies. Previous research showed that the lowest MAPE was 1.38% for TLKM shares and 0.81% for BBRI. Our proposed framework based on the stored model prediction method shows a MAPE value of 0.67% for TLKM shares and 0.42% for BBRI. While the current model prediction method shows a MAPE value of 0.69% for TLKM shares and 0.89% for BBRI. Furthermore, the stored model prediction method takes 1.0 seconds to process a single prediction request, while the current model prediction takes 220 seconds.
Comparison of correlated algorithm accuracy Naive Bayes Classifier and Naive Bayes Classifier for heart failure classification Subarkah, Pungkas; Damayanti, Wenti Risma; Permana, Reza Aditya
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1148.120-125

Abstract

Heart failure (ARF) is a health problem that has relatively high mortality and morbidity rates in developed or developing countries, including Indonesia. In 2016, WHO stated that 17.5 million people died from cardiovascular disease, while in 2008, HF disease represented 31% of patient deaths worldwide. One of the new breakthroughs for early diagnosis is utilizing data mining techniques. In this study, the Correlated Naive Bayes Classifier (C-NBC) and Naive Bayes Classifier (NBC) algorithms are used to obtaining the best accuracy results so that they can be used for the Heart Failure dataset. Based on the results of the tests that have been carried out, it shows that the Correlated Naive Bayes Classifier (C-NBC) algorithm accuracy of 80.6% obtains higher accuracy than the Naive Bayes Classifier (NBC) algorithm of 67.5%. With the results of this study, the use of the Correlated Naive Bayes Classifier (C-NBC) algorithm can be used to diagnose patients with heart failure (heart failure) because it has a high level of accuracy and is categorized as Good Classification.
Sentiment analysis of customer satisfaction levels on smartphone products using Ensemble Learning Ma’ruf, Muhammad; Kuncoro, Adam Prayogo; Subarkah, Pungkas; Nida, Faridatun
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1377.339-347

Abstract

Increasingly sophisticated technological developments create new ways for people to conduct trading business. An example of this technology application is the use of e-commerce. However, there are conditions where the seller cannot measure the level of satisfaction and identify problems experienced by his customers if it is only based on the rating as the case in smartphones transactions. Therefore, a solution is needed to create a system that can filter negative and positive comments. This study offers a solution to address this issue by using machine learning employing the K-Nearest Neighbors, SVM, and Naive Bayes algorithms with hyperparameters from previous studies. This study applied the ensemble learning method with the Voting Classifier technique, which is an algorithm to combine several algorithms that have been made. From the test results, the highest accuracy was obtained by SVM with an accuracy value of 91.18% while the ensemble learning method obtained an accuracy value of 89.22%. The difference in the accuracy of training and testing for SVM and ensemble learning method is 7.1% and 4% respectively. These results indicate that the ensemble learning method can help improve the performance of sentiment analysis algorithms for comments on smartphone products.
Pendampingan Media Pembelajaran Berbasis Artificial Intelligence Untuk Meningkatkan Kinerja Guru Subarkah, Pungkas; Arsi, Primandani; Rofiqoh, Dayana; Anggraeni, Ratih; Riyanto
Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 7 No. 1 (2026): Vol. 7 No. 1, April 2026
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/society.v7i1.1268

Abstract

One institution that plays a role in education is the school. Their contribution to the development of high-quality human resources for a country's advancement is very important, namely, educators or teachers. Therefore, the position of learning is very important to continue to be the driving force for learning units or schools so that they can continue to improve the quality of learning or the quality of learning for their students. One way to improve the quality of educators or teachers is by improving teacher performance. The purpose of this service is to improve teacher performance through artificial intelligence training, in order to add to and improve teacher performance in the current era at SMA Negeri 1 Banyumas. The methods used to carry out this activity included the preparation stage, the implementation stage, and the evaluation stage. The results obtained from the mentoring of learning media based on artificial intelligence showed that the 31 participating teachers experienced an increase in their knowledge and skills, as evidenced by the post-test results, which scored 91%. It is hoped that similar training will continue to be carried out in the future.
Fine-tuned hyperparameter optimization for phishing website detection: insights into efficiency and performance Rizki Wahyudi; Azhari Shouni Barkah; Siti Rahayu Selamat; Pungkas Subarkah
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.1920

Abstract

The escalation of digital threats has made phishing-site identification a critical aspect of online protection. This study investigates how systematic hyperparameter adjustment through grid search influences both predictive precision and computational efficiency in phishing detection. Nine supervised classifiers from different algorithmic families were analyzed: tree-based models (DT, RF, GB, XGBoost), margin and distance-based learners (SVM, k-NN), probabilistic and neural approaches (NB, MLP), and a linear baseline using logistic regression (LR). Although machine learning (ML) approaches have demonstrated strong predictive capability, their reliability largely depends on precise parameter calibration. Through systematic exploration of parameter combinations, the grid-search approach identifies optimal settings for each model. Using the Kaggle phishing-URL dataset, tuned models achieved noticeable accuracy gains. DT, RF, and k-NN reached 99.1% accuracy with training times of 0.10 s, 1.55 s, and 0.01 s, respectively. MLP yielded 99.0% accuracy but required 2758 s, while SVM and LR achieved 97.8% and 92.9%. NB did the worst (62.7%). The results indicate that careful hyperparameter optimization enhances predictive ability, whereas model complexity heavily impacts runtime. This study’s novelty lies in a balanced assessment of accuracy and efficiency trade-offs, offering guidelines for selecting computationally efficient algorithms in practical phishing-detection systems.
Rancang Bangun Aplikasi Mobile Jaringan Lokal Kendali Jarak Jauh ESP32-CAM Triyo Ginanjar Pamungkas; Pungkas Subarkah; Abdul Azis; Ika Romadoni Yunita
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.8996

Abstract

Penggunaan teknologi Internet of Things (IoT) berbasis mikrokontroler, khususnya modul ESP32-CAM yang mengintegrasikan komputasi dan akuisisi citra visual, semakin meluas di berbagai sektor pemantauan dan keamanan. Namun, ketergantungan pada infrastruktur internet eksternal dan perute nirkabel (router) sering menjadi kendala utama, terutama saat sistem diimplementasikan pada area luar ruangan yang terpencil atau minim sinyal. Penelitian ini bertujuan untuk merancang dan membangun aplikasi mobile berbasis Android sebagai antarmuka sistem kendali jarak jauh untuk modul ESP32-CAM dengan memanfaatkan topologi jaringan lokal mandiri (mobile hotspot) pada smartphone. Metode penelitian yang digunakan meliputi perancangan arsitektur jaringan klien-server berbasis protokol HTTP, perancangan antarmuka pengguna (User Interface) yang intuitif, pengujian fungsionalitas perangkat lunak dengan metode Black-Box Testing, serta pengujian latensi transmisi jaringan pada variasi jarak 1 hingga 10 meter. Hasil penelitian menunjukkan bahwa aplikasi Android yang dikembangkan berhasil terhubung dan merender aliran video streaming dari ESP32-CAM secara stabil tanpa memerlukan akses internet eksternal. Pengujian Black-Box mengonfirmasi bahwa 100% fitur kendali, termasuk navigasi arah dan eksekusi pengambilan gambar, berfungsi secara akurat sesuai rancangan. Lebih lanjut, pengujian kinerja jaringan mencatatkan waktu tunda (latensi) transmisi yang sangat rendah, yaitu rata-rata 45 milidetik pada jarak operasional 1 meter. Kesimpulannya, aplikasi mobile ini terbukti menawarkan solusi antarmuka pemantauan yang portabel, memiliki responsivitas tinggi, dan hemat biaya untuk pengendalian perangkat IoT secara nirkabel, sehingga sangat aplikatif untuk diimplementasikan di lingkungan dengan keterbatasan infrastruktur jaringan seperti lahan pertanian.
Akuisisi Citra Penyakit Padi Menggunakan Node Sensor IoT Berbasis ESP32-CAM Yofi Yulianto; Pungkas Subarkah; Abdul Azis; Riyanto Riyanto
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.8998

Abstract

Penyakit pada tanaman padi, seperti hawar daun dan tungro, dapat menyebabkan penurunan hasil panen secara signifikan yang mengancam ketahanan pangan jika tidak dideteksi sejak dini. Untuk melatih model klasifikasi kecerdasan buatan dalam mengenali penyakit tersebut, dibutuhkan dataset citra visual yang berskala besar dan berkualitas tinggi. Sayangnya, pengambilan dataset secara manual di area persawahan memakan waktu, tenaga, dan sangat rentan terhadap inkonsistensi sudut serta jarak pandang pengamat. Penelitian ini bertujuan untuk merancang bangun purwarupa perangkat keras node sensor Internet of Things (IoT) berbasis mikrokontroler ESP32-CAM guna mengotomatisasi proses akuisisi data visual penyakit padi di lingkungan terbuka. Sistem utama mengintegrasikan sensor jarak ultrasonik HC-SR04 untuk memastikan lensa kamera OV2640 selalu berada pada titik fokus yang ideal sebelum pengambilan gambar. Selain itu, perangkat keras ini dilengkapi dengan motor servo sebagai aktuator untuk mengatur sudut pandang kamera secara mekanis dan presisi tanpa perlu memindahkan fisik alat. Catu daya sistem disuplai melalui skema distribusi kelistrikan paralel menggunakan powerbank portabel untuk mencegah terjadinya fenomena penurunan tegangan (brownout reset) pada mikrokontroler saat komponen mekanis beroperasi. Hasil pengujian menunjukkan bahwa arsitektur perangkat keras mampu beroperasi dengan sangat stabil tanpa interupsi daya, dan node sensor berhasil mengakuisisi citra daun padi dengan tajam pada jarak fokus optimal. Alat ini memberikan solusi fisik yang tangguh dan portabel untuk pengumpulan dataset visual secara masif. Ke depannya, purwarupa ini siap diintegrasikan dengan aplikasi kontrol jarak jauh untuk memfasilitasi pemantauan dan pergerakan alat secara nirkabel.
Co-Authors A. Kholil Hidayat Abdallah, Muhammad Marshal Abdul Azis Adam Prayogo Kuncoro Adam Prayogo Kuncoro Adam Prayogo Kuncoro Adhimah, Laily Farkhah Aditya Permana, Reza Afifah, Erika Luthfi Agus Pramono Akhmad Mustolih Ali Nur Ikhsan Alif Nur Fadilah Alifah Dafa Iftinani Alifian , Raditya Sani Alya Khansa Dzakkiyah Amin, M. Syaiful Amira Aida Rashifa Anggi Tri Dewi Septiani Anggraeni, Eling Sekar Anggraini, Nova Anshari, Muhammad Rifqi Anunggilarso, Luky Rafi Arbangi Puput Sabaniyah Argiyan Dwi Pritama Arsi, Primandani Astrida, Deuis Nur Augst Nurandini Aulia Dian Agustina Aulia Shafira Tri Damayanti Aunillah, Puteri Johar Awal Rozaq, Hasri Akbar Awali, Uston Azhar Andika Putra Azhari Shouni Barkah Azizan Nurhakim Azmi, Mohd Sanusi Azzahra, Delia Oktaviana Baehaqi Wahyu Kurniawan Bagus Adhi Kusuma Bagus Adhi Kusuma Bagus Adi Kusuma Baihaqi, Wiga Maulana Banu Dwi Putranto Bibit Raikhan Azzaki Bryan Jerremia Katiandhago Budi Utami, Dias Ayu Busyro, Muhammad Chendri Irawan Satrio Nugroho Chyntia Raras Ajeng Widiawati Cindy Magnolia Damayanti, Wenti Risma Darmo, Cahyo Pambudi Dava Patria Utama Dermawan, Riky Dimas Desi Riyanti Dewi Fortuna Dhanar Intan Surya Saputra Dias Ayu Budi Utami Dias Ayu Budi Utami, Dias Ayu Budi Didit Suhartono Dinar Mustofa Dini Ambarwati Dini Riandini Dominic Dinand Dwi Krisbiantoro, Dwi Dwi Putra, Ruly Niko Eka Nada Rinjani Elistiana, Khoerotul Melina Enggar Pri Pambudi Epri Anggraeni Esti Widianti Fadilah, Alif Nur Fandy Setyo Utomo Faridatun Nida Farizi, Amar Al Febi Dwi Sasmita Fiby Nur Afiana Fiby Nur Afiana Firmanda, Reza Arief Fitriya Maharani, Lulu Amnah Gina Cahya Utami Harun Alrasyid Hellik Hermawan Hendra Marcos Hendra Marcos, Hendra Hidayah, Debby Ummul hidayatulloh, hanif Husna Maulida Ika Romadoni Yunita Ika Romadoni Yunita Ikhsan, Ali Nur Ilham, Fatah Iphang Prayoga Irfan Santiko Irma Darmayanti Isnaini, Khairunnisak Nur Isnaini, Khairunnisak Nur Jali Suhaman Katiandhago, Bryan Jerremia Khoerida, Nur Isnaeni Kholifah Dwi Prasetyo Kartika, Nur Kisma, Atmaja Jalu Narendra Kusuma, Bagus Adhi Kusuma, Velizha Sandy Latifah Adi Triana Lestari, Tri Endah Widi Lestari, Vika Febri Luki Rafi Anuggilarso Maharani Kusuma Dewi Maria Angelina Cahyani Candrakasih Marlita, Reva Ma’ruf, Muhammad Merliani, Nanda Nurisya Mohammad Imron Muflikhatun, Siti Muhammad Marshal Abdallah Muhammad Rifqi Anshari Mustolih, Akhmad Nanda Nurisya Merliani Nandang Hermanto Nandang Hermanto Nasar Ghanim, Nadif Neta Tri Widiawati Nida, Faridatun Nikmah Trinarsih Nur Hidayah, Septi Oktaviani Nur Isnaeni Khoerida Nuraini , Rema Sekar Nurul Hidayati Permana, Reza Aditya Pramudya, Reyvaldo Shiva Prasetya, Eko Budi Prasetyo Kartika, Nur Kholifah Dwi Prastyadi Wibawa Rahayu Prayoga, Iphang Primandani Arsi Primandani Arsi Purba, Mariana Purwadi Purwadi Ragil Wilujeng Ramadani, Nevita Cahaya Ranggi Praharaningtyas Aji Ratih Anggraeni Ratih Anggraeni Rayinda Maya Anjani Refida Septiana Putri Reykha Putri Randika Reza Aditya Permana Reza Arief Firmanda Riandini, Dini Riyanto Riyanto Riyanto Riyanto Riyanto Riyanto Riyanto Rizki Sadewo Rizki Wahyudi Rofiqoh, Dayana Rohman, M. Abdul Romadoni, Nova Salma Rosana Fadilla Sari Rujianto Eko Saputro Sabaniyah, Arbangi Puput Sadewo, Rizki Salma Ngarifatul Khofiyah Salsabiela, Ayuni Saputra, Dhanar Sari, Rida Purnama Sarmini Sarmini Satrio Nugroho, Chendri Irawan Sekhudin, Sekhudin Septi Nurhayati Septi Oktaviani Nur Hidayah Septi Oktaviani Nur Hidayah Sholikhatin, Siti Alvi SITI ALVI SHOLIKHATIN Siti Alvi Solikhatin Siti Alvi Solikhatin Siti Rahayu Selamat Sugiarti Sugiarti Suhaman, Jali Susanto, Wachyu Dwi Syabani, Amin Syamsiar, Syamsiar Tarwoto, Tarwoto Tri Astuti Trian Damai Triana, Latifah Adi Tripustikasari, Eka Tripustikasari Triyo Ginanjar Pamungkas Umma, Rofiqul Utami, Melida Ratna Utomo, Anwar Tri V, Jay Velizha Sandy Kusuma Wachyu Dwi Susanto Wahyu, Herta Tri Wanda Fitrianingsih Wenti Risma Damayanti Wenti Risma Damayanti Widiawati, Neta Tri Wiga Maulana Baihaqi Yanuar Wardanu Yofi Yulianto Yuli Purwati Yunita, Ika Romadhoni Zahra Revadinika Apriliani