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All Journal Jurnal Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnas Nasional Teknologi dan Sistem Informasi ANDHARUPA Jurnal Informatika Jurnal Pilar Nusa Mandiri CogITo Smart Journal Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) 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 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 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 Decode: Jurnal Pendidikan Teknologi Informasi Indonesian Journal of Innovation Studies Jurnal Pengabdian Kepada Masyarakat Abdi Nusa Jurnal Minfo Polgan (JMP) Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Algoritma Jurnal Pengabdian Mitra Masyarakat (JPMM) JOMPA ABDI: Jurnal Pengabdian Masyarakat Digital Transformation Technology (Digitech) ABDINE Jurnal Pengabdian Masyarakat 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
Pungkas Subarkah
Universitas Amikom Purwokerto
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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.
DETECTION OF MICRO-VIRAL CONTENT ON TIKTOK THROUGH SOCIAL LISTENING AND MACHINE LEARNING Ratih Anggraeni; Purwadi; Pungkas Subarkah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7472

Abstract

The phenomenon of micro-virality on TikTok illustrates how content can rapidly spread on a small scale before reaching broader virality. Understanding its driving factors is essential for supporting digital marketing strategies, managing content creators, and analyzing social media trends. This study aims to detect and predict the potential of micro-virality in TikTok videos by integrating a social listening approach with machine learning techniques. The dataset consists of approximately 4,000 TikTok posts enriched with 20 features across five categories, including user metadata (author popularity, follower ratio), temporal features (posting time and day), network features (hashtags and mentions), content features (text length and keywords), and contextual elements (trending music and video duration). To ensure objective labeling, a quantile-based threshold was applied, categorizing videos in the top 25% of view counts (≥ 26,300,000 views) as viral, resulting in a class distribution of 24.74% viral and 75.26% non-viral. To address this imbalance, the SMOTENC technique was used to oversample the minority class and enhance data representativeness. Three machine learning algorithms Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were implemented. Experimental results show that Random Forest improved from 88% to 92%, XGBoost maintained strong performance at 95%, and ANN increased significantly from 92% to 93% after SMOTENC application. These findings indicate that SMOTENC effectively improves model generalization and reduces bias toward majority classes, supporting more reliable early-stage virality prediction. Overall, the study enriches social media analytics research and provides practical insights for optimizing TikTok content strategies and early trend detection.
Sentiment Analysis in User Reviews of Tourist Attractions in East Nusa Tenggara Using Machine Learning Classification Aulia Dian Agustina; Primandani Arsi; Pungkas Subarkah; Irfan Santiko
Journal of Multimedia Trend and Technology Vol. 5 No. 1 (2026): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v5i1.82

Abstract

This study aims to analyze user review sentiments for six tourist attractions in East Nusa Tenggara Province by utilizing a large amount of review data obtained from Google Maps. Data was collected through a scraping process using Serp API, followed by cleaning and text pre-processing to improve data quality. Sentiment labeling was performed automatically using the Indo-BERT model to obtain three sentiment classes: positive, negative, and neutral. Text feature representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, then classified using the baseline Support Vector Machine (SVM) model and the optimized SVM model with Grid-Search CV. The evaluation results showed that the baseline SVM model produced an accuracy of 83.87%, but showed an imbalance in performance between classes with a Macro F1-score of 0.4287. After parameter optimization using Grid-Search CV, the optimized SVM model produced an accuracy of 78.27% with an increase in the Macro F1-score value to 0.4818. This increase indicates an improvement in the model's ability to recognize minority sentiment classes despite a decrease in overall accuracy. Overall, the optimized SVM model provides more balanced and representative classification results in describing tourists' perceptions based on online reviews, so it can be used as a basis for sentiment analysis in the tourism sector.
Implementasi Tiketing Wisata Berbasis Flutter dan Laravel dengan Fitur Dashboard Analitik Pengunjung Bibit Raikhan Azzaki; Pungkas Subarkah; Nandang Hermanto
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3229

Abstract

This study aims to design and implement a digital-based tourism ticketing system at the Curug Pinang tourist attraction as a replacement for the existing manual recording system that still relies on tear-off tickets and conventional bookkeeping. The manual system has the potential to cause errors in recording visitor and transaction data and makes it difficult for managers to compile periodic visitor and revenue reports, particularly monthly reports. The research method used is system development with the Waterfall model, which includes the stages of requirements analysis, system design, implementation, testing, and deployment. The system is developed using the Flutter framework as a mobile-based ticketing and cashier application, and Laravel as the backend integrated with a database and a web-based analytics dashboard. System testing is conducted using the Black Box Testing method to ensure that all system functions operate in accordance with the specified requirements. The results show that the developed digital ticketing system is able to replace manual recording, reduce transaction recording errors, and facilitate the automatic recapitulation of visitor and revenue data. Therefore, this system improves data management efficiency and supports operational decision-making at the Curug Pinang tourist attraction.
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 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 Anggraeni, Epri Anggraini, Nova Anshari, Muhammad Rifqi Anunggilarso, Luky Rafi Arbangi Puput Sabaniyah Arief Rachman Hakim Arsi, Primandani Astrida, Deuis Nur Aulia Dian Agustina 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 Baihaqi, Wiga Maulana Bibit Raikhan Azzaki Bryan Jerremia Katiandhago Budi Utami, Dias Ayu Busyro, Muhammad Chendri Irawan Satrio Nugroho Chyntia Raras Ajeng Widiawati Cindy Magnolia Damayanti, Aulia Shafira Tri 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 Dwi Krisbiantoro, Dwi Dwi Putra, Ruly Niko Elistiana, Khoerotul Melina Enggar Pri Pambudi Fadilah, Alif Nur Fandy Setyo Utomo Faridatun Nida Farizi, Amar Al 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 Ika Romadoni Yunita Ikhsan, Ali Nur Ilham, Fatah Iphang Prayoga Irfan Santiko Irma Damayanti Irma Darmayanti Isnaini, Khairunnisak Nur Isnaini, Khairunnisak Nur Jali Suhaman Katiandhago, Bryan Jerremia Khoerida, Nur Isnaeni Khofiyah, Salma Ngarifatul 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 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 Prastyadi Wibawa Rahayu Prayoga, Iphang Primandani Arsi Primandani Arsi Pritama, Argiyan Dwi Purba, Mariana Purwadi Ragil Wilujeng Ramadani, Nevita Cahaya Ranggi Praharaningtyas Aji Ratih Anggraeni Ratih Anggraeni Rayinda Maya Anjani 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 Sandy Kusuma, Velizha Saputra, Dhanar Sari, Rida Purnama Sarmini Sarmini Satrio Nugroho, Chendri Irawan Sekhudin, Sekhudin Septi Oktaviani Nur Hidayah Septi Oktaviani Nur Hidayah Septiana Putri, Refida 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 Wachyu Dwi Susanto Wahyu, Herta Tri Wanda Fitrianingsih Wenti Risma Damayanti Wenti Risma Damayanti Widiawati, Neta Tri Yofi Yulianto Yuli Purwati Yunita, Ika Romadhoni Yunita, Ika Romadoni