Article Per Year (5 Year)
p-Index From 2021 - 2026
14.069
P-Index
This Author published in this journals
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
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 109 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 109 Documents
Search

 7   8   9   10   11 
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.
Perbandingan Naive Bayes dan Support Vector Machine untuk Klasifikasi Sentimen Pengelolaan Sampah Banyumas Baehaqi Wahyu Kurniawan; Pungkas Subarkah; Dinar Mustofa
Jurnal Minfo Polgan Vol. 15 No. 2 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i2.16189

Abstract

Waste management is an important environmental issue because it is closely related to environmental cleanliness, public health, and quality of life. Public opinion on waste management is increasingly expressed through social media and produces large volumes of unstructured text data. This study aims to analyze public sentiment regarding waste management in Banyumas Regency and compare the performance of the Naive Bayes and Support Vector Machine algorithms in classifying sentiment into positive, negative, and neutral classes. The data were collected from X, YouTube, TikTok, and Threads using relevant keywords related to waste management in Banyumas. The dataset was processed through data selection, manual sentiment labeling, text preprocessing, feature extraction using TF-IDF, dataset splitting with an 80:20 ratio, and class imbalance handling using SMOTE. Model performance was evaluated using confusion matrix, accuracy, precision, recall, and F1-score. The results showed that Support Vector Machine achieved a higher accuracy of 68.63%, while Naive Bayes obtained 61.76%. However, Naive Bayes produced a better macro F1-score of 0.4573 compared to 0.3039 achieved by Support Vector Machine. These findings indicate that although Support Vector Machine performs better in recognizing the majority class, Naive Bayes provides more balanced performance across all sentiment classes. Therefore, Naive Bayes is considered more suitable for classifying imbalanced multi-platform sentiment data on waste management in Banyumas.
SENTIMENT ANALYSIS OF IPUSNAS REVIEWS USING NAIVE BAYES AND K-NEAREST NEIGHBOR ALGORITHMS Augst Nurandini; Zahra Revadinika Apriliani; Eka Nada Rinjani; Pungkas Subarkah
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.528

Abstract

This study aims to analyze the sentiment of iPusnas application user reviews using the classification method with the K-Nearest Neighbor (KNN) and Naive Bayes algorithms. The data used are secondary data in the form of user reviews obtained from the Google Play Store in the period of January to December 2025 totaling 2415 reviews. This study uses a text mining approach with text preprocessing stages, feature extraction using the Bag of Words (BoW) method, and sentiment clas, sification using the Naive Bayes and K-Nearest Neighbor (KNN) algorithms. Model evaluation uses Test and ScoreConfusion Matrix, and Word Cloud. The results show that the Naive Bayes method has better performance with an accuracy value of 0.705 compared to K-Nearest Neighbor (KNN) with an accuracy value of 0.615. Testing the K parameter in the KNN algorithm shows that the best K value is obtained at K = 6 with an accuracy of 0.615. This study shows that Naive Bayes is more effective in classifying sentiment in iPusnas application user reviews.
Analisis Kepuasan Pengguna Aplikasi Kehadiran Panda menggunakan Metode System Usability Scale (Studi Kasus: PT. Puskomedia Indonesia Kreatif) Aulia Shafira Tri Damayanti; Ika Romadoni Yunita; Pungkas Subarkah
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 4 (2025): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i4.511

Abstract

PT Puskomedia Indonesia Kreatif has developed the Panda Attendance Application, a digital technology system for village administration with a focus on the smooth flow of employee infor-mation. This application utilizes QR codes and GPS technology for the attendance or absence of village employees at registered coordinates. One of the issues at PT Puskomedia is that village operators face challenges such as needing to contact the PT Puskomedia system operator for manual attendance tracking, leading to a decline in users. Currently, there are various challenges in the use of the Panda Attendance Application by village government institutions. Several com-plaints have been raised by users regarding technical issues and unsatisfactory user experience. The purpose of this study is to evaluate the level of user acceptance of the Panda application. The method used to analyze user satisfaction is the System Usability Scale (SUS) method. The results of this study yielded a SUS score of 46.22, indicating a low category (Grade E), with a percentile rank of 10%, and classified as poor (Grade E). The nature (adjective) of the application falls into the “Poor” category, and the score of 46.22 places the application in the “Not Acceptable” catego-ry according to the level of acceptance. Recommendations for improvement from this analysis in-clude enhancing system stability and technical improvements such as accelerating the QR code reading process. Responding to user feedback and implementing these improvements is expected to enable the Panda attendance application to achieve a higher level of usability and gain better acceptance from users.
Pendampingan e-Smart Early Warning untuk Peringatan Dini Banjir di Wisata Desa Karangsalam Lor Nandang Hermanto; Pungkas Subarkah; Dini Riandini; Refida Septiana Putri; Salma Ngarifatul Khofiyah; Bagus Adhi Kusuma; Primandani Arsi
Jurnal Medika: Medika Vol. 4 No. 4 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/0yyt8272

Abstract

The Juneng Mijil Community Self-Help Group (KSM) in Karangsalam Lor Village, Baturraden District, Banyumas Regency is a village tourism manager, one of which is Juneng Waterfall. The problem with the partners is that there is no technology used for early flood warning at the Juneng Waterfall and Twin Waterfall tourist sites, as well as low community literacy regarding early flood management. This activity aims to optimize the use of Android-based information technology and the Internet of Things (IoT) applied at Juneng Waterfall and Kembar Waterfall, through KSM Juneng Mijil in Karangsalam Lor Village. The implementation methods in this community service include the Pre-Implementation Stage, Implementation Stage, and Evaluation Stage. The results of the activity showed high enthusiasm among participants, as well as an increase in understanding and knowledge regarding the benefits, usage, and maintenance of the Internet of Things (IoT) and Android. This activity is important in the utilization of technology, particularly in optimal and safe flood warning systems for the community.
KOMPARASI ALGORITMA KNN DAN RANDOM FOREST UNTUK KLASIFIKASI PENYAKIT DISLEKSIA MENGGUNAKAN SMOTE-ENN: COMPARISON OF K-NN AND RANDOM FOREST ALGORITHMS FOR DYSLEXIA DISEASE CLASSIFICATION USING SMOTE-ENN Ali Nur Ikhsan; Pungkas Subarkah
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7626

Abstract

The classification of dyslexia has become a significant challenge in the field of artificial intelligence, particularly when dealing with imbalanced datasets between dyslexic and non-dyslexic individuals. This study aims to compare the performance of two machine learning algorithms, namely K-Nearest Neighbors (KNN) and Random Forest (RF), in classifying dyslexia using the SMOTE-ENN (Synthetic Minority Oversampling Technique–Edited Nearest Neighbours) data balancing technique. The dataset was obtained from the Kaggle platform, consisting of 220 initial samples and 197 features. The preprocessing stages included data subsetting, label encoding, and feature normalization using MinMaxScaler, followed by an 80% training and 20% testing data split. The results show that the application of SMOTE-ENN successfully improved the class distribution balance and enhanced the performance of both models. The Random Forest algorithm achieved the best performance with an accuracy of 92.5%, recall of 94.0%, F1-score of 92.5%, and ROC-AUC of 0.97, while KNN achieved an accuracy of 87.5% with a ROC-AUC of 0.90. The improvement in recall and F1-score demonstrates the effectiveness of SMOTE-ENN in enhancing model performance for the minority class. Overall, this study proves that the combination of machine learning algorithms with data balancing techniques can improve classification accuracy and serve as a potential solution for early detection of dyslexia based on cognitive and digital behavioral data.
RANCANG BANGUN GAME EDUKASI BAHASA ARAB 2D BERBASIS UNITY MENGGUNAKAN METODE MDLC PADA MAN PURBALINGGA Husna Maulida; Pungkas Subarkah; Banu Dwi Putranto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7843

Abstract

Arabic language learning at MAN Purbalingga still relies heavily on conventional lecture-based methods and printed textbooks, causing 62.9% of eleventh-grade students to feel disengaged from the current vocabulary (mufradat) memorization approach. This study aims to design and implement a mobile educational game entitled Arabic Quest as an interactive and engaging learning medium for Hajj and Umrah mufradat. Development was carried out using Unity 2022 LTS and C# programming language, following the Luther-Sutopo version of the Multimedia Development Life Cycle (MDLC) method, which encompasses six stages: concept, design, material collection, assembly, testing, and distribution. The final product is an Android application (minimum version 8.0 Oreo) that integrates 2D adventure exploration mechanics, a trigger-based quiz system, and a Hajj and Umrah ticket reward as feedback for mission completion. Beta Testing was conducted with 33 eleventh-grade students of MAN Purbalingga using a Likert scale 1–5 questionnaire across four indicators. Results show an average feasibility score of 86.1%, with individual scores of 86.7% for Visual Quality, 86.2% for Usability, 85.9% for Engagement, and 85.8% for Content Quality, all classified as Very Feasible. It is concluded that Arabic Quest is suitable as an effective self-learning medium and represents an innovative alternative to conventional Arabic language instruction at the madrasah aliyah level.
Analisis Sentimen Game Genshin Impact pada Play Store Menggunakan Naïve Bayes Clasifier Primandani Arsi; Pungkas Subarkah; Bagus Adhi Kusuma
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol. 3 No. 1 (2023): Maret: Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v3i1.1962

Abstract

Online games are an entertainment medium that cannot be separated from some groups of people, especially during the co-19 pandemic. The existence of a policy of violence reduces the interaction of people in the world, including in Indonesia, resulting in an increase in online activity, especially playing games. Game is an interesting online gaming platform, where players can still interact socially boldly. Genshin Impact is a mobile game that provides other platforms such as PC, PlayStation and Nintendo Switch. Although some people like this game, others are not satisfied with the game play. Sentiment analysis is also very useful when game developers want to know what users think about the game experience. This research aims to build a model of the classification of reviews on the Genshin Impact game available on the Google Play platform, so that the resulting model can provide recommendations for developers for improvement. The results obtained are reviews on the Google Play Store tend to be positive with an accuracy score of 87%, precision of 67%, memory of 98%, and f1 score of 67%. Evaluation is done by comparing the model that has been obtained in this study with the previous model with the same algorithm.
Enhancing Waste Classification with MobileNetV2: Adding a Plastic Sachets Class for Sustainable Management Argiyan Dwi Pritama; Velizha Sandy Kusuma; Wiga Maulana Baihaqi; Pungkas Subarkah
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.18931

Abstract

The issue of waste management remains a critical concern due to its adverse impact on the environment. This research enhances a deep learning-based waste classification model by introducing a new class, namely plastic sachets, to broaden the classification scope and increase the model's relevance to waste types commonly found in the community. The dataset used is an extended version of a previous open-source dataset, comprising 2,968 images divided into seven classes. Data preprocessing steps include stratified data splitting, data augmentation to increase image diversity, and pixel normalization. The model adopts the MobileNetV2 architecture through a transfer learning approach, utilizing 2D Global Average Pooling and Dense layers with softmax activation for multi-class classification. Evaluation using precision, recall, and F1-score demonstrated strong performance, with an overall accuracy of 97%. While the model performs well across most classes, further improvement is needed for minority classes such as plastic sachets. This study highlights the promising potential of deep learning in supporting automated waste sorting to promote sustainable waste management practices in Indonesia.
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