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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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Penerapan Algoritma Apriori Pada Transaksi Penjualan Untuk Rekomendasi Menu Makanan Dan Minuman Merliani, Nanda Nurisya; Khoerida, Nur Isnaeni; Widiawati, Neta Tri; Triana, Latifah Adi; Subarkah, Pungkas
Jurnal Nasional Teknologi dan Sistem Informasi Vol 8 No 1 (2022): April 2022
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v8i1.2022.9-16

Abstract

Semakin pesatnya pertumbuhan bisnis bidang kuliner, membuat persaingan bisnis dibidang ini juga semakin ketat. Warung tenda atau yang biasanya disebut warten banyak menyajikan menu dan minuman, namun perlunya pelaku bisnis berusaha menghasilkan inovasi produk demi memberikan pelayanan memuaskan kepada pelanggan. Pada kondisi tersebut dibutuhkan sebuah teknik pengolahan data untuk mengetahui rekomendasi menu pada Warung Tenda. Metode analisis yang digunakan adalah teknik data mining dengan algoritma Apriori, dimana algoritma ini untuk mennetukan himpunan data yang paling sering muncul (frequent itemset). Hasil dari penelitian didapatkan bahwa Nilai Support dan Confidence tertinggi ialah Es Teh Manis dan Mendoan dengan nilai Support 50% dan Confidence 76%. Hal ini dapat menjadi rekomendasi kombinasi menu dari data yang telah dikumpulkan dan diterapkan algoritma apriori sehingga diharapkan dapat digunakan untuk evaluasi pelayanan serta mampu meningkatkan kepuasaan pelanggan agar Warung tenda dapat berkembang lebih pesat.
Optimizing the Implementation of the Greedy Algorithm to Achieve Efficiency in Garbage Transportation Routes Hidayatulloh, Hanif; Subarkah, Pungkas; Dermawan, Riky Dimas; Rohman, M. Abdul
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i4.16612

Abstract

Until now, the waste problem is still a crucial problem, including in the Banyumas Regency area. The uncontrolled accumulation of garbage at the TPS will of course greatly disturb the comfort of the community around the TPS. As is the case with the accumulation of garbage at TPS (Garbage Disposal Sites) in North Purwokerto District. When searching for this garbage transportation route, the Greedy Algorithm works by finding the smallest weight point by calculating the route passed and calculating the weight depending on the weight of the stages that have been passed and the weight at the stage itself. Based on the results of the system testing that has been made, the shortest route for transporting waste from the starting point of the Banyumas Environment Office is to go to the final disposal site in Tipar, and return to the starting point of the Banyumas Environmental Office. So that the total distance traveled to return to the starting point is 53 km long. Based on the findings and discussions of this research, the results obtained are the determination of the shortest route from node A back to node A. Specifically, the route involves traveling from the DLH Banyumas Regency to TPS Grendeng, TPS Karangwangkal, TPS Pabuwaran, TPS Sumampir, TPS Purwanegara, TPS Bobosan, to TPA Tipar, and then returning to DLH Banyumas Regency. These results have implementable implications in the context of waste management in this area, with a total distance traveled of 53 kilometers.
Optimasi Klasifikasi Gaya Belajar Mahasiswa Inklusif Berdasarkan Model VAK dengan Stratified Split dan Multilayer Perceptron Kusuma, Velizha Sandy; Setyo Utomo, Fandy; Baihaqi, Wiga Maulana; Subarkah, Pungkas
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 5: Oktober 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Identifikasi gaya belajar mahasiswa dengan mempertimbangkan fitur disabilitas memiliki peran penting dalam menciptakan pengalaman belajar yang inklusif dan personal. Namun, ketidakseimbangan data dalam kategori gaya belajar dan disabilitas menimbulkan tantangan yang signifikan bagi model klasifikasi. Penelitian ini bertujuan mengatasi tantangan tersebut dengan menerapkan teknik stratified split untuk menjaga keseimbangan distribusi kelas, khususnya pada variabel disabilitas dan gaya belajar. Algoritma Random Forest dan Multilayer Perceptron (MLP) digunakan untuk mengklasifikasikan gaya belajar mahasiswa berdasarkan model Visual, Auditory, dan Kinesthetic (VAK). Data yang digunakan berasal dari Open University Learning Analytics Dataset (OULAD), yang diproses melalui penggabungan data, pengkodean label, dan transformasi fitur untuk meningkatkan kinerja model. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model MLP mencapai kinerja sempurna dengan skor 100% pada semua metrik, sementara Random Forest menunjukkan performa sangat baik dengan skor 99%. Implementasi stratified split terbukti efektif dalam menjaga keseimbangan distribusi data, memastikan representasi yang memadai untuk semua kelas, termasuk mahasiswa dengan disabilitas. Penelitian ini memberikan kontribusi penting dalam mengembangkan model klasifikasi gaya belajar yang lebih akurat dan mendukung pendekatan pembelajaran yang lebih inklusif.   Abstract Identifying students' learning styles by considering disability features plays an important role in creating an inclusive and personalized learning experience. However, the imbalance of data in learning style and disability categories poses significant challenges for classification models. This research aims to overcome these challenges by applying a stratified split technique to maintain a balanced class distribution, especially in the disability and learning style variables. Random Forest and Multilayer Perceptron (MLP) algorithms are used to classify student learning styles based on the Visual, Auditory, and Kinesthetic (VAK) model. The data used comes from the Open University Learning Analytics Dataset (OULAD), which is processed through data merging, label coding, and feature transformation to improve model performance. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results showed that the MLP model achieved perfect performance with a score of 100% on all metrics, while Random Forest showed excellent performance with a score of 99%. The implementation of stratified split proved effective in maintaining the balance of data distribution, ensuring adequate representation for all classes, including students with disabilities. This research makes an important contribution in developing more accurate learning style classification models and supporting more inclusive learning approaches.
Design and Build Chatbot Application for Tourism Object Information in Bengkulu City Rohman, M. Abdul; Subarkah, Pungkas
TECHNOVATE: Journal of Information Technology and Strategic Innovation Management Vol. 1 No. 1 (2024): January 2024
Publisher : PT.KARYA GEMAH RIPAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52432/technovate.1.1.2024.28-34

Abstract

As technology develops and the number of new arrivals to the city of Bengkulu increases, the need for information regarding information related to tourism in Bengkulu is also increasing. The growth of Bengkulu tourism increases very rapidly every year as many local and foreign tourists visit the city of Bengkulu. Therefore, the author intends to build a chatbot that functions as a Virtual Assistant for Bengkulu tourists and those outside the city of Bengkulu. This chatbot is able to provide information to tourists through data stored in the system. The implementation and design of this software produces a chatbot that is built using the Extreme Programming (XP) method. Chatbots are also able to answer questions according to the abilities embedded in them. The application of chatbots provides fast information in a relatively short time to obtain information because the questions asked can be answered directly.
Analisis Sentimen Media Sosial X Terhadap Kenaikkan PPN di Indonesia Menggunakan Algoritme Naïve Bayes dan Support Vector Machine (SVM) Ikhsan, Ali Nur; Pungkas Subarkah; Alifah Dafa Iftinani; Alif Nur Fadilah
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2518

Abstract

One of the ways to increase state revenue is by raising the Value-Added Tax (VAT). However, implementing a VAT hike policy often elicits both positive and negative responses from the public. With the presence of social media, people can voice their opinions about government policies, including through social media platform X. This study aims to analyze public sentiment on social media X using the Naïve Bayes and Support Vector Machine (SVM) algorithms. The research compares the highest accuracy results before and after the balancing process. The dataset comprises 2,852 rows in CSV format. The findings indicate that the SVM algorithm achieves an accuracy of 98% before balancing and 97% after balancing, while Naïve Bayes achieves an accuracy of 97% before balancing and 90% after balancing. Overall, both algorithms demonstrate good and balanced performance.
Improving K-Means Clustering Accuracy for Academic Success Investigation With Extreme Gradient Boosting Algorithm Darmayanti, Irma; Adhimah, Laily Farkhah; Sadewo, Rizki; Hidayati, Nurul; Subarkah, Pungkas
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.26657

Abstract

Human Resources (HR) has a very important role in the development of the nation, so to improve the quality of human resources, education is needed. Education has a role in developing science, disseminating, socializing, and applying it. So that education is one of the important factors in advancing a nation. However, there are still many challenges in achieving quality education, especially in developing countries such as Indonesia, such as parental education level, socioeconomic status, and environmental conditions can also affect the quality of education and students' opportunities for academic success. The research methods used in this research are problem identification, data collection, data analysis, and evaluation. The results in this study are an increase in accuracy of 38.55% from the difference in the K-Means accuracy value of 14% resulting from the David Bounded Index and the use of the extreme gradient adaboost algorithm.
Early Prediction of Stroke Disease Diagnosis Patients Using Data Mining Algorithm Comparison Subarkah, Pungkas; Damayanti, Wenti Risma; Sabaniyah, Arbangi Puput
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.25955

Abstract

Stroke constitutes a medical emergency of paramount significance, characterized by a notably elevated mortality rate, and stands as the foremost cause of mortality within hospital settings. The dataset employed for this analysis is sourced from Kaggle, denoted as the Brain Stroke Dataset, encompassing a total of 4981 records. This research aims to carry out early prediction of stroke sufferers using several algorithms including the ANN algorithm, CART, KNN, and the NBC algorithm. The results obtained in the ANN algorithm obtained an accuracy of 93.53%, in the CART algorithm of 95.02%, in the KNN algorithm of 91.09% and in the NBC algorithm of 88.44%. With the outcomes of this research, the use of the cart set of rules may be used for early evaluation of stroke sufferers because it has a good degree of accuracy and is listed inside the excellent type kind
IMPROVEMENT OF NAIVE BAYES ALGORITHM IN SENTIMENT ANALYSIS OF SHOPEE APPLICATION REVIEWS ON GOOGLE PLAY STORE Elistiana, Khoerotul Melina; Bagus Adhi Kusuma; Subarkah, Pungkas; Awal Rozaq, Hasri Akbar
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1486

Abstract

Reviews of the shopee application on the google play store are included in useful information if processed properly. Old or new users can analyze app reviews to get information that can be used to evaluate services. The activity of analyzing application reviews is not enough just to see the number of stars, it is necessary to see the entire contents of the review comments to be able to know the intent of the review. A sentiment analysis system is a system used to automatically analyze a review to obtain information including sentiment information that is part of an online review. The data is classified using Naive Bayes. A total of 1,000 shopee app user reviews were collected to form the sample dataset. The purpose of this study is to determine the sentiment analysis of shopee application reviews in the Google Play Store using the Naive Bayes algorithm. The stages of this research include, data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage there are 6 stages, namely Cleaning, Case folding, Word Normalizer, Tokenizing, Stopword Removal and Stemming. TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighting. The data will be grouped into two categories, namely negative and positive. The data will then be evaluated using accuracy parameter testing. The test results show an accuracy value of 81%, this result shows that shopee application reviews tend to be negative.
Sentiment Analysis of Twitter Cases of Riots at Kanjuruhan Stadium Using the Naive Bayes Method Katiandhago, Bryan Jerremia; Mustolih, Akhmad; Susanto, Wachyu Dwi; Subarkah, Pungkas; Satrio Nugroho, Chendri Irawan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i1.2196

Abstract

Sentiment analysis is a process carried out to analyze opinions, sentiments, judgments, and emotions from the riot case at the kanjuruhan stadium. The purpose of this research is to find out public opinion about the tragedy that is currently happening at the Kanjuruhan Stadium. the data was obtained from social media Twitter using the Twitter API, then after that, an analysis was carried out. data from the results of the analysis will be classified using the Naive Bayes method. The classification process is divided into 7 (seven) stages, namely Crawling, Cleansing, pre-processing, labeling, classification, data training, and data testing. In the labeling process, data is classified into 2 (two) classes, namely the positive class and the negative class. The data obtained before the preprocessing process was 1963 tweets, after the preprocessing the data obtained was 1001 tweets. The data will be trained and tested using the naive Bayes classification method. classification results obtained precision values of 82% for negative data and 65% for positive data, recall values obtained 74% for negative data and 75% for positive data, F1-score values obtained 78% for negative data and 70% for positive data, while accuracy value obtained 74%.
Performance Comparison of CART And KNN Algorithms for Analyzing Early Predictions of Mental Health Anggraeni, Eling Sekar; Fitriya Maharani, Lulu Amnah; Desi Riyanti; Aji, Ranggi Praharaningtyas; Pungkas Subarkah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4232

Abstract

Currently, mental health is an unresolved mental health problem both at the national and international levels. Mental health disorders are conditions where a person has difficulty in adjusting to the conditions around them. Mental Health is an important aspect of overall health. Efforts to maintain and improve it can help a person achieve better well-being in everyday life.  This research aims to conduct Early Prediction Analysis related to mental health problems experienced by students by measuring the accuracy level of the analysis. This research was conducted using the CART (Classification and Regression Trees) and KNN (K-Nearest Neighbor) algorithms with a set of Mental Health Datasets consisting of 11 attributes and 101 data.  The data is processed using the Weka Application and the accuracy results of each algorithm are obtained, amounting to 94.0594% for the CART Algorithm and 91.0891% for the KNN Algorithm. From this achievement, it can be concluded that the performance of the CART and KNN algorithms falls into the Excellent Classification category. Judging from the accuracy obtained, the CART algorithm has a higher accuracy value than the KNN algorithm, so the CART algorithm has a high performance for analyzing early prediction of mental health of students who do not take steps in seeking mental health support.
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