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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Simantec Jurnal Informatika dan Teknik Elektro Terapan Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komtika (Komputasi dan Informatika) Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Nasional Komputasi dan Teknologi Informasi CSRID (Computer Science Research and Its Development Journal) Jurnal IMPACT: Implementation and Action Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] Jurnal Informasi dan Teknologi JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Tematik : Jurnal Teknologi Informasi Komunikasi Journal of Computer Networks, Architecture and High Performance Computing Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Walisongo Journal of Information Technology Jumat Informatika: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK Jurnal Media Informatika TIERS Information Technology Journal Jurnal Informatika Dan Tekonologi Komputer (JITEK) Digital Transformation Technology (Digitech) Adpebi Science Series INSTALL: Information System and Technology Journal INTECH (Informatika dan Teknologi) Jurnal Komtika (Komputasi dan Informatika) Jurnal Informatika Dan Tekonologi Komputer
Finki Dona Marleny
Universitas Muhammadiyah Banjarmasin
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Neuroscience Social Sciences Transportation Other

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Rancang Bangun Alat Musik Tradisional Berbasis Android Mambang Mambang; Subhan Panji Cipta; Septian Eka Prastya; Muhammad Zulfadhilah; Finki Dona Marleny; Ropikah Ropikah; Muhammad Riduan Syafi’i; Nur Meilianti Maulida; Sandro Nesta Pembriano; Risma Risma; Muhammad Zaini Bakri; Kartika Kartika; Putri Putri
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 2 (2022): April 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i2.4036

Abstract

Peradaban yang semakin maju dengan adanya teknologi digital telah membawa kita semua pada era baru, dimana perubahan terjadi dimana dan terasa sangat cepat. Perkembangan dan kemajuan teknologi digital sangat mempengaruhi perkembangan ilmu pengetahuan dari berbagai aspek. Teknologi telah mempengaruhi kehidupan ini dan tidak bisa dihindari, karena IPTEK memberikan banyak manfaat dan memudahkan pekerjaan. Rancang bangun alat musik daerah berbasis android yang dilakukan dalam penelitian ini adalah mengembangkan salah satu lat musik tradisional dari kalimantan selatan yaitu panting yang penggunaannya secara digital atau berbasis android. Pada penelitian ini motede yang kami gunakan adalah metode waterfall. Metode waterfall merupakan model pengembangan sistem informasi yang sistematik dan sekuensial. Pada penelitian ini menghasilkan sebuah aplikasi berbasis android yang berfungsi untuk memberikan kemudahan kepada masyarakat agar dapat mengetahui informasi mengenai alat musik panting, sehingga dengan adanya aplikasi berbasis android ini, dapat meningkatkan minat masyarakat khususnya generasi muda dalam melestarikan budaya lokal atau budaya daerah.
Explanatory Data Analysis to Evaluate Keyword Searches for Educational Videos on YouTube with a Machine Learning Approach Mambang, Mambang; Hidayat, Ahmad; Wahyudi, Johan; Marleny, Finki Dona
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11502

Abstract

One of the most important parts of data science is the process of explanatory data analysis. This study aims to analyze learning videos on YouTube using search keywords such as learning biology, chemistry, physics, computers, mathematics, management, accounting, citizenship, history, and culture. The method used is the explanatory data analysis technique with a Machine Learning approach. The dataset used in this study uses learning video search keywords found on the YouTube digital platform. After doing a thorough analysis of all existing variables, we found that in the context of searching for learning video keywords on YouTube, the viewing variable has a heatmap correlation of 0.97 on the likes variable, 0.97 on the subscribers variable, -0.15 on the duration variable and 0.95 on the comment variable. The duration variable negatively correlates with all variables based on the analysis using a correlation heatmap using the seaborn library. Our analysis found that the number of learning videos with the search keyword Mathematics had the highest number of views among other variables. Further research can use existing variables or also add variables and add search keywords on YouTube. The data analysis approach can also be done using SPSS, R and also a Machine Learning approach with different libraries.
Prediction of linear model on stunting prevalence with machine learning approach Mambang, Mambang; Marleny, Finki Dona; Zulfadhilah, Muhammad
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4028

Abstract

An increase in the number of residents should be anticipated including in the health sector, especially the problem of stunting. Stunting in children disrupts height and lack of absorption of nutrients. Information and data drive change in many areas such as health, entertainment, economics, business, and other strategic areas. The stages carried out in this study are initiating, developing linear models, and making prediction results on linear machine learning models. The results of testing with the scikit-learn linear model with a minimum variable of 19 get the best test results, namely the polynomial regression with pipeline model with mean absolute percentage error (MAPE) 0.02, root mean square error (RMSE) 3.32, and coefficient of determination (R2) 1,00. Testing with the scikit-learn linear model with a maximum variable of 48 gets the best test results, namely the polynomial regression with pipeline model with MAPE 0.00, RMSE 3.79 and R2 1.00. Testing with the scikit-learn linear model with an average variable of 32 gets the best test results, namely the polynomial regression model with MAPE 0.01, RMSE 3.32, and R2 1.00. The results of testing with the scikit-learn linear model with the minimum, maximum, and average variables get the best test results, namely the polynomial regression with pipeline model.
Artificial Intelligence and Digital Economy: Comparative Adoption of Regions and Populations in ASEAN Countries Using EDA Samita, Mambang; Mambang; Muhammad Zulfadhilah; Septyan Eka Prastya; Finki Dona Marleny
Adpebi Science Series 2022: 1st AICMEST 2022
Publisher : ADPEBI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The purpose of this paper is to make a comparative analysis of artificial intelligence adoption and the potential of the digital economy in ASEAN countries. The regions of countries and populations of the ASEAN Region correlate with the adoption of artificial intelligence and the potential of the digital economy. This paper uses qualitative methods and experiments with secondary data sources from online websites. The data used has been validated with other online sources that are credible and follow global information provisions. This proposed paper has four variables used as indicators in data visualization related to AI Adoption, Area, Population, and the digital economy. The four countries analyzed are members of ASEAN. The results of exploratory data analysis using the Seaborn library using the Python programming language obtained correlation results consisting of the variables Adoption of AI, Area, Population, and Digital Economy. The correlation of the Adoption of AI variables with the Digital Economy correlates 0.94. Adoption of AI with Population correlates 0.93. Adoption of AI with an Area of 0.86. Furthermore, the Area or region variable has a correlation value of 0.97 with the digital economy. Areas with a population have a correlation value of 0.98. The Population variable has a very strong correlation with the digital economy of 1. Further research can add several variables such as the potential for future jobs and the number of countries so that it is not limited to ASEAN countries alone.
Analisis Kesadaran Masyarakat Terhadap Bahaya Cybercrime di Media Sosial dengan Metode Machine Learning Putri, Putri; Mambang, Mambang; Prastya, Septyan Eka; Marleny, Finki Dona
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 5 (2024): Oktober 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i5.8139

Abstract

Abstrak -Meningkatnya pengguna media sosial di seluruh dunia, maka dari itu para pelaku kejahatan dunia maya pun mulai melancarkan aksinya untuk mencari keuntungan dari para pengguna media sosial. Salah satunya dengan melakukan phising. Phising merupakan suatu bentuk kegiatan yang mengancam atau menjebak seseorang dengan cara mengiming-imingi orang dengan cara menipu seseorang sehingga secara tidak langsung orang tersebut memberikan informasi pribadi yang dibutuhkan oleh pelaku kejahatan. Penelitian yang dilakukan bertujuan untuk menganalisis tingkat kewaspadaan masyarakat terhadap bahaya kejahatan dunia maya di media sosial khususnya di Instagram dengan menggunakan metode machine learning. Penelitian dilakukan di Banjarmasin, Kalimantan Selatan dengan menggunakan metode algoritma K-Nearest Neighbor (K-NN) untuk mengukur tingkat kewaspadaan masyarakat terhadap bahaya phising di media sosial Instagram. Hasil penelitian menunjukkan bahwa dari 210 data responden, sebanyak 137 orang di Banjarmasin menyadari adanya bahaya phising di media sosial Instagram, sedangkan 73 orang menunjukkan hasil tidak sadar. Pengujian algoritma K-NN dengan 70% data latih dan 30% data uji menghasilkan akurasi sebesar 98%. Selanjutnya, uji K-NN dengan Cross Validation menggunakan k-fold 5 memberikan akurasi sebesar 94%, dan matriks konfusi menunjukkan hasil metrik presisi 100%, recall 97%, dan skor f1 98%. Dengan demikian, algoritma K-NN terbukti efektif dalam pemodelan dengan data kesadaran masyarakat terhadap bahaya kejahatan siber phishing di media sosial Instagram. Penelitian yang telah dilakukan diharapkan dapat meningkatkan kesadaran masyarakat dan memberikan arahan dalam mengurangi risiko serangan phishing di media sosial..Kata kunci: Kesadaran Masyarakat, Cybercrime, Phising, Media sosial, Machine learning, K-Nearest Neighbor Abstract - The rise of social media users around the world, therefore cybercriminals have also begun to launch their actions to seek profits from social media users. One of them is by phishing. Phishing is a form of activity that threatens or traps someone by luring people by deceiving someone so that the person indirectly provides personal information needed by criminals. The research conducted aims to analyze public awareness of the dangers of cybercrime on social media, especially on Instagram, using machine learning methods. The research was conducted in Banjarmasin, South Kalimantan using the K-Nearest Neighbor (K-NN) algorithm method to measure the level of public awareness of the dangers of phishing on Instagram social media. The results of the study showed that out of 210 respondent data, 137 people in Banjarmasin were aware of the dangers of phishing on Instagram social media, while 73 people showed unconscious results. Testing the K-NN algorithm with 70% training data and 30% data testing resulted in an accuracy of 98%. Furthermore, the K-NN test with Cross Validation using k-fold 5 provides an accuracy of 94%, and the confusion matrix shows the results of 100% precision metrics, 97% recall, and 98% f1 score. Thus, the K-NN algorithm has proven to be effective in modeling with public awareness data on the dangers of cybercrime phishing on Instagram social media. The research that has been carried out is expected to increase public awareness and provide guidance in reducing the risk of phishing attacks on social media.Keywords: Public awareness, Cybercrime, Phising, Media sosial, Machine learning, K-Nearest Neighbor 
Analisis Penerapan Teknik Search Engine Optimization (SEO) pada Website Universitas Sari Mulia untuk Meningkatkan Indikator Visibility Webometrics Tiara, Astia Rahma; Nurhaeni, Nurhaeni; Mambang, Mambang; Prastya, Septyan Eka; Marleny, Finki Dona
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 4 (2024): Agustus 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i4.7779

Abstract

Abstrak - Universitas Sari Mulia mempunyai website sebagai wadah promosi serta membantu memperkenalkan dan mempromosikan kampus Universitas Sari Mulia agar lebih dikenal ketika pengguna internet mencari kata kunci ”Universitas Sari Mulia” dengan nama domain unism.ac.id. Pencarian website yang terdapat di internet diperlukan sebuah mesin pencari atau search engine salah satunya ialah google. Namun, tidak semua website dapat muncul pada halaman pertama dari sebuah mesin pencari, maka perlu adanya penerapan teknik search engine optimization (SEO) dengan tujuan untuk meningkatkan indikator visibility webometrics agar website bisa optimal berada pada halaman pertama atau baris pertama saat melakukan pencarian dengan kata kunci ”Universitas Sari Mulia”. Metode yang digunakan yaitu menerapkan teknik search engine optimization (SEO) dengan hasil penelitian website unism.ac.id ketika menerapkan teknik search engine optimization (SEO) mampu mengoptimalkan website berada pada halaman pertama dan traffic website unism.ac.id mendapati peringkat Indonesia pada urutan 323. Penerapan ini juga meningkatkan indikator visibility website dengan angka 9041 pada webometrics pemeringkatan Perguruan TinggiKata kunci: Universitas Sari Mulia, search engine optimization, website,visibility, webometrics Abstract - Sari Mulia University has a website as a promotional forum and helps introduce and promote the Sari Mulia University campus so that it is better known when internet users search for the keyword "Sari Mulia University" with the domain name unism.ac.id. Searching for websites on the internet requires a search engine, one of which is Google. However, not all websites can appear on the first page of a search engine, so it is necessary to apply search engine optimization (SEO) techniques with the aim of increasing webometrics visibility indicators so that websites can optimally be on the first page or first line when searching with keywords. "Sari Mulia University". The method used is applying search engine optimization (SEO) techniques with the results of research on the unism.ac.id website. When applying search engine optimization (SEO) techniques it is able to optimize the website to be on the first page and unism.ac.id website traffic finds Indonesia's ranking in the order of 323. This implementation also increases the website visibility indicator with the number 9041 in the University ranking webometrics.Keywords: Sari Mulia University, search engine optimization, website, visibility,webometrics
SISTEM PEMANTAUAN KETINGGIAN AIR SUNGAI UNTUK TANGGAP BENCANA BANJIR BERBASIS INTERNET OF THINGS Marleny, Finki Dona; Sari, Novita; Ansari, Rudy; Fitri, Aulia; Mambang, Mambang
PENDIDIKAN SAINS DAN TEKNOLOGI Vol 12 No 1 (2025)
Publisher : STKIP PGRI Situbondo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47668/edusaintek.v12i1.1427

Abstract

Banjir dengan dampak berskala luas melanda beberapa provinsi di Kalimantan. Pada Januari 2021 dari data BNPB tercatat sebagai banjir besar yang melanda Provinsi Kalimantan Selatan, ribuan rumah terendam dan banyak fasilitas umum rusak akibat bencana banjir. Peristiwa banjir di provinsi Kalimantan selatan berasal dari beberapa kabupaten di provinsi. Pada pertengahan tahun, bencana banjir melanda di provinsi Kalimantan Utara dan Kalimantan Barat, sedangkan pada Agustus 2021 bencana banjir tercatat telah merendam di beberapa daerah di provinsi Kalimantan Timur dan Kalimantan Tengah. Banyak korban dan fasilitas yang rusak, jika tidak ditangani dengan benar, akan menghambat, mengganggu dan membahayakan masyarakat. Instrumen yang diakui untuk mengelola peristiwa bencana memiliki siklus respons manajemen risiko yang cepat. Provinsi dengan tingkat risiko rawan banjir menunjukkan bahwa pencegahan banjir dan situasi pemantauan curah hujan penting untuk deteksi bencana banjir sehingga provinsi sekitarnya dapat mendukung provinsi lain dalam keadaan darurat, Informasi cepat dalam memulai deteksi bencana banjir dapat mengurangi risiko kerusakan pasca-banjir. Penelitian ini bertujuan untuk memantau keadaan cuaca dan batas ketinggian air sungai yang sebagaian besar menjadi pemicu terjadinya banjir di wilayah Kalimantan. Sistem Pemantauan menggunakan perangkat mobile dengan menghimpun data secara Real-Time dari sensor-sensor cerdas yang tertanam pada sistem berbasis Internet of Things pada daerah yang rawan akan banjir dan sistem terintegrasi dengan ponsel cerdas untuk tanggap bencana banjir.
PELATIHAN DIGITAL MARKETING PADA KELOMPOK PENGRAJIN PEMULA SASIRANGAN DESA SEI JINGAH Ahadi Ningrum, Ayu; Dona Marleny, Finki; Ansari, Rudy; Windarsyah; Kamaruddin; Gazali, Mukhaimy; Saubari, Nahdi; Maulida, Ihdalhubbi
Jurnal IMPACT: Implementation and Action Vol. 5 No. 1 (2022): Jurnal Impact
Publisher : Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/impact.v5i1.14781

Abstract

Sasirangan merupakan kain khas Kalimantan Selatan yang memiliki potensi pasar yang sangat bagus. Ratusan industri perajin sasirangan dari industri rumah tangga, skala mikro dan makro sudah mulai berkembang di Kalimantan Selatan dengan total omzet ratusan juta per bulan. Pesatnya perkembangan industri ini juga berbanding lurus dengan persaingan di pasar kain khusus Banua yang juga semakin ketat. Kegiatan pengabdian masyarakat pelatihan pemasaran digital ini bertujuan untuk memberikan pemahaman tentang pemasaran produk berbasis teknologi dan keterampilan menggunakan teknologi kepada pengrajin sasirangan pemula. Peserta yang terlibat dalam pelatihan pengabdian masyarakat digital marketing ini terdiri dari 15 orang yang merupakan pengrajin kain sasirangan di Desa Sasirangan, Sei Jingah Banjarmasin. Materi dalam upaya pengembangan usaha berbasis teknologi bagi Pengrajin Sasirangan di Desa Sei Jingah melalui pelatihan digital marketing antara lain: 1) Menumbuhkan jiwa wirausaha dan memberikan inspirasi kesuksesan bisnis online, 2) Sharing session, 3) Pelatihan digital marketing (melalui sosialisasi media dan e-commerce).
Intelligent Monitoring System Framework for Peatland Management in IoT-Integrated Precision Agriculture Marleny, Finki Dona; Novriansyah, Irvan; Maulida, Ihdalhubbi; Ansari, Rudy; Mambang, Mambang; Saubari, Nahdi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2955

Abstract

Peatlands have excellent air retention capabilities and are crucial for environmental health. They act as natural sponges, absorbing and releasing air, which helps maintain soil moisture levels vital for crops. However, peatlands are highly sensitive ecosystems often threatened by unsustainable agricultural practices. When managed sustainably, peatlands scattered across the globe can be utilized for various farming activities. Managing peatlands for food crops presents an alternative to agriculture in peatland areas, enhancing economic growth in rural regions. This research aims to introduce a framework that integrates IoT into the intelligent monitoring of peatland management for precision agriculture. The primary challenge is implementing effective monitoring and management strategies for sensitive peatlands within precision agriculture. The main principle of precision agriculture is data-driven decision-making, supported by modern agricultural management that employs technology and data analysis to optimize farming practices. The proposed system framework can be utilized to identify the best types of food crops for making new decisions while ensuring high yields at the agricultural level. Precision agriculture principles are then applied to enhance the accuracy of monitoring peatland management, focusing on suitable land potential and food crops planted in areas with the highest potential. The results indicate that prioritizing peatlands for food crops reduces inappropriate decisions in selecting food crops. Furthermore, the efficiency of agricultural management can be improved with lower management costs. This framework provides a practical and user-friendly basis for informing all stakeholders on automating Peatland agriculture for food crops using precision agriculture systems integrated with IoT. Management practices that apply information technology aim to optimize crop inputs based on temporal and spatial variability. The cost-effectiveness from this perspective creates transition opportunities for communities, positioning our framework as a solution for designing Peatland management with intelligent monitoring.
Comparison of Evaluation Image Segmentation Metrics on Sasirangan Fabric Pattern Marleny, Finki Dona; Mambang, Mambang
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i2.1479

Abstract

Sasirangan fabric is a typical fabric from the South Kalimantan area. Sasirangan fabric patterns or motifs have a unique archetype that is different from other typical fabrics in Indonesia. The design of Sasirangan fabric is formed from the process of juju or seam. The pattern of Sasirangan fabric that has this uniqueness can be segmented into a more meaningful shape so that it is easy to analyze. The image segmentation that will be tested is the basic pattern of Sasirangan fabric with a random sample to compare the results of the evaluation of the metric evaluation of the image segmentation process from the Sasirangan fabric pattern. Image segmentation is a different segmentation with certain characteristics, namely using the compact watershed approach, canny filter, and morphological geodesic active contours method in the evaluation of image segmentation metrics using precision-recall, which serves to evaluate the quality of the classifier's output. After the image segmentation process is evaluated, the Sasirangan fabric pattern is grouped using the K-means algorithm as a different labelling strategy. This labelling process uses the K-means algorithm to better match details but can be unstable because it relies on random initialization. Alternatives to balance the unstable labelling process using the means algorithm can use discretization. The addition of the K-means method with discretization can create fields with geometric shapes that are pretty flat. The segmentation with Sasirangan fabric with a full motif or data number four 741.78s, results in processing the fastest and the longest computational time on data number two 120.79s.
Co-Authors Ade Putri Maharani Adha, Muhammad Iqbal Ahadi Ningrum, Ayu Ahmad Faisal Hamidi Ahmad Hidayat Ahmad Hidayat Ahmad Nawawi Ahmad Riki Renaldy Akhmad Baddrudin Antonia Yenitia Aqli, Ahmad Aulia Fitri Aulia Fitri Aulia Fitri, Aulia Ayu Ahadi Ningrum Bambang Lareno, Bambang Bayu Nugraha Bima Wicaksono Damayanti, Alfisah Dixky Dixky Elisa Fitriana Fatahulrahman, Maman Fitriansyah, Muhammad Gazali, Mukhaimy Hamdani Hamdani Haniffah Sri Rinjani Hudatul Aulia Ihdalhubbi Maulida Ihsanudin Indah Wulandari Jaya Hari Santoso Johan Wahyudi Johan Wahyudi, Johan Kamaruddin Kamarudin Kamarudin Kamarudin Kartika Kartika Liliana Swastina Lufila, Lufila M Samsul Hasbi M Samsul Hasmi Maman Fatahulrahman Mambang Mambang Fitriansyah Maria Ulfah Maulida, Ihdalhubbi Meila Izzana, Meila Melda Melda Miranda Miranda Muhammad Khairul Akbar Muhammad Noval Muhammad Riduan Syafi’i Muhammad Satrio Ayuba Muhammad Tantowi Jauhari Muhammad Zaini Bakri Muhammad Ziki Elfirman Muhammad Ziki Elfirman Muhammad Ziki Elfirman, Muhammad Ziki Muhammad Zulfadhilah Mukhaimy Gazali Mutmainah Mutmainah Nahdi Saubari Nalo Valentino Ningrum, Ayu Ahadi Nor Azizah Novita Sari Novriansyah, Irvan Nur Hafiz Ansari Nur Meilianti Maulida Nurhaeni Nurhaeni Prastya, Septyan Eka Putri Putri Putri Putri, Putri Rahmini Rahmini Reni Emiliya Ricardus A P, Ricardus A Risma Maulida Risma Risma Rismawati Rismawati Rizkian Muhammad Fikri Ropikah Ropikah Rudy Ansari Rudy Ansari Rudy Ansari, Rudy Samita, Mambang Sandro Nesta Pembriano Sa’adah Sa’adah Septian Eka Prastya Septyan Eka Prastya Septyan Eka Prastya Subhan Panji Cipta Susanti, NurAina Tasya Salsabila Theresia Kurniati Seran Tiara, Astia Rahma Tumanggor, Agustina Hotma Uli Winda Astria Nuansa Saputri Winda Astria Nuansa Saputri Windarsyah Windarsyah Wulandari Febriani Yulisa Suryana Yuslena Sari, Yuslena