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All Journal TEKNIK INFORMATIKA Sainteks Jurnal Pseudocode SMATIKA Jurnal Ilmiah KOMPUTASI Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA InComTech: Jurnal Telekomunikasi dan Komputer Jurnal SOLMA Prosiding Seminar Nasional Teknoka Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Sisfokom (Sistem Informasi dan Komputer) IJID (International Journal on Informatics for Development) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Linguistik Komputasional Jurnal ICT : Information Communication & Technology Building of Informatics, Technology and Science FIKROH: JURNAL PEMIKIRAN DAN PENDIDIKAN ISLAM Jurnal Teknologi Dan Sistem Informasi Bisnis Zonasi: Jurnal Sistem Informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Teknosains : Jurnal Sains,Teknologi dan Informatika Infotech: Journal of Technology Information Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan Jurnal Penyuuhan dan Pemberdayaan Masyarakat (JPPM) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer SmartComp Fikroh: Jurnal Pemikiran dan Pendidikan Islam JURNAL TEKNIK INFORMATIKA DAN KOMPUTER Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Cosmic Jurnal Teknik BHAKTI JIVANA Smatika Jurnal : STIKI Informatika Jurnal
Firman Noor Hasan
Universitas Muhammadiyah Prof. Dr. Hamka
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Implementasi Business Intelligence untuk Menganalisis Data Tujuan Populer Untuk Bali Tahun 2022 menggunakan Aplikasi Tableau Public Ahmad Syahril; E Erizal; Firman Noor Hasan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 4 (2023): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i4.235

Abstract

The most important part in a business is data management, it's just that the steps are not easy. In the development of technology, there are many ways to process and manage data. Business intelligence (BI) may be one of them. Maybe according to estimates the tourist attractions in Bali are the world's top tourist attractions. TripAdvisor named Bali as the world's top destination in the Traveler's Choice award. According to Google Reviewrs, tours in Bali show the highest number of 4,800 and the lowest 4,200. Providing highly effective information is the goal of tourism research in Bali, Indonesia, which is based on business intelligence. For this study, data analysis was performed using Tableau Public software. In Google Reviews, Bali Safari and Marine Park has the highest score with 16,042 and Banjar Hot Spring has the lowest score with 2,422. According to Google Maps, the highest rating rating in Bali is the Penglipuran Village area, with a value of 4,800 and the lowest rating is occupied by an area called Goa Gajah with a value of 4,200.
Implementasi business intelligence untuk visualisasi kekuatan sinyal internet di Indonesia menggunakan platform tableau Ammar Rusydi; Firman Noor Hasan
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 10 No 1 (2023): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK-Sekolah Tinggi Teknologi Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v10i1.378

Abstract

Internet menjadi suatu kebutuhan dalam pemenuhan kebutuhan masyarakat diera digital. Namun masih ada provinsi yang belum mendapatkan sinyal internet, sehingga masyarakat setempat belum bisa menggunakan internet. Artikel ini bertujuan untuk memvisualisasikan 34 provinsi di Indonesia berdasarkan kekuatan sinyal internet yang diterima oleh Desa/Kelurahan menurut provinsi di Indonesia. Oleh sebab itu, dibutuhkan implementasi Business Intelligence (BI) yang dapat memberikan visualisasi terhadap masalah tersebut dalam bentuk Dashboard menggunakan Platform Tableau Desktop 2019. Metode yang digunakan dalam artikel ini yaitu mengolah dataset dari www.bps.go.id dengan rentang antara bulan Januari 2021, sampai dengan bulan Desember 2021. Hasil akhir dari artikel ini adalah Dashboard yang menampilkan kekuatan sinyal internet yang diterima Desa/Kelurahan menurut provinsi di Indonesia. Kesimpulan artikel ini adalah didapatnya informasi bahwa terdapat 78938 Desa/Kelurahan di Indonesia, dan yang sudah tercover sinyal 4G mencapai 78,45%, dengan provinsi yang paling banyak menerima sinyal 4G adalah Jawa Tengah, sebanyak 7765 Desa/Kelurahan, dan Desa/Kelurahan yang belum menerima sinyal 4G mencapai 21,55%, dengan provinsi yang paling banyak belum menerima sinyal internet adalah Papua, sebanyak 938 Desa/Kelurahan
Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5) Putri, Kirana Alyssa; Febriawan, Dimas; Hasan, Firman Noor
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1943

Abstract

Graduating on time is what every student wants to accomplish in college. Students of Prof. Dr. Hamka Muhammadiyah University are one of those who have this dream. Based on 2020 graduates data from the Tracer Study, 60% said the university had a high enough impact  on improving competence.  This data indicates that university needs to evaluate improvement of academic quality. Often, students have difficulty finding information about important factors that support achieving timely graduation. A prediction analysis is needed to provide information about the student's graduation study period. For this analysis, data mining is implemented using the classification function of the decision tree (C4.5) algorithm with RapidMiner tools. The methodology for implementing data mining follows the stages of Knowledge Discovery In Database (KDD), beginning with data collection, preprocessing, transformation, data mining, and evaluation. The research findings consist of visualization and decision tree rules that reveal GPA as the most influential factor in determining a student's study period.There is other information, namely, students graduated on time (less than equal to 4 years) amounted to 170 or 54.5% and students did not graduate on time (more than 4 years) amounted to 142 or 45.6%. Testing the performance of decision tree (C4.5) utilizing confusion matrix through RapidMiner tools, resulted in accuracy reaching 83.87%, with precision of 87.50% and recall of 91.18%. Provides evidence that the decision tree algorithm (C4.5) has optimal performance to provide valuable information about predicting student graduation in order to increase student enrollment with the right study period.
Sentiment Analysis of Society Towards the Child-free Phenomenon (Life Without Children) on Twitter Using Naïve Bayes Algorithm Nurhaliza, Siti; Febriawan, Dimas; Hasan, Firman Noor
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1944

Abstract

The difference in societal perspective regarding personal well-being and understanding life choices is genuinely diverse. Lately, there is a prevalent thought where individuals believe that personal well-being can be achieved by choosing to live without children. Most of them prefer to prioritize their careers, education, or other activities that they believe can bring greater happiness and well-being to their lives. This topic has become a frequently discussed subject in almost every region of Indonesia, especially in urban areas. Not only facing negative stigma, the choice to live a life without children in Indonesia also carries positive connotations. Views on child-free in Indonesia are highly diverse, considering the many differences in social environments and each individual’s personal experiences. In this research, the Naïve Bayes algorithm is used as a sentiment classifier in the form of textual data collected through Twitter using the Rapid Miner. The data collection period spanned from May 3rd to May 10th, 2023. The research aims to analyze and present data regarding public sentiment towards the child-free phenomenon in Indonesia. The results of this research reveal the presence of 320 positive sentiments and 180 negative sentiments, with the accuracy value of the Naïve Bayes algorithm in conducting sentiment analysis on the child-free phenomenon reached 95.00%.
Analisis Sentimen Terkait Konflik Palestina-Israel Pada Media Sosial X Menggunakan Algoritma Naïve Bayes Classifier Simamora, Silvia Damayanti; Irwiensyah, Faldy; Hasan, Firman Noor
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5344

Abstract

The conflict between Palestine and Israel has been ongoing for approximately 76 years, during which the Zionist movement has attempted to establish a Jewish homeland in Palestinian territory. In October 2023, news about this conflict resurfaced and has continued up until June 2024. This issue has drawn global attention, including from the Indonesian public. On the social media platform X, numerous comments and posts both negative and positive regarding the Palestine-Israel conflict have appeared as a result of the ongoing challenges faced by Palestine. This study aims to analyze the sentiment expressed on the social media platform X regarding the Palestine-Israel conflict. The data collected focuses solely on comments and posts from Indonesia, totaling 1,715 entries. The study employs the Naïve Bayes Classifier algorithm, with an 80% to 20% ratio of training data to test data, following a pre-processing phase. The results of this study indicate an accuracy of 94%, precision of 91%, recall of 100%, and an F1-Score of 95%. The analysis reveals a positive sentiment, suggesting that the Indonesian public's response on the social media platform X predominantly shows positive support towards Palestine
Analisis Sentimen Terhadap Program Makan Siang & Susu Gratis Menggunakan Algoritma Naive Bayes Saputra, Ramadani; Hasan, Firman Noor
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 3 (2024): Juli 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i3.1378

Abstract

The utilization of social media platforms like Twitter has become crucial for the public to voice opinions regarding political programs, including the Free Lunch and Milk Program advocated by Presidential Candidate Pair number 02, Prabowo-Gibran, in the 2024 Presidential Election. This research employs the Naive Bayes classification method with the assistance of the RapidMiner application to analyze public sentiment towards the program. Out of the 785 Twitter data examined, approximately 81.7% displayed negative sentiment, while 6.6% were neutral, and 11.7% exhibited positive sentiment. Despite the prevalence of negative sentiment, there was also support for the program. Model evaluation utilizing 10-fold cross-validation, alongside SMOTEUP sampling and TF-IDF implementation, revealed an accuracy of 92.96%, recall of 85.30%, and precision of 94.57%. These results indicate that the model performs well in classifying sentiment from the test data.
Analysis of Public Sentiment Towards POLRI's Performance using Naive Bayes and K-Nearest Neighbors Handika, Yusuf; Hanif, Isa Faqihuddin; Hasan, Firman Noor
IJID (International Journal on Informatics for Development) Vol. 13 No. 1 (2024): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4500

Abstract

Using Twitter as a platform for sharing information includes tracking public perceptions of the performance of the Indonesian National Police (POLRI). Public sentiment assists as a gauge for evaluating POLRI's operational capabilities and supports decision-making processes to enhance the organization's reputation. However, raw public opinion data often requires careful analysis for decision-making. Hence, conducting sentiment analysis of Twitter data is crucial. This analytical process involves extracting and classifying opinions into neutral, positive, and negative sentiments. This study employs two distinct sentiment analysis methods: the Naive Bayes algorithm and the K-Nearest Neighbors. Analysis of 1285 tweets reveals prevailing satisfaction with POLRI's performance, indicated by many positive sentiments. However, there is also a notable number of negative feelings. The assessment from confusion matrix results demonstrate that the Naive Bayes algorithm achieves 99.03% accuracy, while the K-Nearest Neighbors algorithm achieves 95.33% accuracy. By leveraging insights from public opinion data, POLRI can make more accurate and timely decisions, enabling it to better fulfill the community's needs and expectations. This strategic use of data enhances service quality and bolsters POLRI's favorable image among the public fosters more harmonious relationships and enhances public trust in law enforcement agencies.
Sentiment Analysis of TIMNAS Indonesia's Participation in the Asian Cup U23 2024 on X Using Naive Bayes and SVM Fathurrohman, Sewin; Afandi, Irfan Ricky; Hasan, Firman Noor
IJID (International Journal on Informatics for Development) Vol. 13 No. 1 (2024): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4504

Abstract

This study aims to analyze the sentiment of the Indonesian public regarding the participation of the Indonesian National Team in the 2024 U-23 Asian Cup through the social media platform X. Sentiment analysis is crucial for understanding public perception and its impact on support for the national team. The research methodology involves collecting user comments on X related to the team's performance during the tournament, followed by data cleaning. The dataset is manually labeled, with 80% used as training data for algorithmic model training and the remaining 20% as test data, classified using Naive Bayes and Support Vector Machine algorithms. The analysis results indicate that the SVM algorithm achieves a higher % accuracy rate of 95% compared to Naive Bayes, which achieves 87%. The majority of the 3367 opinions analyzed express positive or satisfactory sentiments towards the national team's participation. However, there are fewer negative sentiments, highlighting areas requiring team management's attention. This study provides valuable insights into public perception of the Indonesian National Team. Furthermore, these findings can inform policymakers and team managers' decision-making to enhance the team's quality and performance in the future.
Penerapan Business Intelligence Untuk Analisis Kematian di Indonesia Tahun 2000-2022 Abdillah, Allif Rizki; Muflih, Hilmy Zhafran; Pranata, Ananda Bagas; Hasan, Firman Noor
Jurnal Informatika Vol 10, No 2 (2023): October 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i2.16569

Abstract

Kematian terus menerus terjadi dan sebagai manusia biasa kematian tidak dapat dihindari. Seiring berjalannya waktu, jenis kematian juga semakin bertambah khususnya di Indonesia, faktor kematian yang disebabkan oleh bencana alam, bencana non-alam atau penyakit dan bencana sosial yang di dalamnya memuat jenis-jenis penyebab kematian juga semakin bertambah jenis kematian yang baru. Seperti kemarin Indonesia mendapatkan jenis kematian baru yaitu covid-19 yang menelan ratusan ribu korban jiwa. Tujuan dari penelitian ini untuk mengidentifikasi serta menganalisa penyebab kematian yang ada di Indonesia dengan rentang tahun 2000 sampai tahun 2022. Peneliti memperoleh dataset untuk penelitian ini dari situs www.kaggle.com untuk dibuat data visualisasinya dengan mengimplementasikan Business Intelligence menggunakan platform Tableau dalam pembuatan visualisasinya. Hasil dari penelitian ini adalah laporan berupa dashboard yang di dalamnya memuat Total kematian berdasarkan kategori, total kematian beserta jenis kematiannya, total kematian per tahun dari tahun 2000 sampai tahun 2022, dll. Sehingga dapat memudahkan proses pengambilan keputusan. Kesimpulan dari penelitian ini yaitu terdapat 777.076 korban yang meninggal karena bencana non alam atau penyakit, terdapat 185.290 korban yang meninggal akibat bencana alam dan 261 korban yang meninggal akibat bencana sosial. Total kematian di Indonesia pada rentang tahun 2000 sampai tahun 2022 sejumlah 962.627. Death keeps happening and as an ordinary human being death cannot be avoided. Over time, the types of death have also increased, especially in Indonesia, the factor of death caused by natural disasters, non-natural disasters or diseases and social disasters which contain the types of causes of death. Like yesterday, Indonesia got a new type of death, namely Covid-19 which claimed hundreds of thousands of lives. The purpose of this research is to identify and analyze the causes of death in Indonesia from 2000 to 2022. Researchers obtained the dataset for this study from the website www.kaggle.com to make data visualization by implementing Business Intelligence using the Tableau platform in making the visualization. The results of this study are reports in the form of dashboards which contain total deaths by category, total deaths and types of deaths, total deaths per year from 2000 to 2022, etc. So that it can facilitate the decision-making process. The conclusion of this study is that there were 777,076 victims who died due to non-natural disasters or diseases, there were 185,290 victims who died as a result of natural disasters and 261 victims who died as a result of social disasters. The total number of deaths in Indonesia between 2000 and 2022 is 962,627.
Implementasi Business Intelligence untuk Visualisasi Laju Indeks Pembangunan Manusia Kota Cirebon Menggunakan Google Collab Rozak, Bahrul; Febriawan, Dimas; Hasan, Firman Noor
Sainteks Vol 21, No 1 (2024): April
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/sainteks.v21i1.21356

Abstract

Indeks Pembangunan Manusia (IPM) merupakan ukuran penting untuk mengevaluasi kesejahteraan dan kemajuan suatu wilayah dalam aspek kesehatan, pendidikan, dan ekonomi. Kota Cirebon adalah salah satu wilayah di Indonesia yang memiliki IPM yang terus mengalami perkembangan. Penelitian ini bertujuan untuk menganalisis kemajuan dalam aspek kesehatan, pendidikan, dan ekonomi di Kota Cirebon selama periode tahun 2010 hingga 2020 serta dampak pandemi COVID-19 terhadap IPM. Metodologi yang digunakan melibatkan pengambilan dataset dari data.kotacirebon.go.id, yang mencakup angka harapan hidup, angka harapan lama sekolah, angka rata-rata lama sekolah, dan pengeluaran per kapita per tahun. Analisis dilakukan menggunakan Google Collab untuk visualisasi data yang fleksibel. Hasil analisis menunjukkan kemajuan dalam aspek kesehatan dan pendidikan di Kota Cirebon selama periode tahun 2010 hingga 2020, dengan angka harapan hidup, angka harapan lama sekolah, dan angka rata-rata lama sekolah mengalami peningkatan. Namun, pengeluaran per kapita per tahun mengalami penurunan, yang mungkin terkait dengan dampak pandemi COVID-19. Trend laju IPM Kota Cirebon mengalami peningkatan antara tahun 2010-2019 namun mengalami penurunan pada tahun 2020, hal ini salah satunya terkait dengan dampak pandemi COVID-19. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan visualisasi data untuk memahami perkembangan IPM kota Cirebon dan meningkatkan pemahaman terhadap pentingnya IPM dalam mengukur kemajuan suatu wilayah.
Co-Authors Abdillah, Allif Rizki Abdul Syakir Achmad Ramadhan Achmad Sufyan Aziz Afandi, Irfan Ricky Affandi, Irfan Ricky Afikah, Prista Afnan Sabili, Dian Ainurrafik Agus Fikri Agus Fikri Ahmad Rizal Dzikrillah Ahmad Rizal Dzikrillah Ahmad Roshid Ahmad Syahril Ahmad Syahril Al Ghozi, Dhiyauddin Alfandi Safira Alim, Endy Sjaiful Allif Rizki Abdillah Allif Rizki Abdillah Allif Rizki Abdillah Ammar Rusydi Ananda Prasta Warasati Janah Ananda, Ridha Faiz Andika Saputra Andriani, Vivi Anhari, Tirta Anwar Hidayat Ari Wibowo Arief Wibowo Arien Bianingrum Rossianiz Arvin Rafialdo Aulia, Muhammad Fathan Avorizano, Arry Azhar Haikal Anwar Azhar Haikal Anwar Azis Styo Nugroho Bagas Kembar Rezkyllah Bahrul Rozak Bahrul Rozak Dan Mugisidi Dandie Triyanto Desty Afni Dewi Mayangsari Dian Ainurrafik Afnan Sabili Dian Ainurrafik Afnan Sabili Diana Fitri Lessy Diana Fitri Lessy Dimas Febriawan Dimas Febriawan Dimas Febriawan Dion Parisda Ray Djeli Moh Yusuf Doni Gunawan Rambe E Erizal Erizal Erizal Estu Sinduningrum Estu Sinduningrum Fachri Zaini Fadli Al Gani Fadli Hardiyanto Putra Fadli, Khairul Faisal Parsakh Nursyamsi Faisal Parsakh Nursyamsyi Fajar Sidik Faldy Irwiensyah Faldy Irwiensyah Faldy Irwiensyah, Faldy Farhan Bias Purnama Putra Farhan Nufairi Farhan Nufairi Fathurrohman, Sewin Fauzan Setya Ananto Fauzi Kurniawan Fayakun Kun Febriandirza, Arafat Febriawan, Dimas Gusnul Mahesa Hafizh Dhery Al Assyam Handika, Yusuf Hanif, Isa Faqihuddin Hardyatman, Intan Diah Hazbi Santoso Hibatullah Faisal Hibatullah Faisal Hibatullah Faisal Hilmi Ammar Hilmy Zhafran Muflih I Ketut Sudaryana, I Ketut Ibnu Suhada Indra Ramadhan Indra Ramadhan Indriyanti, Prastika Intania Widyaningrum Irawati Irawati Irfan Ricky Afandi Irfan Ricky Affandi Irma Wahyuningtyas Isnan Wisnu Prastiyo Kamayani, Mia kivandi Nugroho Krisna, Mohammad Dito Dwi Kurniyati Nur Lathifah Dini Rachmawati Lingga Lingga Lingga Lita Astri Pramesti Luqman Abdur Rahman Malik Lutfi Triyuli Evana Rizki Luthfi Akbar Ramadhan M. Asep Rizkiawan Meliyawati MILASARI, LISA ASTRIA Mohammad Akhdaan Juliandra Muchammad Sholeh Muchammad Sholeh Muflih, Hilmy Zhafran Muhamad Saiful Arif Muhammad Abid Fajar Muhammad Ardhi Ryan Saputra Muhammad Ikhwan Muhammad Ikhwan Muhammad Rafly Al Fattah Zain Muhammad Ridwan Muhammad Rifansyah Mukti, Avis Tantra Mutiara Zahra Arifin Nisa Qonita Rizkina Nofendri, Yos Nugroho, Dendy Aprilianto Nunik Pratiwi Oktarina Heriyani Pamungkas, Dimas Panji Islami Anakku Pavita, Rachma Pranata, Ananda Bagas Prisilia Talakua Prista Afikah Purnamaningsih, Ine Rahayu Putri, Kirana Alyssa Rafli Erlangga Rahman Malik, Luqman Abdur Rahmatullah, Ahmad Faiz Ramadhita, Nindia Fitri Ramzah, Harry Reisa Inayah Rian gustini Ridwan Bagus Andreyanto Ridwan Maulana Subekti Rika Nurhayati Riyan Ariyansah Rizki Alamsyah Rizki Kamelia Rizky Ramdhani Rosalina Rozak, Bahrul Saputra, Ramadani Sari, Jessica Windi Sari, Laila Atikah Setiawan, Ahmat Sewin Fathurrohman Simamora, Silvia Damayanti Sinduningrum, Estu Sistani, Muhammad Ghiffar Siti Nurhaliza Sri Fitriani Sunata, Muhamad Hafidz Ardian Syahri, Alfi Tasya Rizki Salsabilla Tia Anggita Sari Transiska, Dwi Wahyu Stiyawan Wahyuningtyas, Irma Wanda Aulia Widyastuti Andriyani Windi Al Azmi Wulandari, Sania Zahra, Khofifah Humaeroh Az Zaini, Fachri Zuhri Halim Zuhri Halim, Zuhri