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All Journal Dinamik SEGMEN Jurnal Manajemen dan Bisnis GEMA TEKNOLOGI Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Syntax Jurnal Informatika Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Mahasiswa FEB Prosiding SNATIF Jurnal Ketahanan Nasional Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Berkala Epidemiologi Seminar Nasional Informatika (SEMNASIF) CESS (Journal of Computer Engineering, System and Science) E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Ilmiah FIFO Jurnal Pilar Nusa Mandiri InComTech: Jurnal Telekomunikasi dan Komputer Prosiding Seminar Nasional Teknoka JRST (Jurnal Riset Sains dan Teknologi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL EDUCATION AND DEVELOPMENT Jiko (Jurnal Informatika dan komputer) Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika JURIKOM (Jurnal Riset Komputer) Jurnal Telematika STRING (Satuan Tulisan Riset dan Inovasi Teknologi) CCIT (Creative Communication and Innovative Technology) Journal Journal of Information System, Applied, Management, Accounting and Research Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Ilmu Komputer dan Bisnis Syntax Idea Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Mnemonic Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi International Journal of Advances in Data and Information Systems Journal of Computer Science and Engineering (JCSE) SKANIKA: Sistem Komputer dan Teknik Informatika Media Gizi Kesmas Jurnal Teknik Informatika (JUTIF) Jurnal Pewarta Indonesia JURNAL KOMUNIKASI DAN BISNIS Ascarya: Journal of Islamic Science, Culture and Social Studies Jurnal PkM (Pengabdian kepada Masyarakat) Humantech : Jurnal Ilmiah Multidisiplin Indonesia Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Journal Of Human And Education (JAHE) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Economic Reviews Journal Jurnal Algoritma Jurnal Ticom: Technology of Information and Communication Berita Kedokteran Masyarakat Journal of Systems Engineering and Information Technology J-Icon : Jurnal Komputer dan Informatika Jurnal Teknik Indonesia Research Horizon IIJSE Jurnal Relawan dan Pengabdian Masyarakat REDI Jurnal Pengabdian Masyarakat Nasional Health Dynamics Jurnal Ticom: Technology of Information and Communication The Indonesian Journal of Computer Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Prosiding SeNTIK STI&K Journal of Medical and Health Science Jurnal Ilmu Kesehatan Immanuel Jurnal Analogi Hukum
Arief Wibowo
Universitas Budi Luhur
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KLASTERISASI DATA HASIL STUDI PELACAKAN TENTANG KARIR DAN PEKERJAAN LULUSAN PERGURUAN TINGGI MENGGUNAKAN ALGORITMA K-MEANS Sutrisno, Joko; Wibowo, Arief; Pratama, Bayu Satria
J-Icon : Jurnal Komputer dan Informatika Vol 11 No 2 (2023): Oktober 2023
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v11i2.12031

Abstract

Higher education has a responsibility to produce quality graduates. One indicator of the quality of graduates is the status of getting a job, the condition of the suitability of the field of work with the educational program pursued, and the waiting period to get the job. What is being done to find out these conditions is to conduct a tracer study for graduates. This study analyzes data from a college graduate tracking study about careers and jobs using a data mining clustering algorithm, namely K-Means. The results showed that the analysis of the tracking study data formed several graduate clusters with an evaluation value of the Davies-Bouldin Index (DBI) reaching 0.287 in the first trial and 0.291 in the second trial. The clusters formed consist of groups of graduates with status still needing to be working or currently working. The profile of graduates from each cluster can be identified in the form of a relatively short waiting period of less than six months to get a first job or a relatively slow waiting period of more than one year. Another cluster specification that is formed is about the profile of graduates with the level of compatibility between the education attained and the field of work carried out. The results of this study serve as feedback for study program managers to measure the quality of graduates and the improvements in the educational process that need to be made.
PENERAPAN ALGORITMA MULTICLASS SUPPORT VECTOR MACHINE DAN TF-IDF UNTUK KLASIFIKASI TOPIK TUGAS AKHIR Wibiyanto, Alif Dewan Daru; Wibowo, Arief
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 6 No 1 (2023): Jurnal SKANIKA Januari 2023
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v6i1.2999

Abstract

Dalam penyelesaian jenjang strata atau diploma, mahasiswa harus melewati tahap akhir yaitu penyelesaian tugas akhir. Dengan banyaknya mahasiswa yang mengajukan tugas akhir, proses pengarsipan dokumen tugas akhir berdasarkan topik membutuhkan klasifikasi yang dilakukan oleh Perpustakaan Universitas. Klasifikasi topik tugas akhir secara manual dapat menghambat proses lain yang harus diselesaikan oleh staff perpustakaan. Oleh karena itu, diperlukan adanya klasifikasi dokumen tugas akhir secara otomatis, cepat dan akurat untuk klasifikasi topik tugas akhir. Penelitian ini memanfaatkan pembobotan TF-IDF serta pendekatan algoritma Multiclass Support Vector Machine. Pembobotan TF-IDF ini merupakan formula yang merepresentasikan signifikansi suatu kata (term) dalam sebuah dokumen dan korpus. Pemobotan yang sudah dihitung dengan TF-IDF, kemudian menghitung hasil prediksi bobot menggunakan Support Vector Machine. Dikarenakan Support Vector Machine hanya bisa menentukan 2 kelas, diperlukannya metode one against rest dalam algoritma support vector machine untuk menentukan lebih dari 2 kelas. Penelitian ini bertujuan untuk membuat sistem yang dapat mengelompokkan topik tugas akhir secara otomatis, cepat dan akurat untuk pengarsipan tugas akhir di Perpustakaan. Hasil dari perhitungan pengujian dengan data latih dan data uji menggunakan pembobotan TF-IDF dan algoritma multiclass support vector machine menghasilkan persentase sebesar 90.71%.
Internet Service Provider User Customer Lifetime Segmentation Analysis using RFM and K-Means Algorithm Amri, Muhammad Febrian Rachmadhan; Umam, Mohamad Hafidhul; Wibowo, Arief; Ramayu, I Made Satrya
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13024

Abstract

The characteristics of each customer can be segmented using RFM (Recency, Frequency, Monetary) which means customer's last transaction time, number of customer transactions, and amount of money spent. The Lifetime and K-Means methods are used to perform the process of clustering or grouping customers based on segmentation through RFM. The results will be divided into 4 clusters namely Gold, Silver, Platinum and Diamond. The results of clustering are visualized with graphs and cluster tables containing the results of segmentation and clusters or groups of From the results obtained from the previous stage, of the 104 customers in the Retail & Distribution Services (RDS) sector, 4 segments resulted in 43 customers with Platinum class, 39 customers with gold class, 14 customers with silver class, and 8 customers with platinum level. The most popular services services or product is high speed dedicated internet services, VPN IP package, and service network package as top 3 results. The largest amount of revenue services or product is transponder full time use services, support network and contact center application as top 3 results.
PERBANDINGAN METODE ALGORITMA C4.5 DAN NAIVE BAYES UNTUK MEMPREDIKSI PENJUALAN KOSMETIK PADA TOKO JELITA Al Fatach, M Khabib; Wibowo, Arief
Jurnal Mnemonic Vol 7 No 2 (2024): Mnemonic Vol. 7 No. 2
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v7i2.10730

Abstract

Tujuan dari penelitian ini adalah untuk membantu memprediksi masalah yang terjadi di toko Jolita, yang menjual produk kecantikan. Peneliti melakukan penelitian dengan menggunakan dua algoritma—algoritma C4.5 dan Naive Bayes—untuk membantu menentukan produk mana yang paling diminati pelanggan karena toko sering kehabisan stok produk, membuat pelanggan tidak dapat mendapatkan produk yang mereka cari. Digunakan algoritma C4.5 dan Naive Bayes untuk mendapatkan data penjualan toko Jolita, evaluasi kinerja menunjukkan bahwa produk Wardah adalah yang paling diminati. Hasil pengujian confusion matrix C4.5 menunjukkan akurasi sebesar 100% untuk data produk Wardah dengan 51 prediksi paling laku menggunakan data kolom harga dan nama barang dan kategori. Dibandingkan dengan produk lain seperti Kahf, Makeover, dan OMG, pengujian Naive Bayes menunjukkan akurasi sebesar 98% untuk 51 data yang menunjukkan bahwa Wardah adalah produk Dari dua algoritma, C4.5 memiliki hasil akurasi yang lebih baik daripada algoritma Naive Bayes.
NEW STUDENT CLUSTERIZATION BASED ON NEW STUDENT ADMISSION USING DATA MINING METHOD Diana, Anita; Ariesta, Atik; Wibowo, Arief; Risaychi, Diva Ajeng Brillian
Jurnal Pilar Nusa Mandiri Vol 19 No 1 (2023): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v19i1.4089

Abstract

The process of admitting new students to the Faculty of Information Technology (FTI) at Universitas Budi Luhur produces a large amount of student data in the form of student profile data and other data. This happens causing a buildup of new student data, thus affecting the search for information on that data. This study aims to classify regular undergraduate admissions data at the Faculty of Information Technology (FTI) Universitas Budi Luhur by utilizing the data mining process using the clustering technique. The algorithm used for clustering is the K-Means algorithm. K-Means is a non-hierarchical clustering data method that can group student data into several clusters based on the similarity of the data, so that student data with the same characteristics is grouped in one cluster and those with different characteristics are grouped in another cluster. An implementation using RapidMiner is used to help find accurate values. This research produced a description of what clusters were formed from data on regular undergraduate admissions at the Faculty of Information Technology (FTI) at Universitas Budi Luhur. This will help recommend decision-making to determine the marketing promotion strategy for each study program at Universitas Budi Luhur. Based on the results of the K-Means algorithm cluster, it can also be seen which majors or study programs are of interest in each school from which new students come.
CLUSTERING OF POPULAR SPOTIFY SONGS IN 2023 USING K-MEANS METHOD AND SILHOUETTE COEFFICIENT Rohman, Nur; Wibowo, Arief
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.4937

Abstract

The rapid advancement of technology and globalization in this era has brought about comprehensive and easily accessible music streaming services, one of which is Spotify. According to Kompas.com, Spotify has experienced a rise in subscribers up to 130 million, as a platform that offers various features besides music streaming. Spotify also provides a better user experience and has the ability to compete with other music streaming platforms. The mission of this research is to classify popular Spotify song data in 2023, which can aid in a deeper understanding of listener preferences or music trends. Based on the test results, there were 2 clusters obtained with cluster 0 containing 863 data and cluster 1 containing 90 data. From the testing results conducted in the K-Means analysis, a Silhouette Coefficient of 0.81 was obtained, which falls into the category of Strong Structure. From these results, it can be suggested that cluster formation was done very well to provide more personalized and relevant music recommendations to Spotify platform users. By understanding the preferences and patterns of listeners revealed through clustering, streaming services can enhance user experience by providing more tailored content.
Komparasi Naïve Bayes dan SVM Analisis Sentimen RUU Kesehatan di Twitter Widyanto, Tetrian; Ristiana, Ina; Wibowo, Arief
SINTECH (Science and Information Technology) Journal Vol. 6 No. 3 (2023): SINTECH Journal Edition December 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i3.1433

Abstract

This research focuses on sentiment analysis regarding the plan to ratify the Health Bill which has become a hot topic of conversation on social media, especially Twitter. This research aims to classify tweets that reflect various opinions regarding the Health Bill, including support, rejection and neutrality. In this research, the author uses two types of classification algorithms, namely the Multinomial Naïve Bayes Algorithm and the Support Vector Machine (SVM) Algorithm. Previously, tweets were labelled using the Lexicon InSet dictionary. The research was conducted in the Python programming language and using Google Collaboratory. Data validation was carried out using the K-fold cross-validation method. The research results indicate that both algorithms predominantly produce positive sentiments over negative ones. However, SVM with a linear kernel achieves a higher accuracy rate of 0.87, compared to Multinomial Naïve Bayes, which has an accuracy of 0.82. SVM also records a precision of 0.87, recall of 0.97, and an F1-score of 0.91, while Multinomial Naïve Bayes shows a precision of 0.81, recall of 0.98, and an F1-score of 0.89. Overall, this research confirms that SVM excels in text sentiment classification, while Multinomial Naïve Bayes also provides good results in recognising positive and negative sentiment. These results have important implications for understanding public sentiment regarding the Health Bill on the Twitter platform.
Perbandingan Kinerja Algoritma K-Means dan K-Medoids Dalam Klasterisasi Jumlah Tindak Pidana Kejahatan Berbasis Wilayah Kepolisian Daerah Nurcahya, Gelar; Wibowo, Arief; Kristanto, Dwi
SINTECH (Science and Information Technology) Journal Vol. 6 No. 3 (2023): SINTECH Journal Edition December 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i3.1457

Abstract

Criminal acts are often a problem that occurs in Indonesia. Where currently the number of reports handled by the police regarding criminal acts is always there every day. Indonesia's population is increasing and the background of perpetrators who are unemployed is often one of the reasons why the police find it difficult to resolve criminal acts that occur due to limited human resources. To overcome this problem, information is needed that provides areas in Indonesia where criminal acts frequently occur so that the police can make decisions to allocate human resources to protect those jurisdictions from criminal acts that occur. Using data on criminal offenses and the employment of criminal offenders, namely not working from 2021, data was taken from the National Police Criminal Investigation Unit's Pusiknas Annual Journal. The data will be clustered using data mining techniques using the K-Means and K-Medoids algorithms. These 2 algorithms produced 2 clusters with the smallest Davies Bouldin index value found in the K-Means algorithm with a value of 0.272. With the research results which produced 2 clusters, it can be concluded that there are categories of high crime and low crime.
Perbandingan Metode K-Medoids dan Metode K-Means Dalam Analisis Segmentasi Pelanggan Mall Rohman, Nur; Wibowo, Arief
SINTECH (Science and Information Technology) Journal Vol. 7 No. 1 (2024): SINTECH Journal Edition April 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v7i1.1507

Abstract

Memahami pelanggan sangat penting untuk mengelola operasi perusahaan. Dengan mengetahui dan memahami setiap pelanggan, dapat meningkatkan komunikasi layanan produk dengan menyesuaikan kebutuhan dan layanan kepada setiap pelanggan. Namun, analisis pelanggan sangat luas sehingga sulit untuk memahami kebutuhan masing-masing pelanggan. Hal ini dapat mencakup berbagai karakteristik dan perilaku pelanggan. Oleh karena itu, diperlukan segmentasi pelanggan untuk mengelompokkan pelanggan berdasarkan perilaku dan karakteristiknya. Tujuan dari penelitian ini adalah membandingkan metode clustering untuk mendapatkan metode yang lebih baik dan optimal dalam mengelompokkan cluster untuk segmentasi pelanggan. Dari permasalahan tersebut, peneliti menerapkan metode CRISP-DM dengan focus pada analisis cluster atau pengelompokkan dengan membandingkan algoritma K-Means dan K-Medoids terhadap Analisa segmentasi pelanggan pada mall. Pada penerapan perbandingan metode K-Means dan K-Medoids, digunakan metode elbow untuk menentukan jumlah cluster yang optimal. Hasil dari metode elbow menunjukkan bahwa penggunaan lima cluster untuk metode K-Means dan empat cluster untuk metode K-Medoids merupakan pilihan yang tepat dalam kasus ini. Langkah selanjutnya adalah mencari nilai Silhouette Coefficient setiap metode yang digunakan dalam perbandingan untuk menentukan metode clustering yang lebih optimal.  Hasil nilai yang diperoleh dari metode Silhouette Coefficient masing-masing metode adalah k-means adalah 0,553 dan k-medoid adalah 0,485, sehingga algoritma pengelompokan segmentasi pelanggan terbaik pada penelitian ini adalah algoritma K-means karena memiliki nilai koefisien siluet maksimum.
Komunikasi word of mouth (wom) sebagai penentu keputusan pembelian konsumen Wibowo, Arief; Satiri; Poppy Ruliana; Kresno Yulianto
Humantech : Jurnal Ilmiah Multidisiplin Indonesia Vol. 2 No. 3 (2022): Humantech : Jurnal Ilmiah Multidisiplin Indonesia
Publisher : Program Studi Akuntansi IKOPIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32670/ht.v2i3.1473

Abstract

The purpose of this study was to determine the effect of Word Of Mouth (WOM) communication on purchasing decisions at NanoKomputer. NanoKomputer is a shop that has a focus on selling electronic goods in the form of computers and other office needs, researchers argue that NanoKomputer uses a method of delivering messages to consumers by word of mouth, because NanoKomputers do not have open sales suppression activities, but NanoKomputers often become recommendations from several sources as well as several information technology forums. The method used is to conduct a survey given to the group object to be studied. This research design has a purpose to find the effect between two variables, namely Variable (X) is Word of Mouth while variable (Y) is Purchase Decision. Respondents involved in this study were employees of the Depok Kartini Office Complex as many as 96 people. The sampling method used purposive sampling. Data collection using a questionnaire will be given to several employees in the Kartini Citayam Office Complex, Depok. The results of the study indicate that there is a positive influence between the Word Of Mouth (WOM) variable on purchasing decisions which is supported by the results of surveys and statistical calculations using SPSS software, in other words all employees of the Kartini Depok Office Complex who buy computer needs at NanoKomputer are influenced by communication. Word of Mouth.
Co-Authors - Arientawati - Sumardianto Achadi, Abdul Haris Adita, Ita Afifah Khaerani Afifatussalamah, Rizka Ahmad Sururi Ahmad Sururi Akbar, Ahmad Aldizar Al Fatach, M Khabib Anggraini, Julaiha Probo Anita Diana Antika Zahrotul Kamalia Anugrah Sandy Yudhasti Anuqman Fitriadi Apriati Suryani Ardhianto, Angga Ardianah, Eva Ari Wibowo Arief Umarjati Asep Permana Atik Ariesta Bayu Sadewo Bayu Satria Pratama Binarto, Antonius Jonet Bintang, Bagus Boerhan Hidayat, Boerhan Danar Wido Seno Danniswara, Ahmad Darki, Ni Wayan Yustika Agustin Deni Mahdiana Diah Indriani Didik Hariyadi Raharjo Didin Muhidin Dwi Kristanto Dwi Yulianti Dyah Retno Utari Dyah Retno Utari, Dyah Retno Ebine, Masato Eko Aprianto Endah Sarah Wanty Fajar Siddik Chaniago Farah Chikita Venna Farid Setiawan Farid Setiawan, Farid Febrilliani, Jihan Sastri Fenny Irawati Fernando, Donny Firman Noor Hasan Firmanty Mustofa, Vina Fitri Nur Masruriyah, Anis Fitri Rachmilah Fadmi Fitriadi, Rifqi Fitriani, Netty Fransiska Vina Sari Frenda Farahdinna Fried Sinlae Ghapur, Abdul Gurdani Yogisutanti Hadidtyo Wisnu Wardani Hananto, Agustia Handoko, Andy Rio Hanindita, Meta Herdiana Hari Basuki Notobroto Haris Achadi, Abdul HARIYANTO HARIYANTO Harun Nasrullah Hassan, Shiza Hayatul Khairul Rahmat Henry Henry Herriyawan, Herriyawan Hidayat, Manarul Hidayat, Sarifudlin Huda, Ratu Najmil I MADE MINGGU WIDYANTARA, I MADE MINGGU Indah Rizky Mahartika Indra Indra Inge Virdyna Irfan Hadi Irfan Nurdiansyah Istiqoomatun Nisaa Jasmine, Meuthia Joko Sutrisno Jovansgha Avegad Jumaryadi, Yuwan Kanasfi, Kanasfi Karma, Ni Made Sukaryati Karyaningsih, Dentik KRESNO YULIANTO Kresno Yulianto KUNTORO Kuntoro Kuntoro Kurnia Setiawan Kutanto, Haronas Larasati, Pamela Linda Lingga Desyanita Luthfi Akbar Ramadhan Mahmudah Mahmudah Mailana, Agus Maria Adiningsih Marlina, Hesti Martens, Brigitta Griselda Maskur A, Moch Riyadi Megananda Hervita Permata Sari Megawati, Rina Miftahul Arifin Miftahul Arifin Mochammad Rizky Royani Moh Makruf Monica, Silvi Muhamad Fadel Muhammad Bagus Bintang Timur, Muhammad Bagus Bintang Muhammad Febrian Rachmadhan Amri Muhammad Risky Mulyati Mulyati Nazihah, Fasya Nendi, Nendi Ningrum, Yogi Ajeng Nugroho, Angelika Pratiwi Widya Nur Aisiyah Widjaja, Nur Aisiyah Nur Rohman Nurcahya, Gelar Nurfadhiilah, Annisa Nurfidaus, Yasmine Nursyi, Muhamad Pattipeilohy, William Frado Pattipeilohy, William Frado Pebriaini, Prisma Andita Popalia, Qamarullah Poppy Ruliana Pradiptha, Anindya Putri Prastiyo, Krisna Probo Anggraini, Julaiha Purwadi Purwadi Putra, Andi Agung Putra, Rinaldi Febryatna Duriat Rachmah Indawati Rahman, Fathin Aulia Rahmawati, Nur Anisah Rakhman, Abdulah Rakhmat Rakhmat Rakhmat Rakhmat RAMAYU, I Made Satrya Rangkuti, Muhammad Yusuf Rizqon Ratna Ayu Sekarwati Ratna Ayu Sekarwati Relawanto, Bowo Ria Puspitasari Rika Nurhayati Riki Ramdani Saputra Rina Megawati Ririh Yudhastuti Risaychi, Diva Ajeng Brillian Ristiana, Ina Riza, Yeni Rizkiyanto, Muhamad Ardiansyah Roedi Irawan Rojakul, Rojakul Rosita Dewi, Erni Ruliana, Poppy Rusdah Ruwirohi, Jan Everhard Ryo Tanaka Sabirin, Sahril Sadewo, Bayu Santoso, Febrina Mustika Saptari Wijaya Mulia Sari Anggar Kusuma Melati Sari, Fransiska Vina Sari, Wulan Novita Sasongko, Raden Satiri Satiri, Satiri Selly Rahmawati Selly Rahmawati Septian Firman S Sodiq Septiani, Riska Setya Haksama Setyowati, Erlin Shofinurdin Shofinurdin Siddik Chaniago, Fajar Sigit Ari Saputro Sigit Budi Nugroho Siregar, Sutan Syahdinullah SITI NURUL HIDAYATI Sitti Aliyah Azzahra Soenarnatalina Melaniani Sudewo, Andika Hasbigumdi Sugiyarta, Ahmad Sujiharno Sujiharno Sumarna, Presma Dana Scendi Suntoro, Dimas Fahmi Tarmudzi, Rizky Tiaharyadini, Rizka Triantoro, Ery TRISNAWATI, WULAN Tulus Yuniasih Umam, Mohamad Hafidhul Vasthu Imaniar Ivanoti Wahyu Cesar Wahyu Desena Wahyudi, Widi Wahyuni, Chatarina Unggul Wangsajaya, Yosia Heartha Dhalasta Wasis Budiarto Wibiyanto, Alif Dewan Daru Widiyaningrum, Diyah Kiki Widyanto, Tetrian Windhu Purnomo Yahya Darmawan Yudanto, Satyo Zakaria Anshori Zaqi Kurniawan