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All Journal Techno.Com: Jurnal Teknologi Informasi Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro Jurnal Media Wahana Ekonomika PREDIKSI Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Sistem dan Informatika InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL MANAJEMEN BISNIS Jurnal Literasiologi Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Tekno Kompak Jurnal Ilmiah Kebidanan Indonesia (Indonesian Midwifery Scientific Journal) Jurnal Teknologi Informasi dan Multimedia Jurnal Abdidas Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Jurnal Pengabdian UNDIKMA Journal of Electrical Engineering and Computer (JEECOM) Science in Information Technology Letters Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Jurnal Silva Samalas: Journal of Forestry and Plant Science Jurnal Elkasista ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal PRIMED:Primary Education Journal atau Jurnal Ke-SD An Journal of Engineering, Technology and Computing (JETCom) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Management, Economic and Accounting (JMEA) Insand Comtech : Information Science and Computer Technology Journal International Journal of Engineering Continuity Valid Jurnal Pengabdian Journal of Economics and Management Scienties Jurnal Riset Rumpun Ilmu Pendidikan (JURRIPEN) Prosiding Seminar Nasional Unimus UPGRADE: Jurnal Pendidikan Teknologi Informasi Jurnal Ilmu Komputer dan Teknologi Informasi ABDIMASTEK Prosiding Seminar Nasional CORISINDO Media Pendidikan Matematika
Hairani Hairani
Universitas Bumigora, Mataram, Indonesia
Religion 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 Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Pengembangan Game Pembelajaran Lagu Anak-Anak Untuk TK Mutiara Hati Mataram Menggunakan Smartphone Android Madani, Miftahul; Rahman, Mochamad Farhan Caesar; Rosanensi, Melati; Mayadi, Mayadi; Mardedi, Lalu Zazuli Azhar; Hairani, Hairani; Innuddin, Muhammad
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 6 No. 2 (2025): ADMA: Jurnal Pengabdian dan Pemberdayaan Mayarakat: In-Progress
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v6i2.5541

Abstract

Pemanfaatan teknologi multimedia memegang peranan penting dalam pendidikan. Saat ini, proses pembelajaran musik di TK Mutiara Hati Mataram masih dilakukan secara konvensional menggunakan buku lirik, sehingga memicu kebosanan dan penurunan minat belajar siswa. Penelitian ini bertujuan merancang "Game Edukasi Bernyanyi Lagu Anak-Anak" untuk mengatasi masalah tersebut. Metode pengembangan sistem menggunakan Multimedia Development Life Cycle (MDLC) yang terdiri dari tahapan concept, design, material collecting, assembly, testing, dan distribution. Hasil pengujian menunjukkan bahwa responden sangat setuju aplikasi ini diterapkan sebagai media edukasi. Kesimpulannya, penggunaan aplikasi ini efektif meningkatkan minat belajar anak-anak di TK Mutiara Hati Mataram. 
Sosialisasi pemanfaatan herbal lokal sasak untuk materi kelas ibu hamil Triandini, I Gusti Agung Ayu Hari; Isviyanti, Isviyanti; Hairani, Hairani; Hidayati, Diana; Kandisa, Amelia; Wangiyana, I Gde Adi Suryawan
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 2 (2024): June
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i2.23516

Abstract

Abstrak Masa kehamilan merupakan masa terjadinya perubahan fisik, fisiologis dan psikologis yang signifikan pada wanita. Beberapa keluhan umum selama kehamilan meliputi mual dan muntah (morning sickness), kelelahan, nyeri punggung, pembengkakan kaki, sakit kepala, perubahan suasana hati, kesulitan tidur, dan sembelit. Dalam kesehariannya, ibu hamil lebih memilih mengatasi keluhan-keluhan ringan tersebut dengan terapi holistik komplementer karena dinilai lebih efektif dan minim efek samping. Dalam mengoptimalkan pelayanan sinergis kebidanan, Klinik Bumi Sehat Lombok telah menerapkan pelayanan holistik komplementer untuk mendukung persalinan humanis. Tim pelaksana bekerja sama dengan tim Klinik Bumi Sehat dalam kegiatan ini bertujuan untuk memberikan pelatihan untuk meningkatkan pengetahuan mitra dari kalangan ibu hamil, kader dan bidan di sekitar lingkungan klinik sekaligus memberikan pengkayaan materi kelas ibu dan perawatan holistik komplementer pada ibu hamil secara mandiri untuk mengurangi keluhan-keluhan saat masa kehamilan. Metode pengabdian yaitu dengan penyuluhan dan pelatihan praktek pembuatan dan contoh pengaplikasian ke ibu hamil. Tahap pelaksanaan kegiatan meliputi: 1. survei lapangan; 2. perizinan; 3. pengenalan konsep kegiatan kepada mitra; 4. praktek lapangan; 5. diskusi & interaksi aktif, 6. promosi keselamatan, 7. evaluasi. Setelah melakuan edukasi dan praktek pengolahan dan terapi herbal pada ibu hamil dan dilakukan evaluasi mengenai ketidaknyamanan yang dialami oleh ibu, diketahui ibu hamil sudah bisa mengurangi ketidaknyaman yang dialami. Terjadi peningkatan pengetahuan tentang terapi komplementer tersebut yang awalnya umumnya pada kategori kurang (80.94%) menjadi kategori baik (100%). Kata kunci: hamil, herbal; kelas ibu hamil; pengabdian masyarakat; sasak. Abstract Pregnancy is a time of significant physical, physiological and psychological changes in women. Some common complaints during pregnancy include nausea and vomiting (morning sickness), fatigue, back pain, leg swelling, headaches, mood swings, difficulty sleeping, and constipation. In their daily lives, pregnant women prefer to treat these minor complaints with complementary holistic therapy because it is considered more effective and has minimal side effects. In optimizing synergistic midwifery services, Bumi Sehat Lombok Clinic has implemented complementary holistic services to support humanistic childbirth. The implementing team collaborates with the Bumi Sehat Clinic team in this activity which aims to provide training to increase the knowledge of partners from pregnant women, cadres and midwives around the clinic environment while providing enrichment material for maternal classes and complementary holistic care to pregnant women independently to reduce complaints. -complaints during pregnancy. The service method is through counseling and training on manufacturing practices and examples of application to pregnant women. The activity implementation stage includes: 1. field survey; 2. licensing; 3. introduction of the activity concept to partners; 4. field practice; 5. active discussion & interaction, 6. safety promotion, 7. evaluation. After conducting education and practicing herbal processing and therapy for pregnant women and evaluating the discomfort experienced by the mother, it is known that pregnant women can reduce the discomfort they experience. There was an increase in knowledge regarding complementary therapies, which initially were generally in the poor category (80.94%) to the good category (100%). Key words: pregnancy; herbal; pregnant women's class; community service; sasak.
Aplikasi Pemetaan Kualitas Pendidikan di Indonesia Menggunakan Metode K-Means Gibran Satya Nugraha; Hairani Hairani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 17 No. 2 (2018)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v17i2.84

Abstract

Aplikasi Pemetaan Kualitas Pendidikan Di Indonesia Menggunakan Metode K-Means adalah sebuah aplikasi yang dirancang untuk memudahkan pemetaan kualitas pendidikan di Indonesia. Aplikasi ini dapat membuat sebuah cluster dari kualitas pendidikan di Indonesia berdasarkan sejumlah parameter yaitu Angka Partisipasi Kasar, Angka Partisipasi Murni, Angka Putus Sekolah, Angka Kelulusan, Angka Melanjutkan, Jumlah Sekolah, Rasio Siswa/Sekolah, Rasio Siswa/Kelas, Rasio Kelas/Guru, Rasio Kelas/Sekolah, Rombongan Belajar/Ruang Kelas, dan Jarak Sekolah. Keluaran atau output dari sistem berupa peta yang mengelompokkan daerah-daerah sesuai dengan kualitas pendidikan yang dimilikinya. Analisis perancangan yang digunakan dalam pembuatan sistem ini menggunakan UML (Unified Modeling Language) dimana setiap aktivitas pada sistem akan dikelompokkan secara sendiri-sendiri di dalam sebuah use case diagram dan alur dari sistem digambarkan dalam bentuk flowchart. Perancangan sistem yang dilakukan antara lain perancangan basis data. Perancangan berdasarkan spesifikasi kebutuhan, dan perancangan antarmuka. Secara umum aplikasi ini dapat menghitung data parameter kualitas pendidikan dengan menggunakan metode K-Means Clustering, dan menampilkan hasilnya dalam bentuk peta, sehingga dinas pendidikan atau lembaga-lembaga yang menangani pendidikan di Indonesia dapat membandingkan kualitas pendidikan setiap provinsi di Indonesia Key word : Clustering, k-means, pemetaan
Graduation Prediction System on Students Using C4.5 Algorithm Donny Kurniawan; Anthony Anggrawan; Hairani Hairani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 19 No. 2 (2020)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v19i2.685

Abstract

Bumigora University College there are several things that are not balanced between the entry and exit of students who have completed their studies. Students who enter in large numbers, but students who graduate on time below the specified standards. As result, there was a huge accumulation of students in each graduation period. One solution to overcome the problem above needs a data mining based system in monitoring or utilizing student development in predicting graduation using the C4.5 algorithm. The stages of this research began with problem analysis, data collection, data requirement analysis, data design, coding, and testing. The results of this study are the implementation of the C4.5 algorithm for predicting student graduation on time or not. The data used is the data of students who have graduated from 2010 to 2012. The level of acceptance generated using the confusion matrix is ​​93,103% accuracy using 163 training data and 29 testing data or 85% training data and 15% testing data. The results of research and testing that has been done, C4.5 algorithm is very suitable to be used in student graduation prediction.
Penanganan Ketidak Seimbangan Kelas Menggunakan Pendekatan Level Data Abdurraghib Segaf Suweleh; Dyah Susilowaty; Hairani Hairani; Khairan Marzuki
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 20 No. 1 (2020)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v20i1.846

Abstract

Setiap tahun bagian kemahasiswaan Universitas Bumigora melakukan seleksi mahasiswa yang berhak mendapatkan Beasiswa Peningkatan Prestasi Akademik (Beasiswa PPA). Dalam proses seleksi pemilihan penerima Beasiswa PPA terdapat permasalahan seperti kesulitan dalam menentukan mahasiswa yang berhak menerima beasiswa, dikarenakan jumlah kuota beasiswa lebih sedikit dibandingkan jumlah mahasiswa yang mendaftar beasiswa. Kumpulan data hasil seleksi Beasiswa PPA sebanyak 150 instance. Terdapat ketidak seimbangan kelas pada data yang digunakan yaitu 85 instance kelas tidak layak dan 65 instance kelas layak. Solusi yag ditawarkan adalah menggunakan pendekatan level data untuk menyeimbangkan kelasnya seperti metode SMOTE dan k-means-SMOTE. Adapun tujuan penelitian ini adalah menangani permasalahan ketidak seimbangan kelas pada data beasiswa PPA Universitas Bumigora menggunakan pendekatan level data untuk meningkatkan kinerja metode C4.5. Tahapan-tahapan penelitian ini terdiri dari pengumpulan data Beasiswa PPA, data preprocesing, klasifikasi, dan pengujian kinerja. Berdasarkan hasil pengujiannya, pendekatan level data menggunakan metode k-means-SMOTE dan metode C4.5 memiliki kinerja terbaik untuk klasifikasi penerima Beasiswa PPA dengan akurasi 81.3%, sensitivitas 84.9%, dan spesifisitas 77.6%. Dengan demikian, metode k-mean-SMOTE dan metode C4.5 memiliki kinerja terbaik berdasarkan akurasi, sensitivitas, dan spesifisitas.
Segmentasi Lokasi Promosi Penerimaan Mahasiswa Baru Menggunakan Metode RFM dan K-Means Clustering Dyah Susilowati; Hairani Hairani; Indah Puji Lestari; Khairan Marzuki; Lalu Zazuli Azhar Mardedi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i2.1542

Abstract

Persaingan penerimaan mahasiswa baru antar kampus swasta sangat ketat untuk menarik calon mahasiswa sehingga membutuhkan strategi. Strategi Universitas Bumigora adalah mengirimkan tim promosi ke sekolah-sekolah di pulau Lombok maupun pulau sumbawa. Permasalahan pihak panitia Penerimaan Mahasiswa Baru selama ini adalah tidak melakukan segmentasi sekolah yang menjadi skala prioritas untuk dikunjungi agar efektif dan efisien. Tujuan penelitian ini adalah melakukan segmentasi tingkat potensial sekolah sebagai strategi untuk memilih lokasi promosi penerimaan mahasiswa baru Universitas Bumigora menggunakan analisis model RFM dan metode K-means. Tahapan penelitian terdiri dari persiapan data penerimaan mahasiswa baru tahun 2019 dan 2020, pra-pengolahan data, penerapan model Recency (R), Frequency (F), dan Monetary (M)implementasi metode K-means, dan analisa hasil. Hasil penelitian ini adalah terbentuk 3 klaster tingkat potensial sekolah yang dapat dijadikan skala prioritas untuk lokasi promosi penerimaan mahasiswa baru Universitas Bumigora yaitu kurang potensial, potensial, dan sangat potensial. Klaster sangat potensial (C2) terdapat 28 sekolah, klaster potensial (C3) terdapat 90 sekolah, dan klaster kurang potensial (C1) terdapat 152 sekolah.
Comparison of Machine Learning Methods for Classifying User Satisfaction Opinions of the PeduliLindungi Application Putu Tisna Putra; Anthony Anggrawan; Hairani Hairani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.2860

Abstract

Since the emergence of the Covid-19 virus, the Indonesian government urged people to study, work, and worship or work from home. The social restriction policy has changed people's behavior which requires physical distance in social interaction. The government developed an application to minimize the spread of Covid-19, namely the PeduliLindungi application. The PeduliLindungi application is a tracking application to prevent the spread of Covid-19. The government's policy of implementing the PeduliLindungi application during Covid-19 aroused pros and cons from the public. The volume of PeduliLindungi application review data on Google Play was increasing, so manual analysis could not be done. New analytical approaches needed to be carried out, such as sentiment analysis. This research aimed to analyze user reviews of the PeduilLindungi application using classification methods, namely Support Vector Machine (SVM), Random Forest, and Naïve Bayes. The methods used were Synthetic Minority Oversampling Technique (SMOTE), Random Forest, SVM, and Naïve Bayes. SMOTE was used to balance user review data on the PeduliLindungi application. After the data had been balanced, classification was carried out. The results of this study showed that the Random Forest method with SMOTE got better accuracy than the SVM and Naive Bayes methods, which was 96.3% based on the division of training and testing data using 10-fold cross-validation. Thus, using the SMOTE method could improve the accuracy of classification methods in classifying opinions of user satisfaction with the PeduliLindungi application.
Data Mining Earthquake Prediction with Multivariate Adaptive Regression Splines and Peak Ground Acceleration Dadang Priyanto; Bambang Krismono Triwijoyo; Deny Jollyta; Hairani Hairani; Ni Gusti Ayu Dasriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3061

Abstract

Earthquake research has not yielded promising results because earthquakes have uncertain data parameters, and one of the methods to overcome the problem of uncertain parameters is the nonparametric method, namely Multivariate Adaptive Regression Splines (MARS). Sumbawa Island is part of the territory of Indonesia and is in the position of three active earth plates, so Sumbawa is prone to earthquake hazards. Therefore, this research is important to do. This study aimed to analyze earthquake hazard prediction on the island of Sumbawa by using the nonparametric MARS and Peak Ground Acceleration (PGA) methods to determine the risk of earthquake hazards. The method used in this study was MARS, which has two completed stages: Forward Stepwise and Backward Stepwise. The results of this study were based on testing and parameter analysis obtained a Mathematical model with 11 basis functions (BF) that contribute to the response variable, namely (BF) 1,2,3,4,5,7,9,11, and the basis functions do not contribute 6, 8, and 10. The predictor variables with the greatest influence were 100% Epicenter Distance and 73.8% Magnitude. The conclusion of this study is based on the highest PGA values in the areas most prone to earthquake hazards in Sumbawa, namely Mapin Kebak, Mapin Rea, Pulau Panjang, and Pulau Saringi.
Application of RFM Model and K-Means Algorithm in Customer Loyalty Segmentation of Indonesian Regional Water Utility Company (PDAM) Hairani, Hairani; Wahyudi, Rizki; Gede Yogi Pratama; M.Khaerul Ihsan
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 1 (2026): March 2026
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i1.6087

Abstract

Customer loyalty is an important factor in maintaining the sustainability of the Regional Drinking Water Company (PDAM) business, because it directly affects customer retention and company revenue. However, PDAM faces challenges in understanding the diverse patterns of customer loyalty. Therefore, this study aims to group customer loyalty based on using Recency, Frequency, Monetary (RFM) analysis and the K-Means clustering method. The methods used are RFM and K-Means which use 20,000 PDAM customer transaction data. Based on the results of the RFM analysis which was then clustered with K-Means, three customer loyalty clusters were obtained, namely High Value customers (Cluster 0) with 4900 data, Medium Value (Cluster 2) with 10,600 data, and Low Value (Cluster 1) with 4500 data. The results of the analysis show that the High Value cluster consists of customers who have recently made transactions, have a high purchase frequency, and spend large purchase values. On the other hand, the Low Value cluster includes customers with low recency, frequency, and monetary values, indicating a low level of loyalty. The implication of this study is that PDAM customer segmentation using RFM analysis and the K-Means algorithm produces three loyalty clusters, namely High Value, Medium Value, and Low Value, which allow the company to design more targeted service and marketing strategies according to the characteristics of each customer group.
Sentiment Classification of Football Supporters Using NusaBERT Embeddings, BiLSTM, and BiGRU Methods Guntara, Muhammad; Gusti Ayu Diah Gita Kartika Santi, I; Hairani, Hairani; Lalu Zazuli Azhar Mardedi
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 1 (2026): March 2026
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i1.6151

Abstract

Class imbalance and the use of non-standard language in football supporters’ opinions on social media constitute major obstacles to producing accurate sentiment classification for evaluating federation performance. This study aims to identify the most effective bidirectional recurrent architecture for capturing public opinion after applying data balancing techniques. Using a primary dataset of 1,039 instances (604 positive and 435 negative samples), the proposed method integrates a pre-trained NusaBERT model with hybrid Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) layers. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to the training data, with dataset partitioning using a stratified split ratio of 70:30. The results indicate that the NusaBERT-BiLSTM model achieves the best performance, with a testing accuracy of 70.83% and an F1-score of 0.6990, outperforming the BiGRU variant, which attains an accuracy of only 64.74%. Furthermore, NusaBERT-BiLSTM demonstrates greater reliability in detecting negative sentiment, achieving a recall value of 0.6336 compared to 0.4504 for BiGRU. In conclusion, combining NusaBERT's semantic strength with SMOTE-based balancing and BiLSTM layers significantly enhances the model’s sensitivity to minority opinions without causing data leakage. This study contributes a more objective classification model for national team management to accurately map public criticism and aspirations on social media.
Co-Authors Abdillah, Mokhammad Nurkholis Abdul Hadi Abdurraghib Segaf Suweleh Abdurraghib Segaf Suweleh Abu Tholib Adam, M. Awaludin Adawiyah, Rabi'atul Adrianto, Muhammad Subhan Dwi Afrig Aminuddin Ahmad Ahmad Ahmad Fathoni Ahmad Zuli Amrullah Aleeka Jasmine Amelia, Bengi Ameylan Verina Tabun Amin, Farda Milanda Andani, Nazwa Putri Andi Sofyan Anas Andi, Moh syaiful Andini, Nisha Anggarawan, Anthony Anthony Anggrawan Arfa, Muhammad Arifah Ulayya Ashadi, Diki Astuti, Ni Luh Budi Ayu Dasriani, Ni Gusti Bukran Bukran Candra, M. Ade Christine Eirene Christopher Michael Lauw Christopher Michael Lauw Dadang Priyanto Dedi Aprianto Dedy Febry Rachman Dedy Febry Rahman Dekki Widiatmoko Deny Jollyta Diah Ekawati Dian Syafitri Didik Dwi Prasetya Diki Ashadi Dirgantara, Bhintang Djoko Rahardjo Donny Kurniawan Dyah Susilowati Dyah Susilowaty ED. Yunisa Mega Pasha ED. Yunisa Mega Pasha Efendi, Muhamad Masjun Eka Setiawan, Rian Putra Ezra Azzahra Fahry, Fahry Fathorazi Nur Fajri Fatimatuzzahra Fatimatuzzahra Febriana, Annisa Dwi Fikrulia, Hidayah Jihan Firdaus, Adhitya Fitra Rizki Ramdhani Galih Hendro Martono Gede Yogi Pratama Gibran Satya Nugraha Gibran Satya Nugraha Guntara, Muhammad Gusti Ayu Diah Gita Kartika Santi, I Gustiya, Sherly Dwi Guterres, Juvinal Ximenes Hadi, M Fawazi Hammad, Rifqi Hartono Wijaya Haryono Haryono Hasanah, Maulida Hasbullah Hasbullah Herawati, Baiq Candra Heru Kurnianto Tjahjono Hery Widijanto Hidayati, Diana Huda, Dias Nabila Husain Husain I Gusti Agung Ayu Hari Triandini I Nyoman Switrayana Ida Putu Andika Ifnaldi Ifnaldi Iis Sopiah Suryani Ilham Saifuddin Indah Puji Lestari Indradewa, Rhian Irawan, Dudi Isviyanti, Isviyanti Janhasmadja, Mengas Jauhari, M. Thonthowi Jupriadi, Jupriadi Juvinal Ximenes Guterres Juvinal Ximenes Guterres Juvinal Ximenes Guterres Juvinal Ximenes Guterres Kandisa, Amelia Kasiyanto Kasiyanto Kasiyanto Kasiyanto, Kasiyanto Khairan marzuki Khairil Ihsan Khasnur Hidjah Khurniawan Eko Saputro Kurniadin Abd Latif Kurniawan Kurniawan Lalu Ganda Rady Putra Lalu Zazuli Azhar Mardedi Lestari, Jumiati Indah Lilik Nurhayati lnnuddin, Muhammad M. Ade Candra M. Rasyid Ridho M. Rizki M. Thoriq Panca Mukti M.Khaerul Ihsan M.Khaerul Ihsan Maariful Huda, Muhammad Malika, Riwayati Mamay Maulana Mamay Maulana Mardedi, Lalu Zazuli Azhar Mardedi, Lalu Zazuli Azhar Mayadi Mayadi Mayadi Mayadi Mayadi, Mayadi Mayasari, Astri Melati Rosanensi Mia Nisrina Anbar Fatin Michael Lauw, Christopher Miftahul Madani Mubarak, Ahmad Nazhif Mudawil Qulub Muhamad Azwar Muhamad Azwar, Muhamad Muhamad Reza Pahlevi Muhamad Reza Pahlevi Muhamad Wisnu Alfiansyah Muhammad Arfa Muhammad Fahmi Muhammad Ghifari, Muhammad Muhammad Innuddin Muhammad Maariful Huda Muhammad Ridho Akbar Muhammad Ridho Hansyah muhammad Syahbudi, muhammad Muhammad Tahir Muhammad Turmuzi Muhammad Zulfikri Muhammad Zulfikri Muhammad Zulkarnaen Haris Mujahid Mujahid Neny Sulistianingsih Ni Made Gita Gumangsari Nindya Alifia Khumaira Nisa, Rahayu Noor Akhmad Setiawan Novitasari Tsamrotul Fuadah Nur Intan Hayati Nur Intan Hayati Nurhayati, Lilik Nurul Azmi Nurvianti, Nurvianti Nuzululnisa, Bq Nadila Pahrul Irfan Pratama, Gede Yogi Putu Tisna Putra Qososyi, Sayidina Ahmadal Rahayun Amrullah Husaini Rahman, Mochamad Farhan Caesar Rahmawati, Lela Ramadhanti Ramadhanti Ramadhanti, Ramadhanti Rangga Wijaya Regandara, Ellysia Putri Rhomdani, Rohmad Wahid Rian Putra Eka Setiawan Rifqi Hammad Rio Riswanto Simanjuntak Riosatria, Riosatria Riwayati Malika Rizki Wahyudi Robo, Salahudin rokhim utomo Rosyda, Miftahurrahma RR. Ella Evrita Hestiandari Saifuddin Zuhri Saifuddin, Ilham Saifudin, Ilham Samsul Hadi Santoso, Heroe Shudiq, Wali Ja'far Soepriyanto, Harry Sofiansyah Fadli Soni Muhsinin Sri Farida Utami Sri Winarni Sofya Sri Winarni Sofya Sudi Prayitno Sukron, Moh Sutarman Sutarman Syahrir, Moch. tadianta m., Winardi aries Teguh Bharata Adji Tri Nur Jayanti Tri Nur Jayanti Triwijoyo, Bambang Krismono Triyanna Widiyaningtyas Umi Hanifah Vidiasari, Herlita Vidiasari, Viviana Herlita Vina Vitniawati Wahyuningsih, Rr. Sri Handari Wangiyana, I Gde Adi Suryawan Wening Asih Sutrisno Wening Asih Sutrisno Widhya Aligita Widhya Aligita Widiatmoko, Dekki Wira Hendri Wiyanto, Suko Ximenes Guterres, Juvinal Yuri Ariyanto Yuri Ariyanto Zilullah Nazir Hadi