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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Rekam : Jurnal, Fotografi, Televisi Animasi SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknologi Informasi dan Ilmu Komputer KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Bioedukasi JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Sains Dan Teknologi (SAINTEKBU) CogITo Smart Journal Insect (Informatics and Security) : Jurnal Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Applied Information System and Management ILKOM Jurnal Ilmiah Journal of Economic, Management, Accounting and Technology (JEMATech) KOMPUTIKA - Jurnal Sistem Komputer Jambura Journal of Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Bitnet: Jurnal Pendidikan Teknologi Informasi EDUMATIC: Jurnal Pendidikan Informatika METIK JURNAL Building of Informatics, Technology and Science Gema Wiralodra Indonesian Journal of Business Intelligence (IJUBI) Jurnal Tecnoscienza Generation Journal Jurnal Mnemonic Pangea : Wahana Informasi Pengembangan Profesi dan Ilmu Geografi Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics PRAJA: Jurnal Ilmiah Pemerintahan JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Perangkat Lunak Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Aiti: Jurnal Teknologi Informasi Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Electrical Engineering and Computer (JEECOM) JINAV: Journal of Information and Visualization International Journal of Artificial Intelligence and Robotics (IJAIR) Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal Informatika dan Teknologi Komputer ( J-ICOM) DEVICE Djtechno: Jurnal Teknologi Informasi JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer JURNAL STUDIA KOMUNIKA Jurnal Pengabdian Seni KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Journal Computer Science and Informatic Systems : J-Cosys Jurnal Mandiri IT Sulawesi Tenggara Educational Journal JURNAL PAI: Jurnal Kajian Pendidikan Agama Islam Jurnal Sisfotek Global International Journal Artificial Intelligent and Informatics Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Innovation Research and Knowledge SENTRI: Jurnal Riset Ilmiah Malcom: Indonesian Journal of Machine Learning and Computer Science Nusantara of Engineering (NOE) Jurnal Multidisiplin Sahombu COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi JEC (Jurnal Edukasi Cendekia) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) SmartComp Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Scientific Journal of Informatics Pengabdian Seni Jurnal Sistem Informasi Komputer dan Teknologi Informasi Jurnal TAM (Technology Acceptance Model) Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Komtika (Komputasi dan Informatika)
Kusrini, Kusrini
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EMPOWERING RURAL EDUCATORS THROUGH AI LITERACY: CHATGPT TRAINING AT SD NEGERI 3 SIBETAN KARANGASEM BALI Hamdikatama, Bimantyoso; Kusrini, Kusrini; Utami, Ema
Mitra Mahajana: Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2025): Volume 6 Nomor 2 Juli 2025
Publisher : LPPM Universitas Flores

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37478/mahajana.v6i2.5853

Abstract

The advancement of Artificial Intelligence (AI) has had a significant impact on various sectors, including education. However, the adoption of AI in Indonesia remains uneven, particularly in remote and rural areas. This study aims to assess the effectiveness of a training program on the use of ChatGPT as a teaching aid for elementary school teachers at SD Negeri 3 Sibetan, Karangasem, Bali. The training was designed to enhance teachers' understanding, practical skills, and perceptions of AI integration in education. Using a quantitative approach with a one-group pretest-posttest experimental design, data were collected through conceptual knowledge tests, practical skill observations, and perception questionnaires. The results revealed a significant increase in teachers' knowledge, with average posttest scores rising from 32.2 to 78.0. Additionally, practical skills improved notably, as indicated by a posttest average score of 73.0. Positive perception also increased, with 71% of participants expressing enthusiasm for using ChatGPT in the classroom. Despite limited infrastructure, the training successfully introduced AI-based tools to rural educators, demonstrating the transformative potential of AI in promoting equitable, innovative, and interactive education. This study contributes to the discourse on AI in education and underscores the importance of contextualised teacher training in rural settings.
Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Menggunakan Metode Svm (Studi Kasus: Universitas KH A Wahab Hasbullah Jombang Abdullah, Mochamad Fadillah; Kusrini, Kusrini; Arief, M. Rudyanto
SAINTEKBU Vol. 14 No. 01 (2022): Vol. 14 No. 01 January 2022
Publisher : KH. A. Wahab Hasbullah University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/saintekbu.v14i01.1096

Abstract

Kelulusan mahasiswa merupakan salah satu yang harus diperhatikan karena masuk dalam Standar Penjaminan Mutu Internal suatu perguruan tinggi . Fakultas Teknologi Informasi merupakan salah satu fakultas yang di universitas KH A Wahab Hasbullah Jombang. Untuk kelulusan terdapat standar yang akan dicapai oleh fakultas tersebut yaitu waktu studi selama 4 tahun dan IPK minimal 3,00. Untuk dapat mencapai mutu kelulusan tersebut dibutuhkan suatu prediksi tingkat kelulusan dengan standar yang telah ditetapkan untuk mahasiswa yang masih menjalankan studi sehingga dapat dilakukan antisipasi dari awal sehingga dapat menanggulangi terjadinya permasalahan dalam bidang akademik. Untuk memprediksi tingkat kelulusan dan IPK standar tersebut digunakan metode data mining dengan fungsi klasifikasi. Metode klasifikasi yang digunakan menggunakan metode SVM. Perangkat yang digunakan untuk mengolah data yaitu software Rapid Miner.
Prediksi Tingkat Keberhasilan Studi Kinerja Santri Menggunakan Algoritma C 5.0 Miftachuddin, Achmad Agus Athok; Kusrini, Kusrini; Luthfi, Emha Taufiq
SAINTEKBU Vol. 13 No. 01 (2021): Vol. 13 No. 01 Januari 2021
Publisher : KH. A. Wahab Hasbullah University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/saintekbu.v13i01.2523

Abstract

The success of pesantren education institutions can be measured by the success of their students. By predicting the possible outcomes of the learning process based on prediction results can help an Islamic boarding school, by adjusting the factors that contribute and influence the success rate of students' performance studies. And by utilizing data mining techniques that can be used to increase the level of success and reduce the failure of students. this can greatly help pesantren educational institutions to improve their graduates 'skills, because data mining is the best solution to find hidden patterns and can predict the success of students' performance studies. This research presents a model based on decision tree classification algorithm C 5.0 used in this model with alumni tracer study filled by santri alumni. In this study also used the k-folds cross validation test scenario with k values of 2,3,6,10 and 15 with a total of 300 alumni data and 84 data used for validation tests without cross validation. Determination of the criteria for the classification results using a confusion matrix form the measurement of the classification results obtained, namely the highest value in this study is 95% resulting from 15 folds the scenario 1. And form the results of testing the validation data without cross validation, the corresponding results are 73.81%, when compared to the k-folds, there was an increase of 21.19% and it can be ignored that the C 5.0 algorithm is able to classify well. So that pesantren educational institutional can provide a foundation in the arrangement for their students in deciding the right school choice.
Komparasi Algoritma Naive Bayes dan K-Nearest Neighbor untuk Membangun Pengetahuan Diagnosa Penyakit Diabetes Nurmalasari, Maulidya Dwi; Kusrini, Kusrini; Sudarmawan, Sudarmawan
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5140

Abstract

Diabetes is caused by a deficiency of the hormone insulin, which is secreted by the pancreas to lower blood sugar levels. The factors that trigger the occurrence of diabetes are derived from various factors such as a combination of genetic and environmental factors. The phenomenon of the emergence of various beverage brand outlets can be one of the triggers for blood sugar levels in humans. Normal blood sugar levels in the body range from 70-130 mg/dL before eating, less than 180 mg/dL two hours after eating, less than 100 mg/dL after not eating or surviving for eight hours, and 100-140 mg/dL at bedtime. This research aims to determine which algorithm is suitable for building knowledge about diabetes using the Naïve Bayes and K-Nearest Neighbor (KNN) algorithm. The accuracy results from Naïve Bayes are 85.60% and K- Nearest Neighbor of 91.61%. The results showed that K-Nearest Neighbor proved to have the best accuracy.
Pengukuran Kinerja Algoritma K-Means dan Hierarchical Aglomerative Clustering Dalam Pengelompokan Perkara Dispensasi Kawin di Wilayah Pengadilan Tinggi Agama Makassar Slamet, Slamet; Kusrini, Kusrini
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 4 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i4.8441

Abstract

Dispensasi kawin atau dispensasi nikah merupakan sebuah upaya bagi masyarakat yang ingin menikah, namun belum memenuhi persyaratan batas usia untuk menikah yang ditetapkan oleh pemerintah, sehingga perlu mengajukan proses dispensasi kawin ke Pengadilan Agama melalui proses persidangan. Melalui algoritma K-Means Clustering dan Hierarchical Aglomerative Clustering dilakukan klasterisasi terhadap data dispensasi kawin di wilayah Pengadilan Tinggi Agama Makassar. Hasil Klasterisasi di evaluasi menggunakan metode Davies-Bouldin Index. Proses klasterisasi yang dilakukan pada data dispensasi kawin Pengadilan Tinggi Agama Makassar, dihasilkan jumlah klaster yang sama dari dua algoritma yang digunakan yakni 4 klaster. Nilai evaluasi terhadap klaster yang dihasilkan oleh dua algoritma yang digunakan, algortima K-Means menghasilkan nilai Davies-Bouldin Index 1,898 dan algoritma Hierarchical Aglomerative Clustering dihasilkan nilai Davies-Bouldin Index 1,906. Mengacu pada nilai evaluiasi Davies-Bouldin Index terhadap dua algoritma tersebut, dikatakan proses klasterisasi yang dihasilkan cukup baik.Kata Kunci: Dispensasi Kawin; K-Means Clustering; Hierarchical Aglomerative Clustering; Pengadilan Agama; Davies-Bouldin Index
Development of an IoT-Based Electric Safety Buoy with Autonomous Navigation System for Coastal Water Rescue Operations Hari Muktafin, Elik; Abdullah Sukri, M Iqbal; Aziz Muzani, Ma'ruf; Sulistiyono, Mulia; Kusrini, Kusrini; Setiaji, Bayu
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11674

Abstract

This research aimed to develop and evaluate an IoT-based electric safety buoy equipped with an autonomous navigation system to support Search and Rescue (SAR) operations in coastal environments. The system integrates dual‐thruster propulsion, GPS and Inertial Measurement Unit (IMU) sensors, IoT telemetry, and a Return-to-Home (RTH) mechanism, enabling both manual and autonomous operation modes. Prototype testing was conducted in a controlled aquatic environment under light wave conditions (10–25 cm) and mild surface currents (0.18–0.32 m/s), with calm weather and unobstructed line-of-sight communication. The buoy was evaluated in both unloaded and 2 kg payload conditions, traveling at an average speed of 1.25–1.35 m/s across test sessions lasting 12–18 minutes. Three predefined GPS waypoints were used to assess navigation accuracy, motion stability, RTH reliability, and telemetry performance. Results show that the autonomous mode achieved a mean positioning error of 1.12 m, a cross-track deviation of 0.35 m, and a waypoint success rate of 96%, outperforming manual navigation by 52%. The RTH function maintained a success rate of 100% under low-battery conditions and 92% during communication loss, while IoT telemetry remained stable up to 200 meters with less than 1% packet loss. These findings confirm that integrating IoT-based telemetry with adaptive autonomous navigation enhances rescue mission efficiency and operational safety, while indicating the need for further validation under more challenging open-sea conditions.
Deep Learning-Based Soybean Leaf Disease Classification Using DenseNet121, Xception, and MobileNetV2 Helmawati, Nita; Buana, Yopy Tri; Darmawan, Eko Rahmad; Kusrini, Kusrini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6271

Abstract

This study is driven by the challenge of soybean leaf diseases, which significantly reduce agricultural productivity and pose a threat to food security. To address this issue, we developed a deep learning–based classification model for soybean leaf disease detection, employing three prominent architectures: DenseNet121, Xception, and MobileNetV2. The dataset comprised 770 images representing six disease categories and one healthy category, which was expanded to 5,880 images using data augmentation techniques. The dataset was evaluated under three experimental scenarios with splits of 70% training, 10% validation, and 20% testing. Experimental results demonstrated that the DenseNet121 model, optimized with AdamW, achieved the highest accuracy at 90.14%, outperforming MobileNetV2 (85.48%) and Xception (65.37%). Moreover, DenseNet121 exhibited the most consistent performance in classifying the diverse categories of soybean leaf diseases.
PREDIKSI KUALITAS UDARA MENGGUNAKAN MODEL KOMBINASI ALGORITMA GAUSSIAN PROCESS REGRESSION DAN GENETIC ALGORITHM Hartanto, David Budi; Kusrini, Kusrini
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 4 (2025): EDISI 26
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i4.6486

Abstract

Peningkatan polusi udara di wilayah perkotaan berdampak signifikan terhadap kesehatan masyarakat, sehingga prediksi kualitas udara menjadi krusial untuk mitigasi risiko polusi. Penelitian ini mengembangkan model prediksi Air Quality Index (AQI) berbasis Gaussian Process Regression (GPR) yang dioptimasi menggunakan Genetic Algorithm (GA) guna memperoleh hyperparameter terbaik. Dataset berasal dari kota-kota besar di India dengan 1.672 hasil pembersihan data, meliputi fitur polutan seperti PM2.5, PM10, NO?, O?, dan lainnya. Proses preprocessing mencakup penghapusan missing values, outlier dan pembersihan noise, serta pemilihan fitur terbaknya. Evaluasi menunjukkan bahwa GA memberikan peningkatan kinerja pada model GPR, dimana R², RMSE, MAPE, MAE dengan nilai 0.95, 18.65, 9.06, dan 12,71. Keunggulan penelitian ini dibandingkan penelitian sebelumnya terletak pada kemampuan model untuk menghasilkan kesalahan prediksi absolut dan kuadrat rata-rata yang lebih rendah (RMSE dan MAE), meskipun nilai R² lebih rendah. Hal ini membuktikan bahwa integrasi GA pada GPR tidak hanya meningkatkan efisiensi hyperparameter tuning, tetapi juga meminimalkan error secara signifikan. Dengan demikian, model ini lebih efektif dalam mengurangi kesalahan prediksi, stabil terhadap variasi data, dan relevan untuk pengambilan keputusan terkait kualitas udara di wilayah perkotaan.
Implementasi Pengembangan Sistem Model Water Fall Untuk Data Warehouse Akademik Sofan Tohir, Arik Sofan Tohir; Kusrini, Kusrini; Sudarmawan, Sudarmawan
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 1 No 2 (2017): Vol. 1 No. 2 Agustus 2017
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (234.158 KB) | DOI: 10.29407/intensif.v1i2.837

Abstract

Data warehouse is a concept and a technology to store transactional data from several sources that have been through the process of filtering and selection of data. By using the Ectract, Transform and Load (ETL) process in the data warehouse, OLTP data is processed to produce good data and ready for use for the analysis process. For the design of this warehouse data will be built by using the Nine Step Method from Kimbal, so that the resulting warhouse data can be as expected. For the development of life flow system (SDLC) with waterfall model. By using the wate fall model will be built a prototype to implement the data warehouse design results.
Yayak Kartika Sari Prediksi Customer Churn Berbasis Adaptive Neuro Fuzzy Inference System Sari, Yayak Kartika; Kusrini, Kusrini; Wibowo, Ferry Wahyu
Generation Journal Vol 2 No 1 (2018): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (905.836 KB) | DOI: 10.29407/gj.v2i1.12054

Abstract

Abstrak – Customer Churn adalah pelanggan yang berhenti berlangganan dan pindahpada perusahaan lain, karena berbagai faktor. Customer churn merupakan masalah yang sangatpenting yang harus dihadaapi oleh perusahaan karena berhentinya pelanggan akan berdampakpada retensi perusahaan. Oleh sebab itu, dibuatkan sistem prediksi customer churn untukmengetahui tingkat pelanggan yang churn, apabila customer churn dapat diketahui terlebih dahulu,maka akan menguntungkan bagi pihak CRM untuk mengatur strategi-strategi mencegah pelangganyang melakukan churn. Untuk menentukan prediksi customer churn menggunakan teknik datamining dengan algoritma ANFIS. Algoritma ANFIS merupakan gabungan antara jaringan syaraftiruan dengan fuzzy inference system. Model prediksi yang dibangun dengan metode ANFISmenggunakan pembelajaran alur maju dan pembelajaran alur mundur, sehingga untuk melakukanprediksi dibutuhkan nilai parameter fuzzy baru yang diperoleh dari proses pelatihan. Setelah nilaiparameter fuzzy baru didapatkan, maka akan dilakukan tahap pengujian. Pada tahap pengujiandilakukan dengan proses pembelajaran maju untuk mendapatkan nilai prediksinya, sehingga padaprosesnya nilai prediksi yang berupa angka dan status prediksi. Pelatihan dan pengujian ANFISuntuk semua produk menghasilkan perbandingan nilai error rata-rata pelatihan sebesar 8,316 %
Co-Authors AA Sudharmawan, AA Abdillah, Yahya Auliya Abdullah Sukri, M Iqbal Abdullah, Mochamad Fadillah Achmad Oddy Widyantoro Ade Pujianto, Ade Adhani, Muhammad Azmi Agastya, I Made Artha agung budi AGUS PURWANTO Ahmad Yusuf Aji Santoso, Bayu Aji Susanto Anom Purnomo Alfatta, Hanif Alva Hendi Muhammad anas, hasni Andi Muhammad Irfan Andi Sunyoto Andika, Roy Andriyanto, Rifki Angga Kurniawan Anggit Dwi Hartanto, Anggit Dwi Anggraeni, Meita Dwi Ardana, Wildan Muhammad Ardana, Wildan Muhammmad Ardiansyah, Fachri Ari Yuana, Kumara Arief Setyanto Arief, M Rudyanto Arief, Muhammad Rudyanto Arifuddin, Danang Arik Sofan Tohir Aris Subadi Arli Aditya Parikesit Asnawi, Muhamad Fuat Asri, Saffinah Indah Atin Hasanah Azi, Amanda Aziz Muzani, Ma'ruf Aziz, Moh Abdul Azkar, Azkar Bayu Setiaji Béjar, Rodrigo Martínez Bentar Candra P Bernadhed, Bernadhed Bisono, Hadi Hikmadyo Braeken, An Buana, Yopy Tri Candra, Kurnia Khoirul da Silva, Bruno Darmawan, Eko Rahmad David Agustriawan DHANI ARIATMANTO Dzulhijjah, Dwi Ahmad Eko Pramono Eko Purwanto Ema Utami Emha Taufiq Luthfi Fatkhurrochman, Fatkhurrochman Fauzi, Moch Farid Fauzy, Marwan Noor Febrianti, Winda Febriyanti, Nada Rizki Ferry Wahyu Wibowo fitriyanto, nur Gifari, Okta Ihza Halimi, Ahmad Hamdikatama, Bimantyoso Hanafi Hanafi Hanif Al Fatta Hari Muktafin, Elik Haris, Ruby hartanto, david budi Hartono, Anggit Dwi Haryo, Wasis Hasan, Nur Fitrianingsih Hasan, Nurul Rahmawati Hasirun, Hasirun Helmawati, Nita Herawati, Maimi Heri Abijono, Heri Herlinawati, Noor Hulvi, Alfajri I Made Adi Purwantara Ikhwanudin, Aolia Ilmawati, Fahma Inti Indarto, Aan Jeki Kuswanto Jumaris Jumaris, Jumaris Juwariyah, Siti Kasman, Haris Saktiawan Kharisma, Rizqi Sukma Kurniasari, Iin Kusnawi , Kusnawi Kusnawi Kusnawi Lewu, Retzi Y. Linda, Kumara Dewi Listyanto, Ahmad Wildan López, Alba Puelles M. RUDYANTO ARIEF M. Suyanto, M. Madhika, Yudha Randa Mahendra, Awanda Putra Majid Rahardi Mangun, Syamsul Syahab Maradona, Maradona Mardiana Mardiana Martínez-Béjar, Rodrigo Masruri, Nizar Haris Masud, Ibnu maulana, fahrizal Megantara, Muhamad Arldi MEI PARWANTO KURNIAWAN Metha, Halifa Sekar Miftachuddin, Achmad Agus Athok Mohamad Firdaus, Mohamad Mohammad Rezza Pahlevi Moningka, Nirwan Muflich, Alwie Mufti Ari Bianto Muhamad Iksan, Muhamad Muhammad Resa Arif Yudianto Muktafin, Elik Hari Mulia Sulistiyono Mulyaningtyas, Widya Muzakir, Muhammad MZ, Reza Rafiq Nasiri, Asro Ngaeni, Nurus Sarifatul Ni Nyoman Utami Januhari, Ni Nyoman Nugroho, Agung Nugroho, Hanantyo Sri Nuk Ghurroh Setyoningrum Nurmalasari, Maulidya Dwi Oktafiqurahman, Andi Olajuwon, Sayyid Muh. Raziq Onde, Mitrakasih La ode Oscar Samaratungga Pamoengkas, Muhamad Agoeng Pamungkas, Sapto Pradipta, Dody Prameswari, Sonia Anjani Prasetio, Agung Budi Prastyo, Rahmat Pratama, Muhammad Egy Puri, Fiyas Mahananing Purnamasari, Resti Putra, Andriyan Dwi Rachmawati Oktaria Mardiyanto RAMADHAN, SYAIFUL Rasyid, Magfirah Raynald Alfian Yudisetyanto Riduan, Nor Rizkayati, Anisa S, Muhamad Rois S, Muhammad Sabri Saleh, Robby Febrianur Samponu, Yohakim Benedictus Santosa, Hendriansyah SANTRI SANTRI Saputro, Moh. Rizal Bayu Saputro, Uyock Anggoro Sarawan, Tommy Sari, Yayak Kartika Selvy Megira, Selvy Semma, Andi Bahtiar Sentoso, Thedjo Setiawan, Moh. Arif Ma'ruf Setyanto, Arif Siswo Utomo, Mardi Slamet . Solikin, Arif Fajar Sudarmawan, Sudarmawan Sudarto Sudarto Swastikawati, Claudia Syafutra, Arif Dwi Syaiful Huda Tala, WD. Syarni Tampubolon, Jandri Tamuntuan, Virginia Toifur, Tubagus TONNY HIDAYAT Tri Nugroho, Arief Tukan, Ewaldus Ambrosius Ula, M. Izul Wahyu Pujiharto, Eka Wahyudi, Alfian Cahyo Wangsa, Sabda Sastra Wicaksono, Nikko Listio Wijaya, Jodi Wiwi Widayani, Wiwi Yanuargi, Bayu Yossy Ariyanto Yuana, Kumara Ari Yuza, Adela Zakaria Zakaria Zuhri, Muhammad Rafli Zulkarnain, Imam Alfath Zumarni, Zumarni