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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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Heri Abijono Analisis Perbandingan Metode Simple Additive Weighting dan Profile Matching dalam Sistem Pendukung Keputusan Abijono, Heri; Kusrini, Kusrini
Generation Journal Vol 2 No 2 (2018): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (447.633 KB) | DOI: 10.29407/gj.v2i2.12242

Abstract

Peneliti telah menerapkan sistem pendukung keputusan berupa suatu aplikasiuntuk mendistribusikan dana Bantuan Siswa Miskin (BSM) di tahun 2015 dengan menggunakanmetode Simple Additive Weighting. Peneliti kemudian mengembangkan sistem ini di tahun 2017dengan menambahkan ketentuan kepemilikan Kartu Perlindungan Sosial ataupun Surat KeteranganRumah Tangga Miskin untuk mempertimbangkan prioritas pemberian dana bantuan itu, selainmempertimbangkan empat macam kriteria yang telah ada pada sistem sebelumnya. Penelitian kaliini ditujukan untuk membandingkan algoritma dari dua metode, Simple Additive Weighting danProfile Matching, untuk menentukan metode mana yang cocok dipakai dalam pendistribusian danaBSM. Peneliti membuat analisis berupa perhitungan-perhitungan sesuai algoritma dari dua buahmetode yang diperbandingkan dengan menunjukkan cara kerja proses dari tiap-tiap metode yangdiperbandingkan. Keluaran sistem adalah berupa informasi perankingan prioritas siswa untukmemperoleh dana BSM.
Meningkatkan Keamanan Pesan Menggunakan Enkripsi Arnold Cat Map Dan Steganografi Pixel Value Differencing Masruri, Nizar Haris; Kusrini, Kusrini; Sunyoto, Andi
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 3 No. 1 (2019): PROSIDING SEMNAS INOTEK Ke-III Tahun 2019
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v3i1.522

Abstract

Pesan tidak hanya berupa text, namun juga berbentuk gambar. Sebuah pesan gambar terkadang merupakan informasi yang sangat rahasia contohnya gambar informasi barang bukti. Untuk itu dibutuhkan teknik untuk melindungi pesan tersebut agar tidak diketahui oleh pihak lain. Pixel Value Differencing (PVD) merupakan salah satu teknik penyisipan pesan ke dalam data digital seperti gambar (citra) dengan kelebihan kapasitas penampung yang besar. PVD menghitung selisih nilai piksel dengan cara membagi piksel-piksel citra menjadi blok-blok yang terdiri dari dua buah piksel yang posisinya berdekatan yang digunakan sebagai tempat penyisipan pesan. Untuk meningkatkan keamanan, maka dilakukan enksripsi pada pesan citra agar konstruksi citra menjadi tidak beraturan sehingga tidak mudah untuk diketahui dan dimanipulasi oleh pihak lain. Paper ini akan menggabungkan steganografi PVD dan metode enskripsi Arnold Cap Map (ACM). Untuk mengetahui kualitas citra yang tersisipi pesan, maka dilakukan evaluasi kualitas citra dengan perhitungan nilai Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR). Hasil pengujian menunjukkan bahwa citra dengan resolusi 512x512 piksel menghasilkan nilai MSE : 0.36311 dan PSNR (db): 57.3356, sedangkan citra dengan resolusi 256x256 piksel menghasilkan nilai MSE : 11.1786 dan PSNR(db) : 42.4521.
IMPLEMENTASI MOORA PADA SELEKSI DOSEN TERBAIK BERDASARKAN HASIL PENILAIAN DALAM PEMBELAJARAN KULIAH Hasirun, Hasirun; Kusrini, Kusrini; Kusnawi, Kusnawi
Indonesian Journal of Business Intelligence (IJUBI) Vol 6 No 1 (2023): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v6i1.3331

Abstract

Lecturer performance assessment is one of the activities of monitoring and evaluating performance with the aim of supervising the learning process and ensuring that lecturers carry out their duties in accordance with policies and teaching materials that have been determined. Lecturer performance assessment is carried out by students at the end of each semester by assessing lecturers based on criteria related to lecture learning. The criteria assessed in college learning are learning aspects, technological proficiency, integrity, and inspiration. The results of the student assessment will be reported to the learning development and quality assurance institution, which can later be used to determine the best lecturer performance. In this research, we apply the MOORA method to help determine the best lecturer based on assessment results in lecture reasoning. In its implementation, the MOORA method performs calculations based on criteria and weight values that have been determined and produces a ranking that can be used to determine the best lecturer's performance. In this study, the highest ranking was on the VPB alternative with a final value of 0.138, while the lowest value was on the DAM alternative with a final value of 0.108.
Forecasting the Highest and Lowest Prices in Financial Markets Using a VMD-LSTM Hybrid Model Purwantara, I Made Adi; Kusrini, Kusrini; Setyanto, Arief; Utami, Ema
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.963.295-310

Abstract

Accurate forecasting of the lowest and highest prices in financial markets poses a considerable challenge due to the inherent nonlinear behaviour, non-stationarity, and high noise levels of financial time series data. Most prior studies focus only on closing prices, with limited attention to the simultaneous prediction of high and low prices. Yet, predicting the lowest and highest prices is essential for investors to make informed trading decisions. To address this gap, this study proposes a hybrid DL framework that integrates VMD and LSTM networks for predicting daily high and low prices simultaneously. This study used 12 years of daily data from three diverse assets: AUD/USD, TLKM, and XAU/USD. The data underwent preprocessing, VMD-based decomposition, and were input into the LSTM. The dataset was split 80% for training and 20% for testing. Experiments varied the number of decomposition modes (K = 7, 10, 12) and sliding window sizes (5, 15, 30, 45, 60, 90). Results show that the VMD-LSTM model exhibits improved performance in most of the tested scenarios compared to traditional LSTM. These findings underscore that the use of VMD decomposition can help enhance the accuracy of forecasting the highest and lowest prices in the financial market.
Karakteristik Sosial Ekonomi Masyarakat Dufa-Dufa Kusrini, Kusrini; Jumaris, Jumaris
Pangea : Wahana Informasi Pengembangan Profesi dan Ilmu Geografi Vol 7, No 2 (2025): Pangea: Wahana Informasi Pengembangan Profesi dan Ilmu Geografi
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/pangea.v7i2.11241

Abstract

Kelurahan Dufa-Dufa merupakan salah satu kota yang secara administrasi masuk di Kecamatan Ternate Utara. Perkembangan sosial ekonomi masyarakat Dufa-Dufa kecenderunganya meningkat dilihat dari keanekaragaman aktivitas ekonomi masyarakatnya. Jenis sampel yang digunakan dalam penelitian ini adalah purposive random sampling. Berdasarkan hasil analisis menunjukan bahwa mata pencaharian masyarakat di Kelurahan Dufa-Dufa terbagi atas nelayan (39%), pedagang (25%), wiraswata (19%) dan PNS (15%) sedangkan dari tingkat kesejateraan masuk dalam kategori Sejahtera dan prasejahtera.
Application of Convolutional Neural Network Based on ResNet18 for Alzheimer Disease Classification Indarto, Aan; Kusrini, Kusrini
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1504

Abstract

Alzheimer's disease is a form of progressive dementia that significantly impacts the quality of life of patients and their families. Early detection based on Magnetic Resonance Imaging (MRI) can support faster and more accurate diagnosis, but manual classification requires high expertise and is subjective. This study aims to develop an Alzheimer's MRI image classification model using a Convolutional Neural Network (CNN) based on ResNet18 with transfer learning to classify data into four categories: Mild Demented, Moderate Demented, Non-Demented, and Very Mild Demented. The MRI dataset was processed through pre-processing involving 128×128 grayscale conversion, pixel intensity normalization, and class balancing using class weighting. The model was trained using the Adam optimizer (lr=0.0001) with Early Stopping (patience=7) over 50 epochs. Evaluation using the validation set showed that the model achieved high accuracy for the Non-Demented class. The result indicates that ResNet18 with transfer learning can achieve an accuracy of 94.4%, making this model an effective approach for medium-scale classification of Alzheimer's MRI images.
AirDisinfeX: Pengembangan IoT pada Sistem Pencegahan Penyebaran COVID-19 melalui Udara Kharisma, Rizqi Sukma; Kusrini, Kusrini; Saputro, Uyock Anggoro; Sulistiyono, Mulia; Rahardi, Majid; Bernadhed, Bernadhed; Muktafin, Elik Hari
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 1 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i1.4483

Abstract

Ruang tertutup merupakan tempat yang memiliki potensi lebih tinggi dalam penyebaran virus COVID-19. Hal ini dikarenakan virus COVID-19 dapat terbawa udara. Ruang tertutup membuat udara semakin lama di ruang tersebut. Terlebih ruang tertutup sangat banyak digunakan untuk beraktivitas seperti rumah, sekolah, mall, kantor, tempat ibadah, dll. Sehingga untuk ruang tertutup harus mendapat perhatian serius untuk dapat menghindari penyebaran virus. AirDisinfeX adalah alat berbasis IoT dengan dilengkapi sinar UVC yang dapat membunuh virus termasuk virus COVID-19. Alat ini dapat dikendalikan dari jarak jauh secara manual atau timer. Sehingga alat bisa diaktifkan terlebih dahulu sebelum ruang tertutup digunakan. Penelitian ini juga menggunakan mikrokontroler ESP32 dalam mendukung pengembangan alat AirDisinfeX berbasis IoT. Hasil penelitian ini dengan akurasi sistem IoT AirDisinfeX sebesar 96,10% dan waktu respon rata-rata 5,32 detik
Comparative Analysis of Machine Learning-Based Software Defect Prediction in Object-Oriented and Structured Paradigms Using Apache Camel and Redis Datasets Nasiri, Asro; Setyanto, Arief; Utami, Ema; Kusrini, Kusrini
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5315

Abstract

Software Defect Prediction (SDP) is a crucial component of software engineering aimed at improving quality and testing efficiency. However, the majority of SDP research often overlooks the fundamental influence of the programming paradigm on the nature and causes of defects. This study presents a comparative analysis to identify the most influential software metrics for predicting defects across two distinct paradigms: Object-Oriented (OOP) and Structured. To ensure modern relevance and reproducibility, we constructed two new datasets from large-scale, open-source projects: Apache Camel (Java) for OOP and Redis (C) for Structured which exhibited realistic defect rates of 14.4% and 21.8%, respectively. The dataset creation process involved mining Git repositories for defect labeling and automated metric extraction using the CK and Lizard tools. Correlation analysis and baseline modeling using Random Forest revealed significant differences between the paradigms. In the OOP system, dominant defect predictors were related to the complexity of the class interface and features (e.g., uniqueWordsQty, totalMethodsQty, WMC, CBO). Conversely, defects in the structured system were strongly correlated with size and algorithmic complexity (e.g., file_tokens, file_loc, file_ccn_sum). Although the baseline models performed well (ROC–AUC = 0.82–0.87), the significant class imbalance resulted in low recall (44–50%). This motivates the need for more context aware approaches. These findings underscore that effective SDP strategies must be tailored to the underlying programming paradigm.
Deteksi Email Spam Menggunakan Multinomial Naive Bayes dengan Teknik Bag of Words Mulyaningtyas, Widya; Kusrini, Kusrini
SENTRI: Jurnal Riset Ilmiah Vol. 5 No. 2 (2026): SENTRI : Jurnal Riset Ilmiah, Februari 2026
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v5i2.5650

Abstract

Email is a means of communication within internal networks and the internet for the exchange of information. Email is still used today because of its ease of use. However, with the increase in the number of incoming emails, the problem of spam has arisen, requiring effective methods for detecting spam so that users can manage their email more efficiently and avoid potential fraud and disruption. This study aims to analyze the thematic and linguistic patterns of email messages based on their content using text classification techniques with the Multinomial Naive Bayes algorithm, which is believed to have good accuracy in detecting spam emails. The research consists of collecting a dataset related to Indonesian-language spam emails, preprocessing the data, training the model by dividing it into two scenarios (with and without stemming), and evaluating the model. Features from the email text will be converted into numerical representations using the Bags-of-Words method. Classification performance evaluation is carried out using accuracy, precision, recall, F1-Score, and confusion matrix metrics. Experimental results demonstrate that the Multinomial Naive Bayes model without stemming achieved the highest performance with an Accuracy of 92.5%, Precision of 91.0%, and F1-Score of 91.7%. These findings indicate that stemming in short texts like spam emails eliminates crucial semantic features (affixes) characteristic of spam. This study contributes to providing optimal pre-processing recommendations for Indonesian short text classification.
Identifikasi Penyakit Daun Pisang Berbasis Citra Warna dengan Ekstraksi ResNet50 dan Support Vector Machine Muflich, Alwie; Kusrini, Kusrini
Insect (Informatics and Security): Jurnal Teknik Informatika Vol. 12 No. 01 (2026): Maret 2026
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/insect.v12i01.5460

Abstract

Penyakit daun pisang merupakan salah satu faktor utama yang menyebabkan penurunan produktivitas tanaman pisang. Identifikasi penyakit secara manual masih memiliki keterbatasan karena bersifat subjektif, membutuhkan keahlian khusus, dan kurang efisien pada skala besar. Oleh karena itu, penelitian ini mengusulkan sistem identifikasi penyakit daun pisang berbasis citra warna dengan pendekatan ekstraksi fitur menggunakan Convolutional Neural Network (CNN) berbasis ResNet50 dan klasifikasi menggunakan Support Vector Machine (SVM). Dataset yang digunakan terdiri dari empat kelas, yaitu daun sehat, Sigatoka, layu Fusarium, dan bercak Cordana, dengan pembagian data training 70%, validasi 15%, dan testing 15%. Tahapan penelitian meliputi pra-pemrosesan citra, augmentasi data, ekstraksi fitur menggunakan CNN, serta klasifikasi menggunakan SVM dengan beberapa variasi kernel. Selain itu, dilakukan analisis perbandingan ruang warna RGB dan HSV untuk mengetahui representasi warna yang paling efektif dalam mendukung proses klasifikasi. Hasil penelitian menunjukkan bahwa kombinasi CNN dan SVM kernel RBF memberikan akurasi validasi sebesar 99,52% dan akurasi testing sebesar 99,04%, serta nilai precision, recall, dan F1-score yang seimbang. Analisis fitur warna menunjukkan bahwa ruang warna HSV lebih stabil terhadap variasi pencahayaan, namun kombinasi RGB dan HSV mampu memberikan representasi warna yang lebih lengkap. Hasil penelitian ini menunjukkan bahwa metode CNN–SVM efektif dalam mengidentifikasi penyakit daun pisang secara akurat dan berpotensi diterapkan pada sistem deteksi dini berbasis kecerdasan buatan di bidang pertanian.
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