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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 Jurnal Sains Dan Teknologi (SAINTEKBU) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Applied Information System and Management ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer 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 Dinasti International Journal of Education Management and Social Science Jurnal Tecnoscienza Jurnal Mnemonic 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) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Perangkat Lunak Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat 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 Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Bangkit Indonesia Jurnal Multidisiplin Sahombu COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi JEC (Jurnal Edukasi Cendekia) 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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An AI-integrated IoT-based Self-Service Laundry Kiosk with Mobile Application Kusrini, Kusrini; Muhammad, Alva Hendi; Fauzi, Moch Farid; Kuswanto, Jeki; Bernadhed, Bernadhed; Widayani, Wiwi; Pramono, Eko; Muktafin, Elik Hari; Ariyanto, Yossy
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2050.382-393

Abstract

This paper proposes KILAO, an IoT-based self-service laundry kiosk connected with a mobile application that aims to improve the laundry experience by improving user convenience and operational efficiency. This study aims to streamline the washing process using autonomous payment systems, real-time monitoring, and AI-based queue management, resulting in better resource utilization and higher user satisfaction. The development technique comprises identification and requirement gathering, development of both software and hardware prototypes, and evaluation of the prototype. In the requirement-gathering phase, the design of a kiosk machine that consists of hardware and software is defined by combining regular washing machines with IoT technologies for remote control and monitoring. We also developed a mobile application to engage with the kiosk machine. The kiosk simplifies the choice of laundry bundles and accepts various payment options, including cash, cashless transactions, and card-based purchases. The evaluation procedure of the prototype was conducted by using expert evaluations. They are from academics and industry professionals who verified the system’s effectiveness and market potential. The results have shown several unique selling features for KILAO. Extensive payment options and self-service operations were highlighted from the customer’s perspective as key benefits. From the seller’s perspective, its interoperability with traditional washing machines enables a low-cost shift to intelligent, self-service operations, eliminating the need for pricey coin-operated machines. Also, the automatic monitoring system that detects cycle completion can reduce waiting times and improve energy efficiency. In summary, KILAO presents a significant advancement in laundry automation by integrating IoT and AI. Moreover, the Gradient boosting algorithm forecasts waiting times and gives real-time information on machine availability, removing the need for physical queueing. The research demonstrates that KILAO’s capability to provide self-service laundry by providing a user-friendly mobile application can enhance user experience, operational efficiency, and energy utilization.
Meningkatkan Hasil Belajar Siswa pada Pelajaran IPA melalui Metode Eksperimen Kelas IV SD Negeri 1 Kambowa Onde, Mitrakasih La Ode; Rizkayati, Anisa; Kusrini, Kusrini; Febrianti, Winda
Sulawesi Tenggara Educational Journal Vol 4 No 3: Desember (2024)
Publisher : Fakultas Keguruan dan Ilmu Pendidikan Universitas Sulawesi Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54297/seduj.v4i3.861

Abstract

Penelitian ini fokus pada meningkatkan hasil belajar siswa pada pelajaran IPA melalui metode eksperimen di kelas IV SD Negeri 1 Kambowa, Tujuan penelitian ini adalah untuk meningkatkan hasil belajar siswa pada pembelajaran IPA melalui metode pembelajaran eksperimen di SD Negeri 1 Kambowa. Metode penelitian menggunakan desain penelitian tindakan kelas (PTK) dari Kemmis & MC Taggart yang terdiri dari perencanaan, pelaksanaan, observasi dan refleksi. Subjek penelitian adalah siswa kelas IV SD Negeri 1 Kambowa yang berjumlah 20 orang siswa yang terdiri dari 9 orang laki-laki dan 11 siswa perempuan. Teknik pengumpulan data yang digunakan yaitu melalui observasi, tes dan dokumentasi. Penelitian ini dilaksanakan semester genap tahun ajaran 2023/2024 yang dilakukan sebanyak 2 siklus, setiap siklusya terdiri dari 2 kali pertemuan. Hasil pembelajaran siswa pada prasiklus sebelum menerapkan metode eksperimen terdapat 7 siswa yang tuntas belajar dengan ketuntasan klasikal 35%. Setelah menerapkan metode eksperimen pada siklu I siswa yang tuntas belajar menjadi 14 siswa dengan ketuntasan klasikal 70%. Kemudian pada siklus II meningkat menjadi 17 siswa yang tuntas belajar dengan ketuntasan klasikal 85%. Berdasarkan hasil penelitian ini dapat disimpulkan bahwa menggunakan metode eksperimen dapat meningkat hasil belajar IPA siswa kelas IV SD Negeri 1 Kambowa.
POTENTIAL ENTRY OF DHF DISEASE BASED ON ENVIRONMENTAL CONDITIONS USING ARTIFICIAL METHODS NEURAL NETWORK PERCEPTION S, Muhammad Sabri; Herlinawati, Noor; MZ, Reza Rafiq; Kusrini, Kusrini
Device Vol 14 No 2 (2024): November
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v14i2.7694

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus transmitted by the Aedes aegypti mosquito. The spread of DHF is greatly influenced by environmental conditions such as temperature, rainfall, humidity, and population density. In Indonesia, DHF has become a significant public health problem, especially in densely populated urban areas. Therefore, it is important to develop a predictive model that can forecast the potential occurrence of DHF based on environmental variables to reduce the impact and control the spread of this disease. The objective of this research is to develop a predictive model using the Artificial Neural Network Perception (ANN) method to predict the potential occurrence of DHF based on environmental variables, and to create an application for predicting the potential of DHF. This model is expected to help authorities make appropriate decisions to prevent and control DHF outbreaks. The research methodology includes the following stages: data collection, data preprocessing, ANN model development, model evaluation, and implementation and validation. The expected output of this research is an ANN model that can accurately predict the potential occurrence of DHF based on environmental conditions. Additionally, it is hoped that a predictive system will be available for authorities to take effective preventive and control measures against DHF. The research is expected to make a significant contribution to public health, particularly in the prevention and control of DHF. The results include an application for predicting the potential occurrence of DHF in a specific area, with features such as a Dashboard Interface, Temperature Interface, Dataset Interface, and Result Model Interface. The RMSE results obtained for this research were 0.01441372. From the research results, it can be concluded that ANN can be used to predict the potential for dengue fever to enter.
Mengukur Faktor Demografi Psikologis: Memprediksi Depresi, Kecemasan, dan Stres dengan menggunakan Machine Learning Juwariyah, Siti; Hulvi, Alfajri; Riduan, Nor; Kusrini, Kusrini
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.11793

Abstract

Mental health is an important aspect of human life. Depression, anxiety and stress are some of the most common mental health disorders. These disorders can negatively impact daily life, including productivity, social relationships, and an individual's quality of life, requiring accurate prediction for early intervention. One of the psychological measurement tools used to assess a person's level of depression, anxiety, and stress is the DASS-42 (Depression Anxiety Stress Scales - Long Form). In addition to the DASS-42 results, demographic factors such as age, gender, education level, and social status are important to analyze to strengthen the analysis. Machine learning (ML) is a powerful tool for analyzing complex data such as predicting psychological demographic factors associated with these mental health conditions. This study explores the potential of ML using a comprehensive dataset, using K-Nearest Neighbor and Support Vector Machine algorithms to assess prediction performance. The findings highlighted the effectiveness of ML models in predicting depression, anxiety and stress with high accuracy. The best algorithm in this study for the classification of depression, anxiety and stress is SVM with 99% accuracy but the use of Exploratory Data Analysis (EDA) technic to process additional variables affects the accuracy of the model so it can be concluded that demographic variables have an influence on the classification of depression, anxiety and stress.
Analisis Pemilihan Calon Penerima Beasiswa Daerah Dengan Metode Analytical Hierarchy Process Dan Profile Matching (Studi Kasus: Bachtiar, Lukman; Kusrini, Kusrini
Jurnal Bangkit Indonesia Vol 7 No 2 (2018): Bulan Oktober 2018
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (734.523 KB) | DOI: 10.52771/bangkitindonesia.v7i2.34

Abstract

Sistem pendukung keputusan (SPK) dengan metode Analytical Hierarchy Process (AHP) menguji nilai Consistency Ratio (CR) menentukan kelayakan tatanan perbandingan derajat kepentingan tiap-tiap kriteria. Pemrosesan metode AHP ini sampai menghasilkan nilai Prioritas tiap-tiap persyaratan. Jika nilai CR telah layak maka dapat dilanjutkan ke tahap pencarian solusi dengan metode Profile Matching (PM) untuk menyeleksi calon penerima beasiswa. Nilai Prioritas tiap-tiap persyaratan dari metode AHP kemudian dikelompokkan oleh metode PM ke dalam grup Core Factor dan grup Secondary Factor berdasarkan urutan descending nilai Prioritas. SPK ini dipakai oleh bagian Kemahasiswaan untuk menyeleksi para mahasiswa untuk diusulkan ke Bagian Kesra kabupaten dalam program Beasiswa Gerbang Mentaya. Keluaran pertama SPK berupa rincian perhitungan AHP mendapatkan nilai CR dengan konfirmasi apakah sudah dapat melakukan pencarian solusi ataukah masih harus menata kembali tatanan nilai-nilai perbandingan derajat kepentingan tiap-tiap persyaratan. Keluaran kedua memuat rincian pemrosesan PM menilai persyaratan para mahasiswa. Keluaran terakhir berupa daftar para mahasiswa yang benar-benar layak diusulkan menerima beasiswa berdasarkan seleksi penilaian, seleksi status warga tetap kabupaten, dan status tidak sedang menerima beasiswa dari pihak lain, yang kemudian diusulkan oleh bagian Kemahasiswaan kepada Bagian Kesra Kabupaten Kotawaringin Timur.
Analisis Perbandingan Kinerja DWT dan SWT dalam Pengenalan Emosi Berbasis EEG Menggunakan XGBoost Prameswari, Sonia Anjani; Kusrini, Kusrini
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.479

Abstract

Emotion recognition from electroencephalography (EEG) signals is crucial for human-computer interaction and diagnosing emotional disorders. This study evaluates the impact of feature extraction methods on the performance of XGBoost in classifying emotions in game players using EEG data. It compares the efficacy of Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) combined with XGBoost, aiming to identify the most effective feature extraction method for improving emotion classification accuracy. Using the GAMEEMO dataset, which includes preprocessed EEG signals from game players, three scenarios were analyzed: XGBoost without feature extraction, XGBoost with DWT, and XGBoost with SWT. The results demonstrate that DWT significantly enhances classification performance, achieving higher accuracy, precision, and recall compared to SWT and no feature extraction. DWT's ability to capture rapid frequency changes in EEG signals is a key factor in its superior performance. Future work should focus on refining data preprocessing techniques, exploring additional feature extraction methods, and optimizing XGBoost hyperparameters to further enhance emotion recognition accuracy. This research provides valuable insights into the comparative effectiveness of different wavelet transform methods for EEG-based emotion classification, emphasizing the potential of DWT for improved performance
Decision Support System of Bonus for Honorary Teachers through the TOPSIS Algorithm Kurniawan, Angga; Kusrini, Kusrini
JINAV: Journal of Information and Visualization Vol. 5 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav2888

Abstract

The proliferation of algorithms facilitates various tasks by allowing for specialization in specific fields. Among these, algorithms designed for calculation and decision-making are particularly useful. Decision Support Systems (DSS) have undergone numerous changes and advancements, leading to the development of new algorithms in this area. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) algorithm enables the determination of criteria and weights to assist in decision-making by calculating solution distances and weight values. This system allows for the adjustment of points, criteria, and weight values based on specific needs. At SMPN 1 Pajangan, the implementation of this system enhances the accuracy and efficiency of supervisors in managing bonuses for non-permanent teachers, mitigating social jealousy through a predefined, system-based distribution of criteria and weights.
HYPERPARAMETER MODEL LSTM-GRU UNTUK PREDIKSI PEMETAAN TINGKAT KEBAKARAN HUTAN maulana, fahrizal; kusrini, kusrini
Jurnal Informatika Vol 9, No 1 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i1.12882

Abstract

Bencana kebakaran hutan merupakan permasalahan besar bagi pemerintah provinsi Kalimantan Tengah. Langkah eksternal maupun internal telah dilakukan melalui kebijakan publik yang dibuat berupa hasil prediksi atau pemetaan kebakaran hutan dimasa akan datang. Dalam penelitian ini dilakukan pengembangan model untuk prediksi tren dan pemetaan tingkat kebakaran hutan dengan fokus penerapan hyperparameter terhadap kombinasi RNN di dua perangkat dan pengaturan rasio dataset berbeda. Dataset yang digunakan merupakan penggabungan dataset MODIS dan Merra2 sebagai end-to-end multivariate fitur dan target. Penggabungan dataset menggunakan asas interpolasi untuk mendukung kontinuitas kekosongan data. Untuk mencapai tujuan penelitian dilakukan eksperimental sebanyak 12 skenario terhadap 6 set pengaturan hyperparameter dengan evaluasi menggunakan performansi regresi MAE dan RMSE. Temuan penelitian menunjukan model kombinasi LSTM-GRU konsisten memperoleh rata-rata error MAE 2% dan RMSE 6% pada P1 dan P2 dengan nilai performa loss pembelajaran terbaiknya berada pada skenario 7, 10, 11 untuk  pembagian kedua dataset dan skenario 8 di rasio dataset 70:30. Pengujian di perangkat berbeda juga tidak mempengaruhi penurunan error pada model terhadap penerapan hyperparameter kecuali lama runtime pembelajaran model. Hasil penelitian ini memberikan gambaran yang komprehensif terhadap pemilihan parameter terhadap kombinasi model RNN yang ideal berdasarkan pembagian rasio dataset serta memberikan pemahaman tentang penerapan hyperparameter pada perangkat berbeda.
Data Ranking Optimization: Hybrid Fuzzy AHP-VIKOR with Information Gain Ratio Attribute Reduction Muzakir, Muhammad; Kusrini, Kusrini; Arief, Muhammad Rudyanto
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 11, No 2 (2024)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v11i2.689

Abstract

Poverty in Indonesia, including in Banjarmasin City, South Kalimantan Province, remains a significant issue despite the government's implementation of various mitigation programs. One of the primary challenges is the distribution of social assistance, which often fails to reach the intended beneficiaries. To address this problem, this study aims to apply the Hybrid Fuzzy Analytic Hierarchy Process (AHP) - Technique for Order Preference by Similarity to Ideal Solution (VIKOR) algorithm to the integrated social welfare data (DTKS) in Banjarmasin City. The hybrid Fuzzy AHP - VIKOR approach is employed in this study to rank social assistance recipients based on the available dataset, which consists of 2,879 records. Fuzzy AHP is utilized for the weighting and classification process, while VIKOR is applied to rank the alternatives. The combination of these two methods is expected to yield a more accurate ranking that aligns with the actual needs identified in the DTKS data for Banjarmasin City. The findings of this study demonstrate that the application of AHP and VIKOR methods produces more accurate rankings in accordance with the real data. These results have significant implications for enhancing the effectiveness of poverty alleviation programs and aiding the Banjarmasin City government in prioritizing the distribution of social assistance. By integrating the Fuzzy AHP and VIKOR methods, this study provides a deeper understanding of the more targeted distribution of social assistance, which is anticipated to improve the effectiveness of poverty alleviation programs in Banjarmasin City and beyond.
ANALISIS PERBANDINGAN ALGORITMA KLASIFIKASI UNTUK IDENTIFIKASI DIABETES DENGAN MENGGUNAKAN METODE RANDOM FOREST DAN NAIVE BAYES Zuhri, Muhammad Rafli; Kusrini, Kusrini; Ariatmanto, Dhani
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 1 (2025): EDISI 23
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i1.5146

Abstract

Penanganan penyakit diabetes menjadi penting karena komplikasi yang dapat terjadi jika tidak ditanggulangi dengan benar. Klasifikasi merupakan salah satu metode yang dapat digunakan untuk mengidentifikasi diabetes. Algoritma klasifikasi ini dapat menganalisis data pasien, seperti usia, jenis kelamin, riwayat kesehatan, dan hasil tes, untuk memprediksi apakah pasien tersebut menderita diabetes. Random Forest dan Naïve Bayes merupakan dua algoritma klasifikasi yang populer. Random Forest adalah metode kompleks yang didasarkan pada penggabungan beberapa pohon keputusan untuk mendapatkan prediksi yang lebih akurat, sedangkan Naïve Bayes merupakan metode pengklasifikasian berdasarkan probabilitas sederhana dan dirancang agar dapat dimanfaatkan denegan asumsi antar variabel penjelas saling bebas (independen). Hasil penelitian menggunakan data sebanyak 70% sebagai data pelatihan dan 30% sebagai data pengujian dari keseluruhan 768 data. keseluruhan yang diperoleh bahwa metode random forest dapat memprediksi penyakit diabetes dengan tingkat persentase sebesar 94% dan tingkat persentase naïve bayes sebesar 78%. Berdasarkan hasil penelitian yang diperoleh metode random forest memiliki tingkat persentase lebih tinggi dibandingkan metode naïve bayes dengan tingkat persentase 94% sedangkan naïve bayes dengan tingkat persentase 78% sehingga dapat disimpulkan bahwa metode random forest merupakan metode terbaik dalam mengindentifikasi penyakit diabetes dibandingkan metode naïve bayes.
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 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 Aris Subadi Arli Aditya Parikesit Asnawi, Muhamad Fuat 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 Helmawati, Nita Herawati, Maimi Herlinawati, Noor Hulvi, Alfajri I Putu Agus Ari Mahendra Ikhwanudin, Aolia Ilmawati, Fahma Inti Jeki Kuswanto Juwariyah, Siti Kasman, Haris Saktiawan Kurniasari, Iin Kusnawi , Kusnawi Kusnawi Kusnawi Lewu, Retzi Y. Linda, Kumara Dewi Listyanto, Ahmad Wildan López, Alba Puelles Lukman Bachtiar M. RUDYANTO ARIEF M. Suyanto, M. Madhika, Yudha Randa Mahendra, Awanda Putra Mangun, Syamsul Syahab Maradona, Maradona Mardiana Mardiana Martínez-Béjar, Rodrigo Masud, Ibnu maulana, fahrizal Megantara, Muhamad Arldi MEI PARWANTO KURNIAWAN Metha, Halifa Sekar Miftachuddin, Achmad Agus Athok Mohamad Firdaus, Mohamad Mohammad Diqi Mohammad Rezza Pahlevi Moningka, Nirwan Mufti Ari Bianto Muhamad Iksan, Muhamad Muhammad Resa Arif Yudianto Muktafin, Elik Hari Mulia Sulistiyono 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 Sarawan, Tommy 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 triadin, Yusrinnatul Jinana Tukan, Ewaldus Ambrosius Ula, M. Izul Wahyu Pujiharto, Eka Wahyudi, Alfian Cahyo Wangsa, Sabda Sastra Wijaya, Jodi Wiwi Widayani, Wiwi Yanuargi, Bayu Yossy Ariyanto Yuana, Kumara Ari Yuza, Adela Zakaria Zakaria Zuhri, Muhammad Rafli Zulkarnain, Imam Alfath Zumarni, Zumarni