Article Per Year (5 Year)
p-Index From 2020 - 2025
10.954
P-Index
This Author published in this journals
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) SITEKIN: Jurnal Sains, Teknologi dan Industri KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi 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) METIK JURNAL Building of Informatics, Technology and Science 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) JIKA (Jurnal Informatika) Jurnal Perangkat Lunak Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) 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) 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 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 Nusantara of Engineering (NOE) Jurnal Bangkit Indonesia Jurnal Multidisiplin Sahombu COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi JEC (Jurnal Edukasi Cendekia) Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Sistem Informasi Komputer dan Teknologi Informasi Jurnal TAM (Technology Acceptance Model) Jurnal Sistem Informasi dan Teknologi Informasi
Kusrini, Kusrini
Unknown Affiliation
Religion Aerospace Engineering Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences 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 Mathematics Mechanical Engineering Social Sciences Transportation Other

Published : 94 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 94 Documents
Search

 1   2   3   4   5 
PERANGKINGAN DALAM PENENTUAN E-COMMERCE TERBAIK DENGAN METODE ANALYTICAL HIERARCHICAL PROCESS Tri Nugroho, Arief; Kusrini, Kusrini; Al Fatta, Hanif
JURNAL PERANGKAT LUNAK Vol 5 No 3 (2023): Jurnal Perangkat Lunak
Publisher : Indragiri Islamic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/jupel.v5i3.2798

Abstract

Revolusi Industri 4.0 menyebabkan adanya pergeseran interaksi antar manusia, diantaranya dalam proses bertransaksi jual beli. Salah satu produk Revolusi Industri 4.0 yang dihasilkan untuk mempermudah proses bertransaksi jual beli adalah e-commerce. E-commerce diprediksi akan terus bertumbuh seiring berkembangnya ekonomi digital di Indonesia. E-commerce yang hadir di Indonesia sudah semakin menjamur dan bersaing dengan memberikan beragam fasilitas yang memanjakan konsumen diantaranya program diskon, cashback, dan, gratis biaya pengiriman. Semakin banyaknya pilihan e-commerce ini membuat konsumen cenderung bingung dan akhirnya mengikuti gelombang tanpa tahu tujuan sebenarnya dalam penggunaan e-commerce. Salah satu cara yang dapat digunakan konsumen untuk memilih e-commerce yang paling efektif dan efisien adalah dengan metode AHP (Analytical Hierarchical Process). Metode AHP akan memberikan rekomendasi e-commerce yang paling tepat dan layak digunakan berdasarkan bobot kriteria yang dihasilkan.
The effect of Gaussian filter and data preprocessing on the classification of Punakawan puppet images with the convolutional neural network algorithm Kusrini, Kusrini; Arif Yudianto, Muhammad Resa; Al Fatta, Hanif
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3752-3761

Abstract

Nowadays, many algorithms are introduced, and some researchers focused their research on the utilization of convolutional neural network (CNN). CNN algorithm is equipped with various learning architectures, enabling researchers to choose the most effective architecture for classification. However, this research suggested that to increase the accuracy of the classification, preprocessing mechanism is another significant factor to be considered too. This study utilized Gaussian filter for preprocessing mechanism and VGG16 for learning architecture. The Gaussian filter was combined with different preprocessing mechanism applied on the selected dataset, and the measurement of the accuracy as the result of the utilization of the VGG16 learning architecture was acquired. The study found that the utilization of using contrast limited adaptive histogram equalization (CLAHE) + red green blue (RGB) + Gaussian filter and thresholding images showed the highest accuracy, 98.75%. Furthermore, another significant finding is that the Gaussian filter was able to increase the accuracy on RGB images, however the accuracy decreased for green channel images. Finally, the use of CLAHE for dataset preprocessing increased the accuracy dealing with the green channel images.
Enhancing COVID-19 forecasting through deep learning techniques and fine-tuning López, Alba Puelles; Martínez-Béjar, Rodrigo; Kusrini, Kusrini; Setyanto, Arief; Agastya, I Made Artha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp934-943

Abstract

In this study, a comprehensive analysis of classical linear regression forecasting models and deep learning techniques for predicting coronavirus disease of 2019 (COVID-19) pandemic data was presented. Among the deep learning models, the long short-term memory (LSTM) neural network demonstrated superior performance, delivering accurate predictions with minimal errors. The neural network effectively addressed overfitting and underfitting issues through rigorous tuning. However, the diversity of countries and dataset attributes posed challenges in achieving universally optimal predictions. The current study explored the application of the LSTM in predicting healthcare resource demand and optimizing hospital management to provide potential solutions for overcrowding and cost reduction. The results showed the importance of leveraging advanced deep learning techniques for improved COVID-19 forecasting and extending the application of the models to address broader healthcare challenges beyond the pandemic. To further enhance the model performance, future work needed to incorporate additional attributes, such as vaccination rates and immune percentages.
ANALISIS TINGKAT KEMATANGAN SISTEM INFORMASI MANAJEMEN AKADEMIK DAN KEMAHASISWAAN IAIN PALANGKA RAYA MENGGUNAKAN COBIT 5 Pamungkas, Sapto; Kusrini, Kusrini; Prasetio, Agung Budi
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 11 No 2 (2021): September 2021
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (266.924 KB) | DOI: 10.33020/saintekom.v11i2.212

Abstract

The use of Information Technology (IT) at a university is needed at this time. There are many advantages in using IT and have a good impact on a university. The use of IT in a university has different roles according to their needs. The utilization of IT in Higher Education theoretically provides appropriate and efficient administration systems (Fernandes Andry and Christianto, 2018). IAIN Palangka Raya is one of the universities that has implemented IT, in this case, the academic and student management information system. The information system at IAIN Palangka Raya is one of the IT applications that is often used to carry out IT governance, however, there are several problems that occur in the information system, including features or tools that are not yet functional. For this reason, the researcher will analyze the information system to get the level of maturity and recommendations so that IAIN Palangka Raya can follow up on the results of this analysis and improve it to get the goal of good information system governance. In this study, the COBIT 5 framework is used with a focus on the EDM domain (Evaluate, Direct, and Monitor). The results of the analysis showed that the capability level was 2.86 (established), which means that the process was carried out, achieved goals, and well managed. With a balance value of 0.96, which means that the expected distance with the current distance is not too far so that features or tools that are not yet functioning are functional. So that the academic and student management information system of IAIN Palangka Raya runs according to its goals and is better.
Sentiment Analysis to Measure Public Trust in the Government Due to the Increase in Fuel Prices Using Naive Bayes and Support Vector Machine Zakaria, Zakaria; Kusrini, Kusrini; Ariatmanto, Dhani
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 2 (2023): November 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i2.7167

Abstract

The study examines public sentiment on the government's fuel price policy using an experimental approach and Twitter data obtained through API scraping. It applies sentiment analysis methods like Naïve Bayes, SVM, and Majority Voting. SVM achieved 85% accuracy, excelling in identifying negative sentiments, while Majority Voting reached 70% by considering confidence levels. Naïve Bayes struggled with neutral sentiments. They are combining methods to enhance the understanding of public sentiments on fuel price changes. The study highlights sentiment analysis' effectiveness in gauging reactions to fuel policies, with SVM offering more profound insights into sentiments related to fuel price hikes. Challenges remain in identifying neutral sentiments due to social media text brevity. These findings underscore the contextual importance of interpreting sentiment analysis. Leveraging these insights, governments can understand public perceptions better and devise improved communication strategies for sensitive economic policies like fuel price hikes, fostering better government-citizen interactions. The study aims to guide stakeholders in comprehending public perspectives within public policy, emphasizing the relevance of sentiment analysis for policy evaluation.
ANALISIS SENTIMEN PENGGUNA APLIKASI BANK SYARIAH INDONESIA DENGAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) Rachmawati Oktaria Mardiyanto; Kusrini, Kusrini; Ferry Wahyu Wibowo
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 1 (2023): Juni 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i1.85

Abstract

Several new Islamic banking products have been created as a result of the increasing popularity of Islamic banking in Indonesia. Due to the technical advances made possible by the globalization era, all operations, including transactions, can be carried out easily and practically. One of the sharia banks that offers mobile banking services is Bank Syariah Indonesia. BSI Mobile still occupies the 49th position in the banking category according to statistics taken from Google Playstore, while other institutions are already in the top 20 positions. 5 linguists will annotate or manually label the app's 55,416 user-submitted reviews and ratings. 13568 review and rating data collected by app users after annotating or labeling and eliminating duplicate data will be used in this research. In the early stages of the sentiment analysis process, case folding, punctuation mark re-moval, stop word removal, and stemming were carried out on review and rating data. The Support Vector Machine (SVM) approach is used to evaluate training data and data testing using stemmed findings. In this study, the results of the training and precision tests were each worth 87.309%, and the results of the training and memory tests were both worth 86.958%. The training accuracy value is 85.87%, the projected sentiment analysis results have an accuracy rate of 85.87%, and the training results and precision testing are each worth 86.958%.
TATA KELOLA TEKNOLOGI INFORMASI MENGGUNAKAN FRAMEWORK COBIT 2019 DOMAIN ALIGN PLAN AND ORGANIZE STUDI KASUS: AKADEMI KOMUNITAS DARUSSALAM BLOKAGUNG BANYUWANGI Aziz, Moh Abdul; Kusrini, Kusrini; Nasiri, Asro
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 2 (2023): Desember 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i2.112

Abstract

The role of information technology (IT)continues to experience a very significant increase in building and facilitating the performance of a company or institution. The use of IT in educational institutions is theoretically believed to be able to provide convenience and efficiency in administration. Akademi Komunitas Darussalam (AKD) Blokagung Banyuwangi is a relatively new higher education institution, with the 2019 Establishment Decree proving that the institution is not yet five years old. Like a new institution, many unresolved issues must be addressed, one of which is in the field of IT governance in optimizing the performance of human resources in the field of information technology. Therefore, it is necessary to design information technology governance related to the management of IT human resources. This research will design governance using the COBIT 2019 framework. The focus of this research is the Align, Plan, and Organize (APO) domain in the APO07 Manage Human Resources sub-domain. The results of the gap analysis carried out resulted in design recommendations on the people aspect in the form of adjustments to organizational structure, details of main tasks and functions, planning training and workshops, as well as communication in the form of meetings. In the process aspect, it produces SOPs, reporting schemes, and alignment of problem-solving. On the technology aspect, in the form of recommendations for human resource information system tools. This research is expected to assist the AKD Blokagung in carrying out recommendations based on the roadmap that has been adjusted to the AKD Blokagung long-term plan so that it can prioritize IT human resource management in the IT sector according to the needs of the AKD Blokagung. Keywords: Information Technology, COBIT 2019, IT Governance, APO07.
Optimasi Algoritma Naive Bayes Menggunakan Metode Cross Validation Untuk Meningkatkan Akurasi Prediksi Tingkat Kelulusan Tepat Waktu Samponu, Yohakim Benedictus; Kusrini, Kusrini
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 1 No. 2 (2017)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v1i2.29

Abstract

Education at this time is an important requirement in facing the demands of an increasingly advanced era in technolo-gy. To compensate this, the existing educational standards in universities must also be improved, this is a bit much affect the pattern of teaching from universities that produce qualified graduates who can compete in the world of work later and indirectly give a positive impact on the university itself. Qualified graduates are of course not only depending on the role of a university but also majors and quality of education as long as students are still in high school / vocational school also plays an important role. Results of the on-time graduation rate prediction research can be used as an information to im-prove the quality and optimization of the education system but it requires a maximum degree of accuracy. This research predicts on time graduation rates by conducting analysis using data mining classification techniques. Naïve Bayes algo-rithm that are used for this research will be discussed as a reference in conducting research. The author performs a series of different experimental scenarios / cross validation to perform comparisons that can give a difference in the level of ac-curacy gained from this research. The results of this research indicate that with the addition of Cross Validation testing scenario there is an increase of 2% accuracy of the test.
Perbandingan Additive dan Multiplicative Exponential Smoothing Terhadap Prakiraan Kualitas Udara di Banjarmasin Yusuf, Ahmad; Kusrini, Kusrini; Muhammad, Alva Hendi
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 6 No. 1 (2022)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v6i1.507

Abstract

Human health concerns are one of the important consequences of low air quality. The low air quality of each city will have long-term impacts such as global warming and anthropogenic greenhouse effects. Air quality usually occurs in areas that are in some parts of the country such as Kalimantan Island. As the third largest island in the world, Kalimantan can be said to be the lungs of the world like the haze problem that enveloped the city of Banjarmasin in 2019. This condition can result in high sufferers of Acute Respiratory Tract Infection (ISPA). Decision making by stakeholders needs to be studied in depth to prevent this. One of the efforts that can be done is the air quality forecast that will occur. Data obtained from BMKG Banjarmasin is the initial material for the forecast. Air quality forecast will use Triple Exponential Smoothing with 2 types of modeling namely additive and multiplicative, so this study aims to conduct air quality forecasts in Banjarmasin City in 2021 and 2022 using Additive and Multiplicative Triple Exponential Smoothing. In forecasts using this method, weighting the constant values α, β, γ can result in small error values. To determine the accuracy comparison of the two modeling is done with an RMSE value. The results showed that air quality conditions in Banjarmasin during 2021 and 2022 for CO, O3, and PM pollutants were in the category of safe for human health, while for pollutants NO2 and SO2 were declared to have a high index so that air quality can harm the health of living things. In comparison, multiplicative modeling on CO forecasts (α= 0.5, β = 0.001, and γ = 0.149), NO2 (α = 0.5, β = 0.024, and γ = 0.022), and SO2 (α = 0.5, β = 0.001, and γ = 0.037) has high accuracy and small error values compared to additive modeling. In contrast, additive modeling in O3 (α = 0.5, β = 0.001, and γ = 0.06) and PM (α = 0.434, β = 0.001, and γ = 0.213) have high accuracy and low error values compared to multiplicative modeling. The conclusion obtained is the difference in forecast results between additive and multiplicative modeling on air quality forecasts in Banjarmasin because multiplicative modeling is used when there is a trend or sign that seasonal patterns depend on the size of the data. In other words, seasonal patterns enlarge as the data size increases. Additive models are used if this trend does not occur.
Aplikasi Prediksi Banjir Menggunakan Algoritma XGBoost Berbasis Website Asnawi, Muhamad Fuat; Bisono, Hadi Hikmadyo; Megantara, Muhamad Arldi; Kusrini, Kusrini
Journal of Economic, Management, Accounting and Technology (JEMATech) Vol 7 No 2 (2024): Agustus
Publisher : Fakultas Teknik dan Ilmu Komputer, Universitas Sains Al-Qur'an (UNSIQ) Wonosobo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32500/jematech.v7i2.7644

Abstract

Penelitian ini bertujuan untuk mengembangkan model prediksi risiko banjir menggunakan algoritma XGBoost dengan memanfaatkan dataset yang tersedia di Kaggle. Dataset tersebut mencakup berbagai faktor yang mempengaruhi risiko banjir seperti kualitas bendungan, pengikisan sistem drainase, longsor, dan hilangnya lahan basah. Proses penelitian dimulai dengan pengumpulan data, diikuti oleh preprocessing yang meliputi penanganan missing values, pemilihan fitur menggunakan regresi untuk memastikan fitur yang paling berpengaruh, dan normalisasi data. Model XGBoost kemudian dilatih dengan data yang telah diproses dan dievaluasi menggunakan beberapa metrik evaluasi. Hasil evaluasi menunjukkan bahwa model memiliki performa yang sangat baik dengan nilai Cross-Validation RMSE sebesar 0.00097, Mean Squared Error (MSE) sebesar 1.0336, Root Mean Squared Error (RMSE) sebesar 0.001017, Mean Absolute Error (MAE) sebesar 0.000801, dan Mean Absolute Percentage Error (MAPE) sebesar 0.1605%. Nilai-nilai ini mengindikasikan kesalahan prediksi yang relatif kecil. Visualisasi hasil juga menunjukkan bahwa model tidak memiliki bias sistematis dan kesalahan prediksi tersebar merata. Penelitian ini mendesak mengingat peningkatan frekuensi dan dampak banjir akibat perubahan iklim dan urbanisasi yang pesat. Model ini diharapkan dapat digunakan secara efektif untuk memberikan peringatan dini dan membantu dalam perencanaan tata ruang yang lebih baik untuk mengurangi dampak bencana banjir.
Co-Authors AA Sudharmawan, AA Abdillah, Yahya Auliya Adhani, Muhammad Azmi Agastya, I Made Artha Ahmad Yusuf Alfatta, Hanif Alva Hendi Muhammad Andi Muhammad Irfan Andika, Roy Andriyanto, Rifki Angga Kurniawan Anggraeni, Meita Dwi Ardana, Wildan Muhammmad Ari Yuana, Kumara Arief Setyanto Arief, M Rudyanto Arief, Muhammad Rudyanto Arifuddin, Danang Aris Subadi Asnawi, Muhamad Fuat Azi, Amanda Aziz, Moh Abdul Bayu Setiaji Béjar, Rodrigo Martínez Bentar Candra P Bernadhed, Bernadhed Bisono, Hadi Hikmadyo Braeken, An Candra, Kurnia Khoirul da Silva, Bruno DHANI ARIATMANTO Dzulhijjah, Dwi Ahmad Eko Pramono Eko Purwanto Ema Utami Emha Taufiq Luthfi Fatkhurrochman, Fatkhurrochman Fauzi, Moch Farid Fauzy, Marwan Noor Febrianti, Winda Ferry Wahyu Wibowo fitriyanto, nur Gifari, Okta Ihza Halimi, Ahmad Hamdikatama, Bimantyoso Hanif Al Fatta Haris, Ruby Hartono, Anggit Dwi Haryo, Wasis Hasan, Nur Fitrianingsih Hasan, Nurul Rahmawati Herawati, Maimi Herlinawati, Noor Hulvi, Alfajri I Putu Agus Ari Mahendra Ilmawati, Fahma Inti Jeki Kuswanto Juwariyah, Siti Kasman, Haris Saktiawan Kurniasari, Iin Kusnawi , Kusnawi Kusnawi Kusnawi Lewu, Retzi Y. 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 maulana, fahrizal Megantara, Muhamad Arldi MEI PARWANTO KURNIAWAN Metha, Halifa Sekar Mohamad Firdaus, Mohamad Mohammad Diqi Mohammad Rezza Pahlevi Moningka, Nirwan Mufti Ari Bianto Muhammad Resa Arif Yudianto Muktafin, Elik Hari Muzakir, Muhammad MZ, Reza Rafiq Nasiri, Asro Ni Nyoman Utami Januhari, Ni Nyoman Nugroho, Agung Nugroho, Hanantyo Sri 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 Putra, Andriyan Dwi Rachmawati Oktaria Mardiyanto Riduan, Nor Rizkayati, Anisa S, Muhamad Rois S, Muhammad Sabri Saleh, Robby Febrianur Samponu, Yohakim Benedictus Santosa, Hendriansyah Saputro, Moh. Rizal Bayu Sarawan, Tommy Selvy Megira, Selvy Semma, Andi Bahtiar Setiawan, Moh. Arif Ma'ruf Setyanto, Arif Solikin, Arif Fajar Sudarmawan, Sudarmawan Sudarto Sudarto Swastikawati, Claudia Syafutra, Arif Dwi Syaiful Huda Tampubolon, Jandri Tamuntuan, Virginia TONNY HIDAYAT Tri Nugroho, Arief triadin, Yusrinnatul Jinana Tukan, Ewaldus Ambrosius Ula, M. Izul Wahyu Pujiharto, Eka Wahyudi, Alfian Cahyo Wiwi Widayani, Wiwi Yossy Ariyanto Yuana, Kumara Ari Yuza, Adela Zakaria Zakaria Zuhri, Muhammad Rafli