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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Jurnal Peternakan Integratif Elkom: Jurnal Elektronika dan Komputer Journal of Education and Learning (EduLearn) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika Scientific Journal of Informatics Sisforma: Journal of Information Systems Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization AdBispreneur Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Informatika Aptisi Transactions on Management JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Aptisi Transactions on Technopreneurship (ATT) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mnemonic Journal Sensi: Strategic of Education in Information System Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Computer Science and Information Technologies Jurnal Bumigora Information Technology (BITe) Aiti: Jurnal Teknologi Informasi Infotech: Journal of Technology Information Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Applied Research (IJAR) Journal of Applied Data Sciences JOINTER : Journal of Informatics Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of Information Technology (JIfoTech) Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Jurnal Algoritma Nusantara of Engineering (NOE) Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat Jurnal Rekayasa elektrika Jurnal INFOTEL SmartComp Jurnal Indonesia : Manajemen Informatika dan Komunikasi Blockchain Frontier Technology (BFRONT) Scientific Journal of Informatics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Irwan Sembiring
Universitas Kristen Satya Wacana
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology 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 Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Digital Image Object Detection with GLCM Multi-Degrees and Ensemble Learning Kurniati, Florentina Tatrin; Purnomo, Hindriyanto Dwi; Sembiring, Irwan; Iriani, Ade
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5597

Abstract

Object detection in digital images has been implemented in various fields. Object detection faces challenges, one of which is rotation problems, causing objects to become unknown. We need a method that can extract features that do not affect rotation and reliable ensemble-based classification. The proposal uses the GLCM-MD (Gray-Level Co-occurrence Matrix Multi-Degrees) extraction method with classification using K-Nearest Neighbours (K-NN) and Random Forest (RF) learning as well as Voting Ensemble (VE) from two single classifications. The main goal is to overcome the difficulty of detecting objects when the object experiences rotation which results in significant visualization variations. In this research, the GLCM method is used to produce features that are stable against rotation. Furthermore, classification methods such as K-Nearest Neighbours (KNN), Random Forest (RF), and KNN-RF fusion using the Voting ensemble method are evaluated to improve detection accuracy. The experimental results show that the use of multi-degrees and the use of ensemble voting at all degrees can increase the accuracy value, and the highest accuracy for extraction using multi-degrees is 95.95%. Based on test results which show that the use of features of various degrees and the ensemble voting method can increase accuracy for detecting objects experiencing rotation
Optimizing Multilayer Perceptron with Cost-Sensitive Learning for Addressing Class Imbalance in Credit Card Fraud Detection Priatna, Wowon; Hindriyanto Dwi Purnomo; Ade Iriani; Irwan Sembiring; Theophilus Wellem
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5917

Abstract

The increasing use of credit cards in global financial transactions offers significant convenience for consumers and businesses. However, credit card fraud remains a major challenge due to its potential to cause substantial financial losses. Detecting credit card fraud is a top priority, but the primary challenge lies in class imbalance, where fraudulent transactions are significantly fewer than non-fraudulent ones. This imbalance often leads to machine learning algorithms overlooking fraudulent transactions, resulting in suboptimal performance. This study aims to enhance the performance of Multilayer Perceptron (MLP) in addressing class imbalance by employing cost-sensitive learning strategies. The research utilizes a credit card transaction dataset obtained from Kaggle, with additional validation using an e-commerce transaction dataset to strengthen the robustness of the findings. The dataset undergoes preprocessing with RUS and SMOTE techniques to balance the data before comparing the performance of baseline MLP models to those optimized with cost-sensitive learning. Evaluation metrics such as accuracy, recall, F1 score, and AUC indicate that the optimized MLP model significantly outperforms the baseline, achieving an AUC of 0.99 and a recall of 0.6. The model's superior performance is further validated through statistical tests, including Friedman and T-tests. These results underscore the practical implications of implementing cost-sensitive learning in MLPs, highlighting its potential to significantly enhance fraud detection accuracy and offer substantial benefits to financial institutions.
Pemodelan Pengaruh E-Learning Pada Performa Akademik Mahasiswa Dengan Technology Acceptance Model Dan Analisis Structural Equation Modelling Daniawan, Benny; Wijono, Sutarto; Manongga, Danny; Sembiring, Irwan; Krismiyati, Krismiyati; Wellem, Theophilus
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 3: Juni 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025128229

Abstract

E-learning telah menjadi alat yang penting dalam pendidikan tinggi, memungkinkan perguruan tinggi untuk menyediakan aksesibilitas dan fleksibilitas dalam pengajaran dan pembelajaran. Salah satu platform e-learning yang populer adalah Moodle yang digunakan oleh banyak universitas di indonesia. Namun, penggunaan Moodle di universitas belum banyak diteliti secara mendalam, terutama dalam konteks pengaruh e-learning terhadap indeks prestasi akademik. Oleh karena itu, penelitian ini bertujuan untuk melakukan pengujian e-learning Moodle di lingkungan Universitas Buddhi Dharma yang berlokasi di Tangerang dengan metode Technology Acceptance Model (TAM) menggunakan lima variabel Perceive Usefulness (PU), Perceive Ease of Use (PEOU), Attention Towards Using (ATU), Behaviour Intention to Use (BITU), dan Actual System Using (ASU) ditambah dengan dua variabel lain yaitu Level of Confidence (LC) dan Academic Performance (AP). Seluruh variabel dianalisis menggunakan Structural Equation Modelling (SEM). Hasilnya menunjukkan bahwa persepsi kemudahan penggunaan (PEOU), dan persepsi kebermanfaatan (PU) memiliki pengaruh positif secara signifikan terhadap sikap (ATT) dan niat perilaku pengguna (BITU) serta aktual penggunaan sistem (ASU) dalam Moodle sebagai platform e-learning dengan nilai t-statistik yang melebihi nilai t-tabel dan p-value. Namun dalam hal ini penerimaan teknologi e-learning yang baik tidak mempengaruhi tingkat kepercayaan diri (LC) dan performa akademik pengguna (AP) secara aktual.   Abstract E-learning has become an essential tool in higher education, enabling universities to provide accessibility and flexibility in teaching and learning. One of the popular e-learning platforms is Moodle, which many universities in Indonesia use. However, the use of Moodle at universities has yet to be studied in depth, especially in the context of the influence of e-learning on the academic performance index. Therefore, this research aims to test Moodle e-learning in the Buddhi Dharma University environment located in Tangerang using the Technology Acceptance Model (TAM) method using the five variables Perceive Usefulness (PU), Perceive Ease of Use (PEOU), Attention Towards Using (ATU), Behavioral Intention to Use (BITU), and Actual System Using (ASU) plus two other variables, namely Level of Confidence (LC) and Academic Performance (AP). All variables were analyzed using Structural Equation Modeling (SEM). The results show that perceived ease of use (PEOU) and perceived usefulness (PU) have a significant positive influence on attitudes (ATT) and user behavioral intentions (BITU) as well as actual system use (ASU) in Moodle as an e-learning platform with a value of t -statistics that exceed the t-table value and p-value. However, in this case, a good acceptance of e-learning technology does not affect the Confidence Level (LC) and user Academic Performance (AP).
A Dual-Fusion Hybrid Model with Attention for Stunting Prediction among Children under Five Years Hadikurniawati, Wiwien; Hartomo, Kristoko Dwi; Sembiring, Irwan; Arthur, Christian
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.831

Abstract

Malnutrition remains a persistent global health challenge, especially among children under five. Traditional assessment methods often rely on static anthropometric measures, which are limited in capturing complex growth patterns. This study aims to develop a robust classification model for predicting the nutritional status of children under five years old, addressing the critical public health challenge of stunting. The model contributes to the growing need for accurate, data-driven early detection systems in child health monitoring by introducing a hybrid framework that combines deep learning and classical machine learning techniques. The proposed approach integrates automatically extracted features from a One-Dimensional Convolutional Neural Network (1D-CNN) with classical anthropometric indicators. These combined features are processed through an additive attention mechanism, highlighting the most informative attributes. The attention-weighted representation is then classified using an ensemble stacking method that aggregates predictions from multiple base classifiers, including decision trees, nearest neighbor algorithms, support vector machines, etc. Synthetic Minority Over-sampling Technique (SMOTE) is applied to the training dataset to mitigate data imbalance, particularly the underrepresentation of severe and moderate malnutrition cases. The research utilizes a dataset comprising 2,789 records of children under five years old collected from community health posts in Indonesia. Data preprocessing included cleaning, normalization, and gender encoding. The model’s performance was evaluated using 5-fold cross-validation and measured by accuracy, precision, recall, and area under the curve metrics. The results show that the proposed model achieved an average accuracy of 99.70% and an area under the curve of 99.99%. An ablation study further demonstrated the significant contribution of each component, feature extraction, fusion mechanism, and ensemble classifier to the final performance. This approach reveals a robust and scalable solution for early nutritional status prediction in healthcare settings.
SOP of Information System Security on Koperasi Simpan Pinjam Using ISO/IEC 27002:2013 Andriana, Myra; Sembiring, Irwan; Hartomo, Kristoko Dwi
Jurnal Transformatika Vol. 18 No. 1 (2020): July 2020
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v18i1.2020

Abstract

Information security problems always increase every year. One way to minimize problems related to information system security is to establish an SOP. This study was conducted in koperasi simpan pinjam for several reasons that there has never been an assessment related to the level of security of the information system used, there are threatshave occured, and there do not have documented information system security procedures. The SOPs compiled in this study are based on the ISO/IEC 27002:2013 framework. The method used is qualitative with the OCTAVE framework to process the information obtained. Meanwhile, to calculate the value of each risk, FMEA is used. This study shows that 22% of the risks invloved in koperasi simpan pinjam have low categories, 59% medium categories and 19% high categories. The final result of the stiff research is the proposed 8 policies and 12 information system security procedures for koperasi simpan pinjam.
Sensitivitas Sistem Pencarian Artikel Bahasa Indonesia Menggunakan Metode n-gram Dan Tanimoto Cosine Supriadi, Candra; Purnomo, Hidriyanto Dwi; Sembiring, Irwan
Jurnal Transformatika Vol. 18 No. 1 (2020): July 2020
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v18i1.2184

Abstract

The human need for technology and the availability of adequate infrastructure is evidence that technology is now a part of basic human needs. The increasing number of journals and scientific papers, it must be more selective in selecting and sorting even though there are already many online service providers and journal portals. Research on search engines and plagiarism and recommendation systems has been carried out with various methods deemed appropriate to improve the performance of the system itself, this paper has the purpose of calculating the similarity between one article with another article by implementing n-gram and tanimoto cosine. The number of articles tested was forty-three titles and abstracts, tested fifty times with randomly selected keywords, by breaking down each title and abstract sentence into n characters (n = 2 to 8) including spaces and punctuation, then counted similarity with the query or keyword used for system testing. The test was conducted using several threshold variations from n = 2 to 8. After observing fifty times the threshold test of 0.15 has the highest accuracy at n = 4 at 0.92, the highest precision at n = 3 at 0.42 and the highest recall at the test n = 2 = 0.44 .
REVOLUTIONIZING DIGITAL TRUST: NETWORK INTRUSION DETECTION SYSTEMS FOR IDENTITY AND SECURITY ASSURANCE IN THE METAVERSE Ginting, Jusia Amanda; Sembiring, Irwan; Putra, Yonathan Rahadi; Marvelino, Matthew
Infotech: Journal of Technology Information Vol 11, No 1 (2025): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i1.381

Abstract

Digital security has become a major challenge in the metaverse, an interactive virtual space that integrates augmented reality and virtual reality. This study develops a machine learning-based Network Intrusion Detection System (NIDS) to enhance security reliability within the metaverse. K-Means and Apriori algorithms are applied to optimize rules in the Snort IDS, enabling more accurate detection of Distributed Denial of Service (DDoS) and Malware Command and Control (CNC) attacks. The results show that rule optimization using machine learning increases detection accuracy for DDoS attacks from 60% to 75% and for CNC attacks from 35% to 40%. Furthermore, this approach successfully reduces the false positive rate. The implementation of the optimized NIDS provides a significant contribution to securing activities in the metaverse, ensuring a safer and more reliable virtual environment.
Optimizing Automated Machine Learning for Ensemble Performance and Overfitting Mitigation Migunani, Migunani; Setiawan, Adi; Sembiring, Irwan
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 3 (2025): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i3.763

Abstract

Automated Machine Learning (AutoML) has revolutionized model development, but its impact on ensemble diversity and overfitting reduction remains underexplored. This Systematic Literature Review (SLR) analyzes 107 studies published between 2020 and 2024 to explore how AutoML enhances ensemble diversity, mitigates overfitting, and the challenges hindering its integration. Unlike previous reviews focusing on AutoML or ensemble methods independently, this study synthesizes their intersection and identifies key research trends. The findings reveal that AutoML improves ensemble robustness through automated hyperparameter tuning, meta-learning, and algorithmic blending while facing trade-offs in computational cost and interpretability. Four main themes emerge, integration mechanisms (19.6%), overfitting mitigation (26.2%), performance trade-offs (28.6%), and integration barriers (26.2%). Empirical results indicate that AutoML ensembles outperform traditional models by 22–41% in accuracy but require approximately 3.2 times higher computational resources. Hybrid AutoML and Explainable AI frameworks are recommended to balance accuracy and transparency. Theoretically, this study advances understanding of the synergy between AutoML and ensemble learning, while practically providing guidance for deploying reliable AI systems in sectors like healthcare, finance, and digital business. Policy implications align with the EU AI Act and the US Executive Order on trustworthy AI, supporting Sustainable Development Goals 9 and 8.
Implementation of Tensor Flow in Air Quality Monitoring Based on Artificial Intelligence Rahardja, Untung; Aini, Qurotul; Manongga, Danny; Sembiring, Irwan; Girinzio, Iqbal Desam
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v6i1.430

Abstract

Chemicals that cannot be controlled today can pollute resources and the environment. Common sources of pollutants are due to public transportation, cigarette smoke, volcanic activity that emits volcanic ash, factory smoke, forest fires, biogas, or carbon dioxide. The purpose of this paper is to monitor air quality, detect air and anticipate pollution levels. With the specified algorithms, three algorithms will be used to create a good and accurate model where four different gasses are predicted: carbon dioxide, sulfur dioxide, and nitrogen dioxide, in this paper, there are four algorithms used for the Air Qualification Index which are Support Vector Regression, Linear Regression, and Ensemble Gradient Boosted Decision Tree. This research also includes quantitative research which is hypothesized to be evaluated against Root Mean Squared Error, Mean Squared Error, and Mean Absolute error, depending on the performance of the measurements made by artificial intelligence, and the lower error value is selected. Based on the algorithm to be predicted in this air quality monitoring, there are 5 air pollutants like Carbon dioxide, Sulfur dioxide, and Nitrogen dioxide, and the sensors to be used are two sensors like PM2.5 and PM10 that can be predicted.
Ekstraksi Knowledge tentang Penyebaran #Ratnamiliksiapa pada Jejaring Sosial (Twitter) menggunakan Social Network Analysis (SNA) Tomasoa, Lyonly; Iriani, Ade; Sembiring, Irwan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 6: Desember 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4200.212 KB) | DOI: 10.25126/jtiik.2019661710

Abstract

Memasuki tahun politik 2018-2019, Indonesia mengalami darurat hoax  dimana isu-isu politik menyebar dengan sangat cepat terutama pada jejaring sosial yang merupakan wadah untuk menghubungkan setiap individu di seluruh dunia. Twitter sebagai salah satu jejaring sosial yang sering dipakai masyarakat Indonesia, menyebabkan isu-isu politik pun ikut terbawa dalam bentuk tagar (#). Tagar #RatnaMilikSiapa yang merupakan isu politik dari kasus hoax kebohongan penganiayaan RS di kota Bandung dijadikan sebagai tunggangan para pengguna twitter untuk membangun opini di masyarakat sehinga menjadikan hal tersebut sebagai hoax menjelang pemilihan Presiden 2019. Opini-opini dari setiap pengguna twitter tersebut telah menciptakan jaringan-jaringan komunikasi yang membahas tentang kasus politik penganiyayaan RS dengan tagar #RatnaMilikSiapa . Penelitian ini bertujuan untuk mengidentifikasi aktivitas penyebaran tagar #RatnaMilikSiapa dengan menggunakan metode Social Network Analyis (SNA) pada jejaring sosial twitter. Dalam penelitian ini, dilakukan identifikasi terhadap aktor utama dengan melakukan perhitungan sentralitas tingkatan atau degree Centrality (BC) sehingga dapat ditemukan aktor yang berpengaruh dalam terbentuknya kelompok-kelompok jaringan tweet #RatnaMilikSiapa pada jejaring sosial twitter. Hasil dari penelitan ini adalah ditemukannya 3 aktor  kunci (creator & influencers) yang berasal dari 5 aktor utama penyebaran tweet  #RatnaMilikSiapa dengan mengidentifikasi adanya pertukaran berita yang dilakukan oleh para aktor utama dan didukung dengan perhitungan nilai sentralitas keperantaraan atau betweenness Centrality (BC). Kemudian juga ditemukannya 32 aktor boundary spanner yang merupakan dampak dari aktivitas pertukaran berita atau information exchange  yang dilakukan oleh aktor kunci pada jaringan komunikasi dalam jejaring sosial twitter.AbstractEntering the political year of 2018-2019, Indonesia is facing a hoax crisis where political issues spread rapidly, especially on social media as a place for connecting people all over the world. Twitter as one of the popular social media, which is frequently used by the society of Indonesian, leads the political issues spread widely through the hashtag (#).  #RatnaMilikSiapa which was a hoax case about RS persecution in Bandung turned as a way for Twitter users creating a judgment in the society so that that issue became a hoax approaching the Presidential Election of 2019. The opinions of Twitter users had created a communication network discussed RS persecution as a political issue with #RatnaMilikSiapa.  This research intends to identify the #RatnaMilikSiapa deployment activity with the using of Social Network Analyis (SNA) method on Twitter. This research conducts the identification toward the main actors with degree Centrality (BC) calculation until the actor who influenced the establishment of #RatnaMilikSiapa tweet network groups on Twitter can be found. The results of this research are the researcher had found the three key actors (creator and influencers) which originated from 5 main actors who spread #RatnaMilikSiapa tweet. The researcher identifies the information exchange which had been done by the main actors and the results supports by the value of betweenness Centrality (BC) calculation. Later, the researcher had found 32 actors of boundary spanner which was the impact of information exchange done by the key actors on the communication network of Twitter.
Co-Authors Abas Sunarya, Po Ade Iriani Adi Setiawan Adriyanto Juliastomo Gundo Agus Sugiarto Agustinus, Ari Aji, Bintang Kristianto Andriana, Myra April Lia Hananto Apriliasari, Dwi Ardaneswari, Awanda Arthur, Christian Astawa, I Wayan Aswin Dew Ayu Sanjaya, Yulia Putri Bayu Setyanto Pamungkas Budhi Kristianto Budi Santoso Budi, Reza Setya Cahyaningtyas, Christian Daniawan, Benny Danny Manongga Danny Sebastian Dedy Prasetya Kristiadi Dwi Hosanna Bangkalang Dwi Setiawan Edi Suharyadi Efendy, Rifan Eka Purnama Harahap Eko Sediono Eko Sediyono Eleazer Gottlieb Julio Sumampouw Elmanda, Vonda Erick Alfons Lisangan Esti Zakia Darojat Evangs Mailoa Evi Maria Faisal Hakim Amrullah Faturahman, Adam Fauzi Ahmad Muda Ferry Alamsyah Fian Yulio Santoso Florentina Tatrin Kurniati Gallen cakra adhi wibowo Gerry Santos Lasatira Girinzio, Iqbal Desam Gudiato, Candra Hamdan . Hasnudi . Henderi Henderi . Hendry Hendry, - Henuk, Yusuf Leonard Herdin Yohnes Madawara Hindriyanto Dwi Purnomo Huda, Baenil I Gusti Ngurah Suryantara Ignatius Agus Supriyono Ilham Hizbuloh Indrastanti Ratna Widiasari Iwan Setiawan Iwan Setiawan Iwan Setyawan Iwan Setyawan Joko Listiawan Sukowati Joko Siswanto Jonas, Dendy Joseph Teguh Santoso Julians, Adhe Ronny Juneth Manuputty Jusia Amanda Ginting Krismiyati Kristoko Dwi Hartomo Kusumajaya, Robby Andika Limbong, Josua Josen Alexander Marsyel Sampe Asang Marvelino, Matthew Mau, Stevanus Dwi Istiavan Maya Sari Merryana Lestari Migunani Migunani Mira Mira Mira Mohammad Ridwan Muhamad Yusup Nanle, Zeze Nazmun Nahar Khanom Nina Setiyawati Ninda Lutfiani Nining Fitriani Nugroho, Samuel Danny Nuryadi, Didik Nurzainah Ginting Pamungkas, Bayu Setyanto Phillnov Yohanes Pinontoan Pinontoan, Phillnov Yohanes Priatna , Wowon Purbaratri, Winny Purnomo, Hidriyanto Dwi Putra, Yonathan Rahadi Qurotul Aini Qurotul Aini Rahardja.,M.T.I.,MM, Dr. Ir. Untung Raymond Elias Mauboy Rimes Jopmorestho Malioy Roy Rudolf Huizen Saian, Septovan Dwi Suputra Sandry Lanovela Pasaribu Santoso, Nuke Puji Lestari Setiawan Hakim Sri Ngudi Wahyuni Sri Ngudi Wahyuni, Sri Ngudi Sri Yulianto Joko Prasetyo Suharyadi Sulistio Sulistio Sumampouw, Eleazer Gottlieb Julio Supriadi, Candra Susanti, Novita Dewi Sutarto Wijono Suwijo Danu Prasetyo Teady Matius Surya Mulyana Teguh Indra Bayu Teguh Wahyono Theopillus J. H. Wellem Tintien Koerniawati Tio Nurtino Tirsa Ninia Lina Tomasoa, Lyonly Tri Wahyuningsih Tri Wahyuningsih Tukino, Tukino Untung Rahardja Untung Rahardja Wibowo, Mars Caroline Wijaya, Angga Zakharia Wiwien Hadikurniawati Wiwin Sulistyo Yerik Afrianto Singgalen Yessica Nataliani Yohan Maurits Indey Yohnes Madawara, Herdin Yolan Dita Dewi Pramudita Yulian Hany Makaruku