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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 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 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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Predicting Transjakarta Passengers with LSTM-BiLSTM Deep Learning Models for Smart Transportpreneurship Siswanto, Joko; Hendry, Hendry; Rahardja, Untung; Sembiring, Irwan; Lisangan, Erick Alfons
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 1 (2025): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i1.440

Abstract

Travel pattern variations pose challenges in building a prediction model that accurately captures seasonal patterns or precision of BRT passenger numbers. An approach that integrates sophisticated prediction algorithms with high accuracy is needed to address the Transjakarta BRT passenger number prediction model problem. The proposed prediction model with the best accuracy is sought using deep learning on 8 models. The prediction model is used for short-term and long-term predictions, as well as looking for correlations in the prediction results of 13 Transjakarta corridors. The Python programming language with the Deep Learning Tensor Flow framework is run by Google Colaboratory used in the prediction simulation environment. The combination of BiLSTM-CNN was found to have the best accuracy of the evaluation value (SMAPE = 15.9387, MAPE = 0.598, and MSLE = 0.0425), although it has the longest time (134 seconds). Fluctuations in short-term predictions of passenger numbers evenly occur simultaneously across all corridors. Fluctuations in long-term predictions evenly occur simultaneously across all corridors, except in February. There is no negative correlation in the 13 prediction results and there are 8 corridors that have a close positive correlation. The prediction results can be used by transportation operators and the government to optimize resource planning and transportation policies to support sustainable community and economic mobility.
IoT-Based Community Smart Health Service Model: Empowering Entrepreneurs in Health Innovation Jonas, Dendy; Purnomo, Hindriyanto Dwi; Iriani, Ade; Sembiring, Irwan; Kristiadi, Dedy Prasetya; Nanle, Zeze
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 1 (2025): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i1.461

Abstract

The Indonesian government aims to improve public health by integrating a unified health platform with regional systems for effective decision-making. However, the existing health information system is inadequate for broader decision-making needs, focusing primarily on individuals with existing health issues and not adequately addressing the needs of disaster victims, such as those affected by floods, accidents, and burns. Tangerang City, located in Banten Province, is a flood-prone area that faces annual disasters, highlighting this gap. To address this issue, this study proposes the development of a Health Internet of Things (HIoT) model designed to support rapid decision-making and enhance community health services. The proposed IoT-based network will be implemented in residential complexes, private clinics, schools, and places of worship, enabling real-time monitoring of health conditions and facilitating disaster or pandemic mitigation. Data collected from these communities will be transmitted to nearby hospitals for immediate medical assistance. Preliminary findings suggest that the IoT-based e-health system offers significant benefits, including faster patient care, improved data accuracy, and reduced operational costs. These results underscore the potential of HIoT to enhance community-based health services. The study provides a foundation for future research and practical applications. Further investigation will be conducted to evaluate the scalability of the system in diverse communities and its impact on long-term health outcomes.
Analisis Verifikasi Proof of Stake (POS) NFT dengan Teknologi Smart Contract Eleazer Gottlieb Julio Sumampouw; Irwan Sembiring
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 1 (2024): EduTIK : Februari 2024
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/edutik.v4i1.9214

Abstract

ABSTRAK Penelitian mengenai Analisis Verifikasi Proof of Stake (PoS) NFT dengan Teknologi Smart Contract, yang dilakukan melalui metode eksperimental, menghasilkan pencapaian yang sesuai dengan tujuan penelitian. Peneliti berhasil mengembangkan dan menjalankan sistem sesuai dengan tujuan yang diinginkan. Beberapa pencapaian utama mencakup implementasi berhasil dari proses verifikasi PoS, serta proses Stake, Unstake, dan Claim yang menggunakan integrasi Web3 dan dompet Metamask. Rekam transaksi dengan akurat mencatat waktu pengirim dan penerima bersama dengan prosedur verifikasi pemilik. Lebih lanjut, penelitian ini menyajikan analisis perbandingan antara Proof of Work (PoW) dan Proof of Stake (PoS). Temuan penelitian menunjukkan keunggulan Proof of Stake (PoS) dalam efisiensi waktu transaksi, biaya transaksi yang lebih rendah, peningkatan keamanan melalui pemilihan validator yang cermat, dan ketahanan terhadap berbagai jenis serangan. Secara keseluruhan, penelitian ini mengukuhkan keefektifan dan keunggulan implementasi Proof of Stake (PoS) dalam konteks Non-Fungible Tokens (NFTs) menggunakan Smart Contract. ABSTRACT The research on the Analysis Verification of Proof of Stake (PoS) NFT Smart Contract Technology, conducted through experimental methods, has yielded successful outcomes aligning with the research objectives. The researcher has successfully developed and executed the system, achieving the intended goals. Key accomplishments include the successful implementation of the PoS verification process, as well as the Stake, Unstake, and Claim processes, utilizing Web3 and Metamask wallet integration. Transaction records accurately capture the timing of sender and receiver actions, alongside owner verification procedures. Furthermore, the research presents a comparative analysis between Proof of Work (PoW) and Proof of Stake (PoS). The findings underscore the superiority of Proof of Stake (PoS) in terms of transaction time efficiency, lower transaction costs, enhanced security through meticulous validator selection, and resilience against various types of attacks. Overall, the research substantiates the efficacy and advantages of implementing Proof of Stake (PoS) in the context of Non-Fungible Tokens (NFTs) using Smart Contracts.
Implementasi dan Analisis Deteksi Serangan Jaringan pada Web Server NFT Menggunakan Suricata Phillnov Yohanes Pinontoan; Irwan Sembiring
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 1 (2024): EduTIK : Februari 2024
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/edutik.v4i1.9428

Abstract

ABSTRAK Penelitian ini berfokus pada masalah keamanan jaringan yang menjadi krusial bagi perusahaan teknologi blockchain dan Non-Fungible Token (NFT) yang rentan terhadap serangan siber seperti DDoS, injeksi SQL, dan malware. Serangan ini tidak hanya menyebabkan kerugian finansial tetapi juga merusak reputasi dan kepercayaan pengguna. Suricata, sebagai sistem deteksi dan pencegahan intrusi open-source, menawarkan berbagai fitur untuk memonitor dan menganalisis lalu lintas jaringan secara real-time. Penelitian ini mengevaluasi efektivitas Suricata dalam mendeteksi ancaman pada web server NFT melalui pendekatan eksperimental. Pengujian dilakukan dengan metode scanning port, web penetration testing, DDoS, dan identifikasi kerentanan sistem web server menggunakan alat seperti NMap, Hping3, Nikto, dan Metasploit. Hasil menunjukkan bahwa Suricata mampu mencatat aktivitas mencurigakan dan mencegah anomali dengan integrasi firewall PFsense. Implementasi Suricata memberikan informasi deteksi serangan web scanning, meskipun tidak memiliki aturan shared object seperti perangkat lunak intrusi lainnya. Penelitian ini memberikan rekomendasi bagi pengembang dan operator platform NFT untuk melindungi aset digital mereka dari serangan siber, serta berkontribusi pada peningkatan keamanan jaringan di sektor NFT. ABSTRACT This research focuses on the critical issue of network security for blockchain technology and Non-Fungible Token (NFT) companies, which are vulnerable to cyberattacks such as DDoS, SQL injection, and malware. These attacks not only cause financial losses but also damage reputation and user trust. Suricata, an open-source intrusion detection and prevention system, offers various features to monitor and analyze network traffic in real-time. This study evaluates the effectiveness of Suricata in detecting threats on NFT web servers through an experimental approach. Testing methods include port scanning, web penetration testing, DDoS, and identifying web server vulnerabilities using tools such as NMap, Hping3, Nikto, and Metasploit. The results show that Suricata can log suspicious activities and prevent anomalies when integrated with the PFsense firewall. While Suricata provides information on web scanning attacks, it lacks shared object rules found in other intrusion software. This research offers recommendations for NFT platform developers and operators to protect their digital assets from cyberattacks and contributes to improving network security in the NFT sector. Thus, this study is highly relevant in the digital era, where information and data security are top priorities for business continuity and user privacy protection.
Pemodelan Knowledge Dalam Proses Pemberian Beasiswa Bagi Mahasiswa Menggunakan Soft System Methodology (SSM) (Studi Kasus : Fakultas Keguruan dan Ilmu Pendidikan Universitas Pattimura Ambon) Yulian Hany Makaruku; Eko Sediyono; Irwan Sembiring
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 1 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i1.1587

Abstract

The process of scholarship awarding especially for the university students through the sub-section of student affairs is influenced by the students’ background and achievements. The background of the students and the process of scholarship awarding in the sub-section of students affairs have the important role to support students’ achievements and academic finance. The method that used in designing knowledge method to this scholarship awarding is the kualitative method with the soft system methodology approach and the steps that added in accordance with the case study. Knowledge management of FKIP Unpatti Ambon is ecpected to work well if there is an interaction between each component and there is no imbalance of the three components, they are knowledge management plot, appropriate technology, and conducive workplace habits. Knowledge management that is modeled using the SSM approach can provide opportunities to FKIP Unpatti Ambon in catching and analysing the information in the faculties. Faculties can implement it strategically in the form of warehousing, and decision support system. The establishment of a process for accessing information to all outside societies through the internet, groupware, and group decision support system is very needed, so that the stakeholders in the fakulties are informed properly, informatively and inovatively. This makes the motivation of knowkedge accumulated from organizational experience.
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.
Co-Authors Abas Sunarya, Po Ade Iriani Adi Setiawan Adriyanto Juliastomo Gundo Agus Sugiarto Agustinus, Ari Alamsyah, Ferry 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 Eko Sediono Eko Sediyono Eleazer Gottlieb Julio Sumampouw Elmanda, Vonda Erick Alfons Lisangan Esti Zakia Darojat Evangs Mailoa Evi Maria Faturahman, Adam Fauzi Ahmad Muda Fian Yulio Santoso Florentina Tatrin Kurniati Gallen cakra adhi wibowo Gerry Santos Lasatira Ginting, Jusia Amanda Girinzio, Iqbal Desam Gudiato, Candra Hamdan . Hany Makaruku, Yulian Hasnudi . Henderi Henderi . Hendry Hendry, - Henuk, Yusuf Leonard Hindriyanto Dwi Purnomo Huda, Baenil Ignatius Agus Supriyono Ilham Hizbuloh Indrastanti Ratna Widiasari Iwan Setiawan Iwan Setiawan Iwan Setyawan Joko Listiawan Sukowati Joko Siswanto Joko Siswanto Jonas, Dendy Julians, Adhe Ronny Juneth Manuputty Krismiyati Kristoko D Hartomo Kristoko Dwi Hartomo Kusumajaya, Robby Andika Limbong, Josua Josen Alexander Madawara, Herdin Yohnes Manongga, Daniel H.F Manongga, Daniel H.F. Manongga, Daniel HF 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 Nurtino, Tio Nuryadi, Didik Nurzainah Ginting Pamungkas, Bayu Setyanto Phillnov Yohanes Pinontoan Pinontoan, Phillnov Yohanes Priatna , Wowon Purbaratri, Winny Purnama Harahap, Eka 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 Sediyono, Eko - Setiawan Hakim Sri Ngudi Wahyuni Sri Ngudi Wahyuni, Sri Ngudi Sri Yulianto Joko Prasetyo Suharyadi Sulistio Sulistio Sumampouw, Eleazer Gottlieb Julio Supriadi, Candra Suryantara, I Gusti Ngurah Susanti, Novita Dewi Sutarto Wijono Suwijo Danu Prasetyo Teady Matius Surya Mulyana, Teady Matius Teguh Wahyono Theopillus J. H. Wellem Tintien Koerniawati Tirsa Ninia Lina Tomasoa, Lyonly Tri Wahyuningsih Tri Wahyuningsih Tukino, Tukino Untung Rahardja Untung Rahardja Wibowo, Mars Caroline Wijaya, Angga Zakharia Wiwien Hadikurniawati Yerik Afrianto Singgalen Yessica Nataliani Yohan Maurits Indey Yohnes Madawara, Herdin Yulian Hany Makaruku