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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu dan Teknologi Kelautan Tropis IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Informatika Jurnal Penelitian dan Evaluasi Pendidikan JURNAL SISTEM INFORMASI BISNIS Proceedings of KNASTIK Jurnal Simetris Elkom: Jurnal Elektronika dan Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Proceeding SENDI_U Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta INOVTEK Polbeng - Seri Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Aptisi Transactions on Management JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Aptisi Transactions on Technopreneurship (ATT) EDUMATIC: Jurnal Pendidikan Informatika Magisma: Jurnal Ilmiah Ekonomi dan Bisnis Progresif: Jurnal Ilmiah Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Journal Sensi: Strategic of Education in Information System Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Abdimasku : Jurnal Pengabdian Masyarakat INFOKUM Aiti: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Startupreneur Business Digital (SABDA Journal) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Dimensi DKV Seni Rupa dan Desain Jurnal Ilmiah Sains Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Eduvest - Journal of Universal Studies CENDEKIA PENDIDIKAN Jurnal Rekayasa elektrika Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Journal of Technology Informatics and Engineering Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pendidikan Teknologi Informasi (JUKANTI) INTERNAL (Information System Journal) Pendekar: Jurnal Pendidikan Berkarakter Blockchain Frontier Technology (BFRONT) Scientific Journal of Informatics Jurnal Lentera Edukasi Greenation International Journal of Law and Social Sciences BACA: Jurnal Dokumentasi dan Informasi International Journal of Information Technology and Business JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal DIMASTIK International Journal of Marketing and Digital Creative (IJMADIC) JOT
Danny Manongga
Universitas Kristen Satya Wacana
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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 Neuroscience Physics Social Sciences Transportation Other

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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.
A Comparison Support Vector Machine, Logistic Regression And Naïve Bayes For Classification Sentimen Analisys user Mobile App Baihaqi, Kiki Ahmad; Setyawan, Iwan; Manongga, Danny; Purnomo, Hendryanto Dwi; Hendry, Hendry; Fauzi, Ahmad; Hananto, Aprilia
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.962

Abstract

Data is the most important thing, the use of data can be useful to get an evaluation from the user of a system or application that is built based on mobile. Not only, the assessment or acceptance results of mobile applications during the trial stage are considered important, assessments and comments from direct users are also important things that can be input for mobile application developers. Data mining, or known in English as data mining, is the answer to the process of retrieving data on any media. In this research, data mining is carried out on the media mobile application download service provider Google Playstore, which provides data in the form of comments and ratings. After scraping the data and obtaining the latest data parameters determined by the latest 2000 comments, the data is pre-processed by removing the emot icon character and eliminating unneeded variables so that the data obtained can be processed to the next stage, namely classification based on ratings and sentiment comments. The algorithms used or compared in this research are Support Vector machine, logistic regression and naïve bayes which are known to be reliable in data mining processing. In this research, the accuracy results are 88% for SVM, 90.5% for Logistic Regression and 91% for naïve bayes.
Model Konseptual bagi Pengembangan Knowledge Management di SMA Menggunakan Soft System Methodology Perdana, Eric Megah; Manongga, Danny; Iriani, Ade
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 2: April 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3529.171 KB) | DOI: 10.25126/jtiik.201962932

Abstract

Terhambatnya transformasi knowledge antara pengajar dan siswa di SMAK 1 Penabur Jakarta ditemukanlah suatu kendala, yaitu perbedaan karakter antara yayasan, guru, dan siswa. Berdasarkan fakta diatas, SMAK 1 Penabur Jakarta memerlukan suatu Knowledge Management yang dapat membantu kinerja yang sedang berjalan. Metode yang digunakan dalam penelitian ini adalah pendekatan Soft System Methodology. Terdapat langkah dalam Soft System Methodology yang dilakukan yaitu menggunakan analisis CATWOE terhadap holon yang ditemui di SMAK 1 Penabur Jakarta. Pemodelan yang dihasilkan berupa komponen yang dibagi menjadi 4 bagian yaitu, manusia, proses, teknologi, dan content.AbstractThe obstructed of knowledge transformation between the educator and student in SMAK 1 Penabur Jakarta was found the constraints, it is the differences character between foundation, teacher, and student. According to the fact above, SMAK 1 Penabur Jakarta needs a knowledge management in order to help the teaching and learning proses. This research is using the method. The method that is used is Soft System Methodology (SSM) approach. It has some steps in Soft System Methodology by using CATWOE analyzation to holon in SMAK 1 Penabur Jakarta. The model-produced is divided into 4 components, those are human, content, technology, and process.
Perancangan Aplikasi Knowledge Management di Instansi Kearsipan Berbasis Model Choo-Sense Making Hermansyah, Eko Nur; Manongga, Danny; Iriani, Ade
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0812451

Abstract

AbstrakIntansi Kearsipan memiliki berbagai pengetahuan yang digunakan untuk pengelolaan arsip yang dimilikinya, knowledge management digunakan untuk mengumpulkan, mengelola, dan menyebarluaskan pengetahuan yang dimiliki, sehingga pengetahuan yang dimiliki oleh instansi kearsipan dapat digunakan untuk kemajuan intansi dan tidak hilang. Penelitian ini dilakukan di Dinas Perpustakaan dan Kearsipan Kota Salatiga. Pengumpulan data dilakukan dengan wawancara petugas kearsipan untuk mengumpulkan data tentang pengetahuan yang dimiliki dan cara penyimpanan serta penyebarluasan yang diterapkan di intansi kearsipan. Analisis data dilakukan dengan mengelompokkan pengetahuan yang dimiliki oleh intansi kearsipan sesuai dengan model Choo-Sense Making, untuk kemudian diterapkan di Confluence sesuai dengan hasil dari pengolahan data dengan model Choo-Sense Making. Hasil dari penelitian ini untuk model Choo-Sense Making pengetahuan di intansi kearsipan dibagi atas 3 tahap yaitu Sense Making yang berisi tentang pengetahuan yang berasal dari luar intansi dibuatkan wadah sebagai media diskusi, chatting, Knowledge Creating berisi tentang pengetahuan-pengetahuan yang dimiliki intansi kearsipan yang telah di dokumentasikan diubah dalam betuk softfile kemudian diunggah kedalam space untuk memudahkan penyimpanan serta penyebarluasan pengetahuan yang dimiliki, dan Decision Making yang berisi tentang jadwal-jadwal intansi dan evaluasi yang dilakukan intansi kearsipan. Hasil dari model Choo-Sense Making dimasukan ke Confluence, memperoleh hasil space yang dapat memudahkan menyimpan pengetahuan yang dimiliki berupa file aplikasi, softfile, serta memudahkan dalam pencarian kembali dan penyerluasan pengetahuan yang dimiliki. Penerapan Choo-Sense Making selain untuk mempermudah penyimpanan dan penyerbaluasan serta komunikasi, dapat mengurangi resiko kehilangan pengetahuan yang dimiliki oleh intansi kearsipan. Kata kunci: Knowledge Management, Model Choo-Sense Making, Confluence, Perpustakaan dan ArsipAbstractArchival Agency has several knowledge that are used to manage the owned archive, knowledge management is used to collect, manage and disseminate the owned knowledge so that the knowledge that the archival agency has can be used for the agency progress and it will not missing. The research is conducted in Dinas Perpustakaan dan Kearsipan Kota Salatiga. Data collecting is conducted by interviewing the archival officer to gather data related to its knowledge, the storage system and dissemination applied in this archival agency. Data analysis is conducted by categorizing the agency knowledge according to Choo-Sense Making model and then it is applied in Confluence in accordance with the result of the data analysis from the Choo-Sense Making model. The result of this research, for Choo-Sense Making model, the knowledge in the archival agency is divided into 3 steps; Sense Making, Knowledge Creating and Decision Making. Sense Making contains knowledge coming from the outside of the agency that has forum as discussion media, chatting. Knowledge Creating contains knowledge that owned by the archival agency that has been documented and changed in the form of softfile then uploaded into space to ease the storage and the knowledge dissemination. Decision Making is about agency schedules and evaluation toward the activity in this archival agency. The result of Choo-Sense Making Model is input into Confluence, get space result that ease to save the knowledge in the form of application file, softfile, and ease to search and disseminate the owned knowledge. The application of Choo-Sense Making eases the storage system, dissemination, and communication. It also reduces the risk of losing knowledge owned by the archival agency. Keywords: Knowledge Management, Model Choo-Sense Making, Confluence, Library and Archive
Clustering Tingkat Kemiripan Curah Hujan di Indonesia Berdasarkan Provinsi Menggunakan Metode Hierarchical Clustering dan GeoMap Runtulalo, Yahya Supit; Manongga, Daniel H. F.
Progresif: Jurnal Ilmiah Komputer Vol 20, No 1: Februari 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v20i1.1583

Abstract

Indonesia's geographical location has a big influence on the usual rainfall patterns. BMKG data summarized in the 2023 Indonesian statistical data by the Central Statistics Agency, notes that Indonesia will often experience rain from around August to February. Rainfall is an important factor in determining the climate of an area and has a significant impact on various sectors of life, such as agriculture, water resource management and disaster mitigation. The aim of the research is to find out which provinces in Indonesia have similar levels of low, medium and high rainfall characteristics using the Hierarchical Clustering method and GeoMap visualization. After processing the data, 3 cluster levels were obtained, namely cluster 1 with 8 members in areas with low rainfall levels, cluster 2 with 3 members in areas with high rainfall, and cluster 3 with 22 members in areas with moderate rainfall. Then the test was evaluated using the Silhouette Coefficient which produced cluster 1 (medium), cluster 2 (strong), and cluster 3 (medium).Keywords: Rainfall; Hierarchical Clustering; GeoMap AbstrakLetak Geografis Indonesia mempunyai pengaruh besar terhadap pola curah hujan yang biasa terjadi. Data BMKG yang dirangkum dalam data statistik Indonesia 2023 oleh Badan Pusat Statistik, dicatat bahwa Indonesia akan kerap diguyur hujan sekitar bulan Agustus sampai Februari. Curah hujan merupakan faktor penting dalam menentukan iklim suatu daerah dan memiliki dampak yang signifikan terhadap berbagai sektor kehidupan, seperti pertanian, pengelolaan sumber daya air, dan mitigasi bencana. Adapun yang menjadi tujuan dalam penelitian untuk mengetahui provinsi di Indonesia yang memiliki tingkat kemiripan karakteristik curah hujan rendah, sedang, dan tinggi  menggunakan metode Hierarchical Clustering dan visualisasi GeoMap. Setelah melakukan olah data, didapatkan 3 tingkat cluster yaitu cluster 1 dengan 8 anggota wilayah dengan tingkat curah hujan rendah, cluster 2 dengan 3 anggota wilayah dengan  curah hujan tinggi, dan cluster 3 dengan 22 anggota wilayah dengan curah hujan sedang.Kemudian di evaluasi pengujian menggunakan Silhouette Coefficient yang menghasilkan cluster 1 (medium), cluster 2 (strong), dan cluster 3 (medium).Kata kunci: Curah Hujan; Hierarchical Clustering; GeoMap
Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators Siswanto, Joko; Manongga, Danny; Sembiring, Irwan; Wijono, Sutarto
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1245

Abstract

The bus public transportation system has low reliability and ability to predict the number of passengers. The accuracy of predicting the number of passengers by public transport bus operators is still weak, which results in failure to implement solutions by operators. A prediction model with LSTM based on deep learning is proposed to predict passengers for 4 bus public transportation operators (Go Bus, New Zealand Bus, Pavlovich, and Ritchies) which are evaluated by MSLE, MAPE, and SMAPE with variations in epoch, batch size, and neurons. The dataset is a CSV performance report on Auckland Transport (AT) New Zealand metro patronage buses (01/01/2019-07/31/2023). The best prediction model was obtained from the lowest evaluation value and relatively fast time at variations of epoch 60, batch size 16, and neurons 32. The prediction results on training and testing data improved with the suitability of the model tuning. The proposed prediction model performs predictions 12 months later for 4 predictions simultaneously with predicted fluctuations occurring simultaneously. Strong negative correlation on New Zealand Bus-Pavlovich, strong positive correlation on Go Bus with Ritchies and Pavlovich. Predictions that are less closely related and dependent are New Zealand Bus against Go Bus, Pavlovich, and Ritchies. The proposed prediction modeling can be used as a basis for creating operator policies and strategies to deal with passenger fluctuations and for the development of new prediction models.
IMPLEMENTATION OF MULTI-NODE SENSOR DATA DELIVERY USING THE MASTER-SLAVE METHOD IN LORA COMMUNICATION Hendry, Hendry; Manongga, Daniel
Journal of Technology Informatics and Engineering Vol. 3 No. 2 (2024): Agustus : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i2.179

Abstract

This research explains the application of sending data from various sensor nodes using the master-slave method in Long Range (LoRa) communication. This system was created to increase efficiency and reliability in collecting sensor data spread across several locations. Sensor nodes function as slaves that collect and send data to the master. The master then processes and combines the data before sending it to a central server. Experimental results show that this method is successful in reducing latency and increasing data transmission speed and shows great potential for Internet of Things (IoT) applications that require wide communication range and low power consumption.
Deteksi Anomali dalam Penipuan E-commerce Menggunakan Hybrid Autoencoder-Transformer Frameworks Priatna, Wowon; Prasetyo, Sri Yulianto Joko; Wijono, Sutarto; Maria, Evi; Manongga, Danny
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 11, No 1 (2025): Volume 11 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v11i1.82330

Abstract

Peningkatan e-commerce telah menyebabkan peningkatan aktivitas penipuan, seperti pencurian identitas dan transaksi palsu, yang menimbulkan risiko signifikan terhadap keamanan transaksi online. Penelitian ini mengusulkan kerangka kerja hybrid yang menggabungkan Autoencoder (AE) untuk reduksi dimensi dan representasi laten data, serta Transformer untuk menangkap ketergantungan global dan lokal melalui mekanisme self-attention. Pendekatan ini dirancang untuk mengatasi keterbatasan metode tradisional dalam mendeteksi pola data kompleks dan meningkatkan kinerja deteksi anomali. Evaluasi menggunakan dataset transaksi e-commerce menunjukkan bahwa Hybrid AE-Transformer mencapai akurasi sebesar 95,2%, precision sebesar 89,0%, recall sebesar 74,0%, F1 score sebesar 80,0%, dan AUC sebesar 82,0%. Model ini menunjukkan peningkatan precision sebesar 12,0%, recall sebesar 7,0%, F1 score sebesar 8,0%, dan AUC sebesar 1,0% dibandingkan model terbaik lainnya seperti Ensemble. Validasi statistik melalui Uji Friedman dan Uji T-Test mengonfirmasi bahwa Hybrid AE-Transformer secara signifikan mengungguli model konvensional seperti DNN, LSTM, dan RNN dalam mendeteksi anomali pada transaksi e-commerce.
Exploring Data Analytics in Attendance Systems: Unveiling Machine Learning Techniques, Patterns, Practices, and Emerging Trends Santoso, Joseph Teguh; Manongga, Danny; Setyawan, Iwan; Purnomo, Hindriyanto Dwi; Hendry
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3438

Abstract

Purpose: The research aims to identify patterns and trends in attendance management through the application of reward and punishment systems as innovative solutions for improving employee attendance and well-being. Methods: This research utilizes a descriptive analysis approach with the application of Machine Learning (ML) techniques to enhance the accuracy of attendance pattern prediction and ML models for the classification of emerging trends and patterns. Research data were obtained through the company's attendance system and divided into two segments (80% for training and 20% for testing) while maintaining a balanced class proportion, then processed using SPSS and Python software with the Scikit-learn library. Result: The results of the study show that employee attendance is increased from 86.52% to 90.44% when the reward and punishment method is applied to the employee attendance system. Proper reward allocation can increase employee motivation to adhere to work schedules and consistently attend, while punishment tends to lead to lower attendance rates. Novelty: This research emphasizes the optimization of attendance management through data analytics approaches and the implementation of advanced technology in attendance systems with the application of ML techniques to analyze attendance data comprehensively and detect significant patterns.
IT Governance Design in XY University using Cobit 2019 Framework Mangoki, Willson; Manongga, Danny; Iriani, Ade
Jurnal Sistem Informasi Bisnis Vol 14, No 2 (2024): Volume 14 Nomor 2 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss2pp111-122

Abstract

The management and control of information and technology at the university were required for IT's finest use, but in line with organization goals, they can be realized with the use of IT governance. This research uses a qualitative approach with techniques such as interviews, observation, expert judgment, and literature studies that are relevant to the concept of IT governance with the COBIT framework and its application in various fields. This research presents an IT governance design that is considered suitable to be applied at XY University using COBIT 2019 Framework. 10 design factors and 40 IT processes listed in COBIT 2019 are used as parameters. The results obtained from four processes with scores ranging from 50 to 100 with capability levels 3 and 4, namely APO04-Managed Innovation, APO03-Managed Enterprise Architecture, APO07-Managed Human Resources, and BAI07-Managed IT Change Acceptance and Transitioning, are translated into recommendations for actions that need to be taken in implementing IT governance.
Co-Authors Abas Sunarya, Po Abdi Samuel Mango Ade Iriani Adhe Ronny Julians Adriyanto Juliastomo Gundo Agni Isador Harsapranata Agung Wibowo Albert Kriestian Novi Adhi Nugraha Aldi Lasso Alrafi Syammajaya Andreas Resdianto Angela Atik Setiyanti Anton Hermawan Antonius Mbay Ndapamuri Anumi, Maria Grassella Anwar, Ananda Pradana Putra April Lia Hananto Apriliasari, Dwi Ardaneswari, Awanda Arny Lattu Astriyer J. Nahumury Atmoko Nugroho Ayu Sanjaya, Yulia Putri Baihaqi, Kiki Ahmad Bani, Benediktus Benediktus Bani Budhi Kristianto Budi Santoso Cahyaningtyas, Christian Charitas Fibriani Cut Amalia Saffiera Daniawan, Benny Daniel D. Kameo Darmawan Utomo Dendy Kurniawan Destiyani, Gati Dian Widiyanto Chandra Dwi Hosanna Bangkalang Efendy, Rifan Eko Nur Hermansyah Eko Sediyono Elfira Umar Elmanda, Vonda Erwianta Gustial Radjah Erwien Christianto Evangs Mailoa Evi Maria Faturahman, Adam Fauzi Ahmad Muda Filimdity, Elsa K. Flawelna Falerery Pesulima Florentina Tatrin Kurniati Frederik Samuel Papilaya Girinzio, Iqbal Desam Gunawan Gunawan Hanita Yulia Harry Agustian Henderi Hendry Hendry . Hendry Hendry Hendry, - Henry Adhi Sulistyo Herdin Yohnes Madawara Hindriyanto Dwi Purnomo Huda, Baenil I Ketut Suada Indrastanti Ratna Widiasari Irwan Sembiring Ivan Kovac Ivanna K. Timotius Iwan Setiawan Iwan Setyawan Johan Jimmy Carter Tambotoh Joko Siswanto Joseph Teguh Santoso Julianingsih, Dwi Julians, Adhe Ronny Krismiyati Kristia Yuliawan Kristoko Dwi Hartomo Lelatobur, Lovely Ezverenzha Lorna Yertas Baisa Lukman Santoso Madawara, Herdin Yohnes Mango, Abdi Samuel Martza Merry Swastikasari Michael Alan Hirdi Pukada Muhamad Yusup Muhammad Ryza Awwali , Sulartopo, Muhammad Ryza Awwali , Muhtarom Nina Setiyawati Nuryadi, Didik Panja, Eben Penidas Fodinggo Tanaem Perdana, Eric Megah Po Abas Sunarya Prasetia, Yoga Agung Prasetio, Nanda Wiryawan Priatna , Wowon Pudjajana, Andre Maureen Purnomo, Hendryanto Dwi Qurotul Aini Qurotul Aini Radius Tanone Rahardja.,M.T.I.,MM, Dr. Ir. Untung Ravensca Matatula Ravensca Matatula Reinhard Alfaries Saemani Reni Veliyanti Rimes Jopmorestho Malioy Rissal Efendi Rivort Pormes Rivort Pormes Rivort Pormes, Rivort Roy Rudolf Huizen Runtulalo, Yahya Supit Saian, Septovan Dwi Suputra Santoso, Joseph Teguh Santoso, Nuke Puji Lestari Selfiana Pandie Sophia Tri Satyawati Sri Yulianto Joko Prasetyo Stefanus Christian Relmasira Suharyadi Sulistyo, Henry Adhi Sutarto Sutarto Sutarto Wijono Swastikasari, Martza Merry Takumi Sase Theopillus J. H. Wellem Tri Wahyuningsih Tukino Tukino, Tukino Untung Rahardja Victor Peter Lodewyk Duan Willson Mangoki Winny purbaratri Winsy C.D Weku Wiwien Hadikurniawati Yari Dwikurnaningsih Yerik Afrianto Singgalen Yessica Nataliani Yohana Andianti Yudo Devianto