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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 Simetris Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Jurnal Ilmiah Matrik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Indonesian Journal of Computing and Modeling JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal Sensi: Strategic of Education in Information System JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Aiti: Jurnal Teknologi Informasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Information Technology (JIfoTech) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Jurnal Nasional Teknik Elektro dan Teknologi Informasi IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Sri Yulianto Joko Prasetyo
Universitas Kristen Satya Wacana
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Evaluasi Kinerja Pembelajaran Learning Management System Menggunakan COBIT 4.1 pada Universitas STEKOM Semarang Haikal Nur Rachmanrachim Achaqie; Eko Sediyono; Sri Yulianto Joko Prasetyo
Elkom: Jurnal Elektronika dan Komputer Vol. 15 No. 1 (2022): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v15i1.788

Abstract

Universitas Ilmu Komputer dan Teknologi (STEKOM University) telah menggunakan Learning Management System (LMS) sejak tahun 2018, namun hingga saat ini pengukuran kinerja Learning Management System (LMS) belum dilakukan. Penelitian ini bertujuan untuk mengukur tingkat kematangan Learning Management System (LMS) dengan menggunakan Framework COBIT 4.1 pada domain Delivery and Support (DS) dan Monitoring and Evaluation (ME). Nilai tingkat kematangan pada kondisi eksisting berada pada level rata-rata 2, sedangkan kondisi yang ingin dicapai berada pada level rata-rata 3. Untuk mencapai level yang diharapkan, saran perbaikan mengacu pada Kerangka COBIT 4.1 perlu yang akan diberikan antara lain: pembuatan SOP (Standar Operasional dan Prosedur) LMS, kelengkapan isi LMS, tertib administrasi dokumentasi arsip penting, realisasi pelatihan LMS bagi dosen, alokasi biaya pemeliharaan dan pengujian sistem, penerapan reward and punishment, pembuatan dan penggunaan framework dan E governance - Pembelajaran dan semuanya dilakukan secara rutin minimal setiap 6 bulan sekali.
MONITORING MANGROVE MENGGUNAKAN MODEL NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI): STUDI KASUS DI HALMAHERA UTARA, INDONESIA Singgalen, Yerik Afrianto; Gudiato, Candra; Prasetyo, Sri Yulianto Joko; Fibriani, Charitas
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 13 No. 2 (2021): Jurnal Ilmu dan Teknologi Kelautan Tropis
Publisher : Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jitkt.v13i2.34771

Abstract

Ekowisata berbasis masyarakat menjadi salah satu pendekatan yang efektif dalam menjaga kelestarian hutan mangrove. Strategi untuk menetapkan prioritas pengembangan kawasan mangrove, dapat dilakukan dengan menganalisis kerapatan hutan mangrove. Kawasan mangrove dengan nilai kerapatan paling rendah perlu diprioritaskan sebagai strategi preservasi dan konservasi melalui konsep ekowisata berbasis masyarakat. Artikel ini bertujuan mengidentifikasi sebaran mangrove menggunakan model normalized difference vegetation index (NDVI) di Kabupaten Halmahera Utara, Indonesia. Perspektif penghidupan berkelanjutan digunakan untuk mendiskusikan konteks sosio-kultural masyarakat lokal. Penelitian ini mengadopsi metode campuran. Pengolahan data terbagi menjadi dua tahap yakni: tahap pertama, pemetaan sebaran hutan mangrove berdasarkan tingkat kerapatan; tahap kedua, trianggulasi. Pemetaan sebaran hutan mangrove menggunakan citra satelit Landsat 8 operational land imager (OLI) tahun 2013 dan 2021 serta model NDVI di Tanjung Pilawang, Pulau Kumo, Pulau Kakara, Pulau Maiti, dan Pulau Tagalaya. Hasil penelitian ini menunjukkan bahwa Tanjung Pilawang pada zona 1 dan 2 memiliki Nilai NDVI paling rendah di tahun 2021 yakni 0,22 dengan kategori jarang, sehingga perlu diprioritaskan dalam pengembangan ekowisata mangrove berbasis komunitas sebagai strategi perlindungan kawasan hutan mangrove.
Analisis Pemetaan Jaringan Komunikasi Karyawan Menggunakan Social Network Analysis pada Perusahaan Multifinance Zusrony, Edwin; Purnomo, Hindriyanto Dwi; Prasetyo, Sri Yulianto Joko
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 3 No 2 (2019): Vol. 3 No. 2 Agustus 2019
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (627.187 KB) | DOI: 10.29407/intensif.v3i2.12786

Abstract

Business development in the financial services sector improved competition among companies to give the best service to their customers. Having reliable services with a good communication network in the organization is the critical success of the company. This study aims to find the actors or people who influence organizations through formal and informal communication networks using Social Network Analysis (SNA). Information on casual and formal communication networks can be used by the HR department to measure the level of the social relationship of all employees that can improve their performance in the company. The author researched PT. BFI Salatiga. The results showed that tissue density was below 50% so that relationships were considered weak. The most dominant actor in degree centrality, closeness centrality, and betweenness centrality is the actor id#24 and id#29 from the collection division, actor id#27 from the operation division and actor id#30 from the credit division.
Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM Joko Siswanto; Sri Yulianto Joko Prasetyo; Sutarto Wijono; Evi Maria; Untung Rahardja
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 03 (2024): Vol. 15, No. 03 December (2024)
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i03.p03

Abstract

Accurate predictions of the number of public transport passengers on buses in each region are crucial for operations. They are required by the planning and management authority for bus public transport. A deep learning-based LSTM prediction model is proposed to predict the number of passengers in 4 bus public transportation areas (central, north, south, and west), evaluated by MSLE, MAPE, and SMAPE with dropout, neuron, and train-test variations. The CSV dataset obtained from Auckland Transport(AT) New Zealand metro patronage report on bus performance(1/01/2019-31/07/2023) is used for evaluation. The best prediction model was obtained from the lowest evaluation value and relatively fast time with a dropout of 0.2, 32 neurons, and train-test 80-20. The prediction model on training and testing data improves with the suitability of tuning for four predictions for the next 12 months with mutual fluctuations. The strong negative correlation is central-south, while the strong positive correlation is north-west. Predictions are less closely interconnected and dependent, namely central-south. With its potential to significantly impact policy-making, this prediction model can increase public transport mobility in each region, leading to a more efficient and accessible public transport system and ultimately enhancing the public's daily lives. This research has practical implications for public transport authorities, as it can guide them in making informed decisions about service planning and resource allocation.
Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data Gallen cakra adhi wibowo; Sri Yulianto Joko Prasetyo; Irwan Sembiring
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2677

Abstract

The tsunami is a disaster that often occurs in Indonesia, there are no valid indicators to assess and monitor coastal areas based on functional land use and based on land cover which refers to the biophysical characteristics of the earth's surface. One of the recommended methods is the vegetation index. Vegetation index is a method from LULC that can be used to provide information on how severe the impact of the tsunami was on the area.In this study, an increase in the vegetation index was carried out using machine learning. The purpose of this study was to develop a tsunami vulnerability assessment model using the Vegetation Index extracted from Landsat 8 satellite imagery optimized with KNN, Random Forest and SVM. The stages of study, are: 1)extraction Landsat 8 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction of vegetation indices using KNN, Random Forest, and SVM algorithms. 3) accuracy testing using the MSE, RMSE, and MAE,4) spatial prediction using the Kriging function and 5) tsunami modelling vulnerability indicators. The results of this study indicate that the NDVI interpolation value is 0 - 0.1 which is defined as vegetation density, biomass growth, and moderate to low vegetation health. the NDWI value is 0.02 - 0.08 and the MNDWI value is 0.02 - 0.09 which is interpreted as the presence of surface water along the coast. MSAVI is a value of 0.1 – 0 which is defined as the absence of vegetation. The NDBI interpolation value is -0.05 - (-0.08) which is interpreted as the existence of built-up land with social and economic activities. From the results of research on the 10 areas studied, there are 3 areas with conditions that have a high level of tsunami vulnerability. 2 areas with medium vulnerability and 5 areas with low vulnerability to tsunami.
Perbandingan Kinerja Algoritma Klasifikasi untuk Pemilihan Tempat Promosi upaya Meningkatkan Jumlah Mahasiswa Baru Fadilah, Nurul; Joko Prasetyo, Sri Yulianto; Kristianto, Budhi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Efektivitas strategi promosi menjadi kunci keberhasilan institusi pendidikan tinggi dalam menarik minat calon mahasiswa baru. Namun, banyak institusi menghadapi masalah serius dalam pemilihan lokasi promosi yang tepat, yang mengakibatkan penurunan minat calon mahasiswa dan pemborosan sumber daya. Untuk mengatasi masalah ini, penelitian ini mengembangkan model klasifikasi berbasis data mining yang mampu mengidentifikasi lokasi promosi paling efektif. Tiga algoritma klasifikasi yang digunakan dalam penelitian ini adalah Logistic Regression, Support Vector Machine (SVM), dan Decision Tree (C4.5). Data pendaftaran mahasiswa dikumpulkan dan diproses melalui tahapan pra pemprosesan yang meliputi penggantian nilai hilang, normalisasi data, dan transformasi atribut nominal menjadi numerik. Data kemudian dibagi menjadi subset pelatihan dan pengujian menggunakan metode split data dengan rasio 70:30. Hasil evaluasi menunjukkan bahwa model Decision Tree (C4.5) memberikan performa terbaik dengan accuracy 93.75%, precision 97.37%, dan recall 90.24%. Logistic Regression juga menunjukkan hasil yang memuaskan dengan accuracy 90.00%, precision 92.31%, dan recall 87.80%. Sementara itu, SVM menunjukkan performa yang lebih rendah dengan accuracy 72.50%, precision 80.65%, dan recall 60.98%. Kesimpulannya, model Decision Tree (C4.5) dan Logistic Regression dapat diandalkan untuk mengoptimalkan strategi promosi institusi pendidikan tinggi, memastikan alokasi sumber daya yang lebih efisien dan efektif, serta meningkatkan jumlah pendaftar baru. Penelitian ini juga memberikan kontribusi signifikan dalam literatur terkait penggunaan data mining untuk strategi promosi di sektor pendidikan tinggi.   Abstract The effectiveness of promotional strategies is crucial for higher education institutions in attracting new student enrollments. However, many institutions face serious issues in selecting the appropriate promotional locations, leading to decreased student interest and resource wastage. To address this issue, this study develops a data mining-based prediction model capable of identifying the most effective promotional locations. The three classification algorithms used in this study are Logistic Regression, Support Vector Machine (SVM), and Decision Tree (C4.5). Student enrollment data were collected and processed through pre-processing stages, including missing value replacement, data normalization, and transformation of nominal attributes to numerical. The data were then split into training and testing subsets using a 70:30 split ratio. Evaluation results indicate that the Decision Tree (C4.5) model performed the best with an accuracy of 93.75%, precision of 97.37%, and recall of 90.24%. Logistic Regression also showed satisfactory results with an accuracy of 90.00%, precision of 92.31%, and recall of 87.80%. Meanwhile, SVM demonstrated lower performance with an accuracy of 72.50%, precision of 80.65%, and recall of 60.98%. In conclusion, the Decision Tree (C4.5) and Logistic Regression models are reliable for optimizing promotional strategies of higher education institutions, ensuring more efficient and effective resource allocation, and increasing new student enrollments. This study also makes a significant contribution to the literature related to the use of data mining for promotional strategies in the higher education sector.
ANALISIS PERUBAHAN TUTUPAN LAHAN DI KAWASAN PIK 2 TAHUN 2013 - 2024 MENGGUNAKAN CITRA LANDSAT DAN KLASIFIKASI ISO CLUSTER Oliver Elvino Putra Pratama; Sri Yulianto Joko Prasetyo
Jurnal Pendidikan Teknologi Informasi (JUKANTI) Vol 8 No 2 (2025): JURNAL PENDIDIKAN TEKNOLOGI INFORMASI (JUKANTI) EDISI NOPEMBER 2025
Publisher : Universitas Citra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37792/jukanti.v8i2.1867

Abstract

ABSTRAK Penelitian ini bertujuan untuk menganalisis perubahan tutupan lahan di Kawasan Pantai Indah Kapuk 2 (PIK 2) pada tahun 2013, 2019, dan 2024 menggunakan citra satelit Landsat dan pendekatan klasifikasi unsupervised ISO Cluster. Analisis spasial dilakukan dengan menghitung indeks NDBI (Normalized Difference Built-up Index) untuk memetakan area terbangun. Hasil penelitian menunjukkan bahwa kategori bangunan meningkat secara signifikan dari 15.88 km2 pada tahun 2013 menjadi 31.24 km2 pada tahun 2024 (kenaikan +18.57%). Sebaliknya, lahan terbuka menurun dari 21.07 km2 menjadi 10.25 km2 (penurunan -13.09%). Selain itu, dinamika vegetasi ringan dan vegetasi padat yang menunjukkan bagaimana perubahan lingkungan akibat pembangunan. Evaluasi akurasi klasifikasi menghasilkan nilai Overall Accuracy sebesar 86% dan Kappa Coefficient sebesar 76% yang menunjukkan tingkat keandalan tinggi dalam proses klasifikasi. Secara keseluruhan penelitian ini menunjukkan bahwa SIG dan penginderaan jauh adalah alat yang efektif untuk memantau perubahan tutupan lahan dan membantu pengambilan keputusan dalam perencanaan kawasan pesisir berkelanjutan.Kata kunci : citra Landsat, ISO Cluster, klasifikasi unsupervised, NDBI, Perubahan tutupan lahan, PIK 2 ABSTRACT This research aims to analyze land cover change in Pantai Indah Kapuk 2 (PIK 2) area in 2013, 2019, and 2024. This research uses Landsat satellite imagery and the ISO Cluster unsupervised classification approach. Spatial analysis was conducted by calculating the NDBI index (Normalized Difference Built-up Index) to map the built-up area. The results showed that the built-up category increased significantly from 15.88 km2 in 2013 to 31.24 km2 in 2024 (+18.57%). Conversely, open land decreased from 21.07 km2 to 10.25 km2 (-13.09%). In addition, the dynamics of light vegetation and dense vegetation show how the environment changes due to development. Evaluation of classification accuracy resulted in an Overall Accuracy value of 86% and a Kappa Coefficient of 76%, indicating a high level of reliability in the classification process. Overall, this study confirm that GIS and remote sensing are effective tools to monitor land cover change and assist decision-making in sustainable coastal area planning.Keywords: ISO cluster, land cover change, landsat imagery, NDBI, PIK 2, unsupervised classification
Analysis of Smart City Quick Win Program Implementation Using Fuzzy BWM and TOPSIS in Manado and Tomohon Puteri Justia Kardia Momuat Wahani; Sri Yulianto Joko Prasetyo; Indrastanti R. Widiasari; Johan J. C. Tambotoh
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 3 (2026): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i3.2690

Abstract

This study analyzes the implementation quality of smart city quick win programs in Manado and Tomohon using the Fuzzy Best-Worst Method (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The evaluation covers 12 quick win programs across six smart city dimensions: smart governance, smart branding, smart economy, smart living, smart society, and smart environment. The assessment uses 12 criteria derived from government smart city evaluation elements, including policy alignment, problem and goal clarity, public benefit, implementation readiness, technological readiness, governance ownership, service performance, monitoring and evaluation, and sustainability. Data were collected from official documents, evidence catalogs, interviews, expert assessments from 20 respondents, and government evaluation records. The novelty of this study lies in the development of a program-level evaluation model that integrates Fuzzy BWM for criteria weighting and TOPSIS for ranking quick win programs. Unlike previous studies that mainly evaluate smart city performance at the city or dimension level, this study positions quick win programs as concrete implementation units. The results show that problem and goal clarity, direct public benefit, and service performance are the most influential aspects in determining implementation quality. The TOPSIS results identify Manado 360 and SmartGov/PONTER as the strongest programs, while the city-level comparison shows that Manado has more consistent implementation quality than Tomohon. This study contributes a structured decision-support model for evaluating and improving smart city quick win programs.
Comparison of IDW and Kriging Interpolation Methods Using Geoelectric Data to Determine the Depth of the Aquifer in Semarang, Indonesia Brilliananta Radix Dewana; Sri Yulianto Joko Prasetyo; Kristoko Dwi Hartomo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 2 (2022): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i2.23260

Abstract

Several areas in Semarang City have been unable to get a clean water supply through the Local Water Company (PDAM) channel. One of the solutions that can be done to overcome this problem is by utilizing groundwater, which can be obtained by building a deep well made to obtain rock layers that can accommodate and drain groundwater (aquifer layer). To find out the approximate depth of the aquifer layer, it is necessary to conduct a preliminary investigation before drilling. There are so many methods that can be done, and one of them is by using the geoelectric method. After using the geoelectric method, we can determine the distribution of the depth of the aquifer in Semarang City by using interpolation analysis. In this study, the IDW and Kriging interpolation methods were used. The two methods were then compared to show the difference in the distribution of aquifer depths in areas that lack clean water using the two interpolation methods above. Besides that, we are using RMSE and MAPE analysis to find the error rate of the two methods. The results obtained were the RMSE of the IDW and Kriging methods amounting to 5,829 and 5,433, and the MAPE results were 10.90% and 10.34%. Based on this, the Kriging method tends to have better results when interpolating using geoelectric data. With this research, it is hoped to provide knowledge to determine the most suitable interpolation method used in determining the depth of the aquifer and also can be used as an illustration of the depth of the aquifer in the area that lacked clean water in Semarang City, so that it can be used as a reference in estimating the design of deep good development more accurately.
Object Classification Model Using Ensemble Learning with Gray-Level Co-Occurrence Matrix and Histogram Extraction Florentina Tatrin Kurniati; Daniel HF Manongga; Eko Sediyono; Sri Yulianto Joko Prasetyo; Roy Rudolf Huizen
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26683

Abstract

In the field of object classification, identification based on object variations is a challenge in itself. Variations include shape, size, color, and texture, these can cause problems in recognizing and distinguishing objects accurately. The purpose of this research is to develop a classification method so that objects can be accurately identified. The proposed classification model uses Voting and Combined Classifier, with Random Forest, K-NN, Decision Tree, SVM, and Naive Bayes classification methods. The test results show that the voting method and Combined Classifier obtain quite good results with each of them, ensemble voting with an accuracy value of 92.4%, 78.6% precision, 95.2% recall, and 86.1% F1-score. While the combined classifier with an accuracy value of 99.3%, a precision of 97.6%, a recall of 100%, and a 98.8% F1-score. Based on the test results, it can be concluded that the use of the Combined Classifier and voting methods is proven to increase the accuracy value. The contribution of this research increases the effectiveness of the Ensemble Learning method, especially the voting ensemble method and the Combined Classifier in increasing the accuracy of object classification in image processing.
Co-Authors Adenia Kusuma Dayanthi Adi Setiawan Anna Simatauw Antar Maramba Jawa Antonius Budi Istiadi Antonius Mbay Ndapamury Ardian Ariadi Ardito Laksono Suryoputro Arit Imanuel Meha Arvira Yuniar Isnaeni Aurilia Dinda Permatasari Baali, Gabriel Megfaden Kenisa Bandhu Nusantara Baronio, Nodas Constantine Bintang Lazuardi Bistok Hasiholan Simanjuntak Brian Laurensz Brilliananta Radix Dewana Budhi Kristianto Bunga, Alex Frianco Cahyaningtyas, Christian Charitas Fibriani Christanto, Erwien Christiana Ari Setyaningrum Daniel HF Manongga Danny Manongga Dian Widiyanto Chandra Dwi Hayati Edwin Zusrony Eko Sediyono Elvira Umar Engles Marabangkit Yoesmarlan Erik Wahyu Abdi Nugroho Evan Bagus Kristianto Evan Geraldy Suryoto Evi Maria Evi Maria Fabian Valerian Fajar Ayuningtyas Feibe Lawalata Florentina Tatrin Kurniati Gallen cakra adhi wibowo Gideon Bartolomeus Kaligis Gilbert Yesaya Likumahua Gudiato, Candra Haikal Nur Rachmanrachim Achaqie Haikal Nur Rachmanrachim Achaqie Hindriyanto Dwi Purnomo Ida Ayu Putu Sri Widnyani Indra Yunanto Indrastanti Ratna Widiasari Irdha Yunianto Irwan Sembiring Isnaeni, Arvira Yuniar Johan Jimmy Carter Tambotoh Joko Siswanto Josua Josen Alexander Limbong Kase, Celomitha Putri Welhelmina Kristoko Dwi Hartomo Kurnia Latifatul Nazila Laurentius Kuncoro Probo Saputra, Laurentius Kuncoro Probo Lerry Godwin Nanariain Lobo, Murry Albert Agustin Lyonly Evany Tomasoa Maipauw, Musa Marsel Maya Sari Merryana Lestari Mikhael Dio Eclesi Mila Chrismawati Paseleng Mira Mira Muhamad Yusup Muhammad Rizky Pribadi Muhammad Sholikhan Nadia Renatha Yuwono Nadya Inarossy Novem Berlian Uly Nugroho, Ignatius Dion Nurul Fadilah, Nurul Oliver Elvino Putra Pratama Otniel, Marcelinus Vito Patasik, Eva Sapan Patrick Simbolon Petty, Holbed Joshua Praditya, Al-Farrel Raka Prayitno, Gunawan Priatna , Wowon Prihanto Ngesti Basuki Priyadi Priyadi Purwoko, Agus Puteri Justia Kardia Momuat Wahani Puteri Justia Kardia Momuat Wahani Qurotul Aini Ratu, Herman Huki Ravensca Matatula Raymond Elias Mauboy Riko Yudistira Rina Pratiwi Pudja I. A Rohmad Abidin, Rohmad Rony, Zahara Tussoleha Roy Rudolf Huizen Santoso, Nuke Puji Lestari Sarassati, Dwi Sinta Sebastian, Danny Septian Silvianugroho Septio, Pius Aldi Sri Hartati Stanny Dewanty Rehatta Stevanus Dwi Istiavan Mau Supit, Christanti Ekkelsia Suryasatria Trihadaru Suryasatriya Trihandaru Susatyo, Yeremia Alfa Sutarto Wijono Theopillus J. H. Wellem Tirsa Ninia Lina Triloka Mahesti Triloka Mahesti Untung Rahardja Untung Rahardja Valentino Kevin Sitanayah Que Vinsensius Aprila Kore Dima Wasis Pancoro Wicaksono, Muhammad Ryqo Jallu Winarko, Edi Wiwin Sulistyo Yansen Bagas Christianto Yerik Afrianto Singgalen Yesi Arumsari Yohanes Aji Priambodo Yudo Devianto Yuliawan, Kristia