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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Psikologika : Jurnal Pemikiran dan Penelitian Psikologi dCartesian: Jurnal Matematika dan Aplikasi JURNAL SISTEM INFORMASI BISNIS Prosiding KOMMIT BIOTROPIA - The Southeast Asian Journal of Tropical Biology Jurnal Sains dan Teknologi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Mathematics and Natural Sciences Jurnal Ilmiah Kursor Noetic Psychology JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika Scientific Journal of Informatics Psikodimensia: Kajian Ilmiah Psikologi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Sains Matematika dan Statistika BANGUN REKAPRIMA Proceeding of the Electrical Engineering Computer Science and Informatics MNJ (Malang Neurology Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika Inquiry: Jurnal Ilmiah Psikologi BAREKENG: Jurnal Ilmu Matematika dan Terapan IJEBD (International Journal Of Entrepreneurship And Business Development) JOURNAL SPORT AREA Philanthropy: Journal of Psychology MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Evangelikal: Jurnal Teologi Injili dan Pembinaan Warga Jemaat Aptisi Transactions on Technopreneurship (ATT) Insight: Jurnal Ilmiah Psikologi Jurnal Abdi Insani Computer Science and Information Technologies Jurnal Sains dan Edukasi Sains SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Indonesian Journal of Applied Research (IJAR) Journal of Science and Science Education Yumary: Jurnal Pengabdian kepada Masyarakat JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Riset Pendidikan Bahasa dan Sastra Indonesia (Repetisi) Dinamis Jurnal HPT (Hama Penyakit Tumbuhan) Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Jurnal Bisnis Kompetitif INJURITY: Journal of Interdisciplinary Studies Jurnal Akademik Pengabdian Masyarakat Journal of Community Empowerment ENDLESS : International Journal of Future Studies d'Cartesian: Jurnal Matematika dan Aplikasi Tesseract: International Journal of Geometry and Applied Mathematics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) El-Qisth Jurnal hukum keluarga Islam Community Impact and Society Empowerment Journal

Adi Setiawan

Universitas Kristen Satya Wacana

Author-ID : 376722

Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Public Health Social Sciences Transportation Veterinary Other

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COMPARATIVE ANALYSIS OF VINCENTY AND GEODESIC METHOD APPROACHES IN MEASURING THE DISTANCE BETWEEN SUBDISTRICT OFFICES IN SALATIGA CITY Windarni, Vikky Aprelia; Setiawan, Adi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Salatiga city has four subdistrict offices, namely Argomulyo Subdistrict, Sidorejo Subdistrict, Sidomukti Subdistrict and Tingkir Subdistrict. In this study, a comparative analysis of the distance between subdistrict offices in Salatiga city was conducted using the Vincenty method and Geodesic method with distance obtained from Google Maps. The data is the geographical coordinates of the Earth's surface (latitude and longitude) obtained from Google Earth. The results showed that both Vincenty and Geodesic methods compared with Google Maps calculation results between 95% -105%, so it can be said to be good. The geodesic method gives relatively better results than the Vincenty method because it has an average percentage of 99.58 %. In comparison, the Vincenty method has an average percentage of 99.48 %. However, the results obtained still use relatively less data.

COMPARISON OF ANN METHOD AND LOGISTIC REGRESSION METHOD ON SINGLE NUCLEOTIDE POLYMORPHISM GENETIC DATA Setiawan, Adi; Wijaya, Rachel Wulan Nirmalasari
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

This study aims to determine the goodness of classification using the ANN method on Asthma genetic data in the R program package, namely SNPassoc. SNP genetic data was transformed using codominant genetic traits, namely for genetic data AA, AC, CC were given a score of 0, 0.5 and 1, respectively, while CC, CT and TT were scored 0, 0.5 and 1, respectively. The scoring is based on the smallest alphabetical order given a low score. The average accuracy, precision, recall and F1 score were determined using the neural network method if the genetic code was used with variations in the proportion of test data 10%, 20%, 30% and 40% and repeated B = 1000 times. The results obtained were compared with the logistic regression method. If 20% test data is used and the ANN method is used, the accuracy, precision, recall and F1 scores are 0.7756, 0.7844, 0.9844 and 0.8728, respectively. When all information from various countries is used in the Asthma genetic data, the logistic regression method gives higher average accuracy, precision and F1 scores than the ANN method, but the average recall is the opposite. When a separate analysis is performed for each country, the logistic regression method gives higher accuracy, precision, recall and F1 scores in the ANN method compared to the logistic regression method.

PERFORMANCE COMPARISON OF DECISION TREE AND LOGISTIC REGRESSION METHODS FOR CLASSIFICATION OF SNP GENETIC DATA Setiawan, Adi; Setivani, Febi; Mahatma, Tundjung
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0403-0412

This research was conducted to compare the accuracy when decision tree and logistic regression methods are used on some data. Decision tree is one method of classification techniques in data mining. In the decision tree method, very large data samples will be represented as smaller rules, and logistic regression is a method that aims to determine the effect of an independent variable on other variables, namely dichotomous dependent variables. Both algorithms were written and analyzed using R software to see which method is better between the decision tree method and the logistic regression method applied to SNP (Single Nucleotide Polymorphism) genetic data, namely Asthma data. SNP Genetic Data was obtained from R software with the package name "SNPassoc" and the data name "asthma". Asthma data has 57 features, namely Country, Gender, Age, BMI, Smoke, Case control, and SNP (Single Nucleotide Polymorphism) genetic code. Comparative analysis was carried out based on the results of the accuracy values obtained in the two methods. Variations in the proportion of the test data used were 40%, 30%, 20% and 10% and were simulated 1000 times on the grounds of obtaining a better accuracy value. The results obtained show that the decision tree method obtains an accuracy value of 0.5793, 0.5777, 0.5745, 0.5526, respectively, while the logistic regression method is 0.7696, 0.7729, 0.7763, 0.7788, respectively and they are achieved at the proportion of test data of 40%, 30%, 20%, 10%. Thus it can be concluded that in this case the logistic regression method is better than the decision tree method in classifying Asthma data.

CONSTRUCTION OF SUBSTITUTION BOX (S-BOX) BASED ON IRREDUCIBLE POLYNOMIALS ON GF(2^8) Tita, Faldy; Setiawan, Adi; Susanto, Bambang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0517-0528

In the field of modern encryption algorithms, the creation of S-Box is an essential element that plays an important role in maintaining data security in various industries. This article provides a comprehensive review of various S-Box designs, with particular emphasis on essential parameters such as “Average ”, “Average ” and “Non-linearity value”. The main goal is to determine the most optimal S-Box structure to minimize correlation, thereby improving the security and unpredictability of the cryptographic system. Research results indicate that the S-Box characterized by the 1BD hexadecimal code is superior to its counterparts. It has an average value of 4.1953 and an average value of 0.4756. In contrast, the S-Box represented by hexadecimal code 169 displays a relatively lower level of security, with an average d value of 3.8750 and an average value of 0.5156. These results enable security experts and cryptographers to make the correct choice when selecting the S-Box with the minimum correlation value, thereby strengthening cryptographic systems against emerging cyber threats.

S-BOX CONSTRUCTION IN THE ADVANCED ENCRYPTION STANDARD (AES) DEVELOPMENT ALGORITHM IN GF(2^2), GF(2^4) & GF(2^6) Setiawan, Adi; Tita, Faldy; Susanto, Bambang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2329-2344

This research aims to obtain a method for constructing S-boxes based on GF(22), GF(24) and GF(26). A review of the Galois Field GF(2m) is presented for m=1,2,3,4,5 and 6. Furthermore, it is used to construct an S-box based on GF(22), GF(24) and GF(26). Based on these results, later it can be developed for S-box construction in the AES algorithm which uses the Galois Field GF(2m) for m>=10.

Deep Learning-Based Visualization of Network Threat Patterns Using GAN-Generated Infographic Wibowo, Mars Caroline; Setyawan, Iwan; Setiawan, Adi; Sembiring, Irwan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6717

Despite the growing sophistication of cyberattacks, current network traffic analysis tools often lack intuitive visual support, limiting human analysts’ ability to interpret complex threat behaviors. To address this gap, this study proposes a novel deep learning-based visualization framework using a Deep Convolutional Generative Adversarial Network (DCGAN) to synthesize threat-specific infographics from structured numerical features in the CICIDS 2017 dataset. Unlike conventional methods, such as PCA or static dashboards, which often result in abstract or non-adaptive visuals, our approach generates class-distinct grayscale images that preserve the behavioral patterns of various attacks, including denial-of-service, brute force, and port scanning. The preprocessing pipeline reshapes the selected flow-based features into 28×28 matrices to train the generative model. Evaluation using the Frechet Inception Distance (FID) yielded a score of 28.4, whereas a CNN classifier trained on the generated images achieved 91.2% accuracy, confirming visual fidelity and semantic integrity. Additionally, a panel of human experts rated the interpretability of the generated images at 4.3 out of 5.0. These findings demonstrate that generative visualization can enhance human-centered threat analysis by bridging raw data with interpretable imagery, thereby offering a scalable and explainable approach for integrating AI into real-time security workflows.

Dukungan Sosial, Ketangguhan Pribadi, dan Stres Akulturasi Mahasiswa Nusa Tenggara Timur di Salatiga Keo, Jitro Jemryes; Kristinawati, Wahyuni; Setiawan, Adi
Psikologika: Jurnal Pemikiran dan Penelitian Psikologi Vol. 25 No. 1 (2020)
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/psikologika.vol25.iss1.art2

Penelitian yang berfokus pada pengaruh dukungan sosial dan ketangguhan pribadi (hardiness) terhadap stres akulturasi masih perlu dikaji lebih lanjut. Hipotesis penelitian ini adalah adanya pengaruh simultan dan parsial antara dukungan sosial dan ketangguhan pribadi terhadap stres akulturasi pada mahasiswa perantau asal Nusa Tenggara Timur (NTT) di Salatiga. Alat ukur yang digunakan dalam penelitian ini adalah skala Stres Akulturasi, skala Ketangguhan Pribadi, dan skala Dukungan Sosial. Hasil pengumpulan data penelitian ini dianalisis menggunakan analisis regresi linier. Penelitian ini melibatkan 85 orang mahasiswa asal NTT. Hasil uji statistik menunjukkan bahwa secara simultan, ketangguhan pribadi dan dukungan sosial berpengaruh terhadap stres akulturasi dengan nilai F = 5.32 dan tingkat signifikansi 0,007 (p < .05); tampak secara parsial, ketangguhan pribadi tidak berpengaruh terhadap stres yang ditunjukkan dari nilai t = -1.74 dan p > .05. Di sisi lain, dukungan sosial memberikan pengaruh yang signifikan terhadap stres akulturasi yang ditunjukkan dari nilai t = -2.33 dan p < .05. Selanjutnya, berdasarkan analisis koefisien determinasi (R = .115), variabel ketangguhan pribadi dan dukungan sosial berpengaruh sebesar 11.50% terhadap stres akulturasi dengan sumbangan efektif variabel ketangguhan pribadi sebesar 4.39%, dukungan sosial sebesar 7.12%, dan sisanya sebesar 89.50% dipengaruhi oleh variabel lainnya yang tidak diteliti dalam penelitian ini. Hasil ini menunjukan bahwa dukungan sosial memiliki peran yang penting dalam menurukan stres akulturasi.Kata Kunci: dukungan sosial, ketangguhan pribadi, stres akulturasiSocial Support, Hardiness, and Acculturation Stress among East Nusa Tenggara’s Students in SalatigaAbstract. This research related to the influence between social support and hardiness on acculturative stress is still need to be examined further. The hypothesis of this study indicated that there was a simultaneous and partial influence between social support and hardiness on acculturative stress among overseas students from East Nusa Tenggara (NTT) in Salatiga. The data is collected by using Acculturation Stress scale, Hardiness scale, and Social Support scale as well as analyzed by linear regression analysis. This study involved 85 students from NTT. Statistical test results indicate that simultaneous hardiness and social support affect the acculturative stress with a value of F = 5.32 with a significance level of .007 (p < .05); and it appears that partially hardiness has no effect on stress as indicated by the value of t = -1.74 and p > .05. On the other hand, social support has a significant influence on acculturation stress as indicated by the value of t = -2.33 and p < .05. Furthermore, based on the analysis of the coefficient of determination (R = .115), hardiness and social support variables influence 11.50% on acculturative stress with an effective contribution of hardiness variables at 4.39%, social support at 7.12%, and the remaining 89.50% were influenced by other variables which were not examined in this study. The results indicate that social support contributed greater on acculturative stress compared to hardiness.Keywords: hardiness, social support, stress acculturationArticle History:Received 6 November 2019Revised 24 February 2020Accepted 30 May 2020

Accuracy of long short-term memory model in predicting YoY inflation of cities in Indonesia Leipary, Harfely; Setiawan, Adi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3887-3896

Our research evaluates the effectiveness of the long short-term memory (LSTM) model in forecasting annual year-on-year (YoY) inflation across 82 cities in Indonesia based on time series data from BPS economic reports for 2014-2024. This study tests the accuracy of the model in reconstructing past inflation patterns, then evaluates the capabilities and limitations of the model in various urban area contexts with the root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination(R2) metrics. The findings show that LSTM performs well in metropolitan areas such as Jakarta, Bandung, and Surabaya with R2values >0.8 and the lowest MAPE of 10.91% in Jakarta. However, in small cities with higher economic volatility such as Tanjung Pandan, the model shows significant prediction errors (R²<0.50 and MAPE up to 283.11%). Moderate performance (0.50≤ R²≤0.80) was found in cities such as Palembang, Semarang, and Makassar, reflecting the model's adaptive ability to moderate inflation patterns. These results emphasize the important role of structured economic data in improving the reliability of predictions, so that the policy implications of this study include the use of the LSTM model as an early warning system by fiscal and monetary authorities, as well as the need for a data-based inflation control strategy to strengthen regional and national economic resilience in supporting sustainable development towards Indonesia Emas 2045.

Comparison of Convolutional Neural Network (CNN) Models in Face Classification of Papuan and Other Ethnicities Yenusi, Yuni Naomi; Suryasatriya Trihandaru; Setiawan, Adi
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 1 (2023): April
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i1.46861

Klasifikasi objek pada citra menjadi salah satu problem dalam visi komputer. Komputer diharapkan dapat meniru kemampuan manusia dalam memahami informasi citra. Salah satu pendekatan yang berhasil yaitu dengan menggunakan Jaringan Syaraf Tiruan (JST) dimana pendekatan ini terinspirasi dari jaringan syaraf pada manuasia yang dikembangkan lebih jauh menjadi Deep Learning. Convolutional Neural Network (CNN) merupakan salah satu jenis Deep Learning yang sangat terkenal dengan keemampuannya dalam melakukan klasifikasi citra. Dengan mengimplementasikan beberapa model CNN akan dilakukan perbandingan antara model arsitektur CNN dalam klasifikasi wajah etnis Papua dan wajah etnis lainnya untuk melihat model dengan akurasi terbaik pada kasus ini. Model CNN yang dipilih yaitu VGG16, VGG-19, ResNet-50 dan MobileNet v1 dan Mobilenet v2. Model terbaik adalah model arsitektur Mobile Net v1 untuk Pengenalan Wajah Papua dan Non Papua dengan akurasi 95%. Pada penelitian ini disimpulkan bahwa MobileNet V1 adalah model yang terbaik. Model ini menghasilkan akurasi, precision, recall, dan f1-score dengan nilai 95%, 99%, 91%, dan 94%. Adapun saran untuk penelitian selanjutnya adalah dilakukan modifikasi terhadap layer pada masing-masing molde untuk meninggkatkan performa model arsitektur CNN.

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

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.

Co-Authors Abdul Latief Abadi Abesha, Muhammad Bagas ADELIA, PUTRI Adella Septiana Mugirahayu Aditya Nugraha Putra, Aditya Nugraha Adril, Adril Agatha, Titania Puela Agung Sugeng Widodo Agustiningsih, Maulina Al Jauhary, Muhammad Rifqi Aldian Umbu Tamu Ama Aldian Umbu Tamu Ama Alfida Tegar Nurani Alicia Anggelia Lumbantoruan Alkhinaya, Imelzsa ALOYSIUS JOAKIM FERNANDEZ Andhika, Yosi Arbi, Mokhram Ari Ariani, Dwi Setya Arum, Naiya Giska Fauzhia Sekar Atiek Iriany Atina Rahmatalia Ayu Pratiwi, Ayu AYU WULANDARI Bambang Susanto Baskoro Arie Nugroho Bayu Wijayanto Beni Utomo Christiana Hari Soetjiningsih Christina Maya Indah Susilowati Cintika, Sara Famelia D. B. Nugroho, D. B. Daivi Wardani, Daivi Danang Ariyanto Delsylia Tresnawaty Ufi Denny Indrajaya Denny Indrajaya Deswita, Yenny Dewi Anisa Istiqomah Dewi Lukitasari Diah Wulansari Hudaya, Diah Wulansari Didit Budi Nugroho Djoko Hartanto E. D. Saputri, E. D. Eko Sediyono Elok Waziiroh Elsa Septyana Endang Sulistyaningsih Faldy Tita Fika Widya Pratama Florentina Tatrin Kurniati Gustina, Devi Haay, Happy Alyzhya Hamsani Hamsani, Hamsani Hanna Arini Parhusip Hari Slamet Trianto Hari Slamet Trianto Hariyanto Hariyanto Hartiningsih, Tri Haryadi, Andri Henderi . Henrizal, Henrizal Henry Junus Wattimanela Hidayat, Mario Ignatius Agus Supriyono Ilham Hizbuloh Imansyah, Salmaa R. N Irisa Trianti Irwan Sembiring Iwan Setiawan Iwan Setyawan Joko Siswanto JT Lobby Loekmono Kasmadi Kasmadi Keo, Jitro Jemryes Kurniawan, Johanes Dian Kurniawan, Titus Antonius David Larassati, Dian Sukma Leipary, Harfely Leonardo Refialy Leonardo Refialy, Leonardo Leopoldus Ricky Sasongko Lilik Linawati Lindin Anderson Litra Diantara Luqman Qurata Aini Lydia Soepriyani Fallo masipupu, Frangky Aristiadi Meydelina, Gloria Migunani Migunani Mitha Febby R. Donggori Mitha Febby R. Donggori Mochtar Luthfi Rayes Modjo, Marchella Ellena Mohammad Ridwan Mukti, Audy Desaela Junia Munika, Rani Mustafa Kamal Nafisah Riskya Hasna Nasoetion, Panisean Nasrudienullah, Muhammad Ikhsan Ninda Lutfiani Nizwan Zukhri Nugraha, Irfan Nur Priya Nurul Islami, Nurul Olivia Rumahpasal Pamungkas, Bayu Aji Pane, Pina Andriani Pariama, Aprillia Mauren Pirmansyah Pirmansyah Pradani, Wynona Adita Priatna , Wowon Pronika, Yeni Purbaratri, Winny Purwanto Purwoko, Agus Putra, Reza Qurotul Aini Rachayu, Laras Andriani Rachel Wulan Nirmalasari Wijaya Reniati Reniati Riana Dewi Ridlo, Mahmuddin Riza, Sativandi Rizqon Hasibuan Romauli Basaria Roy Rudolf Huizen Rudhito, Andy Salomina Patty Saputra, Muhammad Dio SARI, EMMA NOVITA Sari, Fariezta Sayuti, M. Setivani, Febi Sri Suwartiningsih Sulistio Sulistio Suryasatriya Trihandaru Sutarto Wijono Syamsul Arifin Syib`'li, Muhammad Akhid Tamaela, Jemaictry Theo Sarita, Fetriks Theopillus J. H. Wellem Tri Wahyuningsih Tundjung Mahatma Uli, Desti Monika Untung Rahardja Untung Rahardja Vikky Aprelia Windarni Vikky Aprelia Windarni Vincentia Pawestri Wahyuni Kristinawati Waney, Natalia Christy Wattimanela, Henry Junus Wibowo, Mars Caroline Wiguna, Edo Wijaya, Maruf Ajisaka Wijayanti, Yunita Puput Windarni, Vikky Aprelia Wisnu Anendya Sekti Yanti Sariasih Yenusi, Yuni naomi Yulius Yusak Ranimpi Yuono, Sukma Setyo Zuliani, Nopita zurman, zurman

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