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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Dinamik Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Bulletin of Electrical Engineering and Informatics Journal of Information Systems Engineering and Business Intelligence Annual Research Seminar Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan Komunikasi Jurnal Teknoinfo JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jurnal Mnemonic Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Journal of Electrical Engineering and Computer (JEECOM) Jurnal Teknologi dan Sistem Informasi Jurnal Pendidikan dan Teknologi Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer EXPLORER Jurnal Ekonomika Dan Bisnis Paradigma SENTRI: Jurnal Riset Ilmiah Malcom: Indonesian Journal of Machine Learning and Computer Science Eduvest - Journal of Universal Studies Jurnal Ilmiah Informatika dan Ilmu Komputer Journal of Information Technology, Software Engineering and Computer Science The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Jurnal Komputasi Journal of Decision Support Systems and Multi-Criteria Decision Making (JODESMA)
Ryan Randy Suryono
Universitas Teknokrat Indonesia
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Comparison of Naïve Bayes, Random Forest, and Logistic Regression Algorithms for Sentiment Analysis Online Gambling Dwi Nanda Agustia; Ryan Randy Suryono
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/prk93630

Abstract

This study aims to compare the performance of Naïve Bayes, Random Forest, and Logistic Regression algorithms for sentiment analysis on the topic of online gambling. The dataset consisted of 4592 entries after preprocessing and applying the SMOTE technique to address class imbalance. The evaluation results show that Random Forest achieved the best performance with an accuracy of 78%, followed by Naïve Bayes and Logistic Regression, both achieving 77%. Random Forest excelled in classifying positive and negative sentiments, while Naïve Bayes demonstrated a significant improvement in recall for neutral sentiment, increasing from 0.45 to 0.82 after the SMOTE application. Logistic Regression showed less optimal performance, particularly for neutral sentiment. This study provides essential guidance for selecting the best algorithms for sentiment analysis in specific domains such as online gambling and highlights the importance of SMOTE in handling imbalanced datasets. The findings of this study can be used by practitioners and policymakers to make more informed decisions in regulating online gambling.
Sentiment Analysis of the Influence of the Korean Wave in Indonesia using the Naive Bayes Method and Support Vector Machine Natasha; Ryan Randy Suryono
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/85x4wd90

Abstract

This study analyzes public sentiment towards the influence of the Korean wave in Indonesia using the Naive Bayes and Support Vector Machine (SVM) methods. The Korean wave, as a popular cultural phenomenon from South Korea, has had a significant influence on various aspects of Indonesian society. The dataset consists of 6,237 tweets obtained through a crawling process on social media X, with 80% data divided for training and 20% for testing. The pre-processing process includes cleaning, case folding, tokenizing, stopwords, and stemming. Data imbalance in sentiment distribution is overcome by the SMOTE technique. The test results show that the SVM model has the highest accuracy of 88%, outperforming the Naive Bayes model with an accuracy of 81%. Performance evaluation using precision, recall, and F1-score shows that SVM is more consistent in classifying positive and negative sentiments. Data visualization is done using bar charts and word clouds to illustrate the main patterns and themes in discussions related to the Korean wave in Indonesia. However, this study has limitations, such as data is only taken from one social media platform, so the results are less representative of public opinion as a whole. Nevertheless, this study provides new insights into how Indonesian society responds to popular culture phenomena online. These findings can also be utilized by policy makers to support the development of creative industries based on popular culture.
Komparasi Berbagai Metode Klasifikasi Teks Untuk Sentimen Pengguna Gawai Di Usia Dini Yovi Meliana; Ryan Randy Suryono
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4439

Abstract

In the context of rapid digital development, the use of gadgets among Indonesian children has become a very important topic to study. This study aims to analyze sentiments related to gadget use by applying classification methods such as Support Vector Machine (SVM), Naïve Bayes, and Decision Tree. To overcome data imbalance, After applying the SMOTE technique, the results of the study revealed that SVM obtained the highest accuracy of 99% with SMOTE, followed by Decision Tree which reached 98% and Naïve Bayes which obtained 94% when SMOTE was applied. In addition, the application of preprocessing techniques such as tokenization, stemming, and filtering contributed to improving data quality. These findings emphasize the importance of choosing the right method in sentiment analysis to understand the impact of gadget use on children's development. This study provides meaningful insights for the development of better policies and practices related to children's digital device use
Peran Artificial Intelligence Dalam Mendorong Inovasi Dunia Bisnis Untuk Mencapai Keunggulan Yang Kompetitif Jelna Anggreni; Turlia Indah Sapitri; Ryan Randy Suryono
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3506

Abstract

The development of Artificial Intelligence (AI) has revolutionized various aspects of the business sector, particularly in driving innovation and creating competitive advantage. This study aims to analyze the contribution of AI to business innovation through a Systematic Literature Review (SLR) approach. The SLR approach was employed to identify, review, evaluate, and synthesize relevant literature related to the research topic. The findings indicate that AI plays an important role in transforming business processes, supporting data-driven decision-making, improving operational efficiency, and strengthening marketing strategies and customer service. In addition, AI enables organizations to become more adaptive and innovative through predictive capabilities and advanced automation. Nevertheless, AI implementation also raises ethical challenges, technological dependence, and the need for adequate organizational capabilities. This study provides both theoretical and practical implications for business practitioners, academics, and policymakers in optimizing the role of AI as an enabler of innovation and the creation of competitive value.Keywords: Artificial Intelligence; Business innovation; Competitive advantage; SLR; Digital transformationAbstrakPerkembangan Artificial Intelligence (AI) telah merevolusi berbagai aspek dalam dunia bisnis, terutama dalam mendorong inovasi dan menciptakan keunggulan kompetitif. Tujuan dari Penelitian ini bertujuan menganalisis kontribusi AI terhadap inovasi bisnis melalui pendekatan Systematic Literature Review (SLR). Pendekatan SLR diterapkan untuk mengidentifikasi, menelaah, mengevaluasi, serta menyusun sintesis dari berbagai literatur yang relevan dengan judul penelitian. Hasil kajian menunjukkan bahwa AI berperan penting dalam hal transformasi mengenai proses sebuah bisnis, serta pengambilan keputusan berbasis data, peningkatan efisiensi operasional, serta penguatan strategi pemasaran dan layanan pelanggan. Selain itu, AI juga mendorong organisasi untuk lebih adaptif dan inovatif melalui kemampuan prediktif dan otomatisasi yang canggih. Namun, implementasi AI juga menimbulkan tantangan etis, ketergantungan teknologi, dan kebutuhan kapabilitas organisasi yang memadai. Studi ini memberikan implikasi teoretis dan praktis bagi pelaku bisnis, akademisi, dan pembuat kebijakan dalam mengoptimalkan peran AI sebagai enabler inovasi dan pencipta nilai kompetitif.Kata kunci: Artificial Intelligence; Inovasi bisnis; Keunggulan kompetitif; SLR; Transformasi digital
Analisis Sentimen Aplikasi BCA Mobile Menggunakan Algoritma Naive Bayes dan Suport Vector Machine Dimas Wahyu Bhatara; Ryan Randy Suryono
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.5536

Abstract

Kemajuan teknologi telah mengubah banyak aspek terutama dalam hal transaksi, dengan aplikasi seperti BCA Mobile menjadi salah satu pilihan utama. Aplikasi ini memungkinkan pengguna untuk melakukan berbagai aktivitas finansial secara online. Dengan popularitasnya yang terus meningkat, mencapai lebih dari 5 juta unduhan di Google Play Store, penelitian ini bertujuan untuk mengevaluasi pandangan pengguna terhadap aplikasi ini, baik positif maupun negatif. Analisis dilakukan menggunakan dua metode utama, yaitu algoritma Naïve Bayes dan Support Vector Machine (SVM), yang kemudian diperbaiki kinerjanya dengan menggunakan Synthetic Minority Over-Sampling Technique (SMOTE). Hasil penelitian menunjukkan bahwa SVM mencapai akurasi 85%, sementara Naïve Bayes 83%. Meskipun keduanya memiliki tingkat akurasi yang hampir serupa terdapat perbedaan dalam kemampuan masing-masing model dalam mengklasifikasikan sentimen positif dan negatif..Naïve Bayes memiliki recall yang sedikit lebih rendah untuk ulasan positif sebesar 81% dibandingkan dengan SVM mencapai 85%, namun memiliki presisi yang sedikit lebih tinggi..Sebaliknya, SVM memiliki recall yang lebih rendah untuk ulasan negatif, namun memiliki presisi yang lebih tinggi..Ini menunjukkan kemampuan SVM dalam menangani distribusi fitur dan kelas yang kompleks, yang tidak dapat ditangani dengan baik oleh Naïve Bayes.
KOMPARASI BERBAGAI MODEL KLASIFIKASI TEKS UNTUK ANALISIS SENTIMEN TENTANG TERPILIHNYA PRESIDEN REPUBLIK INDONESIA 2024 Feri Cahya Setiawan; Ryan Randy Suryono
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6536

Abstract

Pemilihan Presiden Indonesia tahun 2024 menjadi topik hangat di kalangan masyarakat, Baik dalam kehidupan sehari-hari maupun di dunia maya, khususnya pada platform media sosial seperti aplikasi X. Pada tahun 2024 calon presiden Indonesia paslon nomor 2 adalah Prabowo Subianto merupakan Presiden terpilih Indonesia sekaligus Mentri Pertahanan. Oleh sebab itu, muncul beragam pandangan, termasuk opini yang positif maupun negatif. Penelitian ini bertujuan untuk mengevaluasi sentimen opini publik di platform media sosial X terkait pemilihan Presiden Indonesia 2024 dengan memanfaatkan berbagai algoritma machine learning yaitu Naïve Bayes, Support Vector Machine, dan Logistic Regression, nanti kinerjanya kemudian ditingkatkan menggunakan Teknik Oversampling Minoritas Sintetis (SMOTE). Hasil penelitian ini Naïve Bayes mencapai Accuracy 96%, Precision 93%, Recall 100%, F1-Score 96%, Untuk Support Vector Machine mencapai Accuracy 99%, Precision 99%, Recall 100%, F1-Score 100 % dan Logistic Regression mencapai Accuracy 99%, Precision 99%, Recall 100%, F1-Score 100%. Dapat disimpulkan bahwa Support Vector Machine Logistic Regression menunjukan performa terbaik dalam mengklasifikasikan sentimen positif dan negatif secara lebih akurat jika dibandingkan dengan algoritma Naïve Bayes. Namun dalam hasil Confusion Matrix algoritma Support Vector Machine menunjukan performa yang jauh lebih baik. Hal ini memnunjukan kemampuan Support Vector Machine dapat menjadi pilihan yang lebih baik dalam kasus analisis sentimen terhadap terpilihnya Prabowo Subianto sebagai Presiden Indonesia 2024.
KOMPARASI BERBAGAI MODEL KLASIFIKASI TEKS UNTUK ANALISIS SENTIMEN KINERJA PELATIH TIMNAS INDONESIA Aryuda Aryuda; Ryan Randy Suryono
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6535

Abstract

Proses Pelatihan timnas Indonesia mengalami perubahan signifikan dalam beberapa tahun terakhir, terutama sejak kedatangan pelatih Shin Tae-yong pada akhir 2019. Sebelumnya, timnas sering mengalami fluktuasi performa akibat metode pelatihan yang kurang terstruktur dan kurangnya inovasi dalam strategi permainan. Dengan Shin Tae-yong, timnas mulai menerapkan pendekatan pelatihan yang lebih modern, dengan fokus pada teknik dasar, taktik permainan, dan kebugaran fisik pemain. Penelitian ini menganalisis 4.476 data sentimen publik dari pengguna X mengenai kinerja Shin Tae-yong. Peneliti membandingkan tiga model klasifikasi teks, yaitu Naive Bayes, SVM, dan Logistic Regression. Melalui perbandingan ini, penelitian diharapkan dapat menentukan model klasifikasi mana yang lebih baik dalam menganalisis komentar publik terkait kinerja Shin Tae-yong. Dengan menggunakan teknik optimasi SMOTE, data yang digunakan dapat diseimbangkan, di mana pelabelan menghasilkan 4.128 data mayoritas dan 344 data minoritas. Dengan optimasi SMOTE, data sentimen positif dan negatif disesuaikan agar model algoritma dapat bekerja lebih baik. Hasil komparasi menunjukkan bahwa model SVM dan Logistic Regression menghasilkan akurasi yang sama, yaitu 99%, sedangkan Naive Bayes menghasilkan akurasi sebesar 91%. Meskipun demikian, Logistic Regression menunjukkan sedikit keunggulan dalam Confusion Matrix, dengan True Positive (TP) sebesar 1.229 dan True Negative (TN) sebesar 1.222, dibandingkan dengan SVM yang memiliki TP 1.220 dan TN 1.221. Ini menunjukkan bahwa Logistic Regression sedikit lebih baik dalam mengklasifikasikan sentimen dibandingkan dengan Naive Bayes dan SVM.
ANALISIS SENTIMEN HATE SPEECH MENGENAI CALON WAKIL PRESIDEN INDONESIA MENGGUNAKAN ALGORITMA BERT Elvika Alya Junita; Ryan Randy Suryono
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.5625

Abstract

Indonesia sebagai negara demokratis mengalami momen penting menjelang  pemilihan  umum,  khususnya  presiden  dan  wakil  presiden.  Persaingan politik yang sengit diwarnai dengan beragam pandangan dan retorika politik. Namun, munculnya hate speech sebagai bentuk ekstrem dari ekspresi politik mengancam stabilitas sosial dan integritas demokrasi. Hate speech dapat mengganggu harmoni masyarakat, mempengaruhi proses pemilihan umum dengan menyebarkan informasi palsu, dan merusak suasana politik. Analisis sentimen sangat penting dalam mendeteksi dan menangani hate speech terkait dengan calon Wakil Presiden Indonesia. Penelitian ini menggunakan data Twitter untuk mengeksplorasi opini masyarakat terhadap calon presiden dengan kata kunci Imin, Gibran, dan Mahfud MD sebanyak 2692 data. Hasil eksperimen menunjukkan bahwa algoritma BERT memiliki tingkat akurasi yang sangat tinggi, mencapai rata-rata 100% pada dataset Gibran Rakabuming Raka dan Mahfud MD. Analisis juga menunjukkan bahwa proporsi sentimen positif terhadap calon wakil presiden menunjukkan kecenderungan bahwa Muhaimin Iskandar mencapai tingkat akurasi 98,17%.
Comprehensive Evaluation of Distance-Based MCDM Methods Using Response to Criteria Weighting: A Ranking Stability and Sensitivity Analysis Sumanto Sumanto; Ryan Randy Suryono; Riska Aryanti; Hetty Rohayani; Setiawansyah Setiawansyah; Desyanti Desyanti
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) Vol. 4 No. 4 (2026): Volume 4 Number 4 October 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v4i4.377

Abstract

Selecting an appropriate new store location is a complex multi-criteria decision-making problem because potential locations must be evaluated using multiple criteria with different performance characteristics. This study proposes an integrated distance-based Multi-Criteria Decision-Making framework using RECA Weighting combined with TOPSIS, SPOTIS, and EDAS to determine objective criterion importance and evaluate the stability and robustness of alternative rankings. The dataset consists of eight potential store locations evaluated using six criteria: Rental Cost, Building Area, Accessibility, Consumer Traffic, Parking Availability, and Infrastructure. RECA Weighting is applied to determine objective criterion weights based on performance variation, while TOPSIS, SPOTIS, and EDAS are used to generate preference scores and rankings through different distance-based evaluation mechanisms. The results show that Consumer Traffic obtains the highest criterion weight of 0.1824, while Parking Availability receives the lowest weight of 0.1422. The ranking results identify LS5 as the best-performing alternative, followed by LS3, although several alternatives exhibit different ranking positions across the applied methods. Weight-variation analysis using Spearman rank correlation shows that SPOTIS achieves perfect ranking stability with a correlation of 1.0000, TOPSIS demonstrates very strong stability with a correlation of 0.9286, while EDAS produces a lower correlation of 0.1429, indicating greater ranking variation. These findings demonstrate that SPOTIS provides the highest robustness to criterion weight changes, followed by TOPSIS, whereas EDAS is more sensitive to weight variations due to its average-solution-based evaluation mechanism. Overall, the proposed framework provides a comprehensive approach for objective weighting, ranking stability assessment, sensitivity analysis, and robustness evaluation in new store location selection.
TEKNO Method: Total Evaluation based on Knowledge-driven Normalized Optimization for Multi-Criteria Decision Making Yuri Rahmanto; Ryan Randy Suryono; Dedi Darwis; Abhishek R. Mehta; Auliya Rahman Isnain
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 2 (2027): March 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i2.7

Abstract

Multi-criteria decision making (MCDM) methods play an important role in supporting decisions involving multiple criteria with different characteristics and levels of importance. However, differences in normalization procedures, criterion treatment, and aggregation mechanisms can lead to variations in the resulting preference structures. This study proposes a new MCDM method, namely total evaluation based on knowledge-driven normalized optimization (TEKNO), which integrates normalization, relative evaluation, criterion weighting, optimization-based normalization, and total evaluation into a unified decision-making framework. The proposed method is designed to transform heterogeneous decision information into comparable evaluation values while preserving the relative contribution of each criterion. The applicability of TEKNO is evaluated through two decision-making case studies involving new store location selection and leasing customer selection. The evaluation framework includes ranking analysis, comparison with established MCDM methods, Spearman rank correlation analysis, and sensitivity analysis under variations in criterion weights. The results show that TEKNO achieves a Spearman rank correlation coefficient of 1.0000 for the new store location case and 0.9964 for the leasing customer selection case, indicating very strong agreement with the reference rankings. In addition, the ranking remains unchanged across the tested sensitivity scenarios, demonstrating the stability of TEKNO under variations in criterion weights. These findings indicate that TEKNO provides a transparent, systematic, and stable alternative for MCDM applications for practical decision support where reliable ranking, methodological transparency, and robustness across alternative evaluation conditions are required. Nevertheless, broader validation using diverse datasets, decision domains, weighting schemes, and statistical evaluation techniques is required to further establish its generalizability and comparative performance.
Co-Authors ., Bagastian Abhishek R. Mehta Achmad Nizar Hidayanto Ade Dwi Putra Adelia Pratiwi Aditia Yudhistira Agresia, Vania Agus Wantoro AGUSTIAN, VENDRI RAMA Ahmad Ari Aldino Ajie Tri Hutama Al Afif, Satria Amarudin Amri Reza Wahyudin Anadas, Sylvi Ananda, Dhea AndaruJaya, Rinaldi Sukma Ansyah, Ferdi Ariany, Fenty Arshad, Muhammad Waqas Aryuda Aryuda Bagastian Bagastian Bagastian Bagus Reynaldi, Dimas Bakti, Da'i Rahman Budi Santosa Budi Santosa Budi Santosa Budiawan, Aditia Budiman, Ega Christ Mario Christ Mario Cynthia Deborah Nababan Dana Indra Sensuse DAVID KURNIAWAN Dede Krisna Friansyah Dedi Darwis Desi Fitria Desyanti Desyanti Dewantoro, Mahendra Dimas Eko Putro Dimas Wahyu Bhatara Dinda Septia Ningsih Dwi Nanda Agustia Dwi Nanda Agustia Dyah Ayu Megawaty Ega Budiman Eko Putro, Dimas Elin Mayoana Fitri Elvika Alya Junita Eskiyaturrofikoh, Eskiyaturrofikoh Feri Cahya Setiawan Firdaus, Noval Dinda Firmanda, Fabian Fudholi, Muhammad Fahmi Gunawan, Rakhmat Dedi Handini, Meitry Ayu Haryanto, Didik Hasiholan Simamora, Alfred Helma Nopijani Heidy Heni Sulistiani Hermana, BP Putra Hetty Rohayani Hoiri, Muhamad Nur Ignatius Adrian Mastan Indra Budi Isnain, Auliya Rahman Ival Sanjaya Iwan Purwanto Iwan Purwanto Jelna Anggreni Juan Adi Putra Juarsa, Doris Kamrozi Kardita Magda Karimah Sofa Kautsarina Kautsarina Kautsarina Kautsarina Kautsarina Kautsarina Kautsarina Kevinda Sari Krishna Yudhakusuma P.M. Laksono, Urip Hadi M Sahyudi Mahendra Dewantoro Maylanda, Putri Oktaria Megawaty, Dyah Ayu Mesran, Mesran Miranda, Khyntia Mugi Prasetio Muh. Alviazra Virgananda Muhamad Adhytia Wana Putra Rahmadhan Muhammad Fadli Muhammad Fadli Muhammad Fahmi Fudholi Muhammad Ridwan Muhammad Sahyudi Muhammad Surono Mustaqim, Ilham Zharif Natasha Natasha Panca Hadi Putra Pratama, Rangga Rizky Purnama, Putri Intan Purwanti, Dian Sri Putra, Djalu Bintang Putra, Satya Setiawan Putri Oktaria Maylanda Rachmad Nugroho Rachmi Azanisa Putri Rahmat Dedi Gunawan Raihandika, M Rafi Raka Sulistiyo Ramadhani, Bagus Reifco Harry Farrizqy Rias Kumalasari Devi Riska Aryanti Riyama Ambarwati Sampurna Dadi Riskiono Sanriomi Sintaro Saputra, Melian Jefri Saputra, Rizky Herdian Sari, Cici Nurita Kumala Sari, Putri Kumala Sarumpaet, Lisyo Hileria septiana Rahayu Septiana Rahayu Setiawan, Andra Setiawansyah Setiawansyah Setiyana, Beta Agus Simarmata, Yohanes Sobirin, Muhammad Hamdan Sri Murdiawati SUDIARTE, PUTU Sumanto Sumanto Surono, Muhammad Surya Indra Gunawan TAHARA, ANGGIT PRANA Tri Widodo Tria Setyani Turlia Indah Sapitri Ulum, Faruk Vania Agresia Wahyudi, Agung Deni Wang, Junhai Waqas Arshad, Muhammad Yeni Agus Nurhuda Yeni Agus Nurhuda Yovi Meliana Yulia Indriani Yuri Rahmanto Yuspita, Emi