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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) 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 The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Jurnal Komputasi
Ryan Randy Suryono
Universitas Teknokrat Indonesia
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Tinjauan Literatur Sistematis tentang Standarisasi Sistem Pembayaran (QRIS dan Kerangka Kerja Serupa) dalam Ekosistem Kewirausahaan Digital TAHARA, ANGGIT PRANA; AGUSTIAN, VENDRI RAMA; SUDIARTE, PUTU; SURYONO, RYAN RANDY
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3520

Abstract

Advances in the digital era have driven significant transformations in entrepreneurial practices, particularly in the Micro, Small, and Medium Enterprises (MSME) sector. The implementation of the Indonesian Standard Quick Response Code (QRIS) as a digital payment system not only reflects the adoption of technology but also the implementation of technopreneurship principles that emphasize technology-based innovation. This study aims to analyze the contribution of technopreneurship in accelerating the adoption of QRIS by MSMEs, as well as to identify the benefits, obstacles, and research gaps that have not been widely studied. The method used is a Systematic Literature Review (SLR) with the Kitchenham protocol applied to national and international journal articles published in the period 2019–2025. The results of the study show that technopreneurship plays an important role in increasing the digital readiness of MSMEs, encouraging business model innovation, and increasing the effectiveness and reliability of transactions through QRIS. However, challenges in the form of limited digital literacy, supporting infrastructure, and the integration of QRIS with the MSME financial system remain major obstacles. These findings are expected to serve as a theoretical basis for the development of technopreneurship-based MSMEs and a reference for further research.Key Word: Technopreunership; QRIS, MSMEs; Technology adoption; Digital payment Abstrak Kemajuan era digital telah mendorong transformasi signifikan dalam praktik kewirausahaan, khususnya pada sektor Usaha Mikro, Kecil, dan Menengah (UMKM). Penerapan Quick Response Code Indonesian Standard (QRIS) sebagai sistem pembayaran digital tidak hanya mencerminkan adopsi teknologi, tetapi juga implementasi prinsip technopreneurship yang menekankan inovasi berbasis teknologi. Penelitian ini bertujuan untuk menganalisis kontribusi technopreneurship dalam mempercepat adopsi QRIS oleh UMKM, serta mengidentifikasi manfaat, hambatan, dan celah penelitian yang masih belum banyak dikaji. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan protokol Kitchenham terhadap artikel Literatur nasional dan internasional yang dipublikasikan pada periode 2019–2025. Hasil kajian menunjukkan bahwa technopreneurship berperan penting dalam meningkatkan kesiapan digital UMKM, mendorong inovasi model bisnis, serta meningkatkan efektivitas dan keandalan transaksi melalui QRIS. Namun demikian, tantangan berupa keterbatasan literasi digital, infrastruktur pendukung, dan integrasi QRIS dengan sistem keuangan UMKM masih menjadi hambatan utama. Temuan ini diharapkan dapat menjadi dasar teoritis bagi pengembangan UMKM berbasis technopreneurship dan rujukan bagi penelitian selanjutnya.Kata Kunci: Technopreunership; QRIS; UMKM; Adopsi Teknologi; Pembayaran Digital
Analisis Sentimen Isu Redominasi Rupiah Menggunakan Lexicon Based dan Naïve Bayes Muhammad Fadli; Ival Sanjaya; Muhammad Surono; Ryan Randy Suryono
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3436

Abstract

Rupiah redenomination has resurfaced as a strategic issue in Indonesian monetary policy following the announcement of the roadmap by Minister of Finance Purbaya Yudhi Sadewa in 2025. This study aims to analyze public sentiment towards the redenomination issue through a hybrid approach: lexicon based (LBA) based on InSet Lexicon enriched with 217 contextual phrases (“bunker money”, “DPR ketar-ketir”), combined with Naive Bayes based on TF-IDF representation. The primary dataset of 1,087 public comments from YouTube (Isu_Ekonomi_Redominasi.csv) was processed using Sastrawi (stopword removal and stemming). The results show a dominance of positive sentiment (58.3%), driven by the justice frame narrative (redenomination as a tool for exposing corrupt assets), while negative (24.1%) and neutral (17.6%) sentiment reflect concerns over inflation risks and transition confusion. The MNB model achieved an average accuracy of 86.3% in 10-fold cross-validation. The findings reveal that public support is not merely monetary, but rather an expression of collective aspirations for state transparency and accountability. This research demonstrates the effectiveness of a hybrid lexicon-enhanced approach for domain-specific sentiment analysis in Indonesian, while also providing evidence-based policy insights for inclusive policy design.Keywords: Public Sentiment; Twitter Media; Naive Bayes; VisualizationAbstrakRedenominasi rupiah kembali mencuat sebagai isu strategis dalam kebijakan moneter Indonesia pasca pengumuman roadmap oleh Menteri Keuangan Purbaya Yudhi Sadewa pada 2025. Penelitian ini bertujuan menganalisis sentimen masyarakat terhadap isu redenominasi melalui pendekatan lexicon based berbasis InSet Lexicon yang diperkaya dengan 217 frasa kontekstual (misal: “uang bunker”, “DPR ketar-ketir”), dikombinasikan dengan Naive Bayes berbasis representasi TF-IDF. Dataset primer berupa 1.087 komentar publik dari YouTube (Isu_Ekonomi_Redominasi.csv) diproses menggunakan Sastrawi (stopword removal dan stemming). Hasil menunjukkan dominasi sentimen positif (58,3%), didorong oleh narasi justice frame (redenominasi sebagai alat eksposur aset korupsi), sementara sentimen negatif (24,1%) dan netral (17,6%) mencerminkan kekhawatiran atas risiko inflasi dan kebingungan transisi. Model Naive Bayes mencapai akurasi rata-rata 86,3% dalam 10 fold cross validation. Temuan mengungkap bahwa dukungan publik tidak hanya bersifat teknis moneter, melainkan ekspresi aspirasi kolektif terhadap transparansi dan akuntabilitas negara. Penelitian ini membuktikan bahwa pendekatan lexicon efektif untuk analisis sentimen domain spesifik dalam Bahasa Indonesia, sekaligus memberikan policy insight berbasis bukti untuk desain kebijakan inklusif. 
Peran Machine Learning Dalam E-Commerce: Tinjauan Literatur Sistematis Terhadap Penerapan Dan Tantangan Sri Murdiawati; Amri Reza Wahyudin; Juan Adi Putra; Ryan Randy Suryono
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.448

Abstract

Abstrak: Perkembangan e-commerce menghasilkan banyak data yang besar dan rumit, sehingga membutuhkan teknologi canggih untuk membantu pengambilan keputusan dan meningkatkan pelayanan. Machine learning menjadi cara utama yang digunakan karena kemampuannya untuk mempelajari pola perilaku pengguna dan transaksi secara otomatis. Penelitian ini menganalisis penerapan machine learning dalam e-commerce dengan menggunakan metode Systematic Literature Review (SLR) terhadap 20 artikel jurnal dari dalam dan luar negeri. Kebaruan penelitian ini terletak pada sistesis yang menggabungkan berbagai aspek seperti bidang penerapan, metode algoritma, cara mengukur kinerja, tantangan teknis, serta dampak bisnis dalam satu kerangka analisis yang terstruktur. Hasil penelitian menunjukkan bahwa machine learning memiliki peran penting dalam sistem rekomendasi, analisis sentimen, mendeteksi penipuan, serta memprediksi penjualan, meskipun masih menghadapi tantangan seperti kualitas data, kebutuhan komputasi yang besar, dan kemampuan menjelaskan hasil.Kata Kunci: e-commerce; machine learning; systematic literature review; sistem rekomendasi.Abstract: The development of e-commerce has generated large amounts of complex data, requiring advanced technology to aid decision-making and improve services. Machine learning has become the primary method used due to its ability to automatically learn patterns of user behavior and transactions. This study analyzes the application of machine learning in e-commerce using the Systematic Literature Review (SLR) method on 20 journal articles from within and outside the country. The novelty of this research lies in its synthesis, which combines various aspects such as fields of application, algorithm methods, performance measurement methods, technical challenges, and business impacts into a single structured analytical framework. The results show that machine learning plays an important role in recommendation systems, sentiment analysis, fraud detection, and sales prediction, despite still facing challenges such as data quality, large computational requirements, and the ability to explain results.Keywords: e-commerce; machine learning; systematic literature review; recommendation system
Tourists’ Acceptance Analysis Of Tourism Village Website Towards The Motivation To Visit Cynthia Deborah Nababan; Dana Indra Sensuse; Ryan Randy Suryono; Kautsarina Kautsarina
Eduvest - Journal of Universal Studies Vol. 4 No. 7 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i7.1226

Abstract

The development of tourism village websites aims to promote the potential of tourism villages in Indonesia. However, the increasing use of social media has reduced the intensity of websites being used. This research aims to determine whether the tourism village websites still influence tourists to visit the tourism village and whether it is relevant to the government's goals. Data was collected by distributing questionnaires and producing a valid sample of 242 respondents who actively use the Internet and have the potential to travel. The research model design was created by combining the DeLone & McLean IS success model with the Technology Acceptance Model (TAM). Data were analysed using the Structural Equation Modeling Partial Least Square (SEM PLS). The results show that the variables of information quality, service, and design positively affect trust, usability, ease of use, and enjoyment, positively affecting the intention to use the website. However, trust does not significantly influence the intention to use the website on the acceptance model. The frequent use of the tourism village website positively affects the tourist's motivation for the actual visit. Therefore, deeper analysis is needed to determine the variables that affect tourist confidence in developing acceptance models for further research. In addition, this research has practical implications for the government in making decisions regarding developing tourism village websites in terms of interface, user experience, information, and features.
PERBANDINGAN KINERJA MODEL SUPPORT VECTOR MACHINE DAN NAÏVE BAYES UNTUK ANALISIS SENTIMEN SUPER APP POLRI Bagastian; Ryan Randy Suryono; Amarudin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7582

Abstract

Digital transformation of public services has driven the Indonesian National Police to develop the Polri Super App, yet faces user acceptance challenges reflected in diverse reviews. This study aims to compare the performance of Support Vector Machine (SVM) and Naïve Bayes algorithms in classifying user sentiment of the Polri Super App. The research utilized 3,997 reviews from Apple Store undergoing comprehensive preprocessing including normalization, tokenization, stopword removal, and Sastrawi stemming. Sentiment labeling employed InSet Lexicon, yielding 55.0% positive and 45.0% negative reviews. Feature extraction used TF-IDF method with 80:20 data split for training and testing. Evaluation results demonstrate SVM significantly outperforms Naïve Bayes with 91.5% versus 79.0% accuracy (12.5 percentage points difference). SVM maintains balanced F1-scores of 90.6% (negative) and 92.2% (positive), while Naïve Bayes exhibits imbalance with 87.2% recall (negative) but only 72.3% (positive). SVM's superiority stems from hyperplane optimization capability in handling high-dimensional text data without rigid feature independence assumptions. The study recommends SVM implementation for police digital service sentiment monitoring systems and exploration of ensemble algorithms and deep learning for future research.
Analisis Sentimen Pengguna Aplikasi Jamsostek Mobile Berdasarkan Ulasan Google Play Store Menggunakan Algoritma Support Vector Machine dan Naive Bayes: Sentiment Analysis of Jamsostek Mobile Application Reviews on Google Play Store Using Support Vector Machine and Naive Bayes Algorithms Tria Setyani; Kevinda Sari; Helma Nopijani Heidy; Ryan Randy Suryono
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2526

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Jamsostek Mobile (JMO) yang tersedia pada Google Play Store menggunakan algoritma Support Vector Machine (SVM) dan Naive Bayes. Data yang digunakan sebanyak 6.000 ulasan pengguna yang dikumpulkan melalui teknik web scraping. Tahapan penelitian meliputi preprocessing teks (cleaning, case folding, normalisasi, tokenizing, stopword removal, dan stemming), pembobotan fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF), pelabelan data dengan metode lexicon-based, serta klasifikasi sentimen ke dalam tiga kelas, yaitu positif, negatif, dan netral. Evaluasi performa model dilakukan menggunakan confusion matrix dan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma SVM menghasilkan performa yang lebih unggul dengan nilai akurasi sebesar 88,9%, sedangkan Naive Bayes memperoleh akurasi sebesar 64,1%. SVM juga menunjukkan nilai F1-score yang lebih konsisten pada seluruh kelas sentimen dibandingkan Naive Bayes. Dengan demikian, algoritma SVM terbukti lebih efektif dan andal dalam mengklasifikasikan sentimen ulasan pengguna aplikasi JMO.
Analisis Komparatif Sentimen Publik terhadap Liputan Media Terkait Aksi Menteri Keuangan Menggunakan Algoritma SVM dan RoBERTa Budi Santosa; Kardita Magda; Ega Budiman; Ryan Randy Suryono
SENTRI: Jurnal Riset Ilmiah Vol. 5 No. 2 (2026): SENTRI : Jurnal Riset Ilmiah, Februari 2026
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v5i2.5858

Abstract

Public opinion on social media is a crucial representation of government policy legitimacy, especially in the fiscal sector. This study intends to provide a comparative investigation of the efficacy of sentiment categorization on YouTube comments pertaining to the activities of the Indonesian Finance Minister by juxtaposing the Support Vector Machine (SVM) algorithm with the RoBERTa Transformer model. A total of 3,780 comments were acquired from national digital media channels. The research method involves intensive text preprocessing, including stemming using the Sastrawi algorithm and lexicon-based labeling. The results showed that the SVM algorithm with TF-IDF features achieved an accuracy of 83.33% and an F1-score of 76.05%. In contrast, the RoBERTa model showed a significantly lower performance with an accuracy of 29.76%. This study concludes that for datasets dominated by neutral sentiments and informal language in specific Indonesian contexts, traditional machine learning like SVM with optimal feature engineering remains more reliable and efficient than complex Transformer models that require more extensive fine-tuning.
Optimizing Employee Admission Selection Using G2M Weighting and MOORA Method Yuri Rahmanto; Junhai Wang; Setiawansyah Setiawansyah; Aditia Yudhistira; Dedi Darwis; Ryan Randy Suryono
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i1.8224

Abstract

An objective and effective employee admission selection process is a crucial step for the success of the organization in achieving its goals. Problems in employee recruitment selection often arise due to a lack of good planning and system implementation, namely decisions are often influenced by personal preferences, stereotypes, or non-relevant factors, thus reducing objectivity in choosing the best candidates. Objective selection ensures that candidate assessments are conducted based on measurable, relevant, and bias-free criteria, so that only individuals who truly meet the company's needs and standards are accepted. The purpose of developing an optimal approach in employee admission selection using G2M weighting and MOORA is to create a more objective, efficient, and accurate selection process. This approach aims to integrate the calculation of criterion weights mathematically, such as those offered by G2M, in order to eliminate subjective bias in determining criterion prioritization. The MOORA method of evaluating alternative candidates is carried out through ratio analysis that takes into account various criteria simultaneously, resulting in a transparent and data-driven ranking. The results of the employee admission selection ranking based on the criteria that have been evaluated, Candidate 3 obtained the highest score of 0.4177, indicating that this candidate best meets the expected criteria. The second position was occupied by Candidate 6 with a score of 0.3886, followed by Candidate 9 with a score of 0.3528. This research contributes to the recruitment process, by providing a more reliable, transparent, and less subjective way of selecting the right candidates for the positions that companies need.
Modification of Additive Ratio Assessment Method through Distance-Based Weighting Approach for Optimizing Assessment Accuracy Rakhmat Dedi Gunawan; Muhammad Waqas Arshad; Agung Deni Wahyudi; Ryan Randy Suryono; Tri Widodo; Faruk Ulum
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 2 (2025): September 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i2.8810

Abstract

The Additive Ratio Assessment (ARAS) method is one of the approaches in multi-criteria decision making (MCDM) used to determine the best alternative based on a number of predetermined criteria. The drawback of this method is its heavy reliance on the accuracy of the criterion weighting determination; non-objective weights can lead to biased results. This study aims to improve the accuracy of ranking in multicriteria decision-making through the modification of the ARAS method with a distance-based weighting approach called ARAS-D. The ARAS method, known for its simplicity in calculation, was modified to be more responsive to the distribution of alternative data on each criterion. This distance-based weighting approach objectively determines the weight of the criteria based on variations in data performance, thereby reducing subjectivity in the weighting process. A case study was conducted on the selection of a new store location with six main criteria: rental cost, building area, accessibility, consumer traffic, parking availability, and infrastructure. The results of the evaluation show that the ARAS-D method is able to produce more precise ratings than the standard approach. Store locations with the highest utility value are recommended as the best choice, proving the effectiveness of the method in supporting strategic decisions. The results of the New Store Location 5 alternative rating obtained the highest score with a value of 0.9083, indicating that this location is the most optimal choice overall. This is followed by New Store Location 3 with a value of 0.8617 and New Store Location 1 with a value of 0.8415, which also shows excellent performance against the criteria that have been set. This research contributes to the development of more adaptive and data-based decision-making methods.
Transforming the Data Ecosystem through Machine Learning and Artificial Intelligence: A Systematic Review of Innovative Big Data Frameworks Bagastian Bagastian; Dimas Eko Putro; Muhammad Fahmi Fudholi; Ryan Randy Suryono
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1437

Abstract

The digital revolution era has created fundamental transformation in data management and utilization, where machine learning and artificial intelligence integration becomes the primary catalyst in optimizing contemporary data ecosystems. Global data volume predicted to reach 181 zettabytes by 2025 demands innovative approaches in big data management, yet 80% of organizations still experience difficulties integrating AI technology with their existing data infrastructure. This research aims to identify and analyze characteristics of innovative frameworks that integrate machine learning and artificial intelligence in data ecosystem transformation, and formulate comprehensive framework recommendations for the future. The research method employs a qualitative approach with Systematic Literature Review (SLR) on 2021-2022 publications via Google Scholar, with thematic analysis using Critical Appraisal Skills Program (CASP) checklist. Research results identify eight major innovative frameworks including AI for Smart Society 5.0, Big Data-AI-IoT Integration, to Digital Responsibility Accounting, with main characteristics of process automation capabilities, service personalization, edge computing for real-time decision making, and blockchain implementation for data security. Implementation challenges include digital infrastructure limitations, human resource skill gaps, data security, and organizational resistance. Transformation impact proves significant in education, governance, and business intelligence sectors. The conclusion shows that comprehensive future frameworks must be adaptive, ethical, and sustainable by integrating technology, human, and environmental dimensions in a balanced manner. A phased implementation approach is recommended with priority on strengthening digital infrastructure and developing human resource competencies through cross-sector collaboration.
Co-Authors ., Bagastian 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 Dana Indra Sensuse Darmini Darmini DAVID KURNIAWAN Dede Krisna Friansyah Dedi Darwis Desi Fitria 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 Fadli, Muhammad Feri Cahya Setiawan Firdaus, Noval Dinda Firmanda, Fabian Fudholi, Muhammad Fahmi Gunawan, Rakhmat Dedi Handini, Meitry Ayu Hasiholan Simamora, Alfred Helma Nopijani Heidy Heni Sulistiani Hermana, BP Putra Ignatius Adrian Mastan Indra Budi Isnain, Auliya Rahman Ival Sanjaya Iwan Purwanto Iwan Purwanto Jelna Anggreni Juan Adi Putra Juarsa, Doris Junhai Wang 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 Fahmi Fudholi Muhammad Ridwan Muhammad Sahyudi Muhammad Surono Muhammad Waqas Arshad 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 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