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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Journal of Information Systems Engineering and Business Intelligence Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) YUME : Journal of Management TEKNOLOGIA TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Data Science and Its Applications Jurnal Sistem Komputer dan Informatika (JSON) Journal of Applied Data Sciences Mosharafa: Jurnal Pendidikan Matematika Indonesian Journal of Social Science Research Open Science and Technology eProceedings of Engineering Innovative: Journal Of Social Science Research AMMA : Jurnal Pengabdian Masyarakat Jurnal INFOTEL
Adiwijaya
Universitas Telkom
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Study on agronomical characteristics of several introduced cucumber (Cucumis sativus L.) genotypes Sumiahadi, Ade; Adiwijaya, Adiwijaya
Open Science and Technology Vol. 3 No. 2 (2023): Open Science and Technology
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/ost.v3i2.104

Abstract

Cucumber is one of the fruit vegetables favored by the Indonesian people. Its development prospect of commercial cultivation and agribusiness management has been very promising, because the marketing opportunities are not only available domestically, but also abroad. Plant introduction is a process of introducing plants from their place of origin into a new region. This study aims to examine the agronomic characteristics of several introduced cucumber genotypes, and was conducted from October to December 2022 at the experimental field of the Faculty of Agriculture, Jakarta Muhammadiyah University. Randomized Complete Group Design (RCGD) was used with five cucumber genotypes: three introduced genotypes (MIT001, MIT002, and MIT003) and two comparator domestic varieties (Ronaldo and Mercury). The results show that the introduced genotypes produced similar vegetative growth characters to those of their comparator varieties. However, several yield components produced are lower than those of their comparator varieties, as seen from several significantly different parameters. MIT001 and MIT003 produced shorter fruit length than that of their comparator variety (Ronaldo), while MIT002 produced lower plant dry weight and fruit weight per plant than those of its comparator variety (Mercury). MIT003 was able to produce the yield components closest to those of its comparator variety. Mentimun merupakan salah satu sayuran buah yang banyak digemari oleh masyarakat Indonesia. Prospek pengembangan budidaya mentimun secara komersial dan pengelolaannya dalam skala agribisnis semakin cerah, karena peluang pemasaran tidak hanya dilakukan di dalam negeri, tetapi juga mancanegara. Introduksi tanaman merupakan suatu proses memperkenalkan tanaman dari tempat asal tumbuhnya ke suatu daerah (negara) baru. Penelitian ini bertujuan mempelajari karakteristik agronomis beberapa genotipe mentimun hasil introduksi. Penelitian ini dilaksanakan dari bulan Oktober sampai Desember 2022 di lahan percobaan Fakultas Pertanian Universitas Muhammadiyah Jakarta. Rancangan penelitian yang digunakan yaitu Rancangan Kelompok Lengkap Teracak (RKLT) dengan lima taraf genotipe mentimun, yaitu tiga genotipe introduksi (MIT001, MIT002, dan MIT003) dan dua varietas nasional sebagai pembanding (Ronaldo dan Merkuri). Hasil penelitian menunjukkan bahwa setiap genotipe introduksi yang diujikan secara umum memiliki karakter pertumbuhan vegetatif yang sama dengan varietas pembandingnya, namun memiliki beberapa karakter komponen produksi yang lebih rendah dari varietas nasional pembandingnya. Genotipe introduksi MIT001 dan MIT003 memiliki panjang buah yang lebih pendek dari varietas nasional pembandingnya (Ronaldo), sedangkan MIT002 menghasilkan bobot kering tanaman dan bobot buah per tanaman yang lebih rendah dari varietas nasional pembandingnya (Merkuri). Genotipe MIT003 adalah genotipe introduksi yang mampu menghasilkan produksi yang paling mendekati varietas pembandingnya.
SCL LEAD to Improve quality of Student-Centered Learning Process in the Class of Discrete Mathematics Adiwijaya, Adiwijaya; Palupi, Irma
Mosharafa: Jurnal Pendidikan Matematika Vol. 13 No. 2 (2024): April
Publisher : Department of Mathematics Education Program IPI Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31980/mosharafa.v13i2.1947

Abstract

Semakin banyak mahasiswa yang kurang tertarik pada beberapa mata kuliah di universitas, terutama matematika. Studi ini meneliti dampak pendekatan Student-Centered Learning (SCL) dalam pembelajaran Matematika Diskrit. Kami memperkenalkan metode SCL - Lecture’s Encouragement, Assistance, and Stimulating-Deliverance (SCL LEAD) untuk meningkatkan keterampilan belajar dengan mendorong interaksi dan kerja sama mahasiswa. Penelitian ini menggunakan pendekatan kuantitatif melalui pre-test dan post-test untuk mengukur keterlibatan dan kinerja mahasiswa. Data dikumpulkan melalui observasi terstruktur, jaminan kesiapan, dan diskusi kelompok, kemudian dianalisis untuk menilai dampaknya terhadap keterampilan kognitif dan kolaboratif mahasiswa. Perbandingan antara ujian tengah dan akhir semester digunakan untuk mengukur efektivitas SCL LEAD, dengan statistik deskriptif dan inferensial untuk mengidentifikasi perubahan keterampilan dan pencapaian. Temuan menunjukkan bahwa SCL LEAD memotivasi mahasiswa untuk berpartisipasi aktif serta meningkatkan kompetensi dan kinerja dalam Matematika Diskrit, memberi wawasan berharga bagi pengembangan strategi pembelajaran berpusat pada mahasiswa di pendidikan matematika. The increasing disengagement of students in certain university courses, particularly in mathematics, is a growing concern. This study investigates the impact of the Student-Centered Learning (SCL) approach on the learning process for Discrete Mathematics. We introduce the Student-Centered Learning - Lecture’s Encouragement, Assistance, and Stimulating-Deliverance (SCL LEAD) method to enhance learning skills by fostering increased student interaction and cooperation. The study employs a quantitative approach, using pre-tests and post-tests to measure students’ engagement and performance. Data were gathered through structured observations, readiness assurance processes, and group discussions, all of which were documented and analyzed to assess their impact on students’ cognitive and collaborative skills. Comparative metrics between mid-term and final exams were used to determine the effectiveness of the SCL LEAD model, with descriptive and inferential statistics applied to identify changes in students' skills and achievements following the implementation of SCL LEAD. The findings suggest that SCL LEAD motivates active participation and enhances both competence and performance in Discrete Mathematics, offering valuable insights for advancing student-centered strategies in mathematics education.
Klasifikasi Teks Hadis Bukhari Terjemahan Indonesia Menggunakan Recurrent Convolutional Neural Network (CRNN) Abu Bakar, Muhammad Yuslan; Adiwijaya, Adiwijaya
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021853750

Abstract

Hadis merupakan sumber hukum dan pedoman kedua bagi umat Islam setelah Al-Qur’an dan banyak sekali hadis yang telah diriwayatkan oleh para ahli hadis selama ini. Penelitian ini membangun sebuah sistem yang dapat melakukan klasifikasi teks hadis Bukhari terjemahan berbahasa Indonesia. Topik ini diangkat untuk memenuhi kebutuhan umat Islam dalam mengetahui apa saja informasi mengenai anjuran dan larangan yang terdapat dalam suatu hadis. Klasifikasi teks memiliki tantangannya tersendiri terkait dengan jumlah fitur yang sangat banyak (dimensi sangat besar) sehingga waktu komputasi menjadi besar dan mengakibatkan sulitnya mendapatkan hasil yang optimal. Pada penelitian ini, digunakan salah satu metode hibrid dalam dunia deep learning dengan menggabungkan Convolutional Neural Network dan Recurrent Neural Network, yaitu Convolutional Recurrent Neural Network (CRNN). Convolutional Neural Network dipilih sebagai metode seleksi dan reduksi data dikarenakan dapat menangkap informasi spasial yang saling berhubungan dan berkorelasi. Sementara Recurrent Neural Network digunakan sebagai metode klasifikasi dengan mengusung kemampuan utamanya yaitu dapat menangkap informasi kontekstual yang sangat panjang khususnya pada data sekuens seperti data teks dengan mengandalkan ‘memori’ yang dimilikinya. Hasil penelitian menyajikan beberapa hasil klasifikasi menggunakan deep learning, dimana hasil akurasi terbaik diberikan oleh Convolutional Recurrent Neural Network (CRNN), yakni sebesar 80.79%. Abstract Hadith is a source of law and guidance for Muslims after the Qur'an and many hadith have been narrated by hadith experts so far. This research builds a system that can classify Bukhari hadith in Indonesian translations. This topic was raised to meet the needs of Muslims in knowing what information about the suggestions and prohibitions that exist in a hadith. Text classification has its own challenges related to several features whose dimensions are very large so that it increases computing time and causes difficulties in getting optimal results. This research uses a hybrid method in deep learning by combining a Convolutional Neural Network and a Recurrent Neural Network, namely Convolutional Recurrent Neural Network (CRNN). Convolutional Neural Network was chosen as a method of selecting and reducing data that can be determined as spatial information that is interrelated and correlated. While Recurrent Neural Networks are used as a classification method by carrying out capabilities that can be used as very long contextual information specifically on sequential data such as text data by relying on the ‘memory’ it has. This research presents several classification results using deep learning, where the best accuracy results are given by the Convolutional Recurrent Neural Network (CRNN), which is equal to 80.79%.
Implementation of Naïve Bayes and Gini Index for Spam Email Classification Imadudin, Fikri Rozan; Murdiansyah, Danang Triantoro; Adiwijaya
Indonesian Journal on Computing (Indo-JC) Vol. 6 No. 1 (2021): April, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.1.452

Abstract

Email is a medium of information that is still frequently used by people today. At the moment email still has an endless problem that is spam email. Spam email is an email that can pollute, damage or disturb the recipient. In this study, we show the performance and accuracy of Multinomial Naïve Bayes (MNNB) and Complete Gini-Index Text (GIT) for use in spam email filtering. In this study, we used 6 cross-validations as testers for the built classification machines. We found that the average yield can exceed Multinomial Naïve Bayes without using feature selection which only uses 80000 features with a difference of 0.39%. Feature selection also increases speed during classification and can reduce features that are less relevant to the category to be classified.
Hybrid Multi-Objective Metaheuristic Machine Learning for Optimizing Pandemic Growth Prediction Adiwijaya, Adiwijaya; Pane, Syafrial Fachri; Sulistiyo, Mahmud Dwi; Gozali, Alfian Akbar
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.981

Abstract

Pandemic and epidemic events underscore the challenges of balancing health protection, economic resilience, and mobility sustainability. Addressing these multidimensional trade-offs requires adaptive and data-driven decision-support tools. This study proposes a hybrid framework that integrates machine learning with multi-objective optimization to support evidence-based policymaking in outbreak scenarios. Six key indicators—confirmed cases, disease-related mortality, recovery count, exchange rate, stock index, and workplace mobility—were predicted using eight regression models. Among these, the XGBoost Regressor consistently achieved the highest predictive accuracy, outperforming other approaches in capturing complex temporal and socioeconomic dynamics. To enhance interpretability, we developed SHAPPI, a novel method that combines Shapley Additive Explanations (SHAP) with Permutation Importance (PI). SHAPPI generates stable and meaningful feature rankings, with immunization coverage and transit station activity identified as the most influential factors in all domains. These importance scores were subsequently embedded into the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to construct Pareto-optimal solutions. The optimization results demonstrate transparent trade-offs among health outcomes, economic fluctuations, and mobility changes, allowing policymakers to systematically evaluate competing priorities and design balanced intervention strategies. The findings confirm that the proposed framework successfully balances predictive performance, interpretability, and optimization, while providing a practical decision-support tool for epidemic management. Its generalizable design allows adaptation to diverse geographic and epidemiological contexts. In general, this research highlights the potential of hybrid machine learning and metaheuristic approaches to improve preparedness and policymaking in future health and socioeconomic crises.
Pengaruh Kompetensi, keterlibatan Kerja dan Disiplin kerja terhadap Kinerja Guru SMK Negeri 1 Bantaeng Kabupaten Bantaeng Adiwijaya, Adiwijaya; K, Kasnaeny; Sukmawati, St.
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i6.16465

Abstract

Abstract This research aims to investigate and analyze the influence of competence, work engagement, and work discipline on the performance of teachers at SMK Negeri 1 Bantaeng, Bantaeng Regency. The target population for this study comprises individuals related to teacher performance, totaling 83 respondents, all of whom were included in the analysis using multiple linear regression. The results of this study indicate that all variables competence, work engagement, and work discipline have a positive and significant simultaneous effect on the performance of teachers at SMK Negeri 1 Bantaeng, Bantaeng Regency. Furthermore, it was found that the work engagement factor has the most dominant significant influence on the performance of teachers at SMK Negeri 1 Bantaeng, Bantaeng Regency. Keywords: Competence, Work Engagement, Discipline, Teacher Performance
The Role of Transformational Leadership in Managing Human Resources for Organizational Innovation Case Study in A State Electricity Company Amir; Adiwijaya; Firly Juanita Surahman; Syam, Mukhlisah
Indonesian Journal of Social Science Research Vol. 5 No. 1 (2024): Indonesian Journal of Social Science Research (IJSSR)
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/ijssr.05.01.31

Abstract

Transformational leaders provide autonomy and responsibility to employees, allowing them to take initiative and contribute actively to organizational goals. The aim of this research is to understand the role of transformational leadership in influencing human resource management strategies to support innovation within the State Electricity Company. The research method used in this study is Systematic Literature Review (SLR). The results of this study indicate that transformational leadership is not just about managing organizations but also about building a culture that continuously seeks ways to improve and innovate, ensuring that the State Electricity Company remains competitive and relevant in facing the challenges of the future electricity industry.
PSO-Enhanced ensemble techniques for pandemic prediction and feature importance analysis Pane, Syafrial Fachri; Sulistiyo, Mahmud Dwi; Gozali, Alfian Akbar; Adiwijaya, Adiwijaya
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.2091

Abstract

During the pandemic crisis that hit after 2020, Indonesia, like many other countries, faced tremendous challenges in areas such as health, economy, and mobility. An in-depth understanding of the dynamics and changes in these areas is essential to address the impacts of the pandemic. This research is an attempt to deeply analyze the impact of the pandemic and the most effective forecasting methods based on data and phenomena. Indonesia, with its growing economy and constantly adapting health system, faces conventional economic impacts, while its health system response tries to keep up with urgent needs driven by the spread of the virus. In the context of mobility, changes in how people move and interact significantly affect virus transmission. Modeling a pandemic event with all its complexities is not an easy task. Even more so, in finding the right method for prediction, ensemble techniques such as stacking and regression voting are emerging as promising approaches. However, deep learning and particle swarm optimization (PSO) techniques offer new innovations. The results of this study show that the ensemble vote provides the best performance in predicting confirmed positive cases and mortality based on factors of health, economic and population mobility in Indonesia. Through feature importance analysis using MDI and Tree SHAP, we conclude that factors such as active cases, the number of vaccinations, and economic indicators, such as close IDR and close IHSG, have a significant influence on the growth of confirmed positive cases. Meanwhile, recovery factors and vaccination number play an important role in the growth of the number of death cases. This study confirms that a multivariate approach that considers health, economy and mobility is the key to understanding and responding more effectively to the pandemic in Indonesia.
Assessing Large Language Models for Zero-Shot Dynamic Question Generation and Automated Leadership Competency Assessment Gheartha, I Gusti Bagus Yogiswara; Adiwijaya, Adiwijaya; Romadhony, Ade; Ardiansyah, Yusfi
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.970

Abstract

Automated interview systems powered by artificial intelligence often rely on fine-tuned models and annotated datasets, limiting their adaptability to new leadership competency frameworks. Large language models have shown potential for generating questions and assessing answers, yet their zero-shot performance, operating without task-specific retraining remains underexplored in leadership assessment. This study examines the zero-shot capability of two models, Qwen 32B and GPT-4o-mini, within a multi-turn self-interview framework. Both models dynamically generated questions, interpreted responses, and assigned scores across ten leadership competencies. Professionals representing the role of Digital Marketing and Account Manager participated, each completing two AI-led interview sessions. Model outputs were evaluated by certified experts using a structured rubric across three dimensions: quality of behavioral insights, relevance of follow-up questions, and fit of assigned scores. Results indicate that Qwen 32B generated richer insights than GPT-4o-mini (mean = 2.86 vs. 2.62; p less than 0.01) and provided more differentiated assessments across competencies. GPT-4o-mini produced more consistent follow-up questions but lacked depth in interpretation, often yielding generic outputs. Both models struggled with accurate scoring of candidate responses, reflected in low answer score ratings (Qwen mean = 2.35; GPT mean = 2.21). These findings suggest a trade-off between insight richness and scoring stability, with both models demonstrating limited ability to fully capture nuanced leadership behaviors. This study offers one of the first empirical benchmarks of zero-shot model performance in leadership interviews. It underscores both the promise and current limitations of deploying such systems for scalable assessment. Future research should explore competency-specific prompt strategies, fairness evaluation across demographic groups, and domain-adapted fine-tuning to improve accuracy, reliability, and ethical alignment in high-stakes recruitment contexts.
Benchmarking Transformer Architectures for Chest X-ray Classification Pinem, Joshua; Astuti, Widi; Adiwijaya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7132

Abstract

Lung diseases remain a major global health concern, necessitating accurate and timely diagnosis. Chest X-ray (CXR) imaging is widely used but challenging to interpret due to overlapping radiographic features and subjective variability among radiologists. Deep learning approaches, particularly Convolutional Neural Networks (CNNs), have shown promise but are limited in capturing global spatial dependencies. Vision Transformers (ViTs) overcome this limitation through self-attention, making them increasingly attractive for medical image analysis. This study systematically evaluates 13 Transformer-based architectures across three CXR datasets with distinct tasks: Pneumonia (3-class: Normal, Bacterial, Viral), COVID-QU-Ex (3-class: Normal, Non-COVID Pneumonia, COVID-19), and Tuberculosis (2-class: Normal, Tuberculosis). All models were trained under a unified setup with consistent preprocessing, augmentation, and evaluation protocols. To improve robustness, a soft voting ensemble of the top five models was also implemented. Results demonstrate that Transformer-based models provide highly competitive performance. On the Pneumonia dataset, the ensemble achieved an accuracy of 0.8743 and F1-score of 0.8615, surpassing several single models such as DeiT-Base (F1 = 0.8725). On COVID-QU-Ex, the ensemble soft voting obtained 0.9593 accuracy and 0.9582 F1-score, effectively balancing precision and recall. On Tuberculosis, ViT-B/16 and MobileViT-S achieved perfect performance (F1 = 1.0), likely influenced by dataset imbalance. These findings highlight the clinical potential of Transformer-based models, particularly when combined through ensembles, for robust and accurate CXR classification.
Co-Authors A Rakha Ahmad Taufiq Abu Bakar, Muhammad Yuslan Ade Iriani Sapitri Ade Romadhony Ade Sumiahadi, Ade Adhitia Wiraguna Adhitia Wiraguna Aditya Arya Mahesa Adnan Imam Hidayat Adwin Rahmanto Afrian Hanafi Al Faraby, Said Al Mira Khonsa Izzaty Alfian Akbar Gozali Alvi Syah Amalya Citra Pradana Amir Andi Ahmad Irfa ANDI FUTRI HAFSAH MUNZIR Andina Kusumaningrum Andri Saputra Andrian Fakhri Andriyan B Suksmono Anggitha Yohana Clara Aniq Atiqi Aniq Atiqi Rohmawati Anisa Salama Annas Wahyu Ramadhan Annisa Adistania Annisa Aditsania Antika Putri Permata Wardani Aras Teguh Prakasa Ardiansyah, Yusfi Astrid Frillya Septiany Astrima Manik Aziz, Muhammad Maulidan Azmi Hafizha Rahman Zainal Arifin Bambang Riyanto T. Bayu Julianto Bayu Munajat Bayu Munajat Bayu Rahmat Setiaji Bernadus Seno Aji Bernadus Seno Aji Bintang Peryoga Bisma Pradana Brama Hendra Mahendra Chiara Janetra Cakravania Clarisa Hasya Yutika D. R. Suryandari Dana Sulistiyo Kusumo Danang Triantoro Danang Triantoro Murdiansyah Daniel Tanta Christopher Sirait Dany Dwi Prayoga Dany Dwi Prayoga Della Alfarydy Akbar Deni Saepudin Denny Alriza Pratama Desi Sitompul Dewangga, Dhiya Ulhaq Dian Chusnul Hidayati Didi Rosiyadi Didit Adytia Dinda Karlia Destiani Dody Qori Utama Dody Qory Utama Dwi Yanita Apriliyana Dwi Yanita Apriliyana Dwifebri, Mahendra Eko Darwiyanto Eliza Jasin Elza Oktaviana Elza Oktaviana Endro Ariyanto Ergon Rizky Perdana Purba F. A. Yulianto Fachri Pane, Syafrial Fahmi Salman Nurfikri Faris Alfa Mauludy Faris Alfa Mauludy Farudi Erwanda Farudi Erwanda Fathur Rohman Fathurrohman Elkusnandi Fhira Nhita Fikri Rozan Imadudin Firda A. Ma’ruf Firdausi Nuzula Zamzami Firly Juanita Surahman Fuad Ash Shiddiq Gde Agung Brahmana Suryanegara Gheartha, I Gusti Bagus Yogiswara Ghozy Ghulamul Afif Gia Septiana Gia Septiana Gia Septiana Gilang Rachman Perdana Gilang Rachman Perdana Gilang Titah Ramadhani Grace Tika Guntoro Guntoro Guntoro Guntoro Guntoro Guntoro Hadyan Arif Hafidudin . Hafizh Fauzan Hafizh Fauzan Hendro Prasetyo Henri Tantyoko Honakan Honakan I Kadek Haddy W. I Made Riartha Prawira I.G.N.P.Vasu Geramona Ilham Kurnia Syuriadi Ilham Yunirakhman Imadudin, Fikri Rozan Imam Prayoga Indriani Indriani Irene Yulietha Irma Irma Irma Palupi Irwinda Famesa Iyon Priyono Jendral Muhamad Yusuf Zia Ul Haq Jenepte Wisudawati Simanullang K, Kasnaeny Kamal Hasan Mahmud Kemas Muslim Lhaksmana Kemas Rahmat Saleh Raharja Kemas Rahmat Saleh Wiharja Kurnia C Widiastuti Kurniawan W. Handito Laila Putri Lalu Gias Irham Lisa Marianah Lisa Marianah Luke Manuel Daely Mahendra Dwifebri P Mahendra Dwifebri Purbolaksono Mahmud Dwi Sulistiyo Melanida Tagari Melanida Tagari Michael Sianturi Milah Sarmilah Moc. Arif Bijaksana Mochamad Agusta Naofal Hakim Mochammad Naufal Rizaldi Mohamad Irwan Afandi Mohamad Mubarok Mohamad Syahrul Mubarok Mohamad Syahrul Mubarok Mohammad Syahrul Mubarok Monica Triyani Muhammad Afianto Muhammad Enzi Muzakki Muhammad Fauzan Muhammad Feridiansyah Muhammad Ghufran Muhammad Irvan Tantowi Muhammad Kenzi Muhammad Mubarok Muhammad Mujaddid Muhammad Naufal Mukhbit Amrullah Muhammad Nurjaman Muhammad Shiddiq Azis Muhammad Shiddiq Azis Muhammad Surya Asriadie Muhammad Syahrul Mubarok Muhammad Yuslan Abu Bakar Nanda Prayuga Nida Mujahidah Azzahra Nida Mujahidah Azzahra Niken Dwi Wahyu Cahyani Novelty Octaviani Faomasi Daeli Novia Russelia Wassi Nuklianggraita, Tita Nurul Nur Ghaniaviyanto Ramadhan Oscar Ramadhan Pinem, Joshua Pratama Dwi Nugraha Preddy Desmon Purbalaksono, Mahendra Dwifebri Putri, Dinda Rahma Putri, Dita Julaika Raihana Salsabila Darma Wijaya Rendi Kustiawan Reynaldi Ananda Pane Riche Julianti Wibowo Riko Bintang Purnomoputra Riska Chairunisa Rizki Syafaat Amardita Rizky Pujianto Rizma Nurviarelda Roberd Saragih Rosyadi, Ramadhana Said Faraby Satria Mandala Sekar Kinasih Semeidi Husrin Sheila Annisa Shidqi Aqil Naufal Shuni’atul Ma’wa Sigit Bagus Setiawan St.Sukmawati S. Sugeng Hadi Wirasna Suriyanti Suriyanti Syafrial Fachri Pane, Syafrial Fachri Syahrizal Rizkiana Rusamsi Syam, Mukhlisah Syifa Khairunnisa Talitha Kayla Amory Tati LR Mengko Tesha Tasmalaila Hanif Timami Hertza Putrisanni Tita Nurul Nuklianggraita Triyani, Monica Try Moloharto Untari Novia Wisesty Untari Wisesty Untari. N. Wisesty Untary Novia Wisesty Vina Mutiara Purnama Warih Maharani Widi Astuti Widi Astuti Widi Astuti Winda Christina Widyaningtyas Wisnu Adhi Pradana Yana Meinitra Wati Yoga Widi Pamungkas Yuliant Sibaroni Zahra Putri Agusta Zakia Firdha Razak Zulfikar Fauzi