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Aji Prasetya Wibawa

Univeristas Negeri Malang

Author-ID : 2489010

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Indonesian English learning preferences Lisa Ramadhani Harianti; Aji Prasetya Wibawa; Bety Masruroh; Vira Setia Ningrum; Eka Nurcahya Ningsih
International Journal of Education and Learning Vol 6, No 2 (2024): August
Publisher : Association for Scientific Computing Electrical and Engineering(ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijele.v6i2.1352

This study examines the language learning preferences of Indonesians by comparing search trends for popular formal English courses and English learning applications using Google Trends data. The data is visualized and analyzed descriptively, revealing Ruang Guru as the most preferred English language institution in Indonesia. Its popularity is attributed to service promotions, user-friendly websites, and efficient marketing techniques. Simultaneously, Grammarly is observed as the most popular English learning application among Indonesian learners, emphasizing the importance of refining writing skills. A comparison of search results between Grammarly and Ruang Guru highlights Grammarly's dominance and the preference for self-directed and flexible learning options among Indonesians. These findings have significant implications for education policymakers, language application developers, and English course institution managers. The study suggests a need for the formulation of courses that align with the preferences of Indonesian learners, facilitating autonomous and flexible learning experiences. This insight can guide strategic decisions to enhance the effectiveness and relevance of English language education in Indonesia.

An enhanced pivot-based neural machine translation for low-resource languages Sulistyo, Danang Arbian; Wibawa, Aji Prasetya; Prasetya, Didik Dwi; Ahda, Fadhli Almuíini
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.2115

This study examines the efficacy of employing Indonesian as an intermediary language to improve the quality of translations from Javanese to Madurese through a pivot-based approach utilizing neural machine translation (NMT). The principal objective of this research is to enhance translation precision and uniformity among these low-resource languages, hence advancing machine translation models for underrepresented languages. The data collecting approach entailed extracting parallel texts from internet sources, followed by pre-processing through tokenization, normalization, and stop-word elimination algorithms. The prepared datasets were utilized to train and assess the NMT models. An intermediary phase utilizing Indonesian is implemented in the translation process to enhance the accuracy and consistency of translations between Javanese and Madurese. Parallel text corpora were created by collecting and preprocessing data, thereafter, utilized to train and assess the NMT models. The pivot-based strategy regularly surpassed direct translation regarding BLEU scores for all n-grams (BLEU-1 to BLEU-4). The enhanced BLEU ratings signify increased precision in vocabulary selection, preservation of context, and overall comprehensibility. This study significantly enhances the current literature in machine translation and computational linguistics, especially for low-resource languages, by illustrating the practical effectiveness of a pivot-based method for augmenting translation precision. The method's dependability and efficacy in producing genuine translations were proved through numerous studies. The pivot-based technique enhances translation quality, although it possesses limitations, including the risk of error propagation and bias originating from the pivot language. Further research is necessary to examine the integration of named entity recognition (NER) to improve accuracy and optimize the intermediate translation process. This project advances the domains of machine translation and the preservation of low-resource languages, with practical implications for multilingual communities, language education resources, and cultural conservation.

Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine Translation Nada, Anita Qotrun; Wibawa, Aji Prasetya; Putri Syarifa, Dhea Fanny; Fajarwati, Erliana; Putri, Fadia Irsania
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6116

This research seeks to construct an automatic translation between Indonesian and Sundanese languages based on the Neural Machine Translation (NMT) method. The model used in this study is the Long Short-Term Memory (LSTM) type, which carries out an encoder-decoder structure model learned with Bible data. The text translation here was conducted in different epochs to optimize the process, followed by the Adam optimization algorithm. Testing the Adam optimizer with different epoch settings yields a BLEU score for Indonesian to Sundanese translations of 0.991785, higher than the performance of the None optimizer. Experimental results demonstrate that Indonesian to Sundanese translation using Adam optimization with 1000 epochs consistently performed better in BLEU - Bilingual Evaluation Understudy - scoring than Sundanese to Indonesian translation. Limitations of the research were also put forth, particularly technical issues related to the collection of data and the Sundanese language’s complex grammatical features, that the model can only partially express, honorifics, and the problem of polysemy. Also, it must be mentioned that no special hyperparameter selection was performed, as parameters were chosen randomly. In future studies, transformer-based models can be investigated since these architectures will better deal with complex language via their self-attention mechanism.

MPPT Performance Analysis for PV Energy Harvesting Using Grey Wolf Optimization (GWO) Algorithm Aripriharta, Aripriharta; Syabani, Muhiban; Sendari, Siti; Wibawa, Aji Prasetya; Susilo, Suhiro Wongso; Bagaskoro, Muhammad Cahyo; Rosmin, Norzanah
ELKHA : Jurnal Teknik Elektro Vol. 17 No.1 April 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i1.91643

Renewable energy is a key solution to meeting the growing demand for electricity while reducing reliance on non-renewable sources. Among various renewable technologies, photovoltaic (PV) systems are widely used in solar power plants (PLTS) to harness solar energy. However, PV efficiency is affected by environmental factors such as fluctuating solar irradiance and temperature, which cause instability in output voltage and power. To address these issues, Maximum Power Point Tracking (MPPT) techniques are applied to optimize power extraction. This study proposes the Grey Wolf Optimization (GWO) algorithm for MPPT and evaluates its performance through MATLAB/SIMULINK simulations under varying irradiance and temperature conditions. Inspired by the hunting behavior and social hierarchy of grey wolves, GWO dynamically adjusts the converter's duty cycle based on real-time voltage and current measurements to maximize output power. The study focuses on PV systems in Malang, Indonesia, and compares GWO with the Particle Swarm Optimization (PSO) method in terms of accuracy and stability. The results indicate that increased solar irradiance substantially enhances PV power output, while rising temperatures tend to reduce efficiency. The GWO algorithm achieves an average tracking accuracy of 94.5632%, slightly lower than the 96.9851% achieved by PSO. However, GWO demonstrates superior performance in terms of stability, with faster convergence and reduced oscillations during the tracking process. A comparison of system performance before and after applying the GWO method shows notable improvements in tracking consistency and power extraction efficiency, especially under dynamic environmental changes. The novelty of this study lies in its use of real-world environmental data collected over a 30-day period in a tropical setting, which is rarely addressed in previous GWO-based MPPT research. These findings highlight the potential of the GWO-based MPPT strategy to enhance PV system reliability and efficiency in real-time renewable energy applications.

A review of recent deep learning applications in wood surface defect identification Ali, Martina; Hashim, Ummi Raba’ah; Kanchymalay, Kasturi; Wibawa, Aji Prasetya; Salahuddin, Lizawati; Rahiddin, Rahillda Nadhirah Norizzaty
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1696-1707

Wood is widely used in construction, art, and home applications due to its aesthetic appeal and favorable mechanical properties. However, environmental factors significantly affect the growth and preservation of wood, often leading to defects that can reduce its performance and ornamental value. Researchers have introduced machine vision and deep learning methods to address the challenges of high labor costs and inefficiencies in identifying wood defects. Deep learning has shown great success in image recognition tasks, yielding impressive results. This paper reviews previous work on deep-learning strategies for identifying wood surface defects. It also discusses data augmentation techniques to address limited defect data and explores transfer learning to enhance classification accuracy on small datasets. Finally, the paper examines the potential limitations of deep learning for defect identification and suggests future research directions.

Enhancing Teks Summarization of Humorous Texts with Attention-Augmented LSTM and Discourse-Aware Decoding Supriyono, Supriyono; Wibawa, Aji Prasetya; Suyono, Suyono; Kurniawan, Fachrul
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.932

Abstractive summarization of humorous narratives presents unique computational challenges due to humor's multimodal, context-dependent nature. Conventional models often fail to preserve the rhetorical structure essential to comedic discourse, particularly the relationship between setup and punchline. This study proposes a novel Attention-Augmented Long Short-Term Memory (LSTM) model with discourse-aware decoding to enhance the summarization of stand-up comedy performances. The model is trained to capture temporal alignment between narrative elements and audience reactions by leveraging a richly annotated dataset of over 10,000 timestamped transcripts, each marked with audience laughter cues. The architecture integrates bidirectional encoding, attention mechanisms, and a cohesion-first decoding strategy to retain humor's structural and affective dynamics. Experimental evaluations demonstrate the proposed model outperforms baseline LSTM and transformer configurations in ROUGE scores and qualitative punchline preservation. Attention heatmaps and confusion matrices reveal the model's capability to prioritize humor-relevant content and align it with audience responses. Furthermore, analyses of laughter distribution, narrative length, and humor density indicate that performance improves when the model adapts to individual performers' pacing and delivery styles. The study also introduces punchline-aware evaluation as a critical metric for assessing summarization quality in humor-centric domains. The findings contribute to advancing discourse-sensitive summarization methods and offer practical implications for designing humor-aware AI systems. This research underscores the importance of combining structural linguistics, behavioral annotation, and deep learning to capture the complexity of comedic communication in narrative texts.

Unveiling Risk Patterns of Disability Progression A Clustering Based Transition Matrix Analysis Using Indonesian National Data Setiawan, Ariyono; Bin Abdul Hadi, Abdul Razak; Faller, Erwin; Wibawa, Aji Prasetya
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.1868

This study investigates the progression of disability severity from "some difficulty" to "a lot of difficulty" using a transition matrix framework. It aims to identify risk patterns and classify severity clusters based on national survey data from Indonesia between 2010 and 2023. The study draws on the theory of functional limitation progression, which assumes that individuals with mild disabilities face varying probabilities of developing severe limitations depending on contextual and demographic factors. It also incorporates clustering theory to group similar progression behaviors. We utilize 20,604 data points from multiple disability types (cognitive, hearing, mobility, etc.). The transition rate is computed as the ratio of individuals with "a lot" difficulty to the total with "some" and "a lot" difficulty. Statistical analyses include descriptive summaries, Pearson correlation, and K-Means clustering via the FASTCLUS procedure. Heatmaps are generated to observe annual and typological patterns. The average transition rate is 66.77%, with a maximum of 99.6% in some subgroups. Three distinct severity clusters emerged, centered at 31.27%, 58.62%, and 82.20%. Transition rate negatively correlates with "some difficulty" prevalence (r = –0.45, p < .0001), indicating progressive concentration of severity in smaller populations. Heatmaps reveal consistent risk escalation over time, especially in cognitive and self-care disabilities. This study enables policy actors to stratify intervention priorities and monitor disability risk more accurately using dynamic, data-driven indicators. This is the first study in Indonesia to apply a large-scale transition matrix combined with clustering to map functional disability progression. It offers a novel quantitative method to uncover hidden severity patterns and informs future decision-support systems for inclusive health planning.

Internalizing religious tolerance in elementary schools: Reality and alternative solution Rahmadonna, Sisca; Setyosari, Punaji; Kuswandi, Dedi; Praherdhiono, Hendy; Wibawa, Aji Prasetya
International Journal of Education and Learning Vol 6, No 3 (2024): December
Publisher : Association for Scientific Computing Electrical and Engineering(ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijele.v6i3.1903

Religious intolerance is rising globally, including in Indonesia, a pluralistic nation with over 600 ethnic groups and six official religions. Ironically, cases of intolerance have emerged even in Yogyakarta, an educational and cultural barometer renowned for its local wisdom of tepa selira (mutual respect). This study investigates how elementary school teachers in Yogyakarta, Indonesia understand and internalize religious tolerance in classroom practices. Employing a qualitative phenomenological approach, data were collected through interviews, observations, FGDs, and document analysis involving 20 teachers from 10 diverse elementary schools. Findings indicate that most teachers focus on cognitive outcomes and struggle to assess tolerance in the affective domain. However, one multicultural school Pelangi Elementary demonstrates an alternative approach by emphasizing shared human values such as kindness, gratitude, and togetherness, and by removing formal religious instruction to foster inclusivity. The school integrates dialogical pedagogy, parental involvement, and community engagement, making tolerance a lived experience rather than a theoretical concept. This study concludes that religious tolerance education in diverse societies must move beyond textbooks toward character-based, participatory learning rooted in local wisdom and inclusive practice. The Pelangi model offers a promising strategy adaptable to other pluralistic contexts.

Optimizing Text Correction For Voice Based IoT Smart Building Virtual Assistants Shidiqi, Maulana Ahmad As; Hadi, Mokh Sholihul; Wibawa, Aji Prasetya; Mhd. Irvan, Mhd. Irvan
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1085

The integration of Virtual Assistants (VAs) within Smart Building Internet of Things (IoT) ecosystems is increasingly critical, particularly for interpreting user commands via Automatic Speech Recognition (ASR). This paper presents an in-depth performance analysis of text correction algorithms on a Raspberry Pi 4—a cost-effective and widely used computing solution in smart building applications. Due to the absence of GPU acceleration for Python on ARM architecture, a specialized dataset was developed to benchmark algorithmic performance, focusing on correction times and accuracy. Our study utilized a near-real-world experimental setup, deploying Docker containers to simulate IoT MQTT brokers, a Smart Building Platform, and Rasa for dialogue management. Among the algorithms tested—Edit distance, Jaccard, FuzzPartialRatio, FuzzSortRatio, MLE, and Norvig Spell—the Edit distance and Norvig Spell emerged as leaders in accuracy, achieving an 84% success rate in text correction. Notably, the Edit distance algorithm demonstrated superior speed, vital for real-time processing demands. The Fuzz Sort Ratio algorithm distinguished itself with the fastest correction time at 31.6 milliseconds, albeit with a slight compromise on accuracy, attaining a 79% success rate. Consequently, the Edit distance algorithm is recommended for applications where accuracy and response time are paramount, while the Fuzz Sort Ratio is preferable for scenarios where speed is the overriding priority. This research paves the way for future exploration into the computational impacts of these algorithms and the exploration of neural network-based methods to further enhance text correction capabilities in smart building automation systems.

LSTM Model Using Adam’s Optimizer for Indonesian – Bugis Bidirectional Translation System Fajarwati, Erliana; Wibawa, Aji Prasetya; Hernandez, Leonel
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1272

The purpose of this research is to develop a machine translation of Bugis to Indonesian and vice versa in order to preserve the Bugis language. This research utilizes a recent dataset consisting of 30,000 Bugis-Indonesian sentence pairs from the online Bible. This research conducts scraping to compile the corpus which is then followed by manual and automatic pre-processing. The method chosen is Neural Machine Translation (NMT) while for training and testing models Long Short-Term Memory (LSTM) is used. The performance of the model is evaluated by Bilingual Evaluation Understudy (BLEU) score to measure the translation accuracy at various epochs. In addition, this study also compared the use of Adam's optimizer with non-optimizer. The results showed that the use of Adam's optimizer significantly improved the performance of the model where at epoch 2000 the model achieved the highest BLEU score of 0.996261 indicating highly accurate translation quality. In contrast, the model without the optimizer showed lower performance. Other results also found that the translation from Bugis to Indonesian was more accurate than from Indonesian to Bugis. This is due to the more balanced word count difference in the Bugis to Indonesian translation, which makes it easier for the model to match words. In conclusion, the use of NMT with Adam optimizer effectively improves the accuracy of two-way translation from Bugis-Indonesian.

Co-Authors A.N. Afandi Abd. Rasyid Syamsuri Abdullah Sholum Abdur Rohman Achmad Fanany Onnilita Gaffar Adaby, Resnu Wahyu Ade Kurnia Ganesh Akbari Adelia Desyana Eka Putri Adelia Desyana Eka Putri Adelia Khansa Ristiaputri Adelia Khansa Ristiaputri Adhelia Wida Khaidir Adhelia Wida Khaidir Adil Zakaria Aditya Wahyu Setiawan Adjie Rosyidin Adnan, Adam Agung Bella Putra Utama Agung Bella Putra Utama Agung Bella Putra Utama Agung Bella Putra Utama Agung Bella Putra Utama Agung Bella Putra Utama Agung Bella Putra Utama Agustinus Noertjahyana Ahmad 'Ammar Musyaffa' Ahmad Munjin Nasih Ahmad Naim Che Pee Ahmad Taufiq Aindra, Alifah Diantebes Aji, Bayu Kuncoro Akbari, Ade Kurnia Ganesh Akhimullah Akmal Fattah Akhmad Fanny Fadhilla Akrom Tegar Khomeiny Alamsyah, David Satria Aldy Rahmat Yulianto Alfiansyah Putra Pertama Triono Ali, Martina Alifah Diantebes Aindra Amro, Manar Y Andien Khansa’a Iffat Paramarta Andika Dwiyanto, Felix Andini, Nurul Fajriah Andrew Nafalski Andrew Nafalski Andrew Nafalski Andrew Nafalski Andrew Nafalski Andrew Nafalski Andri Pranolo Andriansyah, Muhammad Rizal Angeline, Grace Anik Nur Handayani Anton Prafanto Anusua Ghosh Anusua Ghosh, Anusua Ardha Ardhana Putra Agustavada Ardhana Putra Agustavada Ardiansyah, Jevri Tri Ardiansyah, Mohammad Iqbal Firman Arifin, Mohammad Nazir Aripriharta - Arya Tandy Hermawan Ashar, Muhammad Astuti, Wistiani Atmaja, I Made Ari Dwi Suta Atmaja, Nimas Hadi Ba, Abdoul Fatakhou Bagaskoro, Muhammad Cahyo Bahalwan, Lugas Anegah Baitun Nadhiroh Bambang Widi Pratolo Bella Putra Utama, Agung Betty Masruroh Bety Masruroh Bin Abdul Hadi, Abdul Razak Bin Haji Jait, Adam Cahyo Prayogo, Cahyo Cengiz, Korhan Che Pee, Ahmad Naim Chong , Wan Ni Chuttur, Mohammad Yasser Citra Suardi Citra, Hana Rachma Collante, Leonel Hernandez Dafa Fadhilah Hilmi Danang Arbian Sulistyo Daniar Wahyu David Satria Alamsyah Dedes, Khen Dedi Kuswandi Dedy Kuswandi Denis Eka Cahyani Denna Delawanti Chrisyarani, Denna Delawanti Desi Anggreani Desi Fatkhi Azizah Devi Dwi Purwanto Devita, Riri Nada Dewandra, Aderyan Reynaldi Fahrezza Dewi, Popy Maulida Dhani Wahyu Wijaya Dhani Wahyu Wijaya Dhaniyar Dhaniyar Dhia Rafifah Thifal Dhia Rafifah Thifal Didik Dwi Prasetya Didik Nurhadi Didik Suprayogo Dika Fikri L Dityo Kreshna Argeshwara Dityo Kreshna Argeshwara Drezewski, Rafał Dwi Jaelani, Mardian dwi yasa, arnelia Dwieb, Mohamed Dwiyanto, Felix Andika Dwiyanto, Felix Andika Dyah Lestari Edinar Valiant Hawali Eka Nurcahya Ningsih Elta Sonalitha Endah Setyo Wardani Erna Daniati Erna Daniati Esther Irawati Setiawan Fachrul Kurniawan Fachrul Kurniawan Fachrul Kurniawan Fadhilah, Farhan Fadhilla, Akhmad Fanny Fadhli Almu’iini Ahda Fadia Irsania Putri Faidzin, Ilham Fajar Purnama Fajarwati, Erliana Faller, Erwin Faradini Usha Setyaputri Farid Miftahuddin Farida Nur Kumala Fauzan Cahya Arifin Fauzan Prasetyo Felix Andika Dwiyanto Felix Andika Dwiyanto Felix Andika Dwiyanto Felix Andika Dwiyanto Felix Andika Dwiyanto Ferdinand, Miftakhul Anggita Bima Ferina Ayu Pusparani Fidyah Ajeng Wulandari Filby , Brilliant Filby, Brilliant Fithri, Hidayah Kariima Fitria, Nimas Dian Fitriana Kurniawati Gianika Roman Sosa Graciello, Manuel Tanbica Gülsün Kurubacak Gunawan Gunawan Gwinny Tirza Rarastri Hakkun Elmunsyah Hammad, Jehad A. H. Hammad, Jehad A.H Hari Putranto Haris Anwar Syafrudie Harits Ar Rasyid Harits Ar Rosyid Hariyono Hariyono Hariyono Hariyono Hariyono Hariyono Hariyono Hariyono Hartono, Nickolas Hary Suswanto Hasanuddin, Tasrif Hashim, Ummi Raba’ah Hasihi, Cholisah Erman Haviluddin Haviluddin Haviluddin, - Hechmi SHILI Hendrawan, William Hartanto Heri Pratikto Herman Herman Herman Santoso Pakpahan Herman Thuan To Saurik Heru Nurwarsito Heru Wahyu Herwanto Hery Widijanto Hidayah Kariima Fithri Hidayah, Laily Hidayatul Ma'rifah Hitipeuw, Emanuel Hong, Yeap Chi I Made Wirawan I Nyoman Gede Arya Astawa Idris Idris Ilham Mulya Putra Pradana Imansyah, Pranadya Bagus Imro’aturrozaniyah, Imro’aturrozaniyah Inggar Tri Agustin Mawarni Irsyada, Rahmat Islam, Noorul Islami, Pio Arfianova Fitrizky Islami, Pio Arfianova Fitrizky Islami, Pio Arfianova Fitrizky Ismail, Amelia Ritahani Istiqlal, Adib Izdihar, Zahra Nabila Jabari, Nida Jehad A. H. Hammad Jehad A.H. Hammad Jehad Hammad Jevri Tri Ardiansah Julfikar Mawansyah Junoh, Ahmad Kadri Juwita Annisa Fauzi Juwita Annisa Fauzi Kaki, Gregorius Paulus Mario Laka Kartika Candra Kirana Kasturi Kanchymalay, Kasturi Kelvin Wong Khafit Badrus Zaman Khoiruddin Asfanie Khurin Nabila Kirya Mateeke Moses Kohei Arai Kohei Arai Kurniawan, Fachrul Kurniawan, Novian Candra Kurniawati, Fitriana Kuswandi, Dedy Laily Hidayah Langlang Gumilar Lauretta, Giovanny Cyntia Lazuardi Noorca Rachmadi Leonel Hernandez Leonel Hernandez Leonel Hernandez, Leonel Lestari, Muqodimah Nur Lestari, Muqodimah Nur Lestari, Muqodimah Nur Liang, Yoeh Wen Lisa Ramadhani Harianti Lisa Ramadhani Harianti Ludovikus Boman Wadu Luther Latumakulita M Zainal Arifin, M Zainal M. Alfian Mizar M. Zainal Arifin Mairi, Vitrail Gloria Mansoor Abdul Hamid Mantony, Oslida Mao, Yingchi Marchena, Piedad Marida, Tyas Agung Cahyaning Marji Marji Markus Diantoro Masruroh, Bety Mazarina Devi Meiga Ayu Ariyanti Mhd. Irvan, Mhd. Irvan Mifta Dewayani Miftahul Qiki Winata Miladina Rizka Aziza Ming F. Teng Ming Foey Teng, Ming Foey Mochamad Hariadi Moh. Safii Moh. Zainul Falah Mohamad Rodhi Faiz Mokh Sholihul Hadi Moses, Kirya Mateeke Moses, Kirya Mateeke Moses, Kirya Mateeke Mudakir, Mudakir Muh. Aliyazid Mude Muhamad Arifin Muhammad Busthomi Arviansyah Muhammad Ferdyan Syach Muhammad Firman Aji Saputra Muhammad Iqbal Akbar Muhammad Jauharul Fuadi Muhammad Nu’man Hakim Muhammad, Abdullahi Uwaisu Muladi Munir Munir Muntholib Muqodimah Nur Lestari Mursyit, Mohammad Musyaffa', Ahmad 'Ammar Nabila Izdihar, Zahra Nabila, Khurin Nada, Anita Qotrun Nadhiroh, Baitun Nadia Roosmalita Sari Nafalski, Andrew Nastiti Susetyo Fanany Putri Naufal, Ayyub Naziro Nedic, Zorica Ningsih, Eka Nurcahya Ningtyas, Yana Novia Ratnasari Noviani, Erina Fika Novrindah Alvi Hasanah Nugraha, Agil Zaidan Nur Eva Nur Hidayatullah Nurfadila, Piska Dwi Nurhalifah, Siti Nuril Anwar, Nuril Nurroby Wahyu Saputra Nurul Falah Hashim Nurul Hidayat Nuryana, Zalik Oakley, Simon Okazaki Yasuhisa Okazaki Yasuhisa, Okazaki Oki Dwi Yuliana Omar, Saodah Osamu Fukuda Paramarta, Andien Khansa’a Iffat Patrya, Sukma Paul Igunda Machumu Pio Arfianova Fitrizky Islami Praherdhiono, Hendy Prananda Anugrah Prasojo, Fadillah Pratama, Awanda Setya Sanfajar Puji Santoso Puji Santoso Puji Santoso Punaji Setyosari Pundhi Yuliawati Pundhi Yuliawati Purnawansyah Purnawansyah Purnomo Purnomo Purnomo Purnomo Purwatiningsih, Ayu Putra Utama, Agung Bella Putra, Agung Bella Utama Putri Syarifa, Dhea Fanny Putri, Desy Pratiwi Ika Putri, Fadia Irsania Putri, Nastiti Susetyo Fanany Qonita, Adiba Rahiddin, Rahillda Nadhirah Norizzaty Rahmadhani, Nur Aini Syafrina Raja, Roesman Ridwan Ratnasari, Novia Rendy Yani Susanto Resty Wulanningrum Ridho, Faiz Mohammad Ridwan Shalahuddin Ridwan Shalahuddin Riri Nada Devita Rizal Kholif Nurrohman Rizqini, Fajriwati Qoyyum Roni Herdianto Rosmin, Norzanah RR. Poppy Puspitasari Rully Charitas Indra Prahmana Ruth Ema Febrita Saifullah, Shoffan Salahuddin, Lizawati Salsabila, Reni Fatrisna Santoso, Priyo Aji Saputra, Anggie Wahyu Saputra, Irzan Tri Sarni Suhaila Rahim Seno Isbiyantoro Setiawan, Ariyono Setyadi, Hario Jati Setyaputri, Faradini Usha Setyawan P. Sakti Shahrul, Azzhan Shalahuddin, Ridwan Shiddiqy, Jabar Ash Shidiqi, Maulana Ahmad As Sias, Quota Alief Simbolon, Triyanti Sisca Rahmadonna Siti Helmyati Siti Sendari Soenar Soekopitojo Soraya Norma Mustika Sri Rahmawati ST. Ulfawanti Intan Subadra Stamen Gadzhanov Sucahyo, Cornaldo Beliarding Sugiarto Cokrowibowo Sugiyanto - Suhiro Wongso Susilo Sujito Sujito Sularso Sularso, Sularso Sulistyo, Danang Arbian Sunu Jatmika, Sunu Supeno Mardi Susiki Nugroho, Supeno Mardi Supriadi Supriadi Supriyono Supriyono Suryani, Ani Wilujeng Susilo, Suhiro Wongso Suyono Suyono Suyono Suyono Suyono Syaad Patmantara Syaad Patmanthara Syabani, Muhiban Tantri Hari Mukti Trahutomo, Dinnuhoni Tri Andi, Tri Tri Kuncoro Tri Lathif Mardi Suryanto Tri Lathif Mardi Suryanto Tri Saputra, Irzan Tri Sutanti Tri Sutanti, Tri Triono, Alfiansyah Putra Pertama Triyanna Widiyaningtyas Triyanna Widyaningtyas Triyanna Widyaningtyas, Triyanna Tsukasa Hirashima Tuatul Mahfud Ummi Rabaah Hasyim Uriu, Wako Utama , Agung Bella Putra Utama, Agung Bella Putra Utomo Pujianto Vira Setia Ningrum Vira Setia Ningrum Voliansky, Roman Wadu, Ludovikus Boman Wahyu Arbianda Yudha Pratama Wahyu Nur Hidayat Wahyu Sakti Gunawan Irianto Wahyu Tri Handoko Wako Uriu Wardani, Endah Setyo Wayan Firdaus Mahmudy Wibowo, Danang Arengga Wibowo, Fauzy Satrio Wibowo, Nur Cahyo Widiharso, Prasetya Widiyanintyas, Triyanna Yandratama, Hengky Yasa, Arnelia Dwi Yingchi Mao Yongen Susman Yosi Kristian Yuhefizar Yuhefizar Yuliana, Oki Dwi Yulianto, Aldy Rahmat Yuliawati, Pundhi Yuni Rahmawati Yusmanto, Yunan Zaeni, Ilham Ari Elbaith Zakaria, Adil Zhou, Xiaofeng Zulkham Umar Rosyidin Zulkham Umar Rosyidin

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