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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik Elektro Lontar Komputer: Jurnal Ilmiah Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Education and Learning (EduLearn) Journal of Telematics and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Jurnal Sains Teknologi dan Lingkungan (JSTL) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Computer Science and Informatics Engineering (J-Cosine) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Multimedia Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Abdi Insani Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Begawe Teknologi Informasi Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
I Gede Pasek Suta Wijaya
Informatics Engineering Dept., University Of Mataram, Jl. Majapahit 62 Mataram, Mataram INDONESIA
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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KLASIFIKASI IKAN CAKALANG DAN IKAN TONGKOL MENGGUNAKAN XCEPTION DAN MOBILENET Anugrah, Febrian Rizky; Bimantoro, Fitri; Wijaya, I Gede Pasek Suta
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 7 No 1 (2025): Maret 2025
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v7i1.430

Abstract

This study compares the performance of two deep learning architectures, Xception and MobileNet, for classifying skipjack and mackerel tuna, with a focus on accuracy and computational efficiency. MobileNet achieved an impressive accuracy of 97%, with precision, recall, and F1-score all at 97%, and demonstrated a faster prediction time of 0.06 seconds, making it well-suited for real-time applications. In contrast, Xception achieved an accuracy of 93%, with a precision of 94%, recall of 93%, and an F1-score of 93%. However, its prediction time was slower at 0.13 seconds, indicating a higher computational complexity. Although Xception delivered substantial accuracy, MobileNet outperformed it in terms of efficiency, suggesting that MobileNet is better suited for applications with limited resources or time constraints. The results indicated that MobileNet's lightweight architecture makes it ideal for mobile or embedded systems. At the same time, Xception's more complex structure may be advantageous for tasks that require higher precision in image processing. This research makes a significant contribution to the development of deep learning-based methods for fish species classification, offering improvements in both accuracy and speed.
OPTIMALISASI LAYANAN SISTEM INFORMASI MAHASISWA DENGAN INTEGRASI TELEGRAM : CHATBOT RETRIEVAL-AUGMENTED-GENERATION BERBASIS LARGE LANGUAGE MODEL Hidayat, Lalu Ramdoni; Wijaya, I Gede Pasek Suta; Dwiyansaputra, Ramaditia
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 7 No 1 (2025): Maret 2025
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v7i1.459

Abstract

Kemajuan teknologi telah memberikan dampak yang cukup signifikan dalam berbagai bidang, termasuk salah satunya Pendidikan. Dalam aspek Pendidikan permasalahan yang dihadapi adalah keterbatasan akses mahasiswa terhadap informasi akademik secara cepat dan efisien. Untuk mengatasi hal ini, penelitian ini bertujuan mengembangkan chatbot berbasis Telegram yang mampu memberikan respons informatif, akurat, dan ringkas terhadap pertanyaan pengguna terkait akademik di program studi Teknik Informatika. Chatbot ini memanfaatkan metode Retrieval-Augmented-Generation (RAG) untuk memproses informasi dari dokumen teks secara efisien. Metode RAG digunakan untuk menemukan jawaban yang relevan dari dokumen berdasarkan pertanyaan pengguna, sementara Large Language Model memahami konteks pertanyaan dan menghasilkan jawaban yang sesuai. Penelitian ini menggunakan pendekatan Research and Development (R&D) dengan tahapan meliputi survei questioner kebutuhan mahasiswa, preprocessing data, Pembangunan indeks pencarian berbasis vektor, konfigurasi model LLM, serta integrasi chatbot dengan Telegram. Hasil pengujian menunjukkan bahwa chatbot mampu memberikan jawaban dengan akurasi tinggi dan waktu respons rata-rata 60 detik untuk pertanyaan sederhana hingga kompleks, sehingga chatbot berbasis RAG cukup efektif meningkatkan aksesibilitas informasi secara real-time. Pengembangan lebih lanjut dapat difokuskan pada peningkatan pemahaman terhadap beragam pertanyaan dan personalisasi respons.
Empowering NTB Communities through Korean Language Training and Business Digitalization to Enhance Tourism and SMEs: PEMBERDAYAAN MASYARAKAT NTB MELALUI PELATIHAN BAHASA KOREA DAN DIGITALISASI USAHA UNTUK MENINGKATKAN PARIWISATA DAN UKM Widiartha, Ida Bagus Ketut; Irmawati, Budi; Wijaya, I Gede Pasek Suta; Arimbawa, I Wayan Agus; Albar, Moh. Ali; Murpratiwi, Santi Ika; Jayusman, Dirga; Suhada, Destia
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 6 No. 2 (2025): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v6i2.1459

Abstract

Nusa Tenggara Barat (NTB) memiliki potensi besar di sektor pariwisata dengan kontribusi 19,5% terhadap pendapatan daerah. Namun, rendahnya keterampilan Bahasa Korea dan literasi digital masyarakat serta pelaku UKM menjadi hambatan dalam pelayanan wisatawan, khususnya asal Korea Selatan, dan promosi produk lokal. Padahal, kemampuan berbahasa Korea sangat penting untuk memperkuat interaksi, pengalaman wisatawan, promosi budaya, dan perluasan pasar produk lokal. Untuk menjawab tantangan tersebut, Universitas Mataram melalui Program KKN Internasional bekerja sama dengan Seoul National University Social Responsibility (SNU-SR) dan Universitas Nasional (UNAS). Program ini mencakup pelatihan Bahasa Korea praktis, pengembangan konten digital, strategi pemasaran berbasis media sosial, serta personal branding bagi UKM. Tahapan pelaksanaan meliputi sosialisasi, seleksi, pelatihan hybrid (online–offline), pendampingan, evaluasi, dan pembentukan komunitas keberlanjutan. Hasil yang diharapkan meliputi peningkatan kemampuan komunikasi dasar Bahasa Korea, keterampilan produksi konten digital, penguatan branding usaha, serta terbentuknya komunitas pasca pelatihan. Survei menunjukkan program ini efektif meningkatkan daya saing pariwisata NTB, terutama dalam menarik wisatawan Korea Selatan, sekaligus memperkuat posisi daerah dalam pariwisata dan ekonomi kreatif.
Development of an Integrated Lecturer Workload Monitoring Information System Using the Prototype Model: PENGEMBANGAN SISTEM INFORMASI PEMANTAUAN BEBAN KERJA DOSEN TERINTEGRASI BERBASIS PROTOTIPE Irfan, Pahrul; Wijaya, I Gede Pasek Suta; Akhyar, Halil; Zubaidi, Ariyan; Zafrullah, Ahmad
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 6 No. 2 (2025): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v6i2.1463

Abstract

Pemantauan beban penugasan dosen di Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram masih dilakukan secara manual dan terpisah. Kondisi ini menyulitkan proses pemantauan serta berpotensi menimbulkan ketidakseimbangan distribusi tugas akademik maupun non-akademik. Untuk mengatasi permasalahan tersebut, dilaksanakan kegiatan pengabdian berupa pengembangan sistem informasi terintegrasi menggunakan metode prototipe, yang meliputi tahapan analisis kebutuhan, perancangan, pengembangan, pengujian, dan implementasi awal. Hasil pengujian menunjukkan sistem berfungsi sesuai spesifikasi berdasarkan Blackbox Testing, sedangkan uji System Usability Scale (SUS) terhadap delapan responden memperoleh skor rata-rata 84,06 yang termasuk kategori Excellent. Temuan ini membuktikan bahwa sistem mudah digunakan, diterima pengguna, dan mendukung efektivitas pemantauan beban kerja dosen. Dengan adanya sistem ini, Program Studi dapat memantau distribusi tugas secara lebih menyeluruh, adil, dan efisien. Selain itu, sistem berpotensi dikembangkan lebih lanjut agar dapat diterapkan tidak hanya di Program Studi Teknik Informatika, tetapi juga pada program studi lain dalam rangka meningkatkan kualitas manajemen beban kerja dosen di lingkungan perguruan tinggi.
Analisis Sentimen Ulasan Wisatawan Terhadap Destinasi Gili di KLU Menggunakan K-Means Clustering Obenu, Juanri Priskila; Suta Wijaya, I Gede Pasek; Bimantoro, Fitri
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 7 No 2 (2025): September 2025
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v7i2.475

Abstract

The rise of social media, particularly Twitter, has enabled sentiment analysis in tourism. This study examines tourist sentiments toward Gili, Lombok, using K-Means Clustering on 6,030 tweets, refined to 3,156 after cleaning. The clustering yielded Silhouette Coefficient scores of 0.4382 (positive-English), 0.4173 (positive-Indonesian), 0.4258 (negative-English), and 0.4535 (negative-Indonesian), indicating well-structured clusters. Tourists expressed positive views on natural beauty and activities but raised concerns about water supply issues. This study demonstrates K-Means Clustering’s effectiveness in revealing sentiment trends, offering valuable insights for local governments and tourism stakeholders to enhance visitor experiences and address challenges.
ANALISIS PERFORMA INDOBERTWEET DAN DISTILBERT PADA ANALISIS SENTIMEN DENGAN DATASET BERLABEL MANUAL DAN OTOMATIS Kansha, Lyudza Aprilia; Suta Wijaya, I Gede Pasek; Bimantoro, Fitri
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 7 No 2 (2025): September 2025
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v7i2.484

Abstract

Tourism in North Lombok, particularly the Gili Islands (Trawangan, Air, and Meno), plays a significant role in the regional economy. Understanding public sentiment through social media is crucial for improving tourism services and management. This study compares the performance of two transformer-based models IndoBERTweet and DistilBERT in sentiment analysis of tourism-related tweets from X (Twitter). The dataset used consists of 3,159 preprocessed Indonesian-language tweets, labeled through both manual annotation and automatic classification using DistilBERT. IndoBERTweet was evaluated on both manual and automatic labels, while DistilBERT was only applied to the manually translated dataset. Experimental results show that IndoBERTweet with manual labeling achieved an F1-score of 72.98% and demonstrated more balanced performance across all sentiment classes. Meanwhile, DistilBERT showed lower F1-scores overall (max. 57%) but performed efficiently in terms of computational time. Automatic labeling showed weak agreement with manual annotation (only 31.8% match), leading to bias in model learning, particularly the failure to detect neutral sentiment. Evaluation using new test sentences yielded 80% prediction accuracy, yet revealed that IndoBERTweet struggles with implicit sentiment or subtle dissatisfaction. This research highlights IndoBERTweet's effectiveness in Indonesian sentiment analysis and emphasizes the trade off between computational efficiency and contextual accuracy in lightweight models like DistilBERT.
Pengenalan Ekspresi Wajah Menggunakan DCT dan LDA untuk Aplikasi Pemutar Musik (MOODSIC) Wijaya, I Gede Pasek Suta; Firdaus, Asno Azzawagaam; Dwitama, Aditya Perwira Joan; Mustiari, Mustiari
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 5: Oktober 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2877.237 KB) | DOI: 10.25126/jtiik.201855935

Abstract

Masyarakat modern dengan kesibukan sehari-harinya tentu akan mendapat tekanan emosional yang cukup tinggi. Hal yang dilakukan untuk meredakan emosi tersebut adalah salah satu dengan mendengarkan musik. MOODSIC merupakan sebuah aplikasi yang dapat memutar musik sesuai dengan ekspresi wajah pengguna. Aplikasi MOODSIC dibangun menggunakan mesin pengenalan ekspres wajah berbasis DCT dan LDA serta algoritma klasifikasi statistik. Berdasarkan hasil pengujian secara off-line mesin pengenalan ekspresi wajah berhasil memberikan performa yang baik, dengan akurasi sebesar 100% untuk data masukkan terdiri atas fitur DCT 144 elemen, 6 eigen vektor LDA dan klasifikasi statistik jenis LDA. Mesin pengenalan ekspresi wajah memerlukan waktu pengenalan yang pendek yaitu 1 milidetik. Secara real-time MOODSIC memberikan hasil yang cukup baik dengan akurasi pengenalan ekspresi sebesar 91.51% atau dengan tingkat kesalahan pengenalan 9.49%. AbstractModern society lifestyles face many activities every day, which make people receive a fairly high emotional stress. To reduce such kind of emotions can be treated by listening music. MOODSIC is an application that can play music according to the user's face expression. MOODSIC is developed using face expression recognition machine based on DCT, LDA and statistical classification algorithm. Based on offline testing result, face expression recognition machine successfully give good performance with accuracy of 100% when DCT features are 144 elements, 6 eigen vectors of LDA and kind of statistical classifier is LDA. The face expression recognition engine took shorter time to classification about 1 milliseconds. MOODSIC also give good performance with the accuracy of expression recognition about 91.51% or recognition error of 9,49% for real-time testing.
STUDENT FOCUS DETECTION USING YOU ONLY LOOK ONCE V5 (YOLOV5) ALGORITHM Rosalina, Rosalina; Bimantoro, Fitri; Suta Wijaya, I Gede Pasek
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.1977

Abstract

Education has a very important role in life, student involvement in the learning process in the classroom is an important factor in the success of learning. However, some students pay less attention to the lesson, indicating a lack of productivity in learning. The use of machine learning and computer vision techniques has undergone significant development in the last decade and is applied in a variety of applications, including monitoring student attention in the classroom. One of the commonly used techniques in machine learning and computer vision to detect objects is by applying image processing. One of the algorithms implemented for object detection that can provide good results is You Only Look Once. This research proposes the application of YOLOV5 in real time student focus detection and analyzes the performance and computational load of the five YOLOV5 architectures (YOLOV5n, YOLOV5s, YOLOV5m, YOLOV5l, and YOLOV5x) in student surveillance during classroom learning. The dataset used is video data that has been converted into image form, and 297 images are produced. Where, this dataset is divided into 2 classes, namely the "Focus" and "Not Focus" classes. The results show that YOLOV5x has the highest computational load with large parameter values and GFLOPs. However, in term model performance YOLOV5m provides more optimal results than other architectures, with precision of 83.3%, recall of 85.1%, and mAP@50 of 89.9%. The results of this study show that the proposed YOLOV5 model can be a good performing method in detecting student focus in real time.
Development of a Convolutional Neural Network Method for Classifying Ripeness Levels of Servo Variety Tomatoes Rosalina, Rosalina; Husodo, Ario Yudo; Wijaya, I Gede Pasek Suta
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4168

Abstract

The distribution of tomatoes in Indonesia is huge, making it an important commodity in the agricultural sector. However, manual classification of tomato ripeness can lead to human error and decrease supply chain efficiency. Therefore, an automated system capable of classifying tomatoes quickly and accurately is needed, in order to reduce the potential for human error and improve supply chain efficiency. This research aims to develop the Convolutional Neural Network (CNN) method to improve the accuracy of tomato ripeness detection through modifications to the architecture, such as reducing several layers, adding batch normalization, and adding dropouts. The dataset used in this study consists of 500 images taken by the researcher himself which are divided into 5 classes, namely unriped, half-riped, riped, half-rotten, and rotten, with each class containing 100 images. There are 3 proposed CNN models, namely the standard model, as well as the addition of batch normalization and dropout in the architecture. The results showed that the proposed model 3 with the addition of dropout on several layers of its architecture is the optimal model with a parameter of 2.4 million and using a batch size of 16 resulting in an accuracy of 98%, as well as precision, recall, and F1-score values of 98%. With these results, the proposed CNN model is effective in identifying the ripeness level of tomato fruit. This research is expected to be applied in the agricultural industry to improve the efficiency of sorting and distributing tomato fruits according to the desired quality standards.
Enhanced Identity Recognition Through the Development of a Convolutional Neural Network Using Indonesian Palmprints Aprilla, Diah Mitha; Husodo, Ario Yudo; Wijaya, I Gede Pasek Suta
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4169

Abstract

The use of palmprint as an identification system has gained significant attention due to its potential in biometric authentication. However, existing models often face challenges related to computational complexity and the ability to scale with larger datasets. This research aims to develop an efficient Convolutional Neural Network (CNN) model for palmprint identity recognition, specifically tailored to address these challenges. A novel contribution of this study is the creation of an original palmprint dataset consisting of 700 images from 50 Indonesian college students, which serves as a foundation for future research in Southeast Asia. The dataset includes different scenarios with varying input sizes (32x32, 64x64, 96x96 pixels) and the number of classes (30, 40, 50) to assess the model's scalability and performance. Three CNN architectures were designed with varying layers, activation functions, and dropout strategies to capture the unique features of palmprints and improve model generalization. The results show that the best-performing model, Model 3, which incorporates dropout layers, achieved 95% accuracy, 96% precision, 95% recall, and 95% F1-score on 50 classes with 1.2 million parameters. Model 1 achieved 98% accuracy, 99% precision, 98% recall, and 98% F1-score on 40 classes with 1.7 million parameters. These findings demonstrate that the proposed CNN models not only achieve high accuracy but also maintain computational efficiency, offering promising solutions for real-time palmprint authentication systems. This research contributes to the advancement of biometric authentication systems, with significant implications for real- world applications in Southeast Asia.
Co-Authors Adi Sugita Pandey Afwani, Royana Agitha, Nadiyasari Ahmad Musnansyah Ahmad Zafrullah Mardiansyah Akhyar, Halil Albar, Moh. Ali Aldian Wahyu Septiadi Andy Hidayat Jatmika Anita Rosana MZ Annisa Mujahidah Robbani Anugrah, Febrian Rizky Aprilla, Diah Mitha Aranta, Arik Ariessaputra, Suthami Arik Aranta Arik Aranta Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo Ario Yudo Husodo, Ario Yudo Ariyan Zubaidi Ariyan Zubaidi Awaluddin Ayu Rezki Azizah Arif Paturrahman Belmiro Razak Setiawan Budi Irmawati Budi Irmawati Bulkis Kanata Chaerus Sulton Chandra Adiguna Chandra Adiguna Cipta Ramadhani Darmawan, Riski David Arizaldi Muhammad Dedi Ermansyah Dina Juliani U M, Eka Ditha Nurcahya Avianty Dwitama, Aditya Perwira Joan Dwiyansaputra, Ramaditia Eet Widarini Fa'rifah, Riska Yanu Fachry Abda El Rahman Fadilah . Fahmi Syuhada Faqih Hamami Farhan Yakub Bawazir Fiena Efliana Alfian Firdaus, Asno Azzawagaam Fitrah, Muhammad Dinul Fitri Bimantoro Gibran Satria Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gibran Satya Nugraha Gou Koutaki Gunawan Haidra Rahman Halil Akhyar Hamidi, Mohammad Zaenuddin Hendy Marcellino Heri Wijayanto Heri Wijayanto Heri Wijayanto Hidayat, Lalu Ramdoni I B K Widiartha I Gde Putu Wirarama Wedaswhara W. I Made Budii i Suksmadana I Made Subiantara Putra I Putu Teguh Putrawan I Wayan Agus Arimbawa I Wayan Agus Arimbawa I Wayan Agus Arimbawa, I Wayan Agus Ida Bagus Ketut Widiartha Ida Bagus Ketut Widiartha Ida Bagus Ketut Widiartha Ida Nyoman Tegeh Adnyana Imam Arief Putrajaya Jayusman, Dirga Kadriyan, Hamsu Kansha, Lyudza Aprilia Keeichi Uchimura Keiichi Uchimura Keiichi Uchimura L. A. Syamsul Irfan Lalu Sweta Arif Lalu Zulfikar Muslim Lidia Ardhia Wardani Made Agus Dwiputra Mayzar Anas Maz Isa Ansyori Mega Laely Moh Ali Albar Moh. Ali Albar Muhamad Nizam Azmi Muhamad Syamsu Iqbal Muhammad Daden Kasandi Putra Wesa Muhammad Husnul Ramdani Muhammad Khaidar Rahman Muhammad Mukaddam Alaydrus Muhammad Naufal Rizqullah Muhammad Syulhan Al Ghofany Mulyana, Heru Murpratiwi, Santi Ika Mustiari, Mustiari Ni Nyoman Citariani Sumartha Ni Nyoman Kencanawati Nisa, Aisyah Khairun Novian Maududi Novita Nurul Fakhriyah Nugraha, Gibran Satya Nurhalimah Nurhalimah Obenu, Juanri Priskila Pahrul Irfan Pahrul Irfan Pandu Deski Prasetyo Putra, Chairul Fatikhin Rahmatin, Baiq Anggita Arsya Ramaditia Dwiyansaputra Ramaditia Dwiyansaputra Ramaditia Dwiyansaputra Ramdhani, Ghina Kamilah Ramlah Nurlaeli Rani Farinda Reza Rismawandi Rina Lestari Riska Yulianti Ristirianto Adi Romi Saefudin Rosalina Rosalina Salsabila Putri Rajani Said Santi Ika Murpratiwi Saputra, Muhammad Harpan Teguh Satya Nugraha, Gibran Selvira Anandia Intan Maulidya Setiawan, Lalu Rudi Siti Faria Astari Sri Endang Anjarwani Sri Endang Anjarwani Sri Endang Arjarwani Suhada, Destia Suksmadana, I Made Budi Sulfan Akbar Syaifullah Syaifullah Topan Khrisnanda Tri Erna Suharningsih Ulandari, Alisyia Kornelia Wahyu Alfandi Widodo, Agung Mulyo Wirarama Wedashwara Wisnujati, Andika Yogi Permana Yudo Husodo, Ario Zafrullah, Ahmad Zakiyah Rahmiati Zubaidi, Ariyan Zuhraini, Marlia Zul Rijan Firmansyah