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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan EKSAKTA: Journal of Sciences and Data Analysis Jurnal Ilmiah Informatika Komputer Prosiding SNATIF Jurnal Informatika dan Teknik Elektro Terapan Journal of Information System Sistem : Jurnal Ilmu-Ilmu Teknik INTEGER: Journal of Information Technology JIKO (Jurnal Informatika dan Komputer) JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) JURNAL ILMIAH INFORMATIKA Jurnal Infomedia JURNAL PENDIDIKAN TAMBUSAI Jurnal Teknik Elektro dan Komputer TRIAC JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terpadu JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Informatika dan Rekayasa Elektronik bit-Tech JE-Unisla ILKOMNIKA: Journal of Computer Science and Applied Informatics Generation Journal JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian kepada Masyarakat Nusantara Nusantara Science and Technology Proceedings Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Ilmiah Teknologi Informasi dan Robotika HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Journal of Information System and Technology (JOINT) Jurnal Teknologi dan Manajemen TIERS Information Technology Journal Jurnal Informatika, Komputer dan Bisnis (JIKOBIS) Decode: Jurnal Pendidikan Teknologi Informasi International Journal Of Computer, Network Security and Information System (IJCONSIST) ALINIER: Journal of Artificial Intelligence & Applications Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) SinarFe7 Jurnal Informatika Software dan Network (JISN) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer VARIANSI: Journal of Statistics and Its Application on Teaching and Research STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Journal of Informatics and Electronics Engineering J-Icon : Jurnal Komputer dan Informatika TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Jurnal Informatika Polinema (JIP) VISA: Journal of Vision and Ideas Journal of Innovative and Creativity Journal of Technology and System Information Journal of Software Engineering and Multimedia (JASMED) Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Himpunan: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Brilliant International Journal of Management and Tourism Jurnal Informatika Dan Tekonologi Komputer Jurnal Nasional Teknologi Informasi dan Aplikasinya
Anggraini Puspita Sari
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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 Earth & Planetary Sciences 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 Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Analisis Variasi Daya Tarik Konsumen Menggunakan Metode Repeated Measures Anova Jonathan Teguh Samuel Kaeng; Danu Satrio; Anggraini Puspita Sari
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5384

Abstract

  Penelitian ini bertujuan untuk menganalisis pengaruh variasi jenis kemasan terhadap tingkat ketertarikan konsumen pada produk kebab mini. Penelitian ini menggunakan pendekatan kuantitatif dengan metode eksperimen semu dan desain within-subjects (repeated measures design). Pengumpulan data melalui survei menggunakan Google Form, di mana setiap responden memberikan penilaian berupa rating terhadap tiga jenis kemasan kebab mini, yaitu mika, styrofoam, dan craft box, dengan asumsi harga produk yang sama. Data yang terkumpul dianalisis menggunakan metode Repeated Measures ANOVA pada taraf signifikansi 0,05. Hasil penelitian  menunjukkan terdapat perbedaan pada tingkat ketertarikan konsumen berdasarkan jenis kemasan (p < 0,05). Berdasarkan hasil penelitian ini didapatkan bahwa jenis kemasan berpengaruh terhadap daya tarik konsumen pada produk kebab mini
Diagnosis Awal Gangguan Psikologis Anak Menggunakan Fuzzy Logic dan Backward Chaining Siahaan, Renita Enjel; Anggraini Puspita Sari; Intan Ni'matul Fitri; Amelia Ananda Putri Lestari
Jurnal Informatika Polinema Vol. 12 No. 2 (2026): Vol. 12 No. 2 (2026)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i2.8902

Abstract

Gangguan mental pada anak kerap sulit untuk diidentifikasi sejak awal karena kurangnya kesadaran masyarakat dan keterbatasan akses professional. Penelitian ini bertujuan untuk merancang dan menerapkan sebuah sistem pakar berbasis web yang berfungsi sebagai alat untuk diagnosis awal gangguan mental pada anak. Sistem ini dibangun dengan menggabungkan Fuzzy Logic dan Backward Chaining. Fuzzy Logic dengan efisien untuk mengatasi ketidakpastian serta variasi dalam tingkat keseriusan gejala yang kompleks, sehingga memungkinkan representasi bertahap dari tingkat kemunculan gejala dan penilaian gejala utama yang lebih detail. Di sisi lain, Backward Chaining dipilih sebagai mekanisme inferensi berbasis aturan untuk mengarahkan proses diagnosis dari kemungkinan gangguan kembali ke gejala-gejala yang relevan. Sistem ini dikembangkan untuk mengenali sepuluh jenis gangguan mental anak yang umum, berdasarkan empat puluh gejala utama yang telah diteliti secara menyeluruh dan divalidasi oleh seorang psikolog profesional. Proses validasi ini menjamin relevansi dan ketepatan informasi yang diterapkan dalam sistem. Kontribusi dari penelitian ini adalah penyediaan alat diagnosis awal yang responsif dan mudah diakses, yang mampu menangani pola gejala dengan fleksibel. Hasil pengujian fungsional menunjukkan bahwa sistem dapat dengan baik mencocokkan pola gejala dengan kemungkinan gangguan. Diharapkan sistem ini akan menjadi sumber yang berguna bagi orang tua, guru, dan pihak-pihak terkait lainnya, untuk meningkatkan pemahaman tentang gangguan mental pada anak serta memberikan arahan diagnosis awal yang tepat dan terukur.
Agile-Scrum and Business Model Canvas in it Project Management: Integration, Effectiveness, and Critical Success Factors - Systematic Literature Review Kahpi Baiquni Arifani; Dody Pintarko; Anggraini Puspita Sari
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10070

Abstract

A Information systems (IS)/information technology (IT) project management is a critical aspect of successful technology implementation in organizations. Agile and Scrum methodologies have been proven to improve software development effectiveness, while Business Model Canvas (BMC) helps organizations design innovative and sustainable business models. This Systematic Literature Review (SLR) aims to identify, analyze, and synthesize findings from various empirical studies on the application of Agile, Scrum, and Business Model Canvas in IS/IT project management, and explore the integration of these two approaches. This study uses the PRISMA 2020 protocol with an analysis of 95 journals published between 2020 and 2025. Search databases include Google Scholar, SCOPUS, and local institutional repositories. Inclusion criteria include Indonesian/English language studies with a focus on Agile-Scrum (58 journals) and Business Model Canvas (37 journals). The analysis shows that: (1) Agile-Scrum implementation increases development efficiency by 30-50% with a 61.1% (58/95) increase in team adaptability; (2) Business Model Canvas is effective in business strategy with 38.9% (37/95) adoption in empirical studies; (3) Agile-Scrum and BMC integration is found in 15.8% of journals with more positive results (average score 9.3/10 vs 7.2 for Agile-only); (4) Critical Success Factors include organizational leadership (85%), team capability (82%), and stakeholder involvement (75%). The integration of Agile-Scrum with Business Model Canvas results in a holistic approach that combines operational and strategic aspects, increasing the probability of IS/IT project success by up to 78% compared to a single approach. However, implementation still faces challenges related to organizational culture changes, resource availability, and long-term impact evaluation.
Analisis Penggunaan Logika Fuzzy Mamdani dan Sugeno untuk Memprediksi Shade Foundation Aprinia Salsabila Roiqoh; Hanin Fatma Soraya; Dela Ayu Putri Mayona; Nabila Anggita Luna; Anggraini Puspita Sari
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v03.i01.p04

Abstract

The magnificence industry has developed quickly in later decades, expanding request for items that meet an assortment of shopper needs. One imperative angle in choosing magnificence items is finding an establishment color that suits your skin color. This investigates points to analyze the utilize of fluffy rationale, particularly the Mamdani and Sugeno strategy, in foreseeing the correct establishment shade based on varieties in skin color and suggestion. Definition of membership functions, definition of fuzzy rules, fuzzy interference and defuzzification are used as research methods. The data used was obtained from a random experiment by entering skin tone and undertone values into the program. Research results show that the shade of the foundation is greatly influenced by the color and undertone of the skin. Although there is a significant difference between the Mamdan and Sugeno method values, the final predicted base colors are not significantly different.This research strengthens the position of fuzzy logic as an effective method in improving the quality of products and services in the beauty industry, as well as solving the complex problem of determining the appropriate foundation shade for various skin types. It is hoped that the results of this research will make it easier for consumers and beauty professionals to choose the right foundation shade according to individual needs and preferences. 
Prediksi Kenaikan Penduduk Jawa Timur Menggunakan Metode Long Short Term Memory Atiqur Rozi; Muhammad Rohman Irsyadi; Sandy Nicholas; Anggraini Puspita Sari
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p02

Abstract

This research aims to develop a prediction model for population increase in East Java using the Long Short Term Memory (LSTM) method. Historical population data from the previous period will be used as input to train the LSTM model. This approach is expected to produce accurate predictions about population growth in the East Java region. The LSTM method was chosen due to its ability to handle sequential data and long-term memory, which is in line with the characteristics of demographic data. This research will involve data pre-processing, LSTM model building, and model performance evaluation using relevant metrics. The results of this research are expected to contribute to a better understanding of population growth trends in East Java and provide a basis for more informed decision-making in future regional development planning and social policy. 
LSTM with Attention Optimization for IDR-USD Exchange Rate Forecasting Muhammad Abdullah Hafizh; Anggraini Puspita Sari; Henni Endah Wahanani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3131

Abstract

This study proposes the application of the LSTM-Attention model to forecast the IDR exchange rate against the USD. Exchange rate stability is an important element in national and international economic resilience systems, as currency fluctuations can have a significant impact on trade, investment, banking, and household consumption. In the case of Indonesia, which is highly dependent on imported goods, exchange rate fluctuations cause an increase in import costs, rising inflation, and a decline in the competitiveness of export products in the global market, making accurate forecasting of exchange rate movements essential for economic policy, business strategy, and risk management. Statistical models such as ARIMA have been widely applied in exchange rate forecasting, but they have difficulty capturing the nonlinear of time series data. In recent years, machine learning methods such as Long Short-Term Memory (LSTM) have demonstrated their ability to handle timeseries data. Previous studies have shown that LSTM models generally outperform traditional methods, but they still face limitations in identifying important features across time steps. To overcome this problem, the Attention mechanism allows the model to focus on the most informative parts of the input sequence, thereby improving prediction accuracy. Experimental results show that the LSTM-Attention achieves MAPE of 1.28% and R2 of 0.97 and runtime 45% faster than BiLSTM. While BiLSTM achieved slightly higher accuracy, it’s required nearly twice the training time. Findings indicates that the proposed model offers practical choice for real-time exchange rate forecasting.
Classification Tuberculosis on Chest X-Ray Images Using Backpropagation Neural Network Ananda Ayu Puspitaningrum; Anggraini Puspita Sari; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3197

Abstract

Tuberculosis is an infectious disease that primarily affects the lungs and remains a major health concern due to the difficulty of diagnosis through manual interpretation of chest X-ray images. This study aims to develop an automatic tuberculosis classification system using the Backpropagation Neural Network (BPNN) method to improve diagnostic accuracy. The dataset used in this study was obtained from the Kaggle Tuberculosis (TB) Chest X-ray Dataset, consisting of 7.000 images divided into two classes normal and tuberculosis. The research stages include image preprocessing such as conversion to grayscale, resizing to 256×256 pixels, contrast enhancement using histogram equalization, and noise reduction using a median filter. Experiments were conducted by varying the number of hidden layers 2, 3, and 4 to analyze the effect of network architecture complexity on classification performance. The results showed that the configuration with 2 hidden layers and [100 50] neurons achieved the best performance with an accuracy of 93.57%. The findings indicate that deeper network architectures do not always guarantee higher accuracy and may increase computational load. Overall, this configuration provides an optimal balance between learning capability and accuracy, demonstrating the potential of the BPNN method in supporting early and reliable tuberculosis detection through machine learning based chest X-ray image analysis for clinical decision support.
Implementation of GRU with Attention Mechanism for Classifying Lung Diseases from Respiratory Sounds Kartika Sari; Anggraini Puspita Sari; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3210

Abstract

Early and accurate detection of lung diseases plays a crucial role in improving treatment outcomes and reducing mortality rates, particularly in low-resource healthcare settings. Conventional auscultation using a stethoscope is a fundamental, fast, and affordable method for initial lung examination. However, its effectiveness is limited by subjectivity, as it depends on the examiner’s expertise and can be influenced by environmental noise. To overcome these limitations, this study proposes a deep learning approach for lung diseases classification using a combination of Gated Recurrent Unit (GRU) and Attention Mechanism with log Mel spectrogram as an input based on respiratory sound. Unlike previous works that employed standalone methods such as GRU or CNN, the integration of Attention mechanism in this study allows the model to focus on prominent temporal patterns within respiratory sounds, thereby enhancing classification accuracy. Experiments were conducted on the ICBHI 2017 dataset, which underwent preprocessing stages consisting of minor class removal, recording location restriction, data augmentation, and log Mel spectrogram feature extraction. The test results show that the model produces high performances with an accuracy of 90.85%, precision of 93%, recall of 90.85%, and an F1-score of 91.14%, outperforming several works that reported in prior studies. These results demonstrate the effectiveness of combining GRU and Attention mechanism in capturing the temporal features of respiratory signals. Future research could focus on enhancing model robustness through improved data quality, other model architecture, and multimodal integration for broader clinical applicability.
Feature Augmentation with XGBoost to Improve 1D CNN Performance in Anemia Recognition Raissa Atha Febrianti; Anggraini Puspita Sari; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3282

Abstract

Anemia is one of the most prevalent nutritional and hematological disorders worldwide, characterized by low hemoglobin levels caused by iron deficiency, genetic factors, or chronic diseases. Diagnosis commonly relies on Complete Blood Count (CBC) interpretation, a manual process that is time-consuming and susceptible to human error. This study proposes a novel hybrid framework that integrates Extreme Gradient Boosting (XGBoost) and a One-Dimensional Convolutional Neural Network (1D-CNN) to enhance anemia classification. The methodological novelty lies in employing XGBoost as a feature-augmentation mechanism, where its class-probability outputs are fused with the original CBC features before being processed by the 1D-CNN, enabling richer representation learning compared to conventional single-model approaches. The model was trained and evaluated using a CBC dataset consisting of 364 samples covering four anemia classes (normocytic, microcytic, macrocytic, and normal), with performance assessed through an 80:20 stratified train–test split. Experimental results demonstrate that the proposed XGB–1DCNN model achieves a testing accuracy of 97.26%, precision of 98.68%, recall of 96.46%, and F1-score of 97.48%, outperforming the baseline 1D-CNN model (83.56%). These findings demonstrate that combining ensemble learning and deep learning significantly improves the model’s ability to capture complex nonlinear patterns in CBC data, offering a more reliable solution for AI-based early anemia diagnosis and clinical decision support.
Autoimmune Skin Disease Image Classification using EfficientViT-M1 with AdamW Optimizer Hafiyan Fazagi Adnanto; Anggraini Puspita Sari; Achmad Junaidi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3300

Abstract

Diagnosing autoimmune skin diseases is a clinical challenge because several conditions share overlapping visual characteristics. This study evaluates the EfficientViT-M1 model trained with the AdamW optimizer to classify images from five autoimmune skin disease categories. The dataset contains 3,336 images before augmentation and is divided into 60 percent training, 20 percent validation, and 20 percent testing to ensure stable evaluation and reduce overfitting. The model is trained for 50 epochs with a learning rate of 0.0001, and experiments using batch sizes of 64, 128, and 256 are conducted to analyze the impact of data processing on performance. Performance is measured using accuracy, precision, recall, and F1-score derived from confusion matrix results. The best performance appears at a batch size of 64, achieving 89.25 percent accuracy along with balanced precision, recall, and F1-score. These results show that EfficientViT-M1 can extract relevant lesion features while maintaining computational efficiency. A notable challenge emerges when distinguishing visually similar disease classes, particularly Psoriasis and Lichen, which often share comparable textures and color patterns that contribute to misclassification. This highlights the influence of dataset imbalance and visual overlap on prediction outcomes. The approach offers potential value for clinical practice, especially in underserved areas where automated decision support can help early screening when specialist access is limited. The model demonstrates encouraging potential as a resource-efficient tool for dermatological assessment. Future improvements may include increasing dataset diversity, incorporating clinical metadata, and exploring alternative optimization strategies to enhance diagnostic reliability.
Co-Authors Abd Rabi’ Achmad Junaidi Achmad Junaidi, Achmad Achmad Yusuf Yulestiono Adhi Dwi Saputra Adiguna Yudhanto Adila, Mar’atul Adinda Putri Budi Saraswati Aditya, Wigananda Firdaus Putra Adiyatma, Hesel Faza Afandi, Rizki Baehtiar Afina Lina Nurlaili Afina Lina Nurlaili Afina Lina Nurlaili Agung Darmawansyah Agung Mustika Rizki, Agung Mustika Agussalim, Agussalim Agustiardani, Salsa Pramudhita Ajeng Listya Devani Aji Paringga Jati Akbar, Fawwaz Ali Akbar, M.Azriel Yaqi Al-Ayyubi, Iqbal Alam, Fajar Indra Nur Aldito Restu Wintama Alfajr, Achmad Yuneda Alfi Hendri Alhamda, Denisa Septalian Alif Bayu Ammarizky Alif Ernanda Putra Alvin Rama Saputra Alvin Amelia Ananda Putri Lestari Amrullah, Ahmad Wildan Ana, Vika Rafi Ananda Ayu Puspitaningrum Andre Leto Andreas Nugroho Sihananto Andreas Nugroho Sihananto Anindhyta, Erisa Dwi Xena Aninidta, Sophia ANUGRAH PRASETYA, RAJAWALI SHAKTIKA Aprinia Salsabila Roiqoh Aqil Salim, Mas Muhammad Ar Rafi, Mohammad Hafiz Ardelia, Danika Najwa Ardiansyah, Muhammad Dafa Ardiansyah, Muhammad Naufal Arhinza, Rayhan Saneval Ariando, Aldo Pradana Aries Boedi Setiawan Arif Nur Cahyo Arif Rahman Hakim Arif Widiasan Subagio Arifani, Kahpi Baiquni Arifin, Hilda Desfianty Arini, Andhini Putri Ariningtyas, Imelda Dwi Arryanto, Bahiskara Ananda Arthansa, Radendha Muhammad Aryananda, Rangga Laksana Atiqur Rozi Awang Mohammad Ziadhasya Rizqaarrafi AZMI, ANDRA HUSNUL Azzahra Adelia Sabrina Salsabila Azzahra Asti Khairunnisa Bagus Satrio Wicaksono Basuki Rahmat Masdi Siduppa Bayu Setiawan Belva Cynara Trana Putri, Prudencia Bhaswara, Maulana Muzakki Bimantoro, Ryan Bagus Budiman, Daniel cahyono, wahyu eko Cinta Ramayanti Citra Firdausi, Putri Aulia Damai Arbaus, Damai Damayanti, Natasya Meryl Daniel Gloryo Nadirco Daniswara, Sena Danu Satrio Dea Rajwa Zahra Athaya Dela Ayu Putri Mayona Dela Puspita Lasminingrum Deswita Choirun Nisa Dewi, Shanty Kurnia Dian Maharani, Dian Dimas Satria Prayoga Dody Pintarko Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Eka Maurita Eka Prakarsa Mandyartha Ekawati, Anies Eko Kuncoro Eko Kuncoro EKO WAHYUDI Elizabeth, Caritta Endyarni, Regina Caeli Eva Salsabilla Eva Yulia Puspaningrum Fahlefi, Muhammad Reza Fahri Izzuddin Zulkarnaen Fajrina, Nur Septia Farhans, Muhammad Izzudin Fatchur Rozci Fatma Novalia Kussumarani Fauzan, Daffa Athallah Fina Amru Millati Millati Firdaus Putra Aditya, Wigananda Firmansyah, Fahrul Firmantara, Wahyu Firza Prima Aditiawan Firzannabeel Aqila Rafid Gatot Yulisianto Gatut Yulisusianto Hafiyan Fazagi Adnanto Hamid, Aisyah Amalia Hanin Fatma Soraya Hendri, Alfi Henni Endah Wahanani Hilya ‘Zada Mardhatilla Al Haadiy Hiroshi Suzuki Icham, Maulana Izuddin Audadi idhom, Mohammad Intan Ni'matul Fitri Intan Putri Mansyur Pratama Iqbal Bagus Satriawan Irsyadi, Muhamad Haidir Irsyadi, Muhammad Haidir Irsyadi, Muhammad Rohman Irwansyah, Ferry Ishak Febrianto Ismail, Jefri Abdurrozak Istiqomah, Nerissabila Uswatun Jaka Subagja Jamaludin . Jeki Saputra Jibran, Kemal Fahreza Joko Lasmono Jonathan Teguh Samuel Kaeng Julastri, Bregsi Atingsari Kahpi Baiquni Arifani Kartika Sari Kartini Kartini Kartini Kartini KEZIA, KEZIA Khairul Anwar Khairunnisa Khairunnisa Khofifah, Nada Firda krisna krisnawati wati Krisnawati Kuncoro, Eko Kurniawan, Muh. Irsyad Dwi Ledjap, Adventus Michael Bala Letkol Arh Desyderius Minggu Lina Nurlaili, Afina Lisanthoni, Angela Listanto, Evan Adwitiya Dwi M Julius St M. Rafi Ardiansyah Made Hanindia Prami Swari Maharani, Ardiana Deka MAHARDIKA, NAUFAL INDRA Mahendra, Zenryo Yudi Arnava Darva Maisie Yunita Malva Makarim, Irsyad Fadhil Maliq Reynanda , Revano Marsanda, Dea Ayu Eka Masyhuri, Alif Syahda Adji Maulana, Hendra Maulana, M. Zaky Pria Maurisa Arimbi Putri Mayya, Kalfin Syah Kilau Minggu, Desi Derius Minggu, Desi Derius Moh Avin Dharma Wijaya MOH MARIO SUBAGIO Moh. Misbahul Musthofah Mohammad Idhom Mohammad Quthbul Widad Mohammad, Bawazir Fadhil Muhammad Abdullah Hafizh Muhammad Hilmy Aziz Muhammad Lizamul Arsi Muhammad Muharrom Al Haromainy Muhammad Rohman Irsyadi Muhammad Rudmardiansyah Pratama Putra Muhammad Shaquille Syafiq Muhammad Wifaqul Azmi Mulyani Satya Bhakti Mulyo, Budi Mukhamad Mustofa, Tsabita Safana Nabila Anggita Luna Nachrowie, Nachrowie Nadia, Prasinta Hari Nafis Pratama Putra Nandana Wahyu Rizqullah Nicholas, Sandy Ninis Herawati Noor Imansyah Basoeki, Dandy Norhaslinda Binti Hasim Nur Rachman Nur Rachman Supadmana Muda Nurdiansyah, Titis Fajar Nurdianto, Muhammad Akbar Nurul Hidajati Oktavia Nur Khasanah OKTAVIAN, JAGUAR DEVA NANGGALASAKTI OKTAVIAN Olivia Dewi Ramadhani Suryoningsih Panggih Santri Paramita, Maheswari Dian Pintarko, Dody Prakoso, Akbar Tri Pramudyo, Leon Ddewandaru Prapatoni, Velian Prasetyo, Edi Dwi Pratama Putra, Moch Aditya Pratama, Fabio Arayya Pratama, Hendrico Edhent Surya Pratama, Moch Nasikh Andhyka Prismahardi Aji Riyantoko Putra Dwi Wira Gardha Yuniahans Putra, Chrystia Aji Putri Salsabila, Belia Putri Wardhani, Lintang Sari Putricia Hendra, Ria Amelia Shinta Rachmawati, Siti Naia Hesti Rahman, Fatan Izzatur Rahman, Muhammad Fadhillah Rahmawati, Deisya Dzakiyyah Rahmawati. S, Abel Dwi Raissa Atha Febrianti Ramadhani, Aimee Natya Ramadhani, Neo Rendra Ardika Resti Indah Paramita Sari Revano Maliq Reynanda Riandi Zahra, Muhammad Alvin Ridho Fajar Fahturohman Riky Hermawan Ririn Wanandi Rizki, Agung Mustika Rochmawati, Febriyan Putri Rofiah, Muflichatur Romadhoni, Firman Rozi, Atiqur Ryan Purnomo Sagita, Dhea Intan SALMAN ALFARIZI Samdono, Arif Sampurno Utomo, Moch Wahyu Sandy Nicholas Sanjaya, I Wayan Indra Sakti Sanjaya Santoso, Aries Satriya Yudha Saskia Rafika, Chesa Satrio Dharma Putra Satwika, I Kadek Susila Septyana, Dwitamara Setiawan, Aries Buedi Shafara, Anindya Restu Siahaan, Renita Enjel Siharta, Niken Febrinikmah Silitonga, Paulenta Silvania Sischa Wahyuning Tyas Sischa Wahyuning Tyas Siti Sri Wahyuni Siva Ifin Azzahra Subairi Subairi Sugeng Harianto SUGENG HARIANTO Sugiarto S Suherman Suherman Suryahadi, Farrel Zikri Suryangga, Nova Suryantari, Putu Anggi Sutrisni, Erica Aprilia Syahbana, Ahmad Nadhif Fikri Syahrul Amin, Akhmad Syamjovanka, Revelin Putri Takahiro Kitajima Takashi Yasuno Tatipang, Angeline Riendra Torrilynn Farrell Zuriely Tresna Maulana Fahrudin Ulummuddin, Ikhya Wardana, Nabila Sya’bani Wicaksono, Faris Hakim Widoretno, Astrini Aning Widya Indah Sujatmoko, Amanda Wisnu Murti, Hapsoro Yisti Vita Via Yogi Dwi Arsanti Yossie Triwinanda, Rizqullah Sandya Yunizar, Sri Fatmawati Zahran, Muhammad Sulthan Zidan, Ahmad Ziddan, Muhtasar