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All Journal Jurnal Pendidikan Teknologi dan Kejuruan Voteteknika (Vocational Teknik Elektronika dan Informatika) Proceedings of KNASTIK Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Pekommas Jurnal Edukasi dan Penelitian Informatika (JEPIN) Infotech Journal Sistemasi: Jurnal Sistem Informasi Jurnal Ilmiah Matrik BAREKENG: Jurnal Ilmu Matematika dan Terapan Matrix : Jurnal Manajemen Teknologi dan Informatika JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) INTECOMS: Journal of Information Technology and Computer Science KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Teknologi Terpadu Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Tematik : Jurnal Teknologi Informasi Komunikasi Informatics and Digital Expert (INDEX) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Sosial dan Teknologi Jurnal Locus Penelitian dan Pengabdian Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) J-Icon : Jurnal Komputer dan Informatika IJESPG (International Journal of Engineering, Economic, Social Politic and Government) journal Enrichment: Journal of Multidisciplinary Research and Development Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) RESLAJ: Religion Education Social Laa Roiba Journal In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Yulison Herry Chrisnanto
Program Studi Informatika, Fakultas Sains Dan Informatika Universitas Jenderal Achmad Yani
Religion Aerospace Engineering Arts Humanities Automotive Engineering 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 Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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CRYPTOCURRENCY TIME SERIES FORECASTING MODEL USING GRU ALGORITHM BASED ON MACHINE LEARNING Melina, Melina; Sukono, Sukono; Napitupulu, Herlina; Mohamed, Norizan; Herry Chrisnanto, Yulison; ID Hadiana, Asep; Kusumaningtyas, Valentina Adimurti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1317-1328

Abstract

The cryptocurrency market is experiencing rapid growth in the world. The high fluctuation and volatility of cryptocurrency prices and the complexity of non-linear relationships in data patterns attract investors and researchers who want to develop accurate cryptocurrency price forecasting models. This research aims to build a cryptocurrency forecasting model with a machine learning-based time series approach using the gated recurrent units (GRU) algorithm. The dataset used is historical Bitcoin closing price data from January 1, 2017, to July 31, 2024. Based on the gap in previous research, the selected model is only based on the accuracy value. In this study, the chosen model must fulfill two criteria: the best-fitting model based on the learning curve diagnosis and the model with the best accuracy value. The selected model is used to forecast the test data. Model selection with these two criteria has resulted in high accuracy in model performance. This research was highly accurate for all tested models with MAPE < 10%. The GRU 30-50 model is best tested with MAE = 867.2598, RMSE = 1330.427, and MAPE = 1.95%. Applying the sliding window technique makes the model accurate and fast in learning the pattern of time series data, resulting in a best-fitting model based on the learning curve diagnosis.
Klasifikasi Penyakit Stroke Menggunakan Metode Naïve Bayes Classification dan Chi-Square Feature Selection Benedictus Benny Sihotang; Yulison Herry Chrisnanto; Melina Melina
Voteteknika (Vocational Teknik Elektronika dan Informatika) Vol 12, No 3 (2024): Vol. 12, No 3, September 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/voteteknika.v12i3.129022

Abstract

Penyakit stroke merupakan suatu penyakit yang dapat memutuskan suplai darah menuju otak. Menurut World Health Organization (WHO), stroke merupakan salah satu penyebab kematian tertinggi di dunia. Penelitian ini bertujuan untuk menggunakan teknik klasifikasi untuk mendeteksi tingkat resiko terkena penyakit stroke. Klasifikasi merupakan teknik yang bertujuan untuk memperkirakan kelas dari suatu objek yang kelasnya masih tidak diketahui. Penelitian ini mengkombinasikan salah satu metode dari klasifikasi yaitu Naive Bayes dengan salah satu metode seleksi fitur yaitu Chi-Square untuk meningkatkan akurasi dari klasifikasi Naive Bayes. Hasil penelitian ini menunjukkan bahwa seleksi fitur Chi-Square terbukti dapat meningkatkan akurasi pada klasifikasi Naive Bayes dalam klasifikasi penyakit stroke dengan pembagian data latih dan uji yaitu  75:25. Hasil akurasi meningkat dari 73,55% sebelum menggunakan metode seleksi fitur Chi-Square menjadi 74,94% setelah menggunakan metode seleksi fitur Chi-Square. Penelitian ini diharapkan dapat membuka wawasan baru terkait metode seleksi fitur Chi-Square dalam meningkatkan kinerja dari suatu metode klasifikasi khususnya dalam mendeteksi risiko penyakit stroke sebagai tindakan pencegahan dan penanganan risiko penyakit stroke.Kata kunci: Chi-Square, klasifikasi,, Naive Bayes, Stroke. Stroke is a disease that can cut off the blood supply to the brain. According to the World Health Organization (WHO), stroke is one of the highest causes of death in the world. This study aims to use classification techniques to detect the risk level of stroke. Classification is a technique that aims to estimate the class of an object whose class is unknown. This research combines one of the classification methods, Naive Bayes, with one of the feature selection methods, Chi-Square, to improve the accuracy of Naive Bayes classification. The results of this study show that Chi-Square feature selection is proven to improve the accuracy of Naive Bayes classification on stroke disease classification with a division of training data and test data of 75:25. The accuracy results increased from 73.55% before using the Chi-Square feature selection method to 74.94% after using the Chi-Square feature selection method. This research is expected to open new insights related to the Chi-Square feature selection method in improving the performance of a classification method, especially in detecting the risk of stroke disease as a preventive measure and handling the risk of stroke disease.Keywords: Chi-Square, Classification, Naive Bayes, Stroke. 
Weather Classification in West Java using Ensemble Learning on Meteorological Data Azzahra, Cynthia Nur; Chrisnanto, Yulison Herry; Abdillah, Gunawan
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5343

Abstract

Weather classification in West Java presents several challenges, particularly related to class imbalance in the dataset and the complexity of meteorological variables. This study aims to improve classification accuracy by proposing a stacking classifier approach that combines Support Vector Machine (SVM) and Random Forest as base learners, with Logistic Regression serving as the meta-classifier. To address the class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied, while model optimization was conducted using GridSearchCV. Weather data from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG) for December 2024 was used and processed through transformation, normalization, and outlier handling. The dataset was then split into training and testing sets with ratios of 70:30, 80:20, and 90:10. The stacking classifier without SMOTE achieved the highest accuracy of 86.73%, but suffered from overfitting, indicated by a 13.27% gap between training and validation accuracy. The application of SMOTE improved the recall for minority classes to 76.3% and reduced overfitting, with the accuracy gap narrowing to less than 1%. The most stable performance was achieved with an 80:20 train-test split, where the SMOTE-applied and hyperparameter-optimized model reached an accuracy of 85.97%, an F1-score of 68.99%, and a statistically significant t-test result (p < 0.001). These findings demonstrate that the combination of stacking classifiers, SMOTE, and hyperparameter tuning effectively mitigates class bias and enhances model generalization, outperforming single-model classifiers in handling imbalanced weather data.
Lung Cancer Classification Using the Extreme Gradient Boosting (XGBoost) Algorithm and Mutual Information for Feature Selection Zizilia, Regitha; Chrisnanto, Yulison Herry; Abdillah, Gunawan
SISTEMASI Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5345

Abstract

Lung cancer is one of the deadliest types of cancer worldwide and is often detected too late due to the absence of early symptoms. This study aims to evaluate the impact of feature selection using Mutual Information on the performance of lung cancer classification with the XGBoost algorithm. Mutual Information is employed to select relevant features, including those with linear and non-linear relationships with the target variable, while XGBoost is chosen for its ability to handle large datasets and reduce overfitting. The study was conducted on a dataset containing 30,000 data entries, with data split scenarios of 90:10, 80:20, 70:30, and 60:40. The results show that the testing accuracy before applying Mutual Information ranged from 93.42% to 93.83%, while K-Fold Cross-Validation accuracy ranged from 94.59% to 94.76%. After feature selection, testing accuracy remained stable for the 70:30 and 60:40 split scenarios, at 93.60% and 93.42% respectively. However, K-Fold Cross-Validation accuracy decreased to 89.26% and 90.88%. In contrast, for the 90:10 and 80:20 split scenarios, a decline in accuracy was observed — testing accuracy dropped to 88.63% and 88.85%, and K-Fold Cross-Validation accuracy fell to 88.87% and 90.24%. Feature selection using Mutual Information improves computational efficiency by reducing the number of features, and it can be effectively applied to simplify feature sets without significantly compromising model performance in certain data scenarios, depending on the characteristics of the dataset.
OPTIMALISASI KUALITAS INTEGRITY PADA SISTEM DINAS KESEHATAN KOTA CIMAHI MENGGUNAKAN TEKNIK REFACTORING Fitaloka, Intan; Herry Chrisnanto, Yulison; Yuniarti, Rezki
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13316

Abstract

Proses pengembangan perangkat lunak melibatkan empat kegiatan utama yang penting: spesifikasi, perancangan, implementasi, dan pengujian, juga merupakan hal yang penting untuk meningkatkan kinerja sistem. Refaktorisasi dapat membantu meningkatkan kualitas sistem dengan memperbaiki kode yang ada dan memastikan bahwa kode dapat diuji secara otomatis dengan mudah.. Integritas pada sistem informasi sangat penting untuk memastikan bahwa data dan informasi yang ada dalam sistem akurat dan konsisten, serta untuk melindungi aset informasi dari potensi ancaman. Untuk melihat kekurangan dari sebuah program, metode code smell merupakan salah satu dari metode pendeteksi kesalahan source code. Pada jurnal ini melakukan optimalisasi pada sistem Dinas Kesehatan Kota Cimahi menggunakan metode refactoring. Metodologi yang digunakan dalam penelitian ini meliputi analisa sistem, analisa code smell, implementasi refactoring dan pengujian. Hasil pengujian pada sistem informasi yang telah direfactoring menunjukkan peningkatan. Refactoring dengan metode extract class dan extract method berhasil membuat metode dalam kelas menjadi lebih spesifik dan saling terkait. Kode yang sebelumnya belum terintegrasi keamanannya, menjadi lebih aman. Struktur kode yang lebih baik ini juga mempermudah proses deteksi dan perbaikan bug, yang mengarah pada pengurangan jumlah bug dan peningkatan performa serta stabilitas sistem.
Identifikasi Berita Palsu di Portal Media Online Menggunakan Model IndoBERT dan LSTM Kamal, Angga Mochamad; Chrisnanto, Yulison Herry; Yuniarti, Rezki
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8660

Abstract

The rapid spread of political fake news on Indonesian online media portals poses serious threats to public trust and democratic stability. The main research problem is the limitation of existing models in handling the complexity of Indonesian political narratives containing local idioms and long text structures. The proposed solution employs a hybrid IndoBERT-LSTM model with ensemble stacking approach using logistic regression meta-learner to optimize fake news detection. IndoBERT is selected to capture Indonesian language nuances, while LSTM handles sequential dependencies in long articles. The research objective is to develop an accurate detection system for political fake news by leveraging the complementary strengths of both models. The dataset comprises 32,218 political articles from credible portals (Kompas, CNN Indonesia, Tempo, Detiknews, Viva) and Turnbackhoax.id validation from September 2021 to December 2024. Research results demonstrate that ensemble stacking achieves superior performance with F1-score 0.9544, accuracy 95.41%, and AUC-ROC 0.9936, outperforming standalone IndoBERT (F1: 0.9542) and LSTM (F1: 0.9417). Error analysis identifies 4.59% error rate with 134 false positives and 88 false negatives, particularly in long articles (average 2,739 characters). This model has potential for integration into fact-checking platforms for real-time detection of Indonesian political fake news.
Prediksi Risiko Kesehatan Mental Mahasiswa Menggunakan Klasifikasi Naive Bayes Sumantri, Fithra Aditya; Chrisnanto, Yulison Herry; Melina, Melina
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8648

Abstract

The mental health of university students is a growing concern as academic, emotional, and social pressures contribute to increased psychological risks. This study aims to classify mental health risk levels Low, Medium, and High among students using the Naïve Bayes classification algorithm. A dataset consisting of 1,000 entries and 11 key variables was utilized, covering academic, psychological, and behavioral factors. The preprocessing stage included data cleaning, label encoding, normalization, and rule-based labeling to determine the target classes. Model training and testing were conducted using stratified data splitting to preserve class distribution. The initial model achieved a classification accuracy of 88,67%, with macro average F1-score of 0.87 and weighted average F1-score of 0.88. Grid Search optimization with k-fold cross-validation was applied but showed no significant improvement, indicating the model was already in optimal configuration. Furthermore, probabilistic analysis revealed that Sleep Quality and Study Stress Level were the most influential features in predicting mental health risks. The findings suggest that Naïve Bayes is effective for multi-class classification with interpretable results. This research contributes to early detection efforts and offers a foundation for targeted interventions in university mental health management.
Analisis Sentimen Komunitas Counter-Strike 2 (CS2) Menggunakan Support Vector Machine (SVM) Riyadi, Saiful Faris; Chrisnanto, Yulison Herry; Abdillah, Gunawan
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8620

Abstract

Counter-Strike 2 (CS2) is a game that has received a lot of enthusiasm from the gaming community since its release. User reviews on the Steam platform are the main source for understanding community sentiment towards this game. This study aims to analyze sentiment towards CS2 reviews using the Support Vector Machine (SVM) method. Data was collected through the Apify platform, then cleaned through processes such as tokenization, stopword removal, and lemmatization. Text features were converted into numerical values using Term Frequency-Inverse Document Frequency (TF-IDF) to be used in the SVM model. The SVM model was used to classify review sentiment into three categories: positive, neutral, and negative. Evaluation was conducted by measuring accuracy, confusion matrix, and classification reports. In the evaluation results, the SVM model using the One-vs-Rest (OVR) approach showed that the model without SMOTE produced an accuracy of 81.95%. After applying the Synthetic Minority Over-sampling (SMOTE) technique to the training data to balance the distribution between classes, the model accuracy increased slightly to 82.18%. This study provides valuable insights for game developers in understanding players' opinions about CS2. Additionally, this study demonstrates the potential of SVM in text-based sentiment analysis on user review platforms.
Klasifikasi Multi-Label Jenis dan Warna Buah Menggunakan Convolutional Neural Network (CNN) dengan Encoder Fitur Nida Ulhasanah; Yulison Herry Chrisnanto; Melina; Julian Evan Chrisnanto
TEMATIK Vol. 12 No. 1 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i1.2328

Abstract

Indonesia merupakan negara tropis dengan keanekaragaman buah yang sangat tinggi, baik dari segi jenis maupun warna. Tantangan utama dalam klasifikasi buah secara otomatis terletak pada kompleksitas pengenalan atribut ganda, seperti jenis dan warna, secara simultan di tengah variasi kondisi nyata seperti pencahayaan, latar belakang, dan sudut pandang gambar. Penelitian ini bertujuan untuk mengembangkan model klasifikasi multi-label buah menggunakan arsitektur Convolutional Neural Network (CNN) yang dilengkapi encoder ResNet-50 guna mengenali jenis dan warna buah secara bersamaan dengan tingkat akurasi dan generalisasi yang tinggi. Metode yang digunakan melibatkan pelatihan model pada dataset Fruit-360 yang berskala besar dan memiliki keragaman tinggi, serta penerapan teknik n-fold cross-validation untuk meningkatkan validitas hasil dan mengurangi risiko overfitting. Hasil penelitian menunjukkan bahwa model dengan augmentasi data mencapai akurasi validasi hingga 97%, dengan precision sebesar 98,20% dan recall 97,61%, yang membuktikan efektivitas pendekatan multi-label dalam klasifikasi atribut visual buah secara simultan.
COMPARATIVE ANALYSIS OF TIME SERIES FORECASTING MODELS USING ARIMA AND NEURAL NETWORK AUTOREGRESSION METHODS Melina, Melina; Sukono, Sukono; Napitupulu, Herlina; Mohamed, Norizan; Chrisnanto, Yulison Herry; Hadiana, Asep ID; Kusumaningtyas, Valentina Adimurti; Nabilla, Ulya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2563-2576

Abstract

Gold price fluctuations have a significant impact because gold is a haven asset. When financial markets are volatile, investors tend to turn to safer instruments such as gold, so gold price forecasting becomes important in economic uncertainty. The novelty of this research is the comparative analysis of time series forecasting models using ARIMA and the NNAR methods to predict gold price movements specifically applied to gold price data with non-stationary and non-linear characteristics. The aim is to identify the strengths and limitations of ARIMA and NNAR on such data. ARIMA can only be applied to time series data that are already stationary or have been converted to stationary form through differentiation. However, ARIMA may struggle to capture complex non-linear patterns in non-stationary data. Instead, NNAR can handle non-stationary data more effectively by modeling the complex non-linear relationships between input and output variables. In the NNAR model, the lag values of the time series are used as input variables for the neural network. The dataset used is the closing price of gold with 1449 periods from January 2, 2018, to October 5, 2023. The augmented Dickey-Fuller test dataset obtained a p-value = 0.6746, meaning the data is not stationary. The ARIMA(1, 1, 1) model was selected as the gold price forecasting model and outperformed other candidate ARIMA models based on parameter identification and model diagnosis tests. Model performance is evaluated based on the RMSE and MAE values. In this study, the ARIMA(1, 1, 1) model obtained RMSE = 16.20431 and MAE = 11.13958. The NNAR(1, 10) model produces RMSE = 16.10002 and MAE = 11.09360. Based on the RMSE and MAE values, the NNAR(1, 10) model produces better accuracy than the ARIMA(1, 1, 1) model.
Co-Authors Adam, Marcellino Ade Kania Ningsih Ade Kania Ningsih Ade Kania Ningsih Aditya Prakasa Adryansyah Adryansyah Agung Wahana Agus Komarudin Ahmed Haikal Amellia Fahezha Cahyaningrum Andhika Karulyana Febrian Asep Id Hadianna Asep Saepul Ridwan Ashaury, Herdi Asri Maspupah Azzahra, Cynthia Nur Bania Amburika Benedictus Benny Sihotang Cecep M Zakariya Darmawan, Raja Didik Garbian Nugroho Drl, Indra Raja Eina, Muhammad Fikri Eka Rahmawati Emia Rosta Br. Sebayang Enrico Budi Santoso Fadilah, Rifal Fahmy Akhmad Firdaus Faiza Renaldi, Faiza Fajar Tresnawiguna Fajri Rakhmat Umbara Fajri Rakhmat Umbara Farhan Naufal Febry Ramadhan Fitaloka, Intan Fuji Astari, Dhea Gerliandeva, Alfin Gita Mahesa Gunawan Abdillah Gunawan Abdillah Gunawan Abdillah Gunawan Abdillah Gunawan Abdillah, Gunawan Gunawan Abdullah Gunawan Gunawan Hadiana, Asep Id Herdi Ashaury Herlina Napitupulu Herlinda Padillah Ibadirachman, Rifqi Karunia Id Hadiana , Asep Irma Santikarama Joko Irawan Julian Evan Chrisnanto Kamal, Angga Mochamad Kania Ningsih, Ade Kasyidi, Fatan Kharisma Jevi Shafira Sepyanto Kholidah Syaidah Kukuh Yulion Setia Prakoso Kusumaningtyas, Valentina Adimurti Luthfia Oktasari Melina Melina Melina Melina Melina, Melina Muhammad Munzir Rizkya Mubarak Muhammad Rendy Raihan Mukti Kinani Mulianti, Adhani Musa Asyari Hidayat Jati Nabilla, Ulya Nida Ulhasanah Norizan Mohamed Permana, Hary Permatasari, Nissa Aulia Prawira, Angga Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina, Puspita Nurul Puspo Dewi Dirgantari Putri Alifianti Wiyono, Tiara Putri Eka Prakasawati Raflialdy Raksanagara Rahandanu Rachmat Raja Darmawan Razaki, Adam Rd Muhammad Alfajri Reza Noviandi Rezki Yuniarti Ridwan Ilyas RIDWAN INDRANSYAH Riyadi, Saiful Faris Rizal Dwiwahyu Pribadi Salsa Safira Nur Syamsi Santikarama, Irma Sepyanto, Kharisma Jevi Shafira Siska Vadilah Sukono . Sumantri, Fithra Aditya Tacbir Hendro Pudjiantoro Taufiq Akbar Herawan Teguh Munawar Ahmad Tiara Rahmawati Umbara, Fajri Rakhmat Valentina Adimurti Kusumaningtyas Wahyu Pratama, Raka Wawan Setiawan Widinastia, Audila Gumanty Widiyantoro, Widiyantoro Wildah Fatma Lestari Willy Hanafi Wina Witanti Wisnu Uriawan, Wisnu Yosia Oktavian Pailan Zikri Muhamad Afnan Zizilia, Regitha