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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF JURNAL PASTI (PENELITIAN DAN APLIKASI SISTEM DAN TEKNIK INDUSTRI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Annual Research Seminar JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab BAREKENG: Jurnal Ilmu Matematika dan Terapan JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) INTECOMS: Journal of Information Technology and Computer Science Jiko (Jurnal Informatika dan komputer) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurnal Kreativitas PKM JUMANJI (Jurnal Masyarakat Informatika Unjani) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Manajemen Informatika Jurnal ICT : Information Communication & Technology Building of Informatics, Technology and Science JUTIS : Jurnal Teknik Informatika Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) JOINT (Journal of Information Technology jurnal syntax admiration Tematik : Jurnal Teknologi Informasi Komunikasi Innovation in Research of Informatics (INNOVATICS) Informatics and Digital Expert (INDEX) International Journal of Global Operations Research Jurnal Sosial dan Teknologi Jurnal Ilmiah Wahana Pendidikan Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) International Journal of Quantitative Research and Modeling Jurnal Abdimas Kartika Wijayakusuma International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Journal of Informatics and Communication Technology (JICT) 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) Jurnal Algoritma IJESPG (International Journal of Engineering, Economic, Social Politic and Government) journal Ranah Research : Journal of Multidisciplinary Research and Development Enrichment: Journal of Multidisciplinary Research and Development Journal of Informatics and Communication Technology (JICT) Malahayati International Journal of Nursing and Health Science Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Hadiana, Asep Id
Universitas Jenderal Achmad Yani
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Pemberdayaan UMKM Abon Sapi di Kabupaten Bandung Barat Melalui Pelatihan Sains Data Hadiana, Asep Id; Putra, Eddie Krishna; Yuniarti, Rezki
Jurnal Abdimas Kartika Wijayakusuma Vol 5 No 3 (2024): Jurnal Abdimas Kartika Wijaya Kusuma
Publisher : LPPM Universitas Jenderal Achmad Yani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26874/jakw.v5i3.597

Abstract

Program pengabdian masyarakat yang berjudul "Pemberdayaan UMKM Abon Sapi di Kabupaten Bandung Barat Melalui Pelatihan Sains Data" bertujuan untuk meningkatkan kemampuan pelaku UMKM dalam memanfaatkan sains data dan teknologi digital untuk mendukung pengelolaan bisnis yang lebih baik. Program ini menangani sejumlah masalah utama yang dihadapi UMKM, seperti pencatatan penjualan yang masih manual, pengelolaan stok yang tidak efisien, serta keterbatasan dalam menjangkau pasar yang lebih luas. Melalui penggunaan aplikasi manajemen penjualan khusus dan pelatihan website, program ini membekali UMKM dengan keterampilan untuk mencatat penjualan secara lebih efektif, menganalisis data, serta memperluas jangkauan pemasaran melalui platform digital. Pelatihan juga mencakup teknik forecasting untuk mengoptimalkan produksi berdasarkan analisis data penjualan. Implementasi program ini menghasilkan peningkatan signifikan dalam efisiensi bisnis peserta, baik dari segi pengelolaan stok maupun pencatatan penjualan. Evaluasi dampak dilakukan melalui pre-test dan post-test, yang menunjukkan peningkatan pemahaman peserta dalam menggunakan sains data dan teknologi digital dalam operasional bisnis mereka. Hasil dari program ini menunjukkan pentingnya adopsi teknologi bagi UMKM untuk meningkatkan daya saing di era digital.
Enkripsi Data Lokasi Menggunakan Algoritma Rivest Shamir Adleman dan Advanced Encryption Standard pada Location Based Services Yasmina Azzahra; Hadiana, Asep Id; Kasyidi, Fatan
Journal of Informatics and Communication Technology (JICT) Vol. 6 No. 2 (2024)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Location Based Services atau layanan berbasis lokasi (LBS) telah menjadi bagian penting bagi kehidupan sehari-hari. Keamanan data lokasi pada LBS menjadi perhatian utama karena data tersebut dapat digunakan untuk melacak keberadaan pengguna dan mengungkapkan informasi pribadi. Oleh karena itu, penggunaan algoritma kriptografi sangat penting untuk melindungi data lokasi pada LBS. Dalam penelitian ini, algoritma RSA dan AES digunakan untuk mengenkripsi data lokasi pada LBS. AES digunakan untuk mengenkripsi data lokasi, sedangkan RSA digunakan untuk mengenkripsi secret key AES. Namun, penggunaan RSA dan AES memiliki keuntungan dan kelemahan masing-masing yang perlu dipertimbangkan dalam memilih algoritma yang tepat untuk digunakan pada sistem LBS. Penelitian ini menunjukkan bahwa proses enkripsi dan dekripsi data lokasi menggunakan kombinasi 2 algoritma dapat dilakukan pada database sistem LBS. Hasil pengujian avalanche effect pada AES mendapatkan hasil yang baik dengan rata-rata nilai mencapai 50,78 %, sedangkan pengujian entropy mendapatkan hasil yang kurang baik dimana nilai entropy yang dihasilkan jauh dari nilai entropy yang berkualitas. Pengujian performa sistem juga dilakukan untuk melihat dan membandingkan waktu antara sistem dengan enkripsi dan tanpa enkripsi. Sistem yang diterapkan algoritma kriptografi mengalami peningkatan waktu dengan rata-rata mencapai 1,46 detik.
Perlindungan Privasi Data Lokasi pada Location Based Services menggunakan Advanced Encryption Standard dan Secure Hash Algorithm-3 Nurrokhimah, Siti; Hadiana, Asep Id; Kasyidi, Fatan
Journal of Informatics and Communication Technology (JICT) Vol. 6 No. 2 (2024)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Dalam era transformasi digital, Layanan Berbasis Lokasi (LBS) kini menjadi krusial dalam berbagai platform digital, termasuk website pengiriman untuk pelacakan lokasi paket secara real-time. Namun, penggunaan LBS membawa risiko keamanan data lokasi. Dalam penggunaannya, website ini mengirimkan data ke server dalam bentuk teks biasa, yang menimbulkan kelemahan dalam keamanan data privasi pengguna. Padahal, kerahasiaan data pribadi termasuk data lokasi diatur dalam Pasal 26 UU ITE Revisi 2016 yang mengatur tentang privasi data pengguna. Berdasar hal tersebut, pada penelitian ini diterapkan algoritma AES dan SHA-3 untuk mengenkripsi dan hashing data lokasi pengguna sebelum dikirimkan ke server. Penelitian ini menunjukkan bahwa proses enkripsi dan hashing data lokasi dengan menggunakan kombinasi dua algoritma dapat diterapkan pada database sistem LBS. Pengujian dilakukan dengan melihat waktu tempuh pengiriman data pada seluruh sistem sebanyak 10 kali dengan variasi jumlah input data berbeda, yang menunjukkan adanya peningkatan waktu proses setelah implementasi Algoritma AES dan SHA-3. Selanjutnya, dari hasil pengujian avalanche effect menunjukkan hasil yang kurang baik sehingga tidak memberikan tingkat keamanan yang maksimal. Pengujian integritas juga dilakukan, dengan memeriksa hasil hashing data menggunakan SHA-3. Dari hasil pengujian tersebut, menunjukkan bahawa aspek integritas data lokasi pengguna terpenuhi sesuai dengan hasil pengujian integritas yang dilakukan.
Klasifikasi Sentimen pada Aplikasi Shopee Menggunakan Fitur Bag of Word dan Algoritma Random Forest Ananta Firdaus, Ahnaf; Id Hadiana, Asep; Kania Ningsih, Ade
Ranah Research : Journal of Multidisciplinary Research and Development Vol. 6 No. 5 (2024): Ranah Research : Journal Of Multidisciplinary Research and Development (Juli 20
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/rrj.v6i5.994

Abstract

Analisis sentimen merupakan metode klasifikasi yang digunakan untuk mengelompokkan opini yang terkandung dalam sebuah teks. Terdapat tiga jenis opini, yaitu opini positif, negatif, dan netral. Analisis sentimen sering digunakan untuk mengetahui opini masyarakat terhadap aplikasi e-commerce, seperti Shopee, melalui ulasan yang terdapat di komentar aplikasi. Dalam penelitian ini, digunakan fitur Bag of Words untuk merepresentasikan data teks ke dalam bentuk vektor yang dapat diolah oleh algoritma machine learning. Algoritma yang digunakan adalah Random Forest, yang dikenal memiliki kemampuan yang baik dalam menangani data yang kompleks dan menghasilkan prediksi yang akurat. Hasil dari penelitian ini menunjukkan bahwa model yang dibangun memiliki akurasi sebesar 84,91%. Hasil ini menunjukkan bahwa pendekatan yang digunakan cukup efektif dalam menganalisis sentimen pengguna terhadap aplikasi Shopee. Penelitian ini memberikan kontribusi signifikan dalam penerapan machine learning untuk analisis teks dan membuka peluang untuk pengembangan lebih lanjut dalam bidang ini.
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.
Prediction Of Asteroid Hazard Distance Through Earth's Orbit Using K-Neirest Neighbor Method Firdaus, Syahrul; Witanti, Wina; Melina; Hadiana, Asep Id
International Journal of Global Operations Research Vol. 6 No. 2 (2025): International Journal of Global Operations Research (IJGOR), May 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i2.373

Abstract

The National Aeronautics and Space Administration (NASA) is the U.S. government agency that is responsible forspace program. NASA observes objects in space, including asteroids. Asteroids are small, rocky objects that orbit thesun with irregular shapes and are also called planetoids. The Government agencies observe space objects includingasteroids. In terms of the infinite number of objects in space that will cross Earth's orbit, prediction is needed todetermine the danger and its level when they are crossing Earth's orbit. Prediction is a process to know what willhappen in the future which is aimed to find out the approximate asteroids that will cross the earth in the future. In thisstudy, data mining classification techniques and the K-Nearest Neighbor algorithm are used to create a predictionsystem for the threat of asteroids while crossing the earth. Classification is a grouping by classifying items intodesignated class labels, building a classification model from the data set, building a model that is used to predict futuredata. To determine the distance of the asteroid's threat throughout the earth, data mining classification techniques andthe K-Nearest Neighbor algorithm are used. The results are 57.71% accuracy, 54.89% precision, 81.42% recall, and47.45% missclassification rate.
Advanced Earthquake Magnitude Prediction Using Regression and Convolutional Recurrent Neural Networks Id Hadiana, Asep; Muhammad Sukma, Rifaz; Krishna Putra, Eddie
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5922

Abstract

Earthquake magnitude prediction is critical in seismology, with significant implications for disaster risk management and mitigation. This study presents a novel earthquake magnitude prediction model by integrating regression analysis with Convolutional Recurrent Neural Networks (CRNNs). It utilises Convolutional Neural Networks (CNNs) for spatial feature extraction from 2-dimensional seismic signal images and Long Short-Term Memory (LSTM) networks to capture temporal dependencies. The innovative model architecture incorporates residual connections and specialised regression techniques for sequential data. Validated against a comprehensive seismic dataset, the model achieves a Mean Squared Error (MSE) of 0.1909 and a Root Mean Squared Error (RMSE) of 0.4369, with a coefficient of determination of 0.79772. These metrics, alongside a correlation coefficient of 0.8980, demonstrate the model's accuracy and consistency in predicting earthquake magnitudes, establishing its potential for enhancing seismic risk assessment and informing early warning systems.
Forecasting Stock Returns Using Long Short-Term Memory (LSTM) Model Based on Inflation Data and Historical Stock Price Movements Prasetyo, Nur Faid; Witanti, Wina; Hadiana, Asep Id
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6422

Abstract

The stock market is crucial for economic growth and development, offering profit opportunities that attract investors worldwide. However, its inherent volatility necessitates the inclusion of macroeconomic indicators like inflation, which can affect stock valuation and investor behavior. This study explores predicting stock returns using a Long Short-Term Memory (LSTM) model by incorporating inflation data, historical stock price movements, and calculated returns as input features. The dataset was split into 80% for training and 20% for testing, with hyperparameter tuning conducted using the RMSprop optimizer under varying batch sizes and epoch settings. Experimental results show that the configuration using RMSprop with a batch size of 8 and 200 epochs delivered the best performance, achieving a Root Mean Squared Error (RMSE) of 0.0167 and a Mean Absolute Percentage Error (MAPE) of 25.89%. These results represent a significant improvement over alternative configurations and previous benchmarks. This study underscores the importance of including inflation as a predictive variable, enhancing the model's accuracy. The findings highlight the relevance of incorporating macroeconomic factors into stock return forecasting, providing valuable insights for investors and financial analysts seeking data-driven strategies in decision-making processes.
An Integrated Convolutional Neural Networks and Light Gradient Boosting Approach for Flood Classification Using Sentinel-1 SAR Satellite Imagery Anshori, Siddiq Ahmad; Hadiana, Asep Id; Kasyidi, Fatan
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.13600

Abstract

od classification plays a crucial role in disaster mitigation, particularly in areas frequently affected by floods. This study proposes a novel model combining Convolutional Neural Networks (CNN) using ResNet-50 and Light Gradient Boosting Machine (LightGBM) for classifying flood and non-flood areas using Sentinel-1 SAR imagery. The dataset used consists of 21,016 images, evenly distributed between flood and non-flood classes, and processed through resizing, normalization, denoising, and augmentation. Feature extraction was conducted using the ResNet-50 architecture, which captured spatial and textural patterns efficiently, followed by LightGBM for classification. The proposed model achieved a high accuracy of 96%, with Precision, Recall, and F1-scores exceeding 95% for both classes. The evaluation metrics, including Precision-Recall Curve with an AUC of 0.9852 and a Confusion Matrix, confirmed the model's robustness and balance in classifying both categories. Additionally, comparisons with previous research, such as SAR-FloodNet, demonstrated the superiority of the proposed approach, achieving a 2% improvement in accuracy. Despite these results, limitations such as the exclusive use of Sentinel-1 data and the lack of validation across diverse environmental conditions remain. Future research should explore integrating multispectral Sentinel-2 data and testing on broader datasets to enhance scalability and reliability. The findings underscore the model's potential for real-world applications in flood monitoring and disaster management systems.
Klasifikasi Aktivitas Pengguna yang Berpotensi Menyebabkan Kebocoran Informasi Sensitif Menggunakan Algoritma Random Forest Alda Amorita Azza; Asep Id Hadiana; Agus Komarudin
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.v12i1.2325

Abstract

Sensitive information leaks are a growing concern in cybersecurity, often caused by insider threats. To address this, a Random Forest classification model was developed to detect user activities that may lead to data leaks. By applying SMOTE-ENN for class balancing and optimizing model parameters, the study achieved remarkable accuracy. The model demonstrated a strong performance with an average F1-Score of 0.9167 in cross-validation and 0.9231 on the test data, reflecting its ability to identify abnormal activities with a balanced approach to precision and recall. Specifically, the model detected abnormal activities with Recall of 94.28%, meaning it effectively identified most of the risky activities while minimizing false positives. The AUC-ROC score of 0.9721 highlights the model's ability to distinguish between normal and abnormal behaviors. The results indicate that Random Forest, paired with SMOTE-ENN and parameter optimization, is an effective tool for detecting data leakage risks and insider threats, with potential for use in information security systems to monitor suspicious activities.
Co-Authors Abdillah, Fajrul Abidillah, Gunawan Adelia Siti Rukoyah Agri Yodi Prayoga Agus Komarudin Agus Komarudin Agus Komarudin Alawiah, Siti Nurbayanti Alda Amorita Azza Ali, Moch. Dzikri Azhari Ananta Firdaus, Ahnaf Anggoro, Sigit Anggun Titah Islamiyyah Anshori, Siddiq Ahmad Anwar Fauzi, Mochammad Ardiansyah, Diki Arthur Oliviana Zabka Ashaury, Herdi Ashaury, Herdy Azhari, Moch Dzikri Azy Mushofy Anwary Badrujamaludin, Asep Chrisnanto, Yulison H. Dava Maulana, Muhammad Destiyanti, Fitri Dewi Marini Umi Atmaja Dewi Ratnasari DEWI RATNASARI Diah Tri Wahyuni Eddie Khrisna Putra Edvin Ramadhan Eka Purnama Rijaludin, Muhamad Engko M, Galih Yuga Pangestu Eriyadi, Maulidina Norick Fadilah, Vira Hasna Fahrezi, Rizal Febrian Faiza Renaldi Faiza Renaldi Fajar Firdaus, Fajar Fajri Rakhmat Umbara Fajri Rakhmat Umbara Fajri Rakhmat Umbara Fajri Rakhmat Umbara Fajri Rakhmat Umbara Fajri Rakhmat Febriansyah Istianto, Andrian Ferdiansyah Ferdian Ferina Nur Maulidya Firdaus, Syahrul Firman Alamsyah Galih Jatnika Galih Yuga Pangestu Engko M Gestavito, Rio Grace Christian M. Purba Gunawan Abdillah Gunawan Abdillah, Gunawan Gunawan Abidillah Hadi Apryana Hadimas Aprilian, Doni Haikal Muhammad, Husein Hanief Kuswanto, Muhammad Rafi Helsa Hawariyah Herdi Ashaury Herlina Napitupulu Hidayatulah Himawan Hovi Sohibul Wafa Hovi Hovi, Hovi Sohibul Wafa Humaira, Hana Nazla Idham Pratama Putra Illawati, Adinda Rahma Indah Putriani Fajar Sidik Insan Kamil Nurhikmat Ipan Sugiana Iqbal Dwi Nulhakim Iqbal Prayoga Willyana Irma Santikarama Irma Setiawati Ismafiaty, Ismafiaty Julianthy, Denissya Kafi, Moch. Nurul Kania Ningsih, Ade Kasyidi, Fatan Kharisma S, Moh Iqbal Komarudin, Agus Krishna Putra, Eddie Kusumaningtyas, Valentina Adimurti Lestari, Abdila Lugina Masri M. Purba, Grace Christian Melina Melina Melina Melina Melina, Melina Monica, Taris Muhammad Hasan Thoriq Almuwaffaq Thoriq Muhammad Sukma, Rifaz Muhammad, Azri Mulyasari, Cicik Rafka Mushofy Anwary, Azy Muthmainah, Sekar Ghaida Nabilla, Ulya Nizar Septi maulana Norizan Mohamed Nurrokhimah, Siti Nurul Sabrina, Puspita Oktaviani, Ayu Nur Oliviana Zabka, Arthur Prasetyo, Nur Faid Pryma Saputra Ginting Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Putra, Eddie Krishna Putri Eka Prakasawati Rafli Firdaus Raflialdy Raksanagara Rahmawati, A Lusi Fitri Raihan Martin Permana Rakhmat Umbara, Fajri Ramdani, Maullidan Alfa Rizki Fikri Rezki Yuniarti Rezky Yuniarti Ria Amelia Junandes Ridwan Ilyas Rifaldi Elpry Rizal Rizal Dwiwahyu Pribadi Rizky Bayu Oktavian Rizky Fauzi Achman Rukoyah, Adelia Siti Salsabila Fajriati Romli Salsabila Salsabila, Mira Salsabila, Salsabila Fajriati Romli Santikarama, Irma Sapari, Albi Mulyadi SETIAWAN, YOSEP Sevty Nourmantana Shisi Prayesti Singgih, Dimas Siti Rohaeni Siti Widiani Sopian, Annisa Mufidah Sudrajat, Risqi Sukono Sukono Susanti, Adisti Dwi Syamsi, Salsa Safira Nur Syechru Denny Irja Gotama Szalfa Saadiatus Sakinah Tacbir Hendro P Tacbir Hendro P Tacbir Hendro P Tacbir Hendro Pudjiantoro Tacbir Hendro Pudjiantoro Tacbir Hendro Pudjiantoro Tacbir Hendro Pudjiantoro Tacbir Pudjiantoro Hendro Tachbir Hendro Taufiq Akbar Herawan Thomas Adi Nugroho Tulus Harry Lamramot Tulus Tulus, Tulus Harry Lamramot Umbara, Fajri Rakhmat Valentina Adimurti Kusumaningtyas Wahyuni Rodiyah Risfianti Widiyantoro, Widiyantoro Wina Witanti Wina Witanti Wina Witanti Wina Witanti Wina Witanti Wina Witanti Wina Witanti Winalia Winalia Winta Witanti Yasmina Azzahra Yudi Setiadi Permana Yulianto Dwi Saptohadi Yulison Herry Chrisnanto Yulita, Rita Fitri Yuswandi Yuswandi, Yuswandi