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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) TEKNIK INFORMATIKA Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) JurTI (JURNAL TEKNOLOGI INFORMASI) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Telematika JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH MIND (Multimedia Artificial Intelligent Networking Database) Journal CSRID (Computer Science Research and Its Development Journal) Building of Informatics, Technology and Science Jurnal Mantik Jurnal Manajemen, Ekonomi Sains Jurnal Varian JURNAL PIONIR INFOKUM Journal of Applied Data Sciences Jurnal Ipteks Terapan : research of applied science and education International Journal of Research in Vocational Studies (IJRVOCAS) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Jurnal Pengabdian Masyarakat dan Riset Pendidikan SISFOTENIKA Proceeding Of International Conference On Education, Society And Humanity
Wanayumini
Universitas Asahan
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Pengabdian Masyarakat dalam Pengenalan Dunia Cyber untuk Membangun Kesadaran Literasi Digital Bagi Siswa SMA N 1 Ujung Padang: Pengabdian Wanayumini; Muhammad Azwar Al Ayyub; Dini Farhatun; Triana Puspa handayani; M yoggi saputra; Mhd Fauzan Yafi
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 2 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 2 (October 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i2.3054

Abstract

Kegiatan pengabdian masyarakat ini diselenggarakan di SMA Negeri 1 Ujung Padang dengan tujuan untuk meningkatkan pengetahuan dan kesadaran siswa mengenai pentingnya literasi digital dan pengenalan keamanan cyber. Meskipun perkembangan teknologi digital yang pesat menawarkan banyak manfaat, namun juga menimbulkan sejumlah risiko, termasuk maraknya penyebaran hoaks, pencurian data, dan kejahatan cyber. Oleh karena itu, siswa perlu diajarkan cara menggunakan teknologi secara cerdas, aman, dan bertanggung jawab. Metode yang digunakan dalam kegiatan ini adalah koordinasi dengan pihak sekolah, penyampaian materi dalam bentuk presentasi, diskusi, serta sesi tanya jawab. Kegiatan ini diikuti oleh siswa/i kelas XII IPA dan IPS. Dapat disimpulkan bahwa kegiatan sosialisasi telah sukses dan berhasil dilaksanakan dengan baik. Dimana dalam kegiatan para siswa sangat antusias dan aktif dalam diskusi dan tanya jawab. Siswa memperoleh pemahaman yang lebih baik tentang risiko di dunia digital serta strategi menjaga keamanan data pribadi. Kegiatan ini juga menumbuhkan kesadaran untuk memanfaatkan teknologi tidak hanya untuk hiburan, tetapi juga sebagai sarana pembelajaran dan pengembangan diri.
Sentiment Classification in Imbalanced Data: Trade-Offs Between Metrics and Real-World Relevance Indra Swanto Ritonga; Wanayumini; Dedy Hartama
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46652

Abstract

Sentiment analysis plays a crucial role in assessing public perception, particularly in healthcare services like BPJS Kesehatan, Indonesia’s national health insurance program. However, sentiment classification faces a challenge due to class imbalance, where negative feedback dominates positive responses. This study investigates whether sentiment classification should prioritize traditional evaluation or maintain real-world data representation by preserving the original sentiment distribution. Two feature extraction methods, Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW), were evaluated using Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression with varying maximum feature counts (100–300) to examine the impact of feature dimensionality. Model performance was evaluated using traditional metrics, while sentiment distribution fidelity was assessed by comparing predicted proportions with the dataset. Results show TF-IDF achieves higher precision and recall but fails to capture positive sentiments, leading to a skewed representation of real-world trends, while BoW offers a more balanced distribution with slightly lower accuracy. Paired t-tests and Wilcoxon signed-rank tests confirmed differences in accuracy and recall are significant, but not in precision and sentiment distribution. These findings highlight a trade-off between performance and sentiment diversity, vital in healthcare services and other fields with imbalanced datasets, emphasizing the need to align evaluation metrics with real-world objectives. Future research should investigate advanced models, such as deep learning and transformer-based approaches, to enhance both accuracy and fairness when analyzing imbalanced data.
METODE KLASIFIKASI NAIVE BAYES UNTUK MENILAI KINERJA SISWA BERDASARKAN POLA HASIL UJIAN Syahrizal, Syahrizal; Wanayumini, Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4577

Abstract

Abstract: This research discusses the application of the Naïve Bayes classification method to assess student performance based on exam result patterns at SMK Swasta Animah Silau Laut. The data used were obtained from students' e-report scores, which were then analyzed using the Naïve Bayes algorithm. The results showed that this method can classify students into 'Good', and 'Poor' categories with a fairly high level of accuracy. This study is expected to help schools carry out more objective evaluations and support decision making processes to improve the quality of education. Therefore, the application of the Naïve Bayes method is expected to assist the school in gaining a comprehensive and objective understanding of students’ academic achievements, which can serve as a solid foundation for making informed decisions regarding the planning of learning improvement strategies and other academic policies. Furthermore, this research is expected to provide a reference for future studies related to the implementation of data mining or machine learning techniques in the education field, particularly in predicting and classifying student performance. Keywords: Naïve Bayes, Classification, Data Mining, Student Performance, Education Evaluation. Abstrak: Penelitian ini membahas penerapan metode klasifikasi Naïve Bayes untuk menilai kinerja siswa berdasarkan pola hasil ujian di SMK Swasta Animah Silau Laut. Data yang digunakan diperoleh dari nilai e-raport siswa yang kemudian dianalisis dengan algoritma Naïve Bayes. Hasil penelitian menunjukkan bahwa metode ini dapat digunakan untuk mengklasifikasikan siswa ke dalam kategori 'Baik' dan 'Tidak', dengan tingkat akurasi yang cukup tinggi. Penelitian ini diharapkan dapat membantu pihak sekolah dalam melakukan evaluasi yang lebih objektif dan mendukung proses pengambilan keputusan terkait peningkatan mutu pendidikan. Dengan demikian, penerapan metode Naïve Bayes diharapkan dapat membantu pihak sekolah dalam memperoleh gambaran menyeluruh dan objektif terkait capaian akademik siswa, sehingga dapat menjadi dasar yang kuat bagi pengambilan keputusan yang tepat dalam perencanaan strategi peningkatan kualitas pembelajaran maupun kebijakan akademik lainnya. Penelitian ini juga diharapkan dapat menjadi referensi bagi penelitian selanjutnya yang berkaitan dengan penerapan teknik data mining atau machine learning dalam bidang pendidikan, khususnya dalam melakukan prediksi dan klasifikasi terhadap kinerja peserta didik.. Kata Kunci: Naïve Bayes, Klasifikasi, Data Mining, Kinerja Siswa, Evaluasi Pendidikan
Optimizing Disaster Response: A Systematic Review of Time-Dependent Cumulative Vehicle Routing in Humanitarian Logistics Hartama, Dedy; Wanayumini, Wanayumini; Damanik, Irfan Sudahri
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29686

Abstract

Effective delivery of aid during disasters is crucial for mitigating impacts and ensuring well-being. A major challenge in humanitarian logistics is optimizing vehicle routing to maximize efficiency and minimize delivery times. which included 50 studies published between 2012 and 2022. We used the prism method to guide the process of choosing a study, which started from 200 Abstract which is identified and ends with 50 appropriate studies for in -depth analysis. This systematic literature review (SLR) examines the Time-Dependent Cumulative Vehicle Routing Problem (TDCVRP) in humanitarian logistics, identifying VRP variants, their applications, and effectiveness in disaster scenarios. Using a comprehensive search and PRISMA guidelines, the review highlights the importance of optimization models and advanced algorithms. Applications include aid delivery, evacuation management, and facility location optimization, though challenges like computational complexity and reliance on real-time data persist. The review identifies research gaps and suggests future research should focus on integrating advanced methods and improving practical applicability in disaster responses.
MICROCONTROLLER IMPLEMENTATION ON ULTRASONIC SENSOR BASED AUTOMATIC TRASH CAN SYSTEM Wanayumini, Wanayumini; Isnaini, Fitri; lvindra, Farhan A; Wardana, Revo
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 1 (2024): Desember 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i1.3646

Abstract

Abstract : Waste management in Sei Beluru Village faces challenges due to population growth and increasing waste volume. This research aims to design and implement an automatic waste bin system based on microcontroller using Arduino Uno. The research uses experimental method with hardware and software development stages including system design, component integration, and testing. The developed system integrates HCSR-04 sensors for waste volume detection, infrared sensors for object presence detection, and servo motors for automatic opening-closing mechanism. Test results show that the system successfully detects waste levels with high accuracy and operates the opening-closing mechanism effectively. The implementation of this system proves effective in optimizing waste management in Sei Beluru Village by reducing physical interaction and preventing waste accumulation. Keywords : arduino uno; HCSR-04 sensor; automatic waste bin; HCSR-04 sensor; microcontroller; waste management.  Abstract : Waste management in Sei Beluru Village faces challenges due to population growth and increasing waste volume. This research aims to design and implement an automatic waste bin system based on microcontroller using Arduino Uno. The research uses experimental method with hardware and software development stages including system design, component integration, and testing. The developed system integrates HCSR-04 sensors for waste volume detection, infrared sensors for object presence detection, and servo motors for automatic opening-closing mechanism. Test results show that the system successfully detects waste levels with high accuracy and operates the opening-closing mechanism effectively. The implementation of this system proves effective in optimizing waste management in Sei Beluru Village by reducing physical interaction and preventing waste accumulation. Keywords : arduino uno; HCSR-04 sensor; automatic waste bin; HCSR-04 sensor; microcontroller; waste management. 
REAL - TIME FACE DETECTION USING MATLAB HAAR CASCADE ALGORITHM Jannah, Miftahul; Wanayumini, Wanayumini; Ardana, Abdul Aziz; Selase, Septinur; Nurliana, Nurliana
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 3 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3692

Abstract

Abstract: Face detection remains a challenging task in computer vision due to real-world factors such as uneven lighting, varying viewpoints, distance, and occlusion. This study aims to develop and evaluate a real-time facial feature detection application (detecting face, eyes, nose, and mouth) using MATLAB and a webcam. Detection is performed using the Viola-Jones Cascade Classifier method through the vision.CascadeObjectDetector function. Key parameters that were adjusted include the MergeThreshold (ranging from 4 to 50 depending on the feature) and MinSize (based on estimated feature size within the frame). However, this study does not include tuning of other parameters such as FalseAlarmRate, which constitutes a limitation of the employed method. The adjustment of these parameters proved significant in improving detection accuracy and robustness under varying lighting conditions. Nevertheless, the system still encounters difficulties in detecting facial features in the presence of occlusion. This study also has the potential to serve as a foundation for further developments in face recognition, emotion detection, or biometric authentication.            Keywords: computer vision; haar cascade; MATLAB Abstrak: Deteksi wajah merupakan tantangan dalam visi komputer karena dipengaruhi oleh kondisi nyata seperti pencahayaan tidak merata, sudut pandang, jarak, dan obstruksi. Penelitian ini bertujuan untuk mengembangkan dan menguji aplikasi deteksi fitur wajah secara real-time (wajah, mata, hidung, dan mulut) menggunakan MATLAB dan kamera webcam. Deteksi dilakukan dengan metode Viola-Jones Cascade Classifier melalui fungsi vision.CascadeObjectDetector. Parameter penting yang disesuaikan adalah MergeThreshold (antara 4 hingga 50 tergantung fitur), MinSize (mengikuti estimasi ukuran fitur dalam frame). Namun, penelitian ini tidak mencakup penyesuaian parameter lain seperti FalseAlarmRate, yang menjadi salah satu keterbatasan metode yang digunakan. Penyesuaian parameter ini terbukti signifikan dalam meningkatkan akurasi deteksi dan ketahanan terhadap variasi kondisi pencahayaan. Namun, sistem masih mengalami kesulitan mendeteksi fitur wajah jika terjadi obstruksi. Penelitian ini juga berpotensi menjadi dasar untuk pengembangan lebih lanjut dalam face recognition, emotion detection, atau biometric authentication.  Kata kunci: visi computer; haar cascade; MATLAB 
Impact of Hyperparameter Tuning on CNN-Based Algorithm for MRI Brain Tumor Classification Gea, Muhammad Nasri; Wanayumini, Wanayumini; Rosnelly, Rika
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44147

Abstract

This study examines the impact of hyperparameter tuning on the performance of Convolutional Neural Networks (CNN) in classifying brain tumors using MRI images. The dataset, sourced from Kaggle, underwent preprocessing techniques such as normalization, augmentation, and resizing to enhance consistency and diversity. The study evaluates five hyperparameter configurations, analyzing their effects on classification accuracy, precision, recall, and F1-score. The optimal configuration (batch size: 16, epochs: 10, learning rate: 0.001) achieved an accuracy of 86%, precision of 81%, recall of 85%, and an F1-score of 0.83. Other configurations showed trade-offs, where larger batch sizes increased recall but reduced precision. These findings emphasize the importance of careful hyperparameter tuning to optimize medical imaging classification performance.
Online Shop Product Sales Prediction Using Multilayer Perceptron Algorithm Safitri, Erica Rian; Tanti, Lili; Wanayumini, Wanayumini
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44286

Abstract

This study aims to develop a predictive model for forecasting product sales using the Multilayer Perceptron (MLP) algorithm. The model's performance was evaluated using key metrics, including the Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score. The model achieved an MAE of 0.861, an MSE of 9.521, and an impressive R² score of 0.999, demonstrating its ability to accurately predict product sales with minimal error. Feature correlation analysis identified key variables related to the target prediction, which is the number of products ready for shipment, underscoring the importance of feature selection in enhancing model performance. Prediction results revealed variability among product sales, with products like Foodpak Matte 245 (Code 49) predicted to sell approximately 244.31 units, while others like Stiker Kertas (Code 90) showed lower sales forecasts. The findings suggest that strategic interventions may be necessary to boost sales for underperforming items and capitalize on the demand for popular products. Future improvements, such as optimizing the network architecture, experimenting with activation functions and optimization algorithms, and incorporating external factors such as market trends, could further enhance the model’s accuracy and predictive power. Overall, the MLP model demonstrates strong potential for product sales forecasting, providing valuable insights for business decision-making.
ANALISIS PREDIKSI PENURUNAN PENJUALAN PRODUK PADA MINIMARKET MENGGUNAKAN ALGORITMA NAIVE BAYES Putri, Nazifa; Wanayumini, Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4812

Abstract

Abstract: The decline in product sales is a common issue faced by minimarkets as it affects revenue stability and stock management strategies. To address this problem, a system capable of predicting potential sales decline accurately and efficiently is required. This study aims to implement the Naive Bayes algorithm to predict product sales decline in a minimarket, measure the prediction accuracy, and identify the most influential factors contributing to sales decrease. This research employs a quantitative method with a data mining approach. The dataset used consists of minimarket sales data from 2020 to 2023, including attributes such as product name, product category, quantity sold, price, sales period, and promotion status. The research stages include data preprocessing, data splitting into training and testing sets, applying the Naive Bayes algorithm, and evaluating the results using accuracy, precision, and recall metrics. The system implementation was developed using the Python programming language and the CodeIgniter framework for a web-based interface. The results show that the Naive Bayes algorithm achieved a 100% accuracy rate, indicating excellent performance in predicting sales decline. The most influential factors affecting sales decline are promotion status and product category, where non-promoted products and those belonging to the instant food category are more likely to experience decreased sales. Therefore, the implementation of the Naive Bayes algorithm proves to be effective in helping minimarket management monitor sales trends, design promotion strategies, and improve data-driven decision-making efficiency. Keywords: Naive Bayes, sales prediction, sales decline, data mining, minimarket. Abstrak: Penurunan penjualan produk merupakan permasalahan yang sering dihadapi oleh minimarket karena dapat memengaruhi stabilitas pendapatan dan strategi pengelolaan stok barang. Untuk mengatasi permasalahan tersebut, diperlukan suatu sistem yang mampu memprediksi potensi penurunan penjualan secara akurat dan efisien. Penelitian ini bertujuan untuk menerapkan algoritma Naive Bayes dalam memprediksi penurunan penjualan produk pada minimarket, mengukur tingkat akurasi hasil prediksi, serta mengidentifikasi faktor-faktor yang paling berpengaruh terhadap penurunan penjualan. Metode penelitian yang digunakan adalah metode kuantitatif dengan pendekatan data mining. Data yang digunakan berupa data penjualan minimarket periode 2020–2023 dengan atribut meliputi nama produk, kategori produk, jumlah terjual, harga, waktu penjualan, dan status promosi. Proses penelitian meliputi tahap data preprocessing, pembagian data menjadi data latih dan data uji, penerapan algoritma Naive Bayes, serta evaluasi hasil menggunakan metrik accuracy, precision, dan recall. Implementasi sistem dilakukan dengan bahasa pemrograman Python serta framework CodeIgniter untuk antarmuka berbasis web. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes mampu memberikan hasil prediksi dengan tingkat akurasi sebesar 100%, menunjukkan kemampuan model yang sangat baik dalam memprediksi penurunan penjualan produk. Faktor yang paling memengaruhi penurunan penjualan adalah status promosi dan kategori produk, di mana produk tanpa promosi dan kategori makanan instan lebih sering mengalami penurunan penjualan. Dengan demikian, penerapan algoritma Naive Bayes terbukti efektif untuk membantu manajemen minimarket dalam memantau tren penjualan, merancang strategi promosi, serta meningkatkan efisiensi pengambilan keputusan secara berbasis data. Kata kunci: Naive Bayes, prediksi penjualan, penurunan penjualan, data mining, minimarket. 
RANCANG BANGUN SISTEM REKOMENDASI PEMILIHAN SAHAM LOW-RISK BERBASIS FUZZY TSUKAMOTO PADA PASAR MODAL INDONESIA Maharani, Puan; Wanayumini, Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4814

Abstract

Abstract: The Indonesian capital market demonstrated rapid growth throughout 2025. According to a press release issued by the Indonesia Stock Exchange (IDX) on February 10, 2025, a significant surge in the number of national capital market investors was recorded. By early January 2025, the number of Single Investor Identifications (SIDs) had exceeded 15 million, marking the highest achievement in the development of financial inclusion in Indonesia. The stock data used was limited to companies listed on the Indonesia Stock Exchange and consistently included in the LQ45 index. The objective of this study was to design a low-risk stock selection model that uses the Tsukamoto fuzzy method to manage various criteria based on market data. The result sum_alpha = 0.333 was obtained from the highest rule evaluation value (alpha), and to obtain sum_alpha_z = 8.88, the highest rule evaluation value (alpha_z) was obtained to obtain a z_final value of 26.66 (sum_alpha_z value / sum_alpha value). This stock recommendation system can systematically select low-risk stock recommendations. Keywords: Design, Recommendation System, Low-Risk Stock Selection, Fuzzy Tsukamoto, Indonesian Capital Market Abstrak: Pasar modal Indonesia menunjukkan pertumbuhan yang pesat sepanjang tahun 2025. Berdasarkan siaran pers yang diterbitkan oleh PT Bursa Efek Indonesia (BEI) pada 10 Februari 2025, tercatat lonjakan signifikan dalam jumlah investor pasar modal nasional. Hingga awal Januari 2025, jumlah Single Investor Identification (SID) telah melampaui 15 juta, yang menandai capaian paling tinggi dalam perkembangan inklusi keuangan di Indonesia. Data saham yang digunakan terbatas pada perusahaan-perusahaan yang terdaftar di bursa efek Indonesia dan secara konsisten masuk dalam indeks LQ45. Tujuan dalam penelitian ini adalah merancang sebuah model seleksi saham berisiko rendah yang menggunakan metode fuzzy Tsukamoto untuk mengelola berbagai kriteria berbasis data pasar. hasil sum_alpha=0.333 didapat dari nilai tertinggi rule evaluation (alpha) dan untuk mendapatkan nilai sum_alpha_z=8.88 didapat dari nilai tertinggi rule evaluation (alpha_z) untuk mendapat nilai z_final=26.66 (nilai sum_alpha_z/ nilai sum_alpha) hasil. Sistem rekomendasi saham ini dapat melakukan proses seleksi secara sistem tentang rekomendasi saham yang rendah resiko. Kata Kunci: Rancang Bangun, Sistem Rekomendasi, Pemilihan Saham Low-Risk, Fuzzy Tsukamoto, Pasar Modal Indonesia
Co-Authors Ade Clinton Sitepu Ade Clinton Sitepu Adelina, Mimi Chintya Al Ayyub, Muhammad Azwar Alfitra, Andra Amanda, Windi Winona Andi Zulherry Annas Prasetio Annas Prasetio Ardana, Abdul Aziz Arjuna Ginting ayadi, B. Herawan H B. Herawan Hayadi Darma, Ali Dedy Hartama Desi Irfan Desi Irfan Devy Pratiwi Dini Farhatun Doughlas Pardede Elisabeth S, Noprita Erica Rian Safitri Erlina Erlina Gea, Muhammad Nasri Habib Satria Hanani Hutabarat, Jamina Harahap, Sarwedi Hartama, Dedy Hartono Hartono Hasibuan, Cici Cahyati Husin Sariangsah Ichsan Firmansyah Indra Mawanta Indra Swanto Ritonga Irfan Sudahri Damanik Ismail, Juni isnaini, fitri JAKA KUSUMA Juni Ismail Karina Andriani Khoirunsyah Dalimunthe Lili Tanti Lili Tanti Lili Tanti, Lili Lubis, Cindy Paramitha lvindra, Farhan A M yoggi saputra M. Ari Iskandar Maharani, Puan Margolang, Khairul Fadhli Masri Wahyuni Mhd Fauzan Yafi Miftahul Jannah Muhammad Fachrurrozi Nasution Muhammad Nasri Gea Muhammad Sadikin Muhammad Sayid Amir Ali Lubis Muhammad Zarlis Mutiara S. Simanjuntak Nasution, Ammar Yasir Novendra Adisaputra Sinaga NURLIANA NURLIANA P.P.P.A.N.W. Fikrul Ilmi R.H. Zer Prasetya, Hardi Putri, Nazifa Rahma, Intan Dwi Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly RIKA ROSNELLY Rika Rosnelly, Rika Roesnelly, Rika Rohima, Rohima Roslina Roslina, Roslina Roslina, Roslina Safitri, Erica Rian Sartika Mandasari Selase, Septinur Sihombing, Rotua Simangunsong, Dame Lasmaria Sri Ayu Rosiva Srg Sugeng Riyadi Sugeng Riyadi Sumantri, Ekoliyono Wahyu Syahrizal Syahrizal T S Gunawan Tambunan, Fazli Nugraha Tammamah Lubis, Hartati Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Triana Puspa handayani Triwanda, Eri Vicky Rolanda Wardana, Revo Wulandari, Wulandari Yuni Franciska Br Tarigan Zakarias Situmorang Zer, P.P.P.A.N.W. Fikrul Ilmi R.H.