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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JMM (Jurnal Masyarakat Mandiri) Sebatik JURNAL PENDIDIKAN TAMBUSAI Jurnal Ilmiah Media Sisfo Journal of Information Technology and Computer Engineering JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH EXPLORE Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Teknologi Informasi dan Pendidikan Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech Jurnal Sistem Informasi dan Informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Infortech Jurnal Pendidikan Guru (JPG) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Ipteks Terapan : research of applied science and education Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence EXPLORE Jurnal Komtekinfo Journal of Computers and Digital Business SmartComp JOURNAL OF COMMUNITY SERVICE AND APPLICATION SCIENCE (JCSAS) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Pustaka Robot Sister
Sovia, Rini
Universitas Putra Indonesia YPTK Padang, Padang
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The Development of Affine Transformation Method Using Scale Invariant Feature Transform (SIFT) Hartika Zain, Ruri Hartika; Yuhandri, Yuhandri; Sovia, Rini
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3653

Abstract

Carving is a technique used to create decorative images on wood, stone, and other materials. In Indonesia, wood is a popular choice because of its durability and attractive grain. Examples of wood carvings include floral designs. The carving process can involve changes in color, texture, and scale, which may affect the carving's size and appearance and cause dimensional changes in certain materials. This study addresses the issue of quality control in wood carving on thin veneer layers. Free wood-carving data are provided as 200 flower images that can be used as input images. Affine transformation is used to determine the system behavior and the material transfer function during the production process. Additionally, we propose extending the affine transformation method to use the Scale-Invariant Feature Transform (SIFT). Affine transformations enable correlation analysis, outlier removal, and feature orientation in the affine domain. The SIFT algorithm accounts for scale, rotation, brightness, and perspective. Applications using ASIFT can efficiently process images and handle those with different pixel sizes to create new carvings. Training samples used to update the filter model are changed to the same pose. This enables the flower wood carving filter to represent objects with 98% accuracy. The model is then used to predict the class of the flower-carving data and to compute the distance between the template image's features and those of the input flower-wood-carving image. This research project has successfully developed an Affine Transformation method using SIFT features to create a new engraving application based on the ASIFT approach. 
PEMBERDAYAAN SISWA PERHOTELAN MENGGUNAKAN APLIKASI SPEECH RECOGNITION UNTUK MENINGKATKAN KEMAMPUAN BAHASA INGGRIS Rini Sovia; Shally Amna; Randy Permana
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 6 (2025): Desember
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i6.35886

Abstract

Abstrak Pentingnya penguasaan Bahasa Inggris bagi lulusan SMK jurusan perhotelan Padang menjadi perhatian penting bagi kepala sekolah dan guru, karena mayoritas siswanya masih belum mampu menguasai bahasa Inggris dengan baik. Oleh karena itu, kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kemampuan siswa mulai dari kemampuan dasar kosakata dan pengucapan Bahasa Inggris. Kegiatan ini dimulai dengan memberikan modul dan aplikasi dengan speech recognition software (SRS) yang materinya telah disesuaikan dengan kebutuhan siswa, kemudian memberikan pelatihan dan pendampingan kepada 30 siswa dengan metode partisipatif selama tiga hari. Evaluasi dilakukan dengan melihat peningkatan nilai siswa dari hasil pre-test dan post-test. Hasil evaluasi kegiatan menunjukkan adanya peningkatan nilai dengan selisih peningkatan nilai sebanyak 22,67 poin. Kegiatan PKM ini dinilai dan diakui oleh siswa dan guru memberikan manfaat untuk meningkatkan kemampuan pronunciation dan vocabulary bahasa Inggris siswa, terutama dengan diberikannya modul dan aplikasi pendamping.Abstract: The importance of English proficiency for vocational school graduates majoring in hospitality in Padang is a major concern for school principals and teachers, as the majority of students are still unable to master English well. Therefore, this community service activity aims to improve students' skills, starting with basic English vocabulary and pronunciation. This activity began with providing modules and applications with speech recognition software (SRS) whose material had been tailored to the students' needs, then providing training and assistance to 30 students using participatory methods for three days. The evaluation was carried out by looking at the increase in student scores from the pre-test and post-test results. The results of the activity evaluation showed an increase in scores with a difference of 22.67 points. This PKM activity was assessed and recognised by students and teachers as beneficial for improving students' English pronunciation and vocabulary skills, particularly through the provision of modules and supporting applications.
Penerapan Metode Simple Additive Weighting (SAW) untuk Menilai Kinerja Karyawan di Toko Al-Fazza Cosmetic Ardiansyah, Ricki; Rani, Maha; Rindhani Aditia, Mellya; Sovia, Rini; Christy, Tika
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 2 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i2.1486

Abstract

Karyawan merupakan aset penting dalam keberlangsungan sebuah usaha. Karyawan yang kompeten dan memiliki motivasi tinggi berperan dalam mempertahankan usaha ditengah perkembangan teknologi dan industri. Penilaian kinerja berfungsi untuk mengevaluasi sekaligus menjadi acuan dalam memberikan penghargaan serta menentukan posisi yang ideal bagi karyawan. Tapi penilaian kinerja masih menjadi salah satu tantangan bagi sebuah usaha. Belum adanya standar yang baku serta subjektifitas pemilik usaha dan pihak terkait yang melakukan penilaian sering menimbulkan kecemburuan, ambiguitas, dan kekhawatiran, yang mengakibatkan penurunan stabilitas dan motivasi kerja di toko al-fazza cosmetic. Untuk mempermudah dan mempercepat hasil penilaian kinerja karyawan dirancanglah sebuah sistem pendukung keputusan yang dapat membantu pemilik toko dan pihak terkait yang melakukan penilaian kinerja di toko al-fazza cosmetic. Metode yang digunakan untuk memproses penilaian kinerja di toko al-fazza cosmetic adalah simple additive weighting (saw). Kriteria yang menjadi standar dalam penilaian adalah  absensi, disiplin, tanggung jawab, sikap, layanan, pengetahuan produk, dan penampilan. Dari pengolahan data dengan metode saw sistem pendukung keputusan ini dapat memberikan penilaian kinerja dari beberapa karyawan yang menjadi alternatif dan memberikan perangkingan yang dapat digunakan oleh pemilik toko dan pihak terkait untuk menentukan hasil kinerja karyawan dan menentukan penghargaan terhadap hasil kinerja mereka. berdasarkan proses penilaian kinerja menggunakan metode SAW didapat hasil perangkingan kinerja karyawan dengan nilai tertinggi alternatif pertama Ari dengan total Vector 25.05, peringkat kedua widia dengan total nilai Vector 23.40 selanjutnya, Rizki nilai Vector 23.20, diikuti Mega dengan niali Vector 22.85, terakhir Nora dengan Nilai Vector 21,80.
Robust Predictive Model for Heart Disease Diagnosis Using Advanced Machine Learning Techniques Sovia, Rini; Anam, M. Khairul; Wisky, Irzal Arief; Permana, Randy; Rahmi, Nadya Alinda; Zain, Ruri Hartika
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1092

Abstract

This study presents a hybrid ensemble learning framework designed to enhance the predictive accuracy, robustness, and generalizability of heart disease classification models. The framework integrates three base classifiers: Decision Tree (DT), Gaussian Naive Bayes (GNB), and K Nearest Neighbor (KNN), which are combined using a stacking ensemble method with Logistic Regression (LR) as the meta learner. Each classifier contributes a distinct analytical perspective: DT models nonlinear relationships, GNB provides probabilistic reasoning, and KNN captures similarity-based patterns. Logistic Regression aggregates their outputs to produce a unified predictive decision. To mitigate class imbalance commonly observed in clinical datasets, the Synthetic Minority Oversampling Technique (SMOTE) is applied to generate synthetic samples of the minority class, improving the model’s ability to recognize underrepresented cases. Hyperparameter optimization is performed using the Optuna framework, which applies the algorithm to efficiently explore parameter configurations. The proposed model was evaluated on a publicly available heart disease dataset and achieved an accuracy of 99.61%, precision of 99.62%, recall of 99.59%, F1 score of 99.60%, and specificity of 99.58%, corresponding to a false positive rate of only 0.42 percent. These results demonstrate the framework’s strong ability to accurately identify heart disease cases while minimizing misclassification. The integration of SMOTE, stacking, and Optuna optimization contributes to its superior performance and robustness. Consequently, this approach shows strong potential for integration into clinical decision support systems to assist healthcare professionals in reliable and timely diagnosis.
Optimization of LPG Gas Distribution Routes with a Combination of the Saving Matrix Method and Nearest Neighbor Amin Amirul Mukminin, Andi; Hendrik, Billy; Sovia, Rini
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.656

Abstract

Distribution is an important process in economic activities, which involves the delivery of goods or products from producers to end consumers. Efficiency in the distribution system highly depends on the selection of optimal routes, which can affect costs, time, and the quality of service provided. PT Amartha Anugrah Mandiri, which operates in the distribution of 3 kg LPG, faces significant challenges in terms of inefficient distribution route selection, limited fleet capacity, and unstructured variations in LPG demand. The distribution routes currently used do not consider the aspects of distance, time, and cost efficiency, resulting in the wastage of resources such as fuel and time. This research aims to optimize LPG distribution routes. The methods used in this study are the Saving Matrix and Nearest Neighbor. The Saving Matrix method is used to reduce distribution distance and costs by combining existing delivery routes, while the Nearest Neighbor is applied to determine the order of visits to the nearest bases gradually. Both methods are designed to produce distribution routes that are efficient in terms of time, distance, and cost, as well as to maximize the use of the existing fleet. The data in this study were obtained thru direct observation at PT. Amartha Anugrah Mandiri. The data collected included base locations, LPG demand, vehicle capacity, and operational costs. There are 22 bases served with a total delivery reaching 1120 LPG 3 kg cylinders spread across various sub-districts of Batam City. Deliveries are carried out using trucks with a maximum capacity of 560 cylinders, so in one day, distribution requires more than one trip. Using this data, the distance matrix and savings matrix were calculated to design a more efficient distribution system. The research results show that the application of these two methods successfully reduced the total distance traveled, delivery time, and operational costs significantly, as well as improved the efficiency of LPG distribution. This research is expected to contribute to the company so that the 3 kg LPG delivery process can run optimally.
Convolutional Neural Network Architecture Densenet121 to Identify Tuberculosis Nugraha, Fajri; S, Sumijan; Sovia, Rini
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.662

Abstract

Smoking habits and the normalization of smoking activities are often a problem in many developing countries in the world. Cigarette smoke can cause many health problems that increase the risk of developing diseases and worsen the condition of people with the disease, one of which is Tuberculosis (TB). In Indonesia, based on the WHO Global TB Report 2024, Indonesia ranks second in the world in TB cases, it is estimated that there are more than 1,000,000 new cases every year, this disease is a very serious health problem and has obstacles in the identification process. This research aims to develop a TB disease identification system using Deep Learning. The methods used in this study are Convolutional Neural Network (CNN) and Densenet121 architecture. Convolutional Neural Network (CNN) was chosen for its ability to perform X-ray image analysis for visual validation, while Densenet121 was chosen because of its flexible architecture that can be applied to a wide range of computer vision applications, including image classification, object identification, and semantic segmentation. The research stage includes data collection, then preprocessing the image, namely resize, normalization, and conversion to arrays, then building a Convolutional Neural Network model with the selected architecture, then model training, model performance evaluation using accuracy and AUC metrics and ending with testing and validation by experts. The dataset used in this study is X-Ray data of tuberculosis patients taken from Kaggle to build a Deep Learning model that is able to identify TB through 100 chest X-ray image datasets. The results of the study show that the CNN model is able to identify tuberculosis with an accuracy rate of up to 90%, so it can help speed up early diagnosis or screening so that patients can continue to receive treatment and treatment. Therefore, the application of deep learning with the Convolutional Neural Network (CNN) method and DenseNet121 architecture based on X-Ray image data is an effective approach in the early detection of tuberculosis and seeks to make an important contribution to the control of lung diseases related to exposure to cigarette smoke in Indonesia.
IMPLEMENTASI ALGORITMA FUZZY UNTUK PENILAIAN KEPUASAN NASABAH PNM MEKAR DI PASAMAN Yanti, Rahma; Ramadani, Sela; Selvia, Dina; Sovia, Rini
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.v8i4.4849

Abstract

Customer satisfaction assessment is an essential component in improving the service quality of PNM Mekar, a microfinance institution focused on empowering women through ultra-micro financing. Conventional evaluations rely heavily on subjective perceptions, creating a need for a more structured and objective method. This study applies the Fuzzy Logic algorithm to measure customer satisfaction by transforming numerical data into linguistic variables through fuzzification. Annual operational data, including the number of customers and returning customers, were processed using membership functions and fuzzy rules, followed by defuzzification to obtain a crisp satisfaction value. The results indicate that all satisfaction levels fall into the low category, suggesting the need for service improvement. The fuzzy-based model proves effective in providing adaptive, consistent, and realistic satisfaction evaluation.
ANALISIS SENTIMEN MASYARAKAT MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE TERHADAP PROGRAM BPJS Saputra, Charisman Fajri; Sovia, Rini; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5660

Abstract

Abstract: BPJS Kesehatan is a national health insurance program that plays a vital role in providing public health services in Indonesia; however, its implementation has generated diverse public perceptions reflected on social media. This study analyzes public sentiment toward the BPJS Kesehatan program based on Instagram comments using a text mining and machine learning approach. The research methodology includes Indonesian text preprocessing, feature weighting using Term Frequency–Inverse Document Frequency (TF–IDF), and three-class sentiment classification (positive, negative, and neutral) using Multinomial Naïve Bayes and Support Vector Machine (SVM) algorithms. The dataset consists of 1,461 Instagram comments, which are divided into training and testing data with an 80:20 ratio. The experimental results show that Multinomial Naïve Bayes achieves an accuracy of 80.55%, while SVM yields a higher accuracy of 86.35%. These results indicate that SVM performs better in separating sentiment classes within short and imbalanced Instagram comment data. This study contributes to Indonesian-language sentiment analysis research and provides insights for evaluating public health services through social media data. Keyword: sentiment analysis; BPJS Kesehatan; Instagram; Naïve Bayes; Support Vector Machine. Abstrak: BPJS Kesehatan merupakan program strategis nasional yang berperan penting dalam menjamin akses layanan kesehatan bagi masyarakat Indonesia, namun implementasinya masih memunculkan beragam persepsi publik yang tercermin pada media sosial. Penelitian ini mengkaji analisis sentimen masyarakat terhadap program BPJS Kesehatan berdasarkan komentar pada platform Instagram menggunakan pendekatan text mining dan pembelajaran mesin. Metode penelitian meliputi pra-pemrosesan teks berbahasa Indonesia, pembobotan fitur menggunakan Term Frequency–Inverse Document Frequency (TF–IDF), serta klasifikasi sentimen tiga kelas (positif, negatif, dan netral) menggunakan algoritma Multinomial Naïve Bayes dan Support Vector Machine (SVM). Dataset yang digunakan terdiri dari 1.461 komentar Instagram yang dibagi menjadi data latih dan data uji dengan rasio 80:20. Hasil pengujian menunjukkan bahwa Multinomial Naïve Bayes menghasilkan akurasi sebesar 80,55%, sedangkan SVM mencapai akurasi yang lebih tinggi yaitu 86,35%. Temuan ini menunjukkan bahwa SVM memiliki kemampuan yang lebih baik dalam memisahkan kelas sentimen pada data komentar Instagram yang bersifat pendek dan tidak seimbang. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan analisis sentimen berbahasa Indonesia serta menjadi masukan awal bagi evaluasi layanan publik berbasis media sosial. Kata kunci: analisis sentimen; BPJS Kesehatan; Instagram; Naïve Bayes; Support Vector Machine.
PENERAPAN METODE SIMPLE ADDITIVE WEIGHTING DALAM PEMILIHAN MEDIA PROMOSI SEKOLAH (STUDI KASUS DI MTS LABORATORIUM UIN BUKITTINGGI) Tuti Nabila; Gunadi Widi Nurcahyo; Rini Sovia
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3960

Abstract

Schools play a strategic role in organizing learning and implementing promotional strategies to increase student enrollment. The use of information technology in promotions is crucial for enhancing institutional competitiveness. MTs Laboratorium UIN Bukittinggi faces challenges in determining the most effective promotional media among various alternatives. While several media have been implemented, the selection process lacks a systematic analytical approach, making it difficult to measure effectiveness objectively. This study applies the Simple Additive Weighting (SAW) method to determine the most effective promotional media. This study represents the first application of the SAW method for selecting school promotional media based on multi-criteria decision-making. The methodology includes defining criteria and weights, inputting alternative data, assessing suitability ratings, normalizing the decision matrix, and ranking alternatives. The dataset was collected from MTs Laboratorium UIN Bukittinggi, evaluating five media alternatives based on four criteria: promotion duration, reach, information completeness, and production cost. The results show that direct socialization achieved the highest final score of 0.91, followed by websites (0.51), banners (0.49), brochures (0.472), and social media (0.33). These findings provide practical guidance for schools in selecting promotional media that are both effective and efficient in attracting prospective students, optimizing resource allocation, and enhancing promotional impact. This study confirms that the SAW method effectively selects promotional media and can assist educational institutions in improving their promotional strategies
ANALISIS KUALITAS PRODUKSI AYAM BROILER MENGGUNAKAN METODE K-MEANS CLUSTERING DAN ALGORITMA C4.5 Saputra, Oriza Rama; Sovia, Rini; Yanto, Musli
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5747

Abstract

Abstract: Broiler chicken production is influenced by various factors, such as feed, environment, and maintenance management, which generate large amounts of complex production data. This condition causes the assessment and decision-making processes related to production quality to often be suboptimal and not based on in-depth data analysis. This study aims to implement the K-Means method and C4.5 algorithm to produce an analysis process that can be used as a solution in determining broiler chicken production quality in the South Coast region. The K-Means method is used to classify broiler chicken production data based on similar characteristics to facilitate pattern identification. The C4.5 algorithm was used to build a decision tree model to determine and predict broiler chicken production quality based on the most influential attributes. The research dataset was sourced from farm data in the South Coast region, with a total of 157 data points obtained. The clustering results presented three main segments, namely 94 with good results, 58 data with moderate results, and 5 data with poor results. Meanwhile, the C4.5 algorithm was built based on the clustering results from K-Means. The accuracy was calculated using the F1 score, with an accuracy of 93,75%. Keywords: Broiler Chicken;K-Means; C4.5 Algorithm. Abstrak: Produksi ayam broiler dipengaruhi oleh berbagai faktor, seperti pakan, lingkungan, dan manajemen pemeliharaan, yang menghasilkan data produksi dalam jumlah besar dan bersifat kompleks. Kondisi ini menyebabkan proses penilaian dan pengambilan keputusan terkait kualitas produksi sering kali belum optimal dan kurang didasarkan pada analisis data yang mendalam. Penelitian dilakukan bertujuan untuk Mengimplementasikan metode K-Means dan algoritma C4.5 untuk menghasilkan proses analisis yang dijadikan solusi dalam penentuan kualitas produksi ayam broiler di wiliyah Pesisir Selatan. Metode K-Means digunakan untuk mengklasifikasikan data produksi ayam broiler berdasarkan kesamaan karakteristik untuk memudahkan identifikasi pola. Algoritma C4.5 Digunakan untuk membangun model pohon keputusan dalam menentukan dan memprediksi kualitas produksi ayam broiler berdasarkan atribut yang paling berpengaruh. Dataset penelitian bersumber dari data peternakan di wilayah Pesisir Selatan, Dengan total data ada 157 data yang didapatkan. Hasil klustering menyajikan tiga segmen utama yaitu 94 dengan hasil baik, 58 data dengan hasil sedang dan 5 data dengan hasil buruk, Sedangkan Algoritma C4.5 dibangun berdasarkan hasil klustering dari K-Means. Perhitungan Hasil akurasi dengan f1 score Dengan hasil akurasi 93,75%. Kata Kunci: Ayam Broiler, K-Means, Algoritma C4.5
Co-Authors Abuzar Gafari Adiddo Restiady Adinda Syalsabila Aditra Agung Ramadhanu Agus Salim, David Ahsan Firdaus Ali Nurdiansyah Amin Amirul Mukminin, Andi Anam, M Khairul Anggy Wahyudi Aulia Fitrul Hadi Aulia Fitrul Hadi Ayu Mahessya, Raja Billy Hendrik Borianto, B Chairunnissa Deliva Akbar, Syifa Dede Pratama Deny Suyandi Deval Gusrion Devi Maryuni Devia Kartika Dila, Rahmah Dwi Andhara Valkyrie Dwiki Aulia Fakhri Edo Rinaldi Rais Effendy, Geraldo Revanska Eka Praja Wiyata Mandala Elmi Rahmawati Elmi Rahmawati, Elmi Encik Yoega Renaldi Erlanda, Hadrian Fana, Wulan Stau Fatimah, Noor Firdaus daus Firna Yenila Gema, Rima Liana Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Guslendra Gusriva, Revi Hadi, Aulia Fitrul Hadiyanto, Tegas Hadrian Erlanda Hanippa Prima Putra Harnaranda, Jefri Hartika Zain, Ruri Hartika Hasri Awal Hendri Irawan Hendri Irawan Heriyanto Hoka Muhgrah Sandawa Huda, Ramzil Ika Melinia Sapitri Fitriyanti Ilsa Hidayat Irzal Arif Wisky Islam, Md Ataul Jimmy Febio Julsapargi Nursam Kharisma Utama Putra Khomsi, Ahmad Lidya Adriani Darma Lony Armawati Tambunan Lubis, Fitri Amelia Sari Lusinia, Shary Armonitha maha rani Maha Rani Mardhiah, Sitty Mhd Wedo Muhammad Aidil Rahman Muhammad Ikhsan Al-Arrafi Muhammad Reza Putra Muhammad, Abulwafa Mutiana Pratiwi Nabilla Yasmin Niken Rindiana Nugraha, Fajri Nurdiansyah, Ali Nursam, Julsapargi Nursyahrina Oriza Rama Saputra Permana, Randi Permana, Randy Prihandoko Puja M Alca Putra, Kharisma Utama Putri Melati Putri Melati Rahma Yanti Rahmad Rahmad Rahmad Rahmad Rahman, Muhammad Aidil Rahman, Zumardi Rahmi, Nadya Alinda Ramadani, Sela Randa Mahardika Randy Permana Randy Permana Retno Devita Revi Gusriva Ricki Ardiansyah ricki ardiansyah Ricki Ardiansyah Ricki Ardiansyah, Ricki Ridwan Sutri Ridwan Sutri Rinaldi Chan, Fajri Rindhani Aditia, Mellya Riska Amelia Riyan Saputra Riyan Saputra, Riyan Roza, Yesi Betriana Rozakh, Muhammad Ruri Hartika Zain Ruri Hartika Zain Ruri Hartika Zain S, Sumijan Sandawa, Hoka Muhgrah Saputra, Charisman Fajri Saputra, Oriza Rama Saputra, Randy Sarjon Defit Sarjon Defit Selfi Melisa Selvia, Dina Shally Amna Silky Safira Siregar, Diffri Sulastri Sulastri Sumijan Sumijan Syafri Arlis Syafril Syafril Syafril Syafril Syafril Syafril Syaiffullah, Afif Tika Christy Tsalsabila Jilhan Haura Tsalsabila Jilhan Haura Tuti Nabila Wahyudi, Anggy Widya Nursanty Wifra Safitri Wirdawati, Wira Yanti, Rahma Yanto, Musli Yanto, Musli Yasmin, Nabilla Yesi Betriana Roza Yuhandri Yuhandri, Yuhandri Zainal A. Haris