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All Journal International Journal of Electrical and Computer Engineering Elektron Jurnal Ilmiah TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) SITEKIN: Jurnal Sains, Teknologi dan Industri Riau Journal of Computer Science JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Jurnal Mantik Penusa Rang Teknik Journal Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURTEKSI Jurnal Teknologi Informasi dan Pendidikan bit-Tech Systematics Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika C.I.T. Medicom Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Innovative: Journal Of Social Science Research Jurnal Informatika Ekonomi Bisnis RJOCS (Riau Journal of Computer Science) Jurnal Elektronika dan Teknik Informatika Terapan Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Pengabdian Masyarakat Bangsa
Gunadi Widi Nurcahyo
Universitas Putra Indonesia YPTK Padang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Public Health Other

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Enhancing U-Net for Wrist Fracture Segmentation in X-ray Images using Adaptive Callbacks and Weighted Loss Functions Radillah, Teuku; Defit, Sarjon; Nurcahyo, Gunadi Widi
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.952

Abstract

The detection of wrist fracture through medical imaging is causing considerable challenges due to the subtle and variable manifestation of such ruptures, necessitating precise and reliable segmentation methods. Therefore, this research aimed to propose an improved U-Net model for detecting wrist fracture. The model incorporated two innovations, namely adaptive callback training and weighted loss combination. The adaptive callback mechanism could be performed by dynamically adjusting the training parameters based on the model performance to prevent overfitting and accelerate convergence. At the same time, the loss function combined Dice Loss and Binary Cross-Entropy (BCE) Loss with linear as well as non-linear exponential weighting strategies, ensuring balanced optimization between region-based accuracy and pixel classification. During this analysis, a series of experiments were conducted on a curated wrist X-ray image dataset, and the results showed that the proposed method expressed superior performance in terms of segmentation accuracy when compared with previous U-Net and other state-of-the-art procedures. The proposed method achieved 91% accuracy, 87% precision, 86% recall, and 87% F1 score. Following this discussion, the findings showed the efficacy of the adaptive training design and loss function in improving the strength and sensitivity of the model in detecting wrist fracture
Application of Fuzzy Logic Method and Analytical Hierarchy Process in Assessment of Education Quality at the Madrasah Aliyah Level Andi, Muhammad Yusril Haffandi; Nurcahyo, Gunadi Widi; Hendrik, Billy
Jurnal KomtekInfo Vol. 12 No. 3 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i3.653

Abstract

Madrasas have a strategic role in producing a generation of the nation that is intellectually intelligent as well as spiritually and emotionally mature, but the quality between madrasas is still uneven, especially in rural areas such as Kerinci Regency. The assessment of the quality of madrasas until now tends to be subjective and has not been based on a measurable, systematic, and widely replicated system. This research aims to develop an objective and structured quality evaluation system for madrasah education using a technology-based approach. The methods used are Fuzzy Logic and Analytical Hierarchy Process (AHP) which are combined into Fuzzy AHP. The scope of the research is focused on aliyah madrasas under the coordination of the Ministry of Religious Affairs of Kerinci Regency. The data was obtained through direct interviews with the Head of the Madrasah Education Section and included eight aliyah madrasas as the object of the research, which were assessed based on nine main criteria: quality of teachers, teaching materials, infrastructure, school governance, learning environment, assessment system, leadership of school principals, parental support, and technology and digitalization. A web-based decision support system was developed to automatically manage Fuzzy AHP calculations, so that it can be used as a continuous evaluation tool. The results of the study show that this model is able to produce consistent, objective, and valid assessments with a Consistency Ratio (CR) value of < 0.1. Madrasah MA2 obtained the highest ranking in the assessment of the quality of education. The Fuzzy AHP approach has proven to be effective in multi-criteria education evaluation and can be the basis for policies that are more responsive to local needs.
Large Language Model Method as a Translator Indonesian Into SQL Language Putra, Candra; Arlis, Syafri; Nurcahyo, Gunadi Widi
Jurnal KomtekInfo Vol. 12 No. 3 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i3.658

Abstract

The development of information technology has encouraged the massive implementation of information systems and web-based applications in various sectors, including in the academic environment. However, one of the challenges that are still often faced is the difficulty in extracting or mining information from databases flexibly without having to create additional report modules or write SQL code manually. This problem becomes an obstacle for non-technical users, such as administrative staff or lecturers, who need certain data quickly from academic information systems. In this paper, it is intended to convert Indonesian commands into SQL queries automatically, without the need to add additional programming code. Along with advances in Natural Language Processing (NLP) and Machine Learning technology with the Large Language Model (LLM) method, there is now a new approach that allows users to interact with databases only through commands in natural language. The case study was conducted on the Academic Information System of UIN Padangsidimpuan using a dataset of 1,500 student data. The focus of the research is on the type of Data Query Language (DQL) query in Indonesian form, which is then translated by the model into a SQL command to obtain the desired data. The results showed that this approach was able to achieve results with a Rouge1 conversion precision rate from 0.03 to 0.89. This shows that the integration of LLM technology in academic information systems has great potential in improving data accessibility, operational efficiency, and supporting data-driven decision-making faster and more intuitively, especially for users who do not have a technical background.
Analisis Sentimen Publik Terhadap Program Penurunan Angka Prevalensi Stunting Indonesia Menggunakan Data Twitter Dengan Metode Naïve Bayes Putri, Yozi Aulia; Defit, Sarjon; Nurcahyo, Gunadi Widi
Innovative: Journal Of Social Science Research Vol. 4 No. 5 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i5.15180

Abstract

Abstrak Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap program penurunan angka prevalensi stunting dengan menggunakan data Twitter sebagai sumber informasi. Stunting adalah masalah kesehatan masyarakat yang serius di banyak negara, termasuk Indonesia. Pemerintah Indonesia telah meluncurkan berbagai program untuk mengatasi masalah ini. Penelitian ini menggunakan metode analisis sentimen Naive Bayes untuk memahami persepsi dan pendapat publik terhadap upaya-upaya tersebut. Data Twitter yang dikumpulkan meliputi twit yang berkaitan dengan “stunting dan program pengentasannya”. Dari Hasil Crawling data Twitter didapat data twit sebanyak 2.543, yang kemudian masuk pada proses cleaning data, sehingga didapat sebanyak 2.307 dataset. Penerapan Metode Naïve Bayes berhasil memprediksi sentimen masyarakat dengan membagi kelas positif, netral, dan negatif, Hingga dinilai mampu menggali knowledge bahwa dari jumlah data data 2.307 data twit yang ada diketahui ada sebanyak 975 twit atau 42% yang memberikan sentimen positif, sebanyak 741 twit atau 32% yang bernilai sentimen netral, dan sebanyak 591 twit atau 25% yang memberikan sentimen negatif. Hasil pemodelan Naïve Bayes kemudian dievaluasi hingga mendapatkan nilai accuracy sebesar 79,10%, rata-rata class precision 78,79%, class recall 78,5%, dan F1-Score 78,27%. Hingga dapat diambil kesimpulan bahwa penerapan Naïve Bayes untuk klasifikasi kelas sentimen memiliki akurasi yang baik dan stabil. Kata Kunci: Sentimen Analisis, Publik Sentimen, Stunting, Twitter, Naive Bayes
Simulasi Monte Carlo Untuk Prediksi Tingkat Kebutuhan Aset Penunjang Pembelajaran (Studi Kasus di Smk Negeri 1 Merangin) Ardiani, Novia Sutra; Nurcahyo, Gunadi Widi; Sumijan, Sumijan
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i6.18812

Abstract

Aset merupakan sesuatu yang bernilai ekonomis dari pemanfaatan/pengoperasi yang menghasilkan pendapatan dan siklus umurnya panjang. Bagi sekolah, aset diharapkan menjadi sarana dan prasarana sekolah yang dapat dipergunakan dan dijaga di masa yang akan datang. Jika dalam pelaksanaannya, aset tidak dirawat dan dikelola dengan baik, maka akan dapat menghambat kegiatan sekolah tersebut. Seiring dengan perkembangan dan kemajuan sekolah maka jumlah aset di sekolah akan terus bertambah. Dalam dunia pendidikan proses prediksi sangat diperlukan, salah satunya dapat digunakan untuk kebutuhan aset di sekolah yang akan membatu mengidentifikasi dan memperkirakan kebutuhan aset yang akan datang. Dengan melakukan prediksi penggunaan aset di sekolah, pihak sekolah dapat mempersiapkan kebutuhan aset yang dibutuhkan untuk memastikan bahwa aset-aset yang digunakan selalu dalam kondisi baik, memenuhi kebutuhan siswa dan staf pengajar, serta dapat mendukung proses pembelajaran yang efektif. Oleh karena itu, penelitian ini bertujuan untuk menggunakan metode simulasi Monte Carlo dalam memprediksi kebutuhan aset penunjang pembelajaran di sebuah Sekolah Menengah Kejuruan (SMK). Data yang digunakan dalam penelitian ini adalah data aset tahun 2021 sampai dengan tahun 2023. Berdasarkan hasil pengujian prediksi kebutuhan aset yang telah dilakukan bahwa tingkat akurasi hasil simulasi tahun 2021 dibandingkan dengan data real tahun 2022 mencapai 72%, sementara tingkat akurasi hasil simulasi tahun 2022 dibandingkan dengan data real tahun 2023 meningkat menjadi 77%. Hasil tersebut adalah rata-rata dari hasil tiap prediksi setiap tahunnya. Dengan berhasilnya penerapan metode Monte Carlo ini untuk memprediksi tingkat kebutuhan aset, maka akan memberikan kemudahan bagi sekolah tersebut dalam memprediksi kebutuhan aset di tahun yang akan datang.
Model Deep Learning Berbasis Multilayer Perceptron untuk Identifikasi Demam Berdarah Dengue dan Tifus Nurhadi, Nurhadi; Defit, Sarjon; Nurcahyo, Gunadi Widi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.754

Abstract

Dengue Hemorrhagic Fever (DHF) and Typhus/Typhoid are two infectious diseases often found in tropical areas. In Indonesia, data shows that cases of DHF and typhoid are quite high, so a system is needed that can help doctors make faster and more accurate decisions based on blood test results. Based on the previous explanation, this study aims to apply the Deep Learning Multilayer Perceptron (MLP) method to be able to identify dengue fever and typhus. This study uses a Deep Learning-based Multilayer Perceptron approach for accurate classification of Dengue Fever, Typhoid Fever, and Normal cases using clinical blood parameters and selected symptoms. This methodology consists of several stages: dataset acquisition, preprocessing, model architecture design, training, and evaluation. The dataset was taken from Dumai City Hospital medical record data from 2023 to 2024, totaling 379 patient data used to identify Dengue Fever and Typhus using 7 clinical parameters as the main input obtained from laboratory examination results and patient clinical symptoms: Hemoglobin, Leukocyte, Platelet count, Hematocrit level, Headache, Abdominal pain, and diarrhea. Based on the results obtained, the application showed the best performance in classifying Dengue Fever, which is shown through the achievement of the model evaluation metrics as follows. The test results indicate that an increase in the amount of test data is directly proportional to the percentage of classification success achieved by the system. Based on the test results with 10% validation data, 70 % training data, and 20 % test data, the system showed very good performance with an overall accuracy of: 98.68% (Accuracy = 0.9868), which indicates a high level of success in classifying for the three classes, namely Normal, Dengue Fever, and Typhus.
Analisis Algoritma K-Means Clustering untuk Pengelompokan Rekomendasi Judul Proposal Tugas Akhir Mahasiswa Yulihartati, Sandra; Defit, Sarjon; Nurcahyo, Gunadi Widi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.755

Abstract

The academic process requires speed and accuracy in processing student data, such as submitting final project titles. In the context of final project title recommendations, many universities have not yet implemented the Data Mining approach optimally. Based on this, this study aims to recommend grouping of student final project proposal titles. The K-Means clustering method can be used in grouping data based on similarities between analyzed objects. With the K-Means method, the student grouping process utilizes grade data from the courses of Rock Mechanics, Drilling and Excavation Techniques, Underground Mining Methods, Reserve Modeling and Evaluation, Explosives and Blasting Techniques, Open Pit Mining, Mine Drainage Systems, Mapping Surveys, and Mineral Resources. The results of K-Means are strongly influenced by the k parameter and centroid initialization. The research variables include data mapping of course grades of students in the Mining Engineering Study Program. Based on the K-Means Clustering Method, it has been able to divide 104 student value data into 3 clusters, namely Natural Resource Exploration (C0), Geomechanics (C1) and Mining Environment (C2). The results of Cluster CO are 60, the results of Cluster C1 are 27 and the results of Cluster C2 are 17. The contribution of this research can provide fast, precise and accurate information in grouping recommendations for student final project proposal titles.
Analisis Metode Forward Chaining dan Certainty Factor untuk Diagnosa Penyakit pada Ibu Hamil Yasmin, Nabilla; Yuhandri, Yuhandri; Nurcahyo, Gunadi Widi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.756

Abstract

The high number of complications that occur during pregnancy and childbirth has the potential to significantly increase the risk of morbidity and mortality in pregnant women. The Maternal Mortality Rate (MMR) reflects the condition of pregnant, delivering, and postpartum mothers, which remains relatively high and is a major concern in the health sector. Based on this, this study aims to develop and evaluate an Expert System based on the Forward Chaining and Certainty Factor methods to diagnose diseases in pregnant women at an early stage, thereby providing fast and accurate medical decision support and minimizing the risk of complications during pregnancy. The Forward Chaining and Certainty Factor methods were chosen for their ability to handle rule-based inference processes and provide certainty level calculations in the diagnosis results. Forward Chaining is used to find solutions based on the symptoms entered by users, while the Certainty Factor helps assign confidence weights to the generated diagnosis. The dataset in this study consists of 30 data samples with 30 types of symptoms experienced by patients as variables. The results show that the Forward Chaining and Certainty Factor methods are capable of producing disease diagnoses in pregnant women with an accuracy rate of 95%. The contribution of this research is to improve the quality of maternal health services through fast and accurate diagnoses by medical personnel and to assist pregnant women in obtaining an initial diagnosis of common diseases during pregnancy.
APPLICATION OF THE PROFILE MATCHING METHOD IN RECOMMENDING DOCTORAL CANDIDATES FOR LECTURER (CASE STUDY AT STMIK ROYAL) Amin, Muhammad; Nurcahyo, Gunadi Widi; Yunus, Yuhandri
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 3 (2024): Juni 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.v10i3.3055

Abstract

Abstract: The advancement of information technology and knowledge has facilitated the production of quality information. The use of information technology has penetrated all fields, especially in the teaching domain at higher education institutions, aiding in valuable decision-making processes. This research focuses on STMIK Royal Kisaran, which faces challenges in increasing the number of doctoral-educated lecturers. To address this limitation, the study explores the implementation of a Decision Support System (DSS) using the Profile Matching method. Lecturers in higher education play a crucial role in providing education, conducting research, and contributing to society. In an effort to enhance the qualifications of lecturers, this research designs a Decision Support System using the Profile Matching method. The aim of this research is to provide recommendations for prospective lecturer candidates to pursue a Doctoral degree based on criteria factors such as length of service, functional position, research score, dedication score, age, and recognition score. Data from 46 lecturers at STMIK Royal Kisaran who meet the criteria are used to test the validity and effectiveness of the Decision Support System (DSS). Through structured analysis, it is demonstrated that the Decision Support System using the Profile Matching method successfully provides recommendations for suitable lecturer candidates to pursue doctoral studies.Keywords : decision support systems; higher education; information Technology; lecturer qualifications;  profile matching.  Abstrak: Kemajuan teknologi informasi dan ilmu pengetahuan telah menghadirkan kemudahan dalam menghasilkan informasi yang berkualitas, penggunaan teknologi informasi sudah memasuki segala bidang terutama bidang pengajaran pada perguruan tinggi dan membantu pengambilan keputusan yang bernilai. Penelitian ini berfokus pada STMIK Royal Kisaran yang mengalami kendala dalam meningkatkan jumlah dosen berpendidikan Doktor. Untuk mengatasi keterbatasan tersebut, penelitian ini mengeksplorasi penerapan Sistem Pendukung Keputusan (DSS) dengan menggunakan metode Profile Matching. Dosen pada pendidikan tinggi mempunyai peran penting dalam memberikan pendidikan, melakukan penelitian, dan memberikan kontribusi kepada masyarakat. Dalam upaya meningkatkan kualifikasi dosen, penelitian ini merancang Sistem Pendukung Keputusan dengan menggunakan metode Profile Matching. Penelitian ini bertujuan untuk memberikan rekomendasi kandidat calon dosen untuk mengejar gelar Doktor dengan berlandaskan faktor kriteria seperti lama kerja, jabatan fungsional, nilai penelitian, nilai pengabdian, umur, dan nilai rekognisi. Data dari 46 dosen STMIK Royal Kisaran yang memenuhi kriteria digunakan untuk menguji validitas dan efektivitas Sistem Pendukung Keputusan (SPK). Melalui analisis terstruktur, menunjukkan bahwa Sistem Pendukung Keputusan menggunakan metode Profile Matching berhasil memberikan rekomendasi calon dosen yang layak direkomendasikan untuk melanjutkan studi ke jenjang Doktor.Kata Kunci : kualifikasi dosen; pencocokan profil; pendidikan yang lebih tinggi; sistem pendukung keputusan; teknologi Informasi.
Development of extraction features for Detecting Adolescent Personality with Machine Learning Algorithms Wisky, Irzal Arief; Defit, Sarjon; Nurcahyo, Gunadi Widi
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3091

Abstract

This study aims to develop a Natural Language Processing (NLP)-based feature extraction algorithm optimized for personality type classification in adolescents. The algorithm used is TF-IDF + N-Gram Z, which combines Term Frequency-Inverse Document Frequency (TF-IDF) with the N-Gram Z technique to improve the feature representation of the analyzed text. TF-IDF functions to measure the importance of words in a document, while N-Gram Z enriches the context by considering the order of words that appear sequentially. The dataset in this study consists of 3,200 sentences generated by adolescent respondents through a survey designed to explore aspects of their personality. After the feature extraction process is complete, three variants of the Naïve Bayes method are applied for classification, namely Multinomial Naïve Bayes, Bernoulli Naïve Bayes, and Complement Naïve Bayes. Each variant has distinctive characteristics in handling certain data types, such as binomial and multinomial data. The results of the study show that the combined TF-IDF + N-Gram Z algorithm can produce highly representative features, as evidenced by high classification performance. The Multinomial Naïve Bayes and Complement Naïve Bayes variants each achieved 98% accuracy. These findings provide significant contributions to the development of NLP-based personality classification methods for Detecting Adolescent Personality. The combination of the TF-IDF + N-Gram Z algorithm with various Naïve Bayes variants produces an exceedingly high level of accuracy and can be applied in practice in the fields of psychology and adolescent education.
Co-Authors A Alfarisdon AA Sudharmawan, AA Abdi Rahim Damanik Afifah Cahayani Adha Afriosa Syawitri Agung Ramadhanu Ahmad Zamsuri, Ahmad Alexyusandria alexyusandria Alfarisdon, A Ali Djamhuri Andi, Muhammad Yusril Haffandi Anggraini, Siska Dwi Anita Sindar Apriade Voutama Ardia Ovidius ardialis Ardiani, Novia Sutra Asyhari, Ahmad Aulia Mardhatilla Ayudia, Dina Ayunda, Afifah Trista Bayu Rianto Billy Hendrik Boy Sandy Dwi Nugraha.H Breinda, Engla Budayawan, Khairi Budiarti, Lela Bufra, Fanny Septiani Candra Putra Candra, Yeki Cyntia Lasmi Andesti Cyntia Trimulia Damanik, Abdi Rahim Daniel Theodorus Darma Yunita Darmawi Darnis, Rahmi Dedi Irawan Defi Pebriyanti Deri Marse Putra Dina Ayudia Dinda Permata Sukma Dinul Akhiyar DWI JULISA UTARI Dwi Utari Iswavigra Dyan Mardinata Putra Efendi, Akmar Eka Praja Wiyata Mandala Eka Putra, Dian Elfina Novalia Ely Nurhalizah Nst Erizke Aulya Pasel Faisal Roza Fajri Karim Fanny Septiani Bufra Fauzan Azim Fauzi Erwis Febriani, Widya Febrina, Yerri Kurnia Fernando Ramadhan Fitriani, Yetti Fortia Magfira Gaja, Rizqi Nusabbih Hidayatullah Hafid Dwi Adha Handika, Yola Tri Hartati, Yuli Hasni, Salmi Hazlita, H Hendrik, Billy Honestya, Gabriela Humairoh, Putri Idir Fitriyanto Idir Ilham Effendi Indah Savitri Hidayat INTAN NUR FITRIYANI Ipri Adi Ira Nia Sanita Irzal Arief Wisky Iskandar Fitri Iskandar Fitri, Iskandar Jefri Rahmad Mulia Johan Harlan Jufri, Fikri Ramadhan Jufriadif Na`am, Jufriadif Jufriadif Na’am Juliantho, Dwana Abdi Julius Santoni Julius Santony Julius Santony Julius Santony Julius Santony Julius Santony Karim, Fajri Khelvin Ovela Putra Kholil, Muhammad Irvan Larissa Navia Rani Leony Lidya Lidia Sutra Lova Endriani Zen Lubis, Fitri Amelia Sari Lusi Kestina Luth Fimawahib M Mutia M, Mutia M. Almepal Wanda M. Ibnu Pati M. Iqbal Zuqron Mardayatmi, Suci Mardison Mardison Marfalino, Hari Meilinda Sari Meilinda Sari Melissa Triandini Mhd Wedo Miftahul Hasanah Miftahul Hasanah, Miftahul Miftahul Mardiyah Mike Zaimy Muhammad Amin Muhammad Irvan Kholil Nadia, Nadia Aini Hafizhah Nadya Alinda Rahmi Nasution, Amir Salim Khairul Rijal Nia Nofia Mitra Nissa, Ika Ima Nur Azizah Nur, Rofil M Nurdini, Siti Nurhadi Parinduri, Rezti Deawinda Pati, Muhammad Ibnu Petti Indrayati Sijabat Puji Chairu Sabila Putra, Akmal Darman Putra, Deri Marse Putra, Dyan Mardinata Putri Humairoh Putri, Stefani Putri, Yozi Aulia Putut Wicaksono, Putut Radillah, Teuku Rafiska, Rian Rahmad Supriadi Rahman, Zumardi Ramadhanu, Agung Riati, Itin Rika Apriani Rika Apriani, Rika Rini Sovia rini sovia Ririn Violina Ritna Wahyuni Rizka Hafsari Rizki Mubarak Roby Nurbahri Roni Salambue Rovidatul Rozakh, Muhammad Rusnedy, Hidayati Rustam, Camila S Sumijan Sabil, Muhammad Sahari Sahari Sahri, Alfi Sajida, Mayang Sandi Alam Sandrawira Anggraini Sani, Rafikasani Santriawan, Aji Sari, Fitri P. Sarjon Defit Sarjon Defit Sarjon Defit Septiana Vratiwi Sharon Sintia Sintia Siregar, Diffri Siregar, Fajri Marindra Sisi Hendriani Siska Dwi Anggraini Siswahyudianto Siti Nurdini Sovia, Rini Sri Dewi, Apriandini Sri Handayani Sri Layli Fajri Stefani Hardiyanti Putri Suci Mardayatmi Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Suri, Melati Rahma Sutra, Lidia Syafri Arlis Tesa Vausia Sandiva Tuti Nabila Ulfa, Ulia Ulfatun Hasanah Ulia Ulfa Verdian, Ihsan Vratiwi, Septiana W Wahyudi Wahyu, Fungki Wahyudi Wahid Wahyudi Wahyudi Wendi Robiansyah Weri Sirait Widya Febriani Wijaya, Ronni Andri Yasmin, Nabilla Yeng Primawati Yerri Kurnia Febrina Yetti Fitriani Yolla Rahmadi Helmi Yoni Aswan Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yuhandri Yunus Yuhandri, Y Yuli Hartati Yulihartati, Sandra Yunita Cahaya Khairani Yunus, Yuhandri Yuyu, Yuhandri Zikri, Afdal