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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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Implementasi Algoritma Apriori dalam Data Mining untuk Optimalisasi Stok Obat di Apotik Parinduri, Rezti Deawinda; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurnal KomtekInfo Vol. 11 No. 3 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v11i3.544

Abstract

Data Mining memainkan peran penting dalam mengelola dan menganalisis data besar untuk menemukan pola tersembunyi yang mendukung pengambilan keputusan strategis. Algoritma Apriori, yang dikenal untuk menemukan aturan asosiasi dalam data, menjadi alat yang sangat penting di berbagai sektor, termasuk sektor kesehatan. Dalam pengelolaan stok obat di apotek, terdapat tantangan signifikan seperti kelebihan stok, kekurangan stok, dan risiko kedaluwarsa obat, yang semuanya memerlukan solusi yang tepat dan canggih. Penelitian ini bertujuan untuk menerapkan Algoritma Apriori dalam Data Mining guna meningkatkan efektivitas pengelolaan stok obat, dengan fokus pada beberapa aspek kunci: pertama, memantau dan menganalisis pola pembelian obat secara mendalam; kedua, meningkatkan tata kelola stok melalui penerapan sistem monitoring otomatis yang terintegrasi dengan algoritma tersebut; dan ketiga, mengurangi tingkat kedaluwarsa obat melalui analisis data transaksi yang lebih komprehensif. Data transaksi yang digunakan dalam penelitian ini berasal dari PT Enseval Putera Megatrading Tbk. Cabang Padang, yang meliputi periode 3-7 Juni 2024. Data ini dianalisis menggunakan Microsoft Excel 2010 untuk pengolahan awal dan disimulasikan lebih lanjut dengan RapidMiner untuk memvalidasi hasil. Algoritma Apriori diterapkan untuk menentukan stok obat yang optimal melalui proses yang mencakup penentuan minimum support sebesar 3% dan confidence sebesar 40%, serta eliminasi itemset yang tidak relevan atau yang tidak memenuhi kriteria. Hasil dari analisis ini berhasil menemukan enam aturan asosiasi yang dapat digunakan untuk meramalkan stok obat secara lebih efektif dan efisien. Implementasi Algoritma Apriori diharapkan dapat secara signifikan meningkatkan efisiensi dalam manajemen stok obat, mengurangi risiko kelebihan atau kekurangan stok, serta meminimalkan masalah kedaluwarsa obat. Lebih dari itu, penelitian ini juga berkontribusi pada pengembangan pengetahuan ilmiah dalam bidang Data Mining dan manajemen stok obat, serta memberikan landasan yang kuat bagi penelitian lanjutan dan aplikasi praktis dalam konteks yang serupa. Dengan demikian, hasil penelitian ini tidak hanya memberikan solusi praktis untuk masalah pengelolaan stok obat, tetapi juga memperluas cakrawala pengetahuan dalam penggunaan teknik Data Mining untuk tujuan manajerial di bidang kesehatan.
Development of a machine learning model with optuna and ensemble learning to improve performance on multiple datasets Efendi, Akmar; Fitri, Iskandar; Nurcahyo, Gunadi Widi
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp375-386

Abstract

Machine learning, a subset of artificial intelligence (AI) is vital for its ability to learn from data and improve system performance. In Indonesia, advancements in ML have significant potential to boost competitiveness and foster sustainable development. However, issues like overfitting and suboptimal parameter settings can hinder model effectiveness. This study aims to improve the classification performance of ML models on various datasets. Advanced techniques like hyperparameter tuning with Optuna and ensemble learning with extreme gradient boosting (XGBoost) are integrated to enhance model performance. The study evaluates the performance of K nearest neighbors (KNN), support vector machine (SVM), and Gaussian naïve Bayes (GNB) algorithms across three datasets: academic records from the Islamic University of Riau (UIR), diabetes data from Kaggle, and Twitter data related to the 2024 elections. The findings reveal that the GNB algorithm outperforms KNN and SVM across all datasets, achieving the highest accuracy, precision, recall, and F1-score. Hyperparameter tuning with Optuna significantly improves model performance, demonstrating the value of systematic optimization. This study highlights the importance of advanced optimization techniques in developing high-performing ML models. The results suggest that robust algorithms like GNB, combined with hyperparameter tuning and ensemble learning, can significantly enhance classification performance.
Sentiment Analysis in Platform X with the Support Vector Machine Method for Generation Z Sri Dewi, Apriandini; Defit, Sarjon; Nurcahyo, Gunadi Widi
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.v12i4.659

Abstract

Advances in information technology and the increasing use of social media have significantly influenced the behavior of Generation Z. The generation born between 1997 and 2012 is known to be very familiar with the digital world, but also faces challenges such as lack of in-person social interaction and the risk of mental health disorders. This study aims to identify and classify public sentiment towards Generation Z on social media, especially on platform X (formerly Twitter). The method used is the Support Vector Machine (SVM). This research was carried out through several stages, namely the collection of 1607 data in the form of text using crawling techniques, pre-processing of text (tokenization, case folding, removal of stopwords, stemming, and normalization), and feature extraction using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The processed data is then classified into three sentiment categories: positive, negative, and neutral using SVM. Evaluation was carried out by measuring accuracy, recall value, and F1-score value through a confusion matrix. The results showed that the measurement of an accuracy value of 85%, a precision value of 85%, a value of recall of 95% and an F1-score value of 90% that SVM was able to classify sentiment with high accuracy and stability. In addition, SVM has been shown to be more effective than other methods studied in previous studies. The data analyzed shows that most sentiment towards Generation Z is negative, reflecting public concern about the behavior and mindset of this generation. This research is expected to be a reference for academics, practitioners, and policymakers in understanding public opinion and designing targeted policies for the younger generation. Keywords: Sentiment Analysis, Generation Z, Support Vector Machine, Social Media, Machine Learning.
Decision Support System in Determining TPQ/TQA Teacher Certification Categories Using the SAW Method Zikri, Afdal; Nurcahyo, Gunadi Widi; Sumijan, S
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.v12i4.666

Abstract

TPQ/TQA teacher certification is an effort to improve the quality of educators in deepening their knowledge of the Qur'an. The certification assessment process often faces challenges related to subjectivity and inconsistencies in criteria, thus requiring a decision support system capable of producing more objective and measurable assessment results. Based on the problems described above, this study aims to analyze the TPQ/TQA teacher certification assessment in Padang City. The SAW method is very suitable for this study because of its ability to perform calculations based on predetermined criteria. The research data consists of 60 assessment documents. The analysis process includes determining criteria, normalizing weights, calculations, and rankings. Based on the 60 datasets, 9 individuals obtained a certification score of A, 11 obtained a B, and 40 obtained a C. The results of this study indicate that the decision support system is capable of providing highly accurate, transparent, and efficient results in determining TPQ/TQA teacher certification scores. These findings are expected to be useful for TPQ/TQA management institutions in determining certification scores.
Implementasi SIA Berbasis Digital Dalam Pengelolaan Pembukuan Pada Usaha Toko Keluarga Nipah Wijaya, Ronni Andri; Nurcahyo, Gunadi Widi; Candra, Yeki
Jurnal Pengabdian Masyarakat Bangsa Vol. 4 No. 1 (2026): JURMAS BANGSA
Publisher : Riset Sinergi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jurmas.v4i1.1088

Abstract

Usaha Mikro Kecil dan Menengah (UMKM) Toko Keluarga akan menjadi objek dari Pengabdian Kepada Masyarakat (PKM). UMKM Toko Keluarga Nipah ini berdiri sejak 17 Juli 2020 hingga saat ini. Toko Keluarga ada minimarket yang menjual keperluan sehari-hari rumah tangga. Toko Keluarga merupakan salah satu pilihan para masyarakat untuk membeli keperluan sehari-hari rumah tangga maupun makanan dan minuman. Pada penelitian Pengabdian Kepada Masyarakat (PKM) ini diharapkan pada pengaplikasian sistem informasi akuntansi dengan menggunakan aplikasi berbasis digital dalam pengelolaan pembukuan pada usaha Toko Keluarga Nipah akan memberikan pengetahuan, gambaran, dan pemahaman teori dan praktik terkait penggunaan sistem informasi akuntansi dengan aplikasi digital sebagai objek kegiatan untuk membantu dalam pengambilan suatu keputusan sehingga dapat meningkatkan profitabilitas usaha dan membantu untuk membuat perencanaan usaha dalam jangka waktu yang panjang.
PENERAPAN MACHINE LEARNING MENGGUNAKAN ALGORITMA DECISION TREE UNTUK PREDIKSI TINGKAT KELULUSAN MAHASISWA Ely Nurhalizah Nst; Sumijan; Gunadi Widi Nurcahyo
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.v8i2.3958

Abstract

Students are an integral part of higher education institutions, where graduation rates serve as a key indicator of academic quality and institutional effectiveness. To maintain accreditation and academic standards, universities must optimize student graduation rates. Evaluating the factors influencing graduation is crucial in identifying patterns and key determinants that contribute to academic success. This study aims to predict student graduation using Machine Learning, specifically the C5.0 Decision Tree algorithm. The findings indicate a high reliability in predicting student graduation, with an accuracy of 91.35%. The model's ability to identify on-time graduates is reflected in a recall of 93.85% for the On-Time category and 87.18% for the Delayed category. The prediction accuracy is further demonstrated by a precision of 92.42% for the On-Time category and 89.47% for the Delayed category. The F1-Score, which represents the balance between recall and precision, reaches 93.12% for the On-Time category and 88.32% for the Delayed category. These evaluation metrics indicate that the C5.0 algorithm effectively classifies students based on their likelihood of graduating with high accuracy. The predictions generated can serve as a reference for universities to identify at-risk students early, allowing the implementation of appropriate academic strategies to improve graduation rates, accreditation, and institutional quality.
ANALISIS BIG DATA BEASISWA KIP-K MENGGUNAKAN K-MEANS CLUSTERING Defi Pebriyanti; Sarjon Defit; Gunadi Widi Nurcahyo
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.v8i2.3959

Abstract

The Kartu Indonesia Pintar Kuliah (KIP-K) Scholarship Program is a government initiative to provide higher education access to underprivileged students. It aims to reduce educational disparities and improve access for eligible students. However, the selection process faces challenges, particularly in identifying applicants who truly need financial aid. With the increasing number of applicants each year, a Big Data-based approach is essential to enhance selection efficiency and accuracy. This study analyzes KIP-K scholarship recipients’ profiles using the K-Means Clustering method. This technique groups data based on attribute similarities, allowing an objective and data-driven selection process. The dataset, obtained from Universitas Prima Nusantara Bukittinggi (2024), consists of 479 applicants. It includes attributes such as academic performance, parental income, number of dependents, KIP-K card ownership, and achievements. Results indicate that recipients can be categorized based on document completeness, academic scores above 85, and more than three family dependents. Implementing K-Means Clustering improves the selection process by making it more objective, transparent, and efficient.
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
Optimasi Seleksi Ekstrakurikuler Siswa Menggunakan Metode Profile Matching: Studi Kasus di SMP Negeri 1 Kerinci M. Iqbal Zuqron; Sarjon Defit; Gunadi Widi Nurcahyo
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2211

Abstract

Penerapan metode Profile Matching dalam pengelompokan minat dan bakat ekstrakurikuler siswa di SMP Negeri 1 Kerinci. Pemilihan ekstrakurikuler yang tepat bagi siswa merupakan tantangan tersendiri bagi sekolah, terutama karena belum adanya sistem pendukung keputusan yang terkomputerisasi. Selama ini, pemilihan dilakukan secara manual berdasarkan aspek tinggi badan, berat badan, fleksibilitas, dan kecepatan, yang sering kali tidak objektif dan memakan waktu lama. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem berbasis komputer yang dapat membantu menentukan ekstrakurikuler siswa secara lebih efektif dan efisien. Metode Profile Matching digunakan untuk mencocokkan kompetensi individu dengan standar kompetensi ekstrakurikuler. Proses ini dilakukan dengan mengidentifikasi gap antara nilai profil siswa dan nilai target yang telah ditentukan untuk setiap ekstrakurikuler. Perhitungan dilakukan dengan menentukan bobot pada faktor utama (core factor) dan faktor pendukung (secondary factor), yang masing-masing diberi persentase pengaruh sebesar 60% dan 40%. Dari hasil perhitungan, sistem dapat secara otomatis merekomendasikan ekstrakurikuler yang paling sesuai untuk setiap siswa. Hasil penelitian menunjukkan bahwa sistem berbasis Profile Matching ini dapat meningkatkan akurasi pemilihan ekstrakurikuler hingga 85% dibandingkan dengan metode manual. Selain itu, implementasi sistem berbasis web dengan bahasa pemrograman PHP membantu mempercepat proses seleksi dan meminimalkan subjektivitas dalam pengambilan keputusan. Dengan adanya sistem ini, diharapkan proses seleksi ekstrakurikuler dapat dilakukan dengan lebih objektif, akurat, dan efisien. Persentase keakuratan: 85% (berdasarkan perhitungan metode dan hasil perbandingan dengan sistem manual).
Implementasi Metode Profile Matching dalam Sistem Pendukung Keputusan untuk Seleksi Penerimaan Siswa Baru Mhd Wedo; Gunadi Widi Nurcahyo; Rini Sovia
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2229

Abstract

Kemajuan teknologi informasi telah memberikan kontribusi signifikan dalam berbagai bidang, termasuk pendidikan. Salah satu tantangan dalam dunia pendidikan adalah proses seleksi penerimaan siswa baru yang sering kali memerlukan pengambilan keputusan yang cepat, objektif, dan akurat. Penelitian ini bertujuan untuk mengembangkan Sistem Pendukung Keputusan (SPK) berbasis web dengan menerapkan metode Profile Matching dalam proses penerimaan siswa baru di SMPN 1 Kerinci. Metode Profile Matching dipilih karena kemampuannya dalam membandingkan kompetensi individu dengan standar yang telah ditetapkan, sehingga dapat mengurangi subjektivitas dalam proses seleksi. Penelitian ini menggunakan pendekatan kuantitatif dengan metode eksperimen, yang melibatkan pengumpulan data nilai akademik dan non-akademik calon siswa, serta implementasi algoritma Profile Matching dalam sistem berbasis web. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu meningkatkan efisiensi dan akurasi proses seleksi dengan mengurangi waktu yang dibutuhkan dalam penilaian serta memberikan hasil yang lebih transparan. Pengujian sistem dilakukan menggunakan metode black box testing, yang menunjukkan bahwa semua fitur sistem berfungsi dengan baik. Selain itu, analisis perbandingan dengan metode seleksi konvensional menunjukkan peningkatan objektivitas dalam pengambilan keputusan. Dengan demikian, penerapan SPK berbasis web dengan metode Profile Matching dapat menjadi solusi inovatif bagi institusi pendidikan dalam meningkatkan transparansi, akurasi, dan efisiensi seleksi penerimaan siswa baru. Penelitian ini diharapkan dapat menjadi referensi dalam pengembangan sistem serupa di berbagai lembaga pendidikan lainnya.
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