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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 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 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 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.
An Integrated Text Analytics and Ensemble Machine Learning Framework for Fake Review Detection in Online Marketplaces Eka Praja Wiyata Mandala; Sarjon Defit; Gunadi Widi Nurcahyo
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1143

Abstract

The increasing prevalence of fake reviews on e-commerce platforms undermines consumer trust and affects purchasing decisions, particularly for local products by limited visibility such as those by West Sumatra, Indonesia. This study proposes a hybrid approach combining text analytics and machine learning to enhance the detection of fake reviews. Four classification models—Naive Bayes, Random Forest, Logistic Regression, and K-Nearest Neighbor—were tested on a dataset of 1,500 labeled product reviews. Among these models, Random Forest had the highest starting accuracy of 0.8533. To enhance it, we created a better algorithm called EKAHypeRFor (Enhanced Knowledge Augmentation of Hyperparameter Random Forest). This method uses simple feature engineering and careful tuning of settings by RandomizedSearchCV. The enhanced model reached an accuracy of 0.8778, which is 2.45% higher than the original. It also includes a real-time review sorting tool, making it easy to use on online shopping sites. Tests by a confusion matrix and feature importance drawn the model works well and is easy to understand. This method is simple, fast, and accurate, helping to make online product reviews more trustworthy for small and medium businesses in the area.
Development of Color Segmentation and Texture Analysis Algorithms for Early Detection of Green Vegetable Deterioration in Retail Environments Dinul Akhiyar; Iskandar Fitri; Gunadi Widi Nurcahyo
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1094

Abstract

Vegetable deterioration in retail environments is often accelerated by improper storage conditions, leading to quality degradation, economic losses, and reduced consumer trust. Early detection of deterioration is therefore essential to enable timely preventive actions before visible spoilage becomes severe. This study proposes an integrated image-based framework for early detection of spinach leaf deterioration by combining K-Means++ for robust color segmentation, Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction, and Convolutional Neural Network (CNN) for classification. K-Means++ improves segmentation stability through optimized centroid initialization, GLCM captures subtle texture variations associated with early spoilage, and CNN enables accurate classification by learning complex visual patterns from segmented images. The dataset consists of 642 spinach leaf images captured under controlled lighting for initial calibration and under varying lighting conditions to simulate real-world retail environments. Experimental results show that the standard K-Means algorithm achieved an average classification accuracy of 77%, while the proposed K-Means++ segmentation improved accuracy to 81.86%. Furthermore, CNN-based validation achieved the highest classification accuracy of 94.82%, demonstrating strong generalization capability. The novelty of this work lies in the optimized integration of K-Means++ segmentation under lighting variability, selective GLCM feature utilization validated through ablation analysis, and end-to-end CNN-based validation with real-time deployment feasibility. The proposed framework offers a practical, scalable, and non-destructive solution for automated freshness monitoring in retail environments and can be extended to other leafy vegetables.
Implementation of Isolation forest for Anomaly Detection in Hospital Management Information System Ibnu Putra; Gunadi Widi Nurcahyo; Sarjon Defit
Jurnal KomtekInfo Vol. 13 No. 2 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v13i2.678

Abstract

The digitization of the healthcare sector through the Hospital Management Information System (HMIS) increases the risk of patient data security due to the potential for unauthorized access and misuse of sensitive information. The large volume of activity log data makes conventional sampling-based auditing processes ineffective in identifying security threats in real time. This study aims to implement user data access variables into the Isolation Forest algorithm framework to build an intelligent anomaly detection mechanism in the information system of Dr. M. Djamil Padang General Hospital. The research methodology applies an unsupervised machine learning-based Isolation Forest algorithm to isolate deviant behavior through anomaly scoring on random isolation trees. The pre-processing stage involves extracting request, status, and data size variables and performing numerical transformation using Z-Score standardization to maintain computational stability. The research dataset is sourced from the activity logs of the HMIS web server at Dr. M. Djamil Padang General Hospital, with a total sample of 5000 user access transactions. The analysis results show that the model successfully identified 718 data points, or 14.36%, as anomalies with an accuracy rate identical to that of manual calculations. The application and implementation of the Isolation Forest technique proved to be effective in solving the problem of early detection of suspicious data traffic patterns in large data flows. The contribution of this research enhances hospital information security management through a data-based early warning system to improve the overall accountability of health information access.
Model for identifying high-achieving students using the k-means clustering algorithm and c4.5 classification Kalfinus Waruwu; Gunadi Widi Nurcahyo; Sumijan
Jurnal KomtekInfo Vol. 13 No. 2 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v13i2.681

Abstract

Student achievement refers to academic accomplishments or results obtained by students in the field of education, which can influence the process of determining student academic grades, class achievement, and accomplishments. This process plays a strategic role in supporting objective educational decision-making, especially in the preparation of coaching programs, the establishment of awards, and the continuous development of student potential. Based on this, the purpose of this study is to analyze data on high-achieving students using the K-Means and C4.5 algorithms. The research methods used include K-means, which functions to group student data into different groups. C4.5 classification is capable of analyzing data characteristic similarities, and the results of the decision tree are used as the basis for the decision-making process. The dataset in this study consisted of 345 students from SMK Negeri 3 Padangsidimpuan. Based on the results of this study, it was proven that the application of the K-Means and C4.5 algorithms could achieve an accuracy of 98.59%. This research contributes to identifying high-achieving students at SMKN 3 Padangsidimpuan using the K-Means and C4.5 algorithms, which can assist the school in formulating more effective and targeted guidance policies and presenting the results of identifying high-achieving students after clustering and decision tree analysis. This serves as a basis for decision-making in determining student development programs based on objectively identified academic and non-academic achievement clusters.
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 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 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 Honestya, Gabriela Humairoh, Putri Ibnu Putra Idir Fitriyanto Idir Ilham Effendi Indah Savitri Hidayat Intan Nur Fitriyani Ipri Adi Ira Nia Sanita Irzal Arif Wisky Iskandar Fitri 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 Kalfinus Waruwu Karim, Fajri Khelvin Ovela Putra Kholil, Muhammad Irvan Larissa Navia Rani, Larissa 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 Nurdini, Siti Pati, Muhammad Ibnu Petti Indrayati Sijabat Puji Chairu Sabila Putra, Akmal Darman Putra, Dyan Mardinata Putri Humairoh Putri, Stefani Putri, Yozi Aulia Putut Wicaksono, Putut Radillah, Teuku Rafiska, Rian Rahmad Supriadi Rahman, Zumardi Randy Permana Rezti Deawinda Parinduri Riati, Itin Rika Apriani Rika Apriani, Rika Ririn Violina Ritna Wahyuni Rizka Hafsari Rizki Mubarak Roby Nurbahri Rofil M. Nur Roni Salambue Ronni Andri Wijaya Rovidatul Rozakh, Muhammad Rusnedy, Hidayati Rustam, Camila Sabil, Muhammad Sahari Sahari Sahri, Alfi Sajida, Mayang Sandi Alam Sandrawira Anggraini Sani, Rafikasani Santriawan, Aji Sari, Fitri P. 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, 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 Yeki Candra Yeng Primawati Yerri Kurnia Febrina Yetti Fitriani Yolla Rahmadi Helmi Yoni Aswan Yuhandri Yuhandri, Yuhandri Yuli Hartati Yunita Cahaya Khairani Zikri, Afdal