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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Dinamik Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Paradikma: Jurnal Pendidikan Matematika JURNAL PENELITIAN SAINTIKA ELEMENTARY SCHOOL JOURNAL PGSD FIP UNIMED Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Daya Matematis Jurnal Informatika dan Teknik Elektro Terapan Seminar Nasional Informatika (SEMNASIF) JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal KARISMATIKA Bina Insani ICT Journal JURNAL SAINS INDONESIA Indonesian Journal of Artificial Intelligence and Data Mining INTECOMS: Journal of Information Technology and Computer Science Jurnal Cendekia : Jurnal Pendidikan Matematika Jurnal Perspektif M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika JATI (Jurnal Mahasiswa Teknik Informatika) Community Development Journal: Jurnal Pengabdian Masyarakat Budapest International Research and Critics in Linguistics and Education Journal (Birle Journal) Informatics and Digital Expert (INDEX) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Soft Computing Exploration Djtechno: Jurnal Teknologi Informasi Jurnal Pengabdian kepada Masyarakat Teknik: Jurnal Ilmu Teknik dan Informatika INCODING: Journal of Informatics and Computer Science Engineering J-Intech (Journal of Information and Technology) Kharisma Tech Economic Reviews Journal PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam (JURRIMIPA) Jurnal Umum Pengabdian Masyarakat (JUPEMAS) Journal of Informatics and Data Science (J-IDS) Jurnal KALANDRA Journal of Education Transportation and Business International Journal of Educational Insights and Innovations (IJEDINS) Ulil Albab Jurnal Informatika Dan Tekonologi Komputer
Hermawan Syahputra
Universitas Negeri Medan
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Prediksi Penjualan Produk Makanan dan Minuman Ringan pada PT. Sinar Niaga Sejahtera Menggunakan Metode Holt-Winters Berbasis Website M. Revano Ananda Lubis; Insan Taufik; Said Iskandar Al Idrus; Arnita Arnita; Hermawan Syahputra
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.1024

Abstract

PT. Sinar Niaga Sejahtera is a food and beverage distribution company that still relies on conventional methods to determine stock levels, often facing inventory management challenges due to fluctuations in market demand. This study aims to predict sales of the 15 best-selling products at the Tebing Tinggi branch using the Holt-Winters method, based on a website that provides historical sales data from January 2021 to December 2024. The research stages include problem identification, data collection, application of the Holt-Winters method, model evaluation using Mean Absolute Percentage Error (MAPE), and implementation of a website-based system. The results of the study on one product, Garuda Atom Original, show optimal parameters of α = 0.1, β = 0, and γ = 0.8 with an MAPE value of 5,703115%, which is classified as very good. The implementation of a website-based sales prediction system makes it easier for administrators to manage product data, record sales data, and obtain prediction results in the form of informative graphs and tables, thereby helping the company reduce the risk of overstocking or understocking and supporting more effective data-driven decision-making.
Comparison of Support Vector Machine (SVM) and Random Forest Algorithm for Detection of Negative Content on Websites Hermawan Syahputra; Aldiva Wibowo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25861

Abstract

The amount of negative content circulating on the internet can damage people's morale so that social conflicts arise in society that threaten national sovereignty. Detecting negative content can help identify and prevent harmful events before they occur. This can lead to a safer and more positive online environment. Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm for Detection of Negative Content on Websites. The research contributions are 1) detect negative content on the internet with random forest and SVM, 2) comparing SVM and RF algorithms for detecting negative content on websites, 3) detection of negative content based on text focusing on the categories of fraud, gambling, pornography and Whitelist. The stages of this research are preparing a text content dataset on a website that has been labeled, preprocessing (duplicated data, text cleansing, case folding, stopward, tokenize, label encoding, data splitting, and determine the TF-IDF), finally performing the classification process with SVM and Random Forest. The dataset used in this study is a structured dataset in the form of text obtained from emails that have been registered on the TrustPositive website as negative content.  Negative content includes fraud, pornography and gambling. The results show the accuracy of the SVM is 97%, Precision 90% and Recall 91%, while for Accuracy in Random Forest is 92%, Precision 71%, and Recall 86%. The value obtained is the result of testing using 526 website URLs. The test results show that the Support Vector Machine is better than the Random Forest in this study.
Classification of Purple Passion Fruit Ripeness Levels Using Convolutional Neural Network (CNN) Mochammad Gani Alfa Alkhoiri Siregar; Said Iskandar Al Idrus; Hermawan Syahputra; Insan Taufik; Kana Saputra S
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1787

Abstract

Passiflora edulis Sims (purple passion fruit) is a fruit that offers numerous health benefits and possesses high economic value. However, the manual assessment of ripeness by traders tends to be subjective and inconsistent, leading to post-harvest losses of up to 50%. This study developed a classification model for determining the ripeness level of purple passion fruit using a Convolutional Neural Network (CNN) and implemented it in a web-based application. The CNN model was designed to classify four ripeness stages (unripe, half-ripe, ripe, and rotten) with the addition of a non-passion-fruit class to enhance the system’s robustness. The dataset consisted of 2,000 images divided into five classes: four ripeness levels of purple passion fruit (unripe, half-ripe, ripe, and rotten) and one non-passion-fruit class as a comparator. All images were in JPG and PNG formats. The CNN architecture comprised four convolutional layers with 16, 32, 64, and 128 filters, respectively. Evaluation of various data-splitting ratios (80:20, 70:30, 60:40) and learning rates (0.001, 0.0001, 0.01) showed that the optimal configuration was achieved at a ratio of 80:20 with a learning rate of 0.001, resulting in a training accuracy of 96.72% and a testing accuracy of 95.76%, with a loss value of 0.1811. Validation using 5-Fold Cross Validation produced an average accuracy of 95.40%. The model was integrated into a web application developed using Flask and JavaScript, deployed on the PythonAnywhere cloud platform, enabling users to upload images and automatically obtain ripeness predictions to assist traders in sorting fruits more quickly and accurately.
Perancangan Sistem Penerjemah Bahasa Isyarat bagi Tunarungu dan Tunawicara Berbasis Pengolahan Citra Digital dan Text-to-Speech Nafil Rizq Trianto; Alfarizi Wijaya; Arion Pardede; Daniel Pandiangan; Hermawan Syahputra
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v6i1.1156

Abstract

Communication is an essential human right, yet a significant communication gap persists between individuals with sensory disabilities, specifically the deaf and speech-impaired, and the general public. While many technological solutions have been proposed to translate sign language, existing models primarily rely on heavy deep learning architectures such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN/LSTM). These models often demand high computational power, leading to latency and limiting real-time application on standard devices. This study proposes a lightweight, fast, and highly responsive sign language translation system specifically designed to recognize static alphabets (A-Z) and single-character air writing. The system utilizes MediaPipe for hand tracking, where feature extraction is intelligently processed by calculating the relative spatial coordinates of fingertips to the wrist, reducing dependency on raw camera coordinates. Classification is performed using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, prioritizing computational efficiency without sacrificing accuracy. To enhance user experience, the system introduces three key novelties: smart relative feature extraction, an anti-duplication hold system with a 1-second timer to prevent input spamming, and a non-blocking multithreaded audio execution (Daemon Thread) utilizing Google Text-to-Speech (gTTS), ensuring the webcam feed remains fluid during audio playback. Additionally, an alternative air-writing mode is integrated, utilizing geometric heuristics and PyTesseract OCR to read single drawn letters in the air. The results indicate that the proposed system operates swiftly and efficiently, bridging the communication barrier with a hardware-friendly approach.
ANALISIS EFEKTIVITAS RUANG WARNA HSV DAN LAB DALAM PENGELOMPOKAN KONDISI DAUN TEH MENGGUNAKAN K-MEANS UNTUK PENENTUAN TINGKAT KEMATANGAN Ridho Affandi; Syti Salwaa Nafiisah; Calvin Sahputra Buulolo; Shaqila Rahmayani Gultom; Hermawan Syahputra
Djtechno: Jurnal Teknologi Informasi Vol 7, No 1 (2026): April
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v7i1.8548

Abstract

Penentuan tingkat kematangan daun teh merupakan aspek krusial dalam proses sortasi bahan baku yang berpengaruh langsung terhadap mutu produk akhir. Proses identifikasi kematangan yang dilakukan secara visual oleh manusia enderuna bersifat subiektif dan rentan terhadap inkonsistensi. Oleh karena itu diperlukan pendekatan berbasis pengolahan citra digital untuk memperoleh hasil yang lebih objektif dan terstandar. Penelitian ini bertujuan untuk menganalisis efektivitas ruang warna HSV dan Lab dalam pengelompokan kondisi daun teh menggunakan algoritma K-Means untuk penentuan tingkat kematangan. Tahapan penelitian meliputi akuisisi citra daun teh, praproses citra, <onversi ruang warna dari RGB ke HSV dan Lab, ekstraksi fitur warna, serta proses klasterisasi menggunakan algoritma K-Means. Evaluasi kinerja dilakukan dengan membandingkan hasil pengelompokan terhadap label referensi yang diperoleh melalui penilaian pakar. Hasil penelitian menunjukkan bahwa kedue ruang warna mampu merepresentasikan karakteristik visual daun teh secara memadai dalam proses pengelompokan tingkat kematangan, dengan perbedaan kinerja yang ditunjukkan pada tingkat separabilitas klaster dan konsistensi hasil pengelompokan. Temuan ini diharapkan dapat menjadi dasar dalam bengembangan sistem pendukung keputusan untuk klasifikasi kematangan daur teh berbasis pengolahan citra digital
Analisis Citra Fitur Wajah Untuk Deteksi Dini Depresi  Dengan Teknik Hibrida Berdasarkan GabunganFitur Convolutional Neural Network (CNN) Syahputra, Hermawan; Gultom, Syawal; Arnita; Kiswanto, Dedy; Batubara, Saatira Hilma; Manurung, Jeremia
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 4: Agustus 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.134

Abstract

Depresi merupakan salah satu masalah kesehatan mental yang umum dan menjadi penyebab utama disabilitas di seluruh dunia. Deteksi dini sangat penting karena gejala awal sering tidak dikenali atau diabaikan. Penelitian ini mengusulkan sistem deteksi dini depresi berbasis kecerdasan buatan melalui analisis citra wajah menggunakan pendekatan hibrida Convolutional Neural Network (CNN). Data diperoleh dari rekaman wawancara klinis pasien dengan diagnosis depresi dan individu normal. Ekstraksi fitur dilakukan menggunakan tiga arsitektur CNN, yaitu VGG16, ResNet101V2, dan MobileNetV3, yang kemudian digabungkan untuk membentuk representasi fitur gabungan. Selanjutnya, algoritma Random Forest digunakan sebagai klasifikator. Hasil eksperimen menunjukkan bahwa kombinasi VGG16 dan MobileNetV3 memberikan performa terbaik dengan akurasi 94.55%, presisi 98.04%, dan spesifisitas 98.18%. Temuan ini menunjukkan bahwa pendekatan hibrida CNN dengan Random Forest berpotensi menjadi metode efektif untuk mendukung deteksi dini depresi berbasis citra wajah.   Abstract Depression is a major mental health disorder and one of the leading causes of disability worldwide. Early detection is crucial since initial symptoms are often overlooked or unrecognized. This study proposes an artificial intelligence–based early detection system for depression through facial image analysis using a hybrid Convolutional Neural Network (CNN) approach. Facial data were obtained from clinical interview recordings of patients diagnosed with depression and healthy individuals. Feature extraction was carried out using three CNN architectures VGG16, ResNet101V2, and MobileNetV3 whose features were then combined into a joint representation. A Random Forest algorithm was employed for classification. Experimental results indicate that the hybrid model combining VGG16 and MobileNetV3 achieved the best performance, with 94.55% accuracy, 98.04% precision, and 98.18% specificity. These findings demonstrate that the proposed CNN Random Forest hybrid approach is a promising method for supporting early depression detection based on facial features.
Developing Differentiated Learning Tools Based on Learning Styles and Multiple Intelligences to Improve Mathematical Computational Thinking Skills and High School Students' Motivation Nurul Azmi; Bornok Sinaga; Hermawan Syahputra
Daya Matematis: Jurnal Inovasi Pendidikan Matematika Vol 14, No 2 (2026): Juli
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/jdm.v14i2.88271

Abstract

The purpose of this study is to analyze the validity, practicality, and effectiveness of differentiated learning devices for improving mathematical computational thinking skills and motivation of high school students. To determine the improvement in mathematical computational thinking skills and motivation of high school students in using differentiated learning devices, reviewed from learning styles and multiple intelligences. This research method is Research & Development. The development model used is the 4D model., four researches of SMA Swasta Pembangunan. The time of this research will be carried out in the 2025/2026 academic year. The population in this study is all grade X students of SMA Swasta Pembangunan, while the sample in this study is class X-1 of 30 students. The results of this study are; 1. The validity of the differentiated learning device reviewed from the learning styles and multiple intelligences that have been developed is in the "valid" category, 2. The differentiated learning device reviewed from the learning styles and multiple intelligences that have been developed has met the criteria for the practicality of the learning device, 3. The differentiated learning device reviewed from the learning styles and multiple intelligences that have been developed has met the criteria for the effectiveness of the learning device. This can be seen from the completeness of students' mathematical computational thinking abilities in the first trial, classical completeness from the results of students' mathematical computational thinking abilities was 56% (18 students) and in the second trial it was 87% (26 students). 4. Students' mathematical computational thinking abilities using the differentiated learning device reviewed from the learning styles and multiple intelligences that have been developed have increased.
Automatic Waste Type Detection Using YOLO for Waste Management Efficiency Alfattah Atalarais; Kana Saputra S; Hermawan Syahputra; Said Iskandar Al Idrus; Insan Taufik
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.770

Abstract

The management of waste in Indonesia is currently suboptimal, with only 66.24% being effectively managed, leaving 33.76% unmanaged. This highlights a significant challenge in waste management, primarily due to a lack of understanding in selecting appropriate waste types. Advances in deep learning and computer vision offer promising solutions to this issue. This study employs the YOLOv8l model, a well-regarded deep learning model for object detection, to develop an automated waste type detection system integrated with trash bins. The dataset comprises 2800 images across four classes, each containing 700 images, and is split with an 80:10:5 ratio for training, validation, and testing. Evaluation on test data yields a mean Average Precision (mAP) of 96.8%, indicating robust model performance in object detection. The model's accuracy is further validated with a score of 89.98%. Real-time testing conducted at Merdeka Park, Binjai, demonstrates the system's capability to detect waste with varying confidence levels, consistently above the 0.5 threshold. The highest confidence was observed in bottle detection at 0.94, and the lowest in cans at 0.64, underscoring the system's reliability across different detection scenarios within a 30cm range.
Implementation of MobileNet V3 In Classifying Butterfly Species with Android and Cloud Based Application Development Ihsan Zulfahmi; Said Iskandar Al Idrus; Hermawan Syahputra; Insan Taufik; Kana Saputra S
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.797

Abstract

This research aimed to develop an Android application capable of classifying butterfly species using cloud computing and deep learning technologies. MobileNetV3-Large, a Convolutional Neural Network (CNN) architecture, was employed to process and classify six butterfly species. The dataset was divided into two ratios, 70:30 and 80:20, for training and testing. Evaluation results indicated that the optimal model was achieved with an 80:20 ratio, yielding an accuracy of 94% and precision, recall, and F1-Score values exceeding 90% for each species class. Google Cloud Platform (GCP) was utilized to manage and run the model using the Cloud Run service, enabling the application to function efficiently even with limited resources on Android devices. The application incorporates an encyclopedia of species and a camera scanning feature, making it a valuable educational tool
Web-Based Real-Time Object Detection System with Audio Output for the Visually Impaired Muhammad Iqbal Fahrezzi; Azril Arfansyah; Augis Dinanti; Khairany Zuhriyyah Jinan Hsb; Hermawan Syahputra
KHARISMA Tech Vol 21 No 2 (2026): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v21i2.702

Abstract

Limited access to visual information is a major problem faced by visually impaired individuals, making it difficult to recognize surrounding objects and identify currency denominations independently. This issue highlights the need for a system capable of presenting visual information in a more accessible form. Therefore, this study aimed to develop a real-time object detection system based on a web platform with audio output to improve accessibility. The method included requirement analysis, system design, implementation using digital image processing and deep learning techniques, and system testing. The model applied a detection confidence threshold of ≥ 70% and achieved recognition accuracy of ≥ 85% under normal conditions. The system was also integrated with text-to-speech technology to deliver detection results in audio form. The results indicated that the system operated effectively in real-time with good responsiveness through a web browser without requiring additional installation. Therefore, the developed system proved capable of enhancing accessibility and supporting the independence of visually impaired users.
Co-Authors Abil Mansyur, Abil Ade Amelia, Tasya Adhi Guna, Ekin Adidtya Perdana, Adidtya Agus Harjoko Ahmad Andi Solahuddin Ahmad Hidayat Ajizah Siregar Aldiva Wibowo Alfarizi Wijaya Alfattah Atalarais Amelia Br Siregar, Ririn Amelia Vega S. Meliala, Ruth Ami . Riana Andani D N Andika Maulana, Sandy Angel Tumanggor, Asri Angginy Akhirunisa Siregar Ani Sutiani Annisa Aulia Apiek Gandamana Arion Pardede Arnita Arnita Arnita Arnita Asrin Lubis Augis Dinanti Azril Arfansyah Batubara, Saatira Hilma BORNOK SINAGA Bornok Sinaga Budi Akbar, Muhammad Calvin Sahputra Buulolo citra Claudia Agatha Br. Tarigan Daniel Pandiangan Daulay, Leni Karmila Davina, Sherly Dedy Husrizal Syah, Dedy Husrizal Dedy Kiswanto Defiyanti, Aqilah Delvin Ibo, Martince Deo Demonta Panggabean Dhea Putri Adriani Dina Aulia Luthfiah Dinda Syafitri Drilanang, Mhd Ilyasyah Dwi Zahra Putri, Raisya E. Elvis Napitupulu E. Elvis Napitupulu, E. Elvis Edi Syahputra Edward Perdana Sinaga Elisabet Butarbutar, Lastri Erika Nia Devina Br Purba Fanny Rahmadani Farmawaty Tambunan, Vivielda Fauzi, KMS. Amin Fransiska Sihombing, Esra Frengki Alfredo Matondang Hafiz, Alvin Harefa, Meilinda Suriani Hasratuddin Siregar Hidayatul Arifin, Muhammad Husna Batubara, Shabrina Ihsan Zulfahmi Ika Purnama Sari Imelda, Yusmita Impana Manik, Kristin Indriani.S, Dechy Deswita Insan Taufik Irhamna Irhamna Irmaya, Nia Irya Shakila Syukron, Ananda Iwan Jepri Izwita Dewi Jonathan Rio Gultom Kana Saputra S Karimuddin Hakim Hasibuan Kayla Amelia Putri Khairany Zuhriyyah Jinan Hsb Khairun Nadiah Kms. Amin Fauzi Lasker Pangarapan Sinaga Lazuardi Harahap, Muhammad Luthfiah, Dina Aulia M. Ari Maulana M. Revano Ananda Lubis Mahyuni Mahyuni Manurung, Jeremia Martina Restuati Maulana, Raihan Maya Oktora MHD. Reza M.I. Pulungan Mia Yolanda Siregar Mochammad Gani Alfa Alkhoiri Siregar Muhammad Febrilian Zulrahman Muhammad Iqbal Fahrezzi Muhammad Rizki Andrian Fitra Mukti Hamjah Harahap, Mukti Hamjah Nafil Rizq Trianto Nasution, Dinda Indriani Neysa Talitha Jehian Nico Pasaribu, Michael Niska, Debi Yandra Nova Yanti Panjaitan Nur Wahyuni Nurmala Berutu Nurul Azmi Nurul Maulida Surbakti Oktavia, Grace Palendeo Sitepu, Kalpin Pane, M Iqbal Anata Pane, Yeremia Yosefan Panggabean, Suvriadi Panjaitan, Clara Kresensia Panjaitan, Nova Yanti Permata Putri Pasaribu, Yohanna Prana Walidin, Adamsyach Purba, Boy Hendrawan Purba, Desni Paramitha Putri Mayang Sari Putri Mayang Sari Siregar R Givent A Simanjorang Ramadhan Manik, Albert Ramadhani, Fanny Rambe, Imelda Wardani Rangkuti, Muhammad Aswin Riana, Ami Richi, Alfina Ridho Affandi Sahara Lani Lestari Said . Iskandar SANTI MARIA SIMARMATA Santi Maria Simarmata Sembiring, Rinawati Shaqila Rahmayani Gultom Sinaga, Elya Juni Arta Siregar, Putri Mayang Sari Siti Nabila Panjaitan Solahudin, Ahmad Andi Sri Dewi Sriadhi Sriadhi, Sriadhi Steven Imanuel Naibaho Sukma, Ayman Human Suleho, Febrina Suvriadi Panggabean Syamsah Fitri Syarief Afifi Sumantri Syawal Gultom Sybil Auzi Syti Salwaa Nafiisah Thania Dealva Arsyad Tri Bowo Atmojo Triwanti Andini Hutasoit Veryawan, Veryawan Waliyul M Siregar Warjaya, Angga winsyahputra Ritonga Yazid Noor, Muhammad Yulita Molliq Rangkuti Zul Amry Zulfahrizan, Atta