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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Teknologi dan Manajemen Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Majalah Ilmiah MOMENTUM Jurnal Informatika Upgris Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Sistem Informasi dan Aplikasi Systemic: Information System and Informatics Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Infotekmesin Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Robotics and Control (JRC) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknologi Informasi Cyberku Moneter : Jurnal Keuangan dan Perbankan
Ricardus Anggi Pramunendar
Faculty Of Computer Science, Universitas Dian Nuswantoro
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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RFM Analysis for Customer Lifetime Value with PARETO/NBD Model in Online Retail Dataset Megantara, Rama Aria; Alzami, Farrikh; Akrom, Ahmad; Pramunendar, Ricardus Anggi; Prabowo, Dwi Puji; Wibowo, Sasono; Ritzkal, Ritzkal
Moneter: Jurnal Keuangan dan Perbankan Vol. 11 No. 2 (2023): OKTOBER
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/moneter.v11i2.409

Abstract

In recent years, there has been a growing interest in analyzing Customer Lifetime Value (CLV) due to its ability to provide valuable insights into customer profitability and worth. CLV analysis predicts the net profit attributed to the entire future relationship with a customer. This analysis involves calculating the present value of a customer's expected future spending with the company, facilitating an understanding of the economic value of long-term customer relationships. CLV analysis empowers businesses to identify their most profitable customers and develop strategies for retaining them, ultimately maximizing long-term profitability. CLV analysis relies on various models and techniques, including the RFM analysis categorizes customers based on recency, frequency, and monetary value, helping to segment customers and predict future behavior. Then, The Pareto/NBD model combines probability distributions to estimate CLV and is commonly used for customer base analysis. This research article explores the application of RFM analysis for estimating customer lifetime value using the Pareto/NBD model in an online retail dataset. This metric is crucial for businesses as it assists in identifying valuable customers and formulating retention strategies to maximize long-term profitability.
Pelatihan Desain Poster Promosi untuk UMKM Binaan Dinsospermasdes Kabupaten Jepara Ghozi, Wildanil; Prabowo, Dwi Puji; Rafrastara, Fauzi Adi; Pramunendar, Ricardus Anggi; Sani, Ramadhan Rakhmat
Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 8, No 3 (2025): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v8i3.8379

Abstract

Internet sebagai salah satu dorongan utama dalam perkembangan teknologi memungkinkan setiap manusia untuk menjangakau informasi tanpa batasan ruang dan waktu. Saat ini di Indonesia, lebih dari 196,7 juta penduduk memanfaatkan internet dalam aktivitasnya sehari-hari. Pada provinsi Jawa Tengah terdapat 25,6 juta pengguna internet aktif. Tingginya pengguna internet menjadi peluang untuk memperluas target pemasaran produk UMKM. Pemerintah Kabupaten Jepara, melalui Dinas Sosial, Pemberdayaan Masyarakat dan Desa (Dinsospermasdes) Kabupaten Jepara memiliki tanggung jawab dalam program rehabilitasi Penyandang Masalah Kesejahteraan Sosial (PMKS) dimana salah satu programnya adalah pembinaan UMKM. Saat ini, UMKM binaan Dinsospermasdes belum mampu membuat desain poster promosi yang baik dan menarik pembeli. Penulis memberikan pelatihan desain poster dengan menggunakan aplikasi canva kepada para pelaku UMKM binaan. Pelatihan tersebut telah berhasil meningkatkan pemahaman konsep desain, kemampuan pengambilan foto produk, dan kemampuan membuat desain poster promosi para pelaku UMKM. Poster-poster baru yang dihasilkan pada kegiatan pelatihan menjadi indikator keberhasilan para peserta mengikuti pelatihan. Dengan demikian, diharapkan kemampuan yang telah dimiliki dapat membantu meningkatkan penjualan produk UMKM binaan.
Pemanfaatan Metode CNN Menggunakan Arsitektur Alexnet untuk Peningkatan Kinerja Klasifikasi Penyakit Daun Tomat Prabowo, Dwi Puji; Bastian, Henry; Muqoddas, Ali; Pramunendar, Ricardus Anggi; Agustina, Feri
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 15, No 2 (2024): JURNAL SIMETRIS VOLUME 15 NO 2 TAHUN 2024
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v15i2.12529

Abstract

Tomat adalah salah satu komoditas hortikultura dengan nilai ekonomi yang tinggi, tantang yang dihadapi oleh petani salah satunya dalah kerentanan penyakit tomat terhadap penyakit. Identifikasi secara visual pada daun sulit diuraikan dengan sekali pandang, sehingga menyebabkan asumsi yang tidak akurat tentang penyakit tersebut. Akibatnya, mekanisme pencegahan yang dilakukan petani menjadi tidak efektif dan berdampak merugikan. Penelitian ini mengusulkan identifikasi penyakit tomat secara automatis menggunakan metode Convolution Neural Network. Dalam makalah ini kami melakukan evaluasi pada metode CNN dengan arsitektur Alexnet dengan konfigurasi layer untuk mencari hasil kinerja terbaik dari penggunaan parameter tersebut pada architektur Alexnet. Pada penelitian ini juga melakukan analisis yang diperoleh dari hubungan antara parameter yang digunakan terhadap kinerja akurasi, dan analisis terhadap dampak penggunaan parameter dengan jumlah dataset daun tomat dari dataset PlantVillage.
Peningkatan Performa Ensemble Learning pada Segmentasi Semantik Gambar dengan Teknik Oversampling untuk Class Imbalance Nugroho, Arie; Soeleman, M. Arief; Pramunendar, Ricardus Anggi; Affandy, Affandy; Nurhindarto, Aris
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106831

Abstract

Perkembangan teknologi dan gaya hidup manusia yang semakin tinggi menghasilkan data-data yang berlimpah. Data-data tersebut dapat berbentuk data yang terstruktur dan tidak terstruktur. Data gambar termasuk dalam data yang tidak terstruktur. Aktifitas dan objek yang terekam dalam suatu gambar beraneka ragam. Secara normal, mata manusia dapat dengan mudah membedakan antara foreground dan background dari suatu gambar, tetapi komputer membutuhkan pembelajaran dalam membedakan keduanya. Segmentasi gambar adalah salah satu bidang dalam computer vision yang membahas bagaimana cara komputer mempelajari dan mengenali segmen dari suatu gambar sesuai label yang ditentukan. Dalam kenyataannya banyak data yang mempunyai class atau label yang tidak seimbang, tentunya akan mempengaruhi tingkat akurasi dari suatu prediksi. Dalam riset ini membahas bagaimana meningkatkan akurasi segmentasi semantik gambar pada metode ensemble learning untuk menangani masalah data yang tidak seimbang dalam segmentasi gambar. Teknik yang digunakan adalah sintetis oversampling sehingga menghasilkan data yang seimbang dan akurasi yang tinggi. Metode ensemble learning yang digunakan adalah Random Forest dan Light Gradien Boosting Machine (LGBM). Dengan menggunakan dataset Penn-Fudan Database for Pedestrian yang mengandung imbalanced class. Penggunaan teknik sintetis oversampling dapat memperbaikki tingkat akurasi pada class minoritas. Pada algoritma random forest mengalami peningkatan akurasi sebesar 37 % sedangkan pada algoritma LGBM meningkat sebesar 41 %. AbstractThe development of technology and the increasingly high lifestyle of humans produce abundant data. These data can be in the form of structured and unstructured data. Image data is included in unstructured data. The activities and objects recorded in a picture are varied. Normally, the human eye can easily distinguish between the foreground and background of an image, but computers need learning to distinguish between the two. Image segmentation is one of the fields in computer vision that discusses how computers learn and recognize segments of an image according to specified labels. In reality, a lot of data has unbalanced classes or labels, of course, it will affect the accuracy of a prediction. This research discusses how to improve the accuracy of image semantic segmentation in the ensemble learning method to deal with the problem of unbalanced data in image segmentation. The technique used is synthetic oversampling so as to produce balanced data and high accuracy. The ensemble learning methods used are Random Forest and Light Gradient Boosting Machine (LGBM). By using the Penn-Fudan Database for Pedestrian dataset which contains a imbalanced class. The use of synthetic oversampling techniques can improve the level of accuracy in minority classes. The random forest algorithm experienced an increase in accuracy by 37% while the LGBM algorithm increased by 41%.
Evaluating the Impact of Particle Swarm Optimization Based Feature Selection on Support Vector Machine Performance in Coral Reef Health Classification Bastiaans, Jessica Carmelita; Hartojo, James; Pramunendar, Ricardus Anggi; Andono, Pulung Nurtantio
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3761

Abstract

This research explores improving coral reef image classification accuracy by combining Histogram of Oriented Gradients (HOG) feature extraction, image classification with Support Vector Machine (SVM), and feature selection with Particle Swarm Optimization (PSO). Given the ecological importance of coral reefs and the threats they face, accurate classification of coral reef health is essential for conservation efforts. This study used healthy, whitish, and dead coral reef datasets divided into training, validation, and test data. The proposed approach successfully improved the classification accuracy significantly, reaching 85.44% with the SVM model optimized by PSO, compared to 79.11% in the original SVM model. PSO not only improves accuracy but also reduces running time, demonstrating its effectiveness and computational efficiency. The results of this study highlight the potential of PSO in optimizing machine learning models, especially in complex image classification tasks. While the results obtained are promising, the study acknowledges several limitations, including the need for further validation with larger and more diverse datasets to ensure model robustness and generalizability. This research contributes to the field of marine ecology by providing a more accurate and efficient coral reef classification method, which can be applied to other image classifications.
Enhancing Support Vector Machine Classification of Nutrient Deficiency in Rice Plants Through Particle Swarm Optimization-Based Feature Selection Hartojo, James; Bastiaans, Jessica Carmelita; Pramunendar, Ricardus Anggi; Andono, Pulung Nurtantio
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3762

Abstract

The research focuses on the classification of nutrient deficiencies in rice plant leaves using a combination of Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) methods for feature selection. Image features are extracted using Histogram of Oriented Gradients (HOG), which is then optimized with PSO to select the most relevant features in the classification process. Indonesia is one of the largest rice producers in the world, with food security as a major issue that requires sustainable solutions, especially in the agricultural sector. The growth and yield of rice plants are highly dependent on the availability of nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). However, traditional observation methods to detect nutrient deficiencies in plants become inefficient as the scale of production increases. The dataset used includes images of rice leaves showing nitrogen (N), phosphorus (P), and potassium (K) deficiencies. Experiments show that the SVM model optimized with PSO provides a classification accuracy of 83.19% and a runtime of 129.63 seconds with 1150 best feature combinations out of 2303 extracted features, which is higher accuracy and faster runtime than the model that does not use PSO. These results show that the integration of PSO in the feature selection process not only improves the accuracy of the model, but also reduces the required computation time. This research makes an important contribution to the development of an automated system for the classification of nutrient deficiencies in crops, which can be implemented in large farms or other agricultural fields.
Improving Cervical Cancer Classification Using ADASYN and Random Forest with GridSearchCV Optimization Saputra, Resha Mahardhika; Alzami, Farrikh; Pramudi, Yuventius Tyas Catur; Erawan, Lalang; Megantara, Rama Aria; Pramunendar, Ricardus Anggi; Yusuf, Moh.
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2552

Abstract

Cervical cancer is a leading cause of death among women, with over 300,000 deaths recorded in 2020. This study aims to improve the accuracy of cervical cancer diagnosis classification through a combination of Adaptive Synthetic Sampling (ADASYN) and Random Forest algorithm. The research data was obtained from the Cervical Cancer dataset in the UCI Machine Learning Repository with an imbalanced data distribution of 95% negative class and 5% positive class. ADASYN method was chosen for its ability to handle imbalanced data by focusing on minority data points that are difficult to classify. The Random Forest algorithm was optimized using GridSearchCV to achieve maximum performance. Results show that this combination improved accuracy from 96.5% to 96.8% and recall from 93.7% to 94.3%. Feature importance analysis identified key risk factors such as number of pregnancies, age at first sexual intercourse, and hormonal contraceptive use that significantly influence diagnosis. This research demonstrates the effectiveness of combining ADASYN and Random Forest in enhancing classification performance for early cervical cancer detection.
Prediksi Kerusakan Bangunan Pasca Gempa Bumi Menggunakan Metode Deep Neural Network Fakhrurrozi, Fakhrurrozi; Ratmana, Danny Oka; Winarsih, Nurul Anisa Sri; Saraswati, Galuh Wilujeng; Rohman, Muhammad Syaifur; Saputra, Filmada Ocky; Pramunendar, Ricardus Anggi; Shidik, Guruh Fajar
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 1 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i1.37181

Abstract

Addressing the challenge of predicting earthquake-induced building damage, this study proposes the innovative use of Deep Neural Networks (DNN) as a solution. Focusing on optimizing predictive models, the research evaluates the effectiveness of various optimizers - ADAM, SGD, RMSprop, and Adagrad - coupled with adjustments in the learning rate to determine the most efficient configuration. The experiment was conducted to compare the performance of each optimizer in predicting post-earthquake building damage, a critical issue in disaster mitigation. The results demonstrate that ADAM significantly outperforms other optimizers, achieving the highest accuracy of up to 90.50% at a learning rate of 0.001, with RMSprop as its closest competitor. While SGD and Adagrad yielded lower accuracies, SGD showed improvement with higher learning rates. The variance analysis confirmed that the choice of optimizer significantly impacts model performance, with the p-value indicating strong statistical significance for optimizers (1.23E-09), whereas the learning rate had no significant impact (p-value 0.56098964). These findings underline the importance of selecting the appropriate optimizer to enhance the accuracy of DNN models for building damage prediction, a crucial aspect in emergency response planning and earthquake disaster mitigation efforts. This research contributes significantly to the development of more accurate predictive models, which are essential in minimizing the risks of earthquake disasters.
PREDIKSI JUMLAH PRODUKSI AIR PDAM MENGGUNAKAN METODE ANN DENGAN OPTIMASI PSO akrom, ahmad; Pramunendar, R.A.; Prabowo, D.P.
Jurnal Informatika UPGRIS Vol 7, No 2: Desember 2021
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v7i2.10065

Abstract

Perusahaan Daerah Air Minum (PDAM) merupakan perusahaan milik daerah yang begerak di bidang penyedia, pengolahan, dan pendistribusian air bersih. Sebuah sistem yang akurat untuk prediksi jumlah produksi air untuk masa depan dibutuhkan oleh PDAM untuk menentukan kebijakan dalam bidang produksi air. Penelitian ini menghasilkan sebuah model prediksi untuk  volume produksi air PDAM Kota Semarang. Data yang diolah adalah jumlah penduduk, jumlah pelanggan berdasarkan jenis pelanggan, total volume produksi, kontribusi daerah sumber, volume distribusi, air terjual, dan kehilangan air. Data diperoleh dari laporan bulanan perusahaan selama 6tahun terakhir yaitu mulai tahun 2008-2013. Pendekatan yang digunakan untuk prediksi volume produksi air adalah dengan menggunakan metode Artificial Neural Network dengan optimasi Particle Swarm Optimation. Berdasarkan hasilpenelitian, diperoleh hasil prediksi menggunakanneural network dan particle swarm optimization lebih bagus jika dibandingkan dengan menggunakan neural network saja. Hal ini dibuktikan dengan nilai RMSE menggunakan neural network dan particle swarm optimization sebesar 3,797 sedangkan nilai RMSE dengan neural network saja sebesar 4,943.
PROTOTIPE APLIKASI PENGENALAN WAYANG KULIT MENGGUNAKAN CNN BERBASIS VGG16 prabowo, dwi puji; Ullumudin, D.I.I; Pramunendar, R.A.
Jurnal Informatika UPGRIS Vol 7, No 2: Desember 2021
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v7i2.10485

Abstract

Indonesia has various types of culture and traditional arts. In this era of globalization, local culture and arts have begun to be eroded by the times. One of the diverse Indonesian culture is wayang kulit. Where the shadow puppets in Indonesia vary and vary from region to region. In this case, the puppet characters have different forms and curves, so recognizing the shape of a puppet is very difficult. In the development of technology, computer vision technology began to be widely used to perform object recognition with deep learning learning. So that an object being studied can be detected properly. In this study, a prototype was made with the detection of puppet types using Deep Learning learning using Convolutional Neural Networks to detect shadow puppet objects based on the VGG16 architecture. The results obtained by the CNN and VGG16 methods reached 86%. With the results obtained, a prototype model is made which will later be able to help the community in the introduction of shadow puppets.Keyword: CNN, shadow puppets ,VGG16
Co-Authors Abdul Syukur Abu Salam Ade Yusupa Affandy Affandy Agus Winarno, Agus Agustina, Feri Ahmad Akrom Ahmad Akrom Akrom, Ahmad Al-Azies, Harun ALI MUQODDAS Alvin, Fris Alzami, Farrikh Andi Kamaruddin Apriyanto Alhamad Arie Nugroho, Arie Arifin, Zaenal Arya Rezagama Sudrajat Azzahra, Tarissa Aura Baroroh, Nurul Bastiaans, Jessica Carmelita Brilianto, Rivaldo Mersis Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto D, Ishak Bintang Darmawan, Aditya Aqil De Rosal Ignatius Moses Setiadi Dewi Nurdiyah Diana Aqmala Dibyo Adi Wibowo Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Dzuha Hening Yanuarsari, Dzuha Hening Edi Noersasongko Enrico Irawan Erlin Dolphina Etika Kartikadarma Evanita Evanita, Evanita F. Alzami Fafaza, Safira Alya Fajrian Nur Adnan Fakhrurrozi Fakhrurrozi, Fakhrurrozi Farikh Al Zami Fathorazi Nur Fajri Fatkhuroji Fatkhuroji Fauzi Adi Rafrastara Fikri Diva Sambasri Fikri Diva Sambasri Firmansyah, Muhammad Ilham Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Hamid, Maulana As’an Hartojo, James Harun Al Azies Hasan Asari Haydar, Muhammad Rifqi Fajrul Hendri Ramdan Henry Bastian, Henry I Ketut Eddy Purnama Ifan Rizqa Ika Novita Dewi Imran, Bahtiar Irham Ferdiansyah Katili Iswahyudi Iswahyudi Karim, Muh Nasirudin Karis W. Kartika, Gita khoiriya latifah Khoirunnisa, Emila Khoirur Rizky, Muhammad Ivan Kristhina Evandari Kurnia Prayoga Wicaksono Kurniawan Aji Saputra Kurniawan, Defri Kusumawati, Yupie Lalang Erawan Lesmarna, Salsabila Putri M. Arif Soeleman M. Arif Soleman Maulana, Isa Iant Megantara, Rama Aria Mira Nabila Moch Arief Soeleman Moch. Sjamsul Hidajat Mochamad Arief Soeleman Mochamad Hariadi Moh Yusuf, Moh Moh. Arief Soeleman Moh. Yusuf Mohammad Arif Mohammad Syaifur Rohman Muhammad Naufal Muljono, - Muslih Muslih Muslih Muslih Noor Wahyudi Nuanza Purinsyira Nugroho, Muhammad Bayu Nur Azise Nurhindarto, Aris Nurhindarto, Aris Pergiwati, Dewi Prabowo, D.P. Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Purwanto Purwanto Putu Samuel Prihatmajaya R.A. Megantara Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Ramadhani, Irfan Wahyu Ratmana, Danny Oka Riadi, Muhammad Fatah Abiyyu Rifqi Mulya Kiswanto Ritzkal, Ritzkal Rohman, Muhammad Syaifur Rony Wijanarko Rozada, Akfi Ruri Suko Basuki Santoso, Siane Saputra, Filmada Ocky Saputra, Resha Mahardhika Saraswati, Galuh Wilujeng Sasono Wibowo Sinaga, Daurat Soeleman, M. Arief Sri Winarno Stefanus Santosa Sulistyowati, Tinuk Sutini Dharma Oetomo Tamamy, Aries Jehan Teguh Tamrin Ullumudin, D.I.I Usman Sudibyo Vincent Suhartono Vincent Suhartono Vincent Suhartono Wibowo, Gentur Wahyu Nyipto Wildanil Ghozi Winarsih, Nurul Anisa Sri Yudha Tirto Pramonoaji Yuliman Purwanto Yuslena Sari, Yuslena Yuventius Tyas Catur Pramudi Zainal Arifin Hasibuan