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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Informatika dan Teknik Elektro Terapan Jurnal Eksplora Informatika JURNAL PENDIDIKAN TAMBUSAI JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jifosi PLAKAT : Jurnal Pelayanan Kepada Masyarakat Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Teknologi Informasi dan Robotika JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal Of Computer, Network Security and Information System (IJCONSIST) AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment) Jurnal Ilmiah Teknik Informatika (TEKINFO) Sisfo: Jurnal Ilmiah Sistem Informasi Digital Transformation Technology (Digitech) MASALIQ: Jurnal Pendidikan dan Sains JEECS (Journal of Electrical Engineering and Computer Sciences) Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Journal of Innovative and Creativity Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) International Journal of Information Engineering and Science Jurnal Inovasi Artificial Intelligence & Komputasional Nusantara
Ani Dijah Rahajoe
Department Of Computer Science, Faculty Of Informatics, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Transportation Other

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Waste Classification Using YOLOv8 and One Factor At a Time Muhammad Aldi Maulana; Eva Yulia Puspaningrum; Ani Dijah Rahajoe
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3209

Abstract

Solid waste management has become a significant global environmental challenge that affects both ecosystem sustainability and human well-being. The increasing volume of waste generated from daily human activities highlights the urgent need for technology-based solutions that support efficient waste sorting, recycling, and resource recovery. This study proposes an automatic waste classification system using the YOLOv8 algorithm, a state-of-the-art deep learning model capable of performing real-time object detection with high accuracy. A dataset consisting of 1,800 labeled waste images representing five main categories plastic, glass, metal, paper, and organic was used for model training and evaluation. To enhance performance, the One Factor at a Time (OFAT) approach was applied for hyperparameter optimization, focusing on learning rate, batch size, and number of epochs. Two models were compared: the default YOLOv8 configuration and the optimized YOLOv8 OFAT model. Experimental results show that the optimized YOLOv8 OFAT achieved a mAP@0.5:0.95 of 86.1%, slightly higher than the default YOLOv8 model with 85.8%. Although the improvement of 0.3% appears modest, it indicates better model consistency and reliability across various data conditions. The integration of the OFAT technique into YOLOv8 represents a novel contribution, demonstrating that systematic hyperparameter tuning can significantly enhance the efficiency and robustness of automated waste detection systems, thereby supporting environmental sustainability and the realization of a green economy.
Implementation of PSO Optimization on the LightGBM Algorithm for Air Pollution Classification Muchamad Dicky Alifiansyah; Ani Dijah Rahajoe; Eva Yulia Puspaningrum
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3243

Abstract

The survival of living things is highly dependent on the important role of air. Clean air that is free from pollution is a standard for a quality environment that supports life. The Machine Learning approach can be an alternative in conducting data-based air pollution monitoring to assist in making the right decisions to deal with air pollution early on. This research aims to optimize the performance of the Light Gradient Boosting Machine (LightGBM) algorithm in air pollution classification combined with PSO optimization. The LightGBM or Light Gradient Boosting Machine algorithm is a Gradient Boosting algorithm that has decision tree-based learning, but in its application, LightGBM is prone to overfitting because it is sensitive to hyperparameters. Therefore, optimization techniques are needed to maximize performance. Particle Swarm Optimization (PSO) is an optimization method inspired by the movement of flocks of birds searching for optimal solutions. The data used is the Air Pollution Standard Index data. The research method includes data collection, data preprocessing, splitting the data, PSO optimization, model training, and model evaluation. The results show that PSO optimization can improve the performance of the LightGBM model. The LightGBM model with PSO optimization produced an evaluation matrix with an accuracy of 0.9510, precision of 0.9256, recall of 0.9261, and F1-score of 0.9247, demonstrating the model's ability to accurately classify air pollution. Meanwhile, the LightGBM model without optimization produced an evaluation matrix with an accuracy of 0.9455, precision of 0.9201, recall of 0.9170, and F1-score of 0.9182.
Penguatan Branding UMKMGo-Digital Usaha Eka Jaya Tekstil Ani Dijah Rahajoe; Muchlisiniyati Safeyah; Aninditya Daniar
Plakat : Jurnal Pelayanan Kepada Masyarakat Vol 5, No 2 (2023): Plakat: Jurnal Pelayanan Kepada Masyarakat
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik, Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/plakat.v5i2.13197

Abstract

CV Eka Jaya Tekstil is an MSME which has a business sector which includes clothing convection, uniforms, t-shirts, and trousers whose production is on a large scale but supplies more to resellers than direct sales. The problem that occurs with this partner is that they don't have a logo or brand to strengthen branding to increase consumer loyalty to the products they produce, how their products are sold and promoted by resellers, so there is a need for product digitalization to increase sales. efficiency and productivity of small and medium enterprises (MSMEs). This service aims to design a logo that suits the characteristics of the company and products produced by CV Eka Jaya Tekstil as a strengthening of MSME branding. Data collection is also used to create applications that accommodate product data and product orders so that potential analysts and weaknesses can be analyzed from the results of observations and data collection, while the method used is direct outreach to the MSMEs. By producing new innovations and implementing innovative IP, this branding will become more attractive in the eyes of consumers. This can help MSMEs to strengthen their branding and create a positive image in the eyes of consumers. Thus, the application of science and technology can play an important role in strengthening the branding and products of an MSME that utilizes technology effectively to build a strong image and increase customer trust.CV Eka Jaya Tekstil merupakan UMKM yang mempunyai bidang usaha yang meliputi konveksi pakaian, seragam, kaos dan celana panjang yang produksinya berskala besar namun lebih banyak menyuplai reseller dibandingkan penjualan langsung. Permasalahan yang terjadi pada partner ini adalah belum mempunyai logo atau brand untuk memperkuat branding guna meningkatkan loyalitas konsumen terhadap produk yang dihasilkannya, bagaimana produknya dijual dan dipromosikan oleh reseller, sehingga perlu adanya digitalisasi produk untuk meningkatkan penjualan. efisiensi dan produktivitas usaha kecil dan menengah (UMKM). Pengabdian ini bertujuan untuk merancang logo yang sesuai dengan karakteristik perusahaan dan produk yang dihasilkan oleh CV Eka Jaya Tekstil sebagai penguatan branding UMKM. Pengumpulan data juga digunakan untuk membuat aplikasi yang menampung data produk dan pesanan produk sehingga dapat dianalisis potensi analis dan kelemahan dari hasil observasi dan pengumpulan data sedangkan metode yang digunakan yakni dengan sosialisasi langsung ke UMKM tersebut. Dengan menghasilkan inovasi baru dan menerapkan IP yang inovatif, branding ini akan menjadi lebih menarik di mata konsumen. Hal ini dapat membantu UMKM tersebut untuk memperkuat brandingnya dan menciptakan citra positif dimata konsumen. Dengan demikian, penerapan ilmu pengetahuan dan teknologi dapat berperan penting dalam memperkuat branding dan produk suatu UMKM yang memanfaatkan teknologi secara efektif untuk membangun citra yang kuat dan meningkatkan kepercayaan pelanggan.
Artificial Intelligence-Based Expert System for Recirculating Aquaculture Systems in Tulungagung Regency, East Java Ani Dijah Rahajoe; Yushinta Aristina Sanjaya; Anna Fauziah; Rino Zakaria; Arif Setyo Wibowo
Plakat : Jurnal Pelayanan Kepada Masyarakat Vol 7, No 1 (2025): Plakat: Jurnal Pelayanan Kepada Masyarakat
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik, Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/plakat.v7i1.18861

Abstract

Freshwater fish farming in Indonesia plays a crucial role in supporting food security and the economy. UD Tirta Mas Agung Abadi, based in East Java, is a key player in the breeding and cultivation of freshwater ornamental fish, as well as providing feed and aquaculture equipment. The main challenge lies in the complexity of parameters in the recirculating aquaculture system (RAS), such as weather, water quality, temperature, pH, dissolved oxygen, and other chemical contents. The reliance on experts also makes it difficult for farmers to take quick and measured actions in aquaculture management. The first solution proposed is the development of an intelligent system application based on artificial intelligence (expert systems) to efficiently manage RAS parameters. Trials have shown that 95% of respondents found the application met their needs. The second solution involves enhancing the capacity of freshwater ornamental fish farmers through training and mentoring, with 90% of respondents successfully implementing new techniques at their farming locations. These innovations have the potential to improve the efficiency and sustainability of freshwater fish farming in Tulungagung Regency. Budidaya ikan air tawar di Indonesia memegang peranan penting dalam mendukung ketahanan pangan dan perekonomian. UD Tirta Mas Agung Abadi, yang berbasis di Jawa Timur, merupakan salah satu pelaku utama dalam pembenihan dan pembesaran ikan hias air tawar, serta menyediakan pakan dan peralatan budidaya. Permasalahan utama yang dihadapi adalah kompleksitas parameter dalam sistem akuakultur resirkulasi (RAS) seperti cuaca, kualitas air, suhu, pH, oksigen terlarut, dan kandungan kimia lainnya. Ketergantungan pada pakar juga menyulitkan petani untuk menentukan tindakan budidaya yang cepat dan terukur. Solusi pertama yang ditawarkan adalah pengembangan aplikasi sistem cerdas berbasis kecerdasan buatan (sistem pakar) untuk mendukung pengelolaan parameter RAS secara efisien. Uji coba menunjukkan 95% responden merasa aplikasi ini memenuhi kebutuhan mereka. Solusi kedua adalah peningkatan kapasitas petani ikan hias air tawar melalui pelatihan dan pendampingan, dengan hasil 90% responden berhasil mempraktikkan teknik baru di lokasi budidaya mereka. Inovasi ini berpotensi meningkatkan efisiensi dan keberlanjutan budidaya ikan air tawar di Kabupaten Tulungagung.
Evaluasi dan Penguatan Tata Kelola Sistem Informasi E-Prakerin dengan Framework COBIT 2019: Perspektif Integrasi Kecerdasan Buatan Fajar Indra Nur Alam; Bawazir Fadhil Muhammad; Ani Dijah Rahajoe
MASALIQ Vol 6 No 1 (2026): MASALIQ: Jurnal Pendidikan dan Sains
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/masaliq.v6i1.8337

Abstract

The E-Prakerin Information System at PT. XYZ serves as the digital backbone for the management of industrial work practice, and therefore requires mature information technology governance as well as readiness for artificial intelligence (AI) integration. This study aimed to evaluate the maturity level of E-Prakerin Information System governance based on the COBIT 2019 framework and to analyze the system’s readiness for AI integration. An evaluative approach was employed, with data collected through interviews, observations, and questionnaires, which were mapped onto the five COBIT 2019 domains: EDM, APO, BAI, DSS, and MEA. The maturity level was calculated using a 0–5 scale. The results show that the average maturity level is at 2.48 (Managed Process), indicating that processes are in place but have not yet been fully documented and measured. Gap analysis reveals the largest shortfalls in the strategic planning (APO) and monitoring (MEA) domains. In addition, K-Means clustering of questionnaire responses successfully grouped stakeholder perceptions into three main segments that serve as the basis for determining improvement priorities. Based on these findings, the study proposes a roadmap for enhancing governance toward level 4 (Quantitatively Managed) and a model for integrating AI features, such as automated document verification and quota prediction, to close existing gaps. This study contributes to the development of a holistic evaluation framework that integrates IT governance auditing with readiness analysis for emerging technologies.
Optimizing CNN-Based Transfer Learning through Fine-Tuning and Adaptive Augmentation for Chili Plant Disease Detection Bawazir Fadhil Mohammad; Ani Dijah Rahajoe; Agussalim Agussalim
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27270

Abstract

Chili peppers (Capsicum annuum L.) are a strategic horticultural commodity in Indonesia, but their productivity is often hampered by pathogen infections that cause leaf diseases such as anthracnose, leaf spot, and yellow virus. Early detection by farmers is still dominated by subjective visual observation and prone to misdiagnosis due to the similarity of symptoms between diseases. Although Deep Learning technology through Convolutional Neural Networks (CNN) offers an automated solution, implementation in real-world conditions still faces significant challenges such as lighting variations, complex backgrounds, and limited local datasets. This often leads to a drastic decrease in model performance compared to testing in a controlled environment. To address these issues, this study proposes an optimization of the transfer learning strategy on the MobileNetV2 architecture by integrating progressive layer-wise fine-tuning and adaptive data augmentation techniques. The fine-tuning method is carried out gradually on the pre-trained model layers, while adaptive augmentation dynamically manipulates images based on environmental characteristics to improve model robustness. The results of this study, which include multi-class classification on cross-location image data, are projected to be able to boost the accuracy and generalization ability of the model in heterogeneous field conditions. Practically, this research provides a framework for a more precise and robust disease detection system to accelerate the implementation of precision agriculture in the future.
A Hybrid Stacking Ensemble Approach for Rainfall Time Series Forecasting Ani Dijah Rahajoe; Rangga Laksana Aryananda; Angelo A Beltran; Muhammad Suriansyah
AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment) Vol. 10 No. 2 (2026)
Publisher : Asia Pacific Network for Sustainable Agriculture, Food and Energy (SAFE-Network)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29165/ajarcde.v10i2.1083

Abstract

Rainfall forecasting plays a crucial role in hydrology, agriculture, water resource management, and disaster mitigation. However, rainfall data typically exhibit fluctuating, seasonal, and nonlinear characteristics, which make the forecasting process quite complex. In this study, we propose a hybrid multi-model stacking ensemble to improve rainfall prediction accuracy in Kediri Regency. Our framework integrates statistical models—namely the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Holt-Winters models—with machine learning and deep learning models, specifically Random Forest and Long Short-Term Memory (LSTM). We use Linear Regression as a meta-learner to combine predictions from all base models. The dataset contains monthly rainfall records from 2009 to 2022. Various preprocessing techniques are applied to the dataset, primarily normalization, lag feature construction, stationarity testing, and time-series data transformation, to enable deep learning. We use the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) to evaluate each model's predictions. In the experiments, the ensemble stacking model outperformed the other models, with an MAE of 28.56, MSE of 1053.26, RMSE of 32.45, and MAPE of 13.05%. The results of the models used in the experiments, including the standalone SARIMA and Holt-Winters models, Random Forest, and LSTM, also showed inferior performance. Our model forecasted rainfall over the next 12 months while preserving historical seasons and data fluctuations, supporting the claim that the hybrid stacking ensemble method optimizes the accuracy, stability, and robustness of rainfall prediction for complex time-series data. Contribution to Sustainable Development Goals (SDGs):SDG 2 – Zero HungerSDG 6 – Clean Water and SanitationSDG 13: Climate Action
Optimizing Red Onion TSS (True Shallod Seed) Production in the Lowlands Based on Smart Sensors Ida Retno Moeljani; RR Ani Dijah Rahajoe; Pangesti N
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.115

Abstract

Onion cultivation technology using seeds still needs to be developed and socialized at the farm level. to be socialized at the farmer level, considering that until now farmers still cultivate shallots with consumption seed bulbs because there are still not many TSS produced, especially in the lowlands.In principle, not all shallot varieties are capable of flowering, some shallot varieties are capable of flowering. flowering, some shallot varieties are only 30% capable of flowering. This problem can be solved by optimizing flowering with an automation system. The advancement of Internet of thing (IoT) technology can be applied to optimize flowering by using smart sensors on the onion. flowering by using smart sensors on several varieties of shallots. The lanchor blue variety had no flower bulbs that set fruit and produced TSS seeds. This is because all the flower bulbs of the lanchor blue variety were rotten/damaged by disease due to the use of high watering during the growth period that led to flowering, fertilization, and sprouting. There was no interaction between varieties and application of gibberellic acid + and packlobutrazol on seed yield of TSS (Table4). In Bauji and Maaserati varieties, the percentage of flower bulbs that bear fruit and seed (harvested) is still better than BiruLanchor, only about 59.68 to 70% of the total number of flower bulbs that grow (Table 4). This indicates that the process of fertilization and seed formation of shallots is not optimal.
Feature Engineering Optimization on the Performance of XGBoost, Random Forest, and Support Vector Regression Algoritms in House Price Prediction Brahmantio Widyo Trenggono; I Gede Susrama Mas Diyasa; Ani Dijah Rahajoe
IJCONSIST JOURNALS Vol 6 No 1 (2024): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i1.149

Abstract

As the years go by, the ever-increasing movement of house prices has become an important factor in investment decisions and financial planning to curb inflation. However, fluctuations or increases in house prices can be caused by various factors that can affect the value of house price predictions. This study aims to analyze the influence of optimization and the relationship between feature engineering and modeling in house price predictions. The research stages include data preprocessing, logarithmic transformation, feature engineering, data splitting, and optimization in determining parameters during tuning. Model performance is evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Determination coefficient (R-Squared) metrics. The results show that the Support Vector Regression algorithm produces the best performance with a MAE value of 274 million, an RMSE of 780 million, a MAPE of 7%, and an R-Squared of 98%. This research is expected to serve as a reference for future studies on regression model optimization, particularly in decision-making for more accurate house price predictions.
Oil Palm Crown Detection and Tree Counting Using Roboflow Detection Transformer on UAV Imagery Ani Dijah Rahajoe; Denisa Septalian Alhamda; Angelo A Beltran Jr; Muhammad Suriansyah
AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment) Vol. 10 No. 2 (2026)
Publisher : Asia Pacific Network for Sustainable Agriculture, Food and Energy (SAFE-Network)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29165/ajarcde.v10i2.1151

Abstract

Oil palm plantations require accurate and timely inventory data to support plantation management, productivity assessment, and sustainable agricultural practices. However, manual tree inventory in large plantation areas is labor-intensive, time-consuming, and prone to human error. This study proposes an automated approach for oil palm crown detection and tree counting using high-resolution Unmanned Aerial Vehicle (UAV) imagery. To improve image quality, Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied as a preprocessing step, and the Roboflow Detection Transformer (RF-DETR) was used as the object detection model. The proposed method was evaluated using 1,135 UAV images containing 56,547 annotated oil palm crowns collected from commercial plantations in West Kalimantan, Indonesia. Experimental results demonstrate that the proposed approach achieved mAP@50 of 97.5%, precision of 97.1%, recall of 96.0%, and F1-score of 96.6% for oil palm crown detection. In the tree-counting evaluation, the system successfully detected 1,105 of 1,120 ground-truth trees, achieving an overall accuracy of 98.5%. Furthermore, the proposed method achieved an average detection time of 16.1 ms, indicating high computational efficiency. These results demonstrate that the proposed framework provides an effective and practical solution for automated oil palm inventory and plantation monitoring using high-resolution UAV imagery. Contribution to Sustainable Development Goals (SDGs): SDG 2: Zero Hunger SDG 9: Industry, Innovation and Infrastructure SDG 12: Responsible Consumption and Production SDG 15: Life on Land
Co-Authors Achmad Ario Dwi Maulana Agung Subekti, Mohamad Rafli Agussalim Agussalim Agussalim, Agussalim Ainur Rahim Akash, Fransisco Rivaldi Andre Leto Andreas Nugroho Sihananto Andriano Lukas Angelo A Beltran Angelo A Beltran Jr Angga Dwi Cahyono Aninditya Daniar Anna Fauziah arif arizal Arif Arizal Arif Dwi Putra Arif Setyo Wibowo Aryo Bagus Satrio Wicaksono Azaidane, Dandi Azmi Maulana Mahardika Bawazir Fadhil Mohammad Bawazir Fadhil Muhammad Bimantoro, Bisma Satrio Brahmantio Widyo Trenggono Budi Mukhamad Mulyo Chaurina, Agfanadita Rezkia Denisa Septalian Alhamda Dhian Satria Yudha Kartika Dian Agus Prawinata Eka Prakarsa Mandyartha Eva Yulia Puspaningrum Fairus Irhab Adinata Efendi Fajar Indra Nur Alam Feriza, Reyana Dinda Maulan Fransiska, Amelia Hamdan Yuwafi Mastu Wijaya Henni Endah Wahanani I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa Ida Retno Moeljani Indartono, Taqiyya Irsyad Rafi Naufaldi Jr, Angelo A. Beltran Kartini Kartini Khansa Alyssa Fauziyah M Mahaputra M. Mahaputra Made Hanindia Prami Swari Mas Nurul Hamidah Maulana, Hendra Megantara, Sofia Ramadhani Muchamad Dicky Alifiansyah Muchlisiniyati Safeyah Muhammad Aldi Maulana Muhammad Fahreal Bernov Muhammad Farhan Maulana Muhammad Iqbal Al Afgany Muhammad Rizky Firdaus Muhammad Suriansyah Muttaqin, Faisal Nadia Dita Salsabila Nafiendra Praba Hendyka Nurlaili, Afina Lina Nurlaili, Afina Lina P. Eko Prasetyo Pahlevy, Mohammad Reza Pangesti N Perkasa, Laurensius Gading Surya Piter Rudi Irson Rivaldo Lapon Pradana, Ilham Akbar Pramnesti, Adisty Regina Putra Bramantyo, Adam Putra, Brian Akhdan Rangga Laksana Aryananda Retno Mumpuni Retno Mumpuni Reza Aminullah Rifki Fahrial Zainal Rino Zakaria Satrio Budi Wahyuono Shagi Hisyam Al Fathony Soffiana Agustin, Soffiana Subekti, Mohamad Rafli Agung Suriansyah, Muhammad Suryantari, Putu Anggi Syariful Alim Waskito, Muhammad Rizal Winarko, Edi Yushinta Aristina Sanjaya