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All Journal Publikasi Pendidikan JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Simantec Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sistem Informasi dan Bisnis Cerdas Format : Jurnal Imiah Teknik Informatika Sistemasi: Jurnal Sistem Informasi InComTech: Jurnal Telekomunikasi dan Komputer J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics bit-Tech Journal of Robotics and Control (JRC) ILKOMNIKA: Journal of Computer Science and Applied Informatics JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Indonesian Journal of Data and Science Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Nusantara Science and Technology Proceedings SINTA Journal (Science, Technology, and Agricultural) Jurnal Ilmiah Teknologi Informasi dan Robotika Jurnal Manajemen Informatika Jayakarta Jurnal Teknologi dan Manajemen International Journal Of Computer, Network Security and Information System (IJCONSIST) Algoritme Jurnal Mahasiswa Teknik Informatika Literasi Nusantara Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Kohesi: Jurnal Sains dan Teknologi Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Router : Jurnal Teknik Informatika dan Terapan Modem : Jurnal Informatika dan Sains Teknologi Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Router : Jurnal Teknik Informatika dan Terapan
Eva Yulia Puspaningrum
Department Of Informatics, Universitas Pembangunan Nasional Veteran Jawa Timur, Jl. Raya Rungkut Madya, Surabaya, Indonesia
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Optimization of Ride Routes in a Tourist Attraction Using Dijkstra’s and Genetic Algorithm Firyal Wishal Nabili; Eva Yulia Puspaningrum; Afina Lina Nurlaili
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.v8i2.3264

Abstract

This research presents a hybrid optimization framework that integrates Dijkstra’s Algorithm, the Genetic Algorithm (GA), and a 2-Opt local search procedure to generate optimal and demographically tailored tourist routes at Wisata Bahari Lamongan (WBL). The methodological novelty lies in the layered design of the hybrid pipeline: Dijkstra is used as a pre-processing stage to reconstruct a complete shortest-path distance matrix from partially measured field data, ensuring that GA operates on accurate inter-attraction distances and avoids unrealistic transitions. The GA then performs route evolution using PMX crossover, swap mutation, and elitism, while 2-Opt refines local segments to prevent suboptimal edge structures. Experiments involved 12 parameter-testing scenarios (CR = 0.7–0.9, MR = 0.05–0.1, population sizes of 50 and 100) across three visitor categories children, adults, and seniors. Benchmark validation on ATSP datasets from TSPLIB (BR17, P43, RY48, FT53) resulted in a mean error rate of 6.189%, confirming the robustness and generalizability of the method. The optimal configuration (CR = 0.7, MR = 0.05, PopSize = 100) produced route distances of 184,750 cm (children), 197,340 cm (adults), and 180,190 cm (seniors), yielding efficiency improvements of 30–50% compared to a pure GA and 3–7% compared to the initial measured paths. These findings demonstrate that the proposed hybrid Dijkstra–GA–2Opt framework offers a conceptually distinct, scalable, and empirically validated approach for real-world tourism route optimization.
An SMOTE-Optimized MLP Approach for Classification of Diabetes Health Status Ferry Trilaksana Putra; Eva Yulia Puspaningrum; Muhammad Muharrom Al Haromainy
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.v8i2.3340

Abstract

Diabetes mellitus requires accurate classification systems to support early detection and clinical decision-making. Prior research has explored the use of Multilayer Perceptron combined with SMOTE, yet the methodological gap remains in evaluating its effectiveness on multiclass clinical datasets with significant class imbalance, particularly for the Prediabetes category. This study addresses that gap by examining the performance of an MLP model enhanced with SMOTE to improve overall accuracy and minority-class detection. The dataset includes age, gender, blood pressure, random blood glucose, weight, and height as clinical predictors. The preprocessing pipeline consists of label encoding for categorical variables, feature standardization, and the application of SMOTE to balance class distribution. The evaluation follows a consistent 80 10 10 split for training, validation, and testing, with three repeated experimental runs to ensure result stability. On the original imbalanced dataset, the MLP achieved an accuracy of 85 percent and showed limited capability in identifying Prediabetes. After applying SMOTE, accuracy increased to 91 percent, accompanied by notable improvements in recall and F1 score across all health status categories. These results demonstrate that SMOTE enables the model to capture non-linear patterns in minority classes and strengthens overall generalization. The proposed model can be integrated into clinical screening workflows as a decision-support tool. Its predictions can help clinicians identify at-risk individuals earlier, prioritize follow-up actions, and enhance patient management in healthcare settings.
Rapid Application Development Method for Web-Based Shallot Price Prediction Using Machine Learning Model Rafani Bardatus Salsabilah; Yisti Vita Via; 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.v8i2.3348

Abstract

Fluctuations in shallot prices in Indonesia create uncertainty within the agricultural supply chain and affect farmers, traders, and policymakers. This condition highlights the need for analytical mechanisms capable of accurately monitoring and predicting price dynamics. This study develops a web-based shallot price prediction system using the Rapid Application Development (RAD) method, with the best-performing model obtained from the training process being a combination of Long Short-Term Memory (LSTM) and CatBoost. The model is designed to process historical data along with non-sequential variables including price, production, rainfall, inflation, the Consumer Price Index (CPI), and seasonal indicators using a five-year dataset compiled from various official government sources. The trained model is integrated into a Flask-based backend to generate the next 7-day price forecasts. The system allows users to upload datasets, execute prediction processes, and analyze outputs through interactive charts and prediction tables. The evaluation shows that the model achieves strong performance, indicated by a MAPE of 6.71% and an RMSE of 0.029120, reflecting good accuracy and alignment with the seasonal patterns of shallot prices. Black-box testing confirms that all system functions operate as expected. The RAD method contributes to accelerating the development process through continuous iteration, resulting in a lightweight, responsive, and user-friendly system for non-technical users. Consequently, this system has the potential to serve as a decision-support tool for monitoring and anticipating shallot price dynamics at both regional and national levels.
Application of SARIMA and XGBoost Models in Forecasting International Tourist Arrivals at Ngurah Rai Maisie Yunita Malva; Anggraini Puspita Sari; 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.v8i2.3352

Abstract

The tourism sector constitutes a vital component of Indonesia's economic growth, especially in Bali Province, where Ngurah Rai International Airport functions as the principal entry point for international travelers. Precise prediction of tourist arrivals is critical for strategic planning, resource distribution, and infrastructure development. Nevertheless, conventional statistical techniques often struggle to adequately capture the intricate patterns in tourism data, which exhibit both periodic regularities and non-linear characteristics shaped by external influences, including global economic fluctuations, travel regulations, and the COVID-19 pandemic. This research proposes a hybrid SARIMA-XGBoost framework that combines traditional statistical modeling with machine learning techniques to simultaneously capture linear temporal dependencies and non-linear residual patterns—an integration not previously explored for Bali's tourism forecasting. The study employs 204 monthly records of international tourist arrivals spanning January 2008 to December 2024, integrating seasonal indicators and the COVID-19 pandemic period as external covariates. The SARIMA component extracts linear temporal trends and seasonal structures, whereas XGBoost captures non-linear dynamics embedded in the residuals. The hybrid model achieves substantially higher forecasting precision with MAPE of 3.22%, MAE of 0.0492, and RMSE of 0.0597, outperforming standalone SARIMA (MAPE 25.02%, MAE 0.4305, RMSE 0.5035) and XGBoost (MAPE 7.36%, MAE 0.0736, RMSE 0.0995). These results validate that integrating statistical and machine learning methodologies significantly enhances predictive accuracy. The proposed model offers airport management, tourism boards, and policymakers a robust forecasting instrument for capacity planning and strategic decision-making, facilitating sustainable tourism development and enhancing Bali's competitiveness as an international destination.
Optimizing Plantation Production Prediction Using Category Boosting with Random Search and Walk-Forward Validation Faishal Fernando Hutama; Eva Yulia Puspaningrum; Afina Lina Nurlaili
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.v8i2.3354

Abstract

The plantation subsector is a cornerstone of the national economy, yet its productivity is increasingly volatile due to climate change. Predicting production yields remains challenging as traditional models often fail to capture complex nonlinear temporal dependencies and seasonal cycles. This study aims to improve the prediction accuracy of five major plantation commodities, namely palm oil, rubber, coffee, tea, and sugarcane, by optimizing the Category Boosting (CatBoost) algorithm. The analysis uses monthly data from 2009 to 2024, combining official production and land statistics from the Central Bureau of Statistics (BPS) with national temperature and rainfall records from the Meteorology, Climatology, and Geophysics Agency (BMKG) to ensure transparency. Unlike standard approaches that rely on default parameters and random data splitting, this research applies a rigorous optimization pipeline. Random Search is used for hyperparameter tuning, supported by lag features to capture short term dynamics and sinusoidal transformations to represent seasonal cycles. A Walk Forward Validation technique with an expanding window is employed to prevent look ahead bias and ensure realistic evaluation. The optimized model significantly outperforms the baseline. Sugarcane (R² 0.95) and Coffee (R² 0.97) show excellent accuracy, while Palm Oil improves markedly (R² 0.80) as more historical patterns are learned. Rubber and Tea remain difficult to predict, indicating insufficient explanatory features rather than model limitations. The study concludes that combining hyperparameter optimization with temporal feature engineering enables CatBoost to effectively model agricultural time series data and provides a solid foundation for strategic production planning.
Effect of CBAM Integration on InceptionV3 for Improved Foot and Mouth Disease Detection Accuracy Andrianto, Mochammad Rifky; Mandyartha, Eka Prakarsa; Puspaningrum, Eva Yulia
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3756

Abstract

Foot and Mouth Disease (FMD) is a highly contagious livestock disease that causes significant economic losses. Timely detection is essential to prevent rapid transmission. While deep learning has shown promise in image-based disease identification, the impact of integrating lightweight attention mechanisms, such as the Convolutional Block Attention Module (CBAM), into robust multi-scale backbones, such as InceptionV3, for FMD detection on small, imbalanced primary field datasets remains underexplored. This study contributes by providing a systematic evaluation of CBAM integration under varying data-splitting scenarios, highlighting the interaction between attention mechanisms and data distribution. This study evaluates the integration of CBAM into InceptionV3 for the classification of cattle lesion images. It compares its performance with the baseline InceptionV3 model across three train-validation-test splits (70:20:10, 80:10:10, and 70:15:15). The dataset comprises 798 primary images (514 FMD-positive and 284 healthy), indicating a limited size with moderate class imbalance. Images were resized to 299 × 299 pixels and normalized to [-1, 1], with augmentation applied only to the training set. The InceptionV3-CBAM model achieved the best performance under the 70:15:15 split, with 96.69% accuracy, 96.25% precision, 98.72% recall, and 97.48% F1-score. These findings suggest that CBAM can enhance lesion-focused feature representation and detection sensitivity. However, performance gains were inconsistent across splits and appear influenced by both architectural changes and dataset characteristics. The model demonstrates potential for early FMD screening in resource-limited settings, but further validation on larger, more diverse datasets is essential to confirm robustness and generalizability
Optimizing MobileNetV2 Using Transfer Learning and Fine-Tuning Techniques for Lung Cancer Classification Rozi, Atiqur; Puspaningrum, Eva Yulia; mandyartha, Eka Prakarsa
ILKOMNIKA Vol 8 No 1 (2026): Volume 8, Number 1, April 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i1.871

Abstract

Lung cancer remains one of the leading causes of mortality worldwide, highlighting the importance of early and accurate detection. This study proposes a deep learning-based approach for lung cancer classification using the MobileNetV2 architecture on CT-scan images. Two experimental scenarios were investigated: transfer learning with a frozen base model and fine-tuning by unfreezing selected layers. The dataset was compiled from publicly available sources and balanced to address class imbalance. The model was trained using the Stochastic Gradient Descent (SGD) optimizer and evaluated using accuracy, precision, recall, and F1-score. The results demonstrate that the fine-tuning strategy achieves superior performance across most evaluation metrics compared to transfer learning. In particular, recall shows a significant improvement, indicating enhanced capability in detecting positive cancer cases, although accompanied by a slight decrease in precision. The F1-score also improves, reflecting a better balance between precision and recall. These findings suggest that fine-tuning enhances feature representation and improves classification performance within the experimental setting. However, the results are limited to the dataset used in this study, and further validation on larger and clinically representative datasets is required before considering real-world medical applications.
Weighted Moving Average Berbasis Variasi Window untuk Optimasi Persediaan dan Reorder Point Fiber Optik Ridho Fajar Fahturohman; Budi Nugroho; Eva Yulia Puspaningrum
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3592

Abstract

Poor inventory management is a primary challenge for telecommunications service providers. This study implements the weighted moving average (WMA) algorithm with window size variations to forecast demand for kabel drop core and Optical Network Terminal (ONT) at Fibertrust Madiun, integrating it with Safety Stock (SS) and Reorder Point (ROP) calculations. The data covers 63 kabel drop core transactions and 112 ONT transactions during January–December 2025, with coefficients of variation of 0.384 and 0.567 respectively. Three window sizes (3, 4, and 5 periods) were tested using MAD, MSE, and MAPE. Window 5 achieved the best accuracy with MAPE of 27.11% for kabel drop core and 47.57% for ONT, both in the sufficient category. A 95% service level provides the optimal balance between holding cost and stockout risk. ROP implementation has the potential to reduce stockout incidents by 71–79%, from 28 to 6–8 incidents per year.Keywords: Weighted moving average; Reorder point; Safety stock; Inventory management; StockoutAbstrakPengelolaan persediaan yang tidak tertata dengan baik menjadi tantangan utama perusahaan penyedia layanan telekomunikasi. Penelitian ini mengimplementasikan algoritma weighted moving average (WMA) dengan variasi ukuran window untuk meramalkan permintaan kabel drop core dan Optical Network Terminal (ONT) di Fibertrust Madiun, serta mengintegrasikannya dengan perhitungan Safety Stock (SS) dan Reorder Point (ROP). Data mencakup 63 transaksi kabel drop core dan 112 transaksi ONT selama Januari-Desember 2025, dengan koefisien variasi masing-masing 0,384 dan 0,567. Tiga variasi ukuran window (3, 4, dan 5 periode) diuji menggunakan metrik MAD, MSE, dan MAPE. Window 5 periode mencatat akurasi terbaik dengan MAPE 27,11% untuk kabel drop core dan 47,57% untuk ONT, keduanya berkategori cukup. Service level 95% memberikan keseimbangan terbaik antara biaya penyimpanan dan risiko kehabisan stok (stockout). Penerapan ROP berpotensi menekan insiden stockout hingga 71-79%, dari 28 insiden menjadi 6-8 insiden per tahun. 
PENDEKATAN ENSEMBLE VISION TRANSFORMER – DENSENET121 DALAM KLASIFIKASI LUMPY SKIN DISEASE PADA SAPI Ama Maulidatul Khairah; Eka Prakarsa Mandyartha; Eva Yulia Puspaningrum
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 2 (2026): EDISI 28
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i2.7402

Abstract

Lumpy Skin Disease (LSD) adalah penyakit menular pada sapi yang berdampak pada produktivitas dan nilai ekonomi peternakan. Di sisi lain, metode identifikasi konvensional masih membutuhkan proses yang relatif lama serta biaya yang tidak sedikit.. Tujuan dari penelitian ini adalah untuk mengklasifikasikan LSD pada sapi berbasis citra digital menggunakan model ensemble Vision Transformer dan DenseNet121 dengan pendekatan transfer learning. Data yang digunakan berjumlah 1360 citra yang dipisahkan ke dalam dua kelas, yaitu normal dan lumpy skin. Proses pelatihan dan evaluasi menggunakan K-Fold Cross Validation, optimasi bobot ensemble dilakukan dengan Grid Search. Hasil pengujian menunjukkan bahwa model ensemble mampu memberikan performa lebih baik dibandingkan model tunggal, dengan nilai akurasi 91.10%, precision 91.11%, recall 91.10%, dan F1 – score 91.10%. Hasil ini menunjukkan bahwa kombinasi fitur global dan lokal mampu meningkatkan performa klasifikasi. Dengan demikian, model yang diusulkan berpotensi sebagai solusi deteksi dini LSD pada sapi berbasis citra digital yang lebih cepat dan efisien.
Performance of Contrast Adjustment in Face Recognition with Training Image under Various Lighting Conditions Budi Nugroho; Eva Yulia Puspaningrum
IJCONSIST JOURNALS Vol 3 No 2 (2022): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v3i2.63

Abstract

The lighting factor has a very significant effect on facial recognition performance. To reduce the effect of this lighting factor, at the pre-processing stage the researchers used contrast adjustments to the image to improve facial recognition performance. The histogram equalization technique is generally used for contrast adjustment because of its excellent performance to normalize image illumination which is affected by lighting conditions. In this research, empirical experiments were carried out to determine the effect of contrast adjustment using histogram equalization on face recognition in more detail. This research aims to answer the question whether this technique can be used in all image lighting conditions or not. The Robust Regression method is used in this research to recognize faces, which in many cases have very good performance due to lighting factors. Experiments using images in the AR Face Database related to lighting factors. The testing process is carried out by comparing the results of face recognition using the histogram equalization technique in the pre-processing phase and face recognition without pre-processing in each lighting condition. The experimental results show that the use of the histogram equalization technique in pre-processing gives a better face recognition performance effect in low, medium and high lighting conditions. But in very high (extreme) lighting conditions, the use of the histogram equalization technique in pre-processing turns out to have a worse facial recognition performance effect, with an average accuracy of 93.17%, whereas without pre-processing it produces an average accuracy of 94 , 67%.
Co-Authors Abiyan Naufal Hilmi Achmad Junaidi Adelia Putri Adyani Adityawan, Firza Prima Afina Lina Nurlaili Afina Lina Nurlaili Afina Lina Nurlaili Agung Mujiono, Alfinas Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Ahmad Fahry Hamidy Ahmad Hilman Dani Akbar, Fawwaz Ali Al Danny Rian Wibisono Ali Muhhamad Saleh Baaboud Ama Maulidatul Khairah Ananda Azra Razali Andhika Ahnaf Daniswara Andini Fitriyah Salsabilah Andreas Nugroho Sihananto Andrianto, Mochammad Rifky Anggraini Puspita Sari Ani Dijah Rahajoe Ani Dijah Rahajoe Annisaa Sri Indrawanti annisaa sri indrawanti annisaa sri indrawanti Anny Yuniarti Aqsa Prima Cahya Ariani, Dian Dwi Ariyono Setiawan Aryananda, Rangga Laksana Aswan Aswan Attaqwa, Syukur Iman Awang, Mohd Khalid Azizah, Nabila Wafiqotul Bagus Sutikno Putra Basuki Rahmat Basuki Rahmat Basuki Rahmat Basuki Rahmat Masdi Siduppa Bimantara, Candra Kusuma Muhammad Budi Mukhammad Mulyo Budi Nugroho Budi Nugroho Budi Nugroho Budi Nugroho Budi Nugroho Budi Nugroho Chafid, M Putih Daniswara, Sena Dela Puspita Lasminingrum Devan Cakra Mudra Wijaya Dewi, Deshinta Arrova Dhian Satria Yudha K. Dimas Saputra Diyasa, I Gede Susrama Mas Dwi Anggraeni, Shinta Dwiki Aditama Supangkat Eka Prakarsa Mandyartha Eka Prakarsa Mandyartha, Eka Elzandy, Imeldha Etniko Siagian, Pangestu Sandya Fahmi Al Hafidz, Achmad Faishal Fernando Hutama Fara Disa Durry Faris Syaifulloh Farkhan, Farkhan Ferry Trilaksana Putra Fetty Tri Anggraeny Firyal Wishal Nabili Firyal Wishal Nabili Firza Prima Aditiawan Firza Prima Adityawan Firza Prima Adityawan Fitri Rahmawati Hadi, Surjo Hapsari Wiji Utami Hasby Bik, Ahmad Henni Endah Wahanani Humairah, Sayyidah Humam Maulana Tsubasanofa Ramadhan I Gede Susrama Mas Diyasa I Nyoman Sujana I Wayan Alston Argodi Idhana, Ilham Ainur indrawanti, annisaa sri Irsyad Rafi Naufaldi Karim, Mohammad Daniel Sulthonul Kartini Kartini Lestari, Kusmiyati Lina Nurlaili, Afina M. Syahrul Munir, M. Syahrul Mada Lazuardi Nazilly Made Hanindia Prami Swari Maisie Yunita Malva Mandyartha, Eka Prakarsa Manggala, Herwantoro Arya Marchel Adias Pradana Maulana, Hendra Merdin Risalul Abrori Moch. Hatta Mohammad Idhom Muchamad Dicky Alifiansyah Muhammad Aldi Maulana Muhammad Asyraf Muhammad Fernanda Naufal Fathoni Muhammad Misbachuddin Muhammad Muharrom Al Haromainy Muhammad Syafril Hidayat Nabilah, Qonitah Jihan Nanik Suciati Noor Fitria Azzahra Nugroho, Budi Nugroho, Budi Nugroho, Budi Nurcahyo, Syai'in Bayu Nurul Taukid, Mochamad Pallawabonang, Mahabintang Pratama, Gede Ardi Prisheila Dharmawan, Diaz Putra, Chrystia Aji Putra, Riza Satria Putri, Desya Ristya Rafani Bardatus Salsabilah Retno Mumpuni Ridho Fajar Fahturohman Rizki, Agung Mustika Rizqi Mar'atus Sholiihah, Eka Royan Fajar Sultoni Rozi, Atiqur S J Saputra, Wahyu Safira, Dwi Putri Samuel Krispama Lumbantoruan Saputra, Raka Aji Saputra, Wahyu S J Saputra, Wahyu S J Saputra, Wahyu S. J. Saputra, Wahyu S.J. Satria Yudha Kartika , Dhian Shawn Hafizh Adefrid Pietersz Shofiya Syidada Sukendah, Sukendah Sunarko, Victor Immanuel Surjohadi, Surjohadi Susrama Mas Diyasa, I Gede Syahrul Hidayat Syaifullah JS, Wahyu Taruna Ardianto Tataq Distasianto Utami, Hapsari Wiji Vita Via, Yisti Wafiqotul Azizah, Nabila Wahyu Caesarendra Wahyu Dwi Lestari Wahyu S.J. Saputra Wahyu Syaifullah Jauharis Saputra Wan Awang, Wan Suryani Wan Suryani Wan Awang Widiastuty, Riana Retno Wiji Utami, Hapsari yisti vita via Yisti Vita Via Yisti Vita Via Yogie Wilvren Saragih Yudha K., Dhian Satria Yudhistira Nanda Kumala YUSMI NUR AINI Zacky Yaser Malik Gumiwang Zalfa Ibtisamah Arishandy ZAMAZANI, ZAIN MUZADID Zuhriyah, Sitti