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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 Journal of Information Systems and Informatics bit-Tech Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Indonesian Journal of Data and Science Nusantara Science and Technology Proceedings SINTA Journal (Science, Technology, and Agricultural) Jurnal Pengabdian Masyarakat Indonesia 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 Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 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
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry 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 Neuroscience Physics Public Health Social Sciences Veterinary Other

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Stacking Ensemble of XGBoost, LightGBM, and CatBoost for Green Economy Index Prediction Andini Fitriyah Salsabilah; Basuki Rahmat; Eva Yulia Puspaningrum
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2530

Abstract

Indonesia faces persistent challenges in achieving sustainable development, particularly in harmonizing economic growth with environmental sustainability. The imbalance among economic, social, and environmental dimensions necessitates a comprehensive and reliable measurement tool to assess progress toward a green economy. The Green Economy Index (GEI), developed by the Ministry of National Development Planning (BAPPENAS), serves this function. However, limited data availability at the provincial level, such as in East Java, hampers accurate evaluation and informed policy formulation. This study aims to develop a machine learning-based predictive model for the GEI using a stacking ensemble approach that combines three powerful algorithms: XGBoost, LightGBM, and CatBoost. The model was built using relevant economic, social, and environmental indicators and evaluated on a holdout dataset to assess its predictive accuracy and generalizability. The results show that the stacking ensemble model achieved superior performance compared to the individual models, recording an RMSE of 0.0298, MAE of 0.0225, and the R² score of 0.9774. In comparison, CatBoost, XGBoost, and LightGBM individually performed with slightly lower accuracy. These findings confirm that the stacking ensemble approach is highly effective for predicting GEI values and offers a practical, data-driven solution for supporting sustainable development strategies at the regional level. The study concludes that such predictive tools can significantly enhance policy planning and monitoring of green economic growth, although further research is recommended to validate the model across other provinces.
Fuzzy C-Means Clustering of Regencies and Cities Based on Total Sanitation Society Ananda Azra Razali; Eva Yulia Puspaningrum; Henni Endah Wahanani
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.v8i2.3180

Abstract

The Community Based Total Sanitation (STBM) program is a national initiative designed to enhance public health by promoting clean and healthy living habits. However, its implementation in several regions, including East Java Province, continues to encounter a number of challenges, as several sanitation indicators have yet to reach the desired targets. This study aims to group the sanitation performance of regencies and cities in East Java using the Fuzzy C Means (FCM) algorithm and visualize the outcomes through thematic maps to provide clearer and more informative spatial insights. Six key indicators. Six key indicators CTPS, PAMMRT, PSRT, PLCRT, PKURT, and Healthy Home Access were analyzed as percentages, with variable selection and normalization conducted using the Min Max Scaler to ensure comparable value ranges across datasets. The clustering validity was assessed using the Davies Bouldin Index (DBI), where the lowest value of 0.9134 was achieved for three clusters, indicating the most optimal grouping configuration. The resulting clusters represent regions with high, medium, and low sanitation achievement levels, while spatial visualization reveals that lower-performing regions are largely concentrated in the eastern part and the Madura area. From a practical standpoint, the findings of this study can serve as a foundation for policy formulation, intervention prioritization, and more efficient resource allocation to improve regional sanitation performance in a focused and sustainable manner.
Comparison of Fine-Tuning InceptionV3 and Xception for Eye Disease Classification Based on Fundus Images Irsyad Rafi Naufaldi; 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.v8i2.3195

Abstract

Eye diseases represent a major global health concern that can lead to visual impairment and even blindness if not detected early. The shortage of ophthalmologists and unequal distribution of medical services highlight the need for automatic eye disease detection system increasingly essential. Therefore, the role of Artificial Intelligence (AI), particularly Deep Learning, is highly needed. This study aims to compare the performance of two CNN architectures InceptionV3 and Xception. Unlike previous studies, this paper provides a comparative Fine-Tuning analysis of two CNN models on multiclass eye disease. The approach applied is transfer learning with a fine-tuning technique on several final layers to achieve higher accuracy by optimizing pretrained models using large-scale datasets such as ImageNet. The dataset consists of 4,184 fundus images covering multiple eye disease with balanced class distribution, ensuring diversity that supports model generalization. Divided into train, valid, and test sets with a ratio of 70:15:15. The training employed Adam optimizer, a batch size of 16, a learning rate of 0.0001, and implements early stopping to prevent overfitting. The performance of the model was assessed using evaluation metrics including accuracy, precision, recall, and F1-score. Experimental results indicate that the Xception model achieved superior performance with an accuracy of 87.78%, precision of 0.89, recall of 0.88, and an F1-score of 0.88, outperforming InceptionV3 with an accuracy of 85.56%, indicates the model is reliable for preliminary diagnosis. These findings suggest that the architecture in Xception is more efficient in extracting features from limited yet complex medical datasets.
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.
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.
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 Ananda Azra Razali Andhika Ahnaf Daniswara Andini Fitriyah Salsabilah Andreas Nugroho Sihananto 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 Nugroho Budi Nugroho Budi Nugroho Budi Nugroho Chafid, M Putih Daniswara, Sena 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 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 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 Rizki, Agung Mustika Rizqi Mar'atus Sholiihah, Eka Royan Fajar Sultoni 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