Article Per Year (5 Year)
p-Index From 2021 - 2026
10.134
P-Index
This Author published in this journals
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 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
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 Social Sciences Veterinary Other

Published : 102 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 102 Documents
Search

 7   8   9   10   11 
Convolutional layer exertion on few-shot learning for brain tumor classification Sunarko, Victor Immanuel; Puspaningrum, Eva Yulia; Widiastuty, Riana Retno; Hadi, Surjo; Awang, Mohd Khalid; Mas Diyasa, I Gede Susrama
Jurnal Ilmiah Kursor Vol. 13 No. 2 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i2.430

Abstract

Brain tumors, though relatively rare, pose a significant threat due to their critical location within the brain, impacting essential bodily functions. Accurate and timely diagnosis is vital, but traditional diagnostic methods are time-intensive and rely heavily on large labeled datasets. This study addresses these challenges by proposing a Few-Shot Learning (FSL) framework enhanced with Convolutional Neural Networks (CNNs) to classify brain tumors using MRI images. By employing the Matching Network architecture, the model leverages limited training data through an N-way-K-shot setup. Training results demonstrated accuracy levels of 71.58% (1-shot) and 82.89% (5-shot) for 1-layer CNNs, 66.65% (1-shot) and 84.03% (5-shot) for 3-layer CNNs, and 63.43% (1-shot) and 84.94% (5-shot) for 5-layer CNNs. However, validation accuracy revealed overfitting concerns, with the highest performance at 51.56% (1-layer, 1-shot). These results underscore the potential of FSL in medical imaging while highlighting the need for advanced augmentation and feature representation techniques to improve generalization.
Implementation of Web-Based Regional Innovation Selection Process Automation: A Case Study of Pasuruan Regency Firza Prima Aditiawan; Agung Mustika Rizki; Eva Yulia Puspaningrum
SINTA Journal (Science, Technology, and Agricultural) Vol. 6 No. 2 (2025)
Publisher : Perkumpulan Dosen Muda (PDM) Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37638/sinta.6.2.373-386

Abstract

Digital transformation in the regional innovation selection process is key to improving transparency, efficiency, and accountability. To encourage an innovation ecosystem in Pasuruan Regency, Bappelitbangda held the Pasuruan Maslahat Technology Innovation Competition with three categories: (1) Regional Innovation (governance/public services), (2) Technological and Non-Technological Innovation, and (3) Learning Innovation. The main challenges of the competition are the high volume of proposals, process traceability, and consistency of assessment. This article presents the design and implementation of the Maslahat Innovation and Technology Selection Website system to automate the end-to-end flow: registration, proposal upload, administrative verification, multi-reviewer assessment, weighted score aggregation, nomination determination, and publication of results. The three-layer web-based architecture is designed with role control (admin, secretariat, reviewer, participant, public), audit trail, and proportional information disclosure policy. The assessment method uses Simple Additive Weighting (SAW) with min–max normalization and weighting per category. The expected outcome is improved operational efficiency, accountability, and transparency of the selection process so that the sustainability of the competition can be ensured.
Pencarian Jalur Terpendek Jakarta ke Jawa Barat Berbasis Algoritma Genetika Firyal Wishal Nabili; Eva Yulia Puspaningrum; Afina Lina Nurlaili
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Travelling Salesman Problem (TSP) is a well-known combinatorial optimization problem aimed at finding the shortest route that visits each location exactly once and returns to the starting point. This study aims to determine the shortest travel route from Jakarta to all cities in West Java Province using a Genetic Algorithm (GA). Distance data between cities were obtained from the Central Bureau of Statistics (BPS) of Bekasi Regency and used to construct a distance matrix for distance calculation. The optimization process employed a population size of 100 individuals, a crossover rate of 0.7, a mutation rate of 0.05, and 500 generations. The algorithm used Roulette Wheel Selection for parent selection, PMX (Partially Mapped Crossover) for crossover, swap mutation for mutation, and elitism to preserve the best individuals across generations. Experimental results show that the initial route distance of 2918 km was reduced to 1314 km at generation 110 and remained stable until generation 500. The optimal route found was: Jakarta ? Bekasi ? Karawang ? Tangerang ? Serang ? Pandeglang ? Lebak ? Bogor ? Sukabumi ? Cianjur ? Subang ? Indramayu ? Kuningan ? Cirebon ? Tasikmalaya ? Ciamis ? Majalengka ? Sumedang ? Garut ? Bandung ? Purwakarta ? Jakarta. These results demonstrate that the Genetic Algorithm effectively provides optimal route solutions with fast convergence and high efficiency in solving the TSP.
Perbandingan Algoritma Deep Q-Network dan Local Outlier Factor Untuk Deteksi Anomali Konsumsi Air Minum Pelanggan PUDAM Kabupaten Banyuwangi Andhika Ahnaf Daniswara; Basuki Rahmat; Eva Yulia Puspaningrum
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 4 (2024): Agustus: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i4.243

Abstract

Adequate provision of drinking water in quantity, quality, and continuity is needed to realize a healthy and productive society. A well-managed Drinking Water Supply System (SPAM) is essential to meet this need. Based on Government Regulation Number 122 of 2015, the implementation of SPAM involves the development and management of drinking water which is the responsibility of the local government and PUDAM as the implementer. The main challenges faced by PUDAM include the high level of water loss or Non-Revenue Water (NRW), which reaches 40% in Indonesia. One of the efforts to reduce the NRW level at PUDAM Banyuwangi Regency in the Kalipuro District area is to detect abnormal consumption in customer drinking water consumption. This study uses the Deep Q Network and Local Outlier Factor algorithms to detect anomalies in drinking water consumption, with the aim of comparing the performance of the two algorithms in identifying abnormal consumption patterns at PUDAM Banyuwangi Regency. The results of the study indicate that the Local Outlier Factor algorithm is more suitable for anomaly detection as evidenced by the absence of detection errors and an F1-Score value of 36%.
Image Color Correction for Color Vision Deficiency Using ResNet and CycleGAN Adelia Putri Adyani; Fetty Tri Anggraeny; 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.v8i1.2506

Abstract

Color blindness is a visual impairment that limits an individual's ability to accurately perceive certain colors, particularly red, green, or blue. This condition can hinder daily tasks, especially when color identification is crucial. This study proposes a color correction system designed to enhance color perception for individuals with color vision deficiency (CVD), focusing on important visual areas within an image. The method involves converting RGB images into LMS color space, simulating types of color blindness (protanopia, deuteranopia, and tritanopia), detecting visually important regions using a saliency mask, applying color correction through a ResNet-based deep learning model, and performing a reverse transformation back to RGB using a CycleGAN. A total of 5,020 images were used for evaluation, and the proposed system achieved an average Root Mean Square (RMS) error of 0.0212. The Mean Absolute Error (MAE) ranged from 0.1541 to 0.5582 depending on the CVD type. In addition to quantitative evaluation, qualitative validation was conducted through a GUI-based user test involving 10 color blind participants. The system showed the highest effectiveness for deuteranopia with a color recognition accuracy of 71.666%, followed by tritanopia at 59.666% and protanopia at 46.500%. These results indicate that the proposed system offers significant potential in aiding individuals with CVD to better interpret color-based information, especially in visually important regions of an image. Future work may explore broader datasets and alternative deep learning architectures to further improve accuracy and adaptability.
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.
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 Nugroho Budi Nugroho 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 Faisal Muttaqin 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 Revelin Putri Syamjovanka 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