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All Journal International Journal of Electrical and Computer Engineering Jurnal Sistem Komputer Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Bulletin of Electrical Engineering and Informatics Telematika : Jurnal Informatika dan Teknologi Informasi Sinergi Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi JURNAL NASIONAL TEKNIK ELEKTRO Bulletin of Electrical Engineering and Informatics Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) GERVASI: Jurnal Pengabdian kepada Masyarakat Systemic: Information System and Informatics Journal Journal of Information Systems and Informatics Buletin Ilmiah Sarjana Teknik Elektro International Journal of Engineering, Technology and Natural Sciences (IJETS) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Advances in Data and Information Systems Journal of Innovation Information Technology and Application (JINITA) Science in Information Technology Letters Jurnal INFOTEL Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Saifullah, Shoffan
Universitas Pembangunan Nasional Veteran Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Seasonal meat stock demand used comparison of performance smoothing-average forecasting Tundo, Tundo; Saifullah, Shoffan; Dharmawan, Tio; Junaidi, Junaidi; Devia, Elmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp425-433

Abstract

Seasonal patterns significantly influence the demand for beef stock, especially in rural areas that rely on natural feed. Accurate forecasting is essential for managing this demand due to beef's status as a government-regulated nutritional commodity. Food production, consumption, and income levels affect the demand for beef stocks. This research aims to identify the most precise forecasting method for predicting future beef stock needs. We evaluated multiple techniques, including single exponential smoothing (SES), double exponential smoothing (DES), single moving average (SMA), and double moving average (DMA), using the mean absolute percentage error (MAPE) metric, focusing specifically on beef supplies in Pemalang. The results indicated that the DMA method achieved the highest accuracy with a MAPE value of 5.993% at the 4th -order parameter. Additionally, increasing the data volume improved forecasting accuracy, demonstrating the effectiveness of the DMA method for beef stock prediction.
Implementasi Perancangan dan Pemeliharaan Jaringan Internet Menuju Smart School pada MA Raden Fattah Ahmad Taufiq Akbar; Bagus Muhammad Akbar; Shoffan Saifullah; Andiko Putro Suryotomo; Rochmat Husaini; Hari Prapcoyo
Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial Vol. 2 No. 1 (2025): Februari : Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/karya.v2i1.1079

Abstract

Internet Network is one of the fields in informatics and electronics engineering which is now growing rapidly due to the issue of the industrial revolution 4.0 which is increasingly closely related to Cloud computing technology and the Internet of Things. Without resources and knowledge about computer networks, the Internet of things and Cloud computing are quite impossible to design. Computer networks give birth to internet access which is very much needed by every agency and even the entire community in the world. Especially in educational institutions such as Madrasah Aliyah (MA) Raden Fatah, which is located in Kalasan, Yogyakarta when in the era of the Covid-19 pandemic, it faces the challenge of disruption from offline learning to online learning. To answer the demands of the times, MA Raden Fattah is very enthusiastic in developing its institution towards a quality smart school. The network infrastructure available at MA Raden Fattah has not been optimized, so through this service, network design and management are carried out so that the need for access points that help students and teachers can be met. This service has succeeded in increasing the number of access points, optimizing the management of internet network resources at MA Raden Fattah, and improving the quality of teaching and learning services at the institution
Klasifikasi Ekspresi Wajah Menggunakan Covolutional Neural Network Taufiq Akbar, Ahmad; Akbar, Ahmad Taufiq; Saifullah, Shoffan; Prapcoyo, Hari
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 6: Desember 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024118888

Abstract

Pengenalan ekspresi wajah adalah tantangan penting dalam pengolahan citra dan interaksi manusia-komputer karena kompleksitas dan variasi yang ada. Penelitian ini mengusulkan arsitektur sederhana Convolutional Neural Network (CNN) untuk meningkatkan efisiensi klasifikasi emosi pada dataset kecil. Dataset yang digunakan adalah Jaffe, yang terdiri dari 213 citra berukuran 256x256 piksel dalam tujuh kategori ekspresi. Citra-citra tersebut di-resize menjadi 128x128 piksel untuk mempercepat pemrosesan. Data diproses menggunakan arsitektur CNN yang terdiri dari 3 lapisan konvolusi, 2 lapisan subsampling, dan 2 lapisan dense. Kami mengevaluasi model dengan 5-fold dan 10-fold cross-validation untuk estimasi kinerja yang robust, serta teknik hold-out (70:30, 80:20, 85:15, dan 90:10) untuk perbandingan hasil yang jelas. Hasil menunjukkan akurasi tertinggi sebesar 90.6% dengan learning rate 0.001 pada pembagian 85% data latih dan 15% data uji, melebihi model yang lebih kompleks. Meskipun tidak menggunakan transfer learning atau augmentasi data, model ini tetap unggul dibandingkan pendekatan tradisional seperti Local Binary Pattern (LBP) dan Histogram Oriented Gradient (HOG). Dengan demikian, arsitektur CNN yang sederhana ini terbukti efektif untuk pengenalan ekspresi wajah pada dataset kecil.   Abstract Facial expression recognition is a significant challenge in image processing and human-computer interaction due to its inherent complexity and variability. This study proposes a simple Convolutional Neural Network (CNN) architecture to enhance the efficiency of emotion classification on small datasets. Jaffe's dataset consists of 213 images sized 256x256 pixels across seven expression categories. These images were resized to 128x128 pixels to accelerate processing. The data was processed using a CNN architecture comprising 3 convolutional layers, 2 subsampling layers, and 2 dense layers. We evaluated the model with 5-fold- and 10-fold cross-validation for robust performance estimation and hold-out techniques (70:30, 80:20, 85:15, and 90:10) for clear result comparison. The results indicated the highest accuracy of 90.6% with a learning rate of 0.001 using the 85% training and 15% testing data split, surpassing that of more complex models. Although the model does not employ transfer learning or data augmentation, it still outperforms traditional approaches such as Local Binary Pattern (LBP) and Histogram Oriented Gradient (HOG). Thus, this simple CNN architecture proves effective for facial expression recognition on small datasets.
Analisis Perbandingan Pengolahan Citra Asli Dan Hasil Croping Untuk Identifikasi Telur Shoffan Saifullah; Sunardi -; Anton Yudhana
Jurnal Teknik Informatika dan Sistem Informasi Vol 2 No 3 (2016): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v2i3.638

Abstract

Thermal imaging camera and smartphone camera are the impacts of rapid technological development. This research uses two tools to take pictures of chicken eggs. Images of chicken eggs from the two tools are used to identify of size, determination of object and analysis of image cropping from the samples have used. Process analysis using Matlab prototype for image processing began with histogram, converting the image to grayscale or black white, then the process is carried region props, centroid and the bounding box and labeling. Process analysis using Matlab prototype for image processing began with histogram, converting the image to grayscale or black white, then the process is carried region props, centroid, bounding box and labeling. The process of identification egg objects with region props and labeling can be successfully performed with a 100 % success rate. Images of each sample were conducted to provide data that the cropping process gives the area to the smaller / less identifiable objects provide little value and uniform for any number of the same object. The identification process on the image of the chicken egg thermal cameras and smartphone cameras give equal areas. However, each data cropping of the process is done, the image from the thermal cameras and smartphone cameras give different values. So the cropping process provides the difference in the identification process of chicken eggs. The difference of the image processing of thermal cameras and smartphone cameras lies in the preprocessing of images of thermal cameras needed to complement and process images from the camera smartphone needs to process with the opening before to do the region props and labeling process getting the object is identified.
EfficientNet B0 Feature Extraction with L2-SVM Classification for Robust Facial Expression Recognition Akbar, Ahmad Taufiq; Saifullah, Shoffan; Prapcoyo, Hari; Rustamadji, Heru; Cahyana, Nur Heri
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1071

Abstract

Facial expression recognition (FER) remains a challenging task due to the subtle visual variations between emotional categories and the constraints of small, controlled datasets. Traditional deep learning approaches often require extensive training, large-scale datasets, and data augmentation to achieve robust generalization. To overcome these limitations, this paper proposes a hybrid FER framework that combines EfficientNet B0 as a deep feature extractor with an L2-regularized Support Vector Machine (L2-SVM) classifier. The model is designed to operate effectively on limited data without the need for end-to-end fine-tuning or augmentation, offering a lightweight and efficient solution for resource-constrained environments. Experimental results on the JAFFE and CK+ benchmark datasets demonstrate the proposed method’s strong performance, achieving up to 100% accuracy across various hold-out splits (90:10, 80:20, 70:30) and 99.8% accuracy under 5-fold cross-validation. Evaluation metrics including precision, recall, and F1-score consistently exceeded 95% across all emotion classes. Confusion matrix analysis revealed perfect classification of high-intensity emotions such as Happiness and Surprise, while minor misclassifications occurred in more ambiguous expressions like Fear and Sadness. These results validate the model’s generalization ability, efficiency, and suitability for real-time FER tasks. Future work will extend the framework to in-the-wild datasets and incorporate model explainability techniques to improve interpretability in practical deployment Keywords: Facial Expression Recognition, EfficientNet, SVM, Deep Features, Emotion Classification
Privacy-Preserving U-Net Variants with pseudo-labeling for radiolucent lesion segmentation in dental CBCT Ismail, Amelia Ritahani; Azlan, Faris Farhan; Noormaizan, Khairul Akmal; Afiqa, Nurul; Nisa, Syed Qamrun; Ghazali, Ahmad Badaruddin; Pranolo, Andri; Saifullah, Shoffan
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1529

Abstract

Accurate segmentation of radiolucent lesions in dental Cone-Beam Computed Tomography (CBCT) is vital for enhancing diagnostic reliability and reducing the burden on clinicians. This study proposes a privacy-preserving segmentation framework leveraging multiple U-Net variants—U-Net, DoubleU-Net, U2-Net, and Spatial Attention U-Net (SA-UNet)—to address challenges posed by limited labeled data and patient confidentiality concerns. To safeguard sensitive information, Differential Privacy Stochastic Gradient Descent (DP-SGD) is integrated using TensorFlow-Privacy, achieving a privacy budget of ε ≈ 1.5 with minimal performance degradation. Among the evaluated architectures, U2-Net demonstrates superior segmentation performance with a Dice coefficient of 0.833 and an Intersection over Union (IoU) of 0.881, showing less than 2% reduction under privacy constraints. To mitigate data annotation scarcity, a pseudo-labeling approach is implemented within an MLOps pipeline, enabling semi-supervised learning from unlabeled CBCT images. Over three iterative refinements, the pseudo-labeling strategy reduces validation loss by 14.4% and improves Dice score by 2.6%, demonstrating its effectiveness. Additionally, comparative evaluations reveal that SA-UNet offers competitive accuracy with faster inference time (22 ms per slice), making it suitable for low-resource deployments. The proposed approach presents a scalable and privacy-compliant framework for radiolucent lesion segmentation, supporting clinical decision-making in real-world dental imaging scenarios.
Semi-Supervised Sentiment Classification Using Self-Learning and Enhanced Co-Training Aribowo, Agus Sasmito; Khomsah, Siti; Saifullah, Shoffan
JURNAL INFOTEL Vol 17 No 3 (2025): August
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i3.1344

Abstract

Sentiment classification is usually done manually by humans. Manual senti- ment labeling is ineffective. Therefore, automated labeling using machine learning is es- sential. Building a computerized labeling model presents challenges when labeled data is scarce, which can decrease model accuracy. This study proposes a semi-supervised learn- ing (SSL) framework for sentiment analysis with limited labeled data. The framework integrates self-learning and enhanced co-training. The co-training model combines three machine learning methods: Support Vector Machine (SVM), Random Forest (RF), and Lo- gistic Regression (LR). We use TF-IDF and FastText for feature extraction. The co-training model will generate pseudo-labels. Then, the pseudo-labels from models (SVM, RF, LR) are checked to choose the highest confidence — this is called self-learning. This framework is applied to English and Indonesian language datasets. We ran each dataset five times. The performance difference between the baseline model (without pseudo-labels) and SSL (with pseudo-labels) is not significant; the Wilcoxon Signed-Rank Test confirms it, obtaining a p- value < 0.05. Results show that SSL produces pseudo-labels on unlabeled data with quality close to the original labels on unlabeled data. Although the significance test performs well on four datasets, it has not yet surpassed the performance of the supervised classification (baseline). Labeling using SSL proves more efficient than manual labeling, as evidenced by the processing time of around 10-20 minutes to label thousands to tens of thousands of samples. In conclusion, self-learning in SSL with co-training can effectively label unla- beled data in multilingual and limited datasets, but it has not yet converged across various datasets.
Urban Traffic Volume Prediction using LSTM and Bi-LSTM: Performance Evaluation on the Metro Interstate Dataset Pranolo, Andri; Saifullah, Shoffan; Putra, Agung Bella Utama; Dreżewski, Rafał; Wibawa, Aji Prasetya
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.3001.227-240

Abstract

Urban traffic forecasting underpins the mitigation of congestion, enhancement of road safety, and reduction of emissions in intelligent transportation systems. We benchmark Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models on the Metro Interstate Traffic Volume dataset under an identical preprocessing and training pipeline for a fair comparison. Using a 24-hour multivariate input window (temperature, rainfall, snowfall, cloud cover), LSTM delivers the best overall balance of accuracy and efficiency on the full test sequence (RMSE = 0.196, MAPE = 2.36%, R² = 0.480; 7,344 s training). Bi-LSTM achieves competitive short-window accuracy but underperforms on the full sequence (RMSE = 0.231, MAPE = 2.92%, R² = 0.280; 12,672 s training). We attribute the Bi-LSTM gap to prediction "flattening" over long horizons, i.e., over-smoothed peaks from bidirectional averaging, despite its slightly stronger short-segment fit. Compared with prior RNN/GRU/CNN baselines on the same data, LSTM improves variance explanation while remaining deployable for near-real-time use. We also examine seasonality (daily/weekly cycles), weather effects, and data imbalance (peak versus off-peak) as factors that shape model error. These results support LSTM as a practical default for city-scale forecasting and motivate future work with attention/Transformer encoders and richer exogenous signals (incidents, events). The findings inform policy by enabling proactive traffic management that can reduce delays, emissions, and crash risk through earlier, data-driven interventions.
Fuzzy Inference System Mamdani dalam Prediksi Produksi Kain Tenun Menggunakan Rule Berdasarkan Random Tree Tundo, Tundo; Saifullah, Shoffan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 3: Juni 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022924212

Abstract

Kain tenun merupakan salah satu produk yang diminati oleh banyak orang. Hal ini menjadi pemicu produsen untuk meningkatkan pengelolahannya. Salah satu usaha yang dilakukan adalah memprediksi produksi yang dapat dilakukan untuk mendapatkan jumlah optimal yang diperoleh, sehingga mendapatkan keuntungan yang besar. Dalam penelitian ini, untuk mendapatkan prediksi jumlah produksi kain tenun dilakukan dengan perhitungan komputerisasi menggunakan metode logika fuzzy Mamdani. Metode ini menggunakan konsep pohon keputusan random tree dalam membentuk rule. Rule yang dibuat berdasarkan pada kriteria dalam penentuan jumlah produksi kain tenun, diantaranya yaitu biaya produksi, permintaan, dan stok. Konsep pohon keputusan random tree dalam penelitian ini digunakan untuk membuat rule secara otomatis berdasarkan data yang tersedia. Pembentukan rule ini berdasarkan data-data kain tenun dan diimplementasikan dalam random tree, sehingga tidak perlu menggunakan pakar. Penelitian ini membuktikan bahwa prediksi yang dilakukan dapat membangun rule dengan nilai akurasi sebesar 100%. Hasil perbandingan prediksi dengan produksi sesungguhnya memiliki persentase error sebesar 3% dengan nilai kebenaran sebesar 97% (berdasarkan perhitungan Average Forecasting Error Rate (AFER)). Oleh karena itu ketika diimplementasikan dalam fuzzy Mamdani dapat menghasilkan prediksi produksi kain tenun yang optimal. AbstractWoven fabric is a product that is in demand by many people. It triggers producers to improve their management. One of the efforts made is to predict the production that can be done to get the optimal amount obtained, to get a significant profit. In this study, to obtain a prediction of the amount of woven fabric production is done by computerized calculations using the Mamdani fuzzy logic method. This method uses the concept of a random tree decision tree in forming rules. The rules are made based on the criteria in determining the amount of woven fabric production, including production costs, demand, and stock. The concept of a random tree decision tree in this study automatically generates rules based on available data. This rule's formation is based on woven fabric data and is implemented in a random tree, so there is no need to use experts. This study shows that the predictions made can build rules with an accuracy value of 100%. The comparison of predictions with actual production has an error percentage of 3% with a truth value of 97% (based on the calculation of the Average Forecasting Error Rate (AFER)). When implemented in Fuzzy Mamdani, it can produce optimal woven fabric production predictions with predicted results less than the actual production.
Robust Classification of Beef and Pork Images Using EfficientNet B0 Feature Extraction and Ensemble Learning with Visual Interpretation Taufiq Akbar, Ahmad; Saifullah, Shoffan; Prapcoyo, Hari; Yuwono, Bambang; Rustamaji, Heru Cahya
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.4045

Abstract

Distinguishing between beef and pork based on image appearance is a critical task in food authentication, but it remains challenging due to visual similarities in color and texture, especially under varying lighting and capture conditions. To address these challenges, we propose a robust classification framework that utilizes EfficientNet B0 as a deep feature extractor, combined with an ensemble of Regularized Linear Discriminant Analysis (RLDA), Support Vector Machine (SVM), and Random Forest (RF) classifiers using soft voting to enhance generalization performance. To improve interpretability, we incorporate Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize classification decisions and validate that the model focuses on relevant regions of the meat, such as red-channel intensity and muscle structure. The proposed method was evaluated on a public dataset containing 400 images evenly split between beef and pork. It achieved a hold-out accuracy of 99.0% and a ROC-AUC of 0.995, outperforming individual learners and demonstrating strong resilience to limited data and variation in imaging conditions. By integrating efficient transfer learning, ensemble decision-making, and visual interpretability, this framework provides a powerful and transparent solution for binary meat classification. Future work will focus on fine-tuning the CNN backbone, applying GAN-based augmentation, and extending the approach to multiclass meat authentication tasks.
Co-Authors Abdul Fadlil Adityo Nugroho, Adityo Afiqa, Nurul Agung Tri Utomo Agus Sasmito Aribowo Agus Sasmito Aribowo Ahmad Taufiq Akbar Ahmad Tri Hidayat Aji Prasetya Wibawa Akbar, Bagus Muhammad Alek Setiyo Nugroho Alfiani, Oktavia Dewi Alin Khaliduzzaman Alin Khaliduzzaman Alisya Amalia Putri Hasanah Andi Muhammad Dirham Dewantara Andiko Putro Suryotomo Andri Pranolo Anton Satria Prabuwono Anton Satria Prabuwono Anton Yudhana Arianti, Berliana Andra Arief Hermawan Awang Hendrianto Pratomo Azlan, Faris Farhan Azrul Mahfurdz Bambang Yuwono Bambang Yuwono Betty Yel, Mesra Budi Santosa Devia, Elmi Dharmawan, Tio Dreżewski, RafaÅ‚ Drezewski, Rafal Drezewski, Rafał Dwi Wahyuningrum Dwiyanto, Felix Andika Faqihuddin Al-anshori Felix Andika Dwiyanto Ghazali, Ahmad Badaruddin Haekal, Haekal Hari Prapcoyo Herlina Jayadianti Heru Cahya Rustamaji Hidayat, Ahmad Tri Humairoh, Nanda Lailatul Ismail, Amelia Ritahani Isna Nur Aini Ivana Puspita Sari Japkowicz, Nathalie Judanti Cahyaning Junaidi Junaidi Kaswijanti, Wilis Khaliduzzaman, Alin Kusuma, M. Apriandi Lean Karlo Tolentino Luh Putu Ratna Sundari Mubarak, Zulfikar Yusya Muhammad Nur Hendra Alvianto Nathalie Japkowicz Nisa, Syed Qamrun Noormaizan, Khairul Akmal Nur Heri Cahyana Nuril Anwar, Nuril Nuryana, Zalik Opi Irawansah, Opi Prapcoyo, Hari Putra, Agung Bella Utama Putra, Seno Aji Rabbimov Ilyos Rabbimov, Ilyos Rafal Drezewski Rafal Drezewski Rafal Drezewski Rafal Drezewski Rochmat Husaini Rochmat Husaini Rustamadji, Heru Saidah, Andi Santosa, Budi Satya Ghifari Adipratama Seno Aji Putra Siti Khomsah, Siti Suhirman SUHIRMAN SUHIRMAN Sularso Sularso, Sularso Sunardi - Sunardi - Sunardi Sunardi Sunardi, Sunardi Taufiq Akbar, Ahmad Tri Andi, Tri Tundo, Tundo Tuti Purwaningsih, Tuti Wahyu Adjie Saputra Wilis Kaswidjanti Wilis Kaswidjanti Wilis Kaswijanti Wisnalmawati Wisnalmawati Yuhefizar Yuhefizar Yuli Fauziah Yuli Fauziyah