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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA snimed Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jupiter Semantik Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI IJID (International Journal on Informatics for Development) Aplikasia: Jurnal Aplikasi Ilmu-Ilmu Agama KOMPUTA : Jurnal Ilmiah Komputer dan Informatika JISKa (Jurnal Informatika Sunan Kalijaga) JATI (Jurnal Mahasiswa Teknik Informatika) GUYUB: Journal of Community Engagement Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Engagement: Jurnal Pengabdian Kepada Masyarakat
Shofwatul ‘Uyun
Department Of Informatics, Faculty Of Science And Technology, Universitas Islam Negeri Sunan Kalijaga Yogyakarta, Indonesia
Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Social Sciences Other

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Performance Evaluation of Vehicular Ad Hoc Networks Considering Malicious Node Impact on Quality of Services Metrics Alfarizi, Naufal Faiz; Nuruzzaman, Muhammad Taufiq; Uyun, Shofwatul; Sugiantoro, Bambang; Abdullah, Mohd. Fikri Azli bin
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1568

Abstract

Vehicular Ad Hoc Networks (VANETs), a subset of mobile ad hoc networks (MANETs), is essential for enabling communication between vehicles in intelligent transportation systems. However, their dynamic and decentralized nature exposes them to significant security threats, particularly from malicious nodes. Attacks such as black holes and wormholes can severely degrade network performance by causing packet loss and increasing end-to-end delays. This paper aims to evaluate the impact of malicious node behavior on VANET performance using key Quality of Service (QoS) parameters, including throughput, end-to-end delay, jitter, packet delivery ratio (PDR), and packet loss ratio (PLR). The specific objective is to analyze how black hole and wormhole attacks affect communication efficiency in VANET environments. The main contribution of this work lies in the integration of Simulation of Urban Mobility (SUMO) for realistic traffic scenario generation with Network Simulator 3 (NS-3) for detailed network performance evaluation. This approach enables comprehensive simulation of VANET behavior under attack conditions. The findings provide valuable insights into the vulnerabilities of VANETs and form a basis for the design of more robust and secure vehicular communication systems.
Land Cover Classification in Mountainous Regions Using Multi-Scale Fusion and Convolutional Neural Networks: A Case Study on Mount Slamet Yulis Rijal Fauzan; 'Uyun, Shofwatul
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1612

Abstract

Mount Slamet, located in Central Java, Indonesia, is a high-risk volcanic region where accurate land cover classification is essential for disaster mitigation and sustainable land management. However, satellite imagery in this area often suffers from haze and cloud cover, posing challenges to reliable classification. This study aims to develop an effective land cover classification model using Sentinel-2 imagery by addressing these visual distortions. The specific goal is to classify land cover into five classes—Forest, Settlements, Summit, RiceField, and River—using enhanced satellite images. A total of 1101 labeled images were processed through dehazing with Multi-Scale Fusion (MSF) and smoothing using a Guided Filter to improve image quality. The classification was performed using three Convolutional Neural Network (CNN) architectures: VGG-16, MobileNetV2, and DenseNet121. The main contribution of this study is the integration of a tailored preprocessing pipeline with CNN-based modeling for haze-affected mountainous satellite imagery. Among the models tested, MobileNetV2 achieved the highest accuracy of 85.4%, outperforming DenseNet121 (83.8%) and VGG-16 (82.3%). The results demonstrate the effectiveness of combining image enhancement techniques with lightweight CNN architectures for land cover classification in challenging environments with limited and imbalanced dataset.
Klasifikasi Hewan Anjing, Kucing, dan Harimau Menggunakan Metode Convolutional Neural Network (CNN) Murdifin, Murdifin; Uyun, Shofwatul
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 3 (2025): September 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.3.331-340

Abstract

Animal classification is a complex challenge due to variations in shape, color, and patterns across species. Traditional methods, which rely on manual feature extraction, are often ineffective in handling such complexities. Therefore, this study employs Convolutional Neural Networks (CNNs) as a more accurate approach for automatic feature extraction and image classification. This research aims to develop an animal image classification model, specifically for dogs, cats, and tigers, utilizing CNNs. The dataset consists of 4,800 images obtained from Kaggle, which were divided into training, testing, and validation sets. The CNN model was built using TensorFlow/Keras, trained for 50 epochs, and evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The experimental results show that the model achieved an overall accuracy of 88%, with the highest performance in tiger classification (99% accuracy). However, distinguishing between dogs and cats remains a challenge, with an accuracy of 81% for both classes. The findings indicate that CNNs are effective in automatically classifying animal images, although challenges persist in differentiating visually similar species. This study lays the groundwork for further enhancements, such as refining the model architecture or utilizing data augmentation techniques to boost classification accuracy.
Improving Students’ Knowledge of Biomonitoring through Service Learning in Higher Education Institution Sulistiyowati, Eka; Awaliyah, Dien F.; Uyun, Shofwatul
GUYUB: Journal of Community Engagement Vol 6, No 3 (2025): September
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/guyub.v6i3.12431

Abstract

Service learning in biomonitoring is urgent as it links science with community action to tackle river health issues.. This research aims to explore the application of service learning in enhancing students' knowledge and their ability to carry out river health biomonitoring projects. The study involved students in implementing the service learning curriculum through stages of planning, execution, reflection, and assessment. During the planning phase, students participated in developing the module. The results indicated that the biomonitoring module received a quality score of 3.8, with clarity of content and factual accuracy achieving the highest scores (4.0). The service learning program was conducted through the establishment of ECOFOREST groups, training sessions, and the application of action plans within the community. The effectiveness was measured using a one-group pretest-posttest design, which revealed a significant improvement in student understanding (t(22) = 2.45, p < 0.05). These findings confirm that service learning not only enhances student engagement in the community but also contributes to their technical competency development. This study addresses the gap in literature regarding service learning within more practical experiential learning frameworks in higher education.The result implies that there has been an increase of knowledge among the participants.
High Precision Deep Learning Model for Road Damage Classification using Transfer Learning Ghofur, Muhammad Abdul; Murdifin, Murdifin; Hardandrito, Awan Gumilang; 'Uyun, Shofwatul
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5707

Abstract

Roads are critical infrastructure that frequently experience damage, directly impacting transportation safety and efficiency. Manual road damage inspection is time-consuming and resource-intensive, highlighting the need for automated, image-based approaches. This study compares two Convolutional Neural Network (CNN) architectures—MobileNetV2 with transfer learning and a custom-built CNN—for classifying road surface damage severity. The dataset consists of 1,800 road surface images evenly distributed across three categories: good, minor damage, and severe damage. All images were normalized, augmented, and resized, followed by evaluation using 5-Fold Cross-Validation to ensure robust performance. Experimental results show that MobileNetV2 achieved an accuracy of 98%, outperforming the custom CNN, which achieved 89%. These findings demonstrate the effectiveness of transfer learning in improving classification accuracy with limited data and highlight the potential of MobileNetV2 for efficient, real-time road damage detection systems that can be integrated into intelligent infrastructure monitoring solutions.
Sistem Pengolahan Citra Digital untuk Menentukan Bobot Sapi Menggunakan Metode Titik Berat Mustafid, Ahmad; 'Uyun, Shofwatul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 6: Desember 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3672.809 KB) | DOI: 10.25126/jtiik.201856841

Abstract

AbstrakPenentuan harga sapi umumnya disepakati melalui tawar menawar dan interaksi antara permintaan dan penawaran untuk menentukan harga bukan didasarkan pada bobot sapi yang dijual. Kebanyakan menggunakan perhitungan secara kasar maupun secara kira-kira. Terdapat rumus untuk menghitung bobot sapi, rumus yang ada memerlukan informasi terkait lingkar dada dan panjang badan. Untuk mendapatkan nilai lingkar dada dan panjang badan perlu dilakukan pengukuran secara manual, namun di lapangan hal tersebut tidak mudah dilakukan karena sapi sulit dikondisikan. Oleh karena itu diperlukan alat yang dapat mengukur secara mudah. Tulisan ini merupakan tahap kedua dari penelitian untuk menentukan bobot sapi dari hasil akuisisi citra sapi. Oleh sebab itu pada tahap kedua ini difokuskan pada pemilihan rumus penentuan bobot sapi dan usulan algoritma untuk menentukan bobot dari gambar hasil akuisisi citra. Hasil analisis penentuan bobot sapi menggunakan rumus Schoorl dan rumus Modifikasi memiliki nilai penyimpangan bobot badan sebesar 16,87% untuk rumus Schoorl dan nilai penyimpangan bobot badan sebesar 10,58 % untuk rumus Modifikasi. Hasil perhitungan citra tidak berbeda secara signifikan yaitu dengan faktor ketelitian secara statistis dengan nilai MAE (Mean Absolute Error) sebesar 8,15% untuk panjang badan dan sebesar 4,10% untuk lingkar dada. Aplikasi pengolahan citra digital yang dibagun dapat mengetahui berat badan/bobot sapi dengan nilai MAE (Mean Absolute Error) sebesar 8,97% terhadap rumus Modifikasi. Abstract The price determination of cows is generally agreed through bargaining and interacting with demand and supply to establish the general level of the price but it is not based on the weight of the cow itself. The tool that the most commonly used is by rough calculation or approximation. There were formulas to measure the weight, but it required chest circumference and the length of the body information. The values ware obtained manually using the measuring tool, but the reality is inconvenient to do, because of the difficulty conditioning the cows. Therefore, it required a tool that can calculate easily. This article represented the second stages of the research to determine the weight of cows from the image acquisition. Consequently, at this second stage has been focused on the selection of the cow weighting formula and the proposed algorithm to determine the weight from the result of images that had been processed in the early stages. The result of cow weighting analysis using Schoorl formula and Modification/Lambourne formula had the value of body weight deviation of 16.87% and 10.58. The results of image calculation did not differ significantly with MAE (Mean Absolute Error) equal to 8,15% and 4,10% for body length and chest circumference, respectively. Digital image processing application that has been built was able to know the weight of cow with MAE (Mean Absolute Error) equal to 8,97% towards Modification/Lambourne formula. 
Penerapan Decision Tree J48 dan Reptree dalam Menentukan Prediksi Produksi Minyak Kelapa Sawit menggunakan Metode Fuzzy Tsukamoto Tundo, Tundo; 'Uyun, Shofwatul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 3: Juni 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020731870

Abstract

Penelitian ini menerangkan penerapan decision tree J48 dan REPTree dengan menggunakan metode fuzzy Tsukamoto dengan objek yang digunakan adalah penentuan jumlah produksi minyak kelapa sawit di perusahaan PT Tapiana Nadenggan dengan tujuan untuk mengetahui decision tree mana yang hasilnya mendekati dari data sesungguhnya sehingga dapat digunakan untuk membantu memprediksi jumlah produksi minyak kelapa sawit di PT Tapiana Nadenggan ketika proses produksi belum diproses. Digunakannya decision tree J48 dan REPTree yaitu untuk mempercepat dalam pembuatan rule yang digunakan tanpa harus berkonsultasi dengan para pakar dalam menentukan rule yang digunakan. Dari data yang digunakan akurasi dari decision tree J48 adalah 95.2381%, sedangkan akurasi REPTree adalah 90.4762%, akan tetapi dalam kasus ini decision tree REPTree yang lebih tepat digunakan dalam proses prediksi produksi minyak kelapa sawit, karena di uji dengan data sesungguhnya pada bulan Maret tahun 2019 menggunakan REPTree diperoleh 16355835 liter, sedangkan menggunakan J48 diperoleh 11844763 liter, dimana data produksi sesungguhnya sebesar 17920000 liter. Sehingga dapat ditemukan suatu kesimpulan bahwa untuk kasus ini data produksi yang mendekati dengan data sesungguhnya adalah REPTree, meskipun akurasi yang diperoleh lebih kecil dibandingkan dengan J48.AbstractThis study explains the application of the J48 and REPTree decision tree using the fuzzy Tsukamoto method with the object used is the determination of the amount of palm oil production in the company PT Tapiana Nadenggan with the aim of knowing which decision tree the results are close to the actual data so that it can be used to help predict the amount palm oil production at PT Tapiana Nadenggan when the production process has not been processed. The use of the J48 and REPTree decision tree is to speed up the rule making that is used without having to consult with experts in determining the rules used. From the data used the accuracy of the J48 decision tree is 95.2381%, while the REPTree accuracy is 90.4762%, but in this case the REPTree decision tree is more appropriate to be used in the prediction process of palm oil production, because it is tested with actual data in March 2019 uses REPTree obtained 16355835 liters, while using J48 obtained 11844763 liters, where the actual production data is 179,20000 liters. So that it can be found a conclusion that for this case the production data approaching the actual data is REPTree, even though the accuracy obtained is smaller compared to J48.
Konsep Decision Tree Reptree untuk Melakukan Optimasi Rule dalam Fuzzy Inference System Tsukamoto Tundo, Tundo; 'Uyun, Shofwatul
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.2022922601

Abstract

Penelitian ini menjelaskan tentang decision tree REPTree dalam membuat suatu rule yang terbentuk dari produksi minyak kelapa sawit di PT Tapiana Nadenggan, yang dipengaruhi oleh faktor banyaknya kelapa sawit, permintaan yang ada, serta persediaan yang tersedia. Konsep dari decision tree REPTree adalah konsep awal dari decision tree J48 yang kemudian mengalami pemangkasan kembali, sehingg rule yang yang terbentuk lebih minimal dan praktis. Rule yang minimal dan praktis belum tentu dapat dikatakan terbaik, untuk membuktikan hal itu perlu adanya uji coba dan pembuktian. Pembuktian yang dilakukan dalam penelitian ini salah satunya dengan menggunakan perbandingan decision tree J48 dan Random Tree dengan tujuan untuk mengetahui optimasi rule yang terbentuk dengan menggunakan metode fuzzy inference system Tsukamoto, setelah dihitung bahwa decision tree REPTree mempunyai Average Forecasting Error Rate (AFER) yang lebih kecil sebesar 23,17% dengan nilai kebenaran 76,83%, sedangkan J48 memiliki tingkat error sebesar 24,96%, dengan nilai kebenaran 75,04%,  sementara Random Tree memiliki tingkat error sebesar 36,51%, dengan nilai kebenaran 63,49% pada kasus  prediksi produksi minyak kelapa sawit di PT Tapiana Nadenggan. AbstractThis research explains about REPTree's decision tree in making a rule that is formed from the production of palm oil in PT Tapiana Nadenggan, which is influenced by factors of the amount of palm oil, existing demand, and available supplies. The concept of the REPTree decision tree is the initial concept of the J48 decision tree which then experiences pruning, so that the rules formed are more minimal and practical. A minimum and practical rule may not be the best, to prove that there is a need for trials and proofs. Proof carried out in this research is one of them by using a comparison of decision trees J48 and Random Tree with the aim to find out the optimization of rules formed using the Tsukamoto system's fuzzy inference method, after calculating that the REPTree decision tree has a more average Forecasting Error Rate (AFER) error tree small of 23.17% with a truth value of 76.83%, while J48 has an error rate of 24.96%, with a truth value of 75.04%, while Random Tree has an error rate of 36.51%, with a truth value of 63, 49% in the case of prediction of palm oil production at PT Tapiana Nadenggan.
Klasifikasi Kebutuhan Jumlah Produk Makanan Customer Menggunakan K-Means Clustering dengan Optimasi Pusat Awal Cluster Algoritma Genetika Istianto, Yudi; 'Uyun, Shofwatul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021842990

Abstract

PT. Harum Bakery adalah salah satu perusahaan di Yogyakarta yang bergerak pada bidang produksi dan distribusi produk makanan roti. Setiap konsumen memiliki jumlah kebutuhan roti yang tidak teratur, sedangkan roti hanya dapat bertahan dalam waktu dua hari. Roti yang sudah berusia lebih dari dua hari akan diganti dengan yang baru oleh distributor, sehingga dapat menimbulkan kerugian bagi perusahaan. Penelitian ini mencoba untuk melakukan data mining dengan tujuan mengklasifikasikan jumlah produk makanan kepada customer menggunakan k-means clustering dengan optimasi pusat awal cluster algoritma genetika. Pada penelitian ini digunakan 210 data dari penjualan produk selama tiga minggu. Data tersebut akan diproses dengan menerapkan metode data mining melalui tahap preprocessing kemudian tahap klasifikasi. Preprocessing yang dilakukan antara lain, data transformation dan k-means clustering. Hasil dari clustering yang membutuhkan aturan tertentu lebih efektif dengan optimasi karena dari 210 data terdapat 200 data yang layak masuk tahap klasifikasi. Hasil dari pengujian mendapatkan akurasi terbaik sebesar 58.50 % dan crossvalidation untuk lima fold berhasil mendapatkan rata-rata akurasi sebesar 50.58% lebih besar 2.51 % dari KNN tanpa preprocessing.AbstractPT. Harum Bakery is one of the companies in Yogyakarta engaged in the production and distribution of bakery food products. Every consumer has an irregular amount of bread needs while bread can only last for two days. Bread that is more than two days old will be replaced by a new one by the distributor which causes losses for the company. This study tries to apply data mining to classify the number of customer needs for food products using k-means clustering with optimization initial cluster center genetic algorithm. In this study used 210 data from product sales for three weeks. Data will be processed by applying data mining method with preprocessing before going through classification. Preprocessing includes data transformation and k-means clustering. The results of clustering that require certain rules are more effective with optimization because 210 data have 200 data that are worth entering the classification stage. The results of the test get the best accuracy of 58.50% and crossvalidation for five fold managed to get an average accuracy of 50.58% greater than 2.51% of KNN without preprocessing.
Perbandingan Decision Tree J48, REPTREE, dan Random Tree dalam Menentukan Prediksi Produksi Minyak Kelapa Sawit Menggunakan Fuzzy Tsukamoto Tundo, Tundo; 'Uyun, Shofwatul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 3: Juni 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021833108

Abstract

 Penelitian ini menerangkan analisis decision tree J48, REPTree dan Random Tree dengan menggunakan metode fuzzy Tsukamoto dalam penentuan jumlah produksi minyak kelapa sawit di perusahaan PT Tapiana Nadenggan dengan tujuan untuk mengetahui decision tree mana yang hasilnya mendekati dari data sesungguhnya. Digunakannya decision tree J48, REPTree, dan Random Tree yaitu untuk mempercepat dalam pembuatan rule yang digunakan tanpa harus berkonsultasi dengan para pakar dalam menentukan rule yang digunakan. Berdasarkan data yang digunakan akurasi pembentukan rule dari decision tree J48 adalah 95,2381%, REPTree adalah 90,4762%, dan Random Tree adalah 95,2381%. Hasil dari penelitian yang telah dihitung bahwa metode fuzzy Tsukamoto dengan menggunakan REPTree mempunyai error Average Forecasting Error Rate (AFER) yang lebih kecil sebesar 23,17 % dibandingkan dengan menggunakan J48 sebesar 24,96 % dan Random Tree sebesar 36,51 % pada prediksi jumlah produksi minyak kelapa sawit. Oleh sebab itu ditemukan sebuah gagasan bahwa akurasi pohon keputusan yang terbentuk menggunakan tools WEKA tidak menjamin akurasi yang terbesar adalah yang terbaik, buktinya dari kasus ini REPTree memiliki akurasi rule paling kecil, akan tetapi hasil prediksi memiliki tingkat error paling kecil, dibandingkan dengan J48 dan Random Tree. AbstractThis study explains the J48, REPTree and Tree Random tree decision analysis using Tsukamoto's fuzzy method in determining the amount of palm oil production in PT Tapiana Nadenggan's company with the aim of finding out which decision tree results are close to the actual data. The decision tree J48, REPTree, and Random Tree is used to accelerate the making of rules that are used without having to consult with experts in determining the rules used. Based on the data used the accuracy of the rule formation of the J48 decision tree is 95.2381%, REPTree is 90.4762%, and the Random Tree is 95.2381%. The results of the study have calculated that the Tsukamoto fuzzy method using REPTree has a smaller Average Forecasting Error Rate (AFER) rate of 23.17% compared to using J48 of 24.96% and Tree Random of 36.51% in the prediction of the amount of palm oil production. Therefore an idea was found that the accuracy of decision trees formed using WEKA tools does not guarantee the greatest accuracy is the best, the proof of this case REPTree has the smallest rule accuracy, but the predicted results have the smallest error rate, compared to J48 and Tree Random.
Co-Authors Abdullah, Mohd. Fikri Azli bin Agung Nur Hidayat Ahmad Mustafid Ahmad Subhan Yazid Akbar, Riolandi Akhmad Imam Fahrizal Alfarizi, Naufal Faiz Arif Riyandi Arromdoni, Bad’ul Hilmi Awaliyah, Dien F. Awaliyah, Dien Fitri Bambang Sugiantoro Danang Aji Bimantoro Daru Prasetyawan Diniati Ruaika Dony Fahrudy Dori Gusti Alex Candra Eka Sulistiyowati Eka Sulistyowati Eka Sulistyowati, Eka Elvanisa Ayu Muhsina Elvanisa Ayu Muhsina, Elvanisa Ayu Endra Yuliawan Fahrudy, Dony Fajarendra, Yusril Iza Fauzi, Muhammad Dzulfikar Hardandrito, Awan Gumilang Heni Hapsari Iin Intan Uljanah Imam Riadi Istianto, Yudi Iza Fajarendra, Yusril Lina Choridah Madikhatun, Yuni Mardlian, M. Sa’id Abdurrohman Kunta Maria Ulfah Siregar Mufafaq, Naufal Hafizh MUHAMMAD ABDUL GHOFUR Muhammad Akid Musyafa Muhammad Anshari, Muhammad Muhammad Edi Iswanto Muhammad Fadzlur Rahman Muhammad Rifqi Ma'arif Muhammad Taufiq Nuruzzaman Murdifin, Murdifin Mustafid, Ahmad Nuri Guntur Perdana Nurochman Nurochman Nuruzzaman, Muhammad Taufiq Pratiwi, Millati Qonitat, Ihda Imroatun Qorry Aina Fitroh Rahardyan, Seto Rebeccah Ndungi Ridha , M Noor Tasiur Rijal Fauzan, Yulis Riwanto, Yudha Rizal Fauzan, Yulis Rosalia Susilowati Rosalin, Rizqi Praimadi Saiful Machbub Mutaqin Sanora, Fiki Sari, Luky Vianika Sherly Andini Siti Helmiyah Sri Hartati subanar subanar Sucinta, Hanny Handayani Toni Efendi Tundo, Tundo Ulfah, Aniq Noviciate Yulis Rijal Fauzan Yuni Madikhatun Yuni Madikhatun