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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Natural Science: Journal of Science and Technology The Journal of Experimental Life Sciences (JELS) CAUCHY: Jurnal Matematika Murni dan Aplikasi Bulletin of Electrical Engineering and Informatics JAS (Journal of ASEAN Studies) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Teknik Pengairan: Journal of Water Resources Engineering Dauliyah Journal of Islamic and International Affairs JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Electro Luceat BAREKENG: Jurnal Ilmu Matematika dan Terapan Kuttab: Jurnal Ilmu Pendidikan Islam Edukasi Islami: Jurnal Pendidikan Islam Jurnal al-Ulum : Jurnal Pemikiran dan penelitian ke-Islaman Jurnal Sisfokom (Sistem Informasi dan Komputer) LINGUISTIK : Jurnal Bahasa dan Sastra Nation State : Journal of International Studies Justisia Ekonomika Profit : Jurnal Kajian Ekonomi dan Perbankan Syariah Journal of Electronics, Electromedical Engineering, and Medical Informatics Ulumuna: Jurnal Studi Keislaman RESIPROKAL: Jurnal Riset Sosiologi Progresif Aktual Journal of Robotics and Control (JRC) INSPIRASI: Jurnal Kajian dan Penelitian Pendidikan Islam Islam Universalia : International Journal of Islamic Studies and Social Sciences Jurnal Pendidikan dan Kewirausahaan Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Management - Small and Medium Enterprises (SME's) Al Ghazali: Jurnal Kajian Pendidikan Islam dan Studi Islam Jurnal Teknik Informatika (JUTIF) Ulumuna Jurnal Teknologi Pendidikan : Jurnal Penelitian dan Pengembangan Pembelajaran At Turots: Jurnal Pendidikan Islam Dharmas Education Journal (DE_Journal) MANAJERIAL: Jurnal Inovasi Manajemen dan Supervisi Pendidikan COMMUNITY : Jurnal Pengabdian Kepada Masyarakat Journal Corner of Education, Linguistics, and Literature Indonesian Journal of Peace and Security Studies PAKDEMAS : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Sumber Daya Air Indonesian Journal of Global Discourse Tanfidziya: Journal of Arabic Education JURNAL ILMU PENDIDIKAN Qawanin: Journal of Economic Syaria Law Prosiding University Research Colloquium POACE: Jurnal Program Studi Administrasi Pendidikan Jurnal Ekonomi Syariah dan Hukum Islam Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya KOLONI Innovative: Journal Of Social Science Research Jurnal Pengabdian Masyarakat Bangsa Pengabdian Pendidikan Indonesia (PPI) Indonesian Journal of Mathematics and Applications International Journal of Post-Axial: Futuristic Teaching and Learning Jurnal Kebijakan Pembangunan Daerah (JKPD) Alamtana Aspirasi: Jurnal Ilmiah Administrasi Negara Jurnal Indonesia Mengabdi
Syaiful Anam
School of Political Science and International Studies, Queensland University, Australia
Religion 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 Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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TRADITION OF "DINAH BEGUS" IN THE CONSTRUCTION OF HOUSES: Analysis of Islamic Perspectives and the Integration of Local Wisdom in Ponteh Galis Pamekasan Rosi, Muhammad Fathur; Anam, Syaiful; Suhaimi, Suhaimi; Marsum, Marsum
Jurnal Al-Ulum : Jurnal Pemikiran dan Penelitian Ke-Islaman Vol 12 No 3 (2025): al-Ulum: Journal of Islamic Education, Research and Thought
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/alulum.12.3.2025.199-209

Abstract

This research aims to examine the tradition of Dinah Begus in the construction of houses in Ponteh Village, Galis, Pamekasan, and analyze it from an Islamic perspective. Dinah Begus is a local tradition that has been passed down from generation to generation, where people choose a good day as the right time to start important activities, such as building a house. This belief is based on the hope of blessings, safety, and smoothness in the development process and life in the future. This research uses a descriptive qualitative approach with data collection techniques through observation, in-depth interviews, and documentation studies to understand the practices, symbols, and meanings contained in the tradition. The results of the study show that Dinah Begus contains noble values such as prudence, spirituality, and respect for ancestral traditions. From an Islamic perspective, this practice is not contradictory as long as it is not accompanied by shirk beliefs, but is understood as a form of effort in determining the time that is considered most appropriate based on people's experiences and habits. This research is expected to contribute to the preservation of local culture that is in harmony with Islamic values, as well as enrich the study of the interaction between religion and tradition in Madurese society.
Optimized pap-smear image enhancement: hybrid Perona-Malik diffusion filter-CLAHE using spider monkey optimization Khozaimi, Ach; Darti, Isnani; Muharini Kusumawinahyu, Wuryansari; Anam, Syaiful
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2765-2775

Abstract

Pap-smear image quality is crucial for cervical cancer detection. This study introduces an optimized hybrid approach that combines the Perona-Malik diffusion (PMD) filter with contrast-limited adaptive histogram equalization (CLAHE) to enhance pap-smear image quality. The PMD filter reduces the image noise, whereas CLAHE improves the image contrast. The hybrid method was optimized using spider monkey optimization (SMO PMD-CLAHE). Blind/reference-less image spatial quality evaluator (BRISQUE) and contrast enhancement-based image quality (CEIQ) are the new objective functions for the PMD filter and CLAHE optimization, respectively. The simulations were conducted using the SIPaKMeD dataset. The results indicate that SMO outperforms state-of-the-art methods in optimizing the PMD filter and CLAHE. The proposed method achieved an average effective measure of enhancement (EME) of 5.45, root mean square (RMS) contrast of 60.45, Michelson’s contrast (MC) of 0.995, and entropy of 6.80. This approach offers a new perspective for improving pap-smear image quality.
DEVELOPMENT OF CLASSPOINT-BASED INTERACTIVE LEARNING MEDIA IN SCIENCE SUBJECTS FOR PRIMARY SCHOOL STUDENTS Anam, Syaiful; Fatirul, Achmad Noor; Fiantika, Feny Rita
Jurnal Teknologi Pendidikan : Jurnal Penelitian dan Pengembangan Pembelajaran Vol. 10 No. 3 (2025): Juli
Publisher : UNDIKMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jtp.v10i3.15247

Abstract

This research aims to develop ClassPoint-based interactive learning media in science subjects, especially Solar System material in elementary schools. Media development is carried out using the ASSURE model, which includes analysis, design, development, implementation and evaluation stages. The products that have been developed are then validated by experts in the fields of design, materials and media. Next, trials were carried out in stages through small groups, medium groups and large groups to determine the feasibility and effectiveness of the media. The research results show that the use of ClassPoint-based media has a significant positive impact on learning effectiveness. This media is able to increase students' active participation in the learning process through interactive features such as polls, quizzes and direct assessments, which support the creation of two-way interactions between teachers and students. Apart from increasing learning motivation, this media also strengthens students' conceptual understanding of the material being studied. Thus, ClassPoint can be an innovative solution for creating more interesting, interactive and meaningful learning at the basic education level.
HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION Anam, Syaiful; Putra, M. Rafael Andika; Fitriah, Zuraidah; Yanti, Indah; Hidayat, Noor; Mahanani, Dwi Mifta
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0797-0806

Abstract

The number of claims plays an important role the profit achievement of health insurance companies. Prediction of the number of claims could give the significant implications in the profit margins generated by the health insurance company. Therefore, the prediction of claim submission by insurance users in that year needs to be done by insurance companies. Machine learning methods promise the great solution for claim prediction of the health insurance users. There are several machine learning methods that can be used for claim prediction, such as the Naïve Bayes method, Decision Tree (DT), Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The previous studies show that the SVM has some advantages over the other methods. However, the performance of the SVM is determined by some parameters. Parameter selection of SVM is normally done by trial and error so that the performance is less than optimal. Some optimization algorithms based heuristic optimization can be used to determine the best parameter values of SVM, for example Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). They are able to search the global optimum, easy to be implemented. The derivatives aren’t needed in its computation. Several researches show that PSO give the better solutions if it is compared with GA. All particles in the PSO are able to find the solution near global optimal. For these reasons, this article proposes the health claim insurance prediction using SVM with PSO. The experimental results show that the SVM with PSO gives the great performance in the health claim insurance prediction and it has been proven that the SVM with PSO give better performance than the SVM standard.
DEVELOPMENT OF HEALTH INSURANCE CLAIM PREDICTION METHOD BASED ON SUPPORT VECTOR MACHINE AND BAT ALGORITHM Anam, Syaiful; Guci, Abdi Negara; Widhiatmoko, Fery; Kurniawaty, Mila; Wijaya, Komang Agus Arta
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2281-2292

Abstract

Health insurance industry is very much needed by the community in handling the financial risks in the health sector. The number of claims greatly affects the achievement of profits and the sustainability of the health insurance industry. Therefore, filing claims by insurance users from year to year is important to be predicted in insurance firm. The Machine Learning (ML) method promises to be a good solution for predicting health insurance claims compared to conventional data analytics methods. Support Vector Machine (SVM) is one of the superior ML approaches. Nonetheless, SVM performance is controlled by the suitable selection of SVM parameters. The SVM parameters is typically selected by trial and error, sometimes resulting in not optimal performance and taking a long time to complete. Swarm intelligence-based algorithms can be used to select the best parameters from SVM. This method is capable of locating the global best solution, is simple to implemented, and doesn't involve derivatives. One of the best swarm intelligence algorithms is the Bat Algorithm (BA). BA has a faster convergence rate than other algorithms, for example Particle Swarm Optimization (PSO). Based on this situation, this paper offers the new classification model for predicting health insurance claim based on SVM and BA. The metrics utilized for evaluation are accuracy, recall, precision, f1-score, and computing time. The experimental outcomes show that the proposed approach is superior to the conventional SVM and the hybrid of SVM and PSO in forecasting health insurance claims. In addition, the proposed method has a substantially shorter computing time than the hybrid of SVM and PSO. The outcomes of the experiments also indicate that the new classification model for predicting health insurance claim based on the SVM and BA can avoid over-fitting condition.
LEADERS AND FOLLOWERS ALGORITHM FOR TRAVELING SALESMAN PROBLEM Angmalisang, Helen Yuliana; Anam, Syaiful
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0449-0456

Abstract

Leaders and Followers algorithm is a metaheuristics algorithm. In solving continuous optimization, this algorithm is proved to be better than other well-known algorithms, such as Genetic Algorithm and Particle Swarm Optimization. This paper aims to apply the Leaders and Followers algorithm for the Traveling Salesman Problem (TSP), a well-known combinatorial optimization problem to minimize distance. There are some modifications in order to fit the algorithm in TSP problems. Some most-used-problems in TSP are used to test this algorithm. The result is that the Leaders and Followers algorithm performs well, stable, and guarantees the optimality of the obtained solution in TSP with fewer than 20 cities. In TSP with a bigger number of cities, the proposed algorithm is not stable and might has difficulties in finding the optimal solutions.
OPTIMIZING HEART ATTACK DIAGNOSIS USING RANDOM FOREST WITH BAT ALGORITHM AND GREEDY CROSSOVER TECHNIQUE Ardiyansa, Safrizal Ardana; Maharani, Natasha Clarissa; Anam, Syaiful; Julianto, Eric
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1053-1066

Abstract

Cardiovascular disease stands as one of the primary contributors to global mortality, with the World Health Organization (WHO) reporting approximately 17.9 million deaths annually. Swift and accurate diagnosis of heart attacks is crucial to ensure timely and specialized intervention for patients afflicted by this ailment. A machine learning algorithm that can be employed for addressing such issues is the Random Forest algorithm. However, the efficacy of the model is significantly influenced by the features selected during the training phase. To mitigate this, the Binary Bat Algorithm (BBA) with greedy crossover has been utilized to enhance feature selection within the model. This approach is particularly adept at preventing convergence issues often associated with local minima. The optimal parameters for BBA with greedy crossover are determined to be , , , and . With these parameters, the proposed algorithm identifies the most relevant features, including age, gender, cp, chol, thalach, oldpeak, slope, and ca, achieving an accuracy of 94.19% on the training data and 91.8% on the test data. Furthermore, the precision and recall values for both classes range from 0.87 to 0.96, contributing to an approximate -score of 0.92. The proposed method has increased its -score by 0.05 if compared with the regular Random Forest model. These results underscore the effectiveness of the proposed algorithm in providing accurate and reliable predictions for heart disease diagnosis. As such, this model makes diagnosing heart attack more convenient and effective because it does not require too much medical features or patient data. Hopefully, the results of this research help medical practitioners make better and timely decisions in the diagnosis and treatment of heart attacks, as well as assist in planning more effective public health programs for heart attack prevention.
An Enhanced Particle Swarm Optimization with Mutation for Mean-Value-at-Risk Portfolio Optimization in the Indonesian Banking Sector Anam, Syaiful; Bukhori, Hilmi Aziz; Maulana, Avin; Maulana, M. Idam; Rasikhun, Hady
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5191

Abstract

Portfolio optimization in emerging markets is challenging because high volatility and non-normal return distributions reduce the effectiveness of traditional mean–variance models, which tend to underestimate downside risk. This study aims to develop and evaluate an Enhanced Particle Swarm Optimization with Mutation (PSO with Mutation) for portfolio optimization under the Mean-Value-at-Risk (Mean-VaR) framework in the Indonesian banking sector. The novelty of this approach lies in integrating a mutation operator into standard PSO to maintain population diversity, prevent premature convergence, and improve exploration of the solution space. To evaluate the method, daily adjusted closing prices of 31 Indonesian bank stocks from January 2020 to July 2025 were collected. Preprocessing included removing tickers with incomplete data and computing daily returns. The optimization problem was formulated using Mean-VaR as the risk measure, with portfolio weight constraints. The proposed PSO with Mutation was benchmarked against standard PSO, Genetic Algorithm (GA), Bat Algorithm (BA), BA with Mutation, and classical models (Markowitz and Monte Carlo–based VaR). Performance was assessed using expected return, Mean-VaR, risk-adjusted return, Sharpe ratio, execution time, and stability across 25 independent runs. The results show that PSO with Mutation achieved a competitive expected return (0.0020), the lowest Mean-VaR (0.0311), the highest risk-adjusted return (0.0650), and the lowest variability across runs, while maintaining acceptable execution time. These findings confirm that mutation-enhanced PSO provides a robust, balanced, and efficient solution for portfolio optimization, making it highly relevant for investors in volatile emerging markets and advancing research on hybrid metaheuristics in financial optimization.
GWO-Enhanced Hybrid Deep Learning with SHAP for Explainable TLKM.JK Stock Forecasting Bukhori, Hilmi Aziz; Bukhori, Saiful; Anam, Syaiful; Yusuf, Feby Indriana; Sari, Meylita
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5205

Abstract

This study presents an innovative Grey Wolf Optimization (GWO)-enhanced hybrid deep learning model integrating Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Transformer, combined with SHAP for interpretable stock price forecasting of TLKM.JK from July 29, 2024, to July 29, 2025. Addressing non-linear market dynamics, the model evaluates seven experimental cases, with the GWO-optimized configuration (Case 2) achieving superior performance, with a Root Mean Squared Error (RMSE) of 75.23, Mean Absolute Error (MAE) of 58.14, and Directional Accuracy (DA) of 76.2%, surpassing the baseline by 17.4% in RMSE and 8.1% in DA. Notably, Case 2 excels during the April 2025 surge (11.8% increase, MAE 53, DA 82%) and the high-volume day of May 28, 2025 (531,309,500 shares, MAE 48), leveraging Volume (SHAP 0.45) and RSI (0.28) as key predictors. With a 4-hour convergence time on an NVIDIA RTX 3060 GPU, the model ensures computational efficiency and interpretability, making it a robust tool for traders. Despite limitations in single-stock focus and GPU dependency, this framework advances AI-driven financial forecasting by offering transparent, high-accuracy predictions, paving the way for multi-stock applications and real-time SHAP updates.
Improving Lateral-Movement Intrusion Detection in Virtualized Networks using SHAP Feature Selection, SMOTE, and a Voting Ensemble Classifier Maulana, Avin; Anam, Syaiful; Aziz Bukhori, Hilmi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5233

Abstract

Modern virtualized networks, such as those using VXLAN (Virtual eXtensible LAN), generate heavy east–west traffic, which can conceal the lateral movement of attackers. Detecting such infiltration attacks is challenging due to overlay encapsulation (e.g., VXLAN) and flat subnet architectures create blind spots for traditional IDS.  This study aims to evaluate a robust methodology for addressing class imbalance in intrusion detection by integrating SHAP-driven feature selection with SMOTE in a voting ensemble. We conducted an ablation study on the CICIDS2017 Thursday-WorkingHours-Afternoon-Infiltration subset, which is highly imbalanced (36 infiltration flows vs. 288,566 benign flows), varying SHAP feature sets (Top-5 vs. Top-30), classification thresholds , and SMOTE (Synthetic Minority Over-sampling Technique) balancing. The ensemble combined XGBoost, Random Forest, and Logistic Regression, and was evaluated with ROC-AUC, precision, recall, and F1-score. Results indicate that using more SHAP‑important features improves ROC‑AUC and recall, while SMOTE substantially enhances minority‑class detection. The best configuration is Top‑30 SHAP features with SMOTE at , achieved ROC‑AUC = 0.976 and F1‑score = 0.78, whereas using fewer features or omitting SMOTE significantly reduced recall and F1‑score. This synergy of interpretable feature selection and synthetic oversampling establishes a practical methodology for intrusion detection in highly imbalanced, modern virtualized environments. The novelty lies in demonstrating that SHAP + SMOTE integration yields both transparency and resilience, directly addressing encapsulation challenges in detecting stealthy lateral movement.
Co-Authors A.Mirza Fauzan Gazali, A.Mirza Fauzan Abd. Aziz Abdul Bari Abdul Rouf Alghofari Achmad Taufik Adella Novita Aeri Rachmad Agus Supriyadi Ahmad Afif Supianto Ainol Yaqin, Ainol Aji, Kurniawan Akhmad Khumaidi Akhodiyah, Sulistina Alfian Hidayat Alifiobono, Adeva Amalia Amar, Siti Salama Anam, Afdolul Andreas Andriani , Fitri Angga Rizky, Agus ani ani Ardiyansa, Safrizal Ardana Arifin Arifin Aris Munandar Asyidiqi, Hasbi Asyrofa Rahmi, Asyrofa Ayu Dwi Lestari, Cynthia Ayudaning D , Pamungkas Aziz Bukhori, Hilmi Bukhori, Hilmi Aziz Bustamin, Syamsumar Choa, Yeshua Austin Harvey Deny Tisna Amijaya, Fidia Devita Sari, Nindy Dian Eka Ratnawati Dian Sisinggih Dian Sisingih, Dian Dwi Mifta Mahanani, Dwi Mifta Dwi Ratnasari Edi Satriyanto Fahrul Riza Fajri, Haidar Ahmad Fatirul, Achmad Noor Fauzi, Rahman Ali Feby Indriana Yusuf Fery Widhiatmoko Fiantika, Feny Rita Fisnia Pratami Fitriah, Zuraidah Guci, Abdi Negara Habibi, Nur Syakherul Hadi Wijoyo, Satrio Hady Rasikhun Hamdani, Ibnu Mansyur Hamiduddin, Hamiduddin Hanayanti, Citra Siwi Handayani, Nilam Hasbullah Hasbullah Hdayat, Alfian Helen Yuliana Angmalisang Hosim, Moh Alpa Husni , Valencia Ikhwanudin, Tedy Ilyas, Muhaimin Imadoeddin, Imadoeddin Imam Nurhadi Purwanto Indah Yanti Irma Noervadila Islamiyah, Ummi Habibatul Isnani Darti Jayanti, Luh Putu Dharma Judijanto, Loso Julianto, Eric Karjaya, Lalu Puttrawandi Karsim, Karsim Kasanova, Ria Kasyful Amron Khafid Ismail Khairurrizki, Khairurrizki Khozaimi, Ach. Kurniadi, Harso Kusumawinahyu, Wuryansari M Kusumo, R. Budiarianto Suryo Lailatul Jannah, Noor Lestari, Baiq Ulfa Septi Lestari, Cynthia Ayu Dwi Lestari, Silvya Anggun Lina, Roidah Maharani, Natasha Clarissa Maharani, Natasha Clarrisa mahmudy, wayan f Mar'atun, Chairanil Mardialina, Mala Marsudi Marsudi Marsum Marsum Maulana, Avin Maulana, M. Idam Maulana, Zacki Ibnu Maulida, Ghina Rahmah Miftahus Surur, Miftahus Mila Kurniawaty Muhammad Rivai Muharini Kusumawinahyu, Wuryansari Muhtashor, Imam Munir, Ahmad Mubarak Muzaky, Ahmad Nagib, Rima Abdul Mujib Nahdhiyah, Ulfatun Nalasari , Lista Tri Nanang Rifa'i, Muhamad Ni Wayan Surya Wardhani Nono Hery Yoenanto Noor Hidayat, Noor NUR HAMID Nur Shofianah Nurdiana, Titin Pardede, Hilman Ferdinandus Prasetyo, Onky Puspito, Bayu Putra, M. Rafael Andika R. Suhaimi Rahmawati Rifa'i, Dhila Silvia Rahmawati, Reny Rosalina Ramdani, Ahmad Ratri, Dian Kusumaning Rifa'i, Muhamad Nanang Rifa’i , Muhamad Nanang Rini Aristin, Rini RINI RINI Rizki, Kurnia Zulhandayani robbaniyah, qiyadah Ronaldo, Reza Rosi, Muhammad Fathur Rosid, Muchamad Rosulana, Ahmad Rudiyanto, Mohammad Sabilla, Kinanti Rizsa Safitri, Anisa Dewi Saiful Bukhori Saputri, Levia Sari, Meylita Sa’adah, Umu Shofianah, Nur Siska Siska Sofia Mendez Suhaimi Suhaimi Sukma Umbara Tirta Firdaus, Sukma Umbara Tirta Sukowati, Laila Sundari Sundari Suryani Suryani Syaiful . Syarifatul Azaliyah, Syarifatul Trisilowati Trisilowati, Trisilowati Tuloli, Mohamad Handri Tuminem, Hannan Azka Umbara, Sukma Utomo, M. Chandra Cahyo Utomo, Yudo Bismo Uyun, Nazdrotul Very Dermawan Vicky Zilvan, Vicky Wahada, Listiatul Wayan Firdaus Mahmudy Widiyanto Widiyanto Widyantoro, Didik Wijaya, Komang Agus Arta Wuryasari Muharini Kusumawinahyu Yuli Kartika Dewi Yunanto, Fredy Zabadi, Fairus Zulkarnain Zulkarnain