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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Pendidikan Vokasi Jurnal Sains dan Teknologi Jurnal Sarjana Teknik Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal Pengabdian UntukMu NegeRI Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat Martabe : Jurnal Pengabdian Kepada Masyarakat JURTEKSI Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Jambura Journal of Informatics Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Mobile and Forensics Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Jurnal Informatika dan Rekayasa Perangkat Lunak International Journal of Advances in Data and Information Systems Jurnal REKSA: Rekayasa Keuangan, Syariah dan Audit Innovation in Research of Informatics (INNOVATICS) Humanism : Jurnal Pengabdian Masyarakat Jurnal Pendidikan dan Teknologi Indonesia Prima Abdika: Jurnal Pengabdian Masyarakat Bulletin of Pedagogical Research Jurnal Pengabdian Pada Masyarakat Jurnal Pengabdian Informatika (JUPITA) Bulletin of Social Informatics Theory and Application Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka Mohuyula : Jurnal Pengabdian Kepada Masyarakat Scientific Journal of Informatics
Herman Yuliansyah, Herman
Program Studi Teknik Informatika, Fakultas Teknologi Industri, Universitas Ahmad Dahlan
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ENHANCING DIGITAL COMPETENCIES OF STUDENTS AT MUHAMMADIYAH AL MUJAHIDEEN ISLAMIC JUNIOR HIGH SCHOOL THROUGH PYTHON-BASED CODING INSTRUCTION Darmanto, Darmanto; Pratama, Ridho Haikal; Hazar, Siti; Rajunaidi, Rajunaidi; Hafin, Aqid Fahri; Ridwan, Muhammad; Bidinnika, Muhammad Kunta; Murinto, Murinto; Yuliansyah, Herman
Jurnal Pengabdian Masyarakat Sabangka Vol 4 No 02 (2025): Jurnal Pengabdian Masyarakat Sabangka
Publisher : Pusat Studi Ekonomi, Publikasi Ilmiah dan Pengembangan SDM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62668/sabangka.v4i02.1425

Abstract

In the digital era, programming has become an essential skill for students. This community service activity aimed to introduce Python-based coding instruction to students at Muhammadiyah Al Mujahideen Islamic Junior High School, combining digital literacy with Islamic character development. The activity followed a three-stage model: planning, implementation, and evaluation. During the two-day training, students were taught basic Python concepts such as syntax, variables, and data types using the W3Schools platform. Tasks were designed to evaluate their understanding, including coding exercises to calculate the area of basic geometric shapes. Results showed high enthusiasm and full task completion by all 20 participants, indicating that junior high school students can grasp foundational programming concepts when supported by clear instruction and engaging materials. This program demonstrates the potential of integrating Python into early education to support national education goals and foster future-ready, ethically grounded digital citizens.
A Hybrid Model of Graph Attention Networks and Random Forests for Link Prediction in Co-Authorship Networks Arfiani, Ika; Yuliansyah, Herman
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1382

Abstract

Co-authorship prediction is important in academic network analysis due to it helps to understand patterns of scientific collaboration and supports collaboration recommendation systems. Topology-based approaches, such as connectivity metrics and node distance, have been widely used to model new relationships in networks. However, these approaches often overlook relevant author attributes, such as reputation and productivity. This study develops a co-authorship prediction model by combining a Graph Attention Network (GAT) and a Random Forest. GAT is used to extract topological features from the co-authorship graph, while Random Forest leverages additional attributes such as h-index and the number of publications to improve prediction accuracy. Experiments were conducted on a co-authorship dataset comprising over 10,000 authors and 50,000 publications. The results show that GAT achieved 85% accuracy, while Random Forest reached 80%. The combination of the two yielded 90% accuracy and a higher F1-score, indicating a better balance between precision and recall. The combined model also proved more accurate in predicting collaborations involving highly productive authors. These findings suggest that a hybrid approach can more comprehensively capture the dynamics of academic collaboration and may serve as a foundation for developing more effective collaboration prediction systems in the future.
PREDICTING LOAN ELIGIBILITY WITH SUPPORT VECTOR MACHINE: A MACHINE LEARNING APPROACH Rajunaidi, Rajunaidi; Yuliansyah, Herman; Sunardi, Sunardi; Murinto, Murinto
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 3 (2025): Juni 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3876

Abstract

Abstract: Non-performing loans remain one of the main challenges faced by cooperatives, particularly when the loan eligibility assessment process is still conducted manually. This traditional approach tends to be time consuming, subjective, and prone to inaccurate decisions. This study aims to develop a predictive model for borrower eligibility using the Support Vector Machine (SVM) algorithm as a more efficient and objective machine learning-based solution. A total of 1,000 loan history records were processed using RapidMiner software, taking into account variables such as salary, years of employment, loan amount, monthly installment, employment status, monthly expenses, number of dependents, housing status, age, and collateral value. The model’s performance was evaluated using a confusion matrix and classification metrics including accuracy, precision, recall, and kappa. The results indicate that the SVM model achieved an accuracy of 90.05%, precision of 90.13%, recall of 90.05%, and f1 score of 90,08%, reflecting a strong performance in classifying borrower eligibility. The application of this method makes a significant contribution to the development of data driven decision support systems within cooperative environments. This finding expands the scientific understanding in the field of microfinance and supports the implementation of artificial intelligence technologies in making decisions that are more precise, rapid, and accurate.Keywords: cooperative; eligibility prediction; machine learning; non-performing loan; SVMAbstrak: Kredit macet merupakan salah satu permasalahan utama yang dihadapi koperasi, terutama ketika proses penilaian kelayakan peminjam masih dilakukan secara manual. Pendekatan ini cenderung lambat, subjektif, dan berisiko menghasilkan keputusan yang kurang akurat. Penelitian ini bertujuan untuk membangun model prediksi kelayakan peminjam menggunakan algoritma Support Vector Machine (SVM) sebagai solusi berbasis machine learning yang lebih efisien dan objektif. Sebanyak 1.000 data riwayat pinjaman diolah menggunakan tools RapidMiner dengan mempertimbangkan variabel: gaji, lama bekerja, besar pinjaman, angsuran per bulan, status pegawai, pengeluaran bulanan, jumlah tanggungan, status rumah, umur, dan nilai jaminan. Evaluasi model dilakukan menggunakan confusion matrix dan metrik klasifikasi seperti akurasi, presisi, recall, dan kappa. Hasil menunjukkan bahwa model SVM mencapai akurasi  90,05%, presisi 90,13%, recall 90,05%, dan f1 score 90,08%, yang mencerminkan performa model yang sangat baik dalam mengklasifikasikan kelayakan peminjam. Penerapan metode ini memberikan kontribusi penting dalam pengembangan sistem pendukung keputusan berbasis data di lingkungan koperasi. Temuan ini memperluas wawasan keilmuan di bidang keuangan mikro dan mendukung penerapan teknologi kecerdasan buatan dalam pengambilan keputusan yang lebih tepat, cepat, dan akurat.Kata Kunci: koperasi; kredit macet; machine learning; prediksi kelayakan; SVM  
PELATIHAN PENGENALAN DAMPAK POSITIF DAN NEGATIF DALAM PENGGUNAAN ARTIFICIAL INTELLIGENCE PADA BIDANG PENDIDIKAN Wala, Jihan; Nahdli, Muhammad Fahmi Mubarok; Ardiansyah, Ricy; Umar, Rusydi; Yuliansyah, Herman
Jurnal Pengabdian Informatika Vol. 2 No. 4 (2024): JUPITA Volume 2 Nomor 4, Agustus 2024
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

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

Abstract

Artificial Intelligence (AI) merupakan kecerdasan yang ditunjukan dengan suatu objek buatan. AI memiliki potensi untuk mengubah pendidikan dengan mempersonalisasi pengalaman belajar, menyediakan bimbingan belajar yang cerdas, mengintegrasikan teknologi yang mendalam, dan mengotomatiskan pembuatan konten. Dampak positif AI mencakup peningkatan personalisasi pembelajaran, penghematan waktu bagi tenaga pendidik, serta peningkatan aksesibilitas dan kualitas pendidikan. Dampak negatif penggunaan AI yaitu kurangnya sentuhan manusia, risiko ketergantungan pada teknologi, mengurangi kemampuan berpikir kritis dan pemecahan masalah secara mandiri. Oleh karena itu, diperlukan pelatihan yang bertujuan untuk mengedukasi siswa SMK 2 Al-Hikmah 1 Sirampog, Brebes, Jawa Tengah, tentang dampak penggunaan AI dalam pendidikan. Peningkatan pengetahuan siswa diukur melalui pre-test dan post-test. Kegiatan ini mencakup serangkaian sesi yang dirancang untuk memberikan pemahaman mendalam kepada peserta mengenai pengaruh teknologi AI melalui berbagai aktivitas interaktif, diskusi, dan presentasi dengan total peserta sebanyak 30 siswa. Hasil dari pengabdian ini menghasilkan peningkatan pada kategori pengetahuan "Sangat Paham" meningkat dari 50% menjadi 80%.
SMOTE-SVM for Handling Imbalanced Data in Obesity Classification Biddinika, Muhammad Kunta; Yuliansyah, Herman; Soyusiawaty, Dewi; Razak, Farhan Radhiansyah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 2 (2025): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.103994

Abstract

 Obesity is a significant health issue associated with various chronic diseases, making its early classification critical for effective interventions. This study investigates the performance of Support Vector Machine (SVM) models with Radial Basis Function (RBF) and Linear kernels on imbalanced obesity datasets. To address data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) and Random Undersampling (RUS) were applied. The results reveal that balancing techniques significantly enhance classification performance, with the Linear model achieving the highest accuracy of 96.54% when balanced using SMOTE. However, limitations include reduced recall for minority classes and potential overfitting risks. These findings underscore the importance of balancing techniques in health data classification and offer insights for further optimizing model performance. The study highlights the need for advanced data balancing strategies to improve predictive accuracy and equity across all classes.
Multi-Label Classification for Opinion Mining in The Presidential Election using TF-IDF with NB And SVM Ardiansyah, Ricy; Yuliansyah, Herman; Yudhana, Anton
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1432

Abstract

Public opinion plays a crucial role in presidential elections, shaping voter choices and influencing outcomes. Most sentiment analysis studies focus on binary (positive vs. negative) or multiclass (positive, negative, neutral) classification, which limits their ability to capture opinions that express multiple sentiments simultaneously. In presidential elections, a single opinion may support one candidate while criticizing another. This study proposes a MultiLabelBinarizer model to classify candidate and sentiment labels simultaneously—an approach that remains underexplored. The model combines Naïve Bayes (NB) and Support Vector Machine (SVM) for opinion mining using public data and TF-IDF for feature extraction, applying Multinomial and Linear kernels. Performance is evaluated using Accuracy, Precision, Recall, and F1-score. The study is conducted in two stages: developing a multi-label analysis model for presidential candidates and testing the effectiveness of cross-validation. Results show that multi-label classification is effective for both candidate and sentiment categories. Cross-validation with NB and SVM yields high accuracy. NB achieves 0.89 for candidate labels and 0.86 for sentiment labels. SVM performs better, with 0.93 for candidate labels and 0.94 for sentiment labels. While SVM provides higher accuracy, NB offers faster implementation with still competitive results.
Peramalan Jumlah Pengunjung Wisata Edukasi Museum Menggunakan Kombinasi Moving Average Dan Model Prophet Lifa, Lifa; Yuliansyah, Herman
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 7 (2025): JPTI - Juli 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.898

Abstract

Peramalan jumlah pengunjung wisata edukasi museum berperan penting dalam pengelolaan operasional dan strategi berbasis data. Peramalan berfungsi untuk memperkirakan kebutuhan di masa depan, baik dalam hal kuantitas, kualitas, maupun waktu, sehingga pengelolaan sumber daya dapat lebih optimal. Penelitian sebelumnya menerapkan model Prophet untuk peramalan jumlah pengunjung objek wisata. Namun Prophet kurang efektif dalam menghadapi fluktuasi data yang tinggi, terutama saat terjadi perubahan tren secara tiba-tiba. Untuk mengatasi hal tersebut, penelitian ini mengkombinasikan Moving Average (MA) sebagai teknik preprocessing smoothing, sehingga hasil peramalan Prophet lebih stabil dan akurat. Penelitian ini bertujuan untuk membangun model peramalan dengan mengkombinasikan metode Moving Average dan model Prophet. Model ini dievaluasi menggunakan metrik MAE, MSE, RMSE, dan MAPE untuk mengukur tingkat keakuratan hasil peramalan. Data yang digunakan berasal dari jumlah pengunjung Museum Muhammadiyah pada periode 2023–2024. Tahapan penelitian meliputi cleaning data, transformasi log, smoothing dengan Moving Average, serta penerapan Prophet dengan parameter trend, seasonality, dan holidays. Model terbaik diperoleh pada dataset pengunjung personal harian dengan MAE 0.15, MSE 0.02, RMSE 0.15, MAPE 5.58% dengan hasil peramalan tertinggi tanggal 12 Januari 2025 sebesar 2.79 pengunjung dan terendah tanggal 8 Mei 2025 sebesar 1.31 pengunjung. pada dataset pengunjung grup per bulan, hasil peramalan tertinggi bulan Januari sebesar 3398 pengunjung dan terendah bulan Mei sebesar 1171 pengunjung, MAPE sebesar 29,10%. Hasil menunjukkan bahwa Model Prophet mampu meramalkan jumlah pengunjung Museum Muhammadiyah dan Moving Average mampu meningkatkan performa Prophet. Penelitian ini bermanfaat bagi pengelola museum dalam merencanakan strategi promosi, penjadwalan kegiatan, sehingga dapat meningkatkan kualitas layanan.
Optimasi Aturan Asosiasi Transaksi Penjualan Obat Menggunakan Kombinasi Apriori dan Algoritma Genetika Febiyan, Rifal; Yuliansyah, Herman
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 7 (2025): JPTI - Juli 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.902

Abstract

Analisis pola transaksi dalam penjualan obat sangat penting untuk mengoptimalkan manajemen stok di apotek. Salah satu metode yang umum digunakan dalam data mining adalah algoritma Apriori, yang mampu menemukan aturan pola asosiasi antara item dalam transaksi. Penelitian sebelumnya menerapkan Association Rule Mining pada data transaksi penjualan untuk mengoptimalkan tata letak produk dan meningkatkan penjualan di minimarket. Selain itu, metode ini juga diterapkan dalam Market Basket Analysis (MBA) untuk menganalisis keterkaitan antar produk guna meningkatkan strategi bisnis ritel. Keluaran dari Apriori mudah dipahami dan dapat mengidentifikasi banyak pola baru. Namun, banyaknya aturan asosiasi yang dihasilkan memungkinkan munculnya aturan yang lemah dan interpretasi menjadi sulit. Hal ini karena Apriori memiliki keterbatasan dalam menghasilkan sejumlah besar aturan asosiasi yang dapat mengurangi efisiensi kejelasan hasil. Untuk mengatasi keterbatasan tersebut, peneliti mengusulkan kombinasi algoritma Apriori dan algoritma Genetika (GA) untuk menghasilkan aturan asosiasi yang lebih relevan dan optimal. Penelitian ini fokus pada hasil penerapan Apriori dalam menentukan keterhubungan pola antar itemset, serta menganalisis pengaruh algoritma Genetika dalam optimasi association rules dari Apriori. Penelitian dilakukan melalui tahapan pengumpulan data transaksi, preprocessing, penerapan Apriori, dan optimasi aturan menggunakan GA. Seleksi GA memakai metode roulette wheel, dengan teknik one-point crossover dan mutasi. Berdasarkan rata-rata matriks sepuluh kali percobaan kombinasi Apriori dan GA mendapatkan nilai support 0.137309, confidence 3.369825 dan lift ratio 61.49805. Hasil percobaan menunjukkan bahwa Apriori menghasilkan aturan asosiasi dan pengaruh GA mampu menyeleksi aturan asosiasi yang lebih relevan dan kuat serta mengurangi jumlah aturan yang dihasilkan Apriori, sehingga meningkatkan efektivitas analisis manajemen stok dan strategi pemasaran.
Customer Segmentation Using RFM and K-Means Clustering to Support CRM in Retail Industry Syahra, Yohanni; Fadlil, Abdul; Yuliansyah, Herman
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.14907

Abstract

In today’s highly competitive retail landscape, businesses face increasing challenges in retaining customer loyalty and achieving sustainable growth. A common issue, particularly among small and medium-sized enterprises (SMEs), is the absence of a structured method for identifying and categorizing customers based on their value and behavior. This study addresses the challenge by implementing a data-driven customer segmentation approach using Recency, Frequency, and Monetary (RFM) analysis combined with the K-Means clustering algorithm. The research utilized 2,353 transaction records from 369 unique customers collected over three years from a local retail business. After preprocessing and normalizing the RFM values using Min-Max scaling, the Elbow Method was applied to determine the optimal number of clusters, resulting in four distinct customer segments. Cluster 3, labeled “Loyal Customers,” consisted of customers with high purchase frequency and very high spending; Cluster 1 (“Potential Loyalists”) included those with moderate activity; Cluster 0 represented “At-Risk Customers,” and Cluster 2 comprised “One-Time Buyers.” This segmentation framework supports the development of targeted Customer Relationship Management (CRM) strategies, such as loyalty programs and re-engagement campaigns. However, the approach also has limitations, including potential data bias due to the use of static transaction records and the challenge of interpreting clusters without qualitative customer feedback. Despite these constraints, the study demonstrates the practical utility of combining RFM analysis with clustering techniques to extract actionable insights in environments with limited technical infrastructure.
MoLLe: A Hybrid Model for Classifying Diseases in Chili Plants Using Leaf Images Khoirunnisa, Itsnaini Irvina; Fadlil, Abdul; Yuliansyah, Herman
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.29071

Abstract

Purpose: Leaf diseases are often early indicators of problems in plants. More detailed image information with feature extraction on leaves can improve accuracy. However, MobileNetV2 tends to be less than optimal in capturing the fine texture characteristics of leaves. This research aims to propose a classification model for diseases in chili plants based on leaf images using MobileNetV2 with Local Binary Pattern (LBP), with three fully connected layers (220-120-60 neurons) using the ReLU activation function, referred to as MoLLe. Methods: This research consists of six stages. It begins with a dataset collected from chili farms comprising 900 images, which are then preprocessed into 3,600 images. Next, LBP feature extraction is performed. After that, a comparison between the benchmark architecture and the proposed architecture is conducted. A softmax layer is used to perform three-class classification. The MoLLe model was tested with the MobileNetV2 and MobileNetV2+LBP benchmark architectures and evaluated using a confusion matrix. Result: Based on the evaluation conducted, using batch size 32, learning rate 0.001, and 20 epochs, the MoLLe model experienced early stopping at epoch 11, achieving an accuracy of 0.97 training data, 0.84 validation data, and 0.91 testing data. The evaluation results showed consistent precision, recall, and F1-score values of 0.91, indicating the model's balanced ability to identify the three disease classes. Novelty: The novelty of this research lies in the integration of MobileNetV2 and LBP with modifications to three fully connected layers, which not only reduces the number of training parameters but also accelerates the detection process. This research makes an essential contribution to the development of more efficient and effective plant disease detection systems, with experimental results showing that MoLLe outperforms the benchmark architecture.
Co-Authors Abdul Fadlil Adhi Prahara, Adhi Agus Setiawan, Hisyam ALYA MASITHA Anton Yudhana Apriliani, Evinda Ardiansyah, Ricy Arief Ghozali, Fanani Asti Mulasari, Surahma Ayu Laksmi Pandhita, Ayu Laksmi Bambang Sudarsono Bella Okta Sari Miranda Bidinnika, Muhammad Kunta Darmanto Darmanto Destriana, Rachmat Dewi Soyusiawaty Dewi, Ayu Intansari Donna Setiawati Eko Hari Rachmawanto Fatwa Tentama Febiyan, Rifal Firdaus, Muhammad Khysam Fitriani Mutmainah, Nur Fitriani, Isah Ghozali, Fanani Arief Habie, Khairul Fathan Hafin, Aqid Fahri Hazar, Siti Herman Herminarto Sofyan Hidayat, Muhammad Taufiq Hildayanti, Ica Kurnia Hildayanti, Ica Kurnia Ika Arfiani Imam Riadi Irfan, Syahid Al Jayawarsa, A.A. Ketut Jefree Fahana Jumaedi Nasir, Ardiansyah Khoirul Anam Dahlan Khoirunnisa, Itsnaini Irvina Kintung Prayitno, Kintung Lifa, Lifa Lina Handayani Listyaningrum, Prabandari Mahiruna, Adiyah Muhammad Abdul Aziz Muhammad Dzikrullah Suratin, Muhammad Dzikrullah Muhammad Fahmi Mubarok Nahdli Muhammad Kunta Biddinika Muhammad Ridwan Murinto Murinto Murinto Mutmainah, Nur Fitri Nafiati, Lu'lu' Nafiati, Lu’lu’ NGATIMIN, NGATIMIN Nia Ekawati, Nia Nisa Novianti, Tria Novitasari, Isda Desy Nur Rochmah Dyah Pujiastuti Pamungkas, Gilang Pamungkas, Gilang Pratama, Ridho Haikal Pratama, Wegig Putro, Aldibangun Pidekso R. Hafid Hardyanto, Settings Rachmaliany, Nur Rahmawan, Jihad Rahmawati, Rahmawati Raihan, Habib Aulia Rajunaidi, Rajunaidi Razak, Farhan Radhiansyah Rohmadi, Yusuf Eko Rusydi Umar Salji, Rinday Zildjiani Sri Winiarti Subardjo Subardjo, Subardjo Sukesi , Tri Wahyuni Sulistyawati , Sulistyawati Sulistyawati Sulistyawati Sunardi Sunardi Sunardi Surahma Asti Mulasari Tole Sutikno Tri Wahyuni Sukesi Ulumiyah, Iftitah Dwi Wahyuni Sukesi, Tri Wala, Jihan Wan Ali, Wan Nur Syamilah Yohanni Syahra Yulianto, Dinan Yulisasih, Baiq Nikum