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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Buana Informatika Dinamika Informatika Jurnal Teknologi MAGISTRA Sinergi Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Seminar Nasional Informatika (SEMNASIF) INFORMATIKA Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Creative Information Technology Journal Jurnal Sains dan Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ CSRID (Computer Science Research and Its Development Journal) Informasi Interaktif JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Suara Keadilan Technologia: Jurnal Ilmiah KURVATEK Jurnal Tecnoscienza Respati Jurnal Sistem Komputer & Kecerdasan Buatan Journal of Computer System and Informatics (JoSYC) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Madani : Indonesian Journal of Civil Society JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Electrical Engineering and Computer (JEECOM) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Journal of Applied Sciences, Management and Engineering Technology (JASMET) Jurnal Ekonomi dan Teknik Informatika Transformasi Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Dinamika Informatika (JDI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) PELS (Procedia of Engineering and Life Science) JAIA - Journal of Artificial Intelligence and Applications Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi EXPLORE Prosiding University Research Colloquium COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal of Information Technology and Society (JITS) Teknomatika: Jurnal Informatika dan Komputer Explore Jurnal Teknologi SWAGATI: Journal of Community Service

Andi Sunyoto

Universitas Amikom Yogyakarta

Author-ID : 393117

Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Transportation Other

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Implementation of YOLOv7 Model for Human Detection in Difficult Conditions B, Arijal; Sunyoto, Andi; Hanafi, M.
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10662

The rapid development of artificial intelligence technology in recent decades has led to the development of highly efficient object detection algorithms, including human detection under difficult conditions. Human detection is one of the major challenges in computer vision as it involves various complex factors such as obstructed human objects, pose variations, small low-resolution human objects, as well as the presence of fake human objects such as statues or images. This research uses the SLR (Systematic Literature Review) method to determine the algorithm used, namely YOLOv7. The three YOLOv7 models tested in this study are YOLOv7x.pt, YOLOv7-w6-person.pt, and YOLOv7-w6-pose.pt. These models were selected based on their excellence in detecting human objects and their relevance for complex scenarios. Tests were conducted using 100 images obtained from the internet and divided into four categories of human objects under difficult conditions, which represent various challenges in human detection. Analysis was performed using convusion matrix to evaluate performance metrics such as accuracy, precision, recall, and F1-score. Based on the test results, the YOLOv7-w6-person.pt model showed the best overall performance, especially in detecting humans in obstructed conditions and complex lighting with a precision of 90.4%, Recall 88.7%, and F1-Score 89.5%. This model has higher accuracy, precision, and F1-score than the other models, making it a reliable choice for human detection in difficult scenarios. These findings not only demonstrate the relevance of YOLOv7 as a reliable human detection algorithm, but also provide a basis for further optimization of YOLOv7-based human detection systems, both through improving the model architecture and adapting to more specific datasets. This research makes an important contribution to the development of human detection technologies for real-world applications, such as surveillance, crowd analysis, and automated safety systems.

Pelatihan Pembuatan dan Pengelolaan Website untuk Meningkatkan Skill dan Wawasan IT pada Menwa IAIN Salatiga Windarni, Vikky Aprelia; Nugraha, Anggit Ferdita; Pristyanto, Yoga; Aziza, Rifda Faticha Alfa; Purwanto, Ibnu Hadi; Sunyoto, Andi
SWAGATI : Journal of Community Service Vol. 1 No. 2 (2023): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2023v1i2.1037

Perkembangan teknologi informasi yang semakin pesat membawa perubahan yang signifikan bagi personal maupun organisasi. Hal tersebut juga berlaku pada Organisasi Resimen Mahasiswa (Menwa) IAIN Salatiga, terutama untuk menyebarkan informasi kepada khalayak umum. Website menjadi salah satu teknologi yang dapat membantu menyebarkan dan berbagi informasi secara cepat dan mudah. Sayangnya, Sebagian besar anggota menwa IAIN bukan berasal dari bidang ilmu komputer sehingga awam terhadap proses pembuatan dan pengelolaan website. Disisi lain, adanya kemauan yang kuat untuk belajar perlu didukung melalui proses pelatihan dan pendampingan oleh tenaga ahli sehingga pengelolaan website nantinya dapat dilakukan oleh anggota Menwa secara mandiri. Selain itu, dengan adanya proses pelatihan serta pendampingan dalam pembuatan dan pengelolaan website diharapkan dapat memberikan manfaat bagi organisasi serta dapat meningkatkan skill dan wawasan IT bagi anggota organisasi tersebut

Analisis Prediksi Harga Emas Selama Sepuluh Tahun Terakhir Dengan SVM: Tinjauan Mendalam Pola Pergerakan Pasar Logam Mulia Diansyah, Ahmad Febri; Andi Sunyoto
Jurnal Teknologi Informasi dan Masyarakat Vol 3 No 1 (2025): Journal of Information Technology and Society (JITS)
Publisher : Universitas Muhammadiyah Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35438/jits.v3i1.1418

Pasar emas telah menjadi fokus perhatian analis keuangan dan investor selama bertahun-tahun, dipengaruhi oleh perubahan geopolitik, kebijakan moneter, dan ketidakpastian ekonomi global. Transformasi signifikan dalam sepuluh tahun terakhir menciptakan fluktuasi harga emas yang memunculkan peluang dan risiko bagi para pelaku pasar. Dalam era teknologi modern, metode analisis prediktif, seperti algoritma Support Vector Regression (SVR), semakin penting untuk memahami dan meramalkan pergerakan pasar. Penelitian ini menggunakan SVR untuk menganalisis harga emas selama sepuluh tahun terakhir, menyoroti keunggulan algoritma tersebut dalam mengidentifikasi relasi non-linear dalam data kompleks. Diharapkan hasil analisis ini memberikan wawasan mendalam tentang faktor-faktor yang memengaruhi harga emas, dengan tujuan meningkatkan pemahaman tentang tren pasar keuangan dan memberikan dasar untuk prediksi yang lebih akurat. Penelitian juga membandingkan SVR dengan dua metode lain, LSTM dan Multiple Linear Regression, untuk memperoleh gambaran komprehensif tentang kinerja masing-masing. Evaluasi metrik, seperti akurasi, MAPE, dan MSE, menunjukkan bahwa SVR memiliki tingkat prediksi yang sangat baik dibandingkan dengan metode lainnya, memperkuat potensinya dalam memberikan manfaat praktis bagi para pelaku pasar dan investor emas.

Analisis Perbandingan Klasifikasi dalam Data Mining pada Prediksi Hujan dengan menggunakan Algoritma LSTM dan GRU Cahyo, D. Diffran Nur; Sunyoto, Andi
Jurnal Sains dan Informatika Vol. 11 No. 1 (2025): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v11i1.1212

Hujan memiliki dampak signifikan terhadap berbagai sektor, seperti pertanian, transportasi, dan manajemen sumber daya. Oleh karena itu, akurasi prediksi hujan sangat penting untuk mendukung perencanaan dan pengambilan keputusan yang efektif. Seiring kemajuan teknologi, pemanfaatan metode deep learning seperti Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) semakin berkembang dalam bidang prediksi cuaca. Penelitian ini bertujuan untuk membandingkan performa kedua algoritma tersebut dalam mengklasifikasikan data meteorologi untuk prediksi hujan. Dataset yang digunakan berasal dari Australian Bureau of Meteorology melalui Kaggle, terdiri dari 145.460 data dengan 23 atribut. Setelah melalui tahapan pra-pemrosesan dan resampling menggunakan metode SMOTE, kedua model dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa model LSTM memperoleh akurasi sebesar 81,46%, sedangkan GRU sebesar 81,39%. Nilai F1-score GRU lebih tinggi dibandingkan LSTM, masing-masing sebesar 60,96% dan 58,95%. Hasil ini mengindikasikan bahwa kedua model memiliki performa yang kompetitif dan efektif untuk diterapkan dalam sistem prediksi hujan berbasis deep learning.

Improving Tomato Ripeness Classification Using Knowledge Distillation and Hyperparameter Optimization with Optuna Sholihin, Iasya; Sunyoto, Andi
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.11266

Automatic classification of tomato ripeness plays a crucial role in ensuring post-harvest quality and efficiency in the horticultural industry. This study proposes a combined strategy of Knowledge Distillation (KD) and hyperparameter optimization using Optuna to improve the accuracy of the ResNet50 student model by leveraging the performance of a MobileNetV2 teacher model.We used a publicly available Kaggle dataset containing 8,540 images, categorized into four ripeness levels (green, red, ripe, and rotten), comprising 7,157 training images and 1,383 validation images.Each image was resized to 224×224 pixels; light augmentation techniques (random rotation, brightness–contrast adjustment, flipping, and Gaussian blur) were applied only to the training set to prevent overfitting while maintaining consistency during evaluation.The MobileNetV2 teacher model was initially fine-tuned on the last 20 layers using manual hyperparameters (freeze_until = 20, dropout = 0.6), achieving an accuracy of 85.8%.Subsequent tuning via Optuna identified the optimal configuration (freeze_until = 91, dropout_rate = 0.5055), which improved the teacher’s performance to 89.6%.The resulting teacher model was then used to distill knowledge into the ResNet50 student: under manual settings, the student’s accuracy improved from 55.24% to 73.25%; when the student model was also optimized using Optuna, its accuracy surged to 85.54% nearly matching the teacher.Further evaluation using a confusion matrix and ROC curves revealed an increase in per-class AUC to the range of 0.91–0.99 in the KD + Optuna student model, confirming that this method effectively closes the performance gap between student and teacher.These findings demonstrate that combining KD with Optuna-based hyperparameter optimization is an effective approach for producing a lightweight, fast, and highly accurate tomato ripeness classification model ready for deployment in field applications to support post-harvest decision-making.

Pelatihan Branding dan Digital Marketing untuk Meningkatkan Nilai Jual pada UKM Vigaza Farm Andriani, Ria; Astria, Kadek Kiki; Sunyoto, Andi
Madani : Indonesian Journal of Civil Society Vol. 7 No. 2 (2025): Madani : Agustus 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/madani.v7i2.2875

As part of the Sleman Millennial Farmers Group, Vigaza Farm SME has great potential in developing quail farming products. However, this business is still hampered by issues related to brand identity management, packaging design quality, and suboptimal digital marketing strategies, which have implications for low sales value and competitiveness. Through community service activities, this program focuses on enhancing branding and digital marketing capabilities for the managers of Vigaza Farm. The training was conducted on June 21, 2025, at Building 2 of Amikom University Yogyakarta, attended by 10 participants comprising owners, staff, and internal members. The implementation methods included socialization, training, technology application, as well as mentoring and evaluation. The materials covered basic branding concepts, visual identity creation (logo, brand colors, typography), packaging design development, and an introduction to simple digital marketing strategies. Evaluation results showed a significant improvement in participants' understanding. Before the training, 75% of participants were in the “understand” category (score 4) and 25% in the “very understand” category (score 5). After the training, 75% of participants were in the very knowledgeable category (score 5) and 25% in the knowledgeable category (score 4). This activity produced two ready-to-use packaging designs, namely “Sambal Telur Puyuh Kencana” and “Puyuh Kencana Frozen,” which met branding principles and were suitable for promotion. These results demonstrate the effectiveness of the training in enhancing branding and digital marketing skills, although further guidance is still needed for optimizing online promotions.

Enhancing Deep Learning-based Classification of Cassava Leaf Diseases using CLAHE and SMOTE Tribiakto, Herlandro; Sunyoto, Andi; Pramono, Eko
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.v14i6.5530

Efficient detection of foliar diseases in cassava (Manihot esculenta) is essential for sustaining crop productivity and ensuring food security, particularly in regions vulnerable to environmental stress. However, accurate identification remains a challenge due to the widespread occurrence of diseases such as Cassava Mosaic Disease (CMD), Cassava Bacterial Blight (CBB), and Cassava Brown Streak Disease (CBSD), which continue to threaten cassava yields. This study addresses two major obstacles in cassava disease classification—uneven image quality and imbalanced class distribution—by implementing Contrast Limited Adaptive Histogram Equalization (CLAHE) and the Synthetic Minority Over-sampling Technique (SMOTE). A publicly available dataset from the Cassava Leaf Disease Classification competition on Kaggle was used, and two pretrained convolutional neural networks, EfficientNetV2B2 and DenseNet169, were fine-tuned through transfer learning. The images were resized, enhanced using CLAHE, and augmented before being split into training, validation, and test sets. Both models were trained for 10 epochs using identical configurations. Results indicate that EfficientNetV2B2 achieved higher classification accuracy (88.1%) than DenseNet169 (86.4%), with CLAHE contributing a 2–3% improvement in accuracy. While these results are slightly lower than those reported in previous studies employing extended training durations and advanced techniques such as focal loss, the lightweight approach presented here proves effective under computational constraints. The findings demonstrate the feasibility of developing scalable and resource-efficient disease detection systems, especially for mobile or edge devices. Future research should focus on longer training schedules, advanced loss functions, and validation using field-acquired images to further improve model performance in real-world agricultural settings.

Implementasi TOPSIS Pada Sistem Informasi Pembagian Jam Mengajar Guru SMK PGRI 1 Ngawi Sumafta, Ikhwan Baidlowi; Kusrini, K; Sunyoto, Andi
Prosiding University Research Colloquium Proceeding of The 7th University Research Colloquium 2018: Bidang Teknik dan Rekayasa
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

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

Penjadwalan merupakan salah satu hal penting dalam proses kegiatanbelajar mengajar, karena semua kegiatan guru dan murid bergantungpada jadwal yang ada. Salah satu langkah penting dalam pembuatanjadwal pelajaran adalah pembagian jam mengajar. SMK PGRI 1Ngawi adalah salah satu sekolah swasta terbaik di kabupaten ngawimemiliki 2303 siswa pada tahun pelajaran 2017/2018 dan dibagimenjadi 64 rombongan belajar serta memiliki jumlah guru sebanyak103 guru terbagi dalam dua kelompok guru yaitu 40 guru matapelajaran produktif, 63 guru mata pelajaran normatif dan adaptif.SMK PGRI 1 Ngawi juga memiliki dua gedung pembelajaran danmasing-masing memiliki dua lantai. TOPSIS merupakan salah satumetode pengambilan keputusan multikriteria yang memiliki beberapakelebihan, diantaranya konsepnya yang sederhana dan mudahdipahami, efisiensi komputasi, dan memiliki kemampuan untukmengukur kinerja relatif dari alternatif-alternatif keputusan dalambentuk matematis yang sederhana. Fokus penelitian yang dilakukanadalah bagaimana menentukan pembagian jam mengajar gurunormatif dan adaptif berdasarkan usia guru, status guru, dan lamaguru mengajar di SMK PGRI 1 Ngawi dengan menggunakan metodeTOPSIS. Penelitian menghasilkan jumlah jam mengajar danrombongan belajar untuk masing-masing guru sesuai dengan kriteria.

Perancangan Sistem Pendukung Keputusan Untuk Prediksi Penerima Beasiswa Menggunakan Metode Neural Network Backpropagation Pujianto, Ade; Kusrini, Kusrini; Sunyoto, Andi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 2: April 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Seleksi di Amikom masih mengalami kendala pada proses pengambilan keputusan, banyaknya data menyebabkan pengambil keputusan membutuhkan tools yang dapat membantu dalam menentukan penerima beasiswa, salah satu metode yang sering digunakan adalah artificial neural network (ANN). Metode ini meniru jaringan pemodelan saraf otak manusia berupa neuron-neuron untuk menyelesaikan suatu permasalahan. Salah satu penerapan neural network adalah untuk melakukan prediksi atau peramalan terhadap suatu peristiwa tertentu serta dianggap mampu menyelesaikan masalah yang komplek seperti penalaran otak manusia. Untuk menyelesaiakn masalah yang komplek neural network memerlukan banyak neuron atau yang biasa disebut layer (lapis). Salah satu metode neural network multi lapis adalah backpropagation yang mampu mengoptimalisasi bobot pada neuron dan menyelesaikan masalah yang komplek. Hasil dari penelitian ini adalah sebuah perancangan sistem prediksi dengan menggunakan metode neural network backpropagation untuk melakukan peramalan terhadap mahasiswa yang mendaftar beasiswa. hasil akhir penelitian ini adalah nilai akurasi sebesar 90% dan nilai error terkecil sebesar 0,000101 pada epoch ke 329 dengan jumlah 3000 data dengan pembagian data training 2.250 dan 750 data testing serta konfigurasi learning rate sebesar 0,2 dan momentum 0,2. Kata kunci: Artificial Neural netwok, Backpropagarion, Prediksi, beasiswa, Pengambilan Keputusan. AbstractSelection in Amikom is still constrained in the decision-making process, the number of data causing decision makers need tools that can assist in determining scholarship recipients, one of the most commonly used method is artificial neural network (ANN). This method mimics the neural network modeling of the human brain in the form of neurons to solve a problem. One application of neural network is to make predictions or forecasting of a particular event and is considered capable of solving complex problems such as human brain reasoning. To solve the problem the complex neural network requires many neurons or so-called layers. One method of multi layer neural network is backpropagation that is able to optimize the weight of neurons and solve complex problems. The result of this research is a prediction system design using neural network backpropagation method to forecast the students who apply for scholarship. the final result of this research is the accuracy value of 90% and the smallest error value of 0.000101 on epoch to 329 with the amount of 3000 data with sharing training 2,250 and 750 data testing and learning rate configuration of 0.2 and momentum 0.2.Keywords: Artificial Neural Netwok, Backpropagarion, Prediction, Scholarship, Decision Making.

Klasifikasi Penyakit Pada Daun Cabai Menggunakan Arsitektur VGG16 Mashuri, Ahmad Sanusi; Sunyoto, Andi; Kusnawi, Kusnawi
Journal of Electrical Engineering and Computer (JEECOM) Vol 6, No 2 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v6i2.9116

Penyakit pada tanaman cabai dapat mengancam produktivitas dan kualitas hasil panen jika tidak terdeteksi dan diatasi secara tepat waktu. Untuk meningkatkan deteksi dini penyakit pada tanaman cabai, kami mengembangkan sistem klasifikasi menggunakan arsitektur VGG16, sebuah jaringan saraf konvolusional yang telah terbukti efektif dalam pengolahan gambar kompleks. Penelitian ini memanfaatkan dataset citra daun cabai yang terdiri dari beberapa kelas penyakit yang umum dijumpai, termasuk Healthy, Yellowish, whitefly, leafcurl dan leafspot. Citra-citra ini diolah dan dinormalisasi untuk pelatihan dan pengujian model. Arsitektur VGG16 digunakan sebagai model dasar, yang telah dipre-trained pada dataset ImageNet untuk meningkatkan kinerja klasifikasi. Proses pelatihan model dilakukan dengan memanfaatkan teknik transfer learning, di mana lapisan-lapisan akhir dari VGG16 disesuaikan dengan dataset penyakit daun cabai. Selama pengujian, sistem berhasil mengenali dan mengklasifikasikan penyakit pada daun cabai dengan tingkat akurasi yang tinggi. Hasil evaluasi menunjukkan bahwa arsitektur VGG16 mampu mengenali berbagai penyakit dengan akurasi rata-rata sebesar 0.9962%. sedangkan waktu komputasi yang dibutukan adalah 7 detik.

Co-Authors *, Pramono A.A. Ketut Agung Cahyawan W Aam Shodiqul Munir Abdul Jalil Rozaqi Abdul Jalil Rozaqi Abdul Mizwar A. Rahim Abidarin Rosidi Abidarin Rosidi Ade Kurniawan kurniawan Ade Pujianto, Ade Afis Julianto Afis Julianto Agus Harjoko Agus Harjoko AGUS PURWANTO Aidina Ristyawan, Aidina Alva Hendi Muhammad Alva Hendi Muhammad Alva Hendi Muhammad Muhammad Ana Wati Ndarbeni Anna Baita Annas Al Amin Arif Sutikno Asro Nasiri Asro Nasiri Asro Nasiri Astria, Kadek Kiki B, Arijal Bahri, Saiful Bambang Soedijono Bambang Soedijono WA Banu Dwi Putranto Bayu Anugerah Putra Bayu Setiaji Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono, Bonifacius Vicky Cahyo, D. Diffran Nur Dhanar Intan Surya Saputra Diansyah, Ahmad Febri Dwi Sari Widyowaty Dwi Yuli Prasetyo Eka Yulia Sari Eko Pramono Eko Pramono Ema Utami Emha Taufiq Luthfi Emha Taufiq Luthfi Ferry Wahyu Wibowo Ferry Wahyu Wibowo Firdiyan Syah Fitri Handayani Gagah Gumelar Gori, Takhamo Hanafi Hanafi Hani Atun Mumtahana Hani Atun Mumtahana Hani Setiani Hanif Al Fatta Hanif Al Fatta Hanif Al Fatta Hanif Al Fatta Harianto, Harianto Hidayat Hidayat Huda, Luthfi Nurul Ibnu Hadi Purwanto Ikhwan Baidlowi Sumafta Ikmah Ikmah Indah Nofikasari Irawanto, Indra Ismail, Muhamad Yusuf K Kusrini Kapti . kurniawan, Ade Kurniawan Kurniawan, Mei P Kurniawan, Mei P. Kusnawi Kusnawi Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini, K Kusrini, Kusrini Liana Trihardianingsih Licantik M rudyanto Arief M. Afriansyah M. Rudyanto Arief M. Suyanto M. Suyanto, M. M. Syukri Mustafa Maie Istighosah Mashuri, Ahmad Sanusi Masruri, Nizar Haris Mohammad Suyanto Mudawil Qulub Muhammad Rudyanto Arief Muhammad Setiyawan Muhammad, Anva Hendi Muhartini, Sitti Mumtahana, Hani Atun Mursyid Ardiansyah Nalendra, Adimas Ketut Nasiri, Asro Noordin Asnawi Norlaila2 Nugraha, Anggit Ferdita Nulngafan, Nulngafan Nur Arifin Akbar Parsiyono Parsiyono Patmawati Patmawati, Patmawati Pramono * Putranto, Dinar Wakhid Quratul Ain Rafli Junaidi Kasim Rahim Jamal Rahmat Hidayat Raynaldi Fatih Amanullah Ria Andriani Rifda Faticha Alfa Aziza Rifqi Mulyawan Riyanto, Thomas Pramuji Singgih Riza Marsuciati Rizfi Syarif Rizky Arya Kurniawan Rohim, Ni’matur Rudi Prietno Sahirul Muklis Salmuasih - Samsul Bahri Sari, Rita Novita Setiawan Budiman Sholihin, Iasya Silvi Agustanti Bambang Singgih Arif Widodo Slamet Triyanto Soedijono W A, Bambang Sudarmawan Sudarmawan Sudarmawan, Sudarmawan Sudiana Sudiana Sukresno Sukresno Sulistyowati Sulistyowati Suliswaningsih Suliswaningsih Sumafta, Ikhwan Baidlowi Supomo, Eko Sutejo, Danang Syah, Firdiyan Syah, Firdiyan Syukirman Amir TONNY HIDAYAT Tribiakto, Herlandro Ulinuha, Hinova Rezha W., Bambang Soedijono WA, Bambang Soedijono Wahyu Caesarendra Wahyu Hidayat Wihayati, Wihayati Windarni, Vikky Aprelia Windha Mega Pradnya Dhuhita Wing Wahyu Winarno Yoga Dwi Pambudi Yoga Pristyanto Yohanes Setyo Prabowo, Yohanes Setyo Yusuf Sutanto Zaipin, Zaipin

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