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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) BERKALA FISIKA Media Statistika Jurnal Masyarakat Informatika MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Semantik Jurnal Ilmiah Kursor Jurnal Inovasi Teknologi Pendidikan Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Register: Jurnal Ilmiah Teknologi Sistem Informasi Proceeding of the Electrical Engineering Computer Science and Informatics AKSIOMA Jurnal Teknologi dan Sistem Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Matematika: MANTIK Jurnal Penelitian Pendidikan IPA (JPPIPA) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control BAREKENG: Jurnal Ilmu Matematika dan Terapan Unisda Journal of Mathematics and Computer Science (UJMC) JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Applied Data Sciences Journal of Soft Computing Exploration Interdisciplinary Social Studies Journal of Fundamental Mathematics and Applications (JFMA) Buletin Abdi Masyarakat (BAM) Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Scientific Journal of Informatics
Bayu Surarso
Department Of Mathematics & Graduate School Of Information System
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Fine-tuning bidirectional encoder representations from transformers for the X social media personality detection Khoerunnisa, Selvi Fitria; Surarso, Bayu; Kusumaningrum, Retno
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.pp3395-3403

Abstract

Understanding personality traits can help individuals reach their full potential and has applications in various fields such as recruitment, advertising, and marketing. A widely used tool for assessing personality is Myers-Briggs type indicator (MBTI). Recent advancements in technology have allowed for research on how personalities can change based on social media use. Previous research used machine learning methods, deep learning methods, until transformers-based method. However, these previous approaches must be revised to require extensive data and a high computational load. Although transformer-based methods like bidirectional encoder representations from transformers (BERT) excel at understanding context, it still has limitations in capturing word order and stylistic variations. Therefore, this study proposed integrating fine-tuning BERT with recurrent neural networks (RNNs) consisting of vanilla RNN, long short-term memory (LSTM), and gated recurrent unit (GRU). This study also uses a BERT base fully connected layer as a comparison. The results show that the BERT base fully connected layer approach strategy has the best evaluation results in class extraversion/introversion (E/I) of 0.562 and class feeling/thinking (F/T) of 0.538. then, the BERT+LSTM approach strategy has the highest accuracy for the intuition/sensing (N/S) class of 0.543 and judging/perceiving (J/P) of 0.532. 
BERT Model Fine-tuned for Scientific Document Classification and Recommendation Antariksa, Muhammad Deagama Surya; Sugiharto, Aris; Surarso, Bayu
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6789

Abstract

The increasing number of academic documents requires efficient and accurate classification and recommendation systems to assist in retrieving relevant information. This system is built using the "bert-base-uncased” model from Hugging Face, which has been fine-tuned to improve the classification accuracy and relevance of document recommendations. The dataset used consists of 2.000 academic documents in the field of computer science, with features including titles, abstracts, and keywords, which were combined into a single input for the model. Document similarity is measured using cosine similarity, resulting in recommendations based on semantic proximity. Unlike traditional approaches, which rely primarily on word frequency or surface-level matching, the proposed method leverages BERT’s contextual embeddings to capture deeper semantic meanings and relationships between documents. This allows for more accurate classification and more context-aware recommendations. Evaluation results show that the best model configuration (learning rate 3e-5, batch size 32, optimizer AdamW) achieved 89.5% training accuracy and an F1-score of 0.8947, while testing yielded 91% accuracy and 90% F1-score. The recommendation system consistently produced Precision@k values above 92% for k between 5 and 30, with Recall@k reaching 1.0 as k increased. These results indicate that the system not only performs reliably in classifying complex academic texts but also effectively recommends contextually relevant documents. This integrated approach shows strong potential for enhancing academic document retrieval and supports the development of semantically aware information management systems.
Perbandingan Kinerja Inception- Resnetv2, Xception, Inception-v3, dan Resnet50 pada Gambar Bentuk Wajah Masruroh, Fitriana; Surarso, Bayu; Warsito, Budi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 1: Februari 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023104941

Abstract

Saat ini, klasifikasi bentuk wajah banyak diterapkan dalam berbagai bidang. Dalam bidang industri fashion dapat digunakan untuk pemilihan gaya rambut, pemilihan bingkai kacamata, tata rias, dan mode lainnya. Selain itu, dalam bidang medis bentuk wajah digunakan untuk bedah plastik. Identifikasi bentuk wajah adalah tugas yang menantang karena kompleksitas wajah, ukuran, pencahayaan, usia dan ekspresi. Banyak metode yang dikembangkan untuk memberikan hasil akurasi terbaik dalam klasifikasi bentuk wajah. Deep learning menjadi tren dibidang komputer vision karena memberikan hasil yang paling baik dari pada metode sebelumnya. Makalah ini mencoba menyajikan perbandingan kinerja klasifikasi wajah dengan empat arsitektur deep learning Xception, ResNet50, InceptionResNet-v2, Inception-v3. Dataset yang digunakan berjumlah 4500 gambar yang terbagi lima kelas heart, long, oblong, square, round. Berbagai pengoptimal deep learning diantaranya; transfer learning, optimizer deep learning, dropout dan fungsi aktivasi diterapkan untuk meningkatkan kinerja model. Perbandingan antara berbagai model CNN didasarkan kinerja metrik seperti accuracy, recall, precision dan F1-score. Dengan demikian dapat disimpulkan bahwa model Inception-ResNet-V2 menggunakan fungsi aktivasi Mish dan optimizer Nadam mencapai nilai tertinggi dengan accuracy dan f1-score masing-masing 92.00%, dan penggunaan waktu 65.0 menit. AbstractCurrently, face shape classification is widely applied in various fields. In the fashion industry, it can be used for hairstyle selection, eyeglass frame selection, makeup, and other modes. In the medical field, the face shape is used for plastic surgery. Identification of face shape is a challenging task due to the complexity of the face, size, lighting, age and expression. Many methods have been developed to provide the best accuracy results in the classification of face shapes. Deep learning is becoming a trend in the field of computer vision because it gives the best results than the previous method. This paper attempts to present a comparison of the performance of face classification with four deep learning architectures Xception, ResNet50, InceptionResNet-v2, Inception-v3. The dataset used is 4500 images divided into five classes heart, long, oblong, square, round. Various deep learning optimizers include; transfer learning, deep learning optimizer, dropout and activation functions are implemented to improve model performance. Comparisons between various CNN models are based on performance metrics such as accuracy, recall, precision and F1-score. Thus, it can be concluded that the Inception-ResNet-V2 model using the Mish activation function and the Nadam optimizer achieves the highest value with an accuracy and f1-score of 92.00%, and a time usage of 65.0 minutes. Thus, it can be concluded that the Inception-ResNet-V2 model using the Mish activation function and the Nadam optimizer achieves the highest value with an accuracy and f1-score of 92.00%, and a time usage of 65.0 minutes. 
Penggabungan Best Worst Method, Moora Dan Copeland Score Pada Sistem Pendukung Keputusan Kelompok Penentuan Penerima Bantuan Pada Dinas Sosial Alfajri, Willy Bima; Nugraheni, Dinar Mutiara Kusumo; Surarso, Bayu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 3: Juni 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023106724

Abstract

Dalam beberapa penelitian terdahulu, proses pembobotan kriteria belum menjadi perhatian penting karena masih didasarkan pada hasil wawancara secara subjektif dan belum dihitung secara matematis. Pembobotan kriteria dalam sistem pendukung keputusan kelompok merupakan salah satu proses awal yang dilakukan sebelum mengambil sebuah keputusan, proses ini penting dilakukan untuk memastikan bahwa bobot kriteria yang digunakan sesuai dengan kebutuhan. Dalam penelitian ini, proses pembobotan kriteria dihitung secara matematis dengan best worst method. Untuk dapat membangun sistem pendukung keputusan kelompok yang bisa membantu Dinas Sosial dalam menentukan calon penerima bantuan, metode best worst method digabungkan dengan metode MOORA dan copeland score. Best worst method dipilih karena hasil pembobotan yang diperoleh lebih konsisten serta mudah dalam membandingkan kriteria. Metode MOORA memiliki perhitungan matematis lebih simpel dan hasil yang stabil. Dan metode copeland score memiliki keunggulan efektif sebagai alat dalam sistem voting. Penggabungan metode yang dilakukan, lalu diujikan pada studi kasus penentuan calon penerima bantuan. Untuk menentukan penerima bantuan, ada tiga pengambil keputusan yaitu sektor kesehatan, sektor pendidikan dan sektor sosial yang dilibatkan dalam menentukan peringkat akhir dari masing-masing calon penerima bantuan. Hasil perhitungan menunjukkan bahwa alternatif 2 memiliki skor tertinggi dibandingkan alternatif lainnya dengan nilai akhir 4. Analisis sensitivitas menunjukkan konsep sistem pendukung keputusan dengan penggabungan metode yang diusulkan solid, dengan persentase perubahan rendah yaitu 23,52%. Hasil perhitungan dengan metode ini dapat dijadikan sebagai acuan dalam proses pengambilan keputusan bagi Dinas Sosial, sebab ketepatan pilihan penerima bantuan sosial memiliki pengaruh langsung terhadap pencapaian tujuan perlindungan sosial. AbstractIn several previous studies, the process of weighting criteria has not become an important concern because it is still based on subjective interview results and hasn’t been calculated mathematically. One of the initial procedures in a GDSS before reaching a choice is the weighing of the criteria, this process is crucial to ensure that the weighting of the criteria utilized is appropriate for the demands. The weighting of the criteria in this study is computed quantitatively using the best worst method. Best worst method, MOORA, and copeland score are combined in order to create a GDSS that will help the Office of Social Affairs identify probable beneficiaries. Best worst method was chosen because the weighting results obtained are more consistent and easier to compare criteria. MOORA method has simpler mathematical calculations and stable results. And copeland score method has an effective advantage as a tool in the voting system. The combined methods were then evaluated in a case study involving the identification of aid recipients. The health sector, the education sector, and the social sector are the three decision-makers who establish the ultimate ranking of each potential beneficiary in order to determine recipients. The calculation results show that alternative 2 has the highest score compared to the other alternatives with a final score of 4. The sensitivity analysis shows that the concept of a GDSS by combining the proposed methods is solid, with a low percentage change of 23.52%. The precision of the selection of social assistance beneficiaries has a direct impact on the achievement of social protection goals, so the results of calculations using this method can be used as a reference in the Social Service's decision-making process.
Pengaruh Klasifikasi Sentimen Pada Ulasan Produk Amazon Berbasis Rekayasa Fitur dan K-Nearest Negihbor Putri, Nitami Lestari; Warsito, Budi; Surarso, Bayu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241117376

Abstract

Ulasan online menjadi faktor penting yang mendorong konsumen untuk membeli barang di e-commerce. Dalam e-commerce, ulasan pelanggan sebelumnya dapat membantu pembeli membuat keputusan yang lebih baik dengan memberikan informasi tentang kualitas produk, kekuatan dan kelemahan, perilaku penjual, harga, dan waktu pengiriman. Namun, keberadaan ulasan palsu menimbulkan tantangan dalam menilai sentimen yang diungkapkan oleh pelanggan asli secara benar. Dalam penelitian ini, berfokus pada analisis sentimen dan bertujuan untuk mengeksplorasi peran sentimen dalam ulasan produk Amazon. Penelitian ini menggunakan kombinasi fitur dari konten ulasan dengan menerapkan klasifikasi K-Nearest Neighbor untuk mengklasifikasikan polaritas sentimen ulasan secara akurat. Dalam mengekstrak skor polaritas dari ulasan, penelitian ini menggunakan pendekatan analisis sentimen berbasis leksikon yaitu Textblob Library dan menetapkan label sentimen dari ulasan produk. Hasil dari pemodelan yang diusulkan mencapai tingkat akurasi sebesar 83% yang menunjukkan keefektifan pemodelan yang diusulkan dalam analisis sentimen. Hasil dari penelitian ini dapat membantu konsumen dalam membuat keputusan pembelian dan membantu penjual dalam meningkatkan nilai produk dan layanan mereka berdasarkan feedback yang diberikan oleh pelanggan.
Fuzzy-AHP MOORA approach for vendor selection applications Al Khoiry, I’tishom; Gernowo, Rahmat; Surarso, Bayu
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 1 (2022): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2356

Abstract

Vendor selection is a critical activity in order to support the achievement of company success and competitiveness. Significantly, the company has some specific standards in the selection. Therefore, an evaluation is needed to see which vendors match the company's criteria. The purpose of this study is to evaluate and select the proposed vendor in a web-based decision support system (DSS) by using the fuzzy-AHP MOORA approach. The fuzzy-AHP method is used to determine the importance level of the criteria, while the MOORA method is used for alternative ranking. The results showed that vendor 4 has the highest score than other alternatives with a value of 0.2536. Sensitivity analysis showed that the proposed DSS fuzzy-AHP MOORA concept was already solid and suitable for this problem, with a low rate of change.
User Experience Improvement (MSMEs and Buyers) Mobile AR Using Design Thinking Methods Dwiyanasari, Desty; Nurhayati, Oky Dwi; Surarso, Bayu; Nugraheni, Dinar
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24088

Abstract

Purpose: This research aims to improve the User Experience (UX) of Augmented Reality (AR) mobile applications for MSMEs and buyers through the Design Thinking method. This research solves the problem of suboptimal UX in AR-based mobile applications. This study hypothesizes that the application of Design Thinking can result in significant improvements in the UX of AR mobile applications, which is evidenced by an increase in heuristic evaluation scores. Methods: The Design Thinking approach (Empathize, Define, Ideate, Prototype, Test) is implemented. Data were collected through interviews, observations, and heuristic evaluation questionnaires. Result: Initial heuristic testing showed several usability problems in the developed AR mobile applications, such as Help and Documentation (H10), Recognition Rather than Recall (H6), and Error Prevention (H5). After the application of the Design Thinking method and design iteration, the heuristic testing showed that the results of the evaluation comparison before and after the improvement showed a high effectiveness of the corrective actions taken, with an average decrease in severity score of 37% based on the Nielsen scale (0–4), indicating that the most critical and major issues were successfully reduced to cosmetic or minor levels. Novelty: This research contributes in the form of a practical framework to improve the UX of AR mobile applications for MSMEs and buyers by utilizing the Design Thinking method. The results of this research can be a reference for developers in designing user-friendly AR mobile applications.
MICE Implementation to Handle Missing Values in Rain Potential Prediction Using Support Vector Machine Algorithm Putri, Aina Latifa Riyana; Surarso, Bayu; SRRM, Titi Udjiani
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i4.16699

Abstract

Support Vector Machine (SVM) is a machine learning algorithm used for classification. SVM has several advantages such as the ability to handle high-dimensional data, effective in handling nonlinear data through kernel functions, and resistance to overfitting through soft margins. However, SVM has weaknesses, especially when handling missing values in data. The use of SVM must consider the missing values strategy chosen. Missing values in data mining is a serious problem for researchers because it causes many problems such as loss of efficiency, complications in data handling and analysis, and the occurrence of bias due to differences between missing data and complete data. To overcome the above problems, this research focuses on understanding the characteristics of missing values and handling them using the Multiple Imputation by Chained Equations (MICE) technique. In this study, we utilized secondary data experiments that contain missing values from the Meteorological, Climatological, and Geophysical Agency (called BMKG) related to predictions of potential rain, especially in DKI Jakarta. Identification of types or patterns of missing values, exploration of the relationship between missing values and other variables, incorporation of the MICE method to handle missing values, and the Support Vector Machine Algorithm for classification will be carried out to produce a more reliable and accurate prediction model for rain potential. It shows that the imputation method with the MICE gives better results than other techniques (such as Complete Case Analysis, Imputation Method Mean, Median, Mode, and K-Nearest neighbor), namely an accuracy of 89% testing data when applying the Support Vector Machine algorithm for classification.
Dilated Convolutional Neural Network for Skin Cancer Classification Based on Image Data Khasanah, Uswatun; Surarso, Bayu; Farikhin, Farikhin
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 1 (2023): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i1.11667

Abstract

Skin cancer is a disorder of cell growth in the skin. Skin cancer has a big impact, causing physical disabilities that can be seen directly and high treatment costs. In addition, skin cancer also causes death if nor treated properly. Generally, dermatologists diagnose the presence of skin cancer in the human body by using the Biopsy process. In this study, the Dilated Convolutional Neural Network method was used to classify skin cancer image data. Dilated Convolutional Neural Network method is a development method of the Convolutional Neural Network method by modifying the dilation factors. The Dilated Convolutional Neural Network method is divided into two stages, including feature extraction and fully connected layer. The data used in this study is HAM1000 dataset. The data are dermoscopic image datasets which consists of 10015 images data from 7 types of skin cancer. This study conducted several experimental scenarios of changes in the value of d, which are 2,4,6, and 8 to get the optimal results. The parameters used in this study are epoch = 100, minibatch size = 8, learning rate = 0.1, and dropout = 0.5. The best results in this study were obtained with value of d=2 with the value of accuracy is 85.67% and the sensitivity is 65.48%.
Pengembangan Perangkat Pembelajaran Konstruktivisme Berbasis Humanistik dengan Metode Two Stay Two Stray Berbantuan CD Interaktif pada Materi Geometri Dimensi Dua Kelas X ., Indriastuti, T; Waluya, St. Budi; Surarso, Bayu
AKSIOMA : Jurnal Matematika dan Pendidikan Matematika Vol 3, No 1/Maret (2012): Aksioma
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/aks.v3i1/Maret.229

Abstract

Penelitian ini bertujuan untuk: (1) menghasilkan perangkat pembelajaran konstruktivisme berbasis humanistik dengan metode Two Stay Two Stay berbantuan CD interaktif pada materi geometri dimensi dua yang valid; (2) mengukur efektifitas pembelajaran konstruktivisme berbasis humanistik dengan metode Two Stay Two Stray berbantuan CD interaktif pada materi geometri dimensi dua.Jenis penelitian yang digunakan adalah penelitian pengembangan yang menggunakan modifikasi model 4-D (menjadi 3-D) dengan tahap-tahap: Define, Design, dan Develop. Jenis perangkat pembelajaran yang dikembangkan adalah Silabus, RPP, Buku Pegangan Peserta Didik, Lembar Kerja Peserta Didik (LKPD), CD Interaktif, dan Tes Prestasi Belajar (TPB).Subyek ujicoba penelitian adalah peserta didik kelas X SMK N 11 Semarang tahun Pelajaran 2010/2011 , yang dibagi dalam tiga kelas, yaitu: 1 kelas uji coba soal TPB (32 peserta didik), 1 kelas kontrol (33 peserta didik) dan 1 kelas eksperimen (32 peserta didik). Data penelitian diperoleh melalui: (1) lembar validasi; (2) pengamatan; dan (3) tes prestasi belajar. Data-data tersebut digunakan untuk: (1) mengetahui kevalidan perangkat pembelajaran; dan (2) mengetahui efektivitas perangkat pembelajaran.Variabel aktivitas dan keterampilan proses sebagai variabel independen dan prestasi belajar sebagai variabel dependen. Data diolah dengan deskriptif, uji analisis uji banding, sample t.test dan uji pengaruh. Hasil penelitian menunjukkan (1) setelah melalui proses validasi dan revisi diperoleh perangkat pembelajaran berupa Silabus dengan rataan skor 3,82, RPP dengan rataan skor 3,82, Buku pegangan Peserta Didik dengan rataan skor 3,88, LKPD dengan rataan skor 3,75, CD Interaktif dengan rataan skor 3,72, dan?é?á TPB dengan rataan skor 3,73 pada skor tertinggi 4 jadi dengan skor tersebut termasuk kriteria valid; (2) proses pembelajaran matematika konstruktivisme berbasis humanistik dengan metode Two stay Two Stray berbantuan CD interaktif pada materi Dimensi Dua efektif. Efektifitas ditandai dengan (a) Tercapainya KKM prestasi belajar peserta didik= 75 secara individual ?é?á80% dan klasikal ?é?á75; (b) Aktivitas dan keterampilan proses berpengaruh terhadap prestasi belajar sebesar 79,1%; dan (c) rata-rata prestasi belajar kelas eksperimen (81,88) lebih baik secara signifikan dari pada prestasi belajar kelas kontrol (70,91). Berdasarkan hasil?é?á perangkat yang valid dan pembelajaran efektif menunjukkan pengembangan perangkat?é?á tercapai. Kata Kunci: pengembangan perangkat, konstruktivisme, humanistik, Two Stay Two Stray, CD interaktif, Dimensi Dua.
Co-Authors A. Nafis Haikal Adi Wibowo Adi Wibowo Agus Subagio Ahmad Abdul Chamid Ahmad Aviv Mahmudi Aldi Setiawan, Aldi Alfajri, Willy Bima Ali Bardadi Anak Agung Gede Sugianthara Andi Setiabudi, Nur Antariksa, Muhammad Deagama Surya Arief Hidayat Aris Puji Widodo Aris Sugiharto Aslam Fatkhudin Aulia, Lathifatul Badieah Assegaf Bambang Irawanto Beta Noranita Budi Warsito Budi Warsito Budi Warsito Che Pee, Ahmad Naim Dedy Kurniadi Dinar Mutiara Kusumo Nugraheni Dwi Putri Handayani Dwiyanasari, Desty Edwin Setiawan Eko Adi Sarwoko Eko Sediyono Etna Vianita Fajar Nugraha Fra Siskus Dian Arianto Ghufron Ghufron Harjito - Henny Indriyawati Imam Tahyudin Indah Jumawanti Irfan Santiko I’tishom Al Khoiry Jatmiko Endro Suseno Jumawanti, Indah Jumawanti, Indah Juwanda, Farikhin Khoerunnisa, Selvi Fitria Khusnah, Miftakhul Laily Rahmania, Laily Lili Rusdiana, Lili LM Fajar Israwan, LM Fajar Lucia Ratnasari Masruroh, Fitriana Maunah, Uun Migunani Migunani Muhammad Haris Qamaruzzaman Muhammad Nasrullah Muhammad Sam'an Mustafid Mustafid Mustaqim Mustaqim Mustaqim Mustaqim, Mustaqim Nugraheni, Dinar Oky Dwi Nurhayati Pukky Tetralian Bantining Ngastiti Puspita, Yuanita Candra Putri, Aina Latifa Riyana Putri, Nitami Lestari Putut Sriwasito Rachmat Gernowo Ragil Saputra Ragil Saputra Rahmat Gernowo Rahmawati, Nurhita Ratri Wulandari Rezki Kurniati, Rezki Robertus Heri Sulistyo Utomo Saputra, Ragil Satriani, Rineka Brylian Akbar Siti Alfiatur Rohmaniah St. Budi Waluya Sugiyamto Sugiyamto, Sugiyamto Sulastri Daruni Sulistiyo, Budi Suryono Suryono Suryono Suryono Suryono, Suryono Susi Hendartie Susilo Hariyanto sutimin sutimin Sutrisno, Sutrisno Sutrisno, Sutrisno Syibli, Mohammad T Indriastuti . Titi Udjiani SRRM Tri Retnaningsih Soeprobowati Uswatun Khasanah Vianita, Etna Wahyul Amien Syafei Wicaksono, Mahad Zainal Arifin Hasibuan