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Kusrini

Universitas Amikom Yogyakarta

Author-ID : 302666

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Sistem Pendukung Keputusan Pemberian Tugas Belajar Pegawai Negeri Sipil Tukan, Ewaldus Ambrosius; Kusrini, Kusrini; Al Fatta, Hanif
Jurnal Informa : Jurnal Penelitian dan Pengabdian Masyarakat Vol 9 No 1 (2023): Juni
Publisher : Politeknik Indonusa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46808/informa.v9i1.244

The awarding of study assignment scholarships for civil servants is carried out to improve professional competence and professional skills, taking into account several aspects, namely (1) Service Period, (2) Age, (3) Achievement, (4) Performance, (5) Discipline Knowledge, and (6) Good Behavior, which was initially used in the scholarship selection process for study assignments without calculation.Utilization of a decision support system was chosen as a solution to overcome problems in selecting scholarships for study assignments for civil servants within the scope of local government at the district level so as to minimize the occurrence of errors in giving these learning assignments to civil servants using the Multifactor Evaluation Process (MFEP) method. By making multi-factor decisions, subjective and intuitive decision making can be avoided.The results of the study obtained an alternative with the highest total evaluation factor weight at 14.95 and it was concluded that the calculation of the selection of scholarships for study assignments using a decision support system obtained results that could be accounted for in supporting the decision making carried out by the regional head at the district level concerned.

Prediksi Kebakaran Hutan Ibu Kota Nusantara Menggunakan Produk MODIS dengan Algoritma Regresi Linear, Gradient Boosting dan Decision Tree Mangun, Syamsul Syahab; Kusrini, Kusrini
Jambura Journal of Informatics VOL 7, N0 1: APRIL 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jji.v1i1.30926

Forest Fires in the Capital City of the Archipelago (IKN) threaten environmental sustainability and sustainable development, but accurate predictions are still hampered by the complexity of trigger factors such as weather variability and data uncertainty, so this study aims to develop a machine learning-based forest fire prediction model by utilising MODIS (Moderate Resolution Imaging Spectroradiometer) data, including surface temperature and thermal anomalies, by comparing three algorithms namely Linear Regression, Gradient Boosting Regressor (GBR), and Decision Tree Regressor (DTR). Performance evaluation using RMSE (Root Mean Square Error) and accuracy metrics showed GBR as the best model with 98.84% accuracy followed by DTR 96.39%, while R² values close to 1.0 in both models indicated the ability to explain data variability optimally, in contrast to linear regression which showed significant limitations. Thus, these findings prove the superiority of ensemble algorithms such as GBR in handling non-linearity of forest fire data and have practical implications on its potential integration into early warning systems to improve the effectiveness of fire mitigation around the IKN region.Kebakaran Hutan di Ibu Kota Nusantara (IKN) mengancam kelestarian lingkungan dan pembangunan berkelanjutan, namun prediksi akurat masih terhambat oleh kompleksitas faktor pemicu seperti variabilitas cuaca dan ketidakpastian data, sehingga penelitian ini bertujuan mengembangkan model prediksi kebakaran hutan berbasis machine learning dengan memanfaatkan data MODIS (Moderate Resolution Imaging Spectroradiometer), termasuk suhu permukaan dan anomali termal, dengan membandingkan tiga algoritma yaitu Regresi Linear, Gradient Boosting Regressor (GBR), dan Decision Tree Regressor (DTR). Evaluasi performa menggunakan metrik RMSE (Root Mean Square Error) dan akurasi yang menunjukan GBR sebagi model terbaik dengan akurasi 98,84% diikuti DTR 96,39%, sementara nilai R² mendekati 1.0 pada kedua model mengindikasikan kemampuan menjelaskan variabilitas data secara optimal, berbeda dengan regresi linear yang menunjukkan keterbatasan signifikan, sehingga temuan ini membuktikan keunggulan algoritma seperti GBR dalam menangani non-linearitas data kebakaran hutan dan berimplikasi praktis pada potensi integrasinya ke dalam sistem peringatan dini untuk meningkatkan efektivitas mitigasi kebakaran di sekitar wilayah IKN.

Analisis Sentimen Omnibus Law di Twitter dengan Machine Learning dan Teknik Resampling Syafutra, Arif Dwi; Kusrini, Kusrini
Jambura Journal of Informatics VOL 7, N0 1: APRIL 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jji.v1i1.30935

The Omnibus Law has been controversial in Indonesia since its enactment in 2020, sparking widespread public debate on social media platforms, particularly Twitter. This study aims to classify public sentiment toward the Omnibus Law using machine learning algorithms and resampling techniques to address data imbalance. Twenty thousand tweets were collected via web scraping, processed using Natural Language Processing (NLP) methods, and automatically labeled through a lexicon-based approach. The final dataset consisted of 17,184 tweets categorized into positive and negative sentiments. Sentiment classification models were developed using Support Vector Machine (SVM), Random Forest, and Multinomial Naïve Bayes, with Synthetic Minority Oversampling Technique (SMOTE) and Random Undersampling applied to enhance model performance. Evaluation results show that SVM combined with SMOTE achieved the highest performance with an accuracy of 93.08%, a recall of 92.85%, and a precision of 92.44%, outperforming other algorithms. These findings highlight that resampling techniques, particularly SMOTE, significantly improve classification performance in handling imbalanced datasets. This study emphasizes the importance of selecting appropriate algorithms and balancing strategies to enhance sentiment analysis accuracy based on social media data. Furthermore, the results open opportunities for future research to explore deep learning-based approaches for more complex public opinion analysis.Omnibus Law telah menjadi isu kontroversial di Indonesia sejak pengesahannya pada tahun 2020 yang mendorong perdebatan luas di media sosial, khususnya Twitter. Penelitian ini bertujuan untuk mengklasifikasikan sentimen publik terhadap Omnibus Law menggunakan algoritma machine learning dan teknik resampling untuk mengatasi ketidakseimbangan data. Data sebanyak 20.000 tweet dikumpulkan melalui web scraping, diproses dengan metode Natural Language Processing (NLP), dan dilabeli secara otomatis menggunakan pendekatan berbasis lexicon. Dataset akhir terdiri atas 17.184 tweet dengan kategori sentimen positif dan negatif. Model klasifikasi dikembangkan menggunakan Support Vector Machine (SVM), Random Forest, dan Multinomial Naïve Bayes, dengan penerapan teknik Synthetic Minority Oversampling Technique (SMOTE) dan Random Undersampling untuk meningkatkan performa. Hasil evaluasi menunjukkan bahwa SVM dengan SMOTE menghasilkan akurasi tertinggi sebesar 93,08%, recall 92,85%, dan precision 92,44%, mengungguli algoritma lainnya. Temuan ini menunjukkan bahwa teknik resampling, khususnya SMOTE, secara signifikan memperbaiki performa klasifikasi dalam skenario data tidak seimbang. Penelitian ini menegaskan pentingnya kombinasi antara pemilihan algoritma yang tepat dan strategi balancing data untuk meningkatkan akurasi analisis sentimen berbasis media sosial. Studi ini juga membuka peluang penelitian lanjutan menggunakan pendekatan deep learning untuk klasifikasi opini publik yang lebih kompleks.

KLASIFIKASI PENGENALAN WAJAH SISWA PADA SISTEM KEHADIRAN DENGAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) Sarawan, Tommy; Kusrini, Kusrini
Djtechno: Jurnal Teknologi Informasi Vol 6, No 1 (2025): April
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i1.6017

Pengelolaan kehadiran yang konvensional sering kali memakan waktu dan rentan terhadap kesalahan manusia. Oleh karena itu, diperlukan suatu inovasi untuk meningkatkan sistem pengelolaan kehadiran yang lebih akurat, cepat, dan efisien. Pengenalan wajah telah menjadi salah satu teknologi yang semakin populer dalam berbagai aplikasi, termasuk dalam bidang pendidikan. Teknologi ini menawarkan solusi yang efisien dan inovatif untuk mengatasi tantangan dalam sistem kehadiran siswa. Artificial Intelligence, sebagai cabang dari ilmu komputer, bertujuan untuk menciptakan sistem yang mampu melakukan tugas-tugas yang biasanya membutuhkan kecerdasan manusia, seperti pengambilan keputusan, pengenalan pola, dan pembelajaran dari data. Salah satu algoritma yang dapat digunakan adalah Convolutional Neural Network (CNN) yang telah menunjukkan performa yang sangat baik dalam tugas pengenalan wajah, terutama dalam hal ekstraksi fitur dan klasifikasi gambar. Penelitian ini bertujuan untuk mengembangkan model klasifikasi pengenalan wajah siswa pada sistem kehadiran dengan menggunakan metode Convolutional Neural Network (CNN). Penelitian ini menyimpulkan bahwa melakukan retrain pada 50 layers akhir dan 4 layers custom dapat meningkatkan accuracy untuk arsitektur MobileNetV2 mencapai 25% sedangkan ResNetV2 mencapai 26%. Selain itu, skenario dua yang menggunakan arsitektur MobileNetV2 menghasilkan model terbaik dengan nilai precision 92%, recall 91% dan accuracy 91%.

Analisis Laporan Beban Kerja Dosen Pendidikan Agama Islam (PAI) pada Bidang Penelitian dengan menggunakan Metode Clustering Pratama, Muhammad Egy; Kusrini, Kusrini; Agastya, I Made Artha
JURNAL PAI: Jurnal Kajian Pendidikan Agama Islam Vol 4 No 1 (2025)
Publisher : Prodi Pendidikan Agama Islam IAINU Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33507/pai.v4i1.3150

This study aims to classify research activities of Islamic Education lecturers based on Lecturer Workload Reports using the K-Means clustering method. The dataset includes research activities of PAI lecturers at UIN Sultan Aji Muhammad Idris Samarinda over the past three academic years. Classification was conducted by identifying publication types based on keywords and summarizing each lecturer’s activity. The results show variations in productivity, with many activities falling into the “others” category due to the lack of explicit publication descriptions. The K-Means method grouped lecturers into three clusters: Active, Moderately Active, and Less Active, based on the number of activities and total SKS. These findings can assist faculty leaders in formulating human resource development strategies and enhancing lecturers’ performance in supporting key performance indicators (KPI) and accreditation standards.

DiG-MFV: Dual-integrated Graph for Multilingual Fact Verification Agustina, Nova; Kusrini; Utami, Ema; Hidayat, Tonny
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.v9i4.6695

The proliferation of misinformation in political domains, especially across multilingual platforms, presents a major challenge to maintaining public information integrity. Existing models often fail to effectively verify claims when the evidence spans multiple languages and lacks a structured format. To address this issue, this study proposes a novel architecture called Dual-integrated Graph for Multilingual Fact Verification (DiG-MFV), which combines semantic representations from multilingual language models (i.e., mBERT, XLM-R, and LaBSE) with two graph-based components: an evidence graph and a semantic fusion graph. These components are processed through a dual-path architecture that integrates the outputs from a text encoder and a graph encoder, enabling deeper semantic alignment and cross-evidence reasoning. The PolitiFact dataset was used as the source of claims and evidence. The model was evaluated by using a data split of 70% for training, 20% for validation, and 10% for testing. The training process employed the AdamW optimizer, cross-entropy loss, and regularization techniques, including dropout and early stopping based on the F1-score. The evaluation results show that DiG-MFV with LaBSE achieved an accuracy of 85.80% and an F1-score of 85.70%, outperforming the mBERT and XLM-R variants, and proved to be more effective than the DGMFP baseline model (76.1% accuracy). The model also demonstrated stable convergence during training, indicating its robustness in cross-lingual political fact verification tasks. These findings encourage further exploration in graph-based multilingual fact verification systems.

ANALISIS RUNTUN WAKTU DALAM MEMPREDIKSI PENERIMAAN MAHASISWA BARU MENGGUNAKAN REGRESI LINEAR: Studi Kasus : UNIVERSITAS SATYA WIYATA MANDALA) Mawene, Brenstein; Kusrini; Hidayat, Tonny
Jurnal FATEKSA : Jurnal Teknologi dan Rekayasa Vol. 9 No. 1 (2024): Jurnal FATEKSA, Jurnal Teknologi dan Rekayasa
Publisher : Fakultas Teknologi dan Rekayasa

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

Perlembangan teknologi menghasilkan modernitas dibidang pendidikan serta bidang lainnya dalan kehidupan sehari-hari. Tujuan dari penelitian ini yaitu untuk dapat memprediksi jumlah penerimaan mahasiswa baru ditahun mendatang, dengan menggunakan Metode Regresi Linear dan Gradient Boosting. Prediksi jumlah mahasiswa baru diperlukan untuk membantu Universitas Satya Wiyata Mandala untuk mengetahui jumlah mahasiswa baru di tahun yang akan datang sehingga pihak Universitas dapat melakukan evaluasi untuk mempersiapkan Sarana Dan Prasarana Perkuliahan. Dalam penelitian ini peneliti berfokus pada Jumlah Mahasiswa Baru. Berdasarkan evaluasi komparatif Model Regresi Linear menunjukan performa yang buruk dengan nilai MAE 32.83, RMSE 39.74, R² -0.11 sedangkan Model Gradient Boosting menunjukan performa yang lebih baik dengan nilai MAE 13.16, RMSE 17.13, R² 0.79. secara keseluruhan model Gradient Boosting memberikan hasil prediksi yang lebih baik dibandingkan Model Regresi Linear.

TESTING OF PIJAR SEKOLAH APPLICATION WITH LOAD TESTING METHOD USING LOCUST Prastyo, Rahmat; Kusrini, Kusrini; Kusnawi, Kusnawi
Jurnal TAM (Technology Acceptance Model) Vol 16, No 1 (2025): Jurnal TAM (Technology Acceptance Model)
Publisher : Institut Bakti Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v16i1.1790

The Pijar Sekolah application is an application created to help the learning and teaching process. Pijar Sekolah has several features, such as attendance, assignments, exams, and grades. The feature that is widely accessed by users is the exam feature. Therefore, when many users use the application simultaneously, it is important to conduct performance test on the Pijar Sekolah application. This study purpose is to conduct performance test with the Load Testing method using Locust. Test was carried out with a gradually increasing number of users, the number of users as testers were 50, 100, 200, 400, and 800 with a ramp-up period of 1 second. The testing will be carried out in accordance with the examination process carried out using the Pijar Sekolah application by accessing Login, Login Status, Exam List, Start Exam, Question List, Exam Questions, and Submit Answers. The results of the test show that the performance in terms of response time is stable when testing from 50 to 400, but in RPS (Request Per Second) the average value increases with the number of 800 users getting an average value of 33.83 RPS. However, the test with the number of 400 users get an error in submitting answers. When test with the number of users 800, the response time increases and there are several errors by getting responses of 502 and 422 for 0.033%. The results of this study can be used to determine which processes need to be improved in performance. So that the Pijar Sekolah application can be used by many schools in carrying out the exam process simultaneously.

Implementation of Blockchain for Integrated Civil Service Statistical Data (Case Study: Civil Service and Human Resource Development Agency of Madiun Regency, East Java Province) Huda, Syaiful; Kusrini, Kusrini; Kusnawi, Kusnawi
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.v7i2.12170

Digital transformation in personnel data management demands a transparent, secure, and integrated system to support data-driven decision-making and enhance accountability in personnel services. An integrated information system and personnel statistical data are necessary to assist leaders in analyzing staffing needs and making more accurate and efficient data-based policies, while also strengthening the principles of good governance through improved transparency and accountability. Therefore, the Personnel and Human Resource Development Agency of the Government of Madiun Regency, East Java, requires technology capable of effectively managing personnel information by offering security, transparency, and data integrity through a decentralized mechanism. Blockchain, as a distributed ledger technology, provides an innovative solution for maintaining data integrity and increasing public trust through permanent, encrypted, and validated transaction records within a decentralized network. The implementation of blockchain in the management of personnel statistical data remains limited, despite the technology’s ability to support real-time audit trails and reliable interactive data visualization. This study proposes a framework for integrating a relational database with smart contracts on the Ethereum network, by recording the hash of statistical data in the smart contract as proof of data authenticity. Data is retrieved from the database, hashed, and the hash is stored in the smart contract to ensure its integrity, with the results visualized in interactive charts. This framework is expected to improve transparency, accountability, and trust in personnel statistical data to support more accurate and efficient strategic decision-making.

Currency Exchange Rate Prediction Using Gated Recurrent Unit (GRU) with Historical Data and Economic Factor Adhani, Muhammad Azmi; Kusrini, Kusrini
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.v7i2.12385

This study presents a currency exchange rate prediction model using a Gated Recurrent Unit (GRU) with historical price data and selected economic factors. Historical data, including Open, High, Low, and Close (OHLC) prices, were obtained from Yahoo Finance. Economic factor data, including Non-Farm Payrolls (NFP), Gross Domestic Product (GDP), Purchasing Managers Index (PMI), Retail Sales, and Durable Goods Orders, were collected from Trading View. Data preprocessing involved chronological sorting, missing value handling, feature scaling, and sequence generation. Multiple experiment cases were evaluated: historical data alone, historical data combined with all economic factors, and historical data combined with each individual factor. The GRU model achieved its best performance when incorporating historical data with Durable Goods Orders, indicating that this economic indicator provides significant predictive value, as reflected by the lowest RMSE (0.0076) and MAPE (0.0054), and the highest R² (0.9764) indicating that this economic factor provides significant predictive value. These findings highlight the importance of integrating selected economic factors into exchange rate prediction models to enhance forecasting accuracy.

Co-Authors - Saifulloh A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA ABADI Abda Abda Abdi Firdaus Abdillah, Yahya Auliya Abdul Haris Abdul Malik Zuhdi Abdul Ra'uf Alfansani Abdul Rokhim Abdullah Sukri, M Iqbal Abdullah, Mochamad Fadillah Achmad Agus Athok Miftachuddin Achmad Wazirul Hidayat Achmad Yusron Arif Adadilaga Arya Priwanegara Ade Pujianto, Ade Adhani, Muhammad Azmi Adhien Kenya Estetikha Adiatma, Biva Candra Lutfi Aditya Hastami Ruger Aflahah Apriliyani Afryansih, Nila Agam Saka Jati Agastya , I Made Artha Agastya, I Made Artha Agatha Deolika Agatstya, I Made Artha Agianto Syam Halim agung budi Agung Budi Agung Budi Prasetio Agung Budi Prasetyo Agung Jasuma Agung Jasuma Agung Nugroho Agus Harjoko AGUS PURWANTO Agus Sriyanto AGUS SULISTIYONO Agus Susilo Nugroho Agustina Rahmawati Agustina, Nova Ahmad Almas'ud ZD Ahmad Harmain Ahmad Sanusi Mashuri Ahmad Yusuf Ahmad Zarkasih Ahsan, Muhammad Salimy Aidina Ristyawan, Aidina Aiman Ayadi Aisha Alfani Aji Santoso, Bayu Aji Susanto Anom Purnomo Ajie Kusuma Wardhana Akhmad Syarif Akhmad Syarif Akrilvalerat Deainert Wierfi Alfahmi Muhammad Arif Alfatta, Hanif Ali Asgar Zainal Abidin Alva Hendi Muhammad Amanullah, Raynaldi Fatih Amborowati, Armadyahah Amir Bagja Amir Fatah Amir Fatah Sofyan Amrina Rosyada anas, hasni Anastasia Meyliana Andayani Andayani Andhika Wisnu Widyatama Andi Bahtiar Semma Andi Muhammad Irfan Andi Sunyoto Andi Suyoto Andi Syahrul Ramdana Andika, Roy Andri Rachmatullah Andri, Agung Andria Andria Andris Faesal Andriyan Dwi Putra Andriyanto, Rifki Angga Kurniawan Angga Kurniawan Anggi Muhammad Rifa'i Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Anggit Hartanto Anggraeni, Meita Dwi Aninda Astuti Anjar Anjani Putra Anwar Sadad Aolia Ikhwanudin Aolia Ikhwanudin Apriliyani, Aflahah Aprison Wolla Gole Ardana, Wildan Muhammad Ardana, Wildan Muhammmad Ardiansyah, Fachri Arfiani Nur Khusna Arham Rahim Ari Ari Suhartanto Ari Suhartanto Ari Yuana, Kumara Arief Setyanto Arief Setyanto Arief Setyanto Arief, M Rudyanto Arief, M. Rudyanto Arief, Muh. Rudyanto Arief, Muhammad Rudyanto Arief, Rudyanto Ariel Yonatan Alin Arif Budiman Arif Fajar Solikin Arif Fajar Solikin Arif Fajar Solikin Arifuddin, Danang Arik Sofan Tohir Aris Subadi Arli Aditya Parikesit Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Arnap, Adam Arnila Sandi Arridho, Muhammad Noor Artha Agastya, I Made Arwendra Adi Putra Asnawi, Muhamad Fuat Asri, Saffinah Indah Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasrini Assani, Moh. Yushi Atin Hasanah Awaludin Abid Azi, Amanda Azib Widad Zuhaily Imam Azis Wahyudi Aziz Muslim Aziz Muzani, Ma'ruf Aziz, Moh Abdul Azkar, Azkar B, Isdayani B. R., Endah Bagas Dwi Novianto Bahrun Ghozali Banu Santoso Bayu Setiaji Béjar, Rodrigo Martínez Bentar Candra P Bernadhed, Bernadhed Bimantyoso Hamdikatama Bisono, Hadi Hikmadyo Braeken, An Brenstein Mawene Buana, Yopy Tri Burhanudin Dwi Prakoso Candra Adipradana Candra Rusmana Candra, Kurnia Khoirul Choerun Asnawi Christian Budi Andrianto, Christian Budi Christin Nandari Dengen Christin Nandari Dengen Christin Nandari Dengen Christin Nandari Dengen D. Diffran Nur Cahyo da Silva, Bruno Danang Eko Prayogo Danang Wahyu Widodo Darmawan, Eko Rahmad Darno - David Agustriawan David Junggu Manggala Pasaribu dayat subekti Dede - Sandi Dede Husen Dedi Gunawan Dema Matias L Tobing Dengen, Christin Nandari Desty Yani Destyanto, Febrian Devina Ninosari Dhani Ariatmanto Dhani Ariatmanto DHANI ARIATMANTO Dias Ayu Budi Utami Dibyo Sudarsono Didik Irawan Dimaz Arno Prasetio Dina Maulina Dina Maulina Dina Maulina Dina Maulina Djatmiko, Widdi Doni Briyan Wahyudi Donny Yulianto Dwi Astuti Dwi Cahyani, Atika DWI ISKANDAR Dwi Utami Dwi Yuli Prasetyo Dwinda Etika Profesi Dwinda Etika Profesi Dzalfa Tsalsabila Rhamadiyanti Dzulhijjah, Dwi Ahmad Eddy Nurraharjo Eka Wahyu Pujiharto Eka Wahyu Sholeha Eka Yulia Sari Ekastini Ekastini Ekastini Ekastini Eko Boedijanto Eko Pramono Eko Pramono Eko Pramono Eko Pramono Eko Pramono Eko Pramono Eko Purwanto Eko Purwanto Eko Purwanto Eldas Puspitarini, Eldas Elfandry Bayunanda Elfrida Ratnawati Elik Hari Muktafin Elik Hari Muktafin Elik Hari Muktafin Eliza Staviana Elok Noor Farida, Elok Noor Elvis Pawan Elvis Pawan Ema Utami Ema Utami Ema Utami Ema Utami Emha T aufiq Luthfie Emha Taufik Luthf Emha Taufiq Luthfi EmhaTaufiq Lutfi, EmhaTaufiq Enni Lidrawati Erfan Tongalu Eri Sasmita Susanto Erwin Apriliyanto Erwin Apriliyanto Eva Oktaviani Fahma Inti Ilmawati Fahrullah, Fahrullah Fajar Dwi Insani Fajar Sodik Fajar Sudana Putra Fandli Supandi Fandli Supandi Fansuri, Muhammad Febrian Fareza Aditiyanto Nugroho Fatkhurohman, Ari Fatkhurrochman, Fatkhurrochman Fauji Maulana Ramlan, Fauji Maulana Fauzi, Moch Farid Fauzy, Marwan Noor Febrianti, Winda Febriyanti, Nada Rizki Fendy Nugroho Fendy Prasetyo Nugroho Fendy Prasetyo Nugroho Fernandito, Durand Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Firman Sriyono Firmanda Fasya Firza Septian Fitriani Fitriani Fitriyandhi, Ari fitriyanto, nur Fitrony, Fachri Ayudi Fiyas Mahananing Puri Friden E Neno Fryda Fatmayati Gifari, Okta Ihza Gigih Setyaji Guido Adolfus Suni Gutama, Deden Hardan Hadi Sucipto Hadryan Eddy Hafidz Sanjaya, Hafidz Halimi, Ahmad Hamada Zein Hamdikatama, Bimantyoso Hanafi Hanafi Hanafi Handoko Adji Pangestu Hanif Al Fatta Hari Agustiyo Hari Muktafin, Elik Haris, Ruby hartanto, david budi Hartono, Anggit Dwi Haryo, Wasis Hasan, Nur Fitrianingsih Hasan, Nurul Rahmawati Hasim, Hasim As'ari Hasirun Hasirun Hasirun, Hasirun Hasnan Afif Helmawati, Nita Henderi Henderi . Hendriansyah Santosa Hendrik Hendrik Henri Kurniawan Henri Kurniawan Herawati, Maimi Heri Abijono Heri Abijono, Heri Heri Sismoro Herlinawati, Noor Hery Nurmawan Hery Siswanto Hidayat, Fajar Maula Hidayatunnisa'i Hulvi, Alfajri I Made Adi Purwantara I Made Artha Agastya I Putu Agus Ari Mahendra I Wayan Pandu Ibnu Ickwantoro Ichsan Wasiso Ichsan Wasiso Idris Idris Ikhwan Baidlowi Sumafta Ikhwanudin, Aolia Ikmah Ikmah Ilkham, Sundari Ilkham, Sundari Ilmawati, Fahma Inti Ilmi, M. Bahril Imam Listiono Indarto, Aan Iqbal Irfan Purwanto Irma Darmayanti Irwan Oyong Jangkung Tri Nugroho Jazi Eko Istiyanto Jeki Kuswanto Jeki Kuswanto Jemi Yohanis Babys Jhoanne Fredricka Jimmy H Moedjahedy Joang Ipmawati JOKO PRAYITNO José Ramón Martínez Salio Julia Kurniasih Jumaris Jumaris, Jumaris Jumiati Jumiati Junaidi Junaidi Junaidi Sabtu Juwari Juwari Juwariyah, Siti Kaharuddin Kanafi Kanafi Kasman, Haris Saktiawan Kelik Sussolaikah Kemal Pasha Khoironi, Khoironi Khoirul Islam Khoirun Nisa Khomsatun Khomsatun Khusnawi Khusnawi Kristian, Taufik Aldri Kristina Vaher Kumara Ari Yuana Kumara Ari Yuana Kurni, Samuel Everth Andrias Kurniadi, Harso Kurniasari, Iin Kusnawi , Kusnawi Kusnawi Kusnawi Kusrini, Mashudi Kusuma Chandra Kirana La Aba Laka, Beatus Mendelson Lasimin Lewu, Retzi Y. Liana Trihardianingsih Lili Kartikawati Linda, Kumara Dewi Lisda Lisda Listyanto, Ahmad Wildan López, Alba Puelles Lukman Bachtiar Lutfan Lazuardi M rudyanto Arief M Rudyanto Arief M Vaizul Rahman M. Afriansyah M. Dedi Iskandar M. Idris Purwanto M. Ilham Arief M. Irwan Ukkas Irwan Ukkas Ukkas M. Nurul Wathani M. Rudiyanto Arief M. Rudyanto Arief M. RUDYANTO ARIEF M. Rudyanto Arief M. Rudyanto Arief M. Suyanto M. Suyanto, M. M. Syukri Mustafa M. Zainal Arifin M.Andi Rohmat Madhika, Yudha Randa Mahendra, Awanda Putra MAHMUDI Mahmudi Mahmudi Majid Rahardi Mangun, Syamsul Syahab Mansur Mansur Maradona, Maradona Mardiana Mardiana marhalim, marhalim Marissa Utami Marta Ardiyanto Martínez-Béjar, Rodrigo Marwan Noor Fauzy MASRIYAH Masruri, Nizar Haris Masud, Ibnu Mat Sudir maulana, fahrizal Mawene, Brenstein Maykel Sonobe Maykel Sonobe Megantara, Muhamad Arldi MEI PARWANTO KURNIAWAN Metha, Halifa Sekar Miftachuddin, Achmad Agus Athok Mochamad Fadillah Abdullah Moenawar Kholil Moh Er Kamim Moh Royandi Azka Moh. Badri Tamam Mohamad Firdaus, Mohamad Mohammad Diqi Mohammad Rezza Pahlevi Moningka, Nirwan Muahidin, Zumratul Muchamat Zainal Arifin Muflich, Alwie Mufti Ari Bianto Muh Saerozi Muh. Rudyanto Arief Muh. Rudyanto Arief Muhamad Fatahillah Z Muhamad Iksan, Muhamad Muhamad Kurniawan Muhamad Nanda Utama Muhamad Ryandy Ghonim Asgar Muhamad Yusuf Muhammad Agus Muljanto Muhammad Alfariz Muhammad Anwar Fauzi Muhammad Egy Pratama Muhammad Fahmi Muhammad Fajar Apriyanto Muhammad Fajrian Noor Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Koprawi Muhammad Mariko Muhammad Nafi’Uddin Muhammad Resa Arif Yudianto Muhammad Riandi Widiyantoro Muhammad Riza Eko S Muhammad Rudyanto Arief Muhammad Rudyanto Arief Muhammad Rudyanto Arief Arief Muhammad Salimy Ahsan Muhirin Muktafin, Elik Hari Mukti Ali Mulia Sulistiyono Mulyaningtyas, Widya Muqorobin Muqorobin Muqorobin Muqorobin Muqorobin Muqorobin Muqorobin, Muqorobin Musa Hendri Janto Rahanra Muslihah, Isnawati Musthofa Galih Pradana Mutiara Dwi Anggraini Mutiara Dwi Anggraini Muzakir, Muhammad MZ, Reza Rafiq Nabawi, Isnan Nabila Payapo Nadea Cipta Laksmita Nafis Sururi Nanang Prasetiyantara Nasiri, Asro Neno, Friden Elefri Ngaeni, Nurus Sarifatul Ni Nyoman Utami Januhari, Ni Nyoman Nibras Faiq Muhammad Nikko Listio Wicaksono NINIK TRI HARTANTI Noor Abdul Haris Norhikmah Norhikmah Noto Narwanto Nova Agustina Noviyanti P Noviyanti P. Nugroho, Agung Nugroho, Hanantyo Sri Nugroho, Rakhmat Prasetyo Agung Nuk Ghurroh Setyoningrum Nuk Ghurroh Setyoningrum Nulngafan, Nulngafan Nur Hamid Sutanto Nurcahyo, Azriel Christian Nurdianingsih, Neneng Nurita Evitarina Nurmalasari, Maulidya Dwi Nurwijayanti Oktafiqurahman, Andi Olajuwon, Sayyid Muh. Raziq Onde, Mitrakasih La ode Oscar Samaratungga Paiman Paiman Pamekas, Bondan Wahyu Pamoengkas, Muhamad Agoeng Pamungkas, Prima Giri Pamungkas, Sapto Paradise, Paradise Parsiyono Parsiyono Patmawati Hasan Pawit Srentiyono Perdana Putra, Muhammad Ricky Perdana, Muhammad Ricky Prabowo Budi Utomo Pradipta, Dody Prameswari, Sonia Anjani Pramono Pramono Pramudya Insan Prasetio, Agung Budi Prasetio, Dimaz Arno Prasetyo, Adi Prastowo, Wahit Desta Prastyo, Rahmat Pratama, Bayu Trisna Pratama, Muhammad Egy Profesi, Dwinda Etika Profesi, Dwinda Etika Pujianto, Ade Puri, Fiyas Mahananing Purnamasari, Resti Rachmawati Oktaria Mardiyanto Rachmawati Oktaria Mardiyanto Rahmat Saleh Sukur Rajnaparamitha Kusumastuti Rama Ashari Rama Saktriawindarta RAMADHAN, SYAIFUL Ramlan Ramlan Rantung , Tessa Rasyid, Magfirah Reflan Nuari Rendy Bagus Pratama Retantyo Wardoyo Retzi Yosia Lewu Ridlan Ahmad Riduan, Nor Ridwan Joko Purnomo Rifan Ferryawan Rifqi Hammad Rifqi Hammad Rini Wijayanti Rini, Rini Riovan Styx Roring Ripto Sudiyarno Ririn Putri Damaiyanti Riska Dwi Handayani Risna Sari Rita Wati Ritham Tuntun Riyan Abdul Aziz Rizal Khadarusman Rizal Sapta Dwi Harjo Rizkayati, Anisa Rizki Firdaus Mulya Rizki Mawan Rizky Aries Saputra Rizqi Sukma Kharisma Robi Wariyanto Abdullah Rohman, Arif Nur Rona Guines Purnasiwi Rooy Thaniket Rosadi, Ilham Aziz Rudyanto Arief S, Muhamad Rois S, Muhammad Sabri Safagi, Ardian Yuligar Sahirul Muklis Saifudin, Saifudin Saiful Bahri Saikin Saleh, Robby Febrianur Samponu, Yohakim Benedictus Samponu, Yohakim Benedictus Santosa, Hendriansyah SANTRI SANTRI Saputra, Mahmuda Saputra, Rizky Aries Saputro, Moh. Rizal Bayu Saputro, Uyock Anggoro Sarawan, Tommy Sari, Yayak Kartika Sari, Yunita Sartika Sarji, Sarji Selvy Megira, Selvy Semma, Andi Bahtiar Sentoso, Thedjo Sepriadi - Bumbungan Sepriadi Bumbungan Setianto, Yuni Ambar Setiawan, Moh. Arif Ma'ruf Setyanto, Arif Sewindu Putro Sigit Pambudi Simone Martin Marotta Siswo Utomo, Mardi Siti Delimasari Siti Fatonah Siti Hartinah Siti Maesaroh Siti Nurhayati Siti Rahayu Siti Rihastuti Siti Rokhmah Siti Solekhah Slamet Slamet Slamet Slamet Sobirin, Rahmat Sofyan Pariyasto Solikin, Arif Fajar Sri Handayani Sri Hartati Sri Lestari Rahayu Sri Wahyuningsih Sri Wulandari Sri Wulandari Sri Wulandari Sri Yanto Qodarbaskoro Sriyati, Sriyati Sry Faslia Hamka Sudarmawan Sudarmawan Sudarmawan - Sudarmawan . Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan, Sudarmawan Sudarsono, Dibyo Sudarto Sudarto Sudiana Sudiana Sugi Harsono Sugihandono, Agus Suhardi Marli Suhendri Sukasna . Sukasna, Sukasna Supraba, Laksmita Dewi Supriantara Supriantara Supriatin Supriatin Supriyati Supriyati Surya Wibowo, Sapta Hary Suyono, Ayu Adelina Swardiana, I Wayan Pandu Swastikawati, Claudia Syafutra, Arif Dwi Syah, Firdiyan Syaiful Huda Syaiful Huda Syaiful Ramadhan Syamsu Hidayat Tala, WD. Syarni Tamaulina Br Sembiring Tampubolon, Jandri Tamuntuan, Virginia Teguh Arlovin Teguh Sri Pamungkas Theofanis Orphanoudakis Thiro Kabul Y, Yudhis Tika Dedy Prastyo Tito Prabowo Toifur, Tubagus Tolla, Yefta Tonny Hidayat TONNY HIDAYAT Tri Andi Tri Anggoro Tri Haryanti Tri Nugroho, Arief Tubagus Toifur Tukan, Ewaldus Ambrosius Turmuzi Tutik Maryana Tutut Dwi Prihatin Ula, M. Izul Umam Faqih Zubaedi Umdatur Rosyidah Uyock Anggoro Saputro Veny Cahya Hardita Vera Wati Victor Saputra Ginting Victor Saputra Ginting Vinnesa Patricia Carolina Wahyu Adie Saputro Wahyu Nugraha Wahyu Nur Alimyaningtias Wahyu Pujiharto, Eka Wahyu Sindu Prasetya Wahyu Wijaya Widiyanto Wahyudi, Alfian Cahyo Walidy Rahman Hakim Wangsa, Sabda Sastra Wariyanto Abdullah, Robi Wartono Wartono Wati, Vera wati, Vera Wauran, Alfrets Septy Wicaksono, Kurniawan Widdi Djatmiko Widiyanto, Wahyu Wijaya Widyanto, Agung Wijaya, Jodi Winarnie Winarto, Hendri Wira Dimuksa Wiwi Widayani Wiwi Widayani, Wiwi Wiwit Supriyanti Wulandari, Sri Y. P., Ryan Dwi Yanuargi, Bayu Yeyen Dwi Atma Yoga Handoko Agustin Yohanes Setyo Prabowo, Yohanes Setyo Yossy Ariyanto Yovita Kinanti Kumarahadi Yuana, Kumara Ari Yudha Chirstianto F Yuli Astuti Yuliana Yuliana, Yuliana Yuliani, Enie Yulita Fatma Andriani Yulius Nahak tetik Yuni Ambar S Yunita, Risca Yunita, Selviana Yusrinnatul Jinana triadin Yustian Servanda Yusuf Fadlila Rachman Yusuf Fadlila Rachman Yusuf Wahyu Setiya Putra Yuza, Adela Zaenul Amri Zakaria Zakaria Zenal Muttaqin Zeni Muhamad Noer Zuhri, Muhammad Rafli Zul Hisyam Zulkarnain, Imam Alfath Zulkarnain, Jefri Zulkipli Zulkipli Zulpan Hadi Zumarni, Zumarni

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