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Kusrini

Universitas Amikom Yogyakarta

Author-ID : 302666

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A Systematic Literature Review Of Mental Health Diagnostic Using K-Nearest Neighbour - Whale Optimization Algorithm Septian, Firza; Kusrini , Kusrini; Hidayat, Tonny
SISKOMTI: Jurnal Sistem Informasi Komputer dan Teknologi Informasi Vol. 5 No. 1 (2023): Februari 2023
Publisher : Universitas Lembah Dempo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54342/ptc0pb11

People including unborn infants are negatively impacted by a number of things, such as noise. Noise and the others aspect could affect somebody mental health. Mental health as natural problem might be easier detected using metaheuristic algorithm, K-Nearest Neighbour - Whale Optimization Algorithm (KNN-WOA) is one of them. A variety of trustworthy sources, including IEEE and Scopus, are used to collect the data. In the action research technique, practical applications work as a "Laboratory" for testing hypotheses on synthesized products. There are three fundamental ideas in regard to using WOA for medical purposes. KNN will be used according to the plan for medical diagnostics. WOA, a population-based approach, uses a randomized collectivist humpback whale sample to enhance potential solutions as feature selection while KNN as the main algorithm. Only three of the 94 journals collected met the set standards.

FOREST FIRE LOCATION AND TIME RECOGNITION IN SOCIAL MEDIA TEXT USING XLM-ROBERTA Hafidz Sanjaya; Kusrini Kusrini; Kumara Ari Yuana; Arief Setyanto; I Made Artha Agastya; Simone Martin Marotta; José Ramón Martínez Salio
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6194

Forest fires have become a serious global threat, significantly impacting ecosystems, communities, and economies. Although remote sensing technology shows potential, limitations such as time delays, limited sensor coverage, and low resolution reduce its effectiveness for real-time forest fire detection. Additionally, social media can serve as a multimodal sensor, presenting multilingual text data with rapid and global coverage. However, it may encounter challenges in obtaining location and time information on forest fires due to limitations in datasets and model generalization. This study aims to develop a multilingual named entity recognition (NER) model to identify location and time entities of forest fires in social media texts such as tweets. Utilizing a transfer learning approach with the XLM-RoBERTa architecture, fine-tuning was performed using the general-purpose Nergrit corpus dataset containing 19 entities, which were relabeled into 3 main entities to detect location, date, and time entities from tweets. This approach significantly improves the model's ability to generalize to disaster domains across multiple languages and noisy social media texts. With a fine-tuning accuracy of 98.58% and a maximum validation accuracy of 96.50%, the model offers a novel capability for disaster management agencies to detect forest fires in a scalable, globally inclusive manner, enhancing disaster response and mitigation efforts.

Peramalan Jumlah Penjualan Menggunakan Jaringan Syaraf Tiruan Backpropagation Pada Perusahaan Air Minum Dalam Kemasan Nur Fitrianingsih Hasan; Kusrini Kusrini; Hanif Al Fatta
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 2 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i2.1607

The inhibition of the production and distribution of bottled water has become a serious problem in the survival of the community and the company, so there is a need for a solution to this problem both short-term and long-term solutions. One of the things that can be done by the company or management is that the right amount of production and distribution is by forecasting sales. Sales forecasting is the process of predicting which products will be sold in the future made based on data that has ever happened. This paper aims to determine the level of accuracy of the use of Backpropagation ANN in estimating the sales of bottled water.The ANN architecture used is 12-10-1 with the MSE value of 0,00043743 and the MAPE value of 6.88%. Forecasting sales results of Robong Holo 600ml brand using Backpropagation ANN for 2019 is 2271 pcs in January, 2019 pcs in February, 1358 pcs in March, 917 pcs in April, 462 pcs in May, 324 pcs in June, 739 pcs in July, 370 pcs in August, 367 pcs in September, 1073 pcs in October, 765 pcs in November and 1388 pcs in December. Keywords— AMDK,Backpropagation,Jaringan Syaraf Tiruan,Penjualan,Peramalan

Analisis Cluster Data Interkomparasi Anak Timbangan dengan Algoritma Self Organizing Maps Arif Fajar Solikin; Kusrini Kusrini; Ferry Wahyu Wibowo
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 2 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i2.3698

Intercomparison was conducted to determine the ability and the performance of the laboratory. Intercomparison results are usually expressed in the range of En ratio values (En ?|1|) which express the equivalence of one laboratory with other laboratories. If the laboratory is declared unequal, then it needs to identify the source of the problem by itself. To make it easier, it can be done by Clustering which is one of the data mining techniques. Clustering is done by applying a self organizing map algorithm on the KNIME (Konstanz Information Miner) analytic tools. Several experiments were carried out with different layer size and data normalization status from one experiment to another experiment. The results were analyzed through pseudo F statistical test and icdrate test. The largest pseudo F statistic value was obtained from the 8th experiment (setting the layer size 2x2 without data normalization) with a pseudo F statistic value of 167.53 for 1kg artifacts and a Pseudo F statistic value of 104.86 for 200 g artifacts where the optimum number of clusters are 4. The smallest icdrate value was obtained from the 5th experiment (setting the 2x3 layer size without data normalization) with an icdrate value of 0.0713 for 1kg artifacts and icdrate value of 0.2889 for 200g artifacts with the best number of clusters being 6. From 12 laboratories can be grouped into 6 groups where each group has the same identification. There are groups 1, 3 and 6 have 1 member, while groups 2, 4 and 5 have 3 members.

Perbandingan Kinerja Support Vector Machine dalam Klasifikasi Obesitas dengan Pendekatan Kernel Linear dan Radial Basis Function S, Muhamad Rois; Kusrini, Kusrini
Device Vol 15 No 1 (2025): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v15i1.8903

Obesitas adalah kondisi medis yang ditandai dengan penumpukan lemak tubuh yang berlebihan hingga dapat menimbulkan risiko berbagai penyakit kronis, seperti diabetes, penyakit jantung, dan kanker. Di Indonesia, dalam kurun waktu 10 tahun terjadi peningkatan obesitas yang signifikan, dari 10,5% pada tahun 2007 menjadi 21,8% pada tahun 2018. Secara global, pada tahun 2030 diperkirakan 1 dari 5 wanita dan 1 dari 7 pria akan hidup dengan obesitas, yang setara dengan lebih dari 1 miliar orang di seluruh dunia. Untuk mengatasi permasalahan ini, teknologi kecerdasan buatan digunakan dalam prediksi obesitas guna mengidentifikasi faktor risiko secara lebih akurat. Penelitian ini membandingkan performa klasifikasi obesitas menggunakan algoritma Support Vector Machine (SVM) dengan dua pendekatan berbeda: SVM dengan kernel Linear tanpa hyperparameter tuning dan SVM dengan kernel Radial Basis Function (RBF) dengan hyperparameter tuning. Dataset yang digunakan bersumber dari Universitas Sinop yang tersedia di Kaggle, dengan total 1610 data. Hasil eksperimen menunjukkan bahwa model SVM Linear tanpa hyperparameter tuning memiliki akurasi 72% pada data uji, sedangkan model SVM-RBF dengan hyperparameter tuning C dan gamma mencapai akurasi 83%. Perbedaan performa ini menunjukkan bahwa pemilihan kernel dan penerapan hyperparameter tuning dapat meningkatkan akurasi serta keandalan prediksi obesitas.

Klasifikasi Penyakit Diabetes Melitus Menggunakan Metode Stacking Ensemble Herlinawati, Noor; Kusrini, Kusrini
Device Vol 15 No 1 (2025): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v15i1.9331

Pendeteksian dini terhadap risiko diabetes merupakan tantangan penting dalam dunia medis modern. Penelitian ini bertujuan untuk meningkatkan akurasi klasifikasi pasien diabetes menggunakan metode stacking ensemble, yang menggabungkan tiga model pembelajaran mesin: K-Nearest Neighbors (KNN), Random Forest, dan XGBoost. Dataset yang digunakan adalah Pima Indians Diabetes, yang terdiri dari 768 data pasien. Setelah dilakukan preprocessing, balancing, dan feature selection, model stacking dibangun dengan Logistic Regression sebagai meta-learner. Hasil evaluasi menunjukkan bahwa stacking ensemble mencapai akurasi 77.27% dan ROC AUC 82.91%. Metode ini menunjukkan potensi besar dalam pengembangan sistem diagnosis otomatis yang lebih andal untuk penyakit diabetes..

Comparative performance of LSTM and DNN in sentiment analysis Tampubolon, Jandri; Kusrini, Kusrini
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i4.403

Understanding public sentiment toward online transportation services through social media analysis has gained increasing importance. This study provides a comparison between the effectiveness of Deep Neural Network (DNN), and Long Short-Term Memory (LSTM) models in analyzing user sentiment toward online transportation services in Indonesia using Twitter data. The dataset consists of 10,000 tweets related to Gojek, Grab, Maxim, and InDrive, collected from January to December 2023. Data preprocessing includes noise removal, case folding, tokenization, and stemming. Sentiment labeling was conducted using IndoBERTweet and manually validated. Using K-Fold Cross-Validation, both DNN and LSTM models were trained, and assessed using performance metrics such as accuration, precision, recall, and F1-score, training time, and Mean Absolute Error (MAE). The LSTM model demonstrated superior performances with accuration of 82,15%, precision of 82,21%, recall of 82,15%, specificity of 90,74%, F1-score of 82,10%, and MAE of 23,15%, compared to the DNN model which achieved an accuracy of 81,22%, precision of 81,20%, recall of 81,22%, specificity of 90,18%, F1-score of 81,12%, and MAE of 24,46%. However, DNN outperformed LSTM in training time efficiency (50,435 seconds vs. 148,765 seconds). LSTM shows significant advantages in understanding context and word relationships in sentiment analysis, while DNN offers better computational efficiency. The findings of this study can be utilized by online transportation services providers to improve service quality based on user feedback from social media.

Deteksi Stres dan Depresi Unggahan Media Sosial dengan Machine Learning Tolla, Yefta; Kusrini
JURNAL FASILKOM Vol. 15 No. 1 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i1.9067

Penelitian ini menyajikan sebuah model pembelajaran mesin yang mengintegrasikan Support Vector Machine (SVM), Natural Language Processing (NLP), dan SHapley Additive exPlanations (SHAP) untuk mendeteksi stres dan depresi dari unggahan di Twitter. Dataset ini terdiri dari postingan yang dikumpulkan selama satu tahun terakhir, yang dilabeli ke dalam kategori stres, depresi, dan netral. Teknik NLP-tokenization, penghilangan stopword, stemming, dan Term Frequency-Inverse Document Frequency (TF-IDF)-diterapkan untuk membersihkan teks dan mengekstrak fitur-fitur yang relevan. Langkah-langkah ini memastikan hanya kata-kata yang bermakna yang memengaruhi klasifikasi, sehingga meningkatkan kinerja prediktif. SVM berfungsi sebagai pengklasifikasi utama karena keefektifannya dalam data teks berdimensi tinggi. SHAP meningkatkan kemampuan interpretasi dengan menyoroti fitur-fitur berpengaruh yang mendorong prediksi, sehingga meningkatkan transparansi. Hasil penelitian menunjukkan bahwa kata-kata seperti “stres”, “lelah”, dan “cemas” sangat mengindikasikan stres, sedangkan “depresi”, “kecewa”, dan “menyerah” menandakan kecenderungan depresi. Frasa seperti “lelah secara mental” semakin mendukung proses identifikasi. Model ini mencapai akurasi 96,44%, dengan presisi, recall, dan skor F1 yang tinggi (rata-rata 96%). Matriks kebingungan mengkonfirmasi keefektifan model dalam mengklasifikasikan tiga kategori dengan kesalahan minimal. Integrasi NLP, SVM, dan SHAP tidak hanya meningkatkan akurasi klasifikasi, tetapi juga meningkatkan penjelasan, menjadikan model ini alat yang menjanjikan untuk deteksi dini dan pemahaman kondisi kesehatan mental melalui analisis media sosial.

Implementation of topsis algorithm for evaluating lecturer performance Fatkhurrochman, Fatkhurrochman; Kusrini, Kusrini; Alfatta, Hanif
International Journal Artificial Intelligent and Informatics Vol 1, No 1 (2018)
Publisher : Research and Social Study Institute (ReSSI)

Higher education is an education unit that able to carry out academic, professional and / or vocational programs. Lecturers are professional and scientific educators with the main task of transforming and developing, and disseminating science, technology and art through education, research and community service. The performance evaluation applies seven criterias, namely: attendance, teaching, research, dedication, loyalty, cooperation and responsibility. The problems are; the lecturersâ€™ performance evaluation is not optimal yet because there is no specific method to implement it. Therefore, it is necessary to build a decision support system by applying the Technique For Others Reference by Similarity to Ideal Solution (TOPSIS). This system will later help us in determining the best lecturer in accordance with the regulation. The TOPSIS method uses the principle that the chosen alternative must have the longest (farthest) distance from the negative ideal solution from geometric point of view using the relative proximity of an alternative. The alternative means the lecturersâ€™ performance with predetermined criterias. This method produces a lecturers rankings based on the best performance on numerical scores and sorted by the greatest preference scores. The particular study used 5 lecturers as alternative to be tested. They were Lecturers 1, Lecturers 2, Lecturers 3, Lecturers 4, and Lecturers 5. The results showed that Lecturer 1 was the best lecturer with the biggest preference score of 0.612.

Determination of receipt of UPZ assistance using the SAW method Listyanto, Ahmad Wildan; Kusrini, Kusrini; Sudarmawan, Sudarmawan
International Journal Artificial Intelligent and Informatics Vol 1, No 1 (2018)
Publisher : Research and Social Study Institute (ReSSI)

Zakat has a very strategic position in giving the impact of welfare and improving the community's economy if the collection and distribution are managed in trustworthy, transparent and professional. An institution on zakat management is National Zakat Agency (BAZNAS), which an institution manages the zakat management nationally, and non-structural government institution that is independent and responsible to President through the Minister of Religion. BAZNAS is located in capital city and assisted by zakat collection unit (UPZ). Along this time, they conduct manually in determining the person who will receive the assistance, but it often occur errors in its implementation. Therefore, a decision support system is needed to assist UPZ in determining the person who will receive the assistance. The decision support system was designed using SAW (Simple Additive Weighting) method, then the administrators obtain an alternative data in the form of lists of students who receive the UPZ assistance. The SAW algorithm is an algorithms for decision making. The SAW algorithm is also known as an algorithm with additive weighting method. The method requires normalization process of decision matrix (x) into a scale and can be compared to the entire available alternative ratings. The study showed that SAW can be applied to determine the acceptance of UPZ assistance by the calculation results of 16. The result of calculation and recommendation of decision support system for person who receive the UPZ acceptance, have the same data on output, namely Astin Dwi Wulan.

Co-Authors - Saifulloh A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA Aan Indarto 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 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 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 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 Amanah Azzahra Amanullah, Raynaldi Fatih Amborowati, Armadyahah Amir Bagja Amir Fatah Amir Fatah Sofyan Amrina Rosyada An Braeken Anastasia Meyliana Andayani Andayani Andhika Wisnu Widyatama Andi Bahtiar Semma 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 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 Fitriyandhi Ari Suhartanto Ari Yuana, Kumara Arief Setyanto Arief Setyanto Arief Tri Nugroho 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 Arwendra Adi Putra Asnawi, Muhamad Fuat Asri, Saffinah Indah Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasrini Assani, Moh. Yushi Atika Dwi Cahyani Atin Hasanah Attar Raihan Nazhif 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 Bentar Candra P Bernadhed, Bernadhed Bhanu Sri Nugraha Bimantyoso Hamdikatama Bisono, Hadi Hikmadyo Braeken, An Brenstein Mawene Bruno da Silva 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 Darno - David Agustriawan David Budi Hartanto David Junggu Manggala Pasaribu dayat subekti Dede - Sandi Dede Husen Dedi Gunawan Dema Matias L Tobing Dengen, Christin Nandari Denny Saryanto 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 ISKANDAR Dwi Utami Dwi Yuli Prasetyo Dwinda Etika Profesi Dwinda Etika Profesi Dzalfa Tsalsabila Rhamadiyanti Dzulhijjah, Dwi Ahmad Eddy Nurraharjo 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 Eko Rahmad Darmawan 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 Emha T aufiq Luthfie Emha Taufik Luthf Emha Taufiq Luthfi EmhaTaufiq Lutfi, EmhaTaufiq Endah Herminarimawati Enni Lidrawati Erfan Tongalu Eri Sasmita Susanto Erwin Apriliyanto Erwin Apriliyanto Eva Oktaviani Fahma Inti Ilmawati Fahri Alviansyah Fahrullah, Fahrullah Fajar Dwi Insani Fajar Sodik Fajar Sudana Putra Fandli Supandi Fandli Supandi Fareza Aditiyanto Nugroho Fathoni Mahardika 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 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 Hartono, Anggit Dwi Haryo, Wasis Hasan, Nur Fitrianingsih Hasan, Nurul Rahmawati Hasim, Hasim As'ari Hasirun Hasirun Hasnan Afif Hasni Anas Henderi Henderi . Hendriansyah Santosa Hendrik Hendrik Henri Kurniawan Henri Kurniawan Herawati, Maimi Heri Abijono Heri Abijono, Heri Heri Sismoro Herlambang Priantoro 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 Ilham Aziz Rosadi Ilkham, Sundari Ilkham, Sundari Ilmawati, Fahma Inti Imam Listiono Indra Listiawan 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 Junaidi Junaidi Junaidi Sabtu Juwari Juwari Juwariyah, Siti Kaharuddin Kanafi Kanafi Kasman, Haris Saktiawan Kelik Sussolaikah Kemal Pasha Khoironi, Khoironi Khoirul Islam Khoirun Nisa Kholil, Moenawar Khomsatun Khomsatun Khusnawi Khusnawi 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 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. BAHRIL ILMI 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 Mardi Utomo Mardiana Mardiana marhalim, marhalim Marissa Utami Marselina Endah Hiswati Marta Ardiyanto Martínez-Béjar, Rodrigo Marwan Noor Fauzy MASRIYAH Masruri, Nizar Haris Masud, Ibnu Mat Sudir maulana, fahrizal Mauludil Asri M. Cane Mawene, Brenstein Maykel Sonobe Maykel Sonobe Megantara, Muhamad Arldi MEI PARWANTO KURNIAWAN Melwin Syafrizal Metha, Halifa Sekar Miftachuddin, Achmad Agus Athok Mochamad Fadillah Abdullah 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 Azmi Adhani Muhammad Egy Pratama Muhammad Fahmi Muhammad Fajar Apriyanto Muhammad Fajrian Noor Muhammad Febrian Fansuri Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Koprawi Muhammad Mariko Muhammad Nafi’Uddin Muhammad Resa Arif Yudianto Muhammad Riandi Widiyantoro Muhammad Ricky Perdana Muhammad Riza Eko S Muhammad Rudyanto Arief Muhammad Rudyanto Arief Muhammad Rudyanto Arief Arief Muhammad Salimy Ahsan Muhammad Suyanto Muhirin Muktafin, Elik Hari Mukti Adi Azhari Mukti Ali Mulia Sulistiyono 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 Nibras Faiq Muhammad Nikko Listio Wicaksono NINIK TRI HARTANTI Nita Helmawati Nona Adinda Ariana Ariana 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 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 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 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 Risca Yunita Riska Dwi Handayani Risna Sari Rita Wati Ritham Tuntun Riyan Abdul Aziz Rizal Khadarusman Rizal Sapta Dwi Harjo Rizaldi, Rizaldi Rizkayati, Anisa Rizki Firdaus Mulya Rizki Mawan Rizky Aries Saputra Rizqi Sukma Kharisma Robi Wariyanto Abdullah Rodrigo Martínez Béjar Rohman, Arif Nur Rona Guines Purnasiwi Rooy Thaniket 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 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 Sofyan Pariyasto Solikin, Arif Fajar Sri Handayani Sri Hartati Sri Lestari Rahayu Sri Wahyuningsih 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 Suharto Suharto Suhendri Sukasna . Sukasna, Sukasna Supraba, Laksmita Dewi Supriantara Supriantara Supriatin Supriatin Supriyati Supriyati Suputa Suputa 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 Taufik Aldri Kristian 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 Utami Ema 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 Widya Mulyaningtyas 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 Yopy Tri Buana 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, 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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