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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Techno: Jurnal Penelitian Jurnal Teknologi Informasi dan Ilmu Komputer Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Journal of Information Systems Engineering and Business Intelligence Info-Teknik KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jukung (Jurnal Teknik Lingkungan) JTERA (Jurnal Teknologi Rekayasa) Jurnal Komtika (Komputasi dan Informatika) ILKOM Jurnal Ilmiah Astonjadro Jurnal Ilmu Komputer dan Bisnis JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Buletin Profesi Insinyur Jurnal Sains Komputer dan Teknologi Informasi Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) JUSTE (Journal of Science and Technology) Journal of Advanced Health Informatics Research Jurnal Jalan Jembatan INSTALL: Information System and Technology Journal Jurnal Komtika (Komputasi dan Informatika)

Yuslena Sari, Yuslena

Dosen Program Studi Teknologi Informasi Fakultas Teknik Universitas Lambung Mangkurat, Indonesia

Author-ID : 2437949

Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Optimasi Penjadwalan Mata Kuliah Menggunakan Metode Algoritma Genetika dengan Teknik Tournament Selection Sari, Yuslena; Alkaff, Muhammad; Wijaya, Eka Setya; Soraya, Syarifah; Kartikasari, Dany Primanita
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 1: Februari 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

AbstrakBagi sebuah perguruan tinggi, penjadwalan perkuliahan merupakan suatu kegiatan yang sangat penting untuk dapat terlaksananya proses belajar mengajar yang baik. Dimana dalam proses belajar mengajar dapat dilakukan oleh semua pihak yang terkait, bukan hanya bagi dosen yang mengajar, tetapi juga bagi mahasiswa yang mengambil mata kuliah. Dalam penyusunan jadwal, ada beberapa variabel yang mempengaruhi yaitu: ruangan yang tersedia, jumlah mata kuliah yang diselenggarakan, waktu yang ada dan ketersediaan dosen yang mengajar. Oleh karena itu tujuan dari penelitian ini adalah merancang suatu sistem yang dapat membuat atau menyusun jadwal perkulihaan secara teroptimasi. Metode dalam proses pembuatan jadwal perkuliahan secara otomatis pada penelitian ini menggunakan metode algoritma genetika dengan teknik seleksi turnamen. hasil pengujian sistem dapat memberikan kemudahan dan kecepatan kepada user atau Program Studi Teknologi Informasi dalam proses pembuatan atau penyusunan jadwal untuk perkuliahan, yaitu hanya diperlukan waktu sekitar 14,7 menit dibandingkan dengan proses manual yang memerlukan waktu sekitar 2 (dua) hari.AbstractFor a college, the university course timetabling is is an activity that’s very important for the implementation of good teaching and learning process. In teaching and learning process can be done by all related parties, not only for Lecturers who teach, but also for students who take the course. In the preparation of the schedule, there are several variables that affect the: the available space, the number of courses held, the time available and the availability of lecturers who teach. Therefore, the purpose of this research is to design a system that can create or arrange optimization schedule optimally. Methods in the process of making university course timetabling automatically in this study using genetic algorithm method with tournament selection.

Sistem Pakar Berbasis Android untuk Mendeteksi Jenis Perilaku ADHD pada Anak Alkaff, Muhammad; Khatimi, Husnul; Sari, Yuslena; Darmawan, Puja; Primananda, Rakhmadhany
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 2: April 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

ADHD (Attention Deficit Hyperactivity Disorder) adalah gangguan perkembangan otak pada anak yang mengakibatkan meningkatnya aktifitas motorik sehingga menyebabkan penderitanya menjadi hiperaktif, impulsif dan inatentif. Kondisi ini sering memperlihatkan tingkah laku yang tidak wajar seperti selalu bergerak tanpa tujuan, selalu gelisah, atau tidak bisa duduk dengan tenang. Gangguan ADHD terbagi menjadi tiga jenis yaitu Hiperaktif, Inatentif dan Impulsif. Salah satu cara untuk mendiagnosa jenis ADHD yang diderita oleh anak adalah dengan konseling. Tujuan dari penelitian ini adalah membangun sebuah sistem pakar yang dapat membantu memberikan kesimpulan tentang jenis penyakit ADHD yang diderita oleh anak serta tingkat keyakinan diagnosisnya. Penelitian ini menggunakan metode Dempster-Shafer untuk melakukan perhitungan terhadap nilai keyakinan suatu diagnosa. Hal ini dilakukan dengan cara membandingkan setiap nilai keyakinan dari 2 gejala awal yang terjadi pada anak untuk selanjutnya dibandingkan lagi dengan nilai keyakinan dari gejala-gejala lainnya. Sehingga mengerucut pada suatu gejala yang mengacu kepada suatu jenis dari ADHD disertai dengan nilai keyakinannya seperti layaknya diagnosa seorang pakar psikologi anak. Dalam penelitian ini dibangun sistem pakar berbasis Android dengan basis pengetahuan dari 3 orang pakar untuk memudahkan orang tua anak dalam mendiagnosa gejala-gelaja yang mungkin diderita oleh anaknya. Hasil pengujian sistem terhadap pakar dengan persentase rata-rata sebesar 93,3% dari 3 orang pakar, menunjukan bahwa sistem pakar yang telah dibuat mampu mendiagnosa jenis perilaku ADHD yang diderita oleh anak-anak disertai dengan nilai tingkat keyakinan diagnosisnya.Abstract ADHD (Attention Deficit Hyperactivity Disorder) is a brain development disorder in children resulting in increased motor activity causing the sufferer to become hyperactive, impulsive and inattentive. This condition often shows unnatural behavior like always moving aimlessly, always restless, or unable to sit quietly. ADHD disorders divided into three types, namely Hyperactive, Inattentive and Impulsif. One way to diagnose the type of ADHD suffered by children is by counseling. The purpose of this study is to build an expert sistem that can help provide conclusions about the kind of ADHD that the children had and the diagnosis level of confidence. This research uses Dempster-Shafer method to perform the calculation of confidence value of diagnosis. This is done by comparing each of the confidence values of the two early symptoms that occur in the child to furthermore compare with the belief value of the other symptoms. Therefore, conical to a symptom that refers to a type of ADHD accompanied by the value of the diagnosis beliefs, just like the diagnosis of a child psychologist. In this study, an Android-based expert system with a knowledge base from three experts is built to facilitate the child's parents in diagnosing symptoms that may be suffered by his son. The experimental test of the system with the mean percentage of 90% from 3 experts, indicates that the expert system that has been made can diagnose the type of ADHD behavior suffered by the children accompanied by the value of the diagnosis confidence level.

Perbandingan Metode Pembobotan Tf-Rf Dan Tf-Idf Dikombinasikan Dengan Weighted Tree Similarity Untuk Sistem Rekomendasi Buku Sari, Yuslena; Baskara, Andreyan RIzky; Prakoso, Puguh Budi; Royani, Noorhanida
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 6: Desember 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022935709

Unit Pusat Terpadu Perpustakaan merupakan perpustakaan pusat yang ada di Universitas Lambung Mangkurat. Perpustakaan ini mempunyai sistem pencarian buku namun sistem tersebut belum adanya fitur rekomendasi buku sehingga anggota menjadi kesulitan dalam melakukan pencarian buku yang sesuai dengan keinginan anggota. Oleh karena itu, dengan adanya rekomendasi buku atau saran buku yang lain dapat menjadi alternatif untuk membantu anggota dalam melakukan pencarian buku yang sesuai. Dalam penelitian ini menggunakan perbandingan pembobotan kata TF-IDF dan TF-RF dengan weighted tree similarity sebagai pengukur kemiripan diantara beberapa data dengan parameter tree yang sudah ditentukan dan dilakukan perbandingan perhitungan dengan menghitung tf-idf dengan tf-rf menggunakan perhitungan excel mendapatkan nilai yang berbeda antara tf-idf dengan tf-rf, pembobotan tf-idf dapat mengukur kemiripan antara dokumen dan kata kunci buku yang paling mirip dengan buku yang dianggap paling relevan. Sehingga anggota memasukan kata kunci kemudian akan menemukan kemiripan buku dari kata kunci yang dimasukan sebelumnya namun untuk pembobotan tf-rf memberikan kata kunci dari setiap kategori. Hasil perbandingan yang di dapat yaitu 96% untuk tf-idf dan 98% untuk tf-rf. Sistem ini menggunakan bahasa pemrograman python dengan web framework django. AbstractThe Central Integrated Library Unit is the central library at Lambung Mangkurat University. This library has a book search system but the system does not have a book recommendation feature so that members find it difficult to search for books that match the wishes of members. Therefore, the existence of book recommendations or other book suggestions can be an alternative to assist members in searching for suiTabel books. In this study using a comparison of the weighting of the words TF-IDF and TF-RF with weighted tree similarity as a measure of the similarity between several data and a comparison of calculations is carried out by calculating tf-idf with tf-rf using excel calculations to get different values between tf-idf and tf -rf, tf-idf weighting can measure the similarity between documents and keywords of the book that is most similar to the book that is considered the most relevant. So that members enter keywords and then find the similarity of books from the keywords entered previously but for weighting tf-rf provides keywords from each category. The comparison results obtained are 76% for tf-idf and 80% for tf-rf. This system uses the python programming language with the django web framework.

Penerapan Metode K-Means Berbasis Jarak untuk Deteksi Kendaraan Bergerak Sari, Yuslena; Baskara, Andreyan Rizky; Prakoso, Puguh Budi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 4: Agustus 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022945768

Deteksi kendaraan bergerak adalah salah satu elemen penting dalam aplikasi Intelligent Transport System (ITS). Deteksi kendaraan bergerak juga merupakan bagian dari pendeteksian benda bergerak. Metode K-Means berhasil diterapkan pada piksel cluster yang tidak diawasi untuk mendeteksi objek bergerak. Secara umum, K-Means adalah algoritma heuristik yang mempartisi kumpulan data menjadi K cluster dengan meminimalkan jumlah kuadrat jarak di setiap cluster. Dalam makalah ini, algoritma K-Means menerapkan jarak Euclidean, jarak Manhattan, jarak Canberra, jarak Chebyshev dan jarak Braycurtis. Penelitian ini bertujuan untuk membandingkan dan mengevaluasi implementasi jarak tersebut pada algoritma clustering K-Means. Perbandingan dilakukan dengan basis K-Means yang dinilai dengan berbagai parameter evaluasi yaitu MSE, PSNR, SSIM dan PCQI. Hasilnya menunjukkan bahwa jarak Manhattan memberikan nilai MSE = 1.328 , PSNR = 21.14, SSIM = 0.83 dan PCQI = 0.79 terbaik dibandingkan dengan jarak lainnya. Sedangkan untuk waktu pemrosesan data memperlihatkan bahwa jarak Braycurtis memiliki keunggulan lebih yaitu 0.3 detik. AbstractDetection moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detection moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE = 1.328 , PSNR = 21.14, SSIM = 0.83 and PCQI = 0.79 values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages

Ground Coverage Classification in UAV Image Using a Convolutional Neural Network Feature Map Maulidiya, Erika; Fatichah, Chastine; Suciati, Nanik; Sari, Yuslena
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.206-216

Background: To understand land transformation at the local level, there is a need to develop new strategies appropriate for land management policies and practices. In various geographical research, ground coverage plays an important role particularly in planning, physical geography explorations, environmental analysis, and sustainable planning. Objective: The research aimed to analyze land cover using vegetation density data collected through remote sensing. Specifically, the data assisted in land processing and land cover classification based on vegetation density. Methods: Before classification, image was preprocessed using Convolutional Neural Network (CNN) architecture's ResNet 50 and DenseNet 121 feature extraction methods. Furthermore, several algorithm were used, namely Decision Tree, Naí¯ve Bayes, K-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). Results: Classification comparison between methods showed that using CNN method obtained better results than machine learning. By using CNN architecture for feature extraction, SVM method, which adopted ResNet-50 for feature extraction, achieved an impressive accuracy of 85%. Similarly using SVM method with DenseNet121 feature extraction led to a performance of 81%. Conclusion: Based on results comparing CNN and machine learning, ResNet 50 architecture performed the best, achieving a result of 92%. Meanwhile, SVM performed better than other machine learning method, achieving an 84% accuracy rate with ResNet-50 feature extraction. XGBoost came next, with an 82% accuracy rate using the same ResNet-50 feature extraction. Finally, SVM and XGBoost produced the best results for feature extraction using DenseNet-121, with an accuracy rate of 81%. Keywords: Classification, CNN Architecture, Feature Extraction, Ground Coverage, Vegetation Density.

Identifikasi Penyakit Tanaman Ubi Kayu Berdasarkan Citra Daun Menggunakan Metode Probabilistic Neural Network (PNN) Sari, Yuslena; Alkaff, Muhammad; Arif Rahman, Muhammad
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.4605

Cassava or better known as cassava is one of the staples of rice which is popular in Indonesia. Cassava plants can flourish in almost all regions of Indonesia. However, cassava is a plant that is susceptible to plant disease, which attacks the disease resulting in a decrease in the amount of productivity of tubers produced by cassava plants. The application of identifying cassava disease based on leaf image is expected to be useful as a support for cassava farming in easily detecting cassava disease, so that it can be dealt with more quickly. This study uses the Gray Level Co-occurrence Matrix (GLCM) method as an extraction feature and the Probabilistic Neural Network (PNN) method for identification processes. Based on the results of tests on 6 types of cassava leaf images, obtained an accuracy of 83.33%.

Quality Analysis of the Digital Library Website of Universitas Lambung Mangkurat with Webqual 4.0 Method and User Experience Questionnaire (UEQ) Nurul Huda; Andreyan Rizky Baskara; Nurul Fathanah Mustamin; Yuslena Sari
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 10 No. 1 (2025)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v10i1.464

The current digital literature resources are widely provided by various libraries, especially those associated with higher education institutions. One such example of a digital library is the Lambung Mangkurat University Digital Library (Digilib ULM). Pre-evaluation results indicate that around 50% of the issues are related to the quality of the website, such as its appearance and functionality. Therefore, this research examines the quality of the ULM Digital Library to identify indicators that match user preferences and require improvement. The Webqual 4.0 method and User Experience Questionnaire are uti- lised for this purpose. The research is quantitative with a descriptive approach. Data is collected through a questionnaire, with 100 respondents sampled from the entire population, consisting of ULM students. Webqual 4.0 comprises three dimensions: usability, information quality, and service interaction. Meanwhile, UEQ consists of six aspects: attractiveness, dependability, efficiency, perspicuity, stimulation, and novelty. The research findings from Webqual 4.0 indicate a service interaction score of 2.75, information quality of 2.68, and usability of 2.45. The usability aspect falls into the low category or does not meet user expectations, while the other aspects fall into the moderate category. The UEQ results show an attractiveness score of 1.130, efficiency of 0.648, and novelty of 0.673, all scoring below the average compared to benchmarks, while other aspects score above average. Based on these results, it is evident that the Digilib ULM website does not meet user expectations and can be considered suboptimal, requiring improvement and enhancement.

Identifikasi Sentimen pada Data Teks Media Sosial Melalui Pendekatan Pembelajaran Terawasi Bahit, Muhammad; Sari, Yuslena; Baskara, Andreyan Rizky; Wijaya, Eka Setya; Yunus, Harry Pratama; Armelia, Alysa
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16032

Analisis sentimen pada data teks media sosial menjadi penting untuk memahami opini publik, sehingga penelitian ini bertujuan untuk mengidentifikasi sentimen pada data teks media sosial melalui pendekatan pembelajaran terawasi. Dataset yang digunakan terdiri dari tweet dan ulasan produk yang telah dilabeli sentimen positif maupun negatif. Proses penelitian dilakukan melalui beberapa tahapan, yaitu prapemrosesan data (Removal of Stopwords, Lemmatization and Word Stemming, Spell Correction), Ekstraksi Fitur (N-Grm, Word count dan Tf-Idf Vectorizer) serta penerapan algoritma Multinomial Naive Bayes, dan Support Vector Machine (SVM). Hasil penelitian menunjukkan bahwa penghapusan stopwords menurunkan kinerja model, sehingga tetap menggunakan stopwords. Stemming dan lemmatization juga tidak memberikan pengaruh terhadap kinerja model, sedangkan spell correction dapat meningkatkan akurasi sekitar 2% tetapi dengan trade-off waktu komputasi yang tinggi. Pada tahap ekstraksi fitur, TF-IDF menghasilkan akurasi yang lebih tinggi dibandingkan Word Count. Algoritma Multinomial Naive Bayes menghasilkan akurasi sebesar 79,73% dengan AUC-ROC sebesar 0,85. Sedangkan SVM dengan kernel linear mendapatkan hasil terbaik dengan akurasi 82% dan AUC-ROC 0,88, lebih tinggi daripada RBF kernel yang hanya mencapai akurasi 77,79% dan AUC-ROC 0,82. Hasil penelitian ini dapat disimpulkan bahwa SVM dengan kernel linear lebih sesuai untuk klasifikasi teks berdimensi tinggi.

Evaluation of lambung mangkurat university student academic portal using user experience questionnaire (UEQ) Sari, Yuslena; Novitasari, Novitasari; Pratiwi, Hani
ILKOM Jurnal Ilmiah Vol 13, No 1 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i1.787.45-50

The student academic portal is one of the Academic Information Systems at The University of Lambung Mangkurat (ULM). The ULM student academic portal can only be accessed by the ULM students. It can be used for academic guidance, managing student study plans, printing test cards, filling out questionnaires, and viewing exam results (assessment). However, since its release in 2016, there has been no publication regarding evaluations on the website-based ULM student academic portal. Evaluation is one of the stages in the Software Development Life Cycle (SDLC). This stage allows the user to assess the system. This study aims to evaluate the academic portal of the ULM students. Evaluation is carried out to determine the user's evaluation of the existing system. The evaluation method used is the User Experience Questionnaire (UEQ). With this method, users can assess the ULM Student Academic Portal from various aspects: Novelty, Stimulation, Dependability, Efficiency, Perspicuity, and Attractiveness. The results of this study indicate that the Perspicuity aspect gets a high score while Novelty gets a low score.

Hybrid CNN Feature Extraction and Machine Learning Classification for UAV-Based Vegetation Density Land Cover Mapping in Peatlands Maulidiya, Erika; Sari, Yuslena; Gani, Irham Maulani Abdul; Islami, Achmad Mujaddid; Yunita, Helda
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.518

Land cover classification from UAV imagery has become increasingly important for environmental monitoring, especially in peatland ecosystems where vegetation density is closely related to land openness, degradation risk, and fire vulnerability. This study proposes a hybrid approach that combines CNN-based feature extraction with machine learning classification for vegetation density-based land cover classification. A total of 3,000 UAV images collected from Block 1 of the Liang Anggang Protected Forest, Banjarbaru, were used in this study and categorized into three classes: bare, moderate, and high vegetation density. The images were preprocessed through cropping, resizing, and labeling prior to feature extraction. ResNet-50 and DenseNet-121 were employed as feature extractors, while ten machine learning classifiers were evaluated, namely CalibratedClassifierCV, SVC, NuSVC, LogisticRegression, PassiveAggressiveClassifier, SGDClassifier, LinearSVC, XGBClassifier, Perceptron, and LGBMClassifier. The results show that ResNet-50 generally outperformed DenseNet-121 as a feature extractor. The best and most balanced performance was achieved by the ResNet-50 + SVC combination, which obtained 84% accuracy, 84% F1-score, 91% precision, 77% recall, and a computation time of 8.58 minutes. Although CalibratedClassifierCV achieved the same accuracy, it required substantially longer processing time. These findings indicate that classification performance is influenced not only by the classifier used, but also by the compatibility between feature representation and classification mechanism. Therefore, the combination of ResNet-50 and SVC is recommended for UAV-based vegetation density land cover classification in peatlands.

Co-Authors Adla, Musyfia Ahmad Rivaldy Ahmad Shalludin Akhmad Rivaldy Andreyan Rizky Baskara Ariefah Khairina Islahati ARIF RAHMAN, MUHAMMAD Armelia, Alysa Bambang Lareno, Bambang Bayu Nugraha Chastine Fatichah Dany Primanita Kartikasari Darmawan, Puja Dewi Astria Faroek Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Endi Gunawan Fardian, Muhammad Reza Ferry Pratama Finki Dona Marleny Firmansyah, Muhammad Ilham Gani, Irham Maulani Abdul Gusti Nizar Syafi'i Halimah Halimah Husnul Khatimi Irfan Prasetia Islami, Achmad Mujaddid Johan Wahyudi Johan Wahyudi Mambang Maulidia Arafah Maulidiya, Erika Maya Amalia Meila Izzana, Meila Muhammad Adetya Ashari Muhammad Alkaff Muhammad Andri Firdaus Muhammad Arif Rahman Muhammad Baballe Ahmad Muhammad Bahit Muhammad Faidhorrahman Muhammad Syauqi Al Fath Mutia Maulida Muti`a Maulida Nanik Suciati Noor Razikin Novitasari Novitasari Novitasari Novitasari Nurul Fathanah Mustamin Nurul Huda Nurul Qamaria Prakoso, Puguh Budi Prastya, Septyan Eka Pratiwi, Hani Puguh Budi Prakoso Puguh Budi Prakoso Purwono, Purwono Rahman, Muhammad Arif Rakhmadhany Primananda, Rakhmadhany Rama Aria Megantara Ricardus A P, Ricardus A Ricardus Anggi Pramunendar Rico Silas Dwi Kasanda Royani, Noorhanida Rudy Ansari Rusiana, Novi Subhan Panji Cipta Syarifah Soraya Wenny Puspita Wijaya, Eka Setya Yunita, Helda Yunus, Harry Pratama

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