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Dewi Fatmarani Surianto

Universitas Negeri Makassar

Author-ID : 4084971

Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Enhancing K-Means Clustering for Journal Articles using TF-IDF and LDA Feature Extraction Surianto, Dewi Fatmarani; Surianto, Dewi Fatmawati
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5547

Clustering is a fundamental technique in data analysis, particularly in unsupervised learning, to group data with similar characteristics. However, the effectiveness of the K-Means algorithm in text clustering heavily depends on proper feature extraction. This study proposes an enhanced feature extraction approach by integrating Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) to improve clustering performance on journal article datasets. The dataset consists of 427 journal article abstracts collected from Google Scholar. The preprocessing steps include tokenization, stopword removal, and TF-IDF vectorization, followed by topic extraction using LDA, which serves as input features for the K-Means clustering algorithm. The optimal number of clusters is determined using the Silhouette Score, with the best result obtained at k=9, achieving a score of 0.6806. The practical implications of this study include improved accuracy in academic document clustering, with applications in journal recommendation systems, digital library indexing, and research trend analysis. The results demonstrate that the combination of TF-IDF and LDA produces more informative text representations, significantly enhancing clustering quality. This study contributes to text mining and data science by proposing a systematic preprocessing framework for document clustering. Future research could explore its application to full-text articles, hierarchical clustering, or deep learning-based models to further improve clustering performance.

PCA and t-SNE Implementation for KNN Hypertension Classification Visualization Cahyana Resky, Andi Aulia; Lapendy, Jessica Crisfin; Nur Risal, Andi Akram; Surianto, Dewi Fatmarani; Wahid, Abdul
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6208

Hypertension is a condition that, if allowed to increase, can significantly injure internal organs due to high blood pressure. The objective of this study is to use the K-Nearest Neighbor (KNN) algorithm along with PCA and t-SNE to accurately identify four categories of Hypertension, Normal, Hypertension, Stage 1 Hypertension, and Stage 2 Hypertension. After establishing the scope, a dataset consisting of 7,794 samples was sourced from Labuang Baji Regional General Hospital, Makassar, and contained age, weight, and systolic and diastolic blood pressure parameters. The class distribution is Normal (36.3%), Hypertension (43.12%), Stage 1 Hypertension (8.29%), and Stage 2 Hypertension (12.31%). Experimental results show that the KNN base model achieved 99% accuracy, KNN with PCA reached 100%, and KNN with t-SNE attained 99%. Cross-validation was used to evaluate model generalization, yielding accuracies of 91%, 94%, and 91%, respectively. These findings suggest that KNN, particularly when integrated with t-SNE, is highly effective in visualizing and classifying non-linear data structures. Furthermore, this study demonstrates that incorporating dimensionality reduction techniques enhances the interpretability of classified hypertension data, which is crucial for informed decision-making by mental health committees.

Word2Vec Approaches in Classifying Schizophrenia Through Speech Pattern Azis, Putri Alysia; Andi, Tenriola; Surianto, Dewi Fatmarani; Budiarti, Nur Azizah Eka; Risal, Andi Akram Nur; Zulhajji, Zulhajji
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6323

Schizophrenia is a chronic brain disorder characterized by symptoms such as delusions, hallucinations, and disorganized speech, posing significant challenges for accurate diagnosis. This research investigates an innovative Natural Language Processing (NLP) framework for classifying the speech patterns of schizophrenia patients using Word2Vec, with the aim of determining whether there are significant differences between the two features. The dataset comprises speech transcriptions from 121 schizophrenia patients and 121 non-schizophrenia participants collected through structured interviews. This study compares two Word2Vec architectures, Continuous Bag-of-Words (CBOW) and Skip-Gram (SG), to determine their effectiveness in classifying schizophrenia speech patterns. The results indicate that the SG architecture, with hyperparameter tuning, produces more detailed word representations, particularly for low-frequency words. This approach yields more accurate classification results, achieving an F1-score of 93.81%. These results emphasize the effectiveness of the framework in handling structured and abstract linguistic patterns. By utilizing the advantages of both static and contextual embedding, this approach offers significant potential for clinical applications, providing a reliable tool for improving schizophrenia diagnosis through automated speech analysis.

Digital Literacy Training and Introduction to Artificial Intelligence Ethics to Realize Digital Literate Teachers: Pelatihan Literasi Digital dan Pengenalan Etika Kecerdasan Buatan untuk Mewujudkan Guru Melek Digital Fakhri, M. Miftach; Isma, Andika; Hidayat M., Wahyu; Ahmar, Ansari Saleh; Surianto, Dewi Fatmarani
Mattawang: Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2024)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.mattawang2603

The problems identified are the level of digital literacy that needs to be improved and the need for an understanding of AI ethics among teachers, which can hinder the education process in the digital era. The purpose of this service is to improve the digital competence and understanding of AI ethics of teachers, so that they can be more effective in teaching and guiding students towards responsible use of technology. Through an Action Research approach, the teachers were directly involved in the process of improving and developing their skills in digital literacy and scientific article writing. The methods used in this training involved lectures, discussions, and practical exercises using various interactive media such as Quizziz, videos, infographics, and educational games. The training was attended by 35 teachers on 4 March 2024. The results of the training showed a significant increase in participants' knowledge and skills related to digital literacy and AI ethics. In addition, there was a positive shift in attitudes towards the use of digital technology. The implication of the training results is the improvement of education quality in the participating schools, as more digitally literate teachers can be more effective in integrating technology in the learning process. In addition, these teachers can act as agents of change in society, helping to build a community of smart and responsible digital technology users. Abstrak Masalah yang diidentifikasi adalah tingkat literasi digital yang perlu ditingkatkan dan perlunya pemahaman etika AI di kalangan guru, yang dapat menghambat proses pendidikan di era digital. Tujuan Pengabdian ini adalah untuk meningkatkan kompetensi digital dan pemahaman etika AI para guru, sehingga mereka dapat lebih efektif dalam mengajar dan membimbing siswa menuju penggunaan teknologi yang bertanggung jawab. Melalui pendekatan Action Research, para guru terlibat secara langsung dalam proses peningkatan dan pengembangan kemampuan mereka dalam literasi digital dan penulisan artikel ilmiah. Metode yang digunakan dalam pelatihan ini melibatkan ceramah, diskusi, dan latihan praktis dengan menggunakan berbagai media interaktif seperti Quizziz, video, infografis, dan permainan edukatif. Pelatihan ini diikuti oleh 35 guru pada tanggal 4 Maret 2024. Hasil dari pelatihan menunjukkan peningkatan signifikan dalam pengetahuan dan keterampilan peserta terkait literasi digital dan etika AI. Selain itu, terdapat pergeseran sikap yang positif terhadap penggunaan teknologi digital. Implikasi dari hasil pelatihan ini adalah peningkatan kualitas pendidikan di sekolah-sekolah peserta, karena guru yang lebih melek digital dapat lebih efektif dalam mengintegrasikan teknologi dalam proses pembelajaran. Selain itu, guru-guru ini dapat berperan sebagai agen perubahan dalam masyarakat, membantu membangun komunitas pengguna teknologi digital yang cerdas dan bertanggung jawab.

Pelatihan Peningkatan Kompetensi Guru dalam Pemanfaatan Canva sebagai Media Pembelajaran Digital Surianto, Dewi Fatmarani; S, Nurul Fadhillah; Dzulfadhilah, Fitriani; Wardah, Siti Syarifah Wafiqah; Baso, Fadhlirrahman
Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Volume 2 Issue No. 2: January 2025
Publisher : Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/kreativa.v2i2.20259

Transformasi digital dalam dunia pendidikan menuntut guru untuk mengembangkan keterampilan dalam menciptakan media pembelajaran yang menarik, berkesan, dan relevan bagi generasi digital. Canva sebagai platform desain yang berani dan gratis serta mudah digunakan, menawarkan peluang besar untuk mendukung pengembangan media terbuka visual dan interaktif di berbagai jenjang pendidikan. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kompetensi guru di UPT SPF SMPN 50 Makassar dalam memanfaatkan Canva sebagai perangkat pembelajaran yang optimal. Pelatihan diselenggarakan dalam bentuk workshop aplikasi dengan pendekatan inklusif. Peserta dibekali materi konseptual dan teknis dalam Canva, dilanjutkan dengan sesi pelatihan praktik untuk membuat berbagai desain seperti poster, infografis, dan materi ajar presentasi. Penilaian dilakukan dengan pre-test dan post-test yang diberikan kepada 35 guru peserta. Hasil penelitian menunjukkan peningkatan skor rata-rata yang signifikan dari 53,43 pada pre-test menjadi 81,57 pada post-test. Temuan ini menunjukkan bahwa pelatihan terstruktur dapat meningkatkan keterampilan dan pengetahuan guru secara signifikan. Selain peningkatan kognitif, peserta juga menunjukkan antusiasme yang tinggi dan menyatakan keinginan untuk mengintegrasikan Canva ke dalam proses pembelajaran. Disarankan agar pelatihan ini diulang di sekolah lain sebagai bagian dari strategi untuk meningkatkan literasi digital para pendidik.

Pelatihan Perancangan Modul Digital Berbasis Canva untuk Meningkatkan Kreativitas Mahasiswa dalam Mendukung Kegiatan Mentoring Shabrina Syntha Dewi; Dwi Rezky Anadari Sulaiman; Ninik Rahayu Ashadi; Dewi Fatmarani Surianto; Syahrul
Jurnal Pengabdian Masyarakat Vol. 3 No. 1 (2025): Jurnal Pengabdian Masyarakat (AbdiMas)
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/abdimas.v3i1.8473

Pengabdian kepada masyarakat ini dilaksanakan sebagai upaya untuk meningkatkan kapasitas dan kreativitas mahasiswa dalam mendesain media pembelajaran digital yang mendukung kegiatan mentoring mahasiswa. Kegiatan ini berfokus pada pelatihan perancangan modul digital berbasis Canva, yang dirancang untuk membekali mahasiswa dengan pemahaman teoretis serta keterampilan praktis dalam mengembangkan modul yang menarik, komunikatif, dan efektif. Sasaran kegiatan ini adalah mahasiswa Jurusan Teknik Informatika dan Komputer, Fakultas Teknik, Universitas Negeri Makassar yang terlibat sebagai mentor dalam program pembinaan mahasiswa baru. Melalui pendekatan partisipatif, mahasiswa didorong untuk merancang modul digital yang berfungsi sebagai panduan bagi mentee dalam mengenal nilai-nilai akademik, etika, serta budaya kehidupan kampus. Hasil efektivitas pelatihan diukur melalui pre-test dan post-test, yang menunjukkan peningkatan skor rata-rata dari 62 menjadi 87. Sebanyak 12 kelompok berhasil menghasilkan modul digital dengan konten relevan dan visual yang menarik, dinilai “baik” hingga “sangat baik”. Selain itu, hasil evaluasi juga menunjukkan bahwa 94% peserta merasa pelatihan ini sangat bermanfaat. Kegiatan ini memberikan kontribusi terhadap peningkatan kualitas mentoring dan mendorong mahasiswa menjadi agen pembelajar yang inovatif melalui pemanfaatan teknologi digital.

Classification of Livin' by Mandiri Customer Satisfaction Using MLP with BM25 and TF-IDF Feature Weighting Mardiah, Aina; Dillah, Salsa; Surianto, Dewi Fatmarani; Fadilah, Nur; Zain, Satria Gunawan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2248

The increasing use of mobile banking applications such as Livin' by Mandiri requires an analysis of customer satisfaction based on user reviews. This study classifies customer satisfaction levels using the Multi-Layer Perceptron (MLP) algorithm with two feature extraction methods, namely BM25 and TF-IDF. A total of 1,143 reviews were collected from the Google Play Store and App Store. Three test scenarios were applied: (1) comparison of feature extraction methods, (2) application of Synthetic Minority Over-Sampling Technique (SMOTE), and (3) application of Synonym Replacement-based Easy Data Augmentation (EDA) technique. The evaluation results show that the combination of BM25 and data augmentation produces the highest performance, with 97% accuracy and 98% precision, recall, and F1-score, respectively. BM25 proved to be more effective in understanding the context of reviews, while data augmentation improved the quality of representation, especially for minority classes such as neutral sentiment. These findings make a significant contribution to the improvement of Livin' by Mandiri digital services and serve as a reference for the development of review-based satisfaction classification systems in the digital banking sector.

Evaluasi Pengukuran Semantik Sinonim KBBI Menggunakan Pendekatan Word Embedding Muhammad Rafli Aditya H.; Muhammad Ilham; Dewi Fatmarani Surianto; Abdul Muis Mappalotteng
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 2: Mei 2025
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i2.17117

Kamus Besar Bahasa Indonesia (KBBI) is a primary resource for data in research on determining word-meaning similarity in Indonesian. This study investigates the effectiveness of word embedding methods and the term frequency–inverse document frequency (TF-IDF) weighting technique in assessing the semantic similarity of synonym pairs. The objective is to measure the similarity of synonym word pairs listed in KBBI by applying cosine similarity, leveraging TF-IDF weighting, various word embedding models, and latent semantic analysis (LSA). The methodology involved data collection, followed by a text preprocessing stage consisting of case folding, stopword removal, stemming, and tokenization. The processed data were transformed into vector representations using word embedding models, including Word2Vec, fastText, GloVe, and sentence-bidirectional encoder representations from transformers (S-BERT), and TF-IDF. LSA was employed for dimensionality reduction of the vectors before similarity testing using cosine similarity, with final evaluation of the results. The findings revealed that fastText significantly improved the similarity scores between synonym pairs, achieving an average similarity score of 0.901 for 30 synonym pairs. Evaluation results indicated an accuracy of 0.88, a recall of 1.00, a precision of 0.81, and an F1 score of 0.90. These results suggest that fastText is more effective in enhancing the accuracy of synonym meaning similarity measurements. Future research is encouraged to expand the corpus and further explore the use of word embedding for semantic similarity tasks. This study contributes to the natural language processing advancement and provides a potential foundation for more accurate language-based applications that assess word meaning similarity in KBBI.

Sentiment Analysis of Local Sunscreen Skintific, Somethinc, and Avoskin with Naive Bayes and SVM Clarisha, Windi; Fani, A. Astri Merilsa; Surianto, Dewi Fatmarani; Fadilah, Nur
Jambura Journal of Electrical and Electronics Engineering Vol 7, No 2 (2025): Juli - Desember 2025
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v7i2.30257

Indonesia’s beauty industry, particularly local sunscreen products, has experienced rapid growth alongside increasing public awareness of the importance of skin protection against ultraviolet rays. Consumer reviews on digital platforms have become a vital source of information to understand user perceptions and preferences. This study aims to analyze sentiment toward three local sunscreen brands—Skintific, Somethinc, and Avoskin—by comparing two text classification methods: Naïve Bayes and Support Vector Machine (SVM). To address the imbalance in the number of positive and negative sentiment data, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The results show that applying SMOTE to Naïve Bayes significantly improved the accuracy from 81% to 93%, along with notable enhancements in precision, recall, and F1-score. Conversely, applying SMOTE to SVM slightly reduced accuracy from 92% to 91%, although the performance for positive sentiment remained stable. These findings indicate that the combination of Naïve Bayes and SMOTE is more effective in handling imbalanced data for sentiment analysis of beauty products. The implications of this study can serve as a basis for decision-making in product development and marketing strategies within the beauty industry, particularly in aligning with consumer sentiment.Industri kecantikan Indonesia, khususnya produk sunscreen lokal, menunjukkan pertumbuhan pesat seiring meningkatnya kesadaran masyarakat akan pentingnya perlindungan kulit dari sinar ultraviolet. Ulasan konsumen di platform digital menjadi sumber informasi penting untuk memahami persepsi dan preferensi pengguna. Penelitian ini bertujuan untuk menganalisis sentimen terhadap tiga merek sunscreen lokal—Skintific, Somethinc, dan Avoskin—dengan membandingkan dua metode klasifikasi teks, yaitu Naïve Bayes dan Support Vector Machine (SVM). Untuk mengatasi ketidakseimbangan jumlah data antara sentimen positif dan negatif, digunakan teknik Synthetic Minority Over-sampling Technique (SMOTE). Hasil menunjukkan bahwa penerapan SMOTE pada Naïve Bayes meningkatkan akurasi dari 81% menjadi 93%, serta memperbaiki precision, recall, dan F1-score secara signifikan. Sebaliknya, penerapan SMOTE pada SVM justru sedikit menurunkan akurasi dari 92% menjadi 91%, meskipun performa untuk kategori sentimen positif tetap stabil. Temuan ini menunjukkan bahwa kombinasi Naïve Bayes dengan SMOTE lebih efektif dalam menangani data tidak seimbang untuk analisis sentimen produk kecantikan. Implikasi dari penelitian ini dapat digunakan oleh pelaku industri kecantikan sebagai dasar pengambilan keputusan dalam pengembangan dan pemasaran produk berbasis persepsi konsumen.

Analisis Metode Fuzzy C-Means (FCM) dalam Menentukan Performansi Kinerja Karyawan Lapendy, Jessica Crisfin; Resky, Andi Aulia Cahyana; Surianto, Dewi Fatmarani
TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol Vol 11, No 1 (2025): TELKA
Publisher : Jurusan Teknik Elektro UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/telka.v11n1.29-41

Tercapainya sasaran perusahaan di setiap tahunnya dipengaruhi oleh kualitas sumber daya manusia atau karyawan yang dimiliki oleh perusahaan terkait. Kualitas ini berkaitan dengan kompetensi yang dimilikinya, baik itu dalam aspek skill maupun knowledge. Untuk melihat kualitas dari karyawan yang ada di perusahaan terkait, perlu dilakukan penilaian performansi kinerja karyawan. Oleh karena itu, penelitian ini bertujuan untuk menilai performansi kinerja karyawan yang ada di salah satu perusahaan swasta Makassar yang sebelumnya melakukan penilaian dengan melihat dari segi keuangan dan program kerja yang berhasil dipenuhi oleh setiap divisi. Perlunya penilaian kinerja adalah agar dapat membantu Human Resource Development (HRD) ataupun manajer dalam mengambil keputusan yang berkaitan dengan prestasi yang telah dicapai oleh setiap karyawan. Dalam penilaian kinerja karyawan ini digunakan metode Fuzzy C-Means yang merupakan teknik pengklasteran data yang ditentukan oleh derajat keanggotaan. Setelah tahapan-tahapan metode penelitian dilakukan dengan menggunakan Matlab, dihasilkan 3 klaster yang mengelompokkan kualitas karyawan menjadi karyawan dengan performansi kinerja baik sebanyak 11 karyawan, kinerja sedang sebanyak 11 karyawan, dan kinerja buruk sebanyak 6 karyawan. Hasil pengklasteran tersebut didasarkan pada hasil pengolahan data dari 5 kriteria penilaian, yaitu kejujuran, kedisiplinan, kepemimpinan, kehadiran, dan kualitas kerja. Di antara kelima kriteria tersebut, terdapat 2 kriteria yang cukup mempengaruhi hasil penilaian performansi kinerja karyawan di perusahaan terkait, yaitu kepemimpinan dan kualitas kerja. Adapun hasil evaluasi jumlah klaster dilakukan menggunakan metode silhouette coefficient dengan nilai tertinggi didapatkan yakni 0,5653 pada jumlah klaster adalah 3. The achievement of company goals each year is influenced by the quality of human resources or employees owned by the company. This quality is related to the competence they have, both in terms of skills and knowledge. To see the quality of employees in related companies, it is necessary to assess employee performance. Therefore, this study aims to assess the performance of existing employees in one of Makassar's private companies that previously conducted an assessment by looking at the financial aspects and work programs that were successfully fulfilled by each division. The need for performance appraisal is to be able to help Human Resource Development (HRD) or managers in making decisions related to the achievements that have been achieved by each employee. In this employee performance assessment, the Fuzzy C-Means method is used, which is a data clustering technique determined by the degree of membership. After the stages of the research method were carried out using Matlab, 3 clusters were produced which grouped the quality of employees into employees with good performance as many as 11 employees, moderate performance as many as 11 employees, and poor performance as many as 6 employees. The clustering results are based on the results of data processing from 5 assessment criteria, namely honesty, discipline, leadership, attendance, and work quality. Among the five criteria, there are 2 criteria that are quite influential in the results of employee performance assessment in related companies, namely leadership and work quality. The results of evaluating the number of clusters are carried out using the silhouette coefficient method with the highest value obtained, namely 0.5653 at the number of clusters is 3.

Co-Authors A. Arianugerah Ilham A. Arianugerah Ilham AA Sudharmawan, AA Abdal, Nurul Mukhlisah Abdul Muis Mappalotteng Abdul Wahid Abdul Wahid Adiba, Fathiah Adiba, Fhatiah Adnas, Diny Anggriani Agusyana, Nurrahmah Ahmar, Ansari Saleh Ahnaf Riyandirga Ariyansyah Putra Helmy Aindri Muthmainnah Aindri Rizky Muthmainnah Ainun Zahra Adistia Akbar, Mohammad Arsan Akmal Hidayat Akmal Hidayat Akmal, Muhammad syafruddin Amanda Putri Lestari Amiruddin Amri, Muh. Aidil Amukune, Stephen Ana Sulistiana Alwi Andi Akram Nur Risal Andi Akram Nur Risal Andi Baso Kaswar Andi Baso Kaswar Andi Dio Nurul Awalia Andi Tenri Ola Rivai Andi, Tenriola Andika Isma Anwar Wahid Arifin, Afrisal Arifiyanti, Fitria Asis Nojeng Asri Ismail Athiyyah Anandira Awalia, Andi Dio Nurul Awaliah, Widiarti Ayu Hasnining Ayu Safitri Ayu Safitri Azis, Putri Alysia Azzah Ulima Rahma B., Muhammad Fajar Bahar, Muhammad Mahdinul Bakri, Muh. Fajrin Baso, Fadhlirrahman Cahyana Resky, Andi Aulia Clarisha, Windi Dary Mochamad Rifqie Della Fadhilatunisa Dhaffa Mulya Rahman Dilla, Salsa Dillah, Salsa Dwi Rezky Anadari Sulaiman Edi Suhardi Rahman Edy, Marwan Ramdhany Erva Irianti Fadhlirrahman Baso FADIAH, NUR Fani, A. Astri Merilsa Fathahillah Fhatiah Adiba Fhatiah Adiba Firdaus Firdaus Firdaus Fitriani Dzulfadhilah Fitriyanty Dwi Lestary Fizar Syafaat Furqan Ali Yusuf Haekal Febriansyah Ramadhan Hanum Zalsabilah Idham Hardy M, Galang Hartini Ramli Hidayat M., Wahyu Ilyas, Sitti Nurhidayah Indanasufya Indanasufya Inez Sri Wahyuningsi Manguling Irwandi isma, Nur Ivan Fadillah Akram Iwan Suhardi Jariah S.Intam, Rezki Nurul Jariah, Rezki Nurul Jasruddin Jessicha Pamput Jessicha Putrianingsih Pamput Jumadi Mabe Parenreng Jumadil Ahmad Safi’i Jusniar . Khaerunnisa Nur Fatimah Syahnur Khalil Mubaraq Darwing Kurnia Prima Putra Lapendy, Jessica Crisfin Lutfiah Tri Syahyaningsih M. Miftach Fakhri M. Syahid Nur Wahid Makmur, Haerunnisya Mappangara, Surianto MARDIAH, AINA Marwan Ramdhany Edy Meisaraswaty Arsyad Muh. Juharman Muhammad agung Muhammad Akil Musi Muhammad Fadhil Hani Muhammad Fadhullah Muhammad Fahrul Rosi Ishaq Muhammad Fajar B Muhammad Fardan MUHAMMAD ILHAM Muhammad Nur Yusri Muhammad Rafli Aditya H. Muhammad Rakib Muhammad Try Dharsana Muharni Muharni Muhtadi, Nashiruddin Sahal Muliadi Musda Rida Mulia Muthmainnah, Aindri Muthmainnah, Aindri Rizky Mutmainnah R Nafil Rizqullah Rajab Nafil Rizqullah Rajab Nashiruddin Sahal Muhtadi Nasrullah, Asmaul Husnah Natsir, Nasrah Ninik Rahayu Ashadi NIRMALA, PUTRI Nur Fadiah NUR FADILAH NUR FADILLAH Nur Risal, Andi Akram Nurhidayat Nurhidayat Nurjannah Nurjannah Nurjayanti Nurjayanti Nurrahmah Agusnaya Nurul Fadhilah Nurul Fadhillah S Nurul Fadhillah S Nurul Mukhlisah Abdal Pamput, Jessicha Pamput, Jessicha Putrianingsih Parenreng, Jumadi M. Putri Nanda Sari Putri Nirmala Putri Zhachilia Susanto Raden Mohamad Herdian Bhakti Rahman, Dhaffa Mulya Rahmaniar Rahmat Kurniawan Ramadhan, Haekal Febriansyah Resky, Andi Aulia Cahyana Rezki Angriani Pratiwi Kadir Rezki Nurul Jariah Rezky Anisar, Muh. Alief Ridwan Daud Mahande Ridwan Daud Mahande Risaldi, Muhammad Rosidah Rosidah Rusli, Risvan S, Aprilianti Nirmala S, Muh. Rizal S, Nurul Fadhillah S.Intam, Rezki Nurul Jariah Salsa Dillah Sari Wulandari Sasmita Sasmita Setialaksana, Wirawan - Shabrina Syntha Dewi Shasa Inayah Vega Shasa Inayah Vega Siti Syarifah Wafiqah Wardah Soeharto Soeharto Sri Riski Wulandari Sudarmanto Jayanegara Surianto, Dewi Fatmawati Syahrul Syahrul Syahrul Syahyaningsih, Lutfiah Tri Syam, Abd. Azis Syamsurijal Syamsurijal, Syamsurijal Tenriola, Andi Udin Sidik Sidin Wahid, M Syahid Nur Wahid, M. Syahid Nur Wahid, Yokogeri Abdullah Wahyu Hidayat M Wahyu Hidayat M Wahyudi Warda Wahyuni Wardani, Ayu Tri WULANDARI Wulandari Wulandari Zsolt Lavicza Zulfikar, Muh Ihsan Zulhajji, Zulhajji

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