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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika Teknosains: Media Informasi Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer SIGMA: Jurnal Pendidikan Matematika AlphaMath: Journal of Mathematics Education JOIV : International Journal on Informatics Visualization Al Ishlah Jurnal Pendidikan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPM (Jurnal Pemberdayaan Masyarakat) Faktor Exacta Jurnal Penjaminan Mutu JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) JTAM (Jurnal Teori dan Aplikasi Matematika) International Journal of Pedagogy and Teacher Education CARADDE: Jurnal Pengabdian Kepada Masyarakat JURNAL PENDIDIKAN TAMBUSAI Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURNAL MathEdu (Mathematic Education Journal) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) GERVASI: Jurnal Pengabdian kepada Masyarakat TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Sistem Informasi dan Informatika (SIMIKA) Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Unri Conference Series: Community Engagement Jurnal Dedikasi International Journal of Electronics and Communications Systems Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Online Learning in Educational Research Seminar Nasional Pengabdian Kepada Masyarakat Catimore: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Edutic : Pendidikan dan Informatika Internet of Things and Artificial Intelligence Journal Jurnal Penjaminan Mutu Indonesian Journal of Fundamental Sciences IPTEK: Jurnal Hasil Pengabdian kepada Masyarakat Teknovokasi : Jurnal Pengabdian Masyarakat Vokatek : Jurnal Pengabdian Masyarakat Information Technology Education Journal Pengabdian Jurnal Abdimas Journal of Embedded Systems, Security and Intelligent Systems Ininnawa: Jurnal Pengabdian Masyarakat Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Jurnal Ilmu Pengetahuan dan Teknologi Bagi Masyarakat Jurnal MediaTIK Mekongga: Jurnal Pengabdian Masyarakat Media Elektrik Malaqbiq : Jurnal Pengabdian kepada Masyarakat. Sasambo: Jurnal Abdimas (Journal of Community Service) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Journal of Emerging Research in Computer Science and Artificial Intelligence
Andi Baso Kaswar Andi Baso Kaswar
Universitas Negeri Makassar
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Visual Impaired Assistance for Object and Distance Detection Using Convolutional Neural Networks Parenreng, Jumadi Mabe; Andi Baso Kaswar; Ibnu Fikrie Syahputra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5491

Abstract

Vision is a very valuable gift from God; Most aspects of human needs in the body are dominated by vision. Based on data from the World Health Organization (WHO) there are around 180 million people in the world experiencing visual impairment, while the prevalence of blindness in Indonesia reaches 3 million people (1.5% of Indonesia's population), so we designed a system in the form of a prototype that could detect objects around the user and convey data in the form of sound to the user. This research discusses the application of a machine learning model using the Convolutional Neural Network method to detect objects optimally. The objects that have been collected will be trained on machine learning and produce a model to be embedded in the system's main machine, namely the Raspberry PI 4B. The training of the machine learning model was carried out several times by changing the compositions of several layers until a model with optimal accuracy was obtained; however, the size of the resulting model was quite large, so the researchers carried out SSDMobileNetV2 transfer learning to obtain the optimal model. The optimal model was obtained with a model precision of 92% and a model size of 18 MB. Object detection tests carried out under 3 test conditions resulted in an average object detection accuracy of 84.3%, and distance detection tests carried out under 10 conditions resulted in an average distance detection error of 2.1 cm. The results show that the system was accurate and effective.
Identifying Rice Plant Damage Due to Pest Attacks Using Convolutional Neural Networks Tenriola, Andi; Azis, Putri Alysia; Kaswar, Andi Baso; Adiba, Fhatiah; Andayani, Dyah Darma
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.v9i1.6125

Abstract

Rice (Oryza Sativa) is an important crop for meeting global food needs; however, one of the main challenges in its cultivation is the attack of stem borer pests, which can cause significant damage. This study aims to identify the damage caused by these pest attacks using Convolutional Neural Networks (CNN) methods. We developed and trained several CNN architectures, including the proposed architecture, MobileNet, and EfficientNetB0, to detect pest attacks on rice. The dataset used consists of 700 images per class taken directly from the field, where the images depict rice plants that have been peeled or opened to inspect for the presence of pests, specifically stem borer pests. To enhance the quality and diversity of the dataset, we applied a rigorous selection process, ensuring that only high-quality images were used. Additionally, augmentation techniques such as rotation were employed to expand the dataset to 2000 images per class. Labeling was carried out carefully to ensure that each image accurately reflected the condition of the pest attack. The results of the study indicate that the proposed CNN model can identify damage with high accuracy, thereby contributing to efforts to increase rice production through early detection of pest attacks using computer vision technology.
Pelatihan Pembuatan Video Animasi dengan Aplikasi Animiz untuk Mendukung Pembelajaran Kreatif di Sekolah Dasar Nurjannah, Nurjannah; Kaswar, Andi Baso; Andayani, Dyah Darma; Dirawan, Gufran Darma; Risal, Andi Akram Nur
TEKNOVOKASI : Jurnal Pengabdian Masyarakat Volume 3: Issue 2 (May 2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/teknovokasi.v3i2.8327

Abstract

The training on creating animated videos using the Animiz application at SDN 2 Sinjai aimed to enhance teachers' competencies in developing creative and interactive digital learning media. This activity was conducted on December 16, 2024, through four stages: planning, implementation, observation, and evaluation, using a hands-on training approach combined with intensive mentoring. The results showed that teachers were able to understand the material well, operate the Animiz application independently, and produce animated videos relevant to primary school learning themes. The evaluation indicated that most participants responded very positively regarding the clarity of the material, the relevance of the training, and the ease of using the application. Despite some technical challenges and time constraints, the training was considered effective in improving teachers' motivation and skills in utilizing digital technology. This initiative serves as a strategic first step in promoting digital transformation in primary schools and should be followed up with advanced training and continuous mentoring.
Penerapan Data Science sebagai Upaya Meningkatkan Kompetensi Mahasiswa di Era Industri Modern Rivai, Andi Tenri Ola; Risal, Andi Akram Nur; Edy, Marwan Ramdhany; Adiba, Fhatiah; Kaswar, Andi Baso
TEKNOVOKASI : Jurnal Pengabdian Masyarakat Volume 3: Issue 2 (May 2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/teknovokasi.v3i2.8450

Abstract

Data Science adalah bidang multidisipliner yang menggabungkan statistik, analitik data, dan machine learning untuk mengolah data besar menjadi informasi yang bermakna berbasis Data. Program Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk meningkatkan pemahaman mahasiswa terhadap konsep dan penerapan Data Science melalui workshop berbasis praktik. Kegiatan dilaksanakan dalam bentuk workshop satu hari yang mencakup materi eksplorasi data, visualisasi, dan penerapan algoritma sederhana menggunakan Python dan Google Colab. Peserta yang terdiri dari mahasiswa program studi Teknologi Informasi Universitas Bosowa menunjukkan peningkatan pemahaman terkait Data Science dan keberhasilan dalam mengerjakan mini-proyek berbasis data. Keberhasilan kegiatan ini didukung oleh antusiasme peserta, fasilitas yang memadai, serta pendekatan pembelajaran yang aplikatif dan interaktif. Namun, terdapat beberapa hambatan seperti keterbatasan waktu, variasi tingkat kemampuan peserta, dan kendala koneksi internet saat pelatihan. Secara keseluruhan, pelatihan ini memberikan kontribusi nyata terhadap peningkatan literasi data dan keterampilan digital mahasiswa serta relevan untuk diterapkan secara berkelanjutan di institusi pendidikan tinggi.
EKSPLORASI HUBUNGAN ANTARA LITERASI MATEMATIKA DAN KEMAMPUAN PROBLEM SOLVING PADA SISWA DI ERA DIGITAL Nurjannah, Nurjannah; Kaswar, Andi Baso
SIGMA: JURNAL PENDIDIKAN MATEMATIKA Vol. 17 No. 1: Juni 2025
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/sigma.v17i1.17060

Abstract

Di era digital, literasi matematika dan kemampuan pemecahan masalah menjadi keterampilan penting bagi siswa untuk beradaptasi dalam masyarakat berbasis teknologi. Literasi matematika melibatkan pemahaman konsep dan penerapannya dalam situasi nyata, termasuk pemecahan masalah. Penelitian ini menggunakan metode kuantitatif dengan pendekatan korelasional untuk mengeksplorasi hubungan antara literasi matematika dan kemampuan pemecahan masalah pada siswa di era digital. Sampel terdiri dari 35 siswa kelas XI di SMA Negeri 5 Sinjai yang dipilih dengan teknik cluster sampling, dengan pengumpulan data melalui tes literasi matematika yang diadaptasi dari PISA dan tes pemecahan masalah yang dirancang khusus untuk konteks digital. Uji normalitas dan linearitas memastikan data memenuhi syarat untuk dilanjutkan ke analisis korelasi. Hasil analisis menunjukkan adanya korelasi yang sangat tinggi antara literasi matematika dan kemampuan pemecahan masalah dengan koefisien korelasi Pearson sebesar 0,997 yang mengindikasikan bahwa hampir seluruh variansi kemampuan pemecahan masalah dapat dijelaskan oleh literasi matematika. Temuan ini menegaskan bahwa literasi matematika tidak hanya penting untuk prestasi akademik, tetapi juga mendukung keterampilan kognitif tingkat tinggi yang diperlukan dalam konteks digital. Implikasi dari penelitian ini adalah bahwa pendidikan literasi matematika harus diperkuat dalam kurikulum sekolah dengan pendekatan berbasis proyek dan aplikasi digital, guna mempersiapkan siswa menghadapi tantangan kompleks di masa depan yang semakin berbasis teknologi.
Identifikasi Kualitas Fisik Shuttlecocks Menggunakan Teknologi Pengolahan Citra Digital dengan Jaringan Syaraf Tiruan Farid, Muhammad Miftah; Sam, Muh Hadal Ali; Kaswar, Andi Baso; Andayani, Dyah Darma
TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol Vol 11, No 2 (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.v11n2.167-180

Abstract

Shuttlecock merupakan bola yang dipakai dalam permainan bulutangkis, terbuat dari bulu angsa dan bulu ayam berwarna putih. Bola ini memiliki panjang sekitar 64-66 mm, diameter 25 mm, dan berat berkisar antara 4,74 hingga 5,67 gram. Sebelum digunakan pada pertandingan, shuttlecock dipilih berdasarkan kualitas pada bulu dan kepala shuttlecock. Namun, proses pemilihan tersebut masih dilakukan secara manual oleh penyelenggara pertandingan bulutangkis. Jumlah shuttlecock yang banyak memerlukan tenaga kerja yang besar, sehingga seringkali muncul kesalahan manusia akibat kelelahan dan tekanan waktu yang tinggi. Untuk itu, pemanfaatan teknologi menggunakan citra digital dirasa sangat perlu digunakan untuk mengidentifikasi kualitas fisik pada shuttlecock. Oleh karena itu, dalam penelitian ini diusulkan sistem identifikasi kualitas fisik pada shuttlecock menggunakan teknologi pengolahan citra digital dengan metode jaringan syaraf tiruan. Penelitian ini melalui beberapa tahap diantaranya tahap akuisisi citra, preprocessing, segmentasi, morfologi, ekstraksi fitur serta klasifikasi. Penelitian ini juga, mencoba beberapa skenario pelatihan dan pengujian untuk menemukan kombinasi fitur terbaik. Kombinasi warna RGB (channel blue), tekstur (fitur energy), dan bentuk (fitur area dan perimeter) memberikan hasil optimal dalam klasifikasi citra shuttlecock. Hasil penelitian menunjukkan bahwa dengan melatih sistem menggunakan 140 citra latih, diperoleh akurasi tertinggi sebesar 100% dengan waktu komputasi 0,136 detik per citra. Selanjutnya, hasil pengujian pada 60 citra uji mencapai tingkat akurasi sebesar 100% dengan waktu komputasi 0,123 detik per citra. Hasil tersebut menunjukkan bahwa metode yang diusulkan dapat mengidentifikasi kualitas shuttlecock dengan akurat dan waktu komputasi yang cepat. Shuttlecock is a ball used in badminton made of goose feathers and white chicken feathers, has a length of 64-66 mm and has a diameter of 25 mm with a weight of 4,74 – 5,67 grams. Before being used in a match, the shuttlecock is selected based on the quality of the feathers and shuttlecock head. However, the selection process is still done manually by the badminton match organizer. The large number of shuttlecocks requires a large amount of labor, so it is not uncommon for human error to occur due to fatigue and high time pressure. For this reason, the utilization of technology using digital images is deemed very necessary to be used to identify the physical quality of the shuttlecock. Therefore, this research aims to develop a physical quality identification system on shuttlecocks using digital image processing technology with artificial neural network method. This research goes through several stages including image acquisition, preprocessing, segmentation, morphology, feature extraction and classification. This research also tries several training and testing scenarios to find the best combination of features. The combination of RGB color (channel blue), texture (energy feature), and shape (area and perimeter features) provides optimal results in shuttlecock image classification. The results showed that by training the system using 140 training images, the highest accuracy of 100% was obtained with a time of 100%.
Deteksi Tingkat Kematangan Buah Mangga Berdasarkan Fitur Warna Menggunakan Pengolahan Citra Digital Aksa, Muhammad; Ranggareksa, Andi; Aras, Muh Riski Farukhi; Kaswar, Andi Baso; Andayani, Dyah Darma; Intam, Reski Nurul Jariah S
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.10578

Abstract

The classification of mango Golek ripeness is crucial for ensuring product quality and its economic value, especially in industrial applications. Manual and subjective ripeness determination often leads to inconsistency, resulting in decreased harvest quality and market value. This study aims to classify the ripeness of Golek mangoes into three categories: unripe, semi-ripe, and ripe, using digital image processing based on HSV and LAB color features combined with the K-Nearest Neighbor (KNN) algorithm. The dataset consists of 300 images, split into 80% training data and 20% testing data. The proposed method includes image acquisition, preprocessing, segmentation, morphological operations, feature extraction, and classification. The results show that the combination of HSV and LAB color features is effective in distinguishing ripeness levels, with an accuracy of 81.67% on the testing data and an average precision, recall, and F1-Score of 82%. Consistent color patterns in the unripe and semi-ripe categories enhance accuracy, while fluctuations in color intensity in the ripe category pose challenges. This approach shows potential for implementation in automatic sorting systems in industry.
Klasifikasi Tingkat Kualitas Terung dengan Algoritma Backpropagation Berbasis Fitur Warna dan Tekstur R, Muh Raflyawan; Arifky, Reza; Tenriajeng, Andi Afrah; Kaswar, Andi Baso; Andayani, Dyah Darma; Azis, Putri Alysia
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.10655

Abstract

Manual quality assessment of eggplant is often inconsistent, takes a long time, and is prone to errors due to worker fatigue. This research aims to develop an automated system based on digital image processing to assess eggplant quality efficiently and accurately. The stages begin with image capture using a mobile phone device designed to ensure stable lighting and uniform background. The acquired image is then processed through segmentation using the Otsu thresholding method as well as morphological operations to separate the main object from the background. Color and texture features are extracted through Gray-Level Co-occurrence Matrix (GLCM) analysis and RGB, HSV, and LAB color spaces. Training data amounting to 90% of the total dataset was used to train an artificial neural network-based classification model with a backpropagation algorithm, while the remaining 10% was used for testing. Experimental results showed that the combination of LAB, RGB, HSV, and texture features gave the best results, with a testing accuracy of 86%, recall of 85%, and precision of 92%. This model is very effective in detecting poor quality eggplants with 100% accuracy. This system can support the application of technology in the horticultural sector.
KLASIFIKASI BUAH KELAPA BERDASARKAN WARNA KULIT UNTUK MENGIDENTIFIKASI KETEBALAN DAGING PADA BERBAGAI TINGKAT KEMATANGAN MENGGUNAKAN JARINGAN SARAF TIRUAN (JST) Ahmad Khan, Sardar Faroq; Dina Salam, Fitria Nur; Aulia, Magfirah; Kaswar, Andi Baso; Jariah S.Intam, Rezki Nurul; Wahid, Abdul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 4: Agustus 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.124

Abstract

Kelapa (Cocos nucifera L.) adalah bagian dari suku aren-arenan atau Arecaceae dari marga cocos. Kelapa adalah tanaman yang sering ditemui dan kaya akan manfaat bagi umat manusia, mulai dari daun, batang pohon dan buah kelapanya. Pedagang tradisional dapat menggunakan suara yang dihasilkan dari ketukan tangan untuk mengetahui tingkat kematangan buah kelapa. Namun, dengan cara manual ini ada kemungkinan kesalahan dalam proses pengklasifikasianya. Maka dari itu, pada penelitian ini diusulkan judul Klasifikasi Buah Kelapa Berdasarkan Ketebalan Dagingnya Pada Berbagai Tingkat Kematangan Menggunakan Jaringan Saraf Tiruan (JST). Metode penelitian untuk pengklasifikasian terdiri atas 7 tahap yaitu tahap akuisisi citra, preprocessing, segmentasi, operasi morfologi, ekstraksi fitur, klasifikasi, dan evaluasi. Harapan dari metode yang digunakan untuk memberikan solusi khusunya kepada para petani dan pedagang dalam mengklasifikasi atau menyortir buah kelapa untuk mengetahui kualitas dagingnya dengan bantuan teknologi pengolahan citra digital. Dengan menggunakan 300 dataset citra yang dibagi menjadi 240 citra latih dan 60 citra uji, yang menghasilkan tingkat akurasi 97,91% pada citra latih dan 96,66% pada citra uji. Dengan waktu komputasi 0,31 detik per citra pada citra latih dan 0,21 detik per citra pada citra uji. Sehingga hasil dari pembahasan pada penelitian ini, pengklasifikasian buah kelapa menggunakan metode Jaringan Saraf Tiruan (JST) dengan memanfaatkan fitur warna dapat berjalan dan menghasilkan hasil yang dapat digolongkan baik.Abstract Coconut (Cocos nucifera L.) is part of the Arecaceae tribe of the cocos genus. Coconut is a plant that is often encountered and is rich in benefits for mankind, starting from the leaves, tree trunk and coconut fruit. Traditional traders can use the sound produced by hand tapping to determine the ripeness of the coconut fruit. However, with this manual method there is a possibility of error in the classification process. Therefore, this research proposes the title Classification of Coconut Fruit Based on the Thickness of the Flesh at Various Levels of Maturity Using Artificial Neural Networks (JST). The research method for classification consists of 7 stages, namely image acquisition, preprocessing, segmentation, morphological operations, feature extraction, classification, and evaluation. The hope of the method used to provide solutions especially to farmers and traders in classifying or sorting coconut fruit to determine the quality of the meat with the help of digital image processing technology. By using 300 image datasets divided into 240 training images and 60 test images, which resulted in an accuracy rate of 97.91% on the training image and 96.66% on the test image. With a computation time of 0.31 seconds per image on the training image and 0.21 seconds per image on the test image. So that the results of the discussion in this study, the classification of coconut fruit using the Artificial Neural Network (JST) method by utilizing color features can run and produce results that can be classified as good.
Hyperellipsoid Cluster Merging using Hierarchical Analysis of Hyperellipsoid Cluster for Image Segmentation Kaswar, Andi Baso; Nurjannah, Nurjannah; Djawad, Yasser Abd
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.2815

Abstract

Segmentation is one of the critical stages in digital image processing and computer vision. However, conventional clustering-based segmentation methods, such as K-means and Fuzzy C-means (FCM), are still unable to accurately segment images whose pixels form hyperellipsoid clusters in the feature space. In addition, previous clustering methods based on Mahalanobis distance measurement require a long computational time and still have the potential to fall into local optima. Therefore, in this paper, we propose a new method for segmenting images whose pixels form hyperellipsoid clusters in the feature space, utilizing hyperellipsoid clusters merging through hierarchical analysis of hyperellipsoid clusters. The proposed method comprises eight main steps: histogram extraction, peak and valley identification, elimination of low peaks and valleys, peak combination for centroid initialization, initialization of cluster pixel members, elimination of ineffective clusters, hyperellipsoid cluster merging, and finalization of cluster members. This paper presents a novel approach to segmenting color images by employing an initial centroid discovery process and cluster analysis that considers cluster covariance for cluster merging. Based on the tests conducted using various image characteristics, the proposed method can provide 97.42% accuracy, 98.02% precision, 97.15% recall, 2.58 misclassification error, 97.54 F1-score, 95.29% intersection over union, 97.52% dice coefficient, and 15.37 seconds of computation time. The test results are superior to those of conventional methods, such as K-means and FCM. Based on these results, it can be concluded that the proposed method can effectively segment images with high accuracy. The proposed method can serve as an alternative approach to image segmentation.
Co-Authors A Mutahharah A. Farha Adella A. Muhammad Idkhan A. Mutahharah A. Mutahharah Mutahharah A.Farha Adella Abd. Rahman Patta Abdul Muis Mappalotteng Abdul Wahid Adiba, Fhatiah Afdhaliyah, Mukhlishah Aglaia, Alifya Nuraisyar Agung, Andi Sadri Agus Zainal Arifin Agus Zainal Arifin Agustinus Suria Darme Ahmad Adzan Lain Ahmad Fudhail  Majid Ahmad Khan, Sardar Faroq Ahmad Mustofa Hadi Ahmad Mustofa Hadi Ainun Zahra Adistia Aisyah Ramadani Akbar, Trisakti Aksa, Muhammad Alfian Firlansyah Ananta Dwi Prayoga Alwy Andi Ahmad Taufiq Andi Akram Nur Risal Andi Alamsyah Rivai Andi Fitri Novianti Andi Nurul Izzah Andi Rosman N Andi Tenri Ola Rivai Andi Tenriola Anggy Heriyanti Anggy Heriyanti Annajmi Rauf Anny Yuniarti Aprilianti Nirmala S Aqsha, Ismail Aras, Muh Riski Farukhi Arifky, Reza Arinanda Alviansyah Arliandy, Arliandy Arya Yudhi Wijaya Arya Yudhi Wijaya Aryadi Nurfalaq Ashadi, Ninik Rahayu Asmi Ulfiah Asnidar Asnidar Asrofi, Muhammad Ghufran Aswar Aswar Aulia, Magfirah Awalia, Nur Ayu Futri Azis, Putri Alysia Azis, Salsabila Bantun, Suharsono Bugdady, Andi Jaedil Bukhari Naufal Nur A.G Burhan, Rafli Ananta Chairati, Chairati Cyahrani Wulan Purnama Cyahrani Wulan Purnama Rasyid Darma Andayani, Dyah Darme, Agustinus Suria Della Fadhilatunisa Desitha Cahya Dewi Fatmarani Surianto Dhanendra, Fadhil Dina Salam, Fitria Nur Dirawan, Gufran Darma Edy, Marwan Ramdhany Elva Amalia Elva Amalia Eman Wahyudi Kasim Eriyani, Nindy Sri Fachriansyah, Zaky Farid, Muhammad Miftah Farros Taufiqurrahman Fathahillah Fathahillah Fauzi, A. Arfan Fazli Arif Fhatiah Adiba Fhatiah Adiba Hafidz Muhtar Hanum Zalsabilah Idham Hartanto Tantriawan Heriyanti, Anggy Herman Hermansyah Hermansyah Hersyam, Muh Syachrul Hidayat, Muh. Taufik Ibnu Fikrie Syahputra Idkhan, A. Muhammad Idkhan, Andi Muhammad Idris, Muh Gimnastiar Ihlasul Amal Ikra Ain Fahwa Ilham, Muh Ilham, Muhammad Ryan Ilyas, Muh. Imran, Al Indri Pratiwi Ramadhani Intam, Reski Nurul Jariah S Irwansyah Suwahyu ISHAK Israwati Hamsar Iwan Suhardi Jamaluddin, Bunga Mawar Jamila Jamila Jamila Jariah S.Intam, Rezki Nurul Jasruddin Daud Malago Jayanti Yusmah Sari Jessica Crisfin Lapendy Juliano Nufiansyach Dini Jumadi Mabe Parenreng Jusrawati Jusrawati Jusrawati Kaparang, Adam Indra Kaswar, A Baso Kurnia Prima Putra Kurnia Wahyu Prima Labusab Labusab Labusab Labusab, Labusab Lapendy, Jessica Crisfin M. Miftach Fakhri Makmur, Haerunnisya Mappaita, Al Haytsam Marhayati Marwan Eka Ramdhany Marwan Ramdhany Edy Massie, Gary Jeremi Maulana Muhammad Maulana Muhammad Mawaddah, Arini Ulfa Meisaraswaty Arsyad Muammar Muammar Muh Aldhy Fatahillah Muh Devan Fahresi Muh Fuad Zahran Firman Muh Ilham Suherman Muh Omar Hassan ST Muh. Dirgafa Anugra Rais Muh. Dirgafa Anugrah Rais Muh. Fardika Pratama Putra Muh. Fauzan Arifuddin Muh. Ihsan Zulfikar Muh. Rais Muh. Rasul D Muhammad Agung Muhammad Agung Muhammad Akbar Muhammad Akbar Muhammad Akil, Muhammad Muhammad Atthariq Muhammad Fajar B Muhammad Naim Muhammad Nur Yusri Maulidin Yusuf Muhammad Nur Yusri Maulidin Yusuf Muhammad Rais Muhammad Yahya Muhiddin Palennari Muhira Muhira Muhtar, Hafidz Mukhtar Mukhtar Mulia, Musda Rida Muliaty Yantahin Musdar, Devi Miftahul Jannah Mustari Lamada Naim, Muhammad Nasrullah, Asmaul Husnah NFH, Alifya Ninik Astuti Nirsal Nur Anny S. Taufieq Nur Fadillah Bustamin Nur Inayah Yusuf Nurbaitul Afyan Nurfalaq, Aryadi Nurfitri, Andi Aisyah Nurhidayat Nurhidayat Nurhikma Nurhikma Nurhikma Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurul Amanda Pratiwi Hasbullah Nurul Isra Humaira B Nurul Istiqamah Qalbi Nurul Izzah Dwi Nurul Izzah Dwi Nurdinah Nurwijayanti Patongai, Dian Dwi Putri Ulan Sari Perdana, Am Akbar Mabrur Pramudya Asoka Syukur Pratama, Azir Zuldani Putri Nirmala Putri Ramdani R, Muh Raflyawan R, Ranir Aftar Radha Hasda Halfis Ranggareksa, Andi Ranir Atfar R Rapa, Wiwi Resky, Andi Aulia Cahyana Riana T. Mangesa Ridwan Daud Mahande Ridwansyah Ridwansyah Riswansyah , Muh Fikra Junian Riyama Ambarwati Rosidah Rosidah Rosidah Rusli, Risvan S, Mushawwir Sahribulan Sahribulan Saiful Bahri Musa Sakira, Tiara Putri Sam, Muh Hadal Ali Sanatang Saparuddin Saparuddin Saprina Mamase Saputra, Nikola Sartika Sari Sartika Sari Sasmita Sasmita Sasmita SATRIYAS ILYAS Silvia Andriani Soeharto Soeharto SR, Amin Farid Dirgantara Sri Rahayu St. Fatmah Hiola Suharsono Bantun Suhartono, Suhartono Supria Supria Surianto, Dewi Fatmawati Susiana Sari Syamsuddin Syasikirani. N, Adelia Tenriajeng, Andi Afrah Tenriola, Andi Tri Afirianto Tsabita Syalza Billa Tsabita Syalza Billa Irawan Umar, Nur Fadhilah Wahda Arfiana AR WAHYUDI Wanda Hamidah Wardani, Ayu Tri Wiwi Rapa WULANDARI Yasser Abd Djawad Yuliarni, Tarisa Yusuf, Zulfatni Zsolt Lavicza