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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer Panrita Abdi - Jurnal Pengabdian pada Masyarakat Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Journal of Information Technology and Computer Science (JOINTECS) Jurnal Ilmiah FIFO PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Conference on Innovation and Application of Science and Technology (CIASTECH) JURTEKSI METIK JURNAL Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jusikom: Jurnal Sistem Informasi Ilmu Komputer Systematics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informasi dan Teknologi Buana Information Technology and Computer Sciences (BIT and CS) JATI (Jurnal Mahasiswa Teknik Informatika) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Indonesian Journal of Electrical Engineering and Computer Science Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Infotek : Jurnal Informatika dan Teknologi Journal of Applied Data Sciences Jurnal Cahaya Mandalika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Djtechno: Jurnal Teknologi Informasi Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Mechanical Engineering for Society and Industry Dirgamaya: Jurnal Manajemen dan Sistem Informasi J-Intech (Journal of Information and Technology) Automotive Experiences Journal of Informatics and Communication Technology (JICT) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Informatika Teknologi dan Sains (Jinteks) Abdimas Journal International of Lingua and Technology Jurnal Komtekinfo Jurnal Buana Pengabdian Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Adopsi Teknologi dan Sistem Informasi Innovative: Journal Of Social Science Research JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Jurnal Polimesin Journal of Informatics and Communication Technology (JICT) CSRID Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Jurnal PETISI (Pendidikan Teknologi Informasi) Journal of Information Technology Jurnal Abdimas Mahakam
April Lia Hananto
Universitas Buana Perjuangan Karawang, Indonesia
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Implementasi Algoritma K-means untuk Klasterisasai Data Stunting di Kabupaten Bekasi Fuad anwar yuwono; April Lia Hananto; Fitri Nurapriani; Baenil Huda
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 9 No. 2 (2025): Volume 9 Nomor 2 April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v9i2.14748

Abstract

Stunting masih menjadi masalah kesehatan kritis yang berdampak pada tumbuh kembang anak, terutama di negara berkembang seperti Indonesia, di mana prevalensinya cukup tinggi dan memerlukan perhatian serius dari berbagai pihak. Stunting dapat menyebabkan gangguan perkembangan fisik dan kognitif anak, serta meningkatkan risiko berbagai penyakit kronis di masa depan. Dalam upaya mendukung program penanggulangan stunting, penelitian ini menerapkan algoritma pengelompokan K-Means untuk mengklasifikasikan kasus stunting di Kabupaten Bekasi berdasarkan berbagai atribut demografi dan kesehatan, seperti usia, berat badan, tinggi badan, status gizi, dan kondisi sosial ekonomi keluarga. Dataset yang digunakan dianalisis dengan pendekatan Knowledge Discovery in Databases (KDD), yang mencakup tahapan seleksi data, pra-pemrosesan, transformasi, serta penerapan metode data mining untuk pengelompokan. Melalui tahapan tersebut, data yang semula tidak terstruktur diolah secara sistematis menjadi informasi yang berguna. Hasil dari penerapan algoritma K-Means menunjukkan bahwa jumlah klaster yang optimal dapat secara efektif membagi data ke dalam beberapa kelompok dengan tingkat risiko stunting yang berbeda-beda. Informasi ini dapat membantu pembuat kebijakan dalam mengidentifikasi kelompok anak dengan risiko tinggi, sehingga strategi intervensi yang dilakukan dapat lebih tepat sasaran, efisien, dan berdampak signifikan terhadap penurunan angka stunting.
Implementasi Metode K-Means Untuk Memprediksi Status Kredit Macet Muthia Nur Rizky Fitriani; Bayu Priyatna; Baenil Huda; April Lia Hananto; Tukino Tukino
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5953

Abstract

A credit card is one of the legal payment media owned by a bank in making a payment transaction within the agreed timeframe. In particular, credit services are provided by institutions or bodies that have the authority to distribute funds in the form of financial assistance to individuals and groups. However, in practice there are bound to be obstacles, especially during payback periods that often occur, such as when a customer wants to submit a Repeat Order or apply for funds again. Obstacles that are usually encountered in the process of granting credit are substandard credit and bad credit payments. Before PT Esta Dana Ventura wants to decide to approve applications for re-granting credit cards from prospective repeat order customers, a classification of assessment criteria is needed to determine the feasibility of granting credit to prospective repeat order customers. This study made the decision to use Data mining clustering classification with Rapidminer tools as a tool to obtain accurate results by processing data using the K-Means clustering method to help PT. Esta Dana Ventura in analyzing potential non-performing loans. By comparing survey data for Repeat Order candidates with previous credit granting data and classifying them in the form of bad or non-bad credit classifications.From the results of research using the k-means method it can produce grouping data into 3 criteria, namely (C0) 69 data with current customers, (C1) 3 data with very current customers, and (C2) 52 data with Bad customers..
Pemberdayaan kepada Kelompok Pengolah Sampah Organik sebagai Pakan Cacing di Kecamatan Sukun Kota Malang Aviv Yuniar Rahman; Feddy Wanditya Setiawan; April Lia Hananto
Jurnal Abdimas Mahakam Vol. 4 No. 1 (2020): Januari
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/jam.v4i1.753

Abstract

Pemberdayaan masyarakat saat ini dapat mengubah pengetahuan dan keterampilan. Di Kecamatan Sukun, Kota Malang. Di Kota Malang saat ini memiliki kelompok maupun organisasi  pengolah sampah organik menjadi pakan cacing. Kelompok ini membutuhkan informasi tentang dampak limbah, pengelolaan limbah, dan memanfaatkan limbah menjadi pundi-pundi uang. Dari keterbatasan sumber daya manusia dan keterampilan yang mereka miliki. Ternyata limbah organik dapat digunakan sebagai pakan cacing. Sehingga bisa menambah pemasukan bagi kelompok pengolah sampah organik. Dalam pelaksanaan pemberdayaan ini, dibutuhkan tahapan yang terdiri dari survei, dokumentasi, pembuatan bahan dan modul, konseling dan pelatihan, dan evaluasi. Ukur hasil pemberdayaan dengan membagikan kuesioner sebelum dan sesudah kuesioner kepada 10 peserta. Pertanyaan tentang kuesioner sebelum dan sesudah terdiri dari pengetahuan dan keterampilan. Kuesioner dibagi menjadi tiga tahap acara yang terdiri dari konseling dan pelatihan dalam manajemen keuangan, manajemen pemasaran, dan proses produksi. Hasil yang diperoleh setelah kegiatan adalah jumlah peserta yang mengalami peningkatan 80% dalam pengetahuan dari konseling manajemen keuangan. Untuk hasil konseling manajemen pemasaran memiliki peningkatan pengetahuan sebesar 90%. Sementara hasil perpanjangan di ketahui dari proses produksi mencapai peningkatan pengetahuan 95%.
ANALISIS INTERAKTIF KASUS KEKERASAN ANAK DI INDONESIA MENGGUNAKAN DASHBOARD POWER BI Bilqis Amalia Utari; April Lia Hananto; Agustia Hananto
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 2 (2026): EDISI 28
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i2.7346

Abstract

Kekerasan terhadap anak merupakan permasalahan serius di Indonesia yang berdampak pada perkembangan fisik, psikologis, dan sosial anak. Penelitian ini bertujuan untuk menganalisis data kasus kekerasan terhadap anak di Indonesia serta mengimplementasikan dashboard interaktif berbasis Microsoft Power BI sebagai media visualisasi dan eksplorasi data. Data yang digunakan bersumber dari Sistem Informasi Gender dan Anak (SIGA). Metode penelitian meliputi pengumpulan data, pre-processing, analisis distribusi kasus, pengembangan dashboard interaktif, serta evaluasi menggunakan System Usability Scale (SUS). Hasil penelitian menunjukkan bahwa kekerasan seksual merupakan jenis kekerasan yang paling dominan dengan persentase sebesar 46,05%. Dashboard yang dikembangkan mampu menyajikan informasi secara visual dan interaktif sehingga memudahkan pengguna dalam memahami distribusi kasus berdasarkan wilayah dan jenis kekerasan. Hasil evaluasi terhadap 35 responden menunjukkan skor SUS sebesar 80,2 yang termasuk dalam kategori “Good”. Dengan demikian, dashboard interaktif yang dikembangkan dapat membantu memahami pola distribusi kasus serta mendukung pengambilan keputusan berbasis data dalam upaya pencegahan dan penanganan kekerasan terhadap anak di Indonesia.
DIGITALISASI LAYANAN TOKO LARIS MOTOR DENGAN TEKNOLOGI CHATBOT OTOMATIS BERBASIS AI Gefira Rahmaifha; April Lia Hananto; Elfina Novalia; Baenil Huda
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 2 (2026): EDISI 28
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i2.7374

Abstract

Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem chatbot otomatis berbasis Artificial Intelligence (AI) menggunakan workflow automation N8N sebagai solusi digitalisasi layanan pelanggan pada Toko Laris Motor. Permasalahan yang dihadapi meliputi keterlambatan respons, penumpukan pesan WhatsApp, serta ketidakkonsistenan informasi akibat proses pelayanan yang masih dilakukan secara manual. Penelitian ini menggunakan pendekatan Research and Development (R&D) yang mencakup tahapan analisis kebutuhan, perancangan sistem, pengembangan, pengujian, dan evaluasi. Sistem chatbot dikembangkan dengan memanfaatkan AI Agent untuk memahami konteks percakapan, serta diintegrasikan dengan WhatsApp API dan Google Sheets guna menyediakan informasi produk, harga, dan ketersediaan stok secara real-time. Proses pengujian sistem dilakukan menggunakan metode Blackbox Testing untuk memastikan setiap fungsi sistem berjalan sesuai dengan kebutuhan pengguna tanpa melihat struktur kode program. Hasil pengujian menunjukkan bahwa sistem mampu meningkatkan kecepatan respons layanan, menjaga konsistensi informasi, serta mengurangi beban kerja administratif. Implementasi chatbot ini mendukung transformasi digital pada sektor UMKM ritel otomotif dan menunjukkan bahwa otomatisasi layanan berbasis AI dapat meningkatkan efektivitas operasional serta kualitas interaksi pelanggan secara berkelanjutan.
Improved Hybrid GoogLeNet-Based Deep Learning Optimization for Standardized Straw Mushroom Quality Classification in Indonesia Bayu Priyatna; Titik Khawa Abdurahman; Muhammad Fahmi Miskon; April Lia Hananto; Agustia Tia Hananto; Aviv Yuniar Rahman
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1206

Abstract

Deep learning plays a crucial role in modern computer vision due to its ability to automatically extract hierarchical features from large-scale image data. Among various architectures, Convolutional Neural Networks (CNNs) have been extensively utilized for image pattern interpretation, including in agricultural product inspection. Straw mushrooms (Volvariella volvacea) are important agro-industrial commodities in Indonesia; however, their quality assessment still relies on subjective manual evaluation based on the Indonesian National Standard (SNI:01-6945-2003), leading to inconsistency in grading results. To address this limitation, this research proposes an Improved Hybrid GoogLeNet model integrated with a YOLO-based detection framework and hybrid preprocessing to enhance feature clarity and classification robustness. The system is capable of conducting object detection, 3-class morphological quality classification (Pure White, Oval, and Black Spot/Defect), and automatic diameter measurement using calibrated pixel-to-centimeter conversion. Performance evaluation is carried out by benchmarking the proposed model against several popular deep learning architectures including YOLOv5, LeNet, AlexNet, VGGNet, and ResNet. Experimental results demonstrate that the Improved Hybrid GoogLeNet achieves the highest performance with precision of 97.99%, recall of 96.07%, and F1-score of 96.98%, along with low misclassification rates across all classes. These results indicate that the proposed method provides accurate, reliable, and efficient quality assessment that supports standardized automated grading in industrial applications. Therefore, this study contributes to the advancement of intelligent computer vision solutions for digital transformation in the Indonesian mushroom agro-industry.
Web-Based Warehouse Inventory System Using the Waterfall Method: A Case Study at Satria Wholesale Mart Melisa; Tukino; Agustia Hananto; April Lia Hananto
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.610

Abstract

In the digital era, manual warehouse inventory management is still challenging for many business people, including Satria Wholesale Mart. The main problems also faced are irregularities in recording incoming and outgoing goods, low accuracy of stock data, delays in reporting, and difficulties in tracking stock in real time. This finding aims to design and build an efficient and effective web-based warehouse inventory system using the Waterfall method. The finding method used is applied findings with a descriptive qualitative approach, which also aims to describe in detail and systematically the phenomena that also occur in the field and how new systems can be developed to solve these problems. The findings show that applying the waterfall method in developing a web-based inventory information system at PT Herso Ticep Indonesia has also yielded satisfactory results. The system that has also been developed has succeeded in meeting the needs of companies in inventory management, improving operational efficiency, and optimizing inventory management. These findings imply that companies can improve their operational efficiency and optimize inventory management by implementing this information system. The findings could also guide other companies that want to develop similar systems.
SISTEM INFORMASI MANAJEMEN STOK OBAT BERBASIS WEB PADA APOTEK BIMA FARMA Irsyadul I'bad; Tukino Tukino; April Lia Hananto; Baenil Huda; Fitria Nurapriani
EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Vol 16, No 1 (2026): Juni
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/expert.v16i1.4801

Abstract

Manajemen stok obat di Apotek Bima Farma masih dilakukan secara manual, sehingga menyebabkan selisih data stok mencapai 8%–12% dan memperlambat proses pencarian serta pelaporan hingga sekitar 3 jam. Penelitian ini bertujuan membuat sistem informasi manajemen stok obat yang berbasis web agar bisa mempercepat dan membuat lebih tepat dalam mengolah data, serta mengurangi perbedaan stok hingga di bawah 2%. Sistem tersebut dibuat dengan menggunakan metode Waterfall, dan dalam pembuatan nya dilakukan perancangan menggunakan UML serta ERD. Sistem ini kemudian diimplementasikan dengan menggunakan framework CodeIgniter 3 yang berbasis PHP dan MySQL, serta menerapkan arsitektur MVC. Uji coba dilakukan dengan metode Black Box terhadap 12 skenario dan berhasil mencapai tingkat keberhasilan 100%. Selain itu, juga dilakukan pengujian dengan metode White Box dan diperoleh nilai kompleksitas siklomatis V(G) = 2. Hasil menunjukkan bahwa sistem berhasil mengotomatisasi proses pencatatan transaksi, memberikan informasi stok secara langsung dan segera, serta menghemat waktu dalam pembuatan laporan dari sekitar 3 jam menjadi kurang dari 30 menit. Selain itu, perbedaan antara stok yang ada dan yang diperlukan berhasil dikurangi hingga di bawah 2%. Dengan demikian, sistem yang dikembangkan efektif dalam meningkatkan efisiensi dan akurasi pengelolaan stok obat serta layak diterapkan pada apotek skala kecil hingga menengah
Prediksi Penjualan Barang Menggunakan Metode K-Means dan Regresi Linear Henry Adam; Tukino Tukino; Elfina Novalia; April Lia Hananto
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.541

Abstract

Sales data analysis plays an important role in supporting business decision making, especially to optimise stock management and improve operational efficiency. the main problem faced by Vapestore XYZ in Karawang is the difficulty in accurately predicting the number of product sales, so there is often an imbalance between inventory and market demand. This can cause losses due to overstocks or shortages of goods. Currently, the estimation of stock requirements still relies on intuition and personal experience, without the support of objective data analysis. This research aims to build a sales prediction model by combining the K-Means method for product clustering and Linear Regression for sales quantity prediction. Sales data is taken directly from the store POS application, then goes through the stages of cleaning, labelling, and clustering into three groups, namely ‘Less Sold’, “Sold”, and ‘Very Sold’. Sales prediction is performed using Linear Regression by utilising the clustering results and time variables as inputs. Model performance evaluation is performed using error metrics, namely Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Based on the test results, the developed Linear Regression model obtained MAE of 3.20, MSE of 52.34, and RMSE of 7.23. These error values indicate that the model is able to provide sales estimates that are close enough to the actual data to be reliable in stock planning. Visualisation of the prediction results in the form of tables and heatmaps makes it easy to identify sales trends and compare performance between products. The findings of this study prove that the combination of K-Means and Linear Regression methods is effectively used to support stock decision making and marketing strategies in vape retail stores. Further development is recommended by enriching the dataset and exploring other prediction methods to improve model performance.
Assessment Decision Support System Best Teacher By Using Analytical Hierarchy Process (AHP) Method April Lia Hananto; Bayu Priyatna; Fitria Nurapriani; Ahmad Fauzi; Tukino Tukino; Naufal Zubdi Ahnaf
International Journal of Artificial Intelligence Research Vol 6, No 1.1 (2022)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v6i1.1.572

Abstract

Assessment of teachers by the Principal of SMA Negeri 1 Kedungwaringin certainly needs to be done to determine the best teacher, so that teacher performance is in accordance with the specified competencies because the teacher is the most important and influential role in the world of education in the process of teaching and learning activities. The assessment was carried out not only for civil servants (PNS), including honorary teachers who also participated in the assessment process so that there were no limitations in evaluating teachers at SMA Negeri 1 Kedungwaringin. This assessment process uses the Analytical Hierarchy Process (AHP) Algorithm. The method used aims to determine the best teacher at SMA Negeri 1 Kedungwaringin who is in accordance with the assessment criteria prepared. The results of the ranking show that Dian Purwanti, S.Pd obtained the highest score of 0.342 out of 5 other teachers. This application is expected to assist school principals in determining the best teacher based on the criteria and weight of each existing value.
Co-Authors - Faqih Abdullah Abdullah Abdullahi Tanko Mohammed Abdullahi Tanko Mohammed Adittia Agustian Afra, Alfina Fadhilah Agneresa Agneresa Agustia Tia Hananto Ahmad Fauzi Ahmed Sule Alparizi, Muhamad Iqbal Alpian, Yayan alzahra, alika aziza Anis Fitri Nur Masruriyah Anita Khansa Ramadanti Anthony Chukwunonso Opia Aprilia Putri Nardilasari Arkan Hilman Hakim Asep Haris Atmaja, Rashelin Zahra Attaya Faiharani Aulia, Aldi Aviv Yuniar Rahman Aviv Yuniar Rahman Aviv Yuniar Rahman Aviv Yuniar Rahman Aviv Yuniar Rahman Baenil Huda Baenil Huda Baenil Huda Baenil Huda Baenil Huda Bagus Setyawan Baihaqi, Kiki Ahmad Bayu Priyatna Bayu Priyatna Bayu Priyatna Berkah*, Kamila Bilqis Amalia Utari Candra Zonyfar Catur Nugroho Danny Manongga Dean Ariesta Aziz Deddy Prihadi Deden Wahiddin Detrie Noviani Dhany Hermansyah Dien Noviany Rahmatika Edrina Christine, Natalie Eichler, Luiz Eko Pramono Eko Sediyono Elfina Novalia Elsa Rosalina Esam Abu Baker Ali Fatlun, Aulia Fatmanisa Mumpuni Delta Maharani Fauzi Ahmad Muda Firdaus, Mohamad Ricky Firman Nurdiansyah Firman Nurdiyansyah Fitri Nurapriani Fitria Nur Apriani Fitria Nurapriani Fitria Nurapriani Fuad anwar yuwono Gefira Rahmaifha Guntur, Muhamad Hananto, Agustia Hanny Hikmayanti Handayani Hayati, Cucu Hendry Henry Adam Hibatullah, Muhammad Hafizh Hilabi, Shofa Shofia Hilabi, Shofa Shofiah Hilabi, Shofa Shofiah Hindriyanto Dwi Purnomo Huda Huda Huda, Baenil Ihsan, Mohammad Maftuh Ihwan Ghazali Ilman Kadori Indra Kurniawan Indri Oktapiani Irawan, Bei Harira Irsyadul I'bad Irwan Sembiring Istiadi Isyanto, H. Puji Iwan Setiawan Iwan Setyawan Joko Purwanto Kamila Berkah* Kurnia, Nisa Lutfiah, Siti Mega Tri Kurnia Melisa Miswadi Miswadi Moh Hasan Basri Mohamad Ricky Firdaus Mubarok, Piky Muhamad Djaka Permana Muhammad Abil Fadli Muhammad Fahmi Miskon Muhammad Idris Muhammad Idris Muhammad Idris Muhammad Nova Muhammad Zacky Asy'ari Muthia Nur Rizky Fitriani Naufal Zubdi Ahnaf Nisa Kurnia Novalia, Elfina Novia Cahya Utami Nur ‘Azah Nurapriani, Fitria Nurapriani, Nurapriani Nurfajria, Dera Nursyifa Paryono, Tukino Permana Andi Paristiawan Permana Andi Paristiawan Pradana Rizki Maulana Prasetya, Rafli Prasetyo Ajie Pratama, Daffa Agung Priatna, Bayu Priyatna , Bayu Priyatna, Bayu Purnomo, Hendryanto Dwi Putri Indraswari Reformasi, Era Rieke Retnosary Rukmanta Jayawiguna Ruliansyah Ruliansyah Ruliansyah Ruliansyah, Ruliansyah Saepul Aripiyanto Safarudin Gazali Herawan Sari, Nurnilam Sarina Sulaiman Sarina Sulaiman Setiawan, Feddy Wanditya Setiawan, Pratama Wahyu Shelly Nur Azizah Shofa Shofia Hilabi Shofa Shofia Hilabi Shofa Shofia Hilabi Shofa Shofiah Hilabi Shofa Shofiah Hilabi Shofa Shofiah Hilabi Shofa Sofiah Hilabi Shofiah Hilabi, Shofa Shuaibu Alani Balogun Sigit Widiyanto Silva, Tiago Siti Masruroh Sofia Ramadanti Az-Zahra Soleman, Soleman Sopian, Jajang Sri Mumpuni Ngesti Rahaju Sudrajat, Deden Renhad Suhada, Karya Surala, Lyvia Susilawati, Agnes Dwita Syah Alam Tita Puspita Sari Titik Khawa Abdurahman Tukino Tukino Tukino Tukino Tukino Tukino Tukino Tukino Tukino, Tukino Tukino, Tukino Vina Andini Yazid, Muhammad Abi Yovika Aprianti Yoviyardi, Rama