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All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Informatika dan Teknik Elektro Terapan Jurnal IPTEK Jurnal Sistem Informasi dan Bisnis Cerdas JURNAL INSTEK (Informatika Sains dan Teknologi) Prosiding Seminar Nasional Sains dan Teknologi Terapan JURNAL TEKNOLOGI DAN OPEN SOURCE Data Science: Journal of Computing and Applied Informatics Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal IJEEIT : International Journal of Electrical Engineering and Information Technology Applied Technology and Computing Science Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics Abdimas Universal bit-Tech Jurnal Teknologi Dan Sistem Informasi Bisnis Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) JATI (Jurnal Mahasiswa Teknik Informatika) International Journal of Advances in Data and Information Systems Jifosi Nusantara Science and Technology Proceedings KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Penelitian Inovatif Konstelasi: Konvergensi Teknologi dan Sistem Informasi PELS (Procedia of Engineering and Life Science) Journal of Artificial Intelligence and Engineering Applications (JAIEA) NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Mesin, Industri, Elektro dan Informatika Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Jurnal Ilmiah Teknik Informatika dan Komunikasi Madani: Multidisciplinary Scientific Journal Scientica: Jurnal Ilmiah Sains dan Teknologi Student Research Journal Jurnal Informatika Polinema (JIP) Jurnal Ilmu Komputer dan Sistem Informasi Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal ilmiah teknologi informasi Asia Jurnal Ilmu Teknologi Informasi Indonesia (JITIFNA) Jurnal Pengabdian Masyarakat Multidisiplin
Amalia Anjani Arifiyanti
Universitas Pembangunan Nasional Veteran Jawa Timur
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KLASIFIKASI CALON PENDONOR DARAH POTENSIAL MENGGUNAKAN ALGORITMA DECISION TREE DI UTD PMI KOTA SURABAYA Elfaretta, Syifa Saskia; Arifiyanti, Amalia Anjani; Fitri, Anindo Saka
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4957

Abstract

Darah merupakan komponen yang vital dalam tubuh manusia. Kurangnya jumlah darah pada tubuh akan memengaruhi kerja dari organ lain. Oleh karena itu, PMI berperan aktif dalam menyediakan kebutuhan stok darah nasional. Untuk memastikan bahwa darah yang diterima oleh resipien aman dan berkualitas baik, maka perlu dilakukan klasifikasi calon pendonor darah potensial. Penelitian ini menggunakan beberapa algoritma Decision Tree dalam proses klasifikasi data. Algoritma yang digunakan adalah CART, C4.5, dan Random Forest. Hasil perbandingan dari tiga algoritma menunjukkan bahwa Random Forest memiliki nilai terbaik dibandingkan algoritma lainnya. Algoritma Random Forest mendapatkan akurasi dengan nilai 97% dan AUC ROC dengan nilai 99%. Oleh karena itu, algoritma Random Forest diimplementasikan dalam sistem klasifikasi calon pendonor darah potensial berbasis web. Hasil uji validasi sistem menunjukkan akurasi dengan angka 97%.
RANCANG BANGUN APLIKASI DONOR DARAH DARURAT DONORA BERBASIS ANDROID DENGAN KONSEP GAMIFIKASI MENGGUNAKAN KOTLIN AryaRafa, Daud; Dyar Wahyuni, Eka; Anjani Arifiyanti, Amalia
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.5025

Abstract

Dalam menghadapi permasalahan yang ada di masyarakat terkait keterbatasan pasokan darah darurat, Donora hadir sebagai solusi profesional dan inovatif. Kami mengakui adanya kesulitan yang sering dihadapi oleh masyarakat saat mereka membutuhkan darah darurat dalam situasi darurat seperti kecelakaan atau setelah menjalani operasi besar,salah satu masalah utama yang kami identifikasi adalah kelangkaan stok darah di rumah sakit dan unit transfusi darahAgile Scrum adalah salah satu metode pengembangan produk yang terintegrasi dan berkelanjutan dalam menyelesaikan proyek secara bertahap. Kelebihan utama dari metode ini adalah memungkinkan dengan cepat menyesuaikan dengan perubahan yang mungkin terjadi selama pengembangan produk Ada lima prinsip dari metode pengembangan Agile, yaitu customer involvement, incremental delivery, people not process, embrace change, dan maintain simplicity kami melakukan analisis pesaing untuk menganalisis berbagai fitur yang dapat dikembangkan dalam aplikasi Donora sebagai solusi masalah yang telah diidentifikasi sebelumnya. Dengan melibatkan tim pengembang dan stakeholders, kami menentukan prioritas fitur yang paling penting , memastikan fokus pengembangan pada solusi yang paling efektif danbermanfaat bagi pengguna Donora.Setelah berdiskusi , kami pun membuat product backlog dan menentukan prioritas tiap backlogDalam pengembangan aplikasi donor ini kami menggunakan agile scrum.pada pengembangan mobile apps berbasis android  ini kami menggunakan Bahasa pemrograma kotlin. 
KLASTERISASI TRACER STUDY ALUMNI UNIVERSITAS XYZ MENGGUNAKAN ALGORITMA K-MEANS Fernaldy, Fabiyan Atha; Arifiyanti, Amalia Anjani; Kartika, Dhian Satria Yudha
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5581

Abstract

Penelitian ini bertujuan untuk menganalisis dan mengelompokkan data alumni berdasarkan Indeks Prestasi Kumulatif (IPK) dan masa tunggu untuk mendapatkan pekerjaan menggunakan algoritma K-Means. Metode Elbow dan Silhouette Score diterapkan untuk menentukan jumlah cluster yang optimal. Hasil evaluasi menunjukkan bahwa untuk dataset yang dianalisis, jumlah cluster optimal untuk dataset pertama adalah tiga, sedangkan untuk dataset kedua adalah dua, dengan nilai Silhouette Score tertinggi masing-masing 0.497656 dan 0.502767. Deskripsi hasil clustering mengungkapkan perbedaan karakteristik antara cluster, di mana cluster dengan rata-rata IPK tertinggi memiliki masa tunggu terendah untuk mendapatkan pekerjaan. Temuan ini memberikan wawasan berharga bagi pengembangan kurikulum dan program bimbingan karir, serta meningkatkan pemahaman tentang pola karir alumni. Penelitian ini diharapkan dapat menjadi referensi untuk studi lebih lanjut dalam bidang analisis data dan pengembangan pendidikan.
ASPECT-BASED SENTIMENT ANALYSIS PADA ULASAN APLIKASI ACCESS BY KAI MENGGUNAKAN METODE TF-IDF DAN ALGORITMA SUPPORT VECTOR MACHINE Nur Rachman Nidhi Suryono, Muhammad; Amalia Anjani Arifiyanti; Dhian Satria Yudha Kartika
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 10 No 2 (2025): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v10i2.57155

Abstract

Access by KAI merupakan aplikasi layanan transportasi digital dari PT Kereta Api Indonesia yang mempermudah pengguna dalam mengakses layanan perjalanan kereta api. Untuk meningkatkan kualitas layanan dan pengalaman pengguna, penelitian ini melakukan analisis sentimen terhadap ulasan pengguna aplikasi menggunakan algoritma Support Vector Machine (SVM). Tiga aspek utama yang dianalisis yaitu Financial Transactions, Technical Issues and Performance, serta User Experience and Interface. Penelitian menggunakan kombinasi metode sampling (SMOTE dan Non-SMOTE), kernel (Linear, RBF, Polynomial), dan pembagian data (80:20 dan 70:30) untuk menemukan model terbaik. Hasil terbaik untuk aspek Financial Transactions diperoleh dari model SMOTE dengan kernel RBF dan rasio 70:30 (akurasi 0.9270). Untuk Technical Issues and Performance, model terbaik adalah Non-SMOTE dengan kernel Linear dan rasio 70:30 (akurasi 0.8718). Sedangkan untuk User Experience and Interface, model Non-SMOTE dengan kernel Linear dan rasio 80:20 memberikan akurasi tertinggi sebesar 0.8825. Model terbaik ini diimplementasikan dalam aplikasi web berbasis Flask yang dapat memprediksi sentimen, mengekspor hasil dalam bentuk .csv, serta menampilkan visualisasi data. Hasil implementasi menunjukkan bahwa kombinasi model terpilih mampu memberikan pemetaan sentimen yang konsisten dan terstruktur terhadap ulasan pengguna, sehingga dapat digunakan sebagai dasar evaluasi berbasis data dalam pengembangan fitur aplikasi.
Convolutional Neural Network Approach for Aspect-Based Sentiment Analysis of Tourism Reviews Siti Oktavia Eka Putri; Amalia Anjani Arifiyanti; Abdul Rezha Efrat Najaf
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2582

Abstract

The tourism industry is a key economic sector in Indonesia, with East Java ranking highest in tourist visits. This study aims to enhance tourism development by applying aspect-based sentiment analysis (ABSA) using convolutional neural networks (CNN) to analyze online reviews. CNN was selected for this study due to its proven efficiency in capturing local n-gram features and its relatively lower computational cost compared to other deep learning model. Reviews from TripAdvisor and Google Maps were collected focusing on four aspects: attraction, amenities, access, and price. Five different models were developed in this research: one multilabel aspect classifier designed to identify multiple aspects mentioned within each review, and four sentiment classifiers focused on evaluating the sentiment polarity for each specific aspect. These models were trained and evaluated using various combinations of word embeddings, including static embeddings like Word2Vec, and contextualized embeddings such as BERT and IndoBERT. Additionally, the impact of preprocessing through stemming was investigated to understand how simplifying word forms affects model performance. Results indicate that IndoBERT-CNN attains the best overall sentiment classification, reaching F1-scores up to 0.71 for attraction and 0.93 for amenities, while Word2Vec-CNN with stemming leads multilabel classification. Meanwhile stemming improves performance for static embeddings like Word2Vec by simplifying word forms, it reduces effectiveness in transformer-based models like BERT and IndoBERT that rely on natural language context. These findings highlight the benefit of choosing appropriate embeddings and preprocessing for different tasks, thus providing practical insights for improving tourism services through better tourist reviews analysis.
Comparison of Adam, RMSprop, and SGD on DenseNet121 for Tomato Leaf Disease Classification Heni Lusiana Dewi; Amalia Anjani Arifiyanti; Abdul Rezha Efrat Najaf
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2684

Abstract

Diseases affecting tomato leaves can severely impact agricultural productivity, as they can reduce crop yields and quality significantly. A swift and dependable identification of these diseases is vital for ensuring prompt interventions and the successful implementation of disease control strategies. This study focus on evaluating and comparing the efficiency of three separate optimizers, such as Adam, RMSProp, and SGD on the pretrained Convolutional Neural Network (CNN) architecture DenseNet121. There has been no previous research that directly compares the performance of Adam, RMSProp, and SGD optimizers on the DenseNet121 model for classifying tomato leaf diseases using the Plant Village dataset. These optimizers are crucial in the training process by influencing the model’s ability to converge and generalize well on new, unseen data. Experimental procedures were performed using a labeled dataset of tomato leaf images, which included healthy leaves and various disease classes. Out of the three optimization techniques tested, the DenseNet121 model trained with the Adam optimizer consistently outperformed the others. It achieved the highest evaluation metrics, with an accuracy of 0.9800, precision of 0.9807, recall of 0.9800, and F1-score of 0.9800 on the test set. These outcomes suggest that the model has a strong and balanced classification performance, capable of correctly identifying disease conditions with minimal errors. Based on these findings, the DenseNet121 architecture combined with the Adam optimizer is considered the optimal model used to recognize various tomato leaf diseases in this study.
Analisis Komparatif Embedding Semantik Berbasis Large Language Model Pada Sistem Rekomendasi Buku Serendipitous di Perpustakaan Kampus Rahayu Kartika Sari; Eka Dyar Wahyuni; Amalia Anjani Arifiyanti
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.443

Abstract

The phenomenon of information overload in academic libraries often makes it difficult for users to discover relevant books, which may reduce reading interest. Conventional recommender systems are also prone to filter bubbles and tend to perform poorly under cold-start conditions. This study proposes a sequential recommendation system based on the Self-Attention Based Sequential Recommendation (SASRec) model integrated with five semantic embedding models, namely Word2Vec, BERT Multilingual, OpenAI text-embedding-3-small, Gemini-embedding-001, and Qwen3-Embedding-0.6B, to generate accurate and serendipitous recommendations. In addition, the Serendipity-Oriented Greedy (SOG) re-ranking algorithm is implemented to balance recommendation relevance and serendipity. The data set consists of 14,502 book records and 5,445 user interaction histories after the data cleaning process. Evaluation was conducted under three testing scenarios, namely the all-test set, warm test set, and cold test set, by comparing all model variants before and after the re-ranking process. The results show that the integration of Large Language Model (LLM)-based embeddings consistently improves performance compared to the standard SASRec model and traditional embeddings. Qwen3-Embedding-0.6B achieved the best performance, improving HitRate@10 by up to 282.9% and NDCG@10 by up to 387.8%, while maintaining semantic robustness in cold-start scenarios with an UnSerendipity@K score of 0.613. The implementation of SOG re-ranking reveals a direct trade-off between recommendation accuracy and diversity. Lightweight weighting provides the optimal balance, whereas overly aggressive weighting significantly reduces relevance. The main contribution of this study lies in integrating modern LLM embeddings into a sequential recommendation architecture to improve accuracy and cold-start robustness, while also evaluating the impact of serendipity-oriented re-ranking strategies on balancing recommendation relevance and diversity. Overall, this study demonstrates that modern LLM integration can produce a smarter, more adaptive, and more balanced library recommendation system in terms of both accuracy and serendipity.
Otomatisasi Identifikasi Kesenjangan Keahlian Kerja Melalui Analisis Komparatif Data Lowongan Dan Profil Kandidat Iamho Pegodang Eltiuzy; Geovano Galan Widiatmoko Putra; Wahyu Setiawan; Amalia Anjani Arifiyanti
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 2 No. 2 (2026): JITIFNA - Juli
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v2i2.1681

Abstract

This study addresses the persistent skill mismatch faced by students and new graduates in the Indonesian labor market by proposing an integrated automation framework for skill gap identification, developed as part of the TalentIQ career analytics platform. The framework combines Natural Language Processing, semantic matching, and rule-based set-comparison techniques across three datasets: 10,766 job postings, 10,720 synthetic candidate profiles, and 5,663 online courses. After text normalization and skill standardization, all job and candidate records were encoded into 384-dimensional embeddings using Sentence-BERT (all-MiniLM-L6-v2). A Two-Tower Deep Neural Network was trained on cosine-similarity-based pseudo-labels to predict candidate-job match probability, while a rule-based module compared explicit skill sets using a canonical dictionary of over 250 skills to compute matched, missing, and extra skills. Both scores were combined into a Hybrid Readiness Score (HRS). Results show that Operations & Management (36.6%), Video/Content Creator (14.2%), and Web Developer (9.1%) dominate job demand, while English, Information Architecture, and Excel are the most requested skills. The Two-Tower DNN achieved 98.60% accuracy (MAE = 0.0153) on pseudo-labeled test data. Evaluation on 500 candidate-job pairs revealed a polarized readiness distribution: 44.2% "Not Ready," 31.4% "Fairly Strong," 23.8% "Very Strong," and only 0.6% "Needs Improvement," with an average rule-based skill coverage of 21.95%. The most frequent missing skills were information architecture, social media, English, teamwork, and content creation. These findings demonstrate that combining deep semantic matching with explicit rule-based comparison produces an interpretable and actionable readiness measure, offering practical guidance for job seekers and curriculum development in Indonesia's digital creative sector. 
Implementasi Arsitektur Data Warehouse Dan Dashboard Business Intelligence Untuk Analisis Performa Penjualan Ritel Elektronik Di Myanmar Menggunakan Pentaho Dan Tableau Taqiyuddin Ahmad Al Aufa; Adriano Femaz Rivaldy; Farel Ega Nurroyan; Muhammad Fawwaz Dhiaulhaq Hud; Amalia Anjani Arifiyanti
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 2 No. 2 (2026): JITIFNA - Juli
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v2i2.1717

Abstract

This study aims to implement a Data Warehouse architecture and a Business Intelligence dashboard to analyze the electronic retail sales performance in Myanmar. Raw data sourced from the Electronic Retail Dataset (2020–2023) has high complexity and poor quality (dirty data). The methods used include building a data pipeline with PostgreSQL as a staging area, the Extract, Transform, Load (ETL) process using Pentaho Data Integration, Star Schema modeling, and interactive visualization using Tableau via a Virtual Datamart. The results show that the ETL process successfully cleaned data anomalies and produced an integrated schema. The resulting dashboard reveals strategic insights, such as the revenue dominance of "Laptop" products versus the high sales volume of "Desks", and the market's reliance on physical B2B channels. Furthermore, spatial disparities were identified, with Yangon and Bago serving as revenue centers, while other regions require strategic intervention. This implementation proves that the integration of Pentaho and Tableau can transform complex transactional data into visual information that effectively supports data-driven business decision-making.
Application of Ensemble Machine Learning Methods for Aspect-Based Sentiment Analysis on User Reviews of the Wondr by BNI App Rendi Hardiartama; Amalia Anjani Arifiyanti; Seftin Fitri Ana Wati3
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4297

Abstract

This study analyzes user perceptions of the Wondr by BNI app using Aspect-Based Sentiment Analysis (ABSA) and a stacking ensemble learning approach on user reviews. Data were collected from the Google Play Store and App Store through scraping, then processed and labeled. The study involves two classification stages: aspect identification and sentiment classification for each aspect. The stacking ensemble model without resampling showed the best performance, with F1-scores of 99.4% for UI (User Interface), 99.3% for Authentication, and 99% for Transaction. For sentiment classification, F1-scores reached 82.2% User Interface (UI), 87.8% (Authentication), and 92.4% (Transaction). The use of LIME (Local Interpretable Model-Agnostic Explanations) improved model interpretability by highlighting keywords influencing the classification results. The final output of this research is a website capable of performing aspect-based sentiment classification
Co-Authors Abdul Rezha Efrat Najaf Achmad Fauzi Adriano Femaz Rivaldy Adriano Femaz Rivaldy Aghni Qisthina Al Rahma Agung Brastama Putra Akira Permata Ramadhani Al Rahma, Aghni Qisthina alathoillah, abdul hanif Ananda Lakunti A Andhyni, Cyntia Prisya Anggy Oktaviana Syafira Annisa Lusyani Zahra Anwar Sodik, Anwar Aprilia, Eka Fahira AryaRafa, Daud Audrey Septya Rosanti Bagus Utomo Basma Eno Ketherin Brahmantio Widyo Trenggono Daniar, Ivan Faiz Devi, Ditha Lozera Dewi Safitri, Triyatul Dharmawan, Ega Dhian Satria Yudha Kartika Diana Aqidatun Nisa Ditha Lozera Devi Elfaretta, Syifa Saskia Fachrurrozy Nurqoulby Fandi, Rico Satria Farel Ega Nurroyan Farhan Setiyo Darusman Farhan Setiyo Darusman Fariska, Rahmah Putri Ferdiansyah, Rizky Fernaldy, Fabiyan Atha Fidyah Salsabila Putri Sillehu Firsttama, Risav Arrahman Fitri, Anindo Saka Geovano Galan Widiatmoko Putra Hakiki, Primandika Heni Lusiana Dewi I Gusti Ayu Sri Deviyanti Iamho Pegodang Eltiuzy Indira Setia Amalia Indra Fajar Novian Ivan Faiz Daniar Jannatuzzahra, Khoirunisa Ketherin, Basma Eno Kusumantara, Prisa Marga Kusumantara, Prisa Marga M. Rizal Abdullah Rozi Mahanani, Anajeng Esri Edhi Marga Kusumantara, Prisa Marisca Amanda Hidayat Mashita Kustyani Maulana Arrasyid, Nizar Maulana Kharyska Abadi, Muhammad Mochamad Suhri Ainur Rifky Mochammad Fuad Pandji Mohamad Irwan Afandi Muhammad Burhanuddin F Muhammad Fawwaz Dhiaulhaq Hud Narendra, Efriza Cahya Nilwanda, Leona Elsa Novian, Indra Fajar Nur Rachman Nidhi Suryono, Muhammad Nurisa Rahma Shantika Nurjanti Takarini Oktania Purwaningrum Oktania Purwaningrum Oktania Purwaningrum Pandu Rizki Maulidiah Permatasari, Reisa Pradana, Rhendy May Putra, Satrio Honggonagoro Pramono Putri, Youlan Indira Putu Anggi Suryantari Rafi Dhafin Ersamazaya Rafi Purwa Syahputra Rahayu Kartika Sari Raihana Sakhi Aswanda Rendi Hardiartama Rendi Panca Wijanarko Rhendy May Pradana Rizka Hadiwiyanti Safitri, Eristya Maya Saka Fitri, Anindo Salma, Nabila Maudy Satria, Dhian Seftin Fitri Ana Wati Seftin Fitri Ana Wati3 Sembilu, Nambi Sidhi Pamekas, Afu Siti Oktavia Eka Putri Solehudin Al Ayyubi Sudewantoro N M Sulistyowati Sulistyowati Sulistyowati Sulistyowati Taqiyuddin Ahmad Al Aufa Taqiyuddin Ahmad Al Aufa Tri Diana Rimadhani Tri Luhur Indayanti Sugata Tri Puspa Rinjeni Ubaidillah Fahmi, Rohmat Wahyu Setiawan Wahyuni, Eka Dyar Wati , Seftin Fitri Ana Wati, Seftin Fitri Ana Wibisono, Mahendra Priyo Wibowo, Nur Cahyo Wisnu Mukti Darwansah Wulansari, Anita Yudha Yunanto Putra Yudha Yunanto Putra Zahra, Nabila Athifah