Article Per Year (5 Year)
p-Index From 2021 - 2026
1.048
P-Index
This Author published in this journals
All Journal Jurnal Riset Informatika G-Tech : Jurnal Teknologi Terapan Jurnal Pengabdian West Science Journal Of Artificial Intelligence And Software Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Anindhiasti Ayu Kusuma Asri
Universitas Duta Bangsa Surakarta, Indonesia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Implementasi Sistem Pemberian Pakan Ikan Hias Otomatis Menggunakan Esp32 Berbasis IoT (Internet of Things) Sugiarto, Muhammad Aqsha Rizki; Muhtarom, Moh; Asri, Anindhiasti Ayu Kusuma
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.1008

Abstract

This study aims to design and implement an automatic feeding system for ornamental fish using Internet of Things (IoT) technology, with the ESP32 microcontroller and servo motor. Given Indonesia's extensive water resources and the importance of its fisheries sector, ornamental fish farming has gained significant popularity. Fish owners often struggle with providing timely and consistent feeding. IoT technology offers an effective solution through remote connectivity. The developed system utilizes the Blynk application to schedule and monitor aquarium conditions in real-time. Test results indicate that the integration of ESP32 and the servo motor functions effectively, enabling scheduled automatic feeding. The system was tested and found to operate punctually, supporting a consistent daily feeding cycle. This study contributes to the advancement of innovative solutions in aquaculture, particularly in the application of IoT technology for more efficient and effective aquarium management.
Penerapan Face Recognition dengan Algoritma Viola Jones dalam Sistem Presensi Kehadiran Siswa Berdasarkan Titik Lokasi Berbasis Website Akbar Syarif Pratama Putra; Afu Ichsan Pradana; Anindhiasti Ayu Kusuma Asri; Elia Hari Prasetya
G-Tech: Jurnal Teknologi Terapan Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i3.4512

Abstract

Metode pengenalan wajah Viola-Jones merupakan salah satu teknik yang paling umum digunakan dalam domain pengenalan wajah. Kebutuhan akan inovasi dalam sistem pengenalan wajah menjadi krusial dalam konteks keamanan dan pengawasan, serta memiliki berbagai aplikasi lainnya. Penelitian ini memusatkan perhatian pada evaluasi efektivitas metode tersebut, terutama dalam mengidentifikasi siswa di SMA Negeri 2 Surakarta. Tujuannya adalah untuk mengembangkan suatu kerangka partisipasi siswa dengan menggunakan teknologi pengenalan wajah Viola-Jones, dengan fokus pada pengawasan kehadiran. Meskipun tingkat keberhasilan pengenalan wajah mencapai sekitar 90%, penelitian menunjukkan adanya ketidakpuasan terhadap pencocokan gambar wajah dalam database. Pengujian dilakukan dengan melibatkan 200 gambar dari 20 individu dengan variasi penampilan. Faktor-faktor seperti pencahayaan, jarak, dan posisi wajah terbukti mempengaruhi hasilnya. Pengujian ini dilakukan menggunakan aplikasi digital Laravel 11 pada platform Windows 11.
KNOWLEDGE-BASED HIJAB PRODUCT SELECTION RECOMMENDATION SYSTEM AT CANDY SCARVES Nur Rohmani, Mayda; Hartanti, Dwi; Ayu Kusuma Asri, Anindhiasti
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1835.21 KB) | DOI: 10.34288/jri.v7i3.377

Abstract

The primary objective of this study is to construct a knowledge-driven hijab product selection recommendation system for Candy Scarves. This system is designed to help customers find hijabs that match their criteria by utilizing customer characteristics and product attributes. The study uses a knowledge-based recommendation approach supported by case-based techniques. The construction of the system is orchestrated through the application of the Rapid Application Development (RAD) paradigm, encompassing a sequence of iterative stages—ranging from requirement formulation and architectural design to accelerated prototyping and eventual deployment—thus privileging adaptability and user-centered refinement over linear progression. Data modeling using sample data totaling 25 hijab products and 6 attributes. The system provides recommendations based on criteria for hijab models, materials, hijab colors, skin colors, motifs, and prices. The empirical findings reveal that the hijab item exhibiting the utmost degree of similarity is the Umama Hijab with voal material, mocha hijab color, brown skin color, and plain motifs with a result of 0.90303. The results of this analysis are able to provide personal recommendations effectively and have the potential to increase customer satisfaction and product sales.
Implementation of a Food Menu Recommendation System at Ndalem Uti Restaurant Using Collaborative Filtering Based on User Preferences Rhemadanu, Andreas; Susanto, Rudi; Asri, Anindhiasti Ayu Kusuma
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7119

Abstract

This study aims to implement a menu recommendation system at Ndalem Uti Restaurant using a Collaborative Filtering approach based on user preferences. The main problem faced is that customers have difficulty choosing the best-selling menu because of the many choices and minimal information regarding the popularity of each menu. To overcome this, the Collaborative Filtering method is used with the User-Based Cosine Similarity calculation to measure the similarity of preferences between users. The test results show that the recommendation accuracy level reaches 96%, including for new users. This system is able to provide more personalized menu recommendations based on customer order history and ratings. This implementation is expected to improve user experience, optimize sales, and be a solution for the development of culinary businesses in the future.
Pemanfaatan Chat GPT Untuk Guru PAUD dalam Pengembangan Materi Ajar Bahasa Inggris Purwatiningsih, Agustina; Kusuma Asri, Anindhiasti Ayu
Jurnal Pengabdian West Science Vol 4 No 01 (2025): Jurnal Pengabdian West Science
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/jpws.v4i01.1962

Abstract

Dalam beberapa tahun terakhir, perkembangan kecerdasan buatan (AI) sedikit banyak telah mempengaruhi perubahan alat atau metode pengajaran dalam pendidikan. Salah satu inovasi yang menonjol adalah ChatGPT, sebuah model AI yang dikembangkan oleh OpenAI. ChatGPT membantu guru PAUD (Pendidikan Anak Usia Dini) dengan memberikan ide-ide kreatif dan menarik yang disesuaikan dengan kebutuhan perkembangan anak-anak. Kegiatan PKM yang telah dilaksanakan mendapatkan respon yang positif dari guru sebagai praktisi pengajar di PAUD TPA Buah Hati. Peserta mendapatkan ilmu baru dari penggunaan teknologi AI ChatGPT untuk mengembangkan media belajar bahasa inggris. Pada sebelum pengabdian peserta biasanya membuat materi secara manual, akan tetapi setelah adanya PKM para guru dapat membuat materi ajar langsung bentuk tabel dengan memanfaatkan ChatGPT. Keberhasilan tersebut diharapkan dapat meningkatkan kualitas pembelajaran di TPA Buah Hati Karanganyar, serta menciptakan pengalaman belajar yang lebih menarik dan sesuai dengan kebutuhan siswa.
Performance Comparison of Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) Algorithms in Human Face Classification Yusuf Iskandar Royan; Pramono Pramono; Anindhiasti Ayu Kusuma Asri
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7384

Abstract

Facial expression recognition is crucial in fields like mental health monitoring and human-computer interaction. This study compares Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) in classifying facial images into stress and non-stress categories. Using a preprocessed dataset of labeled facial expressions, CNN was employed for its strength in automatic spatial feature extraction, while SVM served as a traditional machine learning benchmark. Both models were trained and tested on the same dataset split. Results showed CNN outperformed SVM in all performance metrics: CNN achieved 88.94% accuracy, 94.42% precision, 93.25% recall, and an F1-score of 89.85%, while SVM recorded 76.53% accuracy, 77.14% precision, 85.72% recall, and an F1-score of 80.67%. Despite its lower performance, SVM had faster training and a simpler structure, making it suitable for resource-limited scenarios. The study emphasizes the superiority of deep learning for complex image classification tasks.
Co-Authors Afu Ichsan Pradana Akbar Syarif Pratama Putra Dwi Hartanti Elia Hari Prasetya Muhtarom, Moh Nur Rohmani, Mayda Pramono Purwatiningsih, Agustina Rhemadanu, Andreas Rudi Susanto Sugiarto, Muhammad Aqsha Rizki Yusuf Iskandar Royan