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All Journal Jurnal Teliska Jupiter Elkom: Jurnal Elektronika dan Komputer PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Prosiding SNATIF Annual Research Seminar JETT (Jurnal Elektro dan Telekomunikasi Terapan) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING IT JOURNAL RESEARCH AND DEVELOPMENT JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA TEKNOLOGI INFORMASI MIND (Multimedia Artificial Intelligent Networking Database) Journal AMALIAH: JURNAL PENGABDIAN KEPADA MASYARAKAT Building of Informatics, Technology and Science Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Jurnal Digit : Digital of Information Technology Journal of Technology and Informatics (JoTI) Arcitech: Journal of Computer Science and Artificial Intelligence Bulletin of Network Engineer and Informatics (BUFNETS) CSRID
Aryanti Aryanti
Jurusan Teknik Elektro, Program Studi Teknik Telekomunikasi, Politeknik Negeri Sriwijaya
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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Sistem Pengenalan Wajah Real-Time Menggunakan YOLOv7 untuk Akses Gedung TVRI Palembang Berbasis Web Fatia Salsabilla Kyara; Aryanti Aryanti; R.A. Halimatussa'diyah
Journal of Technology and Informatics (JoTI) Vol. 7 No. 2 (2025): Vol. 7 N. 2 (2025)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v7i2.1063

Abstract

The development of information and communication technology today has had a significant impact on various aspects of life, including in the field of security. The use of face recognition is one of the facial recognition techniques, where the results of the camera capture will be matched with photos or facial curve textures that already exist in the database. The system is widely applied using various methods and artificial intelligence, one of which is YOLO (You Only Look Once). The purpose of this study is to design, develop, and identify challenges in implementing a real-time facial recognition system using web-based YOLOv7 that can detect the faces of people entering the TVRI Palembang building, then photos and times when a person's face is not detected will be stored in the database. The data used comes from literature studies, data collection obtained from photos of TVRI television station employees' faces, software design with technology selection, user interface design, and algorithm structures that will be used. After going through these stages, a system implementation was carried out for the application of the system and analysis of the data results obtained. The results showed that the face detection system using YOLOv7 showed very good performance. In 100 training epochs, the system achieved 96,6% face detection accuracy and 90% face recognition accuracy, successfully identifying almost all registered faces and detecting faces in real time. This system produces high accuracy in detecting faces and almost all faces that should be recognized are successfully detected.
Implementation of Hybrid ResNet50 and XGBoost Model for Wheat Plant Disease Classification Aryanti Aryanti; Muhammad Aulia Dzikri; Ahmad Rifqi Nugraha; Khumairah Amira Sari; Dea Oktavia
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13004

Abstract

A hybrid artificial intelligence (AI) system was successfully developed in this study, combining the ResNet50 architecture as an image feature identifier and the XGBoost algorithm for final classification. This model was used to detect six disease variations using 5,505 wheat leaf photographs. To ensure model stability, rigorous testing was conducted using two methods: Stratified 5-Fold Cross-Validation on the entire data set and independent testing using 300 images (equally divided into 50 samples per class).The test results demonstrated very solid performance. The model recorded an average global accuracy of 94.66% (±0.21%) using the K-Fold method, and an accuracy of 94.67% and a Macro F1-Score of 0.9445 in the independent testing. Through confusion matrix mapping, the model successfully classified the Healthy, Black Rust, and Septoria categories perfectly (a score of 1.00). However, there was still a minor error in the case of eight samples being confused between Brown Rust and Yellow Rust due to the visual similarity of the orange-yellowish coloration early in the infection period. Furthermore, the Feature Importance assessment demonstrated that the XGBoost decision base is transparent (Explainable AI). This AI accurately focuses on clinical signs of plants such as chlorosis symptoms and spot texture, while ignoring background objects such as weeds and soil. This combination of methods creates a stable, efficient system with a response time of only 18.5 milliseconds per photo, and a biologically valid decision base.
Design of a Hybrid SVM Ensemble and Large Language Model Chatbot for Multi-Class Intent Classification in Clinic Information Services Vina Rahmadiany; Lindawati Lindawati; Aryanti Aryanti
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/a3kdjj55

Abstract

Clinical information service chatbots require an accurate multiclass intent classification mechanism to handle informal language variations, medical abbreviations, service-related inquiries, and health complaints in Indonesian. This study aims to develop a hybrid chatbot architecture that integrates a Support Vector Machine ensemble for intent classification with Meta-Llama-3.1-8B-Instruct to generate relevant, natural, and context-aware responses. The dataset consisted of 3,293 utterance patterns across 66 intent classes. The proposed approach employed an 80:20 stratified split; semantic augmentation on the training data; preprocessing via abbreviation normalization, stopword removal, and Sastrawi stemming; TF-IDF feature extraction using unigram and bigram word n-grams and character n-grams; chi-squared feature selection; and hyperparameter optimization via grid search. The classification model was constructed by combining three calibrated LinearSVC classifiers via probability-based soft voting. Experimental results achieved an accuracy of 89.83%, a weighted F1-score of 89.90%, a kappa of 0.8966, a macro AUC of 0.9942, and an average response time of 61.87 ms. McNemar's test indicated statistically significant improvements over Complement Naïve Bayes, Logistic Regression, and Decision Tree, while no significant difference was observed compared with Single SVM. Therefore, the proposed architecture is effective at supporting multiclass intent classification and delivering fast, relevant chatbot responses for clinical information services.
IMPLEMENTASI PERANGKAT IOT KAMERA CCTV BERBASIS YOLO DAN KALMAN FILTER UNTUK MANAJEMEN KETERSEDIAAN SLOT DAN JUMLAH KENDARAAN : IMPLEMENTATION OF AN IOT-BASED CCTV CAMERA SYSTEM USING YOLO AND KALMAN FILTER FOR PARKING SLOT AVAILABILITY AND VEHICLE COUNT MANAGEMENT IN HOTEL PARKING AREAS Muhammad Iqbal Falevy; Aryanti Aryanti; Suzan Zefi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8193

Abstract

Real-time parking availability information is an important factor in improving service quality in the hospitality industry. Conventional parking management still has limitations in providing accurate and timely information regarding vehicle occupancy and parking slot availability. This study aims to implement an Internet of Things (IoT) and Computer Vision-based hotel parking monitoring system using ESP32-CAM, the YOLOv8-L deep learning model, Kalman Filter through the SORT algorithm, Firebase Realtime Database, and a web-based monitoring dashboard. ESP32-CAM transmits video streams to the server, where YOLOv8-L performs real-time vehicle detection, while Kalman Filter is used for vehicle tracking and counting. The processing results are synchronized with Firebase Realtime Database and displayed on the monitoring dashboard. Experimental results show that the system successfully detected 14 vehicles out of a total parking capacity of 20 slots, resulting in 6 available parking spaces. The system was also able to perform real-time vehicle tracking and counting while automatically updating parking information on the dashboard. The proposed system can support hotel parking management effectively, efficiently, and in real time.
Sistem Rekomendasi Pupuk Berdasarkan Kondisi Dan Jenis Tanah Menggunakan LightGBM Aryanti Aryanti; Aknes Tasia Pratama; Dava Anugrah Limanda; Hisanah Nakhwah Aulia Faruly; Muhammad Juan Farza Rafly Alganiyu
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 10, No 3 (2026): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v10i3.26964

Abstract

Pemupukan yang tidak tepat masih menjadi permasalahan dalam sektor pertanian karena dapat menurunkan produktivitas tanaman, meningkatkan biaya produksi, serta menyebabkan penggunaan pupuk yang kurang efisien. Oleh karena itu, diperlukan sistem rekomendasi pupuk yang dapat membantu petani menentukan jenis pupuk yang sesuai berdasarkan kondisi tanah. Penelitian ini mengembangkan sistem rekomendasi pupuk berbasis machine learning menggunakan algoritma Light Gradient Boosting Machine (LightGBM) untuk mendukung pertanian presisi. Dataset yang digunakan terdiri dari 10.000 data dengan delapan parameter input, yaitu jenis tanah (clay, silt, sandy, loamy), kelembaban tanah, pH tanah, kandungan karbon organik, serta kadar nitrogen, fosfor, dan kalium. Target prediksi terdiri dari tujuh kelas pupuk. Tahapan penelitian meliputi preprocessing (data cleaning, label encoding, dan normalisasi min-max), pembagian dataset dengan rasio 80:20, pelatihan model LightGBM dengan konfigurasi n_estimators=500 dan learning_rate=0,1, serta evaluasi menggunakan confusion matrix dan classification report. Hasil eksperimen menunjukkan akurasi sebesar 86,35% pada data pengujian. Analisis feature importance menunjukkan bahwa Soil Moisture merupakan fitur paling berpengaruh, diikuti Soil pH, Organic Carbon, dan Nitrogen Level. Model menunjukkan performa sangat baik pada kelas Urea dan DAP dengan f1-score mencapai 0,95. Hasil penelitian membuktikan bahwa LightGBM efektif untuk klasifikasi multi-kelas dalam sistem rekomendasi pupuk dan berpotensi mendukung pertanian presisi yang lebih efisien dan berkelanjutan.
Co-Authors Ade Silvia Handayani Adhelia Febriasari Harahap Ahmad Rifqi Nugraha Aknes Tasia Pratama Amanda Tsabita Putri Aqilla Khairunnisya Asriyadi Asriyadi Asriyadi Asriyadi Aulia Syafitri Aulia Syafitri CIK SADAN Ciksadan Ciksadan Clara Alcahya Palpa Dava Anugrah Limanda Dea Oktavia DEFINA APRILIANI Devi Indah Pujiana Dhea Syafitri Diah Novita Sari DIMAS ANDREANSYAH Dimas Iqbal Fahrozy DWI RAMADHANI Dyah Utari Yusa Wardhani Fatia Salsabilla Kyara Fifteen Angelina Hari Ramadhan Hisanah Nakhwah Aulia Faruly Hj Lindawati Hj. Lindawati Ibnu Asrafi Ikhsan Yuda Pratama Ikhthison Mekongga Imas Ning Zhafarina Irma Salamah Jonathan Farrel Akbar Juriawan Raja Saputra Kayla Luna Pasha Khumairah Amira Sari M. A. Racka Eratama M.Nabil Ar-Rassya Mohammad Fadhli Muhammad Aulia Dzikri Muhammad Ghalib Muhammad Hanif Fatin Muhammad Imansyah Muhammad Iqbal Falevy Muhammad Juan Farza Rafly Alganiyu Muhammad Rizko Justiano Muhammad Zakuan Agung Nabil Alrofi Nadya Aprilia Noval Resti Ardiansyah Nurhajar Anugraha Nurul Mardhiyah Pertiwi Nurul Utami Putri Nabila Putri Nur Azizah R.A. Halimatussa'diyah Roni Suhermawan Sarjana, Sarjana Sholihin Sholihin Siti Salwa Nurhadiva Sulistia Sulistia suzan zefi Taufik Permana THREA MALINDA Vina Rahmadiany WULAN ZAHRA PUTRI Yulia Hapsari Yuni Selvita Suci