Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Rekam Kegiatan Pengabdian Masyarakat (REKAGAMA)
Zahratul Fitri
Malikussaleh University
Computer Science & IT Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
PKM Penguatan Transformasi Pembelajaran Daring melalui Platform Teknologi Informasi di SMK Negeri 3 Lhokseumawe Zahratul Fitri; Nur Faliza; Defi Irwansyah; Armelia Dafrina; Syibral Malasyi; Ilham Sahputra
REKAGAMA Vol. 1 No. 2 (2026): REKAGAMA (Rekam Kegiatan Pengabdian Masyarakat)
Publisher : CV Mazaya Cahaya Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kegiatan Program Kemitraan Masyarakat (PKM) ini difokuskan pada penguatan transformasi pembelajaran daring melalui penerapan platform teknologi informasi di SMK Negeri 3 Lhokseumawe. Perkembangan teknologi digital menuntut institusi pendidikan untuk mampu mengimplementasikan pembelajaran berbasis daring secara efektif, efisien, dan berkelanjutan. Namun, beberapa kendala masih ditemui, seperti keterbatasan pemahaman siswa dalam penggunaan platform pembelajaran daring, pengelolaan materi ajar digital yang belum optimal, kurangnya variasi media interaktif, serta minimnya pemanfaatan teknologi informasi sebagai sarana evaluasi dan komunikasi pembelajaran. Kondisi ini berdampak pada rendahnya efektivitas proses belajar-mengajar, khususnya terkait keterlibatan siswa, kemudahan akses materi, dan capaian hasil belajar. Pelaksanaan kegiatan PKM ini meliputi tahap-tahap strategis, yakni analisis kebutuhan mitra, sosialisasi program, pelatihan penggunaan platform digital, pendampingan pembuatan media pembelajaran daring, simulasi penerapan pembelajaran berbasis teknologi, dan evaluasi terhadap tingkat pemahaman serta keterampilan guru dan siswa. Platform yang digunakan difokuskan untuk mendukung manajemen kelas daring, distribusi materi pembelajaran, pemberian tugas, komunikasi dua arah antara guru dan siswa, serta evaluasi pembelajaran secara lebih sistematis dan terukur. Pendekatan yang diterapkan bersifat partisipatif dengan melibatkan guru dan siswa sebagai pemangku kepentingan utama, sehingga solusi yang diberikan relevan dengan kebutuhan nyata di lingkungan sekolah. Hasil kegiatan menunjukkan adanya peningkatan kompetensi peserta dalam pemanfaatan platform pembelajaran daring. Siswa menjadi lebih terampil dalam menyusun materi digital, mengelola kelas virtual, menyampaikan tugas daring, dan menghadirkan media pembelajaran yang interaktif. Siswa memperoleh kemudahan akses materi, keterlibatan aktif dalam pembelajaran, kemampuan mengumpulkan tugas secara daring, serta komunikasi yang lebih efektif dengan guru. Kegiatan ini juga memberikan kontribusi positif dalam menyiapkan sekolah untuk melaksanakan pembelajaran berbasis teknologi informasi secara berkelanjutan dan berdaya guna. Secara keseluruhan, PKM ini dapat menjadi strategi penting dalam mempercepat digitalisasi pembelajaran, meningkatkan kompetensi teknologi guru dan siswa, serta memperkuat mutu pendidikan vokasi di era transformasi digital.
DETEKSI DAUN HERBAL DAN BERACUN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI TANAMAN HERBAL DAN BERACUN: HERBAL AND POISONOUS LEAF DETECTION USING CONVOLUTIONAL NEURAL NETWORK FOR HERBAL AND POISONOUS PLANT CLASSIFICATION Della Adelia; Zahratul Fitri; Cut Agusniar
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6025

Abstract

Indonesia is one of the tropical countries with the greatest biodiversity in the world, including around 50,000 plant species and around 7,500 used by the community as raw materials for traditional medicine. However, the large number of plant species poses a challenge in distinguishing between edible plants and those that contain toxins, because herbal and toxic plants often have similar morphological characteristics of their leaves. Therefore, this study was conducted to design a classification system to distinguish between herbal and toxic plant leaves, which is expected to be utilized by the community as a preventive measure to reduce the risk of poisoning due to incorrect plant identification. The system was built using a Convolutional Neural Network (CNN) architecture implemented through the Flask framework and equipped with a rule-based system to determine plant categories based on the model's prediction results. The dataset consists of 960 images with 8 classes of local Indonesian plant leaves, where four categories include herbal plants (moringa, mint, betel, and basil) and four categories of poisonous plants (saga rambat, bandotan, gympie-gympie, and jelatang). This study conducted experiments on several CNN architectures, including custom and pretrained models (EfficientNetB0, MobileNetV2, and ResNet50V2). The best results were obtained from the EfficientNetB0 model trained using images with an input shape of 224×224 pixels, a batch size of 24, and the Adam optimizer, achieving a training accuracy of 99.51% and validation accuracy of 98.96%. This model demonstrated superior accuracy compared to other models, such as the custom model (93.88%), MobileNetV2 (99.50%), and ResNet50V2 (99.38%). The evaluation results show that the EfficientNetB0 model has excellent performance, with an overall classification accuracy of 99.00%, precision of 99.00%, recall of 99.00%, and an F1-score of 99.00%.
SISTEM PAKAR DIAGNOSIS PENYAKIT PARU MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DAN RULE BASED SYSTEM: EXPERT SYSTEM FOR LUNG DISEASE DIAGNOSIS USING CONVOLUTIONAL NEURAL NETWORK AND RULE-BASED SYSTEM Putri Syifa; Safwandi; Zahratul Fitri
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6548

Abstract

Lung disease remains a major health problem in Indonesia, accounting for 25.8% of respiratory-related deaths according to the Ministry of Health. Data from Muhammad Ali Kasim Gayo Lues Regional Hospital, Aceh, shows approximately 3,600 cases recorded since 2022. This study designs an artificial intelligence-based diagnostic system combining 224x224 pixel chest X-ray image analysis with clinical parameter evaluation using a rule-based system. The Rule-Based System implements standardized weighting where each clinical manifestation contributes proportionally to the total diagnostic score through normalization to a 100-point scale.The dataset consists of 983 images (786 training, 197 validation) collected during the 2022-2025 period, covering tuberculosis (300 cases), pneumonia (300 cases), and pneumothorax (383 cases) with 25 disease symptoms. Evaluation results show the system achieves 94.97% accuracy with 93.2% sensitivity and 95.4% specificity. The F1-scores for each disease were 0.9375 (Tuberculosis), 0.9125 (Pneumonia), and 0.987 (Pneumothorax). Therefore, this system can assist the diagnostic process and support clinical decision-making through both radiographic image analysis and patient symptom evaluation.
Co-Authors Armelia Dafrina Cut Agusniar Defi Irwansyah Della Adelia Ilham Sahputra Nur Faliza Putri Syifa Safwandi Syibral Malasyi