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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Komputer Terapan Jurnal Pengabdian Masyarakat Multidisiplin Journal of Applied Computer Science and Technology (JACOST) Journal of Artificial Intelligence and Digital Business
Ira Puspita Sari
Universitas Abdurrab
Aerospace Engineering Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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GELANG BEL RUMAH UNTUK PENYANDANG TUNARUNGU MENGGUNAKAN MODUL RF 433MHZ Ira Puspita Sari; Luluk Elvitaria; Syaid Alarbi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 1 (2024): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i1.4090

Abstract

This research addresses the design of hardware to assist individuals with hearing loss. The system consists of a transmitter mounted on a door and a receiver worn on the wrist. The user can connect the device by pressing a button on the wristband, and the transmitter sends a command signal to the receiver. The receiver activates the vibrator motor, which produces vibrations for 2 seconds. The communication range is within a radius of several hundred meters under optimal conditions, using common and less congested radio frequencies. The RF module used in the system operates at a frequency of 433MHz and uses Amplitude Shift Keying (ASK) modulation. The system uses a flashing LED to indicate the command signal. The LED blinks for a specific duration, and the blinking pattern can be controlled. The system also includes a data reception function, where received data is checked for specific messages and processed accordingly.
KLASIFIKASI TELUR CACING BERBASIS GAMBAR MENGGUNAKAN JARINGAN SARAF KONVOLUSIONAL: IMAGE-BASED CLASSIFICATION OF HELMINTHS EGGS USING CONVOLUTIONAL NEURAL NETWORKS Luluk Elvitaria Elvitaria; Ira Puspita Sari; Tengku Imam Buchari
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.v10i2.6207

Abstract

This research aims to develop an image processing-based classification system for helminths eggs using Convolutional Neural Network (CNN) with a transfer learning and finetuning approach. Helminths eggs are important indicators in the diagnosis of helminth diseases in humans. However, the manual classification of helminths eggs requires significant time and effort. Therefore, an automated system that can classify helminths eggs with high accuracy would be highly beneficial in the diagnosis of these diseases. In this study, experiments were conducted using three CNN architectures that have proven effective in image classification tasks, namely EfficientNetB0, MobileNetV3, and ResNet50. Transfer learning method was employed by utilizing pre-trained models on large-scale image datasets. Subsequently, fine tuning was performed on the last layers of the models to adapt them to the helminths egg data. Testing was conducted using a dataset of helminths eggs collected from IEEE Dataport. The experimental results show that all three CNN architectures were able to classify helminths eggs with high accuracy, with EfficientNet-B0 achieving the highest accuracy (95.36%). The developed system in this study has the potential to be used in the efficient and accurate diagnosis of helminth diseases.
KLASIFIKASI JENIS JAMUR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) BERBASIS CITRA DIGITAL Luluk Elvitaria; Ira Puspita Sari; Lasiah Susanti; Zaerinisya Fitri
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.v11i2.6933

Abstract

This reserach proposes a mushroom species classification method based on digital image processing using a Convolutional Neural Network (CNN). The EfficientNet-B4 architecture was adopted as the backbone model, employing a transfer learning approach followed by a fine-tuning process. The dataset consisted of 3,000 mushroom images categorized into 10 classes, with each class containing 300 images. The model implementation was carried out using Google Colab and the Python programming language. Model performance was evaluated using accuracy, precision, recall, and F1-Score metrics. Several model variations were examined by adjusting training parameters and data split ratios. The best-performing model, referred to as Model 1, utilized a customized freeze layer and applied an 80% training, 10% validation, and 10% testing data split, achieving the highest performance with 90.00% accuracy, 90.09% precision, 89.63% recall, and an 89.59% F1-Score. The findings indicate that applying a customized freeze layer effectively reduces the number of trainable parameters, leading to improved model accuracy. Furthermore, the selection of data split ratios contributes to performance differences during the training and testing phases.
Pengujian Akurasi Timbangan Kelapa Sawit Berbasis Load Cell dan Arduino Uno Ira Puspita Sari; Liza Trisnawati
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.9350

Abstract

Industri kelapa sawit merupakan salah satu sektor penting yang berkontribusi besar terhadap perekonomian Indonesia. Dalam proses pengelolaan hasil panen, kegiatan penimbangan memiliki peran yang sangat penting karena berhubungan langsung dengan perhitungan produksi, biaya operasional, dan keuntungan. Namun, metode penimbangan tradisional masih memiliki berbagai keterbatasan, seperti tingkat akurasi yang rendah, proses yang memerlukan waktu lebih lama, serta tingginya kemungkinan terjadinya kesalahan manusia. Oleh karena itu, diperlukan suatu sistem penimbangan yang lebih akurat, efisien, dan mudah digunakan. Penelitian ini bertujuan untuk merancang dan membangun prototipe alat timbangan kelapa sawit berbasis sensor load cell menggunakan mikrokontroler Arduino Uno dengan kapasitas pengukuran hingga 350 kg. Metode penelitian yang digunakan meliputi identifikasi masalah, studi literatur, analisis kebutuhan, perancangan perangkat keras dan perangkat lunak, implementasi sistem, serta pengujian alat. Sistem terdiri dari sensor load cell sebagai pendeteksi beban, modul HX711 sebagai penguat dan konverter sinyal, Arduino Uno sebagai pengolah data, serta LCD sebagai media tampilan hasil pengukuran secara real-time. Proses kalibrasi dilakukan untuk memastikan ketepatan pembacaan berat sebelum alat digunakan dalam pengujian. Hasil penelitian menunjukkan bahwa prototipe yang dikembangkan mampu melakukan pengukuran berat secara konsisten, akurat, dan efisien. Sistem digital yang diterapkan juga mempermudah proses pencatatan data serta mengurangi risiko kesalahan manusia dibandingkan metode penimbangan manual. Dengan demikian, alat ini dapat menjadi solusi yang mendukung otomatisasi proses penimbangan hasil panen kelapa sawit serta meningkatkan produktivitas dan efisiensi kerja di sektor pertanian.
Co-Authors Adrian Adi Putra Al Amin anip Febtriko Arisandi, Diki Berliana Putri Debi Setiawan Eliya Mursyida Jon Fery Sitepu Lasiah Susanti Liza Trisnawati Luluk Elvitaria Luluk Elvitaria Luluk Elvitaria Elvitaria Nia Silviana Reski Lestari Robi Wahyu Salamun Salamun Sukri Sukri Syaid Alarbi Tengku Imam Buchari Tri Rahayuningsih Zaerinisya Fitri