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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmu dan Teknologi Kesehatan Jurnal Pemanfaatan Teknologi untuk Masyarakat
Sabrina Adinda Sari
Telkom University
Computer Science & IT Engineering Health Professions

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Eco Kantin Surya: Implementasi Energi Terbarukan untuk Kantin Hemat Energi di Sekolah Dasar Ayu Qatrunnada Istiqfarri; Sabrina Adinda Sari
JAPATUM: Jurnal Pemanfaatan Teknologi untuk Masyarakat Vol 4 No 5 (2025): Desember 2025
Publisher : MATRADIPTI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59328/JAPATUM.2025.4.5.134

Abstract

Program pengabdian kepada masyarakat Eco Kantin Surya di SDN Sukasari 5 Tangerang berhasil direalisasikan dalam bentuk sistem energi surya yang terdiri atas panel surya, baterai, inverter, terminal listrik, dan monitoring berbasis IoT. Sistem ini dirancang untuk mendukung kebutuhan listrik di area kantin, menyediakan fasilitas charging station bagi warga sekolah dan pengunjung, serta menjadi media pembelajaran kepada siswa/i SDN Sukasari 5 tentang energi terbarukan sejak dini. Secara sosial dan edukatif, program ini meningkatkan kesadaran terhadap energi ramah lingkungan; secara ekonomi, program berpotensi mengurangi ketergantungan terhadap listrik konvensional; dan secara lingkungan, program mendukung penerapan energi bersih di sekolah dasar. Dengan demikian, pengabdian ini menunjukkan bahwa integrasi panel surya, fasilitas charging bersama, dan media pembelajaran berbasis teknologi dapat menjadi model pengabdian yang aplikatif, edukatif, dan berkelanjutan di lingkungan sekolah. Implementasi teknis berjalan baik, mulai dari pemasangan perangkat hingga pengujian fungsi sistem, sehingga fasilitas dapat digunakan secara aman dan optimal.
Pendekatan Hibrida menggunakan Sistem Inferensi Fuzzy dan Pembelajaran Mendalam untuk Klasifikasi Penyakit Alzheimer pada Citra MRI Bagas Wibowo; Andy Maulana Yusuf; Bintang Vieshe Mone; Sabrina Adinda Sari
jitek Vol 13 No 2 (2026): Maret 2026
Publisher : Poltekkes Kemenkes Jakarta III

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32668/jitek.v13i2.2381

Abstract

Early detection of Alzheimer's disease using brain MRI image data can substantially improve clinical intervention and patient management. Our study evaluates the performance of an Alzheimer's classification system based on Fuzzy Inference Systems (FIS), specifically for the Mamdani and Sugeno models, in identifying four patient categories: (1) Non-Dementia, (2) Very Mild Dementia, (3) Mild Dementia, and (4) Moderate Dementia. In addition, this study compares the classification performance and computational efficiency of several deep learning architectures, including a traditional CNN (VGG16), a modern model (EfficientNet-B0), and a hybrid Fuzzy Convolutional Inference Engine (FCIE) that integrates CNN-based feature extraction with fuzzy logic reasoning. The dataset used consists of normalized and augmented Alzheimer's MRI images, and each model was trained and validated using a 70%:15%:15% split for training, validation, and testing. Experimental results show that the Mamdani and Sugeno FIS models achieve validation accuracies of about 32% and 35%, respectively, which highlights the limitations of pure texture-based features in capturing complex classification patterns. In contrast, VGG16 and EfficientNet-B0 produced validation accuracies of 82.81% and 85.22%, respectively, with AUC values of 0.95 and 0.96, respectively. However, when both schemes were combined into a hybrid model FCIE achieved the highest validation accuracy of 98.03% and AUC of 0.99. Comparative analysis of metrics, including precision, recall, F1-score, AUC, and training duration, shows a clear trade-off between accuracy and computational efficiency. This study recommends the FCIE model for clinical applications requiring high diagnostic accuracy, while EfficientNet-B0 is suggested for medical environments with moderate GPU resource constraints.
Investigating Shallow Learning Methods for Optical Character Recognition of Indonesia’s Nusantara Scripts Mahmud Dwi Sulistiyo; Aji Gautama Putrada; Aditya Firman Ihsan; Prasti Eko Yunanto; Donny Richasdy; Hassan Rizky Putra Sailellah; Sabrina Adinda Sari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6648

Abstract

Indonesia has numerous regional scripts—or so-called Nusantara scripts—and recognizing them is important to preserve Indonesia's cultural heritage. The advances of AI and computer vision technologies make it possible for a machine to optically read the handwritten scripts through the Optical Character Recognition (OCR) technique. However, collecting some of the top OCR solutions and comprehensively investigating their performances on the Nusantara scripts is currently lacking. This study investigates and evaluates some shallow learning-based methods on our newly introduced datasets, consisting of more than 38,000-character images across 80 letter classes in total; here, we focus on three regional scripts: Javanese, Sundanese, and Balinese. The methods include Random Forest, SVM, Logistic Regression, and Gaussian Naïve Bayes, as well as boosting techniques such as XGBoost, Light GBM, and CatBoost. A 5-fold cross-validation approach assessed model performance based on accuracy, precision, recall, and F1-score. Based on the experimental results, the methods demonstrated their competitiveness in reaching the best models for scripts; in particular, XGBoost, Light GBM, and Random Forest-Gini were the winners for Javanese, Sundanese, and Balinese scripts, respectively. These findings demonstrate the effectiveness of ensemble learning methods for diverse handwritten scripts. Comparative analysis to prior deep learning studies is also discussed in this paper. In addition, this research also contributes to preserving Indonesian traditional scripts, as well as offers insights for future regional OCR in other countries.
Co-Authors Aditya Firman Ihsan Aji Gautama Putrada Andy Maulana Yusuf Ayu Qatrunnada Istiqfarri Bagas Wibowo Bintang Vieshe Mone Donny Richasdy Hassan Rizky Putra Sailellah Mahmud Dwi Sulistiyo Prasti Eko Yunanto