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All Journal Jurnal TAM (Technology Acceptance Model) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JUTIM (Jurnal Teknik Informatika Musirawas) JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi Jurnal Informatika Global Jurnal Abdimas Mandiri Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Digital Teknologi Informasi Jurnal Pengabdian kepada Masyarakat JIKI (Jurnal Ilmu Komputer dan Informatika) IJAIRTec Aptekmas : Jurnal Pengabdian Kepada Masyarakat Algoritme Jurnal Mahasiswa Teknik Informatika Green Intelligent Systems and Applications Informatics and Enginering Dedication Jurnal Teknologi Sistem Informasi Klik - Jurnal Ilmu Komputer Arcitech: Journal of Computer Science and Artificial Intelligence Jurnal Pengabdian Publikasi Hasil Pengabdian Kepada Masyarakat. Jurnal Ilmiah Teknik Informatika dan Komunikasi MDP Student Conference Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Pengabdian Masyarakat dan Riset Pendidikan Indonesian Journal of Innovation Multidisipliner Research JISCOMP (Journal of Information System and Computer) INOVTEK Polbeng - Seri Informatika International Journal of Artificial Intelligence and Robotic Technology (IJAIRTec) Jurnal TAM (Technology Acceptance Model)
Hartati, Ery
Multi Data Palembang University, South Sumatra
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Klasifikasi Penyakit Alzheimer menggunakan CNN dengan pretrained VGG19 dan SMOTE berdasarkan Citra MRI Otak md, Ramanda; Hartati, Ery
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15122

Abstract

Early detection of Alzheimer's disease is crucial for effective treatment, and the use of brain MRI images has become a common method for diagnosis. However, many previous studies have faced challenges in addressing class imbalance in their datasets, leading to lower accuracy for minority classes. This study aims to address this issue by using a pretrained CNN architecture, VGG19, combined with the SMOTE method to address class integration and improve classification accuracy. This study contributes by introducing SMOTE to the Alzheimer's MRI image dataset to achieve a more balanced class distribution, which has not been fully explored in previous studies. The evaluation results show that the classification accuracy reaches 95%, higher than previous studies using VGG-19 with an accuracy of 77.66%. These results confirm that the use of VGG19 with SMOTE produces better performance, especially in addressing class representation, which is a key contribution of this study. This research has the potential to be applied in more efficient and accurate automated image-based detection systems, especially for the early diagnosis of Alzheimer's disease.
Enhancing Generalization of Tomato Leaf Disease Classification via TDR Model and Field-Conditioned Data Augmentation Fernando Feliansyah; Ery Hartati
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/tvgfx074

Abstract

Tomato leaf diseases significantly affect agricultural productivity, particularly when detection systems are deployed under real-field conditions characterized by illumination variation, background clutter, and image noise. Although deep learning-based models have achieved high accuracy on laboratory datasets such as PlantVillage, their generalization performance often degrades when applied to real-world environments. This study proposes a lightweight CNN-based tomato leaf disease recognition model, referred to as the TDR-Model, combined with field-conditioned data augmentation strategies. The proposed model integrates MobileNetV3 with Convolutional Block Attention Module (CBAM) and Omni-Dimensional Dynamic Convolution (ODC) to enhance feature representation while maintaining computational efficiency. Field-conditioned augmentation using the Albumentations library to simulate real-world visual variations during training. The model is evaluated on the real-world tomato set consisting of 10 classes and 885 leaf images. Experimental results show that the proposed model achieves an overall test accuracy of 82.94%, with precision, recall, and F1-score of 85.06%, 83.04%, and 83.03%, respectively. Furthermore, the model requires only 3.47 million parameters, 0.23 GFLOPs, and an average inference time of 5.15 ms, making it suitable for real-time and resource-constrained agricultural applications. These results indicate that the proposed approach effectively balances accuracy and efficiency for practical tomato leaf disease detection.
Classification of Tomato Fruit Ripeness Level Using Convolutional Neural Network–Support Vector Machine Based on Digital Image Saputra Edika, Nelson; Hartati, Ery
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/1r0wh197

Abstract

Tomato ripeness classification is an important task in post-harvest quality management, as the ripeness level directly influences taste, shelf life, and market value. Conventional ripeness assessment methods based on manual visual inspection are inherently subjective and often yield inconsistent results. To address this limitation, this study proposes an image-based tomato ripeness classification model using a hybrid Convolutional Neural Network–Support Vector Machine (CNN–SVM) approach. In the proposed model, a pretrained ResNet-50 architecture is employed as a fixed feature extractor to derive deep visual representations, while a Support Vector Machine with a Radial Basis Function kernel is utilized for final classification. The model is evaluated using a publicly available tomato image dataset, with the analysis limited to unripe and ripe categories. Image preprocessing procedures include resizing, normalization, and data augmentation, followed by an 80:20 train–test split strategy. Experimental results demonstrate that the proposed CNN–SVM model achieves strong and balanced performance, with an accuracy of 96.56%, a weighted precision of 96.80%, a recall of 96.56%, and an F1-score of 96.57%. These findings indicate that integrating deep feature extraction with an SVM classifier provides an effective and robust solution for tomato ripeness classification, particularly under limited data conditions.
Implementation of the You Look Only Once (YOLOv11) Algorithm to Detect the Ripeness of Golden Melons Tandoballa, Lucky; Hartati, Ery
Green Intelligent Systems and Applications Volume 5 - Issue 2 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i2.934

Abstract

Melon is a horticultural commodity with high economic value, and characteristics such as sweetness, aroma, texture, and phytonutrient content significantly influenced consumer preference. Conventional methods for determining melon ripeness were time-consuming, required considerable expertise, and were often prone to subjective errors, ultimately slowing the production and distribution process. This study aimed to detect the ripeness level of golden melon fruit non-destructively using the YOLOv11 algorithm, focusing on external physical characteristics as the basis for classification. The objectives included applying transfer learning to categorize golden melon into ripe and unripe classes and evaluating model performance using precision, recall, mAP50, mAP50-95, and F1-score. The research methodology consisted of a literature review, dataset collection from previous studies, system design, implementation, and performance testing. The dataset was divided into 70% training, 20% validation, and 10% testing data, and the Adam optimizer was used during the training phase. Based on four experimental scenarios, scenario 3 produced the best and most consistent results, achieving a precision of 90.58%, a recall of 90.79%, an mAP50 of 97.31%, an mAP50-95 of 88.84%, and an F1-score of 92.97%. These findings demonstrated that scenario 3 offered optimal performance for detecting golden melon ripeness. Thus, the model was highly reliable overall.
Aplikasi Absensi Pengenalan Wajah dengan Menggunakan Algoritma YOLOv11 Vanness Bee; Ery Hartati
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 3 (2026): JULY 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i3.5965

Abstract

Manual or semi-manual attendance recording may lead to recap errors, delayed reporting, and misuse such as proxy attendance. This study develops a web-based attendance prototype leveraging You Only Look Once version 11 (YOLOv11) to perform face detection and identity recognition within a single end-to-end pipeline. The research stages include a literature review, data acquisition and pre-processing (640×640 letterbox resize and normalization), transfer-learning-based model training, and system implementation using Laravel and MySQL integrated with a Python inference service exposed via a REST API. Model performance was assessed using standard detection metrics (precision, recall, mAP@0.5, and mAP@0.5:0.95), complemented by black-box functional testing of core application modules (enrollment, attendance logging, and reporting). Internal evaluation demonstrates strong performance with precision of 0.982, recall of 0.975, mAP@0.5 of 0.987, and mAP@0.5:0.95 of 0.963. Nevertheless, performance degrades under challenging real-world conditions (extreme low-light, backlight, mask usage, and partial occlusion) and on external dataset testing, suggesting sensitivity to domain shift. Overall, the proposed system indicates practical potential for real-time attendance automation and reduced recording errors, while highlighting the need for richer, more diverse training data and cross-domain evaluation to improve generalization.
Potato Leaf Disease Classification Using MobileNetV3 Architecture With Adam and Stochastic Gradient Descent Optimizers Pebrian, Hafizh; Hartati, Ery
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1063

Abstract

Potato leaf diseases such as Early Blight and Late Blight reduced productivity and could cause crop failure if they were not detected early. This study analyzed the comparative performance of the Adam and Stochastic Gradient Descent (SGD) optimizers using the MobileNetV3-Large architecture for potato leaf disease classification. The dataset consisted of three categories: healthy leaves, Early Blight, and Late Blight, with a total of 4,072 images. All images were processed through preprocessing stages, including resizing to 224 × 224 pixels and pixel value normalization. The data were divided into training, validation, and testing sets with a ratio of 70:20:10. Random undersampling and data augmentation techniques were applied to the training data to address class imbalance and improve the model’s generalization capability. The model training process was conducted using a transfer learning approach with the MobileNetV3-Large architecture through two stages: feature extraction and fine-tuning. Model performance evaluation was based on accuracy, precision, recall, and F1-score metrics. The results showed that the Adam optimizer achieved a test accuracy of 98.75% with an F1-score of 0.9875, while the SGD optimizer achieved a test accuracy of 96.56% with an F1-score of 0.9635. The Adam optimizer also demonstrated faster and more stable convergence during the training process. This study was expected to serve as a reference for determining an appropriate optimizer for deep learning applications in image classification, particularly in plant disease detection.
PELATIHAN BRANDING, DESAIN KEMASAN DAN PEMASARAN DIGITAL GUNA MENINGKATKAN DAYA SAING UMKM SANTAN KELAPA Kesuma, Dorie P.; Fransen, Lisa Amelia; Udjulawa, Daniel; Hartati, Ery
FORDICATE Vol 5 No 2 (2026): April 2026
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v5i2.15872

Abstract

The Erni Susilo coconut milk processing Micro, Small, and Medium Enterprise, located in Kemuning District, faces challenges regarding low competitiveness compared to similar products. This issue stems from ineffective brand identity management, simple packaging design, and suboptimal digital marketing. This community service project aims to improve the business quality of the owner through training in brand identity, packaging design, and digital marketing. The methodology employs a participatory approach, including focus group discussions, counseling, practical training, and mentoring. Results indicate an increase in the participant's understanding of the importance of brand identity, the ability to design more attractive packaging, and the skills to utilize social media for promotion. In conclusion, the training approach successfully improved the entrepreneur's knowledge and skills in utilizing technology to develop the business, thereby encouraging potential sales growth in the future.
Pelatihan Desain Canva untuk Meningkatkan Kreativitas Siswa SMP Karya Dharma Bhakti: Pengabdian Rionaldo, Daniel; Hermanto , Rio; Ishak , Alvin Leonardo; Lim, Farrel Stefanov; Hartati, Ery
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 4 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 4 Tahun 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i4.6384

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan keterampilan desain grafis siswa SMP Karya Dharma Bhakti Palembang melalui pelatihan penggunaan aplikasi Canva. Permasalahan yang dihadapi peserta adalah kurangnya pemahaman mengenai dasar desain grafis dan penggunaan media digital secara kreatif. Metode kegiatan dilakukan melalui penyampaian materi, praktik langsung, diskusi, dan pendampingan peserta dalam membuat desain sertifikat menggunakan Canva. Hasil kegiatan menunjukkan bahwa peserta mampu memahami fitur dasar Canva, menerapkan prinsip desain sederhana, serta membuat desain secara mandiri. Selain itu, kegiatan ini meningkatkan kreativitas dan literasi digital peserta dalam memanfaatkan teknologi secara positif dan produktif. Kegiatan pelatihan berjalan dengan baik dan mendapat respon positif dari pihak sekolah maupun peserta.
Pemanfaatan Website Untuk Mendukung Promosi Dan Branding Usaha Pada Toko Bintang Terang Sukses 888 Dervin Dervin; Joshua Liu; Michael Peter Chandra; Fikar Penemuan; Ery Hartati
Indonesian Journal of Innovation Multidisipliner Research Vol. 4 No. 2 (2026): April - Juni
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijim.v4i2.751

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman owner dan pegawai mengenai pemanfaatan website sebagai media promosi, branding, dan penyampaian informasi usaha pada Toko Bintang Terang Sukses 888. Permasalahan yang dihadapi mitra adalah belum tersedianya website sebagai sarana informasi dan promosi usaha serta masih rendahnya pemahaman mengenai pentingnya penggunaan teknologi digital dalam mendukung perkembangan usaha di era modern. Kondisi tersebut menyebabkan penyampaian informasi produk dan promosi usaha belum dapat dilakukan secara maksimal kepada pelanggan. Metode pelaksanaan kegiatan dilakukan melalui beberapa tahapan, yaitu persiapan, penyampaian materi, demonstrasi website, praktik langsung penggunaan website, diskusi, tanya jawab, serta pendampingan peserta dalam memahami pengelolaan website usaha. Materi yang diberikan meliputi pengenalan website, manfaat website dan landing page dalam branding usaha, serta dasar pengelolaan konten website sebagai media informasi dan pemasaran digital. Pendekatan pembelajaran berbasis praktik diterapkan agar peserta dapat memahami penggunaan website secara lebih mudah dan efektif. Hasil kegiatan menunjukkan bahwa peserta mampu memahami fungsi dan manfaat website sebagai media promosi dan branding usaha, mengenal fitur dasar website, serta memahami cara mengelola dan memanfaatkan website untuk mendukung pemasaran usaha. Selain itu, kegiatan ini juga meningkatkan literasi digital peserta dalam memanfaatkan teknologi secara positif, efektif, dan produktif untuk mendukung perkembangan usaha. Kegiatan pengabdian berjalan dengan baik serta memperoleh respon positif dari pihak toko dan seluruh peserta kegiatan.
Edukasi Digital Marketing Melalui Instagram Dan Pelatihan Aplikasi Capcut Pada Toko Kemplang Dua Gading Mas Palembang Fierdaus Fiernando Alfarizi; Nardian Varianto; Williams Peter; Fachtur Rachman; Ery Hartati
Indonesian Journal of Innovation Multidisipliner Research Vol. 4 No. 2 (2026): April - Juni
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijim.v4i2.784

Abstract

Perkembangan teknologi informasi mendorong pergeseran paradigma pemasaran dari konvensional menjadi digital. Namun, banyak pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) menghadapi kendala keterbatasan pengetahuan strategi pemasaran digital dan keterampilan teknis memproduksi konten visual yang menarik. Permasalahan tersebut dihadapi oleh Toko Kemplang Dua Gading Mas di Palembang, di mana promosi produknya masih bersifat konvensional dan belum memanfaatkan media sosial secara optimal untuk membangun identitas merek. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk menjembatani kesenjangan keterampilan digital mitra melalui edukasi teoretis digital marketing, pembuatan akun bisnis Instagram, serta pelatihan praktis editing video promosi menggunakan aplikasi CapCut. Metode pelaksanaan kegiatan menggunakan pendekatan partisipatif aktif yang meliputi tahapan observasi situasi, penyuluhan teoretis, demonstrasi fitur, pendampingan praktik mandiri, hingga evaluasi capaian luaran. Hasil kegiatan menunjukkan peningkatan signifikan pada kapasitas literasi digital mitra yang ditandai dengan keberhasilan pembentukan akun Instagram bisnis profesional serta peningkatan keterampilan teknis dalam memproduksi konten visual. Mitra mampu mengoperasikan aplikasi CapCut secara mandiri untuk melakukan pemotongan klip video, sinkronisasi audio, penggunaan transisi, serta penyematan teks informasi varian produk dan harga secara estetis. Konten video pendek hasil suntingan mandiri mitra telah berhasil dipublikasikan melalui fitur Reels Instagram sebagai simulasi awal kemandirian pemasaran digital. Kesimpulan dari pengabdian ini adalah penggabungan manajemen media sosial Instagram dan penguasaan aplikasi CapCut yang ramah pemula terbukti efektif mendobrak batasan inovasi digital serta memberikan fondasi strategi visual marketing yang kuat bagi pelaku usaha mikro secara mandiri dan berkelanjutan.
Co-Authors ., Dewa Adrian Chandra Agustria, Kevin Akhfir, Muhammad Fadly Ukhrowi Amarullah, Rendy Ambrosius Dwi Cahyadi Andreas, Kevin Aneke Windari Ardiansyah, Aldi Ariansyah, Nova Awalludin, Nur Bertnas Valentino, Calvin Budi, Raden George Samuel Candra candra Cerwyn Asyraq Chaesa, Linus Ardel Chandra, Kelvin William Christy, Christy Daniel Udjulawa Deka Putra Pamungkas Derry Alamsyah Dervin Dervin Desy Iba Ricoida Eka Puji Widiyanto Fachtur Rachman Fathimah Azzahra Fatimah Nadia FELICIA Felix Santoso Ferdian Indrahadi Fernando Fernando Feliansyah Fernando Fernando, Fernando Fierdaus Fiernando Alfarizi Fikar Penemuan Firnando, Januar Franko, Billy Fransiska, Julita gasim, Gasim Graciela, Michelle Hafiz Irsyad Hakiki, Muhammad Anugrah Hebert, Hocwin Hermanto , Rio Inayatullah, Inayatullah Intan Sanu Ishak , Alvin Leonardo Ivander Destian Luis Jennifer Jocelyn Jennifer Velensia Santoti Jeovanni Wong Joshua Liu Jumhari Jumhari Kelly, Angel Keristin, Usnia Wati Kesuma, Dorie P. Kevin Antonio Khairani, Siti Kotan, Jendraja Husin Kusuma, Dorie P. Leonardo Leonardo Lim, Farrel Stefanov Lisa Amelia Fransen M Rifqi Virgiansyah M. Kurniawan, M. Maria Elana Maulana, Muhammad Ishaq Md, Ramanda Michael Michael Michael Peter Chandra Micheal Micheal Muhammad Maisep Muhammad Nirraca Mutia, Silvi Nardian Varianto Nataliatara Nataliatara Nicholas Edison Nicholas, Nicholas Nirraca, Muhammad Nurrahman, Wahyu Aji Oktaviani, Ayu Sri Pebrian, Hafizh Peter Reynard Susanto Prana Welas Sukma, Tangguh Prasetyo, Zavier Billy Putra Darmansius, Albertus Dwi Andhika Putra Ganda Dewata Qois Al Qorni Renaldo, Florence Reza Ardana Richie Jonathan Chaniago Ricko Andreas Kartono Ricky Ricky Rikky, Rikky Rionaldo, Daniel Sahpira, Mulia Saputra Edika, Nelson Saputra, Ade Rocky Saputra, Riganda Sasongko, Randie Se, Abd Rosyiid Selvie Selvie Sherdian Djunaidi Sihombing, Mecha Bella Permata Sri Wahyuni Steven Hartanto Sudiadi Sudiadi Sudiadi Sudiadi Suluh Arif Wibowo Tan, Handy Christianto Tandoballa, Lucky Tanzil, Surya Pratama Teo Yulio Sihotang Umar Muhdhor Umi Karolina Vanness Bee Vasco Dee Gamma Bororing Verdy Verrino Adityya Virginia, Callista Widyakusuma, Rafael Lois Wijaya, Frisky Williams Peter Wilyanto, Nicholas Winardi, Eric Agustian Yogie Prakoso Yohannes, Yohannes Yulistia Yulistia