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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JSAI (Journal Scientific and Applied Informatics) Informanika Jurnal Sistem Informasi Dan E-Bisnis Jurnal Ilmu Komputer Dan Informatika Jurnal Pengabdian Kepada Masyarakat Inovasi Teknologi Jurnal Pengabdian Kepada Masyarakat JCOSIS (Journal Computer Science and Information Systems) Jurnal Pengabdian Digital dan Rekayasa Informatika (JARDIRA)
Mariana Purba
Universitas Sjakhyakirti
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Pelatihan Pemrograman Dasar untuk Siswa Sekolah Menengah sebagai Bekal Memasuki Dunia IT Mariana Purba; Pipin Octavia; Rudiansyah; Bakhtiar K; Kemas Welly Anggra Permana
Jurnal Pengabdian Kepada Masyarakat Vol 1 No 02 (2024): Jurnal Pengabdian Kepada Masyarakat
Publisher : Alihsanpublisher

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

Abstract

Pelatihan Pemrograman Dasar untuk Siswa Sekolah Menengah sebagai Bekal Memasuki Dunia IT bertujuan untuk memperkenalkan dan mengembangkan keterampilan dasar pemrograman komputer bagi siswa sekolah menengah. Di era digital yang terus berkembang, pemrograman menjadi keterampilan yang sangat penting dan dibutuhkan di berbagai sektor. Pelatihan ini dirancang untuk memberikan pemahaman dasar mengenai konsep pemrograman, logika komputasi, serta aplikasi praktis menggunakan bahasa pemrograman yang mudah dipahami seperti Python atau Scratch. Melalui pendekatan pembelajaran aktif, berbasis proyek, dan bertahap, siswa akan diajak untuk belajar dengan cara yang menyenangkan dan interaktif, serta dapat mengembangkan keterampilan untuk menyelesaikan masalah secara sistematis. Selain itu, pelatihan ini juga bertujuan untuk menumbuhkan kemampuan berpikir kritis, kreatif, dan analitis yang sangat dibutuhkan di dunia teknologi. Dengan pelatihan ini, diharapkan siswa dapat memiliki dasar yang kuat dalam pemrograman dan siap memasuki dunia IT, baik untuk melanjutkan studi ke jenjang yang lebih tinggi ataupun memulai karir di industri teknologi.
School Branding Design Training : Logos, Banners , and Digital Presentation Templates For Student SMK Negeri 3 Kayuagung Dwi Fitri Brianna; Paisal; Mariana Purba; Bakhtiar .K
JARDIRA – Jurnal Pengabdian Digital dan Rekayasa Informatika Vol. 1, No. 1, July 2025
Publisher : CogniSpectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/jardira.v1i1.9

Abstract

Background: Graphic design is skills important in the digital era, especially in context build and strengthen visual identity of a institutions , including institution education . The school's visual identity — such as logos, banners , and presentation templates — plays a role in emphasize character , vision , and image institutions in the eyes public . Therefore that , understanding and skills in field design graphic become need strategic for students , especially in schools Intermediate Vocational (SMK) which is oriented towards mastery skill practical . Activities training This implemented as form contribution real through community service programs to society , with objective main equip student with skills design relevant and impactful graphics​ direct to identity school . Contribution: Method applied training​ is approach participatory and practical directly (learning by doing). During activities , students in a way active involved start from understanding base about draft design , exploration school visual identity , to the creation process work design use device soft graphic approach​ This selected so that students No only understand theory , but also has experience concrete in apply principles design in a way contextual Method: Method applied training​ is approach participatory and practical directly (learning by doing). During activities , students in a way active involved start from understanding base about draft design , exploration school visual identity , to the creation process work design use device soft graphic approach​ This selected so that students No only understand theory , but also has experience concrete in apply principles design in a way contextual Results: Training results show that student capable produce work creative , functional , and representative digital design to identity school . Products The resulting designs — such as logos, banners , and presentation templates — can be direct used in various activity school . Besides achievements technical , activities this also proves existence improvement motivation , creativity , and caring student to development visual image of the school they.
Klasifikasi Penyakit dan Hama Daun Padi Menggunakan Model ResNet50 pada Dataset AgroGuard AI Mariana Purba
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9987

Abstract

Rice leaf diseases and pests are one of the main factors causing decreased rice productivity. Manual disease identification still relies on the experience of farmers and extension workers, potentially leading to delayed diagnosis and mishandling. This study aims to develop an image-based rice leaf disease and pest classification model using the ResNet50 deep learning architecture. The dataset used comes from AgroGuard AI and consists of seven classes: blast disease, healthy leaves, insect attacks, leaf roller pests, leaf scald disease, brown spot disease, and tungro disease. The dataset is divided into training, validation, and test data with a ratio of 70%:15%:15%, where the test data is balanced with 400 images in each class. The ResNet50 model was trained from scratch without pre-training weights with a batch size of 32, a learning rate of 0.001, and 50 epochs. The evaluation results showed that the model achieved an accuracy of 77.86% on the test data, with a training accuracy of 80.52% and a validation accuracy of 89.38%. Evaluation using a confusion matrix and precision, recall, and F1-score metrics indicated that the model performed quite well and stably across all classes.
Pengenalan Penyakit Tanaman Berdasarkan Citra Daun Menggunakan Arsitektur DenseNet Berbasis Sekuensial Mariana Purba
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9988

Abstract

Plant diseases that affect leaves can significantly reduce crop quality and productivity, making accurate and efficient detection methods essential. This study aims to develop a plant disease recognition model based on leaf images using a sequential DenseNet121 architecture. The dataset consists of 1,530 leaf images categorized into three classes: Healthy, Powdery, and Rust, which are divided into training, validation, and testing sets with a relatively balanced distribution. The model employs DenseNet121 as a base model with pre-trained ImageNet weights, where all base layers are frozen to function as a feature extractor. The classification process utilizes GlobalAverage Pooling2D, Dense, Dropout, and Softmax layers. Experimental results show that the model achieves an accuracy of 98.28% on the training data and 96.25% on the validation data. Evaluation on the test dataset yields an accuracy of 93.33%, indicating that the proposed model demonstrates good generalization capability in classifying plant diseases based on leaf images. These results suggest that the sequential DenseNet architecture is effective for plant disease recognition and has potential for further development as a decision support system in agriculture
Implementasi Grad-CAM pada EfficientNet untuk Deteksi Tingkat Kematangan Buah Sawit Berbasis Citra Digital Erwin Dwika Putra; Mariana Purba
JCOSIS (Journal Computer Science and Information Systems) Vol. 3 No. 1 (2026): May
Publisher : Institute for Research and Community Service

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61567/jcosis.v3i1.275

Abstract

Tujuan : Penelitian ini bertujuan untuk mengimplementasikan metode Explainable Deep Learning menggunakan EfficientNetB0 dan Grad-CAM dalam mendeteksi tingkat kematangan buah kelapa sawit berbasis citra digital. Dataset yang digunakan berasal dari Annotated Datasets of Oil Palm Fruit Bunch Piles for Ripeness Grading yang terdiri dari beberapa kelas kematangan buah sawit. Metode/Design/Pendekatan: Tahapan penelitian meliputi preprocessing data, augmentasi citra, pembagian dataset, pelatihan model EfficientNetB0 berbasis transfer learning, evaluasi performa model, serta visualisasi interpretasi menggunakan Grad-CAM. Hasil/Temuan: Hasil penelitian menunjukkan bahwa model EfficientNetB0 mampu menghasilkan performa klasifikasi yang sangat baik dengan nilai accuracy sebesar 95,21%, precision sebesar 94,87%, recall sebesar 94,53%, dan F1-score sebesar 94,69%. Implementasi Grad-CAM berhasil memberikan visualisasi heatmap yang menunjukkan area citra paling berpengaruh terhadap hasil klasifikasi, dengan fokus utama model berada pada warna buah sawit sebesar 48% dan tekstur buah sebesar 27%. Kebaharuan/Originalitas/Nilai: Hasil penelitian membuktikan bahwa kombinasi EfficientNetB0 dan Grad-CAM mampu menghasilkan sistem klasifikasi kematangan buah sawit yang akurat, efisien, dan interpretable untuk mendukung penerapan Artificial Intelligence pada sektor perkebunan modern.
Co-Authors Ade Sukma Wati Agrinda Ghaniya Andi Nugroho Ayumi, Vina Bakhtiar Bakhtiar .K Bakhtiar K Cahyo Pambudi Darmo Dwi Fitri Brianna Erlangga Erwin Dwika Putra Hadiguna Setiawan Herlambang Kemas Welly Angga Permana Lemi Iryani Lemi Iryani Lemi Iryani M. Syidik Rizky Marissa Utami Nia Umilizah Nia Umilizah Noprisson, Handrie Nur Ani Octavia, Pipin Paisal Pipin Octavia Putra, Erwin Dwika Putri Maharani Rudiansyah Rudiansyah Rudiansyah Sarwati Rahayu Sulis Sandiwarno Syamsiar, Syamsiar Vina Ayumi Yadi