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All Journal TEKNIK INFORMATIKA Jurnal Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Khatulistiwa Informatika Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Techno Nusa Mandiri : Journal of Computing and Information Technology Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Informasi dan Teknologi Jurnal Restikom : Riset Teknik Informatika dan Komputer Journal of Applied Data Sciences Jurnal Responsif : Riset Sains dan Informatika International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Sosial dan Abdimas INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Journal Medical Informatics Technology SWAGATI: Journal of Community Service
Sri Hadianti
Department Of Information System, STMIK Nusa Mandiri, Jl. Raya Jatiwaringin No 2, Cipinang Melayu, Makasar, Jakarta Timur 13620|STMIK Nusa Mandiri
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Pelatihan Pengembangan Desain UI/UX pada Aplikasi layanan Administrasi RW Cipinang Melayu Jakarta Timur Hadianti, Sri; Mayangky, Nissa Almira; Nurfalah, Ridan; Kusumayudha, Mochammad Rizky
SWAGATI : Journal of Community Service Vol. 2 No. 3 (2024): November
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2024v2i3.1720

Abstract

Rukun Warga Cipinang Melayu Jakarta Timur telah memanfaatkan teknologi informasi dan komunikasi (TIK) untuk mendukung dan memfasilitasi kegiatan administratif bagi masyarakat. Pengelolaan arsip masih dilakukan secara konvensional melalui pencatatan di buku besar, yang mengurangi efektivitas dan memperpanjang proses administrasi. Diperlukan perancangan prototipe Aplikasi Layanan Administrasi dengan User Interface dan User Experience (UI/UX) yang baik agar pengguna mudah mengoperasikannya dan mendapatkan pengalaman yang memuaskan. Pelatihan desain menggunakan aplikasi Figma, yang dapat diakses secara gratis melalui web, akan diadakan untuk membuat desain antarmuka. Kegiatan ini diharapkan memberikan kontribusi ilmu dan keterampilan kepada peserta untuk mengimplementasikan desain aplikasi yang dinamis, interaktif, dan user-friendly sebelum membangun Aplikasi Layanan Administrasi, sehingga dapat membantu petugas arsip dalam mengelola data yang akan menjadi nilai penting bagi pihak yang berkepentingan.
Evaluating Deep Learning Architectures for Potato Pest Identification: A Comparative Study of NasNetMobile, DenseNet, and Inception Models Hadianti, Sri; Riana, Dwiza; Sulistyowati, Daning Nur
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.545

Abstract

Manual potato pest identification that is still applied today is often time-consuming and highly dependent on farmer skills in the field. This causes delays in taking action and inaccurate reporting, especially in pest emergencies. In addition, these limitations slow down the response to pest control which ultimately risks reducing crop yields and farmer income. This study aims to develop a more accurate, fast, and consistent deep learning-based approach to identify potato pests, in order to support practical solutions that farmers can implement independently. This study contributes by comparing three deep learning architecture models, namely NasNetMobile, DenseNet, and Inception which are designed to identify pest images. The potato pest image dataset used was collected from various sources equipped with an augmentation process to increase data diversity. The model was drilled using transfer learning techniques to utilize previously learned features on a large dataset. The evaluation model was carried out comprehensively based on accuracy, precision, and inference time efficiency. The results showed that the DenseNet model achieved the highest accuracy of 97% with an inference time of 11 seconds, and this model maintained a relatively stable performance and was superior several times compared to other models. Based on these results, DenseNet was chosen as the most effective and reliable model to be developed for practical applications in the field. This study provides practical implications in the form of providing a model that can be integrated into a mobile-based application that is easy to use by farmers, including in remote areas. This allows farmers to identify pests independently without requiring in-depth technical expertise. In addition, this study is a new benchmark for the development of artificial intelligence-based pest identification systems in other crops and opens up opportunities for integration with IoT-based technologies to support sustainable agricultural practices.
Image Analysis of Skin Diseases Using DenseNet-121 Architecture Putra, Mahesa; Pioni, Pioni; Rosalina, Alya; Aditya, Diyar; Azhari, Azidan Allen Deva; Hadianti, Sri; Nurfalah, Ridan
Journal Medical Informatics Technology Volume 3 No. 2, June 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i2.99

Abstract

Skin diseases such as dermatitis, psoriasis, and tinea often exhibit similar visual characteristics, which can lead to frequent errors in early diagnosis. Accurate diagnosis is critical, as each disease requires different treatment approaches. This study aims to develop an automated classification model for these three skin diseases using a deep learning approach based on the DenseNet-121 architecture, which consists of 121 layers designed to facilitate efficient feature reuse and gradient flow. The dataset consists of 300 labeled images, evenly distributed among the three disease classes. To enhance model generalization, preprocessing steps were applied, including data normalization and augmentation techniques such as image rotation (±20°), horizontal and vertical flipping, random zooming (range 0.8-1.2×), and brightness adjustment (±20%). The model was trained and validated using a stratified 5-fold cross-validation strategy. Experimental results demonstrated an overall classification accuracy of 94.59%, with high precision and recall scores across all classes. These results indicate the potential of using DenseNet-based deep learning models as decision support tools for early skin disease diagnosis. Further validation with larger datasets and clinical input from dermatologists is recommended to ensure reliability in real-world healthcare settings. Visual comparison through Grad-CAM heatmaps was also conducted to enhance interpretability and validate model focus on relevant skin features.
Co-Authors Achmad Baroqah Pohan Achmad Rifai Adilah Purnamasari Aditya Gusti Aditya, Diyar Akmaludin Akmaludin, Akmaludin Aldi Jakaria Arina Selawati Azhari, Azidan Allen Deva Aziz, Faruq Chairul, Yasrizal Chandra Nugraha, Chandra Daniati Uki Eka Saputri Dwiza Riana Dwiza Riana Dwiza Riana Dwiza Riana Dwiza Riana Dwiza Riana Fani Nurona Cahya Fiqi Pranastara Frieyadie Galih Widagdo Heckel Sungkar Iftina Salya Rosa Izni Nur Karimah Jajang Jaya Purnama Kodri, Wan Ahmad Gazali Kurniawantoro Kusumayudha, Mochammad Rizky Laela Kurniawati Lestari Yusuf Mayangky, Nissa Almira Mochamad Rizky Kusumayudha Mochammad Rizky Kusumayudha Mohamad Ragil Ramadhan Muhamad Hasan Muhammad Faitullah Akbar Nanik Wuryani Nila Hardi Nugraha, Fitra Septia Nur Fiyah Nur Sulistyowati, Daning Nurfalah, Ridan Nurul Jannah Salsabila Pioni, Pioni Putra, Mahesa Putri Permata Sari Rafly Pratama Resti Kartika Dewi Ridan Nurfalah Ridan Nurfalah Riski Febrianto Rizka Dahlia Rizky Ade Safitri Rosalina, Alya Rousyati Rousyati Rudini, Edwin Saputra, Ridwan Siti Nurdiani Siti Nurlela siti nurlela Sofiyatul Mu’minah SRI RAHAYU Sri Rahayu Sugiyanti Sugiyanti Sulistyowati, Daning Nur Widagdo, Galih Winda Astariyah Fatimah Windu Gata WITRIANA ENDAH PANGESTI Yutri Amelia