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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Seminar Nasional Informatika (SEMNASIF) Dharma: Jurnal Pengabdian Masyarakat International Journal of Advances in Artificial Intelligence and Machine Learning
Dyah Ayu Irawati, Dyah Ayu
Politeknik Negeri Malang
Computer Science & IT Electrical & Electronics Engineering Engineering Social Sciences

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PEMANFAATAN WEB SERVICES PADA PROTOTIPE INTEGRASI DATA SISWA SMK PENERIMA DANA BANTUAN KHUSUS MURID Irawati, Dyah Ayu; Ashari, Ahmad; Mustofa, Khabib
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 1 (2013): Information System and Application
Publisher : Jurusan Teknik Informatika

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

Abstract

Sekolah Menengah Kejuruan di wilayah Dinas Pendidikan Kabupaten Sleman yang tersebar dan terpisah jarak yang jauh sangat tidak efektif dan efisien dalam hal pendistribusian data siswa penerima BKM. Pendistribusian secara online juga tidak membantu pendistribusian data tersebut menjadi mudah karena harus berpindah-pindah dari satu situs ke situs yang lain untuk mengambil data.Penelitian ini bertujuan membangun sebuah teknologi web services yang mampu mengintegrasikan unit data SMK di wilayah Dinas Pendidikan kabupaten Sleman. Dengan toolkit Netbeans 6.0, dibangun di atas server GlassFish v2.0 dan memanfaatkan XML, maka integrasi data dari SMK di Sleman yang memiliki basis data berbeda dapat dilakukan.                 Dengan web service tersebut, dihasilkan sebuah sistem informasi siswa penerima BKM yang merupakan integrasi dari layanan yang berada di SMK. Layanan sistem informasi tersebut dapat menjadi sumber informasi mengenai data siswa penerima BKM bagi Dinas Pendidikan Kabupaten Sleman.
Perancangan Sistem Pemesanan Menu pada Kedai Teh Berbasis Customer Relationship Management (CRM) Irawati, Dyah Ayu; Zubaeda, Dyah Ayu; Ristyowati, Trismi
Telematika Vol 22 No 1 (2025): Edisi Februari 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i1.14662

Abstract

Tujuan: Penelitian ini bertujuan merancang sistem pemesanan menu mandiri berbasis CRM untuk meningkatkan kinerja operasional dan interaksi pelanggan di Kedai Teh Kaula, sebuah usaha kuliner berskala kecil. Metodologi: Sistem dikembangkan dengan model waterfall dan kerangka kerja CRM Francis Buttle. Pengumpulan data dilakukan melalui observasi, wawancara, dan telaah pustaka, dilanjutkan dengan pembuatan Use Case Diagram, ERD, DFD, dan wireframe antarmuka. Hasil: Desain sistem mendukung desain sistem pemesanan mandiri lewat QR code serta fungsi admin untuk pengelolaan pesanan dan data pelanggan. Desain sistem ini mengatasi antrian panjang, meningkatkan akurasi pesanan, dan memperbaiki pengalaman pengguna. State of the Art: Penelitian ini menunjukkan integrasi praktis strategi CRM pada tahap awal perancangan sistem, memberikan model yang skalabel bagi usaha kecil yang menjalani transformasi digital.
PENDAMPINGAN UMKM KWT SUKA MAJU UNTUK MENINGKATKAN PRODUKSI DAN PEREKONOMIAN MASYARAKAT DUSUN PALIHAN Heriyanto, Heriyanto; Fauziah, Yuli; Irawati, Dyah Ayu
Dharma: Jurnal Pengabdian Masyarakat Vol. 1 No. 2 (2020): November
Publisher : Universitas Pembangunan Nasional "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/dlppm.v1i2.4043

Abstract

The SUKAMAJU Women's Farmer Group (KWT) is a group of women craftsmen of banana tree processing. During the Covid-19 pandemic, sales and marketing of processed banana food were very limited. Online marketing in times of the Covid-19 pandemic is urgently needed and requires support. Community service from UPN Veteran Yogyakarta, in this case, is programmed to help solve problems during the pandemic. Marketing through the internet and social media is very much needed, while the ability of mothers to master social media and the internet is very limited. The service team from UPN Veteran Yogyakarta is trying to help with solutions going into the field to help provide full assistance and also assistance for production equipment so that food processing craftsmen maintain production in KWT. The hope of the community service team is that there will be an increase in sales results by providing full assistance in both marketing media and increasing production equipment with an average increase of 8-9 pieces per day.
Pengembangan Sistem Ekstraksi Metadata Artikel ilmiah secara Otomatis Rahutomo, Faisal; Irawati, Dyah Ayu; Pramudita, Muhammad Aisamuddin Eka
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 2: April 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2241.739 KB) | DOI: 10.25126/jtiik.2019621227

Abstract

Pengarsipan artikel ilmiah di Jurusan Teknologi Informasi Politeknik Negeri Malang menggunakan platform Open Journal Systems (OJS). Pengarsipan tersebut melalui tahapan penulisan metadata artikel ilmiah yang dilakukan satu-persatu. Metadata artikel ilmiah ini berupa judul, penulis, instansi, surel penulis, abstrak, kata kunci, dan daftar pustaka. Diperlukan waktu yang cukup lama untuk memasukkan metadata artikel ilmiah dalam OJS karena prosedur dalam OJS itu sendiri. Untuk itu penelitian ini mengusulkan sebuah sistem tambahan OJS yang bisa menyediakan metadata artikel ilmiah tersebut secara otomatis. Sistem dibangun menggunakan pendekatan rule-based text parsing. Dalam metode tersebut disusun beberapa aturan untuk mengambil teks yang diperlukan oleh isian metadata OJS yang mewakili sebuah artikel ilmiah. Artikel ilmiah diunggah ke dalam sistem tambahan tersebut untuk menghasilkan metadata-nya secara otomatis. Metadata tersebut selanjutnya disimpan dalam format XML. Pada sistem OJS terdapat perangkat native XML plugin yang bisa melakukan export – import metadata suatu artikel ilmiah untuk OJS. Dari hasil pengujian, sistem bisa memudahkan pengarsipan artikel ilmiah lebih cepat 13 kali dibanding pengisian metadata secara manual.AbstractDepartment of information technology, State Polytechnic of Malang archives its scientific article with Open Journal Systems (OJS) platform. Archiving in OJS needs to write the scientific article metadata manually through a form. Metadata of this scientific article includes title, author, agency, writer e-mail, abstract, keywords and bibliography. Inserting scientific articles metadata in OJS manually takes quite a long time because of the procedure in OJS itself. Highlighting this problem, this research proposes a text processing add-on system for OJS that able to extract the scientific article's metadata automatically. The system is built with rule-based text parsing method. In this method, the authors composed some rules to obtain the metadata of scientific article. Scientific articles were uploaded into the system to capture the metadata of the scientific article automatically. The metadata was then stored in XML. In OJS add-on there is native XML plugin tool that able to export - import the scientific article metadata for OJS. The experimental results show the system able to facilitate the archiving of scientific articles 13 times faster.
Plant Disease Detection Using Image Processing and Machine Learning Sirisati, Ranga Swamy; Sravya, J.; Sruthi, D.; Nandhu, A.; Sree, R. Navya; Irawati, Dyah Ayu
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 1 (2026): Forthcoming Issue (March)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i1.433

Abstract

Background: Plant diseases continue to threaten agricultural productivity worldwide, causing significant reductions in crop yield and quality. Traditional visual inspection by farmers or experts is often slow, subjective, and unreliable, especially across large plantation areas. With the increasing availability of digital imaging technologies, automated detection through image processing and machine learning presents a promising alternative.Aims: This study aims to develop an enhanced plant disease detection framework using image processing combined with machine learning algorithms, particularly Support Vector Machine (SVM) and Convolutional Neural Networks (CNN).Methods: A dataset of 54,306 leaf images from the PlantVillage collection was used to train and test the models. Preprocessing steps included resizing, noise removal, background segmentation, and feature extraction. CNNs were trained for end-to-end classification, while SVM operated on manually extracted features. A 10-fold cross-validation procedure was employed to ensure robustness. Fine-tuning strategies and comparative experiments were implemented to evaluate performance consistency across dataset variants.Result: The system demonstrated strong capability in early disease detection, achieving 97% accuracy for healthy leaves and moderate performance (56%) for certain diseased classes due to visual similarity and image noise. Background segmentation improved focus on disease features, while grayscale images reduced reliance on color cues but lowered classification accuracy.Conclusion: The findings confirm that machine learning, particularly CNN-based models, can significantly enhance plant disease diagnosis and support timely agricultural decision-making. Future improvements will explore advanced deep learning architectures, expanded datasets, multimodal imaging, and IoT integration for real-time field deployment.
Co-Authors Abdullah, Sulton Ginanjar Ahmad Ashari Anggraeni, Sari Arief Prasetyo Astiningrum, Mungki Budi Harijanto, Budi Chamdika, Vandi Dinda, Elistya Rahma Faisal Rahutomo Heriyanto Heriyanto Khabib Mustofa Kusbianto P, Deddy Maharany, Fitri Maulana, Adithya Gilang Muhammad R, Aisy Nafis, Ari Mahardika Ahmad Nandhu, A. osiani, Ulla Delfana Padeka Putri, Riza Ivanadi Pramudita, Muhammad Aisamuddin Eka Purwoko A, Deddy Kusbianto Rahmadhian, Bagus Rawansyah, Rawansyah Ridwan Rismanto Sirisati, Ranga Swamy Sravya, J. Sree, R. Navya Sruthi, D. Susi Susanti Trismi Ristyowati Yanuar, Harry Yuli Fauziah Yuri Ariyanto Zubaeda, Dyah Ayu