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All Journal Jurnas Nasional Teknologi dan Sistem Informasi JURNAL NASIONAL TEKNIK ELEKTRO Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKA (Jurnal Informatika) Andalasian International Journal of Applied Science, Engineering, and Technology Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) BACA: Jurnal Dokumentasi dan Informasi Jurnal Pengabdian Rekayasa dan Wirausaha
Sunaryo, Budi
Universitas Bung Hatta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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Uji Coba Aplikasi Mobile untuk Layanan Konseling dan Pengaduan Perempuan dan Anak pada UPTD PPA Kabupaten Agam Hidayat, Hidayat; Pakpahan, Muhammad Yusuf; Arnita, Arnita; Sunaryo, Budi; Satria, Iman
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 2 (2025)
Publisher : Universitas Syiah Kuala

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Abstract

Kasus kekerasan terhadap perempuan dan anak di Kabupaten Agam masih cukup tinggi, sementara keterbatasan akses geografis menyebabkan tidak semua kasus dapat terdata dan tertangani dengan baik. Artikel ini menyajikan inovasi teknis berupa rancang bangun dan uji coba aplikasimobileuntuk layanan konseling dan pengaduan perempuan dan anak pada UPTD PPA Kabupaten Agam. Tujuannya adalah mengembangkan aplikasi terintegrasi berbasis Flutter guna meningkatkan akses dan efisiensi layanan pengaduan. Metode pengembangan yang digunakan adalah model Waterfall, dengan implementasi menggunakan Flutter dan Firebase sebagai backend. Pengujian dilakukan melaluiBlack-box testinguntuk memastikan fungsionalitas sistem sertaSystem UsabilityScale(SUS) terhadap 28 responden untuk menilai kegunaan aplikasi, yang terdiri atas masyarakat umum, petugas UPTD PPA Kabupaten Agam, serta pengguna yang mewakili target pemakai aplikasi. Hasilnya menunjukkan seluruh fitur termasuk pengaduan, konselingreal-time,danchatberbasisArtificial Intelligent(AI) berfungsi baik, dengan skor SUS 70,54 (kategoriacceptable). Aplikasi ini masih berada pada tahap eksperimen dan belum diuji secara luas di masyarakat. Meski demikian, hasil uji coba ini menunjukkan potensi aplikasi sebagai solusi awal dalam peningkatan aksesibilitas dan efektivitas layanan perlindungan perempuan dan anak di Kabupaten Agam.
Plant Disease Identification Using Image Processing: A Systematic Literature Review Minarni, Minarni; Rusydi, Muhammad Ilhamdi; Darwison, Darwison; Nugroho, Hermawan; Sunaryo, Budi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7171

Abstract

This article is a literature review focusing on plant disease identification using image processing techniques. This review aims to provide a comprehensive analysis of dataset sources, preprocessing methodologies, segmentation techniques, feature extraction processes, and various classification methods, along with their associated accuracies. It also discusses challenges encountered and potential future research directions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a literature search was conducted in the Scopus database to obtain primary studies. The search covered Scopus-indexed journals and proceedings published by IEEE, Elsevier, Springer, MDPI, and ACM between 2019 and 2025. The initial identification phase yielded 9,286 studies screened. Further screening was performed based on specific eligibility criteria, including relevance to the topic, year of publication, subject area, document type, and articles written in English, resulting in the selection of 82 studies for the review. The findings indicate that the most commonly used dataset is PlantVillage, followed by field data. The dominant preprocessing techniques include image enhancement and augmentation. For segmentation and feature extraction, the most frequently used methods were k-means and CNN, respectively. Sixty-one studies achieved an accuracy exceeding 90%. However, several key challenges remain: data limitations, methodological issues, and practical constraints. Future research should focus on developing more representative datasets, hybrid approaches that integrate classical and deep learning methods, and lightweight, adaptive decision support systems suitable for real-world agricultural applications. This review supports continued progress in this field by providing valuable insights for researchers developing image-based methods for identifying plant diseases.
Co-Authors Abdul Manab Amirul Luthfi Ariadi Hazmi Arnita Arnita Baharuddin Abbas, Baharuddin Darmawan, Darmawan Darwison Darwison Dessi Mufti, Dessi Eka Putra Waldi Febrian Rusdi, Jack Gumilar Hadiningrat, R. Gurat Baktina, Tedja Hamdi, Khairil Hidayat Hidayat Illahi, Ridho Pratama Imam Gunawan, Imam Laurenty, Fretycia M. Ilhamdi Rusydi Minarni Minarni Moko, Joyo Kilat Muhammad Rusydi H. Nugroho, Hermawan Pakpahan, Muhammad Yusuf putri, silvialista Ramadhan, Teguh Reskya Riska Amelia Rudi . Sammir, Haddad Sandra, Randi Proska Satria, Iman Trisya Septiana Wiyanatra, Reihan Hanafi Yuhefizar Yuhefizar Zulfadli Zulfadli Zulfadli Zulfadli