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All Journal Indonesian Journal of Electronics and Instrumentation Systems Elkom: Jurnal Elektronika dan Komputer Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal CoreIT Jurnal SOLMA Jurnal TAM (Technology Acceptance Model) Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Informasi dan Komputer IJISCS (International Journal Of Information System and Computer Science) Jurnal Cendikia CCIT (Creative Communication and Innovative Technology) Journal JUSIM (Jurnal Sistem Informasi Musirawas) Applied Technology and Computing Science Journal Journal Software, Hardware and Information Technology Jurnal Algoritma Universal Raharja Community (URNITY Journal) RISTEC : Research in Information Systems and Technology Jurnal Teknologi dan Informatika (JEDA) Abdi Akommedia : Jurnal Pengabdian Masyarakat Journal of Information Systems Management and Digital Business Seminar Nasional Teknologi dan Multidisiplin Ilmu Journal of Social Science Utilizing Technology Laporan Upaya Nyata Inovasi Ilmu Komputer (LUNIK) Journal of Social Science Utilizing Technology Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Jurnal TAM (Technology Acceptance Model) IJISCS (International Journal of Information System and Computer Science) Journal of Moeslim Research Technik Journal of Technology and Data Science
Hilda Dwi Yunita
Universitas Mitra Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Physics Social Sciences Transportation Other

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Analisis Performansi Pendekatan Machine Learning pada Deteksi Penyakit Daun Tanaman Kopi Yodhi Yuniarthe; Rosyana Fitria Purnomo; Hilda Dwi Yunita; Fatimah Fahurian; Ahmad Ikhwan
Seminar Nasional Teknologi dan Multidisiplin Ilmu (SEMNASTEKMU) Vol. 5 No. 1 (2025): SEMNASTEKMU
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/p2t2nm71

Abstract

Abstract. Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.   Keywords: Coffee Classification, Image Processing, Machine Learning, Plant Disease Detection.  
Analisis Performansi Pendekatan Machine Learning Pada Deteksi Penyakit Daun Tanaman Kopi Purnomo, Rosyana Fitria; Yodhi Yuniarthe; Hilda Dwi Yunita; Fatimah Fahurian; Ahmad Ikhwan
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 2 (2025): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v18i2.3302

Abstract

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.
Software Engineering for Zakat Management Platforms: A Study on Transparency, Security, and User Trust Zuraida; Farah, Rina; Yunita, Hilda Dwi
Journal of Moeslim Research Technik Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i6.2499

Abstract

The management of zakat is crucial in Islamic finance, and digital platforms have increasingly been adopted to enhance transparency, security, and trust among users. This study examines the software engineering aspects of zakat management platforms, focusing on these critical dimensions. The research aims to identify key software design considerations that can improve transparency, ensure data security, and foster user trust within digital zakat platforms. A mixed-method approach is used, involving both qualitative interviews with zakat management professionals and quantitative analysis of platform users' perceptions. The findings suggest that clear communication regarding financial transactions, robust data protection measures, and user-friendly interfaces are essential for building trust. Furthermore, implementing blockchain technology was found to significantly enhance transparency and security. The study concludes that for zakat platforms to be successful, they must not only comply with Shariah principles but also integrate advanced technology solutions that align with user expectations for security and transparency. This research provides a comprehensive framework for the development of zakat management platforms that can be adopted by stakeholders in the Islamic finance sector.
Co-Authors Ahmad ikhwan Aldesion Denagi Zenda Amelia Hayati Anna, Eva Itma Ardiansyah Farid Aryatama, Apriyan Aulia, Pitri Budi Hartanto budi hartanto Desi Febriyani Devi Cantika Dirayati, Fadhilah Farah, Rina Fatimah Fahurian Fawaati, Teuku Muhammad Hamdan Sukri Hamdan Sukri Hartanto, M Budi Hartanto, M.Budi Hartantom, Budi Helen Aggraini Hendro Pramono, Doni Eko Iin Marliana ikhwan, Ahmad Khozainuz Zuhri M. Budi Hartanto, M. Budi marliana, iin Nitin, Mahon Pangestu, Yoga Putra, Billy Sistian Rahmadi, Putra Refitasari, Ade Nisa Rio Idha Rio Okida Romadhona, Rio Rosyana Fitria Purnomo Rudi Setiawan Satrianah Satrianah Sri Ipnuwati Sukri, Hamdan Tarnando, Kiki Triyugo Winarko Triyugo Winarko Triyugo Winarko, Triyugo Winariyani . Winarko , Triyugo Yodhi Yuniarthe Zuraida