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All Journal Jurnal Pengabdian Pada Masyarakat Infotekmesin Scientific Journal of Informatics Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Jurnal PkM (Pengabdian kepada Masyarakat) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Informatika: Jurnal Pengembangan IT
Muhammad Fikri Hidayattullah
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Social Sciences Other

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Journal : scientific journal of informatics

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Peringkas Otomatis Teks Berbahasa Arab Menggunakan Algoritma TextRank Muhammad Fikri Hidayattullah; Ardhiyan Azizi
Jurnal Ilmiah Informatika Vol. 6 No. 1 (2021): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v6i1.1231

Abstract

Increasingly, the amount of data in the form of text documents scattered on the internet is getting bigger. It took a very long time to get the information from each of these documents. For this reason, several researchers developed the Automatic Text Summarizer to summarize text automatically, so that the time needed to get important information from the entire document can be faster. Research that focuses on automatic summarization of Arabic texts is very rare. In fact, there are more than 300 million Arabic speakers in the world and Arabic is the official language at the United Nations. Therefore, this study develops a model that can perform text summarization automatically using the TextRank algorithm. The test results using Q&A Evaluation show very good results with details of the suitability of the summary results with the original text by 90%, the suitability of the summary results with Arabic grammar is 91.43%, the suitability of the summary results is 90%, the ease of understanding the summary results is 90%. and the useful aspects of the model developed were 91.43%.
Deteksi Bahan Makanan untuk Rekomendasi Resep Masakan pada Program Diet Menggunakan Algoritma CNN Berliani Risqi Dwi Saputri; Fadiyah Desi Asmawati; Asih Rahmawati; Ilham Hatta Manggala; Muhammad Fikri Hidayattullah
Jurnal Ilmiah Informatika Vol. 9 No. 2 (2024): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v9i2.134-141

Abstract

The increasing need for efficient dietary planning has led to the development of automated systems for identifying food ingredients and generating suitable diet recommendations. This study focuses on implementing a Convolutional Neural Network (CNN) using the VGG16 architecture to classify food ingredients and determine appropriate diet recipes. The problem addressed is the difficulty of manually identifying various food ingredients, which can be time-consuming and error-prone, especially in large-scale dietary planning. The proposed solution integrates deep learning technology with a user-friendly application that automates the classification process and generates diet suggestions. The method involves utilizing the VGG16 model pre-trained on the ImageNet dataset. The dataset underwent preprocessing techniques, including Gaussian Blur for noise reduction, normalization, and data augmentation, to improve model generalization. The model was trained over 50 epochs, achieving a training accuracy of 96.28% and a validation accuracy of 95%. This study contributes to the development of intelligent dietary systems, providing significant benefits in enhancing user convenience, accuracy in food classification, and promoting healthier lifestyles.
Penerapan Algoritma Convolutional Neural Network(CNN) Deteksi Kesehatan Mata Rizki Eka mulyani; Muhammad Rafli Erfiyanto; Fathur Rizqi Putra Pratama; Tengku Dimas Aditya; Muhammad Fikri Hidayattullah
Jurnal Ilmiah Informatika Vol. 9 No. 2 (2024): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v9i2.142-155

Abstract

Indra manusia memegang peranan penting dalam mengamati dan berinteraksi dengan lingkungan. Salah satu indera yang paling vital adalah penglihatan, yang difasilitasi oleh mata, yang menyediakan hingga 80% informasi yang dibutuhkan untuk aktivitas sehari-hari. Meskipun penting, kesehatan mata sering kali terabaikan, dan gangguan mata dapat menyebabkan ketidaknyamanan, gangguan penglihatan, atau bahkan kebutaan total. Penyakit seperti katarak, glaukoma, retinopati diabetik, dan kondisi mata umum lainnya menimbulkan ancaman signifikan terhadap kesehatan penglihatan. Indonesia, dengan salah satu tingkat kebutaan tertinggi di dunia, menghadapi tantangan dalam menyediakan perawatan mata yang memadai, terutama di daerah terpencil dengan akses terbatas ke dokter mata. Untuk mengatasi masalah ini, teknik pembelajaran mesin, khususnya Convolutional Neural Networks (CNN), telah digunakan untuk tugas klasifikasi gambar, termasuk analisis gambar medis. Model CNN, terutama arsitektur VGG-19, telah terbukti efektif dalam mengklasifikasikan dan mendiagnosis penyakit mata berdasarkan gambar retina dengan tingkat akurasi 96%. Penelitian ini bertujuan untuk mengimplementasikan metode CNN menggunakan arsitektur VGG-19 untuk mengklasifikasikan berbagai penyakit mata, seperti katarak, glaukoma, retinopati diabetik, dan kondisi mata lainnya. Penelitian ini melibatkan penggunaan kumpulan data berkualitas tinggi yang terdiri dari 800 gambar berlabel di delapan kategori penyakit mata, dengan tujuan mencapai klasifikasi yang akurat. Kumpulan data tersebut diproses terlebih dahulu, dan model pembelajaran mendalam dilatih untuk meningkatkan hasil klasifikasi. Penelitian ini berkontribusi pada potensi penerapan CNN dalam membantu diagnosis penyakit mata.
Co-Authors Ardhiyan Azizi Ardi Susanto Asih Rahmawati Berliani Risqi Dwi Saputri Dairoh Dairoh Dairoh Dairoh Dega Surono Wibowo Dwi Intan Af’idah Dyah Apriliani Elang Bimantoro Elang Bimantoro Fadiyah Desi Asmawati Fathur Rizqi Putra Pratama Ginanjar Wiro Sasmito Ginanjar Wiro Sasmito Ilham Hatta Manggala M Nishom M Shidiq Erdi W M Yoka Fathoni Miftakhul Huda Mohammad Humam Mohammad Khambali Muhammad Rafli Erfiyanto Muhammad Rizki Musnadil Firdaus Musnadil Firdaus Putri Ajeng Imamah Rizki Eka mulyani Sharfina Febbi Handayani Taufiq Abidin Tengku Dimas Aditya Yustia Hapsari Yustia Hapsari Yustia Hapsari Yustia Hapsari Yustia Hapsari