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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Simetris Prosiding SNATIF SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknologi Informasi dan Ilmu Komputer CESS (Journal of Computer Engineering, System and Science) PROSIDING SENATEK FAKULTAS TEKNIK UMP R.E.M (Rekyasa Energi Manufaktur) Jurnal JOIV : International Journal on Informatics Visualization INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Network Engineering Research Operation [NERO] JITK (Jurnal Ilmu Pengetahuan dan Komputer) Multitek Indonesia : Jurnal Ilmiah KOMPUTEK JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Journal of Computer Networks, Architecture and High Performance Computing Journal of Computer, Electronic, and Telecommunication (COMPLETE) Insyst : Journal of Intelligent System and Computation Indonesian Journal of Networking and Security - IJNS Jurnal Abdimas Dosma SinarFe7 Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Abdimas Indonesian Journal Jurnal Pengabdian Masyarakat Bangsa Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif Jurnal Abdimas Dosma Journal Information System and Computer Application KREATIF: Jurnal Pengabdian Masyarakat Nusantara Mathematics, Informatics, Knowledge, And Information Research
Fauzan Masykur
Universitas Muhammadiyah Ponorogo
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Analisis Deteksi Citra Mata Ikan Nila dengan Metode Convolutional Neural Network Arsitektur Alexnet Angga Prasetyo; Fauzan Masykur; Arief Rahman Yusuf; Arin Yuli Astuti; Sugianti Sugianti; Yovi Litanianda; Ismail Abdurrozzaq
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 1 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i1.995

Abstract

Kualitas kesegaran ikan nila terletak pada proses pembekuan. ikan nila memiliki lapisan sisik yang tebal di seluruh permukaan tubuhnya, yang dapat menghambat proses pembekuan secara merata. Ketidakteraturan dalam proses ini berpotensi menurunkan kualitas dan kesegaran ikan selama penyimpanan. Kondisi ini merugikan dan menyulitkan konsumen dalam menilai tingkat kesegaran ikan hanya melalui pengamatan penglihatan secara manual, seperti memeriksa kondisi mata ikan. Oleh karena itu, tujuan utama riset yaitu, membangun sistem deteksi citra mata ikan dengan metode penilaian kesegaran yang cepat, akurat, dan objektif untuk membantu konsumen menjadikanya opsi utama yang harus dilakukan. Model CNN memiliki keunggulan dalam akurasi serta klasifikasi citra, selain itu model CNN dapat ditingkatkan melalui penambahan arsitektur salah satunya arsitektur alexnet. Proses tahapan metodologi klasifikasi dataset yaitu diperoleh dari kaggle berdasarkan citra mata ikan Nila dengan membaginya ke dalam dua kelas, yaitu kelas 'mata ikan nila segar' dan kelas 'mata ikan nila kurang segar' dan preprocessing menghasilkan modeling cnn untuk deteksi citra mata ikan. Hasil analisis diperoleh Gambar ikan nila digunakan sebagai data uji dan diberikan sebagai input ke dalam model yang telah dilatih dengan hanya memerlukan waktu sekitar 68 milidetik per langkah (68 ms/step). Berdasarkan analisis terhadap pola visual, seperti warna mata, tekstur kulit, serta ciri fisik lainnya, model mengkategorikan ikan tersebut dikondisi tidak segar. Untuk kelanjutan riset perlu dilakukan keseimbangan dataset citra dengan menggunakan Bayesian hyperparameter.
Decision Support System for Recommendation of Competition Types for Elementary School/Islamic Elementary School/Equivalent Students Using the TOPSIS Method Shidney, Valentino Shidney; Masykur, Fauzan; Dyah Mustikasari; Raveenthiran Vivekanantharasa
MEKAR : Journal Information System and Computer Application Vol. 2 No. 1 (2026): APRIL
Publisher : PT Mekar Research and Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65475/wc0xrk72

Abstract

The selection of students to participate in competitions at the elementary school/Islamic elementary school/equivalent level has been done manually and subjectively, potentially causing inaccuracies in determining the type of competition that suits the students' abilities. This study aims to develop a web-based Decision Support System (DSS) using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to recommend the right type of competition for elementary school/Islamic elementary school students in Parang District. This system was built using the PHP programming language and MySQL database, with four main criteria: academic grades (weighted 0.35), artistic grades (weighted 0.25), sports grades (weighted 0.20), and student interests (weighted 0.20). The system was developed using the waterfall method and testing was carried out using white box testing. The test results showed that the system was able to produce student rankings based on TOPSIS preference scores with a 100% accuracy rate compared to manual calculations. This system is expected to assist teachers in making decisions more objectively, systematically, and efficiently.
Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Identifikasi Jenis Tanaman Rimpang (Zingiberaceae) Rani Dwi Kartikasari; Mohammad Bhanu Setyawan; Fauzan Masykur; Adi Fajaryanto Cobantoro
MIKIR : Mathematics, Informatics, Knowledge And Information Research Vol. 1 No. 1 (2025): OKTOBER
Publisher : PT Mekar Research and Publishing

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

Abstract

Rhizomes (Zingiberaceae) are modified plant stems that grow horizontally beneath the soil surface and can produce shoots and new roots from their nodes. Rhizome plants (Zingiberaceae) are known as ginger or spice plants. This research article discusses the identification of rhizome plant species using Convolutional Neural Network (CNN) algorithm with VGG19 architecture, involving a total of 10 classes of data samples. The rhizome images underwent data preprocessing, resizing them from 500 x 500 to 200 x 200 pixels. During the model design phase, three different scenarios were tested, considering variations in dataset proportions, number of epochs, and batch sizes. The results of the three scenarios showed that the second scenario performed the best, achieving an accuracy of 90%, a loss of 0.285, precision of 93%, recall of 89%, and F1-Score of 91%. The first scenario obtained an accuracy of 88%, and the third scenario achieved an accuracy of 82%. However, when applying the model to test images and achieving the highest accuracy of 90% during training, the accuracy dropped to 40% when evaluated on 100 testing data. This drop in accuracy can be attributed to several factors, including noise in the dataset used and insufficient amount of training data, leading to the model being less effective in learning and recognizing data patterns.
Co-Authors ., Sugianti Adi Fajaryanto Adi Fajaryanto Cobantoro Adi Fajaryanto Cobantoro Ali Mahmudi Ali Mahmudi Aminuddin, Wildan Muhammad Andy Triyanto Angga Prasetyo Angga Prasetyo Aprilia Cahyanti Arief Rahman Yusuf Arief Rahman Yusuf Astuti, Arin Yuli Beni Yulio Eka Pratama Cobantoro , Adi Fajaryanto Cobantoro, Adi Fajaryanto Cobantoro, Adi Fajaryanto Desriyanti Devi Kartikasari Dyah Mustikasari Eahyu Oktavian, Elang Efi Mukaromah Eka, Novie Ellisia Kumalasari Fajaryanto Cobantoro, Adi Fajaryanto, Adi Fredin Rimba Saputra Ghulam Asrofi Buntoro Ibnu Makruf Pandu Atmaja Indah Puji Astuti Indah Puji Astuti Irfan Agung Nugroho Jamilah Karaman Karaman, Jamilah Karaman, Lazuardi Irham Kelik Sussolaikah Khoiru Nurfitri Kuntang Winangun Kuntang21 Kuntang21 M. Malyadi Makruf Pandu Atmaja, Ibnu Miftakhul Arifin Mohammad Bhanu Setyawan Mohammad Bhanu Setyawan Mohammad Rizqi Rosyadi Muhamad, Fikri Muhammad Furqon Fadli Muhammad Malyadi nabila solihin zaelani, bintang muhammad Novi Indah Riani Novia Anggraini Pradityo Utomo Prasetiyowati, Fiqiana Prasetyo, Angga Rani Dwi Kartikasari Raveenthiran Vivekanantharasa Rendy Cahyono Reza Risky Khamdani Rido Muhamad Nasrudin Riyanto, Didik Rizqi Rosyadi, Mohammad Roikhatul Jannah Setyawan, Moh. Bhanu Shidney, Valentino Shidney Sugianti Sugianti, Sugianti Sulthon Habiby, Jawwad Sumaji Trisnadi Putra, Wawan Wawan Trisnadi Putra Windy Octavia Yofhi, Yofhi Fauda Pradana Yovi Litanianda Yovi Litanianda, Yovi Yulio Eka Pratama , Beni Yusuf, Arief Rahman Zulkarnain, Ismail Abdurrozaq Zulkarnain, Ismail Abdurrozzaq Zulkarnain, Ismail Abdurrozzaq