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Mutaqin Akbar
Universitas Mercu Buana Yogyakarta
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Segmentasi Tutupan Lahan Berbasis CNN pada Citra UAV Gumuk Pasir Parangtritis Yonanta Dwi Hartanto; Mutaqin Akbar
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 4 (2026): Volume 4 Number 4 October 2026 (Issue in Progress)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i4.357

Abstract

Periodic land cover monitoring in the Core Zone of Parangtritis Sand Dunes is crucial for conservation, but manual annotation of UAV imagery is time-consuming and subjective. This study implements a Standard U-Net for automatic semantic segmentation of sand and non-sand classes on UAV images. The training data consists of an integration of manual annotations (463 UAV images from 2019) and semi-automated annotations based on the Segment Anything Model (SAM) on 98 UAV images from 2022, while the test data comprises 80 UAV images from 2022 that the model has not previously encountered. Preprocessing included tiling images and masks into 256×256 patches, with data augmentation via rotation, zoom, brightness/contrast adjustment, and horizontal flip. The model was trained using a combined Binary Cross Entropy and Dice loss, Adam optimizer (learning rate 10⁻⁴), batch size 8, and a fixed threshold of 0.45. Evaluation on 80 independent 2022 test images yielded Pixel Accuracy of 73.31%, Dice Coefficient of 83.28%, and Intersection over Union (IoU) of 71.35%. Qualitatively, the model captured the general shape of sand areas well, though noise appeared in heterogeneous scenes, while the highest accuracy was achieved on homogeneous sand textures. The practical implication is accelerating precise spatial data provision for agencies monitoring sand dune changes. Further research is recommended to improve ground truth quality, integrate shadow correction, explore modern architectures, and apply loss functions handling class imbalance for comprehensive accuracy enhancement.
KLASIFIKASI CITRA SINTETIS HASIL MODEL DIFUSI MENGGUNAKAN GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) dan CONVOLUTIONAL NEURAL NETWORK (CNN) Andri Hardiyanto; Mutaqin Akbar
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.2033

Abstract

Kemajuan teknologi kecerdasan buatan khususnya dalam bidang pengolahan citra telah memungkinkan penciptaan gambar buatan yang sangat menyerupai gambar nyata, sehingga menimbulkan tantangan dalam verifikasi keaslian citra digital. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi citra untuk membedakan antara citra asli dan citra hasil kecerdasan buatan dengan pendekatan hybrid Gray Level Co-occurrence Matrix (GLCM) untuk mengekstraksi enam fitur tekstur dari citra grayscale dengan fitur spasial dari Convolutional Neural Network (CNN) yang kemudian digabungkan untuk membentuk vektor fitur gabungan. Dataset yang digunakan terdiri dari 3.410 citra berwarna yang terbagi secara seimbang ke dalam dua kelas real dan fake. Hasil pengujian CNN murni mencapai akurasi 97%, dengan presisi dan recall antara 0.95-0.99,serta f1-score 0.97. Sementara itu, pada model GLCM-CNN akurasinya mencapai 98% dengan nilai presisi dan recall 0.96-1.00 serta f1-score 0.98. Integrasi fitur tekstur dari GLCM terbukti mampu meningkatkan sensitivitas model terhadap pola mikro pada citra buatan yang tidak dapat ditangkap oleh CNN. Penelitian ini menunjukkan potensi pendekatan hybrid sebagai dasar pengembangan sistem pendeteksi citra sintetis yang adaptif dan akurat di masa mendatang.
Feasibility of Opportunity Material Module with Joymath Cognitive Behavioral Method to Reduce Mathematics Anxiety and Increase Student Self-Efficacy Nafida Hetty Marhaeni; Nanang Khuzaini; Muhammad Rafi Fajar Rizky; Reny Yuniasanti; Mutaqin Akbar; Dian Kartika Sari; Dangin Dangin
Mosharafa: Jurnal Pendidikan Matematika Vol. 13 No. 3 (2024): July
Publisher : Department of Mathematics Education Program IPI Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31980/mosharafa.v13i3.2188

Abstract

Siswa memerlukan media pembelajaran yang dapat membantu siswa mengurangi kecemasan matematika dan meningkatkan efikasi diri. Tujuan penelitian yaitu mengembangkan modul untuk mengurangi kecemasan matematika dan meningkatkan efikasi diri siswa pada materi peluang. Penelitian Research and Development ini menggunakan model ADDIE dengan lima tahap pengembangan yaitu analisis, desain, pengembangan, implementasi, dan evaluasi. Sumber data dan subjek dalam penelitian ini adalah ahli materi, ahli media, guru, dan 1328 siswa dari 24 SMP di Kota Yogyakarta. Teknik pengumpulan data yang digunakan yaitu wawancara, observasi, Focus Group Discussion (FGD), dan angket. Hasil menunjukkan bahwa produk yang dikembangkan yaitu modul materi peluang untuk mengurangi kecemasan matematis dan meningkatkan efikasi diri siswa layak digunakan dalam pembelajaran matematika. Pengambilan keputusan kelayakan produk didasarkan pada hasil analisis penilaian dari validator, ahli materi, dan ahli media yang menunjukkan bahwa modul sangat valid dan berpeluang menurunkan kecemasan matematis dan meningkatkan efikasi diri siswa.Students need learning media that can help students reduce mathematics anxiety and increase self-efficacy. The aim of the research is to develop a module to reduce mathematics anxiety and increase students' self-efficacy in opportunity material. This Research and Development research uses the ADDIE model with five development stages, namely analysis, design, development, implementation and evaluation. The data sources and subjects in this research were material experts, media experts, teachers, and 1328 students from 24 junior high schools in Yogyakarta City. The data collection techniques used were interviews, observation, Focus Group Discussion (FGD), and questionnaires. The results show that the product developed, namely the opportunity material module to reduce mathematical anxiety and increase students' self-efficacy, is suitable for use in mathematics learning. Decision making on product feasibility is based on the results of assessment analysis from validators, material experts and media experts which show that the module is very valid and has the opportunity to reduce mathematical anxiety and increase student self-efficacy.
Klasifikasi Jenis Buah Nanas Menggunakan Convolution Neural Network Jodhy Dwi Marfianto; Mutaqin Akbar
Jurnal Transformatika Vol. 21 No. 1 (2023): July 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.6369

Abstract

Indonesia is one of the countries with gread agricultural potential. One of the products of agriculture in Indonesia is pineapple. Pineapple is a tropical plant with edible fruit and one of the maximum economically vital plants in the Bromeliaceous family. The process of selecting pineapple species is generally very dependent on human perception. The development of technology and science makes it possible to perform classification or in terms of object selection using technology based on digital image-based characteristics. Images are used as a source of information that can be used to classify objects. One of the deep learning methods used is Convolutional Neural Networks (CNN) because they have a high deep network and are widely used to image data. Deep learning in Computer Vision has good capabilities in, one of which is image classification or object classification in images, and the network in CNN has a special layer, namely the convolution layer, The image convolution process in this study uses the keras package on GoogleColab, because making a neural network model using Keras does not need to write code to express mathematical calculations individually. Testing using a sample of 120 pineapple images shows an accuracy rate of 91,66% which is considered to be able to identify 3 types of pineapple fruit.
Co-Authors Adella Maharani, Putri Adwin Nurhasananda Agung Firmansyah Agus Salim Ahsan, Moh An-Naufal Nuha, Alfian Andri Hardiyanto Anisyah Jatu Siti Nurjanah Aprisia Bahagia, Grace Arif Pria Purnama Arifadillah, Elang Arita Witanti Ascha, Nugrah Pratama Astri Wulandari Audita Nuvriasari Auditya, Yonathan Bagus Dwi Kurniawan, Bagus Dwi Bambang Agus Setyawan Budi Sulistiyo Jati Budi Sulistiyo Jati Budianto, Alexius Endy Dangin Dangin Dangin, Dangin Dede Fajriansyah Dian Kartika Sari Dian Kartika Sari, Dian Kartika Diski Ijtima Putri Dwiyati Pujimulyani Elsa Anggraini Maili Febri Rahmadsyah Fiki Ertandi Firdaus Alfajar Sudarsih Hendri Tri Cahya Leksana Hilda Sukma Pertiwi Ichlasia Ainul Fitri Ikram, Rauf Al Indah Susilawati Intan Aulia Irfan Nur Fahrudin Irfan Pratama Jeremias Quintino Tilman Jodhy Dwi Marfianto Junianto Bagas Prasetyo Kafilahudin, Fahrul Advis Kartadinata, Arifqi Khuzaini*, Nanang Kuswandaru, Kuswandaru Muhammad Abdul Gofur Muhammad Ali Ma'mun Muhammad Pratiwo Muhammad Rafi Fajar Rizky Muhammad Syadham, Syahrun Muhammad Syaifudin Musa, Rahmat Nafida Hetty Marhaeni Nanang Khuzaini, Nanang Nanik Triatmi Nur Alamsyah Nurdiarti, Rosalia Prismarini Nusantara, Bondan Surya Pascal Munthe, Thimoty Prasetyaningrum, Putri Taqwa Prima Yalesta Sarumaha Primananda, Muhammad Izra Priscilia Amanda Leza Priyanto Putu Sangyoga, Titus Bintang Pekiek Rahmat Musa Ramos, Sarah Vega Refky Satria Bima Reny Yuniasanti Rio Setya Pambudi Rismanto, Septa Rismaria Sipayung Rivansyah Subagyo, Ibnu Rivka Novi Cahyati Riyanto, Agung Rizky, Muhammad Rafi Fajar Rofiqi, Lutfi Rohmad, Arinadi Nur Rosalia Prismarini Nurdiarti Saputra, Aldi Dwi Saputra, Andika Dwi Sari, Prima Wulan Sedyarsa, Hanif Fauzan Septa Rismanto Setyaningsih, Putry Wahyu Sidiq Purnomo, Agus Sri Muhammad Kusumantomo Subhan Bole Boly Suharjo, Imam Supatman Supatman Umul Aiman Wakidi Wakidi Wibowo, Sigit Heri Wisnu Adi Yulianto Wulandari, Astri Yonanta Dwi Hartanto Yusanto, Yoga Zada Maulana