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All Journal International Journal of Advances in Intelligent Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Samudra Ekonomi dan Bisnis Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Mediasosian : Jurnal Ilmu Sosial dan Administrasi Negara Jurnal Dharma Bhakti Ekuitas Journal Competency of Business PELS (Procedia of Engineering and Life Science) Journal of Community Empowerment Journal of Transportation and Society Empowerment Environment Conflict
Prasetyo, Heri
Universitas Sebelas Maret (UNS)
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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OPTIMALISASI KINERJA PEROLEHAN ZAKAT, INFAK DAN SEDEKAH DENGAN MENGGUNAKAN SIMLAZ Winarno, Winarno; Harjito, Bambang; Wiranto, Wiranto; Prasetyo, Heri; Sihwi, Sari Widya
Journal of Community Empowerment Vol 4, No 1 (2025): Juni
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jce.v4i1.31767

Abstract

ABSTRAK                                                                            Trend berzakat, berinfak di era digital mengalami perubahan yang cukup siginifkan. Baznas telah melaporkan tren berzakat saat ini didominasi oleh anak muda dengan usia antara 25-44 tahun. Pencapaian di tahun 2021 nilai yang dihasilkan sangat besar yaitu mencapai 11,5 trilyun rupiah yang sebagian besar merupakan partisipasi dari anak muda milenial. Besarnya angka Zakat, Infak dan Shodaqoh ini salah satunya dikarenakan mudahnya mengeluarkan zakat dengan menggunakan aplikasi. Laz Nur Hidayah merupakan salah satu lembaga Laz di Kota Surakarta yang berdiri pada 2021. Lembaga baru ini saat ini masih sedang mengembangkan instansinya untuk dapat berkembang mengejar lembaga-lembaga Laz lain. Lembaga Laz Nur Hidayah memiliki potensi yang sangat besar dimana lembaga Nur Hidayah sudah memiliki nama besar di dunia pendidikan di Kota Surakarta. Saat ini Laz Nur Hidayah yang merupakan lembaga baru masih banyak kekurangan dalam manajemen ZIS, baik dari sisi perencanaan program, optimalisasi donatur, manajemen donatur dan pelaporan kepada donatur. Tujuan dari program pengabdian ini adalah mengoptimalisasikan kinerja LazNH dengan pembangunan aplikasi SIMLaz. Permasalahan yang dialami oleh Laz Nur Hidayah tersebut dapat diatasi dengan melakukan digitalisasi pengelolaan Laz Nur Hidayah dengan membangun aplikasi donasi berbasis online yang dapat ditransfer secara langsung secara realtime baik menggunakan akun bank atau dompet digital layaknya OVO, Gopay, Linkaja atau dompet elektronik lain. Hasil evaluasi dari program ini adalah bahwa aplikasi yang telah dibangun sudah diuji dengan metode blackbox dengan tingkat akurasi 100%. Artinya aplikasi ini sudah sesuai dengan kebutuhan fungsi yang diinginkan pengguna. Aplikasi selanjutnya dilakukan evaluasi kualitatif, untuk melihat optimalisasi kinerja lembaga. Hasil evaluasi yang dilakukan dengan wawancara, menghasilkan kesimpulan bahwa aplikasi mempermudah dalam proses mengatur program-program, merekap pendapatan dan mampu memberdayakan sumber daya relawan yang bisa menjadi pekerjaan sampingan. Kata kunci: Simlaz; crowdfunding;zakat; laz; pemberdayaan; sdm; aplikasi zakat. ABSTRACTThe trend of giving charity in the digital era has changed significantly. Baznas has reported that the current trend of providing zakat is dominated by young people aged between 25 and 44. The achievement in 2021 is that the value generated is tremendous, reaching 11.5 trillion rupiah, most of which is duet o the participation of millennial young people. The large number of Zakat, Infaq, and Shodaqoh is partly due to the ease of issuing Zakat using the application. Laz Nur Hidayah is one of the Laz institutions in Surakarta City, which was established in 2021. This new institution is currently still developing to be able to create after other Laz institutions. Laz Nur Hidayah has enormous potential, as Nur Hidayah already has a big name in the world of education in Surakarta. Currently, Laz Nur Hidayah, a new institution, still has many shortcomings in ZIS management in terms of program planning, donor optimization, donor management, and reporting to donors. This service program aims to optimize LazNH's performance with the development of the SIMLaz application. The problems experienced by Laz Nur Hidayah can be overcome by digitizing the management of Laz Nur Hidayah by providing an online-based donation application that can be transferred directly in real time either using a bank account or digital wallet such as OVO, Gopay, Linkaja or other ewallets. The assessment outcomes about this program indicate that the application developed has undergone evaluation utilizing the black box methodology, achieving an accuracy rate of 100%. This means that this application is in accordance with the function requirements that users want. Subsequent applications were subjected to qualitative assessment to ascertain the enhancement of institutional efficacy. The assessment of the interview determined that the application enhances the organization of programs, the documentation of financial inflows, and the augmentation of volunteer resources that may serve as supplementary employment. Keywords: Simlaz; Crowdfunding; Zakat; LAZ; empowerment; HR; zakat application.
Dual Attention and Channel Atrous Spatial Pyramid Pooling Half-UNet for Polyp Segmentation Sarira, Beatrix Datu; Prasetyo, Heri
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.893

Abstract

Colorectal cancer (CRC) is a leading cause of cancer-related deaths, with two million cases detected in 2020 and causing one million deaths annually. Approximately 95% of CRC cases originate from colorectal adenomatous polyps. Early detection through accurate polyp segmentation is crucial for preventing and treating CRC effectively. While colonoscopy screening remains the primary detection method, its limitations have prompted the development of Computer-Aided Diagnostic (CAD) systems enhanced by deep learning models. This study proposes a novel neural network architecture called Dual Attention and Channel Atrous Spatial Pyramid Pooling Half-UNet (DACHalf-UNet) for medical polyp image segmentation that balances optimal performance with computational efficiency. The proposed model builds upon the U-Net framework by integrating Double Squeeze-and-Excitation (DSE) blocks in the encoder after the Ghost Module, Channel Atrous Spatial Pyramid Pooling (CASPP) in the bottleneck and decoder, and Attention Gate (AG) mechanisms within the architecture. DACHalf-UNet was trained and evaluated on the CVC-ClinicDB and Kvasir-SEG datasets for 70 epochs. Evaluations demonstrated superior performance with F1-Score and IoU values of 94.23% and 89.28% on CVC-ClinicDB, and 88.40% and 81.47% on Kvasir-SEG, respectively. Comparative analysis showed that DACHalf-UNet outperforms existing architectures including U-Net, U-Net++, ResU-Net, AGU-Net, CSAP-UNet, PRCNet, UNeXt, and UNeSt. Notably, the model achieves this performance with only 0.56 million trainable parameters and 30.29 GFLOPs, significantly reducing computational complexity compared to previous methods. These results demonstrate that DACHalf-UNet effectively addresses the need for accurate and efficient polyp segmentation, potentially enhancing CAD systems and contributing to improved CRC detection and treatment outcomes.
Improving Accuracy and Efficiency of Medical Image Segmentation Using One-Point-Five U-Net Architecture with Integrated Attention and Multi-Scale Mechanisms Fathur Rohman, Muhammad Anang; Prasetyo, Heri; Yudha, Ery Permana; Hsia, Chih-Hsien
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.949

Abstract

Medical image segmentation is essential for supporting computer-aided diagnosis (CAD) systems by enabling accurate identification of anatomical and pathological structures across various imaging modalities. However, automated medical image segmentation remains challenging due to low image contrast, significant anatomical variability, and the need for computational efficiency in clinical applications. Furthermore, the scarcity of annotated medical images due to high labelling costs and the requirement of expert knowledge further complicates the development of robust segmentation models. This study aims to address these challenges by proposing One-Point-Five U-Net, a novel deep learning architecture designed to improve segmentation accuracy while maintaining computational efficiency. The main contribution of this work lies in the integration of multiple advanced mechanisms into a compact architecture: ghost modules, Multi-scale Residual Attention (MRA), Enhanced Parallel Attention (EPA) in skip connections, the Convolutional Block Attention Module (CBAM), and Multi-scale Depthwise Convolution (MSDC) in the decoder. The proposed method was trained and evaluated on four public datasets: CVC-ClinicDB, Kvasir-SEG, BUSI, and ISIC2018. One-Point-Five U-Net achieved sensitivity, specificity, accuracy, DSC, and IoU of of 94.89%, 99.63%, 99.23%, 95.41%, and 91.27% on CVC-ClinicDB; 91.11%, 98.60%, 97.33%, 90.93%, and 83.84% on Kvasir-SEG; 85.35%, 98.65%, 96.81%, 87.02%, and 78.18% on BUSI; and 87.67%, 98.11%, 93.68%, 89.27%, and 83.06% on ISIC2018. These results outperform several state-of-the-art segmentation models. In conclusion, One-Point-Five U-Net demonstrates superior segmentation accuracy with only 626,755 parameters and 28.23 GFLOPs, making it a highly efficient and effective model for clinical implementation in medical image analysis.
BIBLIOMETRIC ANALYSIS OF NEURAL BASIS EXPANSION ANALYSIS FOR INTERPRETABLE TIME SERIES (N-BEATS) FOR RESEARCH TREND MAPPING Saputro, Dewi Retno Sari; Prasetyo, Heri; Wibowo, Antoni; Khairina, Fadiah; Sidiq, Krisna; Wibowo, Gusti Ngurah Adhi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp1103-1112

Abstract

Bibliometrics is the statistical analysis of articles, books, and other forms of publication. The bibliometrics analysis is performed with data on the number and authorship of scientific publications and articles, and citations to measure the work of individuals or groups of researchers, organizations, and countries to identify national and international networks and map developments in new multidisciplinary fields of science and technology. In addition, bibliometrics assesses and maps the research, organization, and country of researchers at a given time period. The Bibliometric analysis also has advantages which include mapping relationships between concepts, mapping research directions or trends, mapping state of the art (the novelty of the results of research conducted), and providing insights related to fields, topics, and research problems for future works. This study aims to determine the growth and development of N-BEATS publications, their distribution, variable keywords, and author collaboration using a bibliometric network. The research method used in this paper, through screening of articles obtained from the Scopus database page in 2008-2022, is used for citations in the form of metrics. At the same time, they are visualizing the metadata with VOSviewer. Data was collected from the direct science database with the keyword N-BEATS. The results show that 2022 has the highest number of publications, reaching 310 publications (14.90%). The distribution of research publications on N-BEATS shows a perfect distribution. Terms in the N-BEATS variable that often appear and are associated with other variables.
SOSIALISASI NAVIGASI PELAYARAN & PERAWATAN PERMESINAN BAGI KESELAMATAN PELAYARAN NELAYAN DI DESA GEMPOLSEWU KECAMATAN ROWOSARI, KABUPATEN KENDAL, JAWA TENGAH Kristin Anita Indriyani; Prasetyo, Heri; Erliyani, Dian; Yuli Aryani, Desi; Prasetya Anggrahini, Wahyu; Sulistiyowati, Ely; Indah Safiira, Putri
Pengabdian Masyarakat Vol 2 No 1 (2024): JTSE Vol.2 No.1 2024
Publisher : Politeknik Ilmu Pelayaran Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46484/jtse.v2i1.588

Abstract

Penangkapan Penangkapan ikan komersial masih merupakan salah satu pekerjaan yang paling berbahaya di dunia. Nelayan mungkin menghadapi banyak masalah keselamatan. Berbagai aspek keselamatan harus benar-benar dipertimbangkan untuk mengetahui seberapa besar risiko yang dihadapi saat melakukan pekerjaan di tengah laut. Sumber daya manusia maritim, khususnya kompetensi nelayan, diharapkan dapat ditingkatkan dengan memberikan pengetahuan tentang navigasi dan perawatan dan perbaikan permesinan kapal motor. Maka dari itu dilakukanlah Sosialisasi Navigasi Pelayaran & Perawatan Permesinan Bagi Keselamatan pelayaran Di Desa Gempolsewu, Kecamatan Rowosari, Kabupaten Kendal, Jawa Tengah. Sosialisasi ini akan ditujukan bagi nelayan di sekitar pesisir Kendal. Pembicara sosialisasi ini adalah dosen di lingkungan Politeknik Ilmu Pelayaran Semarang. Kegiatan pengabdian kepada masyarakat terutama bagi pekerja nelayan di Kabupaten Kendal, Jawa Tengah untuk lebih meningkatan pengetahuan tentang bernavigasi dan perawatan mesin guna keselamatan para pekerja nelayan. Salah satu bagian dari pelaksanaan tugas Tri Dharma Perguruan Tinggi adalah kegiatan ini.
Problematika impor sampah di Indonesia: Kepentingan politik, ekonomi, atau lingkungan? Ekarini, Diah Fitri; Sakina, Nova Amalia; Erpinda, Mia; Prasetyo, Heri
Environment Conflict Vol. 1 No. 1: (February) 2024
Publisher : Institute for Advanced Science Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/environc.v1i1.2024.464

Abstract

The waste issue in Indonesia remains an unresolved environmental concern, exacerbated by the problem of waste importation from developed countries to Indonesia, a practice that has been ongoing since 1989 until now. Since 2018, China, as the world's largest importer of waste, ceased its waste import activities with the implementation of the National Sword Policy (reducing drastically from 60% to 10%). This had repercussions on Indonesia, a developing country and a recipient of waste imports in Southeast Asia. The increase in the volume of waste imports from developed countries to Indonesia was approximately 320,000 tons in 2018. This article aims to discuss the issues of waste importation in Indonesia from political-ecological, social, and economic perspectives, as well as its environmental impact. The article also reviews the compliance with existing laws regarding the waste import mechanism in Indonesia. The literature review method is employed to compile this article, utilizing various research materials related to waste imports and policies in Indonesia. Based on the findings, it can be concluded that the waste import issue in Indonesia is a manifestation of political-ecological concerns, where environmental problems are greatly influenced by political (ego-sectoral) and economic aspects. The environmental interests mandated by the Basel Convention, which are subsequently translated into policies and regulations in Indonesia, have not been able to achieve their main objectives, namely, the protection of environmental and human health from the impacts of imported waste. The waste import policy in Indonesia needs to be reevaluated concerning the clarity of requirements and effective law enforcement when violations occur.
PENINGKATAN LITERASI DIGITAL MANAJEMEN CONTENT MANAGEMENT SYSTEM BERBASIS WORDPRESS UNTUK MENCEGAH SERANGAN JUDI GACOR PADA WEBSITE PORTAL ORGANISASI PERANGKAT DAERAH Winarno, Winarno; Harjito, Bambang; Wiranto, Wiranto; Prasetyo, Heri; Widya Sihwi, Sari
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 6, No 6 (2023): martabe : jurnal pengabdian kepada masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v6i6.2252-2257

Abstract

Maraknya aksi peretasan di Indonesia saat ini terjadi tidak hanya di Lembaga-lembaga besar, namun juga lembaga pemerintah daerah. Salah satu bentuk peretasan yang sangat marak akhir-akhir ini adalah judi gacor pada Content Managemen System(CMS) berbasis Wordpress. Di Kabupaten Karanganyar masih ada 6.180 konten judi gacor yang terlihat di Google. Untuk menanggulangi masalah tersebut diperlukan Langkah praktik baik bagaimana mencegah dan mengelola sebuah CMS berbasis Wordpress agar terhindar dari serangan siber. Pelaksanaan pengabdian berupa workshop mengelola website mulai dari instalasi sampai implementasi. Hasil workshop menunjukkan peningkatan kemampuan peserta yang semula hanya 40% menjadi 80% paham bagaimana mengelola sebuah website yang aman.
HALF-MAFUNET: A Lightweight Architecture Based on Multi-Scale Adaptive Fusion for Medical Image Segmentation Maula Sandy, Abiaz Fazel; Prasetyo, Heri
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1357

Abstract

Medical image segmentation is a critical component in computer-aided diagnosis systems but many deep learning models still require large numbers of parameters and heavy computation. Classical CNN-based architectures such as U-Net and its variants achieve good accuracy, but are often too heavy for real deployment. Meanwhile, modern Transformer-based or Mamba-based models capture long-range information but typically increase model complexity. Because of these limitations, there is still a need for a lightweight segmentation model that can provide a good balance between accuracy and efficiency across different types of medical images. This paper proposes Half-MAFUNet, a lightweight architecture based on multi-scale adaptive fusion and designed as a simplified version of MAFUNet. The main contribution of this work is combining the efficient encoder structure of Half-UNet with advanced fusion and attention mechanisms. Half-MAFUNet integrates Hierarchy Aware Mamba (HAM) for global feature modelling, Multi-Scale Adaptive Fusion (MAF) to combine global and local information, and two attention modules, Adaptive Channel Attention (ACA) and Adaptive Spatial Attention (ASA), to refine skip connections. In addition, this model incorporates Channel Atrous Spatial Pyramid Pooling (CASPP) to capture multi-scale receptive fields efficiently without increasing computational cost. Together, these components create a compact architecture that maintains strong representational power. The model is trained and evaluated on three public datasets: CVC-ClinicDB for colorectal polyp segmentation, BUSI for breast tumor segmentation, and ISIC-2018 for skin lesion segmentation. All images are resized to 256×256 pixels and processed using geometric and intensity-based augmentations. Half-MAFUNet achieves competitive performance, obtaining mean IoU around 84 85% and Dice/F1-Score around 90 92% across datasets, while using significantly fewer parameters and GFLOPs compared to U-Net, Att-UNet, UNeXt, MALUNet, LightM-UNet, VM-UNet, and UD-Mamba. These results show that Half-MAFUNet provides accurate and efficient medical image segmentation, making it suitable for real-world deployment on devices with limited computational resources.
Medical Image Segmentation Using a Global Context-Aware and Progressive Channel-Split Fusion U-Net with Integrated Attention Mechanisms Widhayaka, Alfath Roziq; Prasetyo, Heri
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1371

Abstract

Medical image segmentation serves as a key component in Computer-Aided Diagnosis (CAD) systems across various imaging modalities. However, the task remains challenging because many images have low contrast and high lesion variability, and many clinical environments require efficient models. This study proposes CFCSE-Net, a U-Net-based model that builds upon X-UNet as a baseline for the CFGC and CSPF modules. This model incorporates a modified CFGC module with added Ghost Modules in the encoder, a CSPF module in the decoder, and Enhanced Parallel Attention (EPA) in the skip connections. The main contribution of this paper is the design of a lightweight architecture that combines multi-scale feature extraction with an attention mechanism to maintain low model complexity and increase segmentation accuracy. We train and evaluate CFCSE-Net on four public datasets: Kvasir-SEG, CVC-ClinicDB, BUSI (resized to 256 × 256 pixels), and PH2 (resized to 320 × 320 pixels), with data augmentation applied. We report segmentation performance as the mean ± standard deviation of IoU, DSC, and accuracy across three random seeds. CFCSE-Net achieves 79.78% ± 1.99 IoU, 87.21% ± 1.72 DSC, and 96.70% ± 0.59 accuracy on Kvasir-SEG, 88.11% ± 0.86 IoU, 93.42% ± 0.55 DSC, and 99.04% ± 0.09 accuracy on CVC-ClinicDB, 69.33% ± 2.66 IoU, 78.80% ± 2.65 DSC, and 96.30% ± 0.51 accuracy on BUSI, and 92.27% ± 0.52 IoU, 95.92% ± 0.30 DSC, and 98.06% ± 0.16 accuracy on PH2. Despite its strong performance, the model remains compact with 909,901 parameters and low computational cost, requiring 3.24 GFLOPs for 256 × 256 inputs and 5.07 GFLOPs for 320 × 320 inputs. These results show that CFCSE-Net maintains stable performance on polyp, breast ultrasound, and skin lesion segmentation while it stays compact enough for CAD systems on hardware with low computational resources.
Co-Authors Antoni Wibowo Arif Senja Fitrani Azzahra, Ummi Fajarina Bambang Harjito, Bambang Basuki, Akbari Indra Dewi Retno Sari Saputro, Dewi Retno Ekarini, Diah Fitri Elidasari, Devi Erliyani, Dian Erpinda, Mia Fathur Rohman, Muhammad Anang Fifi Swandari Hermansyah Hermansyah Heru Priyanto Horng, Shi Jinn Hsia, Chih-Hsien Indah Safiira, Putri Iwan Setiawan Khairina, Fadiah Kristin Anita Indriyani Maula Sandy, Abiaz Fazel Prasetya Anggrahini, Wahyu Prissando, Fendy Artha Rosiyadi, Didi Rr. Eko Giyartiningrum Sakina, Nova Amalia Sari Widya Sihwi, Sari Widya Sarira, Beatrix Datu Sidiq, Krisna Sulistiyowati, Ely Surifah Surifah Teguh Pramono Wahono Wahono Wibowo, Gusti Ngurah Adhi Widhayaka, Alfath Roziq Widya Sihwi, Sari Wijaya, Anugrah Winarno Winarno Wiranto Wiranto Yudha, Ery Permana Yuli Aryani, Desi