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All Journal bit-Tech ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Leverage, Engagement, Empowerment of Community (LeECOM) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Mulyo, Budi Mukhamad
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Social Sciences

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EDUKASI POLA HIDUP SEHAT DI ERA PANDEMI COVID-19 DAN PENINGKATAN BAKAT MINAT MEMBACA KEMBALI PADA SISWA/SISWI SD KARTIKA IV-9 SURABAYA Mulyo, Budi Mukhamad; A., Alif Arsyad; Prasetya, Aditya; N., Bella Risky; Y., Muhamad Andika; B., Muhammad Agung; Alif S., Reza Muhammad; Z., Eva Rahmania; Fitriyah, Fitriyah; R., Qurotul A’yun; Prastika, Ayu Dwi; Aprilia, Yuva Dwi; S., Aprilia Erlinda; S., Widhi Ayu; Marjiansa, Aris; Putra, Anggelus
Jurnal Leverage, Engagement, Empowerment of Community (LeECOM) Vol. 4 No. 1 (2022): Jurnal Leverage, Engagement, Empowerment of Community (LeECOM)
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37715/leecom.v4i1.2952

Abstract

Tema Kuliah Kerja Nyata Tematik (KKN-T) yang kami gunakan adalah Edukasi Pola Hidup Sehat di Era Pandemi Covid-19 dan Peningkatan Bakat Minat Membaca dengan sasaran program siswa sekolah dasar. kami merancang beberapa program khusus yang berkaitan dengan tema KKNT, dengan pertimbangan untuk tetap mematuhi protokol kesehatan yang berlaku. Program-program yang telah penulis rancang diharapkan dapat tercapainya tujuan dari tema yang telah di pilih. Tujuan program-programnya adalah sebagai berikut. (1) Memberikan pengetahuan kepada siswa/siswi SD Kartika IV-9 tentang bahaya virus Covid-19 dan dampak bagi kesehatan, kesejahteraan, dan masa depan. (2) Meningkatkan minat membaca dan kenyamanan belajar. Metode pelaksanaan menggunakan langkah sebagai berikut: identifikasi potensi dan menganalisis permasalahan di dalam masyarakat, perancangan program, penelitian pustaka untuk acuan materi yang digunakan selama pengabdian, metode observasi lapangan dilakukan untuk mengetahui kondisi, dengan mendatangi lokasi secara langsung sekaligus untuk melaksanakan kegiatan KKN.
Rupiah Classification System using Segmented Fractal Texture Analysis and HSV Color Features Rakhmadi, Ardhon; Rahayu, Putri Nur; Thooriqoh, Hazna At; Mulyo, Budi Mukhamad
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.560

Abstract

The crime of forgery of rupiah currency can be anticipated by examining the rupiah banknotes based on traits or features contained on the original paper money. Features that are not owned by the rupiah banknote counterfeit is an ultraviolet sign that are owned by the original paper money. Rupiah banknotes feature extraction consists of a combination of color and texture feature extraction. The proposed method is the HSV color histogram for color feature extraction and Segmented Fractal Texture Analysis (SFTA) for texture feature extraction. The combination of HSV and SFTA is expected to improve the performance of rupiah banknotes feature extraction. Moreover this paper will analyze feature redundancy in Two Threshold Decomposition Algorithm in SFTA Algorithm. Experimental results show the proposed method can reach 100% accuracy. Experiment results also show that redundant features can be removed without affecting the accuracy of of the system so that it can reduce the computational cost.
Implementasi Business Intelligence pada Rekomendasi Produk Agrowisata Durian Sari, Anggraini Puspita; Rozci, Fatchur; Mulyo, Budi Mukhamad; Aqil Salim, Mas Muhammad; Arini, Andhini Putri
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 4 (2025): Edisi Oktober - Desember
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i4.6872

Abstract

Agrowisata Durian memiliki potensi untuk menarik wisatawan yang cukup tinggi, dilihat bagaimana penggemar durian yang ramai disaat-saat panen durian. Dengan adanya pengembangan Agrowisata ini dapat meningkatkan ekonomi lokal melalui pemberdayaan petani dan UMKM. Meskipun demikian, banyak juga tantangan yang perlu diselesaikan dalam mengembangkan agrowisata durian ini. Misalnya Pengelolaan dan pemasaraan yang masih terkendala, terutama dalam bagaimana cara memahami preferensi konsumen dan strategi penjualan yang sesuai. Pengabdian yang dilakukan oleh Tim ini bertujuan mengimplementasikan sistem Business Intelligence sebagai solusi untuk mengelola dan menganalisis data transaksi, preferensi pelanggan, serta pola pembelian yang bertujuan untuk menghasilkan rekomendasi produk durian yang lebih tepat sasaran. Metode yang digunakan antara lain pengumpulan data penjualan dan preferensi, integrasi data dalam data warehouse, pengolahan menggunakan metode Apriori dan Collaborative Filtering, serta penyajian hasil melalui dashbord yang interaktif. Hasil implementasi memperlihatkan sistem Business Intelligence mampu mengidentifikasi produk unggulan, memberikan rekomendasi personalisasi kepada wisatawan, serta mendukung pengambilan keputusan berbasis data untuk pengelola agrowisata. Dengan adanya sistem ini, pengelola dapat meningkatkan efisiensi pemasaran, memperluas jangkauan promosi, dan juga memberikan pengalaman wisata yang lebih personal dan memuaskan bagi pengunjung. Penerapan Business Intelligence pada agrowisata durian diharapkan mampu meningkatkan daya saing destinasi wisata sekaligus mendorong pertumbuhan ekonomi lokal secara berkelanjutan.
A Comparative Analysis of Resource Utilization using ISO/IEC 25010 in REST API File Upload Testing: Postman (GUI-Based) and Cucumber (Code-Based) Thooriqoh, Hazna At; Mulyo, Budi Mukhamad; Rakhmadi, Ardhon
ILKOMNIKA Vol 7 No 2 (2025): Volume 7, Number 2, August 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i2.773

Abstract

To guarantee quality and dependability, testing of REST Application Programming Interfaces (APIs) is an essential part of contemporary software development cycles. However, with a multitude of available testing frameworks, selecting the most efficient tool for specific tasks, such as file uploads, remains a challenge. The decision often overlooks the critical factor of resource utilization, which can significantly impact system performance and cost, particularly in continuous integration environments. This study addresses this problem by providing a comparative analysis of resource utilization between two popular REST API testing frameworks, Postman (GUI-based) and Cucumber (code-based), specifically for a file upload transaction. The research utilizes relevant metrics from the ISO/IEC 25010 standard, which cover processor, memory, I/O, storage, and bandwidth. The findings reveal that while Cucumber exhibits superior bandwidth efficiency, Postman demonstrates more significant efficiency in all other key resource metrics. Quantitatively, Postman's processor utilization was found to be approximately 74% lower, and its memory usage around 80% lower than that of Cucumber. These findings provide crucial empirical evidence for software developers and testers, enabling them to make informed decisions on tool selection based on specific resource efficiency priorities for file upload transactions.
Ekstraksi Fitur Kupu-Kupu dengan Lacunarity, Statistik, HSV dan Random Forest Rakhmadi, Ardhon; Rahayu, Putri Nur; Pamuji, Feby Agung; Thooriqoh, Hazna At; Mulyo, Budi Mukhamad
ILKOMNIKA Vol 7 No 2 (2025): Volume 7, Number 2, August 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i2.780

Abstract

Tujuan penelitian ini adalah mengekstraksi ciri morfologi kupu-kupu menggunakan Random Forest, fitur statistik, lakunaritas, dan HSV. Algoritma Random Forest menggunakan fitur ekstraksi ini sebagai input untuk proses klasifikasi. Fungsi HSV digunakan untuk mengekstrak informasi warna dari citra kupu-kupu, dan fungsi Lakunaritas digunakan untuk mengekstrak tekstur dan meningkatkan representasi visual suatu objek. Persentase akurasi, menurut hasil pengujian adalah 74%. Akurasi ini menunjukkan bahwa pendekatan Random Forest, karakteristik statistik, lakunaritas, dan HSV bekerja sama dengan baik untuk mendeskripsikan citra kupu-kupu untuk kategorisasi kupu-kupu otomatis.
Sustainable Community-Based Tourism dengan Sistem Informasi Manajemen Berbasis Web pada Desa Jubung Anugrah Purba, Claudia Dwi; Rusyda, Safira; Zain, Ardina Mudholivah; Mulyo, Budi Mukhamad
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 4 (2025): Edisi Oktober - Desember
Publisher : Lembaga Dongan Dosen

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

Abstract

Program pengabdian masyarakat di Desa Jubung dilatarbelakangi oleh potensi wisata alam, edukasi, dan agroedukasi yang belum dikelola optimal serta promosi yang masih dilakukan secara manual. Kegiatan ini bertujuan mengimplementasikan sistem informasi manajemen berbasis website yang terintegrasi dengan konsep Sustainable Community-Based Tourism (SCBT) untuk meningkatkan promosi potensi desa, UMKM, akomodasi, dan kegiatan masyarakat secara lebih luas dan interaktif. Stakeholder yang terlibat meliputi perangkat desa, pelaku UMKM, dan masyarakat lokal yang berpartisipasi aktif dalam pengembangan dan pengelolaan sistem. Metode pelaksanaan meliputi observasi lapangan, wawancara, perancangan dan pengembangan sistem menggunakan model Waterfall, sosialisasi, serta evaluasi melalui kuesioner. Hasil evaluasi menunjukkan bahwa lebih dari 70% responden sangat puas terhadap kualitas informasi dan kemudahan akses, serta seluruh responden (100%) menilai website bermanfaat dalam meningkatkan promosi digital dan akses informasi wisata.
Implementasi CNN Untuk Klasfikasi Emosi Dalam Lagu Berdasarkan Fitur Audio Pakpahan, Fredrik Sahalatua; Haromainy, M. Muharrom Al; Mulyo, Budi Mukhamad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3438

Abstract

Music is a powerful art form for conveying and evoking emotions; however, the vast volume of digital music data makes manual emotion categorization difficult. This study aims to implement a Convolutional Neural Network (CNN) to classify emotions in instrumental songs based on audio features. The dataset used is the Database for Emotional Analysis of Music (DEAM), containing 1,802 songs with valence and arousal annotations, which is divided with a 70:15:15 ratio for training, validation, and testing. The feature extraction methods applied include Mel-Frequency Cepstral Coefficients (MFCC) with variations of 13, 24, and 30 coefficients, and Mel-spectrograms with variations of 128, 256, and 512 bins. Data is processed through pre-emphasis and framing stages before being input into a CNN architecture with four convolutional blocks. Evaluation was conducted using 4-quadrant classification scenarios and a simplification into 2 quadrants. The results showed that in the 4-quadrant classification, the best model was achieved using MFCC with 30 coefficients with an accuracy of 66%, but model performance was hindered by extreme minority class imbalance. Conversely, simplifying the emotion space into 2 quadrants (valence or arousal) significantly improved accuracy to 77%. This study concludes that while increasing feature resolution has a minor impact, simplifying emotion dimensions proves more effective in addressing complexity and data imbalance in music emotion classification.
Pengaruh preprocessing citra retina pada klasifikasi diabetic retinopathy berbasis prototypical network Wulyono, Abi Eka Putra; Muttaqin, Faisal; Mulyo, Budi Mukhamad
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11126

Abstract

Diabetic retinopathy is a diabetes complication that can lead to progressive retinal damage and permanent blindness. Early detection through automated fundus image classification is essential but challenged by varying image quality, background noise, and color dominance that reduces lesion visibility. Prototypical networks have demonstrated good performance in few-shot learning settings, yet specialized preprocessing is rarely explored. This study proposes a prototypical network enhanced with modified circle crop to remove irrelevant regions and enhanced green channel to improve microvascular lesion contrast. Experiments were conducted on the APTOS 2019 dataset consisting of 3,662 images, split into 2,929 training and 733 testing samples, using a 5-way 5-shot configuration. The proposed preprocessing increases accuracy from 64.53 percent to 71.35 percent and improves quadratic weighted kappa from 0.5712 to 0.6990. These results indicate that preprocessing enhances feature representation and classification performance under limited data conditions.
Analisis Efisiensi Arsitektur U-Net dengan Encoder MobileNetV2 pada Segmentasi Karat Daun Kopi Adeva, Muhammad; Muttaqin, Faisal; Mulyo, Budi Mukhamad
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11221

Abstract

Coffee Leaf Rust (Hemileia vastatrix) poses a serious threat to Robusta coffee productivity. Manual identification is often slow and subjective, while standard Deep Learning segmentation methods like U-Net with VGG16 encoder bear heavy computational loads (~24.89 million parameters), hindering deployment on resource-constrained devices. This study aims to optimize computational efficiency by proposing a Lightweight U-Net architecture based on the MobileNetV2 encoder. The model's performance was comparatively evaluated against the VGG16 baseline using the PlantSeg public dataset. Experimental results show that MobileNetV2 integration successfully reduced model size massively by 96% (to ~0.95 million parameters) and accelerated inference time by ~20% (76.28 ms). Although there was a slight F1-Score decrease of 0.3% compared to the baseline, the proposed architecture offers the best trade-off between efficiency and accuracy, making it a viable solution for mobile implementation
Co-Authors A., Alif Arsyad Adeva, Muhammad Alif S., Reza Muhammad Anggraini Puspita Sari Anugrah Purba, Claudia Dwi Aprilia, Yuva Dwi Aqil Salim, Mas Muhammad Ardhon Rakhmadi Arini, Andhini Putri B., Muhammad Agung Fahrial, Rifki Zainal Fatchur Rozci Feby Agung Pamuji Fitriyah Fitriyah Marjiansa, Aris Muhammad Muharrom Al Haromainy Muttaqin, Faisal N., Bella Risky Pakpahan, Fredrik Sahalatua Prasetya, Aditya Prastika, Ayu Dwi Putra, Anggelus Putradira, Muhammad Rizki R., Qurotul A’yun Rahayu, Putri Nur Rusyda, Safira S., Aprilia Erlinda S., Widhi Ayu Thooriqoh, Hazna At Wulyono, Abi Eka Putra Y., Muhamad Andika Z., Eva Rahmania Zain, Ardina Mudholivah