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All Journal Jurnal Simetris Scientific Journal of Informatics Proceeding SENDI_U EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat JoMMiT : Jurnal Multi Media dan IT Community: Jurnal Pengabdian Pada Masyarakat MANGGALI Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
Sudibyo, Usman
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences

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Menavigasi Dunia Digital dengan Meningkatkan Literasi Office, TI, dan Internet di Kalangan Siswa-Siswi Pondok Pesantren Raudhatul Qur'an Paramita, Cinantya; Andono, Pulung Nurtantio; Sudibyo, Usman; Rafrastara, Fauzi Adi; Supriyanto, Catur
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 6, No 2 (2023): Mei 2023
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/ja.v6i2.1338

Abstract

Peningkatan popularitas penggunaan perangkat komputer semakin berkembang di berbagai lapisan masyarakat. Pondok pesantren, yang sebelumnya dianggap sebagai tempat yang kurang produktif dan hanya diperuntukkan bagi mereka yang beragama, kini melakukan inovasi untuk meningkatkan peran dan potensi dalam mendukung kemaslahatan lingkungan sekitarnya. Pondok Pesantren Raudhatul Qur’an di Kauman Semarang telah banyak menciptakan siswa yang berhasil menghafal Al-Quran. Setelah menyelesaikan studi di pondok, banyak dari mereka yang melanjutkan pendidikan ke sekolah formal atau menjadi pemuka agama yang memberikan pengajaran dan bimbingan kepada masyarakat dalam memahami agama Islam di lingkungan mereka. Oleh karena itu, pelatihan teknologi komputer diperlukan untuk memberikan pengetahuan dan keterampilan bagi para santri agar dapat dimanfaatkan untuk membantu mengurus keperluan administrasi di pondok pesantren dan berguna bagi masa depan mereka. Sebanyak 53 santri diikutsertakan untuk mengikuti pelatihan yang mencakup pengenalan dasar teknologi informasi [1] seperti hardware, software, penggunaan aplikasi office seperti Word, Excel, dan PowerPoint, serta internet untuk komunikasi dan pengiriman data digital. Berdasarkan hasil pelatihan yang dilaksanakan, para santri memberikan respon positif seperti yang terlihat pada diagram 3 dan 4. Pada diagram 3 menunjukkan bahwa 81,4% dari para santri sangat tertarik dengan pelatihan tersebut, sementara hanya 13,9% yang merasa biasa-biasa saja dan 10,7% yang terpaksa mengikuti. Selain itu, hasil perbandingan pretest dan postest pada diagram 4 menunjukkan peningkatan yang signifikan setelah para santri mengikuti pelatihan tersebut.
Pelatihan Computational Thinking Melalui Soal Tantangan Bebras untuk Siswa SD Herowati, Wise Herowati; Sudibyo, Usman; Budi, Setyo; Kurniawan, Achmad Wahid; Safitri, Aprilyani Nur
Manggali Vol 4 No 1 (2024): Manggali
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat, Universitas Ivet

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31331/manggali.v4i1.2941

Abstract

Proses penyampaian ilmu pengetahuan salah satunya ada di proes pendidikan dasar yang diterapkan pada proses persekolahan. Salah satu yang mendukung untuk mengembangkan pengetahuan adalah menggunakan konsep Computational Thinking (CT) melalui soal-soal Bebras. Pengetahuan mengenai soal bebras dapat membantu guru dan siswa dalam mengembangkan kemampuan dan kreatifitas dalam berpikir kritis dan melakukukan analisis dalam menyelesaikan masalah. Metode pembelajaran berbasis simulasi yang digunakan dalam proses pelatihan yang diharapkan dapat membantu guru dan siswa dalam memvisualisasikan konsep secara lebih baik dan jelas serta diharapkan pula siswa mendapatkan keterampilan yang baru dengan tidak takut jika melakukan kesalahan.
Klasifikasi Motif Batik Nitik Berbasis Fitur Ekstraksi SqueezeNet dengan Reduksi Dimensi PCA–LDA Suciani, Ratih; Sudibyo, Usman
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9437

Abstract

Batik nitik motif classification faces significant challenges due to high intra-class variability and complexity of geometric dot patterns, along with limited samples per class in available datasets. Previous research using handcrafted feature extraction methods such as GLCM and MTCD achieved only 53% accuracy, while BSIF with data augmentation reached 97.70%. This study aims to develop a batik nitik classification method using feature extraction based on SqueezeNet trained on ImageNet to achieve superior accuracy without additional external data augmentation techniques. The Batik Nitik 960 dataset consisting of 960 images (60 classes × 16 samples) inherently contains natural visual diversity for each motif as curated by Minarno et al., enabling deep feature extraction from SqueezeNet to be optimized without extra augmentation. A 1000-dimensional feature vector extracted from SqueezeNet's pool10 layer then underwent dimensionality reduction using PCA, LDA, or PCA+LDA, and was classified with Random Forest, SVM, or KNN. These three classifiers were selected to represent distinct learning paradigms: ensemble method (Random Forest), margin-based classifier (SVM), and instance-based learning (KNN), enabling a comprehensive analysis of the extracted feature space characteristics. Experiments were conducted across various training data sizes (4-14 samples per class). Results showed that 8 out of 9 model combinations achieved perfect 100% accuracy, with LDA+SVM, LDA+KNN, PCA+LDA+SVM, and PCA+LDA+KNN requiring only 4 training samples per class. Only LDA+Random Forest failed to reach 100% (maximum 95.14%). The method's advantages lie in the deep feature extraction capability of SqueezeNet, which produces far more discriminative representations than handcrafted features, combined with the efficiency of supervised dimensionality reduction (LDA) in optimizing class separability. Inference time analysis shows that all model combinations are capable of performing predictions within the range of 0.013–0.173 ms per image, and stability evaluation using 5 random states confirms result consistency with mean accuracy ≥99.70% across 8 combinations (standard deviation ≤0.25%), confirming real-time implementation feasibility. This research establishes a new state-of-the-art for the Batik Nitik 960 dataset and opens opportunities for practical applications in authentication, quality control, and preservation of Indonesian batik cultural heritage. The primary contributions of this research encompass the application of SqueezeNet as a fixed feature extractor without fine-tuning for batik nitik classification a previously unexplored approach in this domain a comprehensive comparative analysis of nine dimensionality reduction and classifier combinations, and the establishment of a new state-of-the-art benchmark for the Batik Nitik 960 dataset, validating that CNN-based deep feature extraction surpasses handcrafted methods even with as few as four training samples per class. These findings pave the way for practical real-time batik identification systems applicable to authentication, quality control, and Indonesian cultural heritage preservation
Machine Learning-Assisted Discovery and Optimization of Sodium-Ion Batteries: A Review Trisnapradika, Gustina Alfa; Al Azies, Harun; Akrom, Muhamad; Sudibyo, Usman; Setiyanto, Noor Ageng
Journal of Multiscale Materials Informatics Vol. 3 No. 1 (2026): April (In Progress)
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v3i1.15954

Abstract

Sodium-ion batteries (SIBs) have emerged as a promising alternative to lithium-ion batteries due to the natural abundance, low cost, and wide geographic availability of sodium resources. However, their practical implementation is hindered by challenges such as lower energy density, slower ion diffusion, and limited cycle stability. In recent years, machine learning (ML) has been increasingly applied to accelerate the discovery, design, and optimization of SIB materials and systems. This review provides a comprehensive overview of ML applications in sodium-ion battery research, including electrode material discovery, electrolyte optimization, performance prediction, and degradation analysis. Various ML techniques, such as supervised learning, unsupervised learning, and deep learning, are discussed in relation to their roles in materials informatics. Additionally, challenges such as data scarcity, model interpretability, and transferability are critically analyzed. Finally, future perspectives on integrating ML with high-throughput experiments and quantum computing are highlighted to guide next-generation sodium-ion battery research.
On the Effectiveness of Lightweight CNN Architectures for Fine-Grained Coffee Bean Classification Burhanudhin, Akbar Muhamad; Sudibyo, Usman; Meindiawan, Eka Putra Agus
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34044

Abstract

Distinguishing coffee bean varieties remains a significant challenge in the agricultural industry due to high inter-class similarity and the subtle morphological differences between species. This study aims to conduct a comparative evaluation of MobileNetV2 and EfficientNetB0 for fine-grained coffee bean classification, specifically investigating how efficiency-oriented architectural mechanisms such as depthwise separable convolution and compound scaling influence feature extraction. The research employed a quantitative experimental method using a private dataset of 2,400 images comprising Arabica, Robusta, and Liberica varieties. Data preprocessing included resizing to 224×224 pixels and augmentation, followed by training the two architectures using transfer learning under a controlled experimental framework. The results showed that EfficientNetB0 achieved superior performance with a testing accuracy of 99.17%, while MobileNetV2 attained a competitive accuracy of 98.33% with lower computational complexity. These results demonstrate that while EfficientNetB0 is optimal for high-precision industrial sorting, MobileNetV2 offers a highly efficient alternative for resource-constrained mobile applications. This study provides a scalable framework for automating quality control, effectively balancing architectural efficiency with the sensitivity required for accurate coffee variety identification.
Co-Authors Achmad Wahid Kurniawan Astuti, Yani Parti Ayu Pertiwi Budi Widjajanto Budi, Setyo Burhanudhin, Akbar Muhamad Catur Supriyanto Christy Atika Sari De Rosal Ignatius Moses Setiadi Desi Purwanti Kusumaningrum Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Erlin Dolphina Etika Kartikadarma Fahmi Amiq Fauzi Adi Rafrastara Harun Al Azies Herawati, Wise Herowati, Wise Herowati Hidayat, Novianto Hidayat, Novianto Nur Meindiawan, Eka Putra Agus Moch Anjas Aprihartha Muhamada, Keny Muhammad Naufal Muljono Muljono Noor Ageng Setiyanto, Noor Ageng Ojugo, Arnold Adimabua Paramita, Cinantya Prabowo, Wahyu Aji Eko Pulung Nurtantio Andono Safitri, Aprilyani Nur Suciani, Ratih Trisnapradika, Gustina Alfa Utomo, Danang Wahyu Yuniarsi Rahayu