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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) Budimas : Jurnal Pengabdian Masyarakat Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat International Journal of Advances in Artificial Intelligence and Machine Learning
Dhidhi Pambudi
Pendidikan Matematika FKIP UNS
Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Mathematics Public Health Social Sciences Other

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Profil Kesulitan Siswa dalam Memecahkan Masalah Matematika Materi Pokok Bangun Ruang Sisi Datar Ditinjau dari Kecerdasan Visual-Spasial Siswa Wijayanti, Ria Wahyu; Sutopo, S; Pambudi, Dhidhi
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2016: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

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

Abstract

Penelitian ini bertujuan untuk mengetahui profil kesulitan siswa dalam memecahkan masalah matematika materi bangun ruang sisi datar ditinjau dari kecerdasan visual-spasial siswa kelas VIII SMP Negeri 1 Jaten Karanganyar pada semester genap tahun ajaran 2013/2014.Bentuk penelitian yang digunakan dalam penelitian ini adalah penelitian kualitatif, dengan strategi penelitian yaitu deskriptif kualitatif. Subjek penelitian menggunakan pemilihan sampel bertujuan (purposive sample), dipilih 6 subjek penelitian, 2 subjek dengan kecerdasan visual-spasial tinggi, 2 subjek dengan kecerdasan visual spasial sedang dan 2 subjek dengan kecerdasan visual spasial rendah. Teknik pengumpulan data yang digunakan wawancara berbasis tugas.Pemeriksaan keabsahan data dengan teknik verifikasi dan triangulasi waktu.Langkah-langkah dalam analisis data adalah reduksi data, penyajian data dan menarik kesimpulan.Hasil penelitian ini dapat dipaparkan sebagai berikut.1) Siswa dengan kecerdasan visual-spasial tinggi memiliki jenis kesulitan (a) kesulitan dalam menerjemahkan masalah kedalam model matematika, (b) kesulitan dalam melakukan prosedur matematik yang benar. (2) Siswa dengan kecerdasan visual-spasial sedang, memiliki jenis kesulitan (a) kesulitan dalam menerjemahkan masalah ke dalam model matematika, (b) kesulitan dalam menentukan strategi penyelesaian masalah, (c) kesulitan dalam melakukan prosedur matematik yang benar. (3) Siswa dengan kecerdasan visual-spasial rendah, memiliki jenis kesulitan (a) kesulitan dalam memahami masalah, (b) kesulitan dalam menentukan strategi penyelesaian masalah, (c) kesulitan dalam menerjemahkan masalah ke dalam model matematika, (d) kesulitan dalam melakukan prosedur matematik yang benar.
The Eye's Signature: Innovative Approaches to Iris Detection Pambudi, Dhidhi; Fadly, Fadly; Kurniawan, Muhammad Hafiz; Haryanto, Haryanto
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 1 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i1.379

Abstract

This research aims to develop and evaluate a deep learning-based iris detection system using a specialized Convolutional Neural Network (CNN) architecture. The research methodology includes data set preprocessing, CNN model design, training using Adam optimization, as well as evaluation using accuracy, precision, recall, and F1 score metrics. The dataset used was obtained from Kaggle and preprocessed before being divided into training, validation, and testing sets. The CNN model consists of three convolutional layers with increasing filter sizes (32, 64, and 128), ReLU activation, batch normalization, and MaxPooling layers for efficient feature extraction, as well as dropout regularization to reduce overfitting. Experimental results show that the proposed model achieves a high classification accuracy of 97.33%, with robust performance against variations and noise in iris images. Comparative analysis with traditional iris recognition methods confirms the superiority of deep learning in handling challenges such as lighting changes and occlusions. Although the results are promising, challenges such as data bias and computational demands are still a concern. Future research will explore more advanced architectures as well as additional pre-processing techniques to improve the generalizability and effectiveness of the system in real-world applications.
3D Box Packing with Heuristics and Metric Analytics Kasem Alqudah, Mashal; Pambudi, Dhidhi; Zakaria, Mohd Zaki
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 2 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i2.409

Abstract

Background of Study: The 3D Bin Packing Problem (3D-BPP) is an NP-hard problem crucial for logistics and supply chain optimization, aiming to efficiently pack boxes into containers while maximizing space and maintaining stability. Traditional heuristics like First Fit and Best Fit are fast but lack optimality and adaptability in dynamic environments. Metaheuristic approaches, such as Genetic Algorithms (GA), offer better solutions but with higher computational costs.Aims and Scope of Paper: This study presents a comparative analysis of First Fit, Best Fit, and a custom Genetic Algorithm as packing strategies for 3D-BPP. It evaluates these methods against multiple performance metrics to understand their trade-offs and proposes future research directions.Methods: The study uses a dataset of 5,000 cargo records from an Indonesian logistics company, including item dimensions and weights, preprocessed for normalization and filtering. A 3D simulation environment built with PyBullet visualizes the packing process. Performance metrics include space utilization, total packed weight, packing time, access efficiency, stability score, and placement success rate. A Wall-Building heuristic acts as a fallback for unplaced items.Result: First Fit provides fast, lightweight solutions suitable for real-time applications. Best Fit shows marginally better space utilization but lacks robustness. The Genetic Algorithm outperforms both heuristics in packing quality, accessibility, and load stability, though with significantly higher computation time. No single algorithm dominates across all metrics.Conclusion: The choice of packing method should align with specific operational constraints: speed, compactness, or quality. A hybrid model combining heuristic initialization with GA refinement is a promising direction for future research to develop more intelligent, context-aware packing systems.
Pelatihan Pengembangan Media Pembelajaran Interaktif Berbasis Lumi Education di SMPI Al Abidin Surakarta Pambudi, Dhidhi; Triyanto; Fitriana, Laila; Pramesti, Getut; Wiraya, Ario
BUDIMAS : JURNAL PENGABDIAN MASYARAKAT Vol. 7 No. 3 (2025): BUDIMAS : Jurnal Pengabdian Masyarakat
Publisher : LPPM ITB AAS Indonesia Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/budimas.v7i3.18193

Abstract

Pembelajaran yang melibatkan partisipasi aktif dan kemandirian siswa merupakan salah satu faktor penentu efektivitas pembelajaran. Oleh karena itu, guru-guru perlu memiliki kemampuan untuk mengembangkan pembelajaran yang bisa memunculkan sikap aktif dan kemandirian siswa yang salah satunya diwujudkan dalam media pembeljaran yang interaktif. Meskipun demikian, tidak setiap guru di SMP Islam (SMPI) Al Abidin Surakarta memiliki kemampuan yang sama dalam hal pemanfaatan teknologi. Untuk mengatasi permasalah tersebut maka dilakukan kegiatan pengabdian kepada masyarakat di SMPI Al Abidin Surakarta. Para guru dilatih agar mampu mengembangkan media pembelajaran interaktif yang sesuai dengan kebutuhan masing-masing dengan bantuan Lumi Education. Pelatihan dibagi menjadi dua tahap, yaitu pengembangan buku interaktif (Interactive Book) dan permainan dengan peta (Game Map). Hasilnya pelatihan mampu meningkatkan kompetensi guru-guru SMPI Al Abidin dalam membuat media pembelajaran dengan Lumi Education. Para guru mampu merancang, mengembangkan, dan menghasilkan media pembelajaran interaktif yang sesuai dengan kebutuhan pembelajaran.
Penguatan Literasi Digital di Era Artificial Intelligence Untuk Mendukung Sustainable Development Goals Pambudi, Dhidhi; Triyanto, Triyanto; Fitriana, Laila; Pramesti, Getut
KOMUNITA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 4 No 4 (2025): November
Publisher : PELITA NUSA TENGGARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60004/komunita.v4i4.313

Abstract

Literasi digital merupakan kompetensi dasar untuk memahami dan mengembangkan AI, sekaligus bekal penting keterampilan abad 21. Penguatan literasi ini mendukung SDG 4 yang menargetkan pendidikan berkualitas, inklusif, dan pembelajaran sepanjang hayat. Pentingnya literasi digital ini juga dirasakan oleh guru-guru di SMA Negeri 1. Meskipun sebagian guru sudah mulai menggunakan AI dalam pembelajaran, namun secara umum guru-guru belum mempunyai pengetahuan yang cukup tentang teknologi digital dan AI. Oleh karena itu, kegiatan pengabdian ini bertujuan untuk menguatkan literasi digital pada guru. Pengabdian ini diwujudkan dalam bentuk pelatihan. Kompetensi literasi digital diukur menggunakan lima aspek, yaitu: (a) pengetahuan dasar literasi digital dan AI, (b) keamanan data dan privasi, (c) deteksi hoax dan disinformasi, (d) UU ITE dan etika digital, dan (e) sikap proaktif. Pengukuran dilakukan sebelum dan sesudah pelatihan untuk mengetahui efektifitas pelatihan. Hasil uji sampel berpasangan menunjukkan bahwa adanya peningkatan yang signifikan pada setiap aspek. Hasil ini menunjukkan bahwa kegiatan pelatihan yang diberikan bisa diterima dan dipahami dengan baik. Aspek proaktif yang meningkat menunjukkan bahwa guru-guru menjadi lebih siap untuk membawakan literasi digital dan AI dalam pembelajaran di kelas.
Fuzzy C-Means Clustering of Student Mathematical Communication Skills and Cognitive Performance and Its Association with Learning Models Pambudi, Dhidhi; Nakano, Sachiko; Damayanti, Alfina
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 2 (2026): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i2.35522

Abstract

This study applies Educational Data Mining (EDM) to examine how students’ mathematical communication skills and cognitive styles interact under different instructional contexts. A quantitative research that applies quasi-experimental design combined with EDM was employed involving 64 seventh-grade students at SMP Negeri 2 Jaten, divided into Learning Cycle 7E (LC7e) and Direct Instruction (DI) groups. The research instruments included a validated essay test for mathematical communication and the Group Embedded Figures Test (GEFT). Data were analyzed using the Fuzzy C-Means (FCM) algorithm to partition students into distinct clusters based on their cognitive-communicative attributes. The analysis resulted in a stable five-cluster model, representing progressive levels of cognitive–communicative integration. The model shows that cognitive profile predominantly creates cluster structure, whereas the Learning Cycle 7E (LC7E) model exerts a moderating influence. Students taught through LC7E were more concentrated in higher-performing clusters than those in DI classrooms. Furthermore, Field-Independent (FI) learners tended to achieve the highest communicative profiles, yet Field-Dependent (FD) learners also benefited meaningfully from LC7E activities that emphasized exploration and reflection. These results demonstrate that the LC7E model supports cognitive and communicative development across the learner spectrum, with differentiated gains linked to cognitive style. These findings highlight the utility of EDM in capturing student heterogeneity and provide a basis for educators to design adaptive learning strategies that accommodate diverse cognitive characteristics. 
Co-Authors Ario Wiraya Ayuningtyas, Winanti Cahyo, Yulianto Dwi Chrisnawati, Henny Ekana Damayanti, Alfina Fadly Fadly Fitriana, Laila Getut Pramesti Hapsari, Wirani Dwi Haryanto, Haryanto Kasem Alqudah, Mashal Kurniawan, Muhammad Hafiz Kuswardi, Yemi Nakano, Sachiko Nur, Dhimifri Fitria Ain Rahmawati, Yunia Ria Wahyu Wijayanti, Ria Wahyu Sugiarto, Bambang Sutopo Sutopo Sutopo, Sutopo Sutopo, Sutopo Sutopo, Sutopo Triyanto Triyanto, Triyanto Wardani, Novita Koes Wijayanti, Ria Wahyu Zakaria, Mohd Zaki