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All Journal Jurnal Buana Informatika Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Journal of Telematics and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics TRANSISTOR Elektro dan Informatika Indonesian Journal of Community Services Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Indonesian Journal of Data and Science Jurnal Pengabdian kepada Masyarakat Nusantara JAST Prosiding Seminar Riset Mahasiswa Jurnal Rekayasa Sistem Informasi dan Teknologi International Journal of Artificial Intelligence in Medical Issues
Poetro, Bagus Satrio Waluyo
Universitas Islam Sultan Agung
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences

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Advancements in Agricultural Automation: SVM Classifier with Hu Moments for Vegetable Identification Waluyo Poetro, Bagus Satrio; Maria, ⁠⁠Eny; Zein, Hamada; Najwaini, Effan; Zulfikar, Dian Hafidh
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.123

Abstract

This study investigates the application of Support Vector Machine (SVM) classifiers in conjunction with Hu Moments for the purpose of classifying segmented images of vegetables, specifically Broccoli, Cabbage, and Cauliflower. Utilizing a dataset comprising segmented vegetable images, this research employs the Canny method for image segmentation and Hu Moments for feature extraction to prepare the data for classification. Through the implementation of a 5-fold cross-validation technique, the performance of the SVM classifier was thoroughly evaluated, revealing moderate accuracy, precision, recall, and F1-scores across all folds. The findings highlight the classifier's potential in distinguishing between different vegetable types, albeit with identified areas for improvement. This research contributes to the growing field of agricultural automation by demonstrating the feasibility of using SVM classifiers and image processing techniques for the task of vegetable identification. The moderate performance metrics emphasize the need for further optimization in feature extraction and classifier tuning to enhance classification accuracy. Future recommendations include exploring alternative machine learning algorithms, advanced feature extraction methods, and expanding the dataset to improve the classifier's robustness and applicability in agricultural settings. This study lays a foundation for future advancements in automated vegetable sorting and quality control, offering insights that could lead to more efficient agricultural practices.
Pemanfaatan MidtrGame Edukasi Petualangan Menggunakan RPG Maker MV dengan Finite State Machineans Sebagai Gateway pada Sistem Pembayaran Administrasi Sekolah Andika, Ardhi Dwi; Mulyono, Sri; Poetro, Bagus Satrio Waluyo
TRANSISTOR Elektro dan Informatika Vol 5, No 3 (2023)
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/ei.5.3.139-146

Abstract

Penelitian Game Edukasi Petualangan Menggunakan RPG Maker Mv dengan berlatarbelakang mengenai game yang bermunculan tahun 2022 banyak mengandung unsur pendidikan. Game ini bergenre RPG berlatar spesifik Unissula dan karakter yakni mahasiswa FTI. Dengan metode Finite State Machine, pemain dituntut menyelesaikan permainan dengan baik, beberapa syarat menjadi pemicu game ini, hingga pemain dapat melanjutkan ke state selanjutnya. Beberapa kekurangan penelitian ini hanya berjalan di PC dan jumlah gender pada pemain hanya ada laki-laki. Tujuan penelitian ini dibuat untuk membuat game edukasi Mahasiswa Baru Unissula. Menganalisa kualitas multimedia dan pixel grafik game Advenducation. Mengimplementasikan metode fsm game. Hasil penelitian ini game Advenducation yang dimainkan pada PC, dengan metode Finite State Machine membuat game Edukasi RPG sangatlah baik, karena player bermain sambil belajar dengan mencar suatu persyaratan agar lanjut ke level selanjutnya. Hal ini akan membuat player secara tidak sadar telah mempelajari bagaimana melakukan sesuatu agar bisa berurutan demi mencapai suatu tujuan tertentu.
Identifikasi Kematangan Buah Jeruk Medan Menggunakan K-Nearest Neighbor berbasis Metrik RGB Putra, Allief Suryatama Jaya; Subroto, Imam Much Ibnu; Poetro, Bagus Satrio Waluyo
TRANSISTOR Elektro dan Informatika Vol 5, No 3 (2023)
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/ei.5.3.155-160

Abstract

Kemajuan pesat inovasi di bidang pengolahan citra semakin membuat aplikasi dan eksplorasi strategi penanganan gambar dibuat. Pengolahan citra mempunyai peranan penting di berbagai bidang. Aplikasi pengolahan citra berkaitan dengan pemrosesan citra berkaitan dengan transformasi warna. Dalam hal ini, metode transformasi ruang warna RGB sebagai bagian dari pengolahan citra membantu dalam mendeteksi warna dalam citra dan mengolahnya. Ruang warna merupakan model matematis yang menjelaskan mengenai warna yang direpresentasikan ke dalam model angka. Dalam penelitian ini, berdasarkan dari hasil pengujian menggunakan citra buah Jeruk Medan untuk mendeteksi jenis kematangannya dengan melakukan transformasi ruang warna RGB lalu mencari nilai rata-rata dari setiap warna dasar yaitu merah, hijau, dan biru kemudian memberikan metode KNN algoritma yang sering digunakan dalam pembelajaran mesin. Algoritma ini digunakan untuk memprediksi kelas suatu objek berdasarkan data pembelajaran yang ada. Algoritma ini bekerja dengan cara mencari objek yang paling mirip dengan objek yang ingin diprediksi kelasnya, lalu menggunakan kelas dari objek-objek tersebut untuk memprediksi kelas dari objek yang ingin diprediksi yang dilakukan dengan menggunakan data sampel sebanyak 180 data buah yang terdiri dari 60 citra buah Jeruk Medan disetiap jenis kematangannya, 60 sampel uji buah Jeruk Medan matang, 20 sampel buah Jeruk Medan setengah matang dan 60 sampel buah Jeruk Medan mentah. Pada penelitian ini mendapatkan nilai hasil dari klasifikasi dari k = 9 juga memiliki presentasi yang tinggi yaitu 87%
Comparative Study on the Performance of the Bagging Algorithm in the Breast Cancer Dataset Fadhila Tangguh Admojo; Waluyo Poetro, Bagus Satrio
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i1.87

Abstract

Breast cancer remains a predominant health concern globally. Early detection, powered by advancements in medical imaging and computational methods, plays a vital role in enhancing survival rates. This research delved into the application and performance of the Bagging algorithm on a Breast Cancer dataset that underwent image segmentation using the Canny method and feature extraction through Hu-Moments. The Bagging algorithm demonstrated moderately consistent performance across a 5-fold cross-validation, with average metrics of 56.9% accuracy, 58.3% precision, 57.7% recall, and 56.6% F-measure. While the results showcased the potential of the Bagging algorithm in classifying breast cancer data, there remains an avenue for further optimization and exploration of other ensemble or deep learning techniques. The findings contribute to the broader domain of machine learning in medical imaging and offer insights for future research directions and clinical diagnostic tool development.
Optimizing Neurodegenerative Disease Classification with Canny Segmentation and Voting Classifier: An Imbalanced Dataset Study Sinra, A.; Waluyo Poetro, Bagus Satrio; Angriani, Husni; Zein, Hamada; Musdar, Izmy Alwiah; Taruk, Medi
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.97

Abstract

This study explores the efficacy of a Voting Classifier, combining Logistic Regression, Random Forest, and Gaussian Naive Bayes, in the classification of neurodegenerative diseases, focusing on Alzheimer's Disease (AD), Parkinson’s Disease (PD), and control groups. Utilizing a dataset pre-processed with Canny segmentation and Hu Moments feature extraction, the research aimed to address the challenges posed by imbalanced datasets in medical image classification. The classifier's performance was evaluated through a 5-fold cross-validation approach, with metrics including accuracy, precision, recall, and F1-Score. The results revealed a consistent recall rate of approximately 46% across all folds, indicating the model's effectiveness in identifying cases of neurodegenerative diseases. However, the precision and F1-Score were notably lower, averaging around 22% and 29%, respectively, underscoring the difficulties in achieving accurate classification in imbalanced datasets. The study contributes to the understanding of machine learning applications in medical diagnostics, specifically in the challenging context of neurodegenerative disease classification. It highlights the potential of using advanced image processing techniques combined with machine learning ensembles in enhancing diagnostic accuracy. However, it also draws attention to the inherent challenges in such approaches, particularly regarding precision in imbalanced datasets. Recommendations for future research include exploring data balancing techniques, alternative feature extraction methods, and different machine learning algorithms to improve the precision and overall performance. Additionally, applying the model to a broader and more diverse dataset could provide more generalizable and robust findings. This study is significant for researchers and practitioners in medical imaging and machine learning, offering insights into the complexities and potential of automated disease classification
Prediksi Penyakit Batu Ginjal dengan Menerapkan Convolutional Neural Network Waluyo Poetro, Bagus Satrio; Mulyono, Sri; Vani Aulia Pramesti
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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

Abstract

Kidney stones are a health problem that requires intensive treatment. If the disease is not treated quickly, it can lead to impaired kidney function and complications to other organs. Computerized Tomography Scan (CT Scan) with high resolution is used to scan the human body for disease diagnosis. The doctor will explain the diagnosis within a few days or one week. This research aims to create a prediction model for the classification of kidney stone disease through CT Scan images by applying the Convolutional Neural Network (CNN) method of DenseNet-121 architecture and deployment using Streamlit. The results of the model in this study with the application of CNN DenseNet-121 architecture are accuracy 98.18%, precision 96.36%, recall 100%, and F1-score 98.14%.
Performance Comparison of CNN and ResNet50 for Skin Cancer Classification Using U-Net Segmented Images Aris Wahyu Murdiyanto; Zulfikar, Dian Hafidh; Waluyo Poetro, Bagus Satrio; Siregar, Alda Cendekia
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.200

Abstract

Skin cancer is a significant global health issue, with melanoma, basal cell carcinoma, and actinic keratosis being the most common types. Early and accurate detection is critical to improve survival rates and treatment outcomes. This study evaluates the performance of Convolutional Neural Networks (CNN) and ResNet50 in classifying segmented images of skin lesions. The dataset, sourced from Kaggle, was pre-processed using U-Net for lesion segmentation to enhance the quality of input data. Both models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. The CNN model demonstrated a balanced performance across classes, with a weighted F1-score of 47%, but suffered from overfitting, as indicated by the divergence between training and validation losses. ResNet50 achieved better recall for basal cell carcinoma (100%) but failed to classify actinic keratosis and melanoma, resulting in a macro F1-score of 23%. The findings reveal that U-Net segmentation improved classification focus but was insufficient to address dataset imbalance and model-specific limitations. This study highlights the challenges of skin cancer classification using deep learning and underscores the importance of addressing data imbalance and overfitting. Future research should explore advanced techniques, such as ensemble methods, data augmentation, and transfer learning, to improve the generalization and clinical applicability of these models. The proposed framework serves as a foundation for further investigation into automated skin cancer detection systems.
Mr. Anggara Putra Meldyantono Implementasi Sistem Absensi Berbasis Pengenalan Wajah Menggunakan Metode CNN dan Model FaceNet: Menggunakan Metode CNN dan Model FaceNet Meldyantono, Anggara Putra; Poetro, Bagus Satrio Waluyo
Jurnal Rekayasa Sistem Informasi dan Teknologi Vol. 2 No. 3 (2025): Februari
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jrsit.v2i3.1857

Abstract

This research implements a face recognition-based attendance system using Convolutional Neural Networks method and FaceNet model. This topic was chosen because face recognition is an effective identification method for attendance applications, but often faces challenges of low illumination and varying object distances, especially on devices with mid-to-low specifications. This system uses Convolutional Neural Networks for facial feature extraction, FaceNet to improve face representation accuracy, and Local Binary Patterns Histogram to analyze facial texture to improve recognition performance. The steps taken include collecting face datasets, applying Convolutional Neural Networks and FaceNet models, and evaluating the system under low lighting conditions and various object distances. The test results showed 100% accuracy with three face images even in low lighting conditions. The system still performs well despite variations in light intensity and object distance. The main contribution of this research is the development of an efficient face recognition system based on Convolutional Neural Networks and FaceNet that can be applied to devices with limited specifications for attendance applications, with a focus on stability in poor lighting and testing in real environments.
SISTEM DETEKSI GAMBAR DEEPFAKE MENGGUNAKAN CNN DENSENET-121 DENGAN WATERMARKING LEAST SIGNIFICANT BIT (LSB) Jiwani, Fatwa Akbar; Poetro, Bagus Satrio Waluyo
Jurnal Rekayasa Sistem Informasi dan Teknologi Vol. 2 No. 3 (2025): Februari
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jrsit.v2i3.1939

Abstract

Penelitian ini bertujuan untuk mengembangkan model deteksi deepfake menggunakan arsitektur Convolutional Neural Network (CNN) DenseNet-121 serta mengevaluasi efektivitas teknik watermarking Least Significant Bit (LSB) dalam meningkatkan keamanan citra digital. Dataset yang digunakan terdiri dari citra asli dan citra deepfake yang diperoleh dari sumber terbuka Kaggle, yang dibagi menjadi subset pelatihan, validasi, dan pengujian dengan total 22.382 gambar. Proses preprocessing melibatkan resizing ke ukuran 128x128 piksel, konversi ke grayscale, normalisasi, serta augmentasi data untuk meningkatkan generalisasi model. Model DenseNet-121 dikompilasi menggunakan optimizer Adam dan loss function categorical crossentropy, dengan evaluasi menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil pelatihan menunjukkan bahwa model mampu mendeteksi deepfake dengan akurasi tinggi. Selain itu, evaluasi watermarking menggunakan PSNR menunjukkan bahwa penyisipan watermark dengan metode LSB tidak mengurangi kualitas visual citra secara signifikan. Penelitian ini memberikan kontribusi dalam meningkatkan deteksi deepfake dan keamanan digital melalui kombinasi metode CNN dan watermarking. Kata Kunci: Deepfake, CNN, DenseNet-121, Watermarking, LSB, Deteksi Citra Digital
Penyempurnaan Web Desa dan Penambahan Aplikasi Digital Pelayanan Mandiri Masyarakat (Pelayanan Online) Desa Manggihan Poetro, Bagus Satrio Waluyo; Kurniadi, Dedy; Haviana, Sam Farisa Chaerul
Indonesian Journal of Community Services Vol 7, No 1 (2025): May 2025
Publisher : LPPM Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/ijocs.7.1.68-75

Abstract

Desa Manggihan memiliki potensi besar untuk berkembang melalui pemanfaatan teknologi informasi, namun menghadapi berbagai kendala dalam penyediaan layanan publik yang efisien dan transparan. Penelitian ini bertujuan untuk meningkatkan kualitas pelayanan desa melalui penyempurnaan situs web desa dan pengembangan aplikasi digital pelayanan mandiri masyarakat. Metode yang digunakan meliputi observasi, perancangan, pengembangan, evaluasi, dan pelaporan. Hasilnya berupa situs web interaktif dengan fitur-fitur baru serta aplikasi digital yang memungkinkan warga melakukan layanan administrasi secara mandiri, seperti pembuatan surat keterangan dan pembayaran pajak. Transformasi ini diharapkan dapat mengatasi permasalahan seperti antrian panjang, keterlambatan pelayanan, dan ketidakpuasan masyarakat. Selain itu, program ini juga memberikan pelatihan kepada aparat desa dan masyarakat untuk meningkatkan pemahaman dan keterampilan dalam memanfaatkan teknologi. Dengan pendekatan ini, Desa Manggihan dapat mewujudkan pelayanan publik yang lebih modern, efisien, dan transparan, sekaligus memberdayakan masyarakat dalam menghadapi era digital.Manggihan Village has significant potential for development through the utilization of information technology but faces various challenges in providing efficient and transparent public services. This study aims to improve the quality of village services by enhancing the village website and developing a digital self-service application for the community. The methods employed include observation, design, development, evaluation, and reporting. The results include an interactive website with new features and a digital application that enables residents to perform administrative services independently, such as certificate creation and tax payments. This transformation is expected to address issues such as long queues, service delays, and public dissatisfaction. Additionally, the program provides training for village officials and residents to enhance their understanding and skills in utilizing technology. Through this approach, Manggihan Village can achieve more modern, efficient, and transparent public services while empowering the community to thrive in the digital era.
Co-Authors Ahmat Yusuf Andika, Ardhi Dwi Angriani, Husni Arief Budiman Aris Wahyu Murdiyanto Ayudhawara, Ahmad Dedy Kurniadi Dian Hafidh Zulfikar Effan Najwaini Fadhila Tangguh Admojo Hamada Zein Imam Much Ibnu Subroto izmy alwiah musdar Jiwani, Fatwa Akbar Maria, ⁠⁠Eny Meldyantono, Anggara Putra Moch Taufik Muhammad Qomaruddin Nicodemus Mardanus Setiohardjo Nur Muhammad Syaifuddin Nurul Hidayah Nuzulia Khoiriyah Putra, Allief Suryatama Jaya Saifurrohman, Ahmad Sam Farisa Chaerul Haviana Sinra, A. Siregar, Alda Cendekia Sri Mulyono Sri Mulyono Sunia Raharja, I Made Suryani Alifah Taruk, Medi Vani Aulia Pramesti Widhya Nugroho Satrioajie