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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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Peningkatan Fitur Layanan Sistem Informasi Pada UMKM Rangkul Semarang Bagus Satrio Waluyo Poetro; Sam Farisa Chaerul Haviana
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 2 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Cv. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v4i2.1013

Abstract

UMKM Rangkul (Rakyat Semarang Kuliner) merupakan suatu wadah perkumpulan bagi para pelaku UMKM dibidang kuliner yang ada di Kota Semarang. Sasaran UMKM Rangkul adalah meningkatkan pemasaran lokal maupun global produk – produk UMKM sebesar rata-rata 5 % per tahun. Namun terbatasnya akses masyarakat di luar anggota UMKM atas informasi produk – produk yang ada pada paguyuban membuat potensi pemasaran sangatlah kecil. Website yang ada pada UMKM Rangkul perlu adanya perbaikan supaya lebih responsif dan nyaman dilihat. Bentuk kegiatan yang cocok pada permasalahan UMKM Rangkul ini adalah dengan berupa penambahan serta pembaruan informasi, dimulai dari penyesuaian halaman, penyesuaian tampilan katalog serta penmbahan fitur tertentu yang dapat memberikan informasi kepada pengguna. Secara umum luaran dari program pengabdian masyarakat ini adalah sebuah website Katalog Online berbasis web yang lebih responsif dan nyaman digunakan yang memuat informasi produk – produk anggota UMKM Rangkul semarang. Pembaruan yang dilakukan antara lain tampilan berita, perbaikan tampilan umkm, penambahan fitur pencarian, shopee food, grab food, umkm dan produk terdaftar, perubahan nama.
Pelatihan Desain Grafis sebagai Langkah Penjaringan dan Regenerasi Pekerja Kreatif pada UMKM GALGIL Indonesia Sam Farisa Chaerul Haviana; Bagus Satrio Waluyo Poetro; Widhya Nugroho Satrioajie
Indonesian Journal of Community Services Vol 5, No 1 (2023): May 2023
Publisher : LPPM Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/ijocs.5.1.35-44

Abstract

Bisnis UMKM GALGIL Indonesia yang bergerak di bidang fashion, dengan produk utamanya Kaos berdesain unik Khas Kota Tegal sangat bergantung pada pekerja kreatifnya. Pekerja desainer grafis yang mampu menghasilkan karya-karya menarik tentunya akan menunjang penjualan produk UMKM GALGIL Indonesia. Selama ini ide desain dan pembuatan desain kaos yang unik dan menarik ditangani langsung oleh pada pendirinya. Namun dengan berkembangnya bisnis GALGIL Indonesia dan visi memajukan bisnis dengan memperkuat peran para pendirinya dalam manajemen, maka perlu ada kaderisasi pekerja kreatif dalam UMKM GALGIL Indonesia. Tantanganya adalah bahwa untuk mendapatkan pekerja kreatif yang potensial, enerjik dan mau bekerja pada level UMKM tidaklah mudah. Untuk merekrut SDM profesional dibidang desain grafis tentunya tidak murah dan mungkin akan sulit menemukan SDM profesional di daerah lokasi GALGIL Indonesia berpusat. Oleh karena itulah, GALGIL Indonesia bekerjasama dengan pelaksana pengabdian khususnya dosen Teknik Informatika UNISSULA untuk melakukan pendekatan lain dalam rangka kaderisasi dan penjaringan pekerja kreatif yaitu melalui program pelatihan desain grafis bagi masyarakat maupun siswa-siswa SMA/SMK/Sederajat atau Mahasiswa bahkan kalangan umum yang memiliki potensi. GALGIL Indonesia dengan visinya tentu juga ingin memberikan kontribusi yang lebih luas dengan memberikan peluang kerja pada masyarakat di kota Tegal dan sekitarnya. The MSME business of GALGIL Indonesia, which is engaged in fashion, with its main product, T-shirts with unique designs from the City of Tegal, relies heavily on its creative workers. Graphic designer workers who are able to produce interesting works will certainly support the sales of GALGIL Indonesia's MSME products. So far, the idea of designing and making unique and interesting t-shirt designs has been handled directly by the founders. However, with the development of GALGIL Indonesia's business and the vision of advancing the business by strengthening the role of its founders in management, there needs to be a regeneration of creative workers in GALGIL Indonesia's MSMEs. The challenge is that to get creative workers who are potential, energetic, and willing to work at the MSME level is not easy. To recruit professional human resources in the field of graphic design is certainly not cheap and it may be difficult to find professional human resources in the area where GALGIL Indonesia is located. Therefore, GALGIL Indonesia collaborates with community service implementers, especially UNISSULA Informatics Engineering lecturers to take another approach in the context of regeneration and recruitment of creative workers, namely through graphic design training programs for the community as well as high school / vocational / equivalent students or students and even the general public who have potency. GALGIL Indonesia with its vision of course also wants to make a wider contribution by providing job opportunities to the people in the city of Tegal and its surroundings.
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.
Co-Authors Ahmad Ayudhawara Ahmad Saifurrohman Ahmat Yusuf Andika, Ardhi Dwi Angriani, Husni Arief Budiman Aris Wahyu Murdiyanto 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 Nur Muhammad Syaifuddin Nurul Hidayah Nuzulia Khoiriyah Putra, Allief Suryatama Jaya Sam Farisa Chaerul Haviana Sinra, A. Siregar, Alda Cendekia Sri Mulyono Sri Mulyono Suryani Alifah Taruk, Medi Vani Aulia Pramesti Widhya Nugroho Satrioajie