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All Journal Katalogis Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Scientific Journal of Informatics
Affandy Affandy
Program Studi Sistem Informasi Fakultas Ilmu Komputer Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Peningkatan Performa Ensemble Learning pada Segmentasi Semantik Gambar dengan Teknik Oversampling untuk Class Imbalance Nugroho, Arie; Soeleman, M. Arief; Pramunendar, Ricardus Anggi; Affandy, Affandy; Nurhindarto, Aris
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106831

Abstract

Perkembangan teknologi dan gaya hidup manusia yang semakin tinggi menghasilkan data-data yang berlimpah. Data-data tersebut dapat berbentuk data yang terstruktur dan tidak terstruktur. Data gambar termasuk dalam data yang tidak terstruktur. Aktifitas dan objek yang terekam dalam suatu gambar beraneka ragam. Secara normal, mata manusia dapat dengan mudah membedakan antara foreground dan background dari suatu gambar, tetapi komputer membutuhkan pembelajaran dalam membedakan keduanya. Segmentasi gambar adalah salah satu bidang dalam computer vision yang membahas bagaimana cara komputer mempelajari dan mengenali segmen dari suatu gambar sesuai label yang ditentukan. Dalam kenyataannya banyak data yang mempunyai class atau label yang tidak seimbang, tentunya akan mempengaruhi tingkat akurasi dari suatu prediksi. Dalam riset ini membahas bagaimana meningkatkan akurasi segmentasi semantik gambar pada metode ensemble learning untuk menangani masalah data yang tidak seimbang dalam segmentasi gambar. Teknik yang digunakan adalah sintetis oversampling sehingga menghasilkan data yang seimbang dan akurasi yang tinggi. Metode ensemble learning yang digunakan adalah Random Forest dan Light Gradien Boosting Machine (LGBM). Dengan menggunakan dataset Penn-Fudan Database for Pedestrian yang mengandung imbalanced class. Penggunaan teknik sintetis oversampling dapat memperbaikki tingkat akurasi pada class minoritas. Pada algoritma random forest mengalami peningkatan akurasi sebesar 37 % sedangkan pada algoritma LGBM meningkat sebesar 41 %. AbstractThe development of technology and the increasingly high lifestyle of humans produce abundant data. These data can be in the form of structured and unstructured data. Image data is included in unstructured data. The activities and objects recorded in a picture are varied. Normally, the human eye can easily distinguish between the foreground and background of an image, but computers need learning to distinguish between the two. Image segmentation is one of the fields in computer vision that discusses how computers learn and recognize segments of an image according to specified labels. In reality, a lot of data has unbalanced classes or labels, of course, it will affect the accuracy of a prediction. This research discusses how to improve the accuracy of image semantic segmentation in the ensemble learning method to deal with the problem of unbalanced data in image segmentation. The technique used is synthetic oversampling so as to produce balanced data and high accuracy. The ensemble learning methods used are Random Forest and Light Gradient Boosting Machine (LGBM). By using the Penn-Fudan Database for Pedestrian dataset which contains a imbalanced class. The use of synthetic oversampling techniques can improve the level of accuracy in minority classes. The random forest algorithm experienced an increase in accuracy by 37% while the LGBM algorithm increased by 41%.
Sentiment Analysis Of Public Comments On Licensing Services Seeks To Use The Naive Bayes Algorithm With Genetic Selection Algorithm Patue, Abdulatif; Sidik, Guruh Fajar; Affandy, Affandy; Ismail, Abdul Rahman
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3550

Abstract

The Investment and One-Stop Integrated Service Office is the Licensing Service service to support an area in terms of business potential and investment value. Presenting MSME Business Actors and Young Entrepreneurs. As time goes by, Licensing Services in the Region, especially PTSP, must know what are the constraints and problems faced by business actors in terms of business services and products issued by the Service. Naïve Bayes is the most common algorithm that we encounter in several libraries. Therefore, this research will discuss the level of accuracy of this algorithm. Then additional selection of Genetic Algorithm features was carried out to increase the accuracy of the Naïve Bayes method. The Naive Bayes Algorithm method with Genetic Algorithm selection is superior compared to only using the Naive Bayes method. This is proven by the acquisition of 83.17% accuracy, 86.38% Precision, and 83.05% recall
Smart Rupiah Recognition: A Mobile Machine Learning Approach for Visually Impaired Users Fadlurrahman, Hanan Nadhif; Affandy, Affandy; Cahyadi, Dede Faiz
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.28930

Abstract

Purpose: Despite advances in assistive technology, low-connectivity areas lack reliable solutions for visually impaired individuals, prompting this study to enhance financial autonomy in cash-based economies. This research addresses high fraud risks and the limitations of online tools like Be My Eyes, which fail in areas with only 40% internet access, by developing a 3MB MobileNetV2 model for offline Rupiah denomination recognition on low-end Android devices. Methods: A MobileNetV2-based Convolutional Neural Network, optimized to 3MB via TensorFlow Lite quantization, was trained on 10,855 augmented images (rotation ±30°, flipping, Gaussian noise, σ=0.1). The Kotlin-based application integrates CameraX for 720p video and Bahasa Indonesia text-to-speech, with a “no object” class. The model was tested on 4–8GB RAM devices, validated through usability evaluations with diverse stakeholders. Result: The model achieves 90% accuracy (F1-score 0.90) at 1000 lux, 85% at <50 lux, 80% at >60° angles, and 88% for “no object,” with 10ms latency. Self-supervised learning (SimCLR) on 2,000 worn notes improves accuracy by 3% (p < 0.05). Usability evaluations yield 95% session success, with TTS and UI Likert scores of 4.2 and 4.0.. Novelty: The 3MB MobileNetV2 model, with 10ms latency and 15% false positive reduction, outperforms YOLOv5 (500MB, 50ms), Vision Transformer (1GB, 200ms), and YOLOv8 (200MB, 30ms). This model shows potential for cross-currency detection throught preliminary exploration (e.g., USD and euro), which may advance edge AI and financial inclusion in developing nations.
The Impact of Squeeze-and-Excitation Blocks on CNN Models and Transfer Learning for Pneumonia Classification Using Chest X-ray Images Yunan, Muhammad; Marjuni, Aris; Affandy, Affandy; Soeleman, Mochamad Arief; Firdaus, Iqbal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6693

Abstract

Pneumonia is one of the leading causes of death due to respiratory tract infections, especially in children and the elderly. Early detection using chest X-ray images is crucial to accelerate diagnosis and treatment, but manual interpretation is often subjective and error-prone. This study evaluates the effect of Squeeze-and-Excitation (SE) Block integration on the performance of a custom Convolutional Neural Network (CNN) model and three popular transfer learning architectures: MobileNetV2, VGG16, and InceptionV3 in X-ray image-based pneumonia classification. A dataset of 5,856 images, taken from Chest X-ray Images (Pneumonia) on Kaggle, was processed through preprocessing, undersampling, and augmentation. Each model was tested in two configurations: without and with SE Block. Evaluation was performed using accuracy, precision, recall, F1-score, and test loss metrics. The results show that SE Block integration improves the performance of most models. The accuracy of the custom CNN increased from 95.17% to 95.88%, MobileNetV2 from 97.18% to 97.59%, and VGG16 from 96.88% to 97.69%. InceptionV3 also saw an accuracy increase from 94.06% to 94.16%, although accompanied by an increase in test loss. SE Block proved effective in strengthening the model's emphasis on important features through an inter-channel recalibration mechanism, especially on efficient architectures like MobileNetV2 and complex models like VGG16. These findings support the development of a more accurate, efficient, and adaptive deep learning-based pneumonia diagnosis system, especially for implementation in healthcare facilities with limited resources.
Co-Authors Abdul Syukur Ahmad Zainul Fanani Al zami, Farrikh Althoff, Mohammad Noval Ardytha Luthfiarta Arie Nugroho, Arie Assaqty, Mohammad Iqbal Saryuddin Ayu Pertiwi Azhar, Saifulloh Basiron, Halizah Cahyadi, Dede Faiz Dliyauddin, Muhammad Erwin Yudi Hidayat Etika Kartikadarma Fadlurrahman, Hanan Nadhif Firdaus, Iqbal Guruh Fajar Shidik Hafidhoh, Nisa'ul Hardiansyah, Raindy Wicaksana Ismail, Abdul Rahman Marjuni, Aris Mochammad Ilham Aziz Muhammad Yunan, Muhammad Nurhindarto, Aris Patue, Abdulatif Prasetya, Rudy Eko Pujiono Pujiono Ricardus Anggi Pramunendar Sari Ayu Wulandari Satya, Mohammad Wahyu Bagus Dwi Sidik, Guruh Fajar Soeleman, M. Arief Soeleman, Mochamad Arief Sukamto, Titien S. Yulianto, Satria Pradana Rizki