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All Journal Semantik Jurnas Nasional Teknologi dan Sistem Informasi Journal of Information System Journal of Development Research Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Abdimasku : Jurnal Pengabdian Masyarakat OISAA Journal of Indonesia Emas Komatika: Jurnal Pengabdian Kepada Masyarakat Jurnal Indonesia Sosial Teknologi Advance Sustainable Science, Engineering and Technology (ASSET)
Affandy Affandy
Dian Nuswantoro University
Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Improving C4.5 Algorithm Accuracy With Adaptive Boosting Method For Predicting Students in Obtaining Education Funding Mohammad Ahmad Maidanul Abrori; Abdul Syukur; Affandy Affandy; Moch Arief Soeleman
Journal of Development Research Vol. 6 No. 2 (2022): Volume 6, Number 2, November 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i2.205

Abstract

The level of accuracy in determining the prediction of the provision of educational funding assistance is very important for the education agency. The large number of data on prospective beneficiaries can be processed into information that can be used as decision support in determining eligibility for education funding assistance. The data processing is included in the field of data mining. One method that can be applied in predicting the feasibility of receiving aid funds is classification. There are several classification algorithms, one of which is a decision tree. The famous decision tree algorithm is C4.5. The C4.5 algorithm can be applied in classifying prospective recipients of educational aid funds. This study uses datasets from student data of SMK Al Fattah Kertosono. The purpose of this study is to increase the accuracy of the C4.5 algorithm by applying adaboost in classifying students who deserve education funding and not, by comparing the results before and after applying adaboost. Validation in this study uses cross validation. While the measurement of accuracy is measured by the confusion matrix. The experimental results show that there is an increase in accuracy of 7.2%. The accuracy of the application of the C4.5 algorithm reaches 91.32%. While the accuracy of the application of the C4.5 algorithm with adaboost reached 98.55%.
Deteksi Dini Covid-19 Melalui Citra CT-Scan Paru-Paru Menggunakan K-Nearest Neighbor dengan Komparasi Jarak Lu'luul Maknun; Abdul Syukur; Affandy Affandy; Moch Arief Soeleman
Jurnal Indonesia Sosial Teknologi Vol. 3 No. 03 (2022): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1049.395 KB) | DOI: 10.59141/jist.v3i03.397

Abstract

Covid -19 yang telah mewabah dan menjadi pandemik secara global yang merupakan masalah utama yang perlu di perhatikan dan di tangani, beberapa cara yang harus di lakukan adalah dengan memutus mata rantai penyebaran virus salah satunya dengan melakukan deteksi dini dan melakukan karantina, dengan CT scan paru-paru. CT scan paru-paru dapat dijadikan jalan alternatif. Berdasarkan permasalahan di atas maka peneliti mengetahui kondisi paru-paru secara detail dan dalam mendiagnosis virus secara dini. Pada penelitian ini pendekatan yang di ajukan menggunakan metode K-NN dengan perhitungan jarak euclidean distance, manhattan distance, miskowski distance untuk deteksi dini Covid -19 melalui citra CT scan paru-paru yang di duga terinfeksi Covid -19 . dalam mendeteksi secara dini evaluasi yang di gunakan untuk mengetahui pervorma yang di usulkan menggunakan coufusion matrix dengan hasil eksperimen menunjukkan hasil dari tiga perhitungan jarak menunjukkan hasil akurasi yang baik dan menggunakan dataset secara publik yaitu euclidean distance berjumlah 83%, Manhattan distance berjumlah 87%, Minkowski berjumlah 76%, di harapkan metode ini dapat di gunakan dan di kembangkan untuk melengkapi dioglosa medis.
Implementasi AI sebagai Asisten Cerdas untuk Meningkatkan Kompetensi Guru dalam Penyusunan Instrumen Asesmen di SMA Negeri 1 Ngadiluwih Galuh Wilujeng Saraswati; Erba Lutfina; Affandy; Ricardus Anggi Pramunendar; Muhammad Syaifur Rohman
Komatika: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2026): May 2026
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Institut Informatika Indonesia Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/komatika.v6i1.1482

Abstract

Perkembangan Artificial Intelligence (AI) yang pesat menuntut adaptasi kompetensi guru dalam menyusun instrumen evaluasi yang adaptif dan inovatif. Pengabdian masyarakat ini bertujuan untuk meningkatkan literasi digital tenaga pendidik di SMA Negeri 1 Ngadiluwih melalui pelatihan pemanfaatan AI sebagai asisten cerdas dalam penyusunan asesmen berbasis Higher Order Thinking Skills (HOTS) dan produksi media pembelajaran kreatif. Metode yang digunakan adalah Participatory Action Research (PAR) yang melibatkan 60 guru dari berbagai rumpun mata pelajaran. Pelatihan dilaksanakan selama 150 menit dengan alur kerja yang mencakup pemaparan teori, demonstrasi prompt engineering, dan praktik mandiri pembuatan video edukasi clay-motion. Hasil kegiatan menunjukkan adanya peningkatan kompetensi kognitif peserta secara signifikan, yang dibuktikan dengan kenaikan rata-rata nilai dari 5,63 pada pre-test menjadi 7,5 pada post-test. Selain itu, mitra berhasil memproduksi draf instrumen asesmen HOTS dan purwarupa media visual yang relevan dengan kebutuhan kurikulum. Meskipun terdapat kendala pada kesenjangan literasi digital antar generasi dan limitasi teknis perangkat, kegiatan ini terbukti efektif dalam mentransformasi peran AI sebagai asisten instruksional yang mampu mereduksi beban administrasi sekaligus meningkatkan kualitas konten edukasi di sekolah.
The Impact of Squeeze-and-Excitation Blocks on CNN Models and Transfer Learning for Pneumonia Classification Using Chest X-ray Images Muhammad Yunan; Aris Marjuni; Affandy Affandy; Mochamad Arief Soeleman; Iqbal Firdaus
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.
A Sustainable Computational Framework for Breast Cancer Screening: Optimizing High-Dimensional Feature Spaces via MVP-PCA for Resource-Constrained Environments Etika kartikadarma; Ahmad Zainul Fanani; Pujiono; Affandy
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.3858

Abstract

The rapid integration of Electronic Health Records (EHR) demands the efficient processing of high-resolution medical images. However, deep learning architectures applied to mammography classification often produce massive, high-dimensional feature spaces susceptible to the curse of dimensionality and anatomical noise. Furthermore, conventional dimensionality reduction approaches tend to cause over-reduction, which destroys crucial microcalcification textures. To address these challenges, this study proposes a CPU-efficient hybrid dimensionality reduction framework integrating Mean Vector Projection (MVP) and Principal Component Analysis (PCA) on features extracted by SqueezeNet. The MVP layer acts as a crucial pre-conditioner to stabilize intra-class variance before PCA decomposition. Experimental results demonstrate that the proposed MVP-PCA framework successfully linearizes the feature space and achieves an extreme compression rate of 99.11%, reducing 264,702 features to 2,355 essential components. The peak accuracy reaches 97.58% with a minimal False Negative rate (2 cases), providing a sustainable diagnostic solution for healthcare facilities with limited technological resources.
Co-Authors Abdul Syukur Adhe Irham Thoriq Ahmad Zainul Fanani Aris Marjuni Ayu Pertiwi Ayu Pertiwi Desi Hanindya Ujianti Erba Lutfina Erika Devi Udayanti Erwin Yudi Hidayat Erwin Yudi Hidayat Etika Kartikadarma Iqbal Firdaus Lu'luul Maknun Moch Arief Soeleman Mochamad Arief Soeleman Mohammad Ahmad Maidanul Abrori Mohammad Faishol Zuhri Much Chafid Muhammad Haris Zuhri Muhammad Syaifur Rohman Muhammad Yunan Muslih Muslih Nurhindarto, Aris Oktania Nandiyati Pujiono Raindy Wicaksana Hardiansyah Ricardus Anggi Pramunendar S. Kholidah Rahayu Maharani Saraswati, Galuh Wilujeng Syafaatun Khasanah Titien Suhartini