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All Journal Jurnal Inotera INTECOMS: Journal of Information Technology and Computer Science JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Jurnal Ilmu Komputer (JIK) OKTAL : Jurnal Ilmu Komputer dan Sains AMMA : Jurnal Pengabdian Masyarakat APPA : Jurnal Pengabdian Kepada Masyarakat JURNAL ILMIAH PENELITIAN MAHASISWA Journal of Innovative and Creativity Journal of Engineering and Science Application
Achmad Lutfi Fuadi
Universitas Pamulang
Aerospace Engineering Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Social Sciences Other

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PERBANDINGAN METODE HASIL EVALUASI PENGELOMPOKAN PENERIMA BANTUAN SOSIAL TUNAI (BST) BERDASARKAN PARAMETER KELAYAKAN BANTUAN SOSIAL MENGGUNAKAN METODE K-MEANS DAN K-MEDOIDS (STUDI KASUS: KECAMATAN SOLEAR) Akbar Suseno Tri Maulana; Achmad Lutfi Fuadi
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 3 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/v9faqe45

Abstract

Cash Social Assistance (BST) is a government program aimed at supporting economically vulnerable communities. Accurate identification of eligible beneficiaries is essential to ensure effective and targeted assistance. This study compares the performance of the K-Means and K-Medoids clustering algorithms in grouping BST beneficiaries based on socioeconomic eligibility parameters in Solear District. A quantitative data mining approach was employed using a dataset of 2,000 beneficiary records processed with RapidMiner. The research included data preprocessing, transformation, normalization, clustering, and evaluation using the Davies-Bouldin Index (DBI). Three clusters were generated based on eighteen socioeconomic attributes. The results show that the K-Means algorithm achieved a DBI value of -2.245, outperforming K-Medoids with a DBI value of -1.694. The lower DBI value indicates that K-Means produced more optimal cluster quality and better separation among beneficiary groups. These findings suggest that K-Means is more suitable for supporting objective and data-driven classification of BST beneficiaries.
Comparative Analysis of Hybrid CNN-ViT and CNN for Brain Tumor Classification Ahmad Fauzi; Achmad Lutfi Fuadi; Agus Heri Yunial
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 1 (2026): January 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5860

Abstract

The automated categorization of brain cancers from MRI is essential for improving diagnostic precision. Traditional Convolutional Neural Networks (CNNs) are proficient in local feature extraction but are constrained in their ability to capture long-range spatial relationships, hence impairing performance on intricate malignancies. We propose a hybrid parallel architecture that merges a CNN with a Vision Transformer (ViT) to combine local and global feature modeling. We assessed our dual-branch model in comparison to a conventional CNN baseline using a curated dataset of 15,000 MRI images categorized into three classes: glioma, meningioma, and pituitary. The hybrid model exhibited enhanced performance, attaining 98.40% accuracy and 0.0783 loss, in contrast to the baseline's 97.40% accuracy and 0.1187 loss. The substantial decrease in misclassifications was validated by additional metrics, such as enhanced recall for the meningioma category. The integration of local and global variables produces a more precise, stable, and generalizable classification framework, demonstrating significant potential as a basis for dependable AI-driven Clinical Decision Support Systems (CDSS) in neuroradiology.
Peningkatan Kompetensi Pemrograman Siswa Melalui Pelatihan Python Di SMK Panti Karya 3 Achmad Lutfi Fuadi; Nardiono; Saprudin
AMMA : Jurnal Pengabdian Masyarakat Vol. 5 No. 5 : Juni (2026): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Mastery of computer programming has become an urgent need in the era of the industrial revolution 4.0, especially for Vocational High School (SMK) students who are prepared to enter the workforce directly. However, SMK Panji Karya 3 still faces obstacles in improving students' programming competencies, particularly in Python, due to limited teaching materials and practical training. This community service activity aims to improve students' programming competencies through structured Python training. The methods used include interactive lectures, hands-on practice, and mentoring on small projects. The results of the activity show a significant increase in the understanding of basic programming concepts (such as variables, branching, looping, and functions) and the ability to solve simple case studies. Furthermore, students' enthusiasm and independence in writing code have increased significantly. This training makes a real contribution to preparing SMK Panji Karya 3 students to face the challenges of industry and the workforce.
Verifikasi Otomatis Bukti Pembayaran SPP Berbasis OCR pada Sistem Informasi Manajemen Sekolah Dadan Nuh Faturahman; Achmad Lutfi Fuadi
Journal of Engineering and Science Application Vol. 3 No. 2 (2026): Mei-Oktober
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v3i2.52

Abstract

This study develops a web-based School Management Information System (SIMS) equipped with a deep learning Optical Character Recognition (OCR) module that extracts data from tuition payment receipts at SMK BIT Bina Aulia, Bogor. The system aims to accelerate transaction verification, reduce manual input errors, and improve administrative transparency. The Research and Development method was applied, with the Waterfall model used to construct the product. The OCR module was built on PaddleOCR PP-OCRv4 with DBNet text detection and SVTR_LCNet text recognition using a CTC decoder, fine tuned on 384 receipt images collected from 14 payment channels and augmented into 14,824 training crops. The best training checkpoint reached 79.04% exact match accuracy with a normalized edit distance of 0.9563 at epoch 90. Evaluated on 940 text crops, the deployed service achieved 94.79% character accuracy, a 5.21% Character Error Rate, a 25.67% Word Error Rate, and 76.60% exact match accuracy, rising to 86.49% when spacing differences are ignored. Fine tuning improved exact match accuracy by 4.05 percentage points, and the proposed model outperformed Tesseract OCR 5 and EasyOCR on every metric. Black box testing of 63 test items and white box basis path testing of 53 independent paths passed without failure.
Benchmarking Deep Learning Models for Multi-Class Fruit Quality and Formalin Contamination Detection in Horticultural Fruits Ahmad Fauzi; Achmad Lutfi Fuadi; Agus Heri Yunial
Jurnal Inotera Vol. 11 No. 2 (2026): July - December 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss2.2026.ID732

Abstract

The adulteration of fresh horticultural produce with formaldehyde (formalin) and the rapid physiological decay of fruit remain persistent food-safety and economic problems within agricultural supply chains. Deep learning offers a non-destructive route to automated fruit-quality screening; however, the literature has largely pursued peak accuracy using progressively heavier network backbones, leaving the computational cost of that accuracy and therefore the feasibility of on-site, embedded deployment insufficiently examined. This study benchmarks three representative visual-recognition paradigms of markedly different scale, namely MobileNetV2 (a depthwise-separable lightweight convolutional neural network), ResNet-50 (a deep residual network), and ViT-B/16 (a patch-based Vision Transformer), on the identical three-class task of distinguishing fresh, rotten, and formalin-mixed fruit. A total of 10,154 images from the public FruitVision dataset were processed under a single fixed preprocessing and training protocol, so that architecture was the only experimental variable. Rather than accuracy alone, performance was assessed through an efficiency lens that normalises accuracy by parameter count and by inference cost (GFLOPs). The lightweight MobileNetV2 attained the highest test accuracy (99.69%) while using approximately one-seventh of the parameters and one-thirteenth of the floating-point operations of ResNet-50, and about one twenty-fifth of the parameters of ViT-B/16; the Transformer recorded the lowest accuracy (99.28%) at the highest cost. These results indicate that, for this medium-scale task, additional architectural capacity yields a negligible accuracy return, and that a compact model can support reliable, safety-critical formalin screening on resource-constrained devices. Per-class reliability and deployment implications, including one residual false negative, are reported without overstatement.
Co-Authors A. Nurul Anwar Ade Napila Agus Heri Yunial Agus Heri Yunial Ahmad Fauzi Akbar Suseno Tri Maulana Andrian Hidayat Dadan Nuh Faturahman Joko Suwarno Mahaska Aiden Wibisono Mahaska Aiden Wibisono Nardiono Saprudin Saprudin Triyono Willis Puspita Sari Yuda