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All Journal 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
Achmad Lutfi Fuadi
Universitas Pamulang
Humanities Astronomy Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics 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.
Co-Authors A. Nurul Anwar Ade Napila Agus Heri Yunial Agus Heri Yunial Ahmad Fauzi Akbar Suseno Tri Maulana Andrian Hidayat Joko Suwarno Mahaska Aiden Wibisono Mahaska Aiden Wibisono Nardiono Saprudin Saprudin Triyono Willis Puspita Sari Yuda