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All Journal Elkom: Jurnal Elektronika dan Komputer Intuisi Jurnal Transformatika Jurnal Informatika Upgris AKSIOMA Fountain of Informatics Journal IJISTECH (International Journal Of Information System & Technology) Jurnal Informatika dan Rekayasa Perangkat Lunak JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknologi Informasi dan Komunikasi Community Empowerment IJISTECH Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat TECHSI - Jurnal Teknik Informatika Paradigma J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Jurnal Informatika: Jurnal Pengembangan IT SWAGATI: Journal of Community Service Advance Sustainable Science, Engineering and Technology (ASSET) Sekolah Dasar: Kajian Teori dan Praktik Pendidikan
Febrian Murti Dewanto
Universitas PGRI Semarang
Arts 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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Penerapan Algoritma K-Means Clustering dalam Analisis Pengelompokan Produk Toko Oleh-Oleh Berdasarkan Data Penjualan Chairunnita, Chairunnita; Handayanto, Agung; Dewanto, Febrian Murti
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7832

Abstract

Toko Oleh-Oleh Dury Weleri faces challenges in inventory management and promotional strategy due to the limitations of a conventional sales data recording system. This study aims to classify products based on sales performance using the K-Means Clustering algorithm by analyzing total sales, average sales, and remaining stock attributes. The optimal number of clusters was determined through a combination of the Elbow Method, Silhouette Score, and Davies-Bouldin Index, resulting in four main clusters. Cluster 0 consists of products with low sales and high stock (indicating potential overstock), Cluster 1 includes products with high sales but low stock (key products), Cluster 2 comprises products with moderate sales and relatively high stock (requiring light promotions), and Cluster 3 contains products with low sales and very low stock (likely seasonal or low-priority items). The clustering evaluation produced a Silhouette Score of 0.47336 and a DBI of 0.72644, indicating a reasonably good grouping quality. Interactive visualization via Streamlit provided strategic insights for decision-making regarding restocking and promotional planning. These findings are expected to support management in optimizing inventory control, improving operational efficiency, and developing more targeted sales strategies.
Facial Skin Disease Classification Using Swin Transformer V2 and ResNet-50 in a Flask-Based System Imaniyah, Shinta Arum; Murti Dewanto, Febrian; Sari, Nur Latifah Dwi Mutiara
Paradigma - Jurnal Komputer dan Informatika Vol. 28 No. 1 (2026): March 2026 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v28i1.12381

Abstract

Facial skin diseases are common health conditions that can significantly affect both physical and psychological well-being. Early identification is essential to minimize the risk of disease progression. However, in many areas, there is still a lack of access to dermatological care. Although deep learning algorithms have been widely used in medical image categorization, few studies offer a direct comparison between convolutional neural networks (CNN) and transformer-based architectures within a cohesive experimental framework, especially concerning the classification of facial skin diseases. This study compares the effectiveness of ResNet-50 with Swin Transformer V2 and develops a deep learning system to classify six different types of skin problems on the face. The models were evaluated using accuracy, precision, recall, and F1-score after the dataset was divided into subsets for testing, validation, and training. According to the trial results, Swin Transformer V2 achieves an astounding accuracy of 97.54%, outperforming ResNet-50, which achieves 94.44%. The training curves indicate stable learning behavior with minimal overfitting. Grad-CAM visualization is applied to improve interpretability by highlighting relevant regions in the images. The best-performing model is implemented in a Flask-based web application as a prototype system for early detection. These results demonstrate how transformer-based architectures can improve classification performance and highlight their potential applications in practical diagnostic support systems
Profil Media Gabidroid pada Pembelajaran Bilangan di Sekolah Dasar Febrian Murti Dewanto; Husni Wakhyudin; Mudzanatun
Sekolah Dasar: Kajian Teori dan Praktik Pendidikan Vol. 27 No. 1 (2018)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um009v27i12018p91-97

Abstract

Abstract: The purpose of this study is to produce gabidroid media for the learning of numbers at primary schools. In this research, the development of media using research and development approach. The research data retrieval by means of observation, questionnaires and interviews. Data validity is done by means of triangulation of sources. Based on the results, the data analysis obtained 3.25 on very well criteria, the results of media legibility trials obtained 3.67 on very well criteria. The gabidroid media is worth to use because it satisfies common aspects, content feasibility, media presentation, language feasibility, and graphic eligibility. Abstrak: Tujuan penelitian ini adalah untuk menghasilkan media gabidroid pada pembelajaran bilangan di sekolah dasar. Pengembangan media pembelajaran dalam penelitian ini menggunakan pendekatan penelitian dan pengembangan. Teknik pengambilan data penelitian dengan cara observasi, angket dan wawancara, keabsahan data dilakukan dengan cara triangulasi sumber. Berdasarkan hasil analisis data diperoleh validasi ahli 3,25 kriteria baik sekali, hasil uji coba keterbacaan media diperoleh 3,67 kriteria baik sekali. Media gabidroid layak digunakan karena memenuhi aspek umum, kelayakan isi, penyajian media, kelayakan bahasa, dan kelayakan grafik.
Co-Authors Achmad Buchori Achmad Buchori Agung Handayanto Ari Tri Jaka Harjanta Aris Tri Jaka Harjanta, Aris Tri Aris Tri Joko Harjanto Bambang Agus Herlambang, Bambang Chairunnita, Chairunnita Desi Maulia Desi Purwaningsih Dini Rakhmawati Dini Rakhmawati Fitria, Alfina Haryo Kusumo Husni Wakhyudin Imaniyah, Shinta Arum Irfan Nur Ridwan Khoiriya Latifah, Khoiriya Khoirul Junianto Kusumo, Haryo Lia Ayu Kusumaningrum Lukman Harun Marlina, Dian Mega Novita Mega Novita Mudzanatun Mudzanatun Noora Qotrun Nada, Noora Qotrun Nur Latifah Dwi Mutiara Sari Ridwan, Irfan Nur Setyoningsih Wibowo Setyoningsih Wibowo Venty Venty Venty Vilda Ana Veria Setyawati Waliyansyah, Rahmat Robi Wobowo, Setyoningsih Yayuk Wahyuningsih