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All Journal Information Technology and Telematics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Informasi dan Teknologi Economics and Digital Business Review International Journal of Social Science Aptekmas : Jurnal Pengabdian Kepada Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Riset Teknik Komputer
Imam Syafii
Diponegoro University
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IMPLEMENTASI SISTEM INFORMASI MANAJEMEN KEUANGAN BERBASIS WEB SEBAGAI UPAYA OPTIMALISASI PENGELOLAAN DANA ZIS (ZAKAT, INFAK, SEDEKAH) DI LAZISNU KECAMATAN LIMPUNG Laksamana Rajendra Haidar Azani Fajri; Imam Syafii; Adhitya Purboyo; Ryan Yunus
Jurnal Riset Teknik Komputer Vol. 2 No. 4 (2025): Desember : Jurnal Riset Teknik Komputer (JURTIKOM)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/mces8w27

Abstract

Currently, the Nahdlatul Ulama Zakat, Infaq, and Alms Collection Institution (LAZISNU) in Limpung District still records all donation data manually in books. This method slows down the process of preparing financial reports, often resulting in delays. Therefore, this research aims to create a web-based financial information system. It is hoped that this system will simplify and expedite the management of donation data and the creation of accurate financial reports. To create this system, researchers used the R&D (Research and Development) method, which encompasses various stages, from problem identification and design to testing and product revision. The system will be built using the PHP programming language and a MySQL database. Essentially, this new system is expected to address the problem of manual recording, making data management and financial reporting at LAZISNU Limpung faster, easier, and more accurate
IMPLEMENTASI SISTEM INFORMASI MANAJEMEN KEUANGAN BERBASIS WEB SEBAGAI UPAYA OPTIMALISASI PENGELOLAAN DANA ZIS (ZAKAT, INFAK, SEDEKAH) DI LAZISNU KECAMATAN LIMPUNG Laksamana Rajendra Haidar Azani Fajri; Imam Syafii; Adhitya Purboyo; Ryan Yunus
Jurnal Riset Teknik Komputer Vol. 2 No. 4 (2025): Desember : Jurnal Riset Teknik Komputer (JURTIKOM)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/mces8w27

Abstract

Currently, the Nahdlatul Ulama Zakat, Infaq, and Alms Collection Institution (LAZISNU) in Limpung District still records all donation data manually in books. This method slows down the process of preparing financial reports, often resulting in delays. Therefore, this research aims to create a web-based financial information system. It is hoped that this system will simplify and expedite the management of donation data and the creation of accurate financial reports. To create this system, researchers used the R&D (Research and Development) method, which encompasses various stages, from problem identification and design to testing and product revision. The system will be built using the PHP programming language and a MySQL database. Essentially, this new system is expected to address the problem of manual recording, making data management and financial reporting at LAZISNU Limpung faster, easier, and more accurate
OPTIMASI K-MEANS CLUSTERING PSO UNTUK PENENTUAN JUMLAH CLUSTER OPTIMAL PADA DATA KANKER PAYUDARA Adhitya Purboyo; Laksamana Rajendra Haidar Azani Fajri; Imam Syafii
Jurnal Riset Teknik Komputer Vol. 3 No. 1 (2026): Maret : Jurnal Riset Teknik Komputer (JURTIKOM)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/drakfm71

Abstract

Breast cancer is one of the most dangerous diseases and a leading cause of death among women worldwide. Clustering methods can assist in diagnosing breast cancer to determine the best course of treatment. K-Means is a widely used clustering algorithm known for its ability to handle large datasets efficiently with fast computational time. However, K-Means has a significant weakness: the number of clusters is determined randomly, resulting in suboptimal clustering outcomes. To overcome this limitation, Particle Swarm Optimization (PSO) is applied for automatic determination of the optimal number of clusters. PSO was selected due to its advantages, including requiring few parameters, ease of implementation, fast convergence, and low computational cost. This study uses the breast cancer dataset from the UCI Machine Learning Repository, consisting of 699 records and 10 attributes. The proposed PSO–K-Means method was evaluated using the Silhouette Coefficient and Davies-Bouldin Index. The results show that the optimal number of clusters is k = 2, achieving a Silhouette Coefficient of 0.92 and a Davies-Bouldin Index of 1.374. These results demonstrate that the PSO–K-Means method significantly outperforms standard K-Means by directly producing optimal clustering results without the need for conducting repeated experiments.
Co-Authors Adhitya Purboyo Adila Safitri Ahmad Aufar Ribhi Arief Setyanto Budiono, Graceilla Kristia Seraphim Dwi Agus Diartono Hanif Abdul Rahman Kalimatussa’diyah Kusworo Adi Laksamana Rajendra Haidar Azani Fajri Liya Yuni Astutik Mohamad Rifqy Roosdhani Mutia Ulfa Nita Ilmiyatul Lailiyah Pamela, Alberta Silvia Rafli Yunan Suryatama Ridha, Alfan Suwanto Raharjo Vincensia Serenade Vioresta Steffiandry Yunus, Ryan