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Perancangan Sistem Informasi Laporan Keuangan pada Sekolah Menengah Pertama Ulya Ilhami Arsyah; Mutiana Pratiwi; Abulwafa Muhammad
Journal Of Indonesian Social Society (JISS) Vol. 1 No. 1 (2023): JISS - Februari
Publisher : PT. Padang Tekno Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1018.863 KB) | DOI: 10.59435/jiss.v1i1.28

Abstract

Sistem informasi laporan keuangan adalah kegiatan pencatatan laporan keuangan yang diterapkan guna mempermudah dalam proses pengolahan data keuangan. Kegiatan ini bertujuan untuk merancang dan membangun aplikasi pengelolaan transaksi keuangan pada sekolah berbasis web. Sistem informasi ini meliputi sistem pencatatan, pembuatan jurnal umum, buku besar, dan neraca saldo. Pihak bendahara sekolah melakukan proses pencatatan masih menggunakan Ms. Excel. Terkait hal tersebut, dirancang sebuah sistem berbasis akuntansi guna membantu dalam proses pencatatan transaksi keuangan pada sekolah menengah pertama (SMP). Lokasi pengabdian dilakukan pada SMP 24 Padang. Pemanfaatan Sistem Informasi Akuntansi (SIA) ini dapat membantu mengurangi redudansidata, menghasilkan informasi secara cepat, tepat dan akurat serta data laporan keuangan dapat disimpan dengan baik. Metode yang digunakan dalam perancangan aplikasi ini adalah Object Oriented Programming (OOP) dengan menggunakan Unified Modeling Language (UML) dalam analisis perancangan sistem. Hasil kegiatan ini adalah berupa rancangan sistem informasi akuntansi yang dapat mempermudah pencatatan transaksi keuangan yang dilakukan oleh pihak sekolah.
Penerapan Data Mining Untuk Pengembangan Destinasi Wisata Menggunakan Algoritma K-Means Clustering Irzal Arief Wisky; Febria Nika Putri; Mutiana Pratiwi
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1337

Abstract

One of the strategic industries that supports community wellbeing and regional economic development is tourism. In West Sumatra's Solok Regency, Alahan Panjang is home to a wide range of natural tourist sites, each with unique characteristics and possibilities for growth. Due to differences in the number of tourists, attractions, accessibility, and amenities, it is still difficult to identify development priorities for each destination. Based on their similarities, this study uses the K-Means Clustering algorithm and the Data Mining approach to categorize tourist attractions. The study uses tourist destination data from Alahan Panjang that includes variables for tourism components and visitor statistics. To ensure a consistent range of values, the data were subjected to Min-Max normalization before clustering. Three types of tourist sites are distinguished by the clustering findings: high-priority, priority, and supporting locations. Additionally, an information system built on PHP and MySQL integrated the clustering results to assist tourism managers and municipal governments in assessing tourist potential and establishing development goals. Through data-driven and objective tourism analysis, the suggested method is anticipated to increase the effectiveness of decision-making.
Penerapan Data Mining Untuk Pengembangan Destinasi Wisata Menggunakan Algoritma K-Means Clustering Irzal Arief Wisky; Febria Nika Putri; Mutiana Pratiwi
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1337

Abstract

One of the strategic industries that supports community wellbeing and regional economic development is tourism. In West Sumatra's Solok Regency, Alahan Panjang is home to a wide range of natural tourist sites, each with unique characteristics and possibilities for growth. Due to differences in the number of tourists, attractions, accessibility, and amenities, it is still difficult to identify development priorities for each destination. Based on their similarities, this study uses the K-Means Clustering algorithm and the Data Mining approach to categorize tourist attractions. The study uses tourist destination data from Alahan Panjang that includes variables for tourism components and visitor statistics. To ensure a consistent range of values, the data were subjected to Min-Max normalization before clustering. Three types of tourist sites are distinguished by the clustering findings: high-priority, priority, and supporting locations. Additionally, an information system built on PHP and MySQL integrated the clustering results to assist tourism managers and municipal governments in assessing tourist potential and establishing development goals. Through data-driven and objective tourism analysis, the suggested method is anticipated to increase the effectiveness of decision-making.