Julia: Jurnal Ilmu Komputer An Nuur

JURNAL Penerapan Sistem Informasi Stok Barang Berbasis Aplikasi Untuk Meningkatkan Efisiensi Pengelolaan Inventaris Pada Toko Sembako

2025-02-24T22:06:52+07:00

Perkembangan teknologi informasi yang berkembang pesat memberikan dampak yang signifikan ke berbagai sektor, seperti pengelolaan inventaris di sektor ritel. penelitian ini bertujuan untuk menerapkan sistem informasi yang berbasis aplikasi untuk stock barang agar meningkatkan efisiensi pengelolaan inventaris pada toko. aplikasi yang di bangun di harapkan dapat mempermudah pemantauan stok barang, mempercepat proses pencatatan transaksi, serta meminimalisir kesalahan dalam mengelola data stock. metode yyang di gunakan dalam penelitian ini adalah pengembangan aplikasi berbasis perangkat lunak yang dapat memberikan hasil laporan inventaris secara akurat. hasil yang di harapkan dari penerapan sistem informasi ini adalah pengurangan tingkat kesalahan, serta pengelolaan stok yang lebih mudah dan cepat. Penelitian ini memiliki kontribusi dalam memberikan solusi bagi toko-toko untuk menghadapi tantangan pengelolaan inventaris yang lebih kompleks di era digital sekarang.

PENGGUNAAN METODE REGRESI LINIER UNTUK ESTIMASI ANGKA PERCERAIAN : STUDI KASUS PENGADILAN AGAMA KABUPATEN GROBOGAN

2024-09-20T19:25:14+07:00

Data mining is the process of analyzing large datasets to discover hidden patterns, trends, and valuable information. This study utilizes data mining to address a social issue, specifically estimating divorce rates in the Religious Court of Grobogan Regency. The method used is multiple linear regression, with the dependent variable being the number of divorces and independent variables including 'cerai talak' (divorce initiated by the husband), 'cerai gugat' (divorce initiated by the wife), and 'dispensasi kawin' (marriage dispensation). The objective of this research is to test and develop a data mining method to estimate divorce rates, thereby aiding the Religious Court of Grobogan Regency in formulating more effective policies, based on divorce data from 2023-2024. The research process includes data collection, pre-processing, algorithm implementation, and result evaluation. The analysis shows that the multiple linear regression model provides reasonably accurate estimates, with a Root Mean Square Error (RMSE) of 6.505 and a Relative Root Squared Error (RRSE) of 0.070. Further analysis reveals that 'cerai talak,' 'cerai gugat,' and 'dispensasi kawin' significantly affect divorce rates, with 'cerai gugat' being the most dominant factor. These findings provide a solid foundation for developing strategic policies to handle divorce cases in Grobogan Regency. To improve model accuracy, data enrichment and additional variables are needed. Collaboration between academics and the Religious Court of Grobogan Regency is also crucial to ensure the successful implementation of this research’s findings.

JURNAL PREDIKSI TINGKAT KELULUSAN PESERTA DIDIK SMK FATHUL ULUM GABUS DENGAN METODE NAIVE BAYES

2025-03-06T16:21:41+07:00

The graduation of students refers to those who are able to complete and meet the graduation requirements set through a graduation meeting based on the decision letter signed by the school principal. Graduation rate data can be used to help make policies and strategies for the school to improve graduation rates in the following year. This study utilizes classification or prediction methods to analyze the graduation rates of students at SMK Fathul Ulum Gabus. The method used in this study is Naive Bayes, using variables such as practical exam scores, school exam scores, competency test scores, student attendance, and student behavior. The purpose of this study is to test the accuracy of the Naive Bayes method in predicting graduation rates based on data collected from 2019 to 2024. The research process includes data collection, data integration, and model training using Naive Bayes, which produces fairly accurate predictions with an accuracy of 94.64%. Based on this accuracy, it can be concluded that the Naive Bayes method can be used to predict graduation rates at SMK Fathul Ulum Gabus.

JURNAL Sistem Informasi Pembuatan Aplikasi Berbasis Web Pada Konveksi ‘Sania Komveksi’

2025-02-24T21:57:37+07:00

Perkembangan teknologi informasi yang pesat telah mempengaruhi berbagai sektor bisnis, termasuk industri konveksi. Sistem informasi berbasis web kini menjadi solusi efektif untuk meningkatkan efisiensi operasional dan manajemen data. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sebuah aplikasi berbasis web pada konveksi ‘Sania Konveksi’ yang dapat membantu dalam pengelolaan data produksi, pemesanan, inventaris, serta pelaporan secara lebih terstruktur dan real-time. Aplikasi ini dibangun dengan menggunakan metode pengembangan perangkat lunak Waterfall, dimulai dari tahap analisis kebutuhan, desain sistem, implementasi, sampai dengan pengujian. Hasil penelitian ini adalah sebuah aplikasi berbasis web yang mampu mengintegrasikan berbagai proses bisnis pada konveksi, memudahkan pihak manajemen dalam memonitor kinerja operasional, serta meningkatkan efisiensi dalam pengelolaan data dan proses produksi. Berdasarkan hasil pengujian, sistem ini terbukti dapat memberikan kemudahan dan efisiensi dalam pengelolaan operasional konveksi ‘Sania Konveksi’.

JURNAL PREDIKSI LUAS PANEN DI KECAMATAN PURWOADADI MENGGUNAKAN ALGORITMA REGRESI LINEAR BERGANDA

2025-03-06T16:21:25+07:00

Agriculture, particularly rice cultivation, is highly vulnerable to climate change because it depends on water cycles and weather conditions to maintain productivity. Climate change affects crop growth, development, and yields, as agricultural activities are heavily dependent on weather and climate. This study utilizes data mining to introduce a new breakthrough in addressing rice farming issues in Grobogan Regency, Purwodadi District. The method used is multiple linear regression, with the dependent variable being harvested area and the independent variables including plxanted area and rainfall. The objective of this research is to test and develop data mining methods to predict yield levels, thereby assisting local governments in decision-making during crop failures, based on agricultural data from 2019-2023. The research process involves data collection, preprocessing, algorithm implementation, and result evaluation. The analysis shows that the multiple linear regression model provides reasonably accurate predictions, with a Root Mean Square Error (RMSE) value of 209.042 and a Relative Root Squared Error (RRSE) of 0.111. Furthermore, the analysis reveals that planted area significantly influence the harvested area. These findings offer insights for local governments as policymakers in providing aid during crop failures.