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Julia Jurnal
Published by Universitas An Nuur
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Julia is an open access journal. Readers may read, download, copy, distribute, print, search, or link to the full text of this article free of charge. All submitted papers will be peer reviewed before being accepted for publication. Authors who wish to submit manuscripts to Julia must follow the norms described in the guidelines.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 27 Documents
 1   2   3 
PENGGUNAAN METODE REGRESI LINIER UNTUK ESTIMASI ANGKA PERCERAIAN : STUDI KASUS PENGADILAN AGAMA KABUPATEN GROBOGAN Alfianaa Khanifiyah; Agus Susilo Nugroho; Andri Tiyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 1 (2025): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i1.1

Abstract

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. 
PREDIKSI TINGKAT KELULUSAN PESERTA DIDIK SMK FATHUL ULUM GABUS DENGAN METODE NAIVE BAYES Wahyudi; Eko Supriyadi; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 1 (2025): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i1.3

Abstract

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.
IMPLEMENTASI ALGORITMA FP-GROWTH UNTUK REKOMENDASI PRODUK DI TOKO LM MART Happy Dewi Ariyantini; Dhika Malita Puspita; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 4 No 1 (2024): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v4i1.4

Abstract

LM Mart merupakan salah satu usaha toko BumDesa yang berlokasi di Jl Raya PurwodadiSemarang Km.13 kecamatan Godong Kabupaten Grobogan. Produk yang dijual meliputi berbagai bahan pangan pokok (sembilan bahan pokok) untuk kebutuhan masyarakat umum. Data disimpan dalam database toko LM Mart. Salah satunya adalah memperbanyak data transaksi. Dengan semakin meningkatnya volume data di LM Mart, fungsi analis yang menganalisis data secara manual harus digantikan dengan aplikasi berbasis komputer. Permasalahan yang ada pada Toko LM Mart adalah pedagang kurang mempunyai kemampuan dalam mengamati keinginan dan kebutuhan konsumen yang tentunya akan berdampak pada peningkatan penjualan produk. Selain itu data transaksi penjualan jika diolah dapat menghasilkan informasi bermanfaat yang dapat menjadi strategi penjualan untuk meningkatkan pemasaran. Algoritma FP-Growth akan digunakan untuk pendekatan asosiasi pada penelitian ini. Algoritma FP-Growth merupakan pengembangan dari algoritma apriori, memperbaiki kekurangan dari algoritma apriori. Untuk mendapatkan kumpulan item yang sering, algoritma apriori harus menghasilkan kandidat. Dari hasil penelitian perhitungan menggunakan RapidMiner dengan nilai Support sebesar 30% dan nilai Confidance sebesar 80% dengan data transaksi sebanyak 800 record menghasilkan 36 rule. 
ANALISIS SENTIMEN PADA TWITTER TENTANG ISU PERILAKU ANTISOSIAL DENGAN ALGORITMA NAÏVE BAYES Retika Nur Fadila; Andri Triyono; Dhika Malita Puspita
Julia: Jurnal Ilmu Komputer An Nuur Vol 4 No 1 (2024): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v4i1.5

Abstract

In 2023, around 78.19% of the 275.77% or 215.63 million Indonesian population will be connected to the internet, with positive impacts such as fast communication, entertainment and new knowledge. The internet makes non-cash transactions easier and has negative impacts such as addiction and antisocial behavior such as indifference to people around you. Teenagers often access social media, especially Twitter, to express opinions and vent both positive and negative. Sentiment analysis is used to determine opinions about antisocial behavior on Twitter by using text mining techniques to analyze teenagers' opinions. Naive Bayes and SVM algorithms are used in sentiment analysis on the Twitter dataset to analyze antisocial behavior. Actions to evaluate the Naive Bayes algorithm in assessing antisocial behavior sentiments had the best accuracy results of 59.71% with k=7 without n-grams. The Naïve Bayes algorithm with k=5 and n-gram n=2 has the best precision of 33.76% and the best recall of 33.45%. Future research can try to use other classification algorithms such as KNN, SVM, etc. To find the best accuracy of the antisocial behavior dataset. 
ANALYSIS USABILITY OF USER EXPERIENCE OF THE SRAWUNG WITH THE USER EXPERIENCE QUESTIONNARE (UEQ) METHOD Achmad Azhar Rifan Nugroho; Erwin Apriliyanto
Julia: Jurnal Ilmu Komputer An Nuur Vol 3 No 1 (2023): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v3i1.6

Abstract

An important aspect when evaluating the quality of an application is usability. The quality level of usability of the application is determined by how easy the user of the application uses it. Applications and products with good usability usually have many loyal users. Conversely, if an app has a low level of usability, users will inevitably give up and switch to another app that has similar functionality. The application analyzed in this study is an android application called Srawung with video conferencing features. In this study, to assess the quality of usability used the User Experience Questionnaire (UEQ) method. The user experience questionnaire (UEQ) uses a six-dimensional scale such as attractiveness, acuity, efficiency, dependability, stimulation, and novelty. Data collection is done by distributing forms to Srawung application users who are also respondents. The questionnaire consists of several questions as indicators of research variables from six dimensions. The result of user satisfaction analysis is that application users can use the Srawung application better and which indicators are good and which are not.
USABILITY ANALYSIS OF AN ANDROID BASED VIDEO CONFERENCE APPLICATION Achmad Azhar Rifan Nugroho; Erwin Apriliyanto
Julia: Jurnal Ilmu Komputer An Nuur Vol 3 No 1 (2023): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v3i1.7

Abstract

In the digital age, data and technology are evolving very quickly. Technology is basically the process of adding value to a given outcome and Android is an application platform and an operating system for smartphones. The use of Android is huge in our life, now educators can use Android to improve teaching supported by smartphones and other Android based gadgets like Srawung video conference. This is very important in our life, Srawung video conference is a very useful alternative application as a cloud based virtual conference that aims to easily communicate with many people without direct contact and can support education in the digital age. It has been shown that this case paper analyzes the usability of Srawung video conference application as a tool to support teachers' learning, the results of this usability analysis are expected to provide insights for future development of the application so as to improve the usability of the application. The test includes five aspects of usability, learnability, effectiveness, memoriability, errors, and satisfaction. The results of the study show that the usability value of Srawung version 1.0 is a percentage value of 96.11%, which indicates that the application is very useful. 
Image Cluster Features Shape and Texture Determinants of Rice Quality Using the K Means Algorithm Eko Supriyadi
Julia: Jurnal Ilmu Komputer An Nuur Vol 2 No 01 (2022): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/pm208j29

Abstract

There is a lot of fraud case in the forgery of ricequality by mixing good quality rice with low quality rice for increasing price. To protect the community from counterfeiting, we conduct research to detect the quality of rice which can later help the community to be able to distinguish good and bad quality. This paper presents a low-costimage processing system for assessing the quality of rice. Many factors affect the quality of rice such as grain fragments, non-uniform color, odor and other factors. This study uses procentage of broken rice grains and color uniformity to determine the quality of rice. We propose texture feature with Otsu segementation for determining the number of broken grains and color distribution for specifying the color uniform. The classification results usingK Fold validation on the original data show the results of K-Nearest Neighbor have 99.70% accuracy.
Information System Using The Web For Garbage Bank Transactions (A Case Study of TPS 3R Sido Makmur, Sidoharjo, Pacitan) Tamara Maharani; Dhodit Rengga Tisna
Julia: Jurnal Ilmu Komputer An Nuur Vol 2 No 01 (2022): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v2i01.9

Abstract

The construction of a web software, is expected to assist users in processing data and obtaining information quickly, precisely and as needed, along with technological developments, especially in the field of information technology This application contains the Garbage Bank Information System at TPS 3R Sido Makmur, Sidoharjo village, Pacitan district. Application is built using HTML, PHP and CSS programming. The database used is MySQL. With this information system, it is expected to be able to handle transactions that run at the waste bank and provide data reports needed by system users, so that the waste bank work process is more effective and efficient.
COMPUTER NETWORK ANALYSIS USING NETWORK  MANAGEMENT SYSTEM AT AN NUUR UNIVERSITY Achmad Rizki Ramadhani; Muhammad Akbar Mustofa; Rahmawan Bagus Trianto
Julia: Jurnal Ilmu Komputer An Nuur Vol 1 No 01 (2021): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v1i01.10

Abstract

During the pandemic, teaching and learning activities have changed. Which originally used the offline format to go online and its combinations. Internet bandwidth usage plays an important role in the success of the teaching and learning process on campus, including at An Nuur University. By using Cacti Network Management System it can be used as a monitoring system to monitor the movement of internet bandwidth whether it meets the needs of the online learning process or not. Internet bandwidth usage is influenced by several factors such as logical topology, physical topology and configuration in computer networks.
COMPARISON OF SVM, KNN, AND NAIVE BAYES METHOD WITH N-GRAM IN TRAFFIC ACCIDENT CLASSIFICATION Dhika Malita Puspita Arum; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 1 No 01 (2021): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v1i01.11

Abstract

Traffic accidents that occur in Indonesia are still relatively high, the information can be easily obtained through social media, one of which is Twitter. The amount of traffic accident information can be processed and classified according to certain categories. Traffic accident data classification is done using SVM, KNN and Naïve Bayes methods using n-gram feature extraction. The results of this study indicate the best accuracy is 87.63 using the KNN method.

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