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Joko Minardi
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Universitas Islam Nahdlatul Ulama Jepara Jl. Taman Siswa No 9, Pekeng, Tahunan, Jepara 59427
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Journal of Information System and Computer
Published by Universitas Islam Nahdlatul Ulama Jepara
ISSN : -     EISSN : 28095995     DOI : https://doi.org/10.34001/jister.v3i1
Core Subject : Science,
Computer Science & IT
Jurnal Jister menyediakan sebuah forum untuk menerbitkan artikel penelitian asli , artikel review dari kontributor , dan berita teknologi baru yang berkaitan dengan sistem informasi. Jurnal ini menampung artikel asli penelitian, artikel review yang meliputi, serta tidak terbatas pada : 1. Bidang Teori Komputasi dan system cerdas Teori Kompilasi Kecerdasan Buatan Jaringan Saraf Tiruan Algoritma Genetik Metode Formal Intelligent Agent Simulated Annealing Grafika Komputer Sistem Pakar 2. Bidang Teknologi jaringan dan Sistem Terdistribusi Wireless and Mobile Technology Remote Sensing Image and Signal Processing Multimedia Teknologi Web Data Center Cluster Computing Teknologi Jaringan Komputer dan Aplikasinya Distributed System Middleware Rekayasa Protokol Arsitektur Komputer Sistem Operasi 3. Bidang Sistem Informmasi dan Rekayasa Perangkat Lunak Rekayasa Perangkat Lunak Sistem Informasi Sistem Informasi Geografi Audit Sistem Informasi Analisis dan Metode Numerik Optimasi dan Teknologi Basis Data Basis Data Multimedia Interaksi Manusia dan Komputer Data Mining 4. Bidang Keamanan Sistem Informasi Kriptografi Keamanan Teknologi Informasi 5. Bidang E-Application e-goverment e-learning e-commerce e-health e-language e- education dan sejenisnya
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 7 Documents
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Search results for , issue "Vol. 4 No. 2 (2024): Desember 2024" : 7 Documents clear
PREDIKSI TINGKAT KELULUSAN MENGGUNAKAN K-MEANS PADA UNIVERSITAS XYZ Dede Brahma Arianto; Erfiana Julietta Sembiring
Journal of Information System and Computer Vol. 4 No. 2 (2024): Desember 2024
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jister.v4i2.1013

Abstract

On-time graduation rate is an important indicator in higher education. This study uses the K-Means Clustering algorithm to cluster students based on academic attributes, such as length of study, number of credits, Semester Achievement Index (IPS), and Cumulative Achievement Index (IPK). The dataset used consists of 4483 student data from the Informatics Study Program. The clustering results show three main groups: (1) high-achieving students with an average GPA of 3.77 and the shortest length of study, (2) students with stable performance (average GPA of 3.51), and (3) at-risk students with an average GPA of 3.20 and the longest length of study. Evaluation with Silhouette Score produces a value of 0.1972, indicating weak cluster separation, but providing insight into graduation patterns. This study is expected to help educational institutions develop data-based intervention strategies to improve student graduation rates.
PENERAPAN K-MEANS CLUSTERING PADA DATA PEMBAYARAN TAGIHAN KARTU KREDIT UNTUK MENGANALISIS POTENSI FRAUD Vilan Purnama; Dede Brahma Arianto
Journal of Information System and Computer Vol. 4 No. 2 (2024): Desember 2024
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jister.v4i2.1199

Abstract

Credit cards are non-cash payment tools that use cards issued by banks. Fraud in credit card payment transactions is a significant issue for the banking and financial industry. With the increasing number of digital transactions, early detection of potential fraud is essential to protect consumers and financial institutions. One of the techniques that can be used is clustering, which allows data to be grouped based on similar characteristics without requiring specific labels. This study aims to analyze potential fraud in credit card bill payments using the K-Means Clustering approach, with model evaluation results including a Silhouette Score of 0.5211 and a Davies-Bouldin Index (DBI) score of 0.8293. This research is expected to provide deeper insights into the use of K-Means Clustering for detecting potential fraud. The study is not limited to identifying fraud in bank data alone but can also be applied in various sectors that are vulnerable to unauthorized or suspicious transactions.
PREDIKSI JUMLAH PENGANGGURAN DI INDONESIA BERDASARKAN DATA TIME SERIES MENGGUNAKAN REGRESI LINEAR Apredo Suranta Singarimbun; Dede Brahma Arianto
Journal of Information System and Computer Vol. 4 No. 2 (2024): Desember 2024
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jister.v4i2.1200

Abstract

Unemployment is a very big problem. The purpose of this research is to predict the number of unemployed people based on the working-age population. To produce an accurate prediction, valid data and a long period of time are needed. This is a reference for forecasting the unemployment population based on the last 5 years. To do forecasting, a method is needed, namely the Linear Regression method. Linear regression is a statistical method that serves to test how far the relationship between the cause variable and the effect variable is. After forecasting, prediction data is obtained in 2025. Where the number of working-age population in 2025 is predicted to be 218.21 million people and the number of unemployed people is 6.77 million people.
APLIKASI UP-KATALOG BERBASIS DIGITAL DALAM BRANDING PRODUK SANTRI SMK ROUDLOTUL MUBTADIIN Ahmad Edi Yahya; Indra Kurniawan
Journal of Information System and Computer Vol. 4 No. 2 (2024): Desember 2024
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jister.v4i2.1210

Abstract

SMK Roudlotul Mubtadiin is a Vocational School under the auspices of the Roudlotul Mubtadiin Balekambang Foundation. SMK Roudlotul Mubtadiin has 6 Vocational Competencies and in each Vocational Competency, there is a Production Unit that has produced many products. Currently, SMK Roudlotul Mubtadiin still uses information media in the form of printed catalogs and through existing exhibition events in promoting and marketing products, this method often causes problems and is considered less effective and efficient in conveying information about products and consuming a lot of budget, therefore a digital-based product catalog application is needed to help solve the problems that exist, as well as increase competitiveness and also optimal service and provide detailed information on the products offered. The researcher designed a digital-based product catalog application in the form of a website using the prototype method which aims to provide input to the running system and produce a better system where the system is designed with the PHP programming language with the Laravel Framework and uses the MySQL Database. With the designed system, it will produce product catalog information and can make it easier for SMK Roudlotul Mubtadiin to introduce or brand the products that have been produced quickly and effectively.
ASISTEN DIGITAL CEPAT DAN PRAKTIS CHATBOT PMB MENGGUNANKAN ALGORITMA NEURAL NETWORK Sabrina, Dinta; Arina Zulfa; Heru Saputro; Alzena Dona Sabilla
Journal of Information System and Computer Vol. 4 No. 2 (2024): Desember 2024
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jister.v4i2.1216

Abstract

Demand for accurate information services, and responsiveness is increasing in the modern era, especially in the process of receiving new students. The limitations of human resources that provide information services in a direct way cause user delays and dissatisfaction. Therefore, an automatic solution that can provide efficient and effective information services, is the chatbot service (PMB) using AI to make it easier for prospective students and educational institutions to communicate. The study created a chatbot that could understand a better natural language by combining the neural convolutional network (CNN) and long short-term memory (LSTM) supported by embedding gloves. To ensure that the neural network's models can process text optimally, development processes involve important stages such as tokenization, padding, and the formation of the embedding matrix. Test results show that models have high training accuracy, but validation charts show overfitting, which is indicated by the big difference between losing training and losing validation. Embedding gloves, however, successfully enhance word representation and help people better understand the context of the text included. The CNN-LSTM PMB chatbot aims to provide a faster, more, relevant, and accurate service to prospective students
OPTIMALISASI PENJADWALAN KULIAH MENGGUNAKAN ALGORITMA GENETIKA UNTUK MENINGKATKAN EFISIENSI JADWAL PADA PROGRAM STUDI SISTEM INFORMASI UNISNU JEPARA Nuradira, Afrida Hilda; Muhammad Roiful Anam; Leni Amrita; Maulidya Zumrotul Izzati; Heru Saputro
Journal of Information System and Computer Vol. 4 No. 2 (2024): Desember 2024
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jister.v4i2.1220

Abstract

The scheduling process in higher education is a complex challenge because it involves time allocation, space management, and human resources. In the UNISNU Jepara Information Systems Study Program, problems such as schedule clashes, uneven distribution of lecturer workloads, and inefficient classroom utilization often occur due to the increasing number of students and variety of courses. This research proposes the application of genetic algorithm as an optimization solution due to its ability to handle problems with various constraints and produce near-optimal solutions through the process of selection, crossover, and mutation. This research includes three main stages: data collection, genetic algorithm implementation, and result evaluation. Data was obtained from academic administration documents, including class schedules, course instructors, and classroom capacity. The evaluation results show that the genetic algorithm is able to reduce schedule conflicts, improve lecturer time efficiency, and maximize the use of classrooms. In conclusion, the application of genetic algorithms not only solves technical problems in scheduling, but also contributes to the development of a modern and adaptive academic information system, supports more effective decision-making, and ensures a smoother teaching-learning process in a college environment.
EFISIENSI PENGELOLAAN PERSEDIAAN STOK MENGGUNAKAN METODE SAFETY STOCK DI KAKI NAGA JEPARA Putri, Natasya; Leni Amrita; Arina Zulfa; Danang Mahendra; Joko Minardi
Journal of Information System and Computer Vol. 4 No. 2 (2024): Desember 2024
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jister.v4i2.1221

Abstract

Inventory management is crucial for any business to ensure smooth operations and customer satisfaction. This study investigates the efficiency of inventory management using the Safety Stock method at Kaki Naga Jepara. The research aims to determine how this method can help maintain optimal inventory levels, reduce stockouts, and minimize carrying costs. Using a combination of quantitative data analysis and case study methodology, the results indicate that the Safety Stock method significantly improves inventory management efficiency by providing a buffer against demand variability and lead time fluctuations.

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