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JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2686228X     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Artikel yang dimuat melalui proses Blind Review oleh Jurnal JOSH, dengan mempertimbangkan antara lain: terpenuhinya persyaratan baku publikasi jurnal, metodologi riset yang digunakan, dan signifikansi kontribusi hasil riset terhadap pengembangan keilmuan bidang teknologi dan informasi. Fokus Journal of Information System Research (JOSH)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 774 Documents
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A Multi-Criteria Decision-Making Approach Using MOORA for Evaluating Job Search Platforms Rogayah, Rogayah; Kusumawijaya, Ike Putri; Ningrum, Witta Listiya
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9117

Abstract

With the development of technology today, it can make it easier for many people to carry out various activities. For example, in searching for work using various job search websites. A job search website is one of the sites where there is an online platform opportunity that allows users to search for and apply for jobs that suit their qualifications and interests. This job search website also has the main aim of helping fresh graduates who have no experience in the field of work. However, this job search website has both positive and negative impacts. Therefore, applicants must first seek detailed information before applying for work. And you also have to look for a truly official platform to avoid fraud. The problems above can make applicants face difficulties in determining which is the best platform to meet their needs. So, to resolve the problems that occur in selecting a job search website, criteria data is really needed, including ease of use, ease of application, detailed information, speed of access, security, and number of site uses. So, a decision support system (DSS) is very necessary as a problem solving tool in selection of the best job search websites. In this research, the author applies the MOORA method to search for final results accurately and is assisted by using the ROC weighting method. Therefore, the one that gets the highest ranking in selecting the best job search website is an alternative. with value.
Decision Support System for Selecting Outstanding Students University using the Method Multi Attribute Utility Theory (MAUT) Sallaby, Achmad Fikri
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9118

Abstract

The Selection of Outstanding Students describes the activities that have been carried out by the Higher Education since 2004. These activities include sorting and distributing awards to students who have achieved great achievements, both curricular and extracurricular achievements. Therefore, in each academy it is necessary to identify students who are able to do both and be given awards as outstanding students, namely by conducting mawapres selection at the high academic level. In the higher academies there is a program where students want to be selected and are also entitled to become outstanding students and are also given awards each year. In the process of achieving students at Budi Darma University, Medan, it is still being tried manually. This allows errors in the evaluation and processing of student data information. For this reason, it is necessary to make a decision support system that can help determine achievements and can take into account all the criteria. the predicate of outstanding students are cases that require an assessment process. So that in solving something that is needed a multi-process decision support system. The trick used in this research is the MAUT procedure. Determine the determination of students to overcome problems that require part of the evaluation process. So that the solution requires a multi-process decision support system. The procedure used in this research is the MAUT method. The MAUT method is to obtain a decision support information system for companies with a large level of data accuracy, so that by using this procedure it is expected to determine student achievement and process information more effectively. With this MAUT method creates the most preference as the best alternative results in the initial ranking which lies in the alternative with the highest preference level of 0.5419 on behalf of Khamsah Anugrah.
Implementasi Arsitektur CNN untuk Klasifikasi dan Identifikasi Penyakit Daun Tanaman Padi Hidayat, Helmy Purnomo; Imaduddin, Helmi
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9192

Abstract

Leaf diseases in rice plants are a serious threat that can reduce productivity and crop quality, thus directly impacting national food security. Farmers still face various obstacles in identifying diseases conventionally, especially in the early stages of infection which can potentially cause delays in treatment. This study aims to develop a Deep Learning-based rice leaf disease classification system by building a Convolutional Neural Network (CNN) architecture independently (from scratch). The dataset used includes 18,445 rice leaf images categorized into ten disease classes, with an allocation of 70% training data, 15% validation data, and 15% test data. All images were resized to 224×224 pixels before being input into the model. Data augmentation was applied to prevent overfitting by rotation (20°), horizontal and vertical shifts (15%), shear (15%), zoom (15%), horizontal flip, and brightness variations (0.8-1.2). The CNN model was designed using five convolution blocks with cascaded filter configurations (32, 64, 128, 256, 512) using a 3×3 kernel and equipped with Batch Normalization, MaxPooling2D, and Dropout. The model was compiled using the Adam optimizer with a learning rate of 0.0001, a categorical cross-entropy loss function, and ReLU and Softmax activation functions. The training process used a batch size of 8 equipped with EarlyStopping and ReduceLROnPlateau callbacks. The experimental results showed that training with 75 epochs produced optimal performance with an accuracy of 97.91%, a precision of 0.9792, a recall of 0.9791, and an F1-score of 0.9790 on the test data. Evaluation per class showed that the Bacterial Leaf Blight and Tungro classes achieved perfect accuracy (100%), while Leaf Blast had the lowest accuracy (93.8%) due to its visual similarity to Brown Spot. The best model was implemented into a web system called Pariku using the Flask framework, which provides automatic diagnosis features, prediction confidence levels, and Integrated Pest Management (IPM)-based treatment recommendations.
Implementasi Metode Random Forest Untuk Memprediksi Jumlah Penjualan Gorden Berdasarkan Data Historis Wijanarko, Amiladito Adhyatma; Imaduddin, Helmi
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9194

Abstract

The rapid development of information technology has encouraged companies, including Tova Gorden, a small business engaged in curtain sales, to adopt technology to improve operational efficiency and competitiveness. Tova Gorden often faces obstacles in fulfilling orders, especially when demand suddenly increases, which is exacerbated by limited stock, raw material difficulties (such as smokers), fabric pre-order systems, and time-consuming production processes. Determining stock that is still based on employee estimates often leads to inefficiencies in the form of shortages or excesses of goods. This condition highlights the urgent need for an accurate prediction system to optimize inventory management. This study aims to implement and test the performance of the Random Forest algorithm, which is an ensemble learning method, to predict the number of curtain sales based on historical sales data. The collected data includes historical information related to curtain sales, including sales weeks, curtain motifs, and sales volumes. Unlike previous studies that generally use Linear Regression and focus on products with stable sales patterns, this study applies Random Forest to address more fluctuating curtain demand patterns. This research method includes several stages, namely Data Collection, Exploratory Data Analysis (EDA), Data Preprocessing, Data Splitting (70% training, 15% validation, 15% testing), Modeling with Random Forest, Evaluation, and Deployment. The evaluation results show that the model has excellent performance, with a coefficient of determination (R²) value of 97.83% on training data, 93.72% on validation data, and 96.64% on test data. Furthermore, the model is integrated into a web-based system using the Flask framework. This system is equipped with data upload features, prediction processes and curtain category grouping, and presentation of model evaluation results.

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