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Recursive Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : -     EISSN : 29866588     DOI : https://doi.org/10.15294/rji
Core Subject : Science,
Computer Science & IT
Recursive Journal of Informatics published by the Department of Computer Science, Universitas Negeri Semarang, a journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. We hereby invite friends to post articles and citation articles in our journals. We appreciate it if you would like to submit your paper for publication in RJI. The RJI publication period is carried out 2 periods in a year, namely in March and September.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1 (2023): March 2023" : 5 Documents clear
Implementation Data Mining with Naive Bayes Classifier Method and Laplace Smoothing to Predict Students Learning Results Pradana, Dany; Sugiharti, Endang
Recursive Journal of Informatics Vol. 1 No. 1 (2023): March 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/hjs1fd70

Abstract

Abstract. The application of information technology in the field of education produces big data. It retains information that can be treated as useful. Having data mining, can be used to model highly useful student performance for educators performing corrective actions against weak students. Purpose: The study was to identify the application and accuracy algorithm Naive Bayes Classifier to predict students' study results. Methods: The prediction system for student learning outcomes was built using the Naive Bayes Classifier and Laplace Smoothing methods using a combination of two Information Gain and Chi Square feature selections. The experiment was carried out 2 times using different dataset comparisons. Result: In the first experiment using a dataset of 80:20, the accuracy Naive Bayes Classifier method with Laplace Smoothing and without Laplace Smoothing showed the same results as 94.937%. On the second experiment to equate dataset 60:40 results of the Naive Bayes Classifier accurate method without Laplace Smoothing only 86.076%, then score a 91.772% accuracy using the Laplace Smoothing. The improvement is caused by a probability of zero that can be worked out with Laplace Smoothing. Novelty: The selection feature process is very important in the classification process. Thus, in this study, information gain and chi square double selections of such features as information gain and so promote accuracy.
Implementation of Synthetic Minority Oversampling Technique and Two-phase Mutation Grey Wolf Optimization on Early Diagnosis of Diabetes using K-Nearest Neighbors Arsyadani, Fathan; Purwinarko, Aji
Recursive Journal of Informatics Vol. 1 No. 1 (2023): March 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/s6qsz079

Abstract

Abstract. Diabetes is a disease attacking the endocrine system characterized by high blood sugar levels. International Diabetes Federation (IDF) estimates that there were 451 million people with diabetes globally in 2017. Without treatment, this number is expected to rise to 693 million by 2045. One method for preventing increases in the number of diabetics is by early diagnosis. In an era where technology has developed rapidly, early diagnosis can be made with the machine learning method using classification. In this study, we propose a diabetes classification using K-Nearest Neighbors (KNN). Before classifying the data, we select the best feature subset from the dataset using Two-phase Mutation Grey Wolf Optimization (TMGWO) and balance the training data using Synthetic Minority Oversampling Technique (SMOTE). After dividing the dataset into training and testing sets using 10-fold cross validation, we reached an accuracy of 98.85% using the proposed method. Purpose: This study aims to understand how to apply TMGWO and SMOTE to classify the early stage diabetes risk prediction dataset using KNN and how it affects the results. Methods/Study design/approach: In this study, we use TMGWO to make a feature selection on the dataset, K-fold cross validation to split the dataset into training and testing sets, SMOTE to balance the training data, and KNN to perform the classification. The desired results in this study are accuracy, precision, recall, and f1-score. Result/Findings: Performing classification using KNN with only features selected by TMGWO and balancing the training data using SMOTE gives an accuracy rate of 98.85%. From the results of this research, it can be concluded that the proposed algorithm can give higher accuracy compared to previous studies. Novelty/Originality/Value: Implementing TMGWO to perform feature selection so the model can perform classification with fewer features and implementing SMOTE to balance the training data so the model can better classify the minority class. By doing classification using fewer features, the model can perform classification with a shorter computational time compared to using all features in the dataset.
C4.5 Algorithm Optimization and Support Vector Machine by Applying Particle Swarm Optimization for Chronic Kidney Disease Diagnosis Ariyanti, Lisa; Alamsyah, Alamsyah
Recursive Journal of Informatics Vol. 1 No. 1 (2023): March 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/cdnb8v88

Abstract

Abstract. Kidneys are one of the organs of the body that have a very important function in life. The main function of the kidneys is to excrete metabolic waste products. Chronic kidney disease is a result of the gradual loss of kidney function. Chronic kidney disease occurs when the kidneys are unable to maintain an internal environment consistent with life and the restoration of useless functions. Data mining is one of the fastest growing technologies in biomedical science and research. Purpose: In the field of medicine, data mining can improve hospital information management and telemedicine development. In the first stage of data mining process, data processing is done with pre-processing by handling missing values ​​and data transformation. Then, the feature selection stage is carried out using the Particle Swarm Optimization algorithm to find the best attributes. Next, it is done by classifying the dataset. Methods/Study design/approach: The algorithm used for classification is the C4.5 Algorithm and the Support Vector Machine. Both classifications are known as algorithms that have a fairly good level of accuracy. This study uses the chronic kidney disease dataset from the UCI Machine Learning Repository. Result/Findings: This research increases the accuracy by 100% for the C4.5 Algorithm and 98.75% for the Support Vector Machine by using 24 attributes and 1 class attribute. Novelty/Originality/Value: The purpose of this study was to determine the level of accuracy of the comparison between the C4.5 Algorithm and the Support Vector Machine after applying the Particle Swarm Optimization algorithm.
Prediction of Student Graduation Predicts using Hybrid 2D Convolutional Neural Network and Synthetic Minority Over-Sampling Technique Wibisono, David Leandro; Abidin, Zaenal
Recursive Journal of Informatics Vol. 1 No. 1 (2023): March 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rwnrr791

Abstract

Abstract. With the rapid growth of technology, educational institutions are constantly looking for ways to improve their services and enhance student performance. One of the significant challenges in higher education is predicting the graduation outcome of students. Predicting student graduation can help educators and academic advisors to provide early intervention and support to students who may be at risk of not graduating on time. In this paper, we propose a hybrid 2D convolutional neural network (CNN) and synthetic minority over-sampling technique (SMOTE) to predict the graduation outcome of students. Purpose: Knowing the results and how the Hybrid 2D Convolutional Neural Network (CNN) and Synthetic Minority Over-sampling Technique (SMOTE) algorithms work in predicting student graduation predicates. This algorithm uses a dataset based on family background variables and academic data. Methods/Study design/approach: This study uses the Hybrid 2D CNN algorithm for the classification process and SMOTE for the minority class over-sampling. Result/Findings: The prediction accuracy of the model using SMOTE is 96.31%. Meanwhile, the model that does not use SMOTE obtains an accuracy of 95.32%. Novelty/Originality/Value: This research shows that the use of a Hybrid 2D CNN algorithm with SMOTE gives better accuracy than without using SMOTE. The dataset used also proves that family background and student academic data can be used as a reference for predicting student graduation predicates.
Adaptive Difficulty in Earthquake Mitigation Game Using Fuzzy Mamdani Ardiadna, Rika Jane; Setiawan, Abas
Recursive Journal of Informatics Vol. 1 No. 1 (2023): March 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/byjq7951

Abstract

Abstract. Earthquake disasters cause a lot of casualties. Therefore, needs to be education on earthquake disaster mitigation to minimize losses. In addition to counseling and teaching in schools, mitigation education can also be through games. Some education games for earthquake disaster mitigation have circulated quite a lot but have disadvantages, namely the difficulty level that hasn't been adaptive. A game requires an adaptive level of difficulty that can adjust between the ability and playing experience of the player with the level of difficulty so that players do not feel bored or frustrated.Purpose: This study aims to provide earthquake disaster mitigation education and discuss making the level of difficulty in the game adaptive to suit the abilities and experience of the player.Method: From the research carried out by applying the Mamdani Fuzzy Logic, the game's difficulty level for each player becomes more adaptive or different for each player according to the ability and experience of each player in the previous stage measured from 6 input parameters.Result: The level of difficulty that is obtained becomes adaptive. It changes according to conditions or is adjusted based on the player's ability. It is from the playtesting experiment conducted on 20 players. The minimum difficulty level's score is five, and the difficulty level's score is 28.36.Novelty: This paper's purpose is an educational game for earthquake mitigation with the feature of adaptive level based on fuzzy Mamdani.

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