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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 7 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2023): September 2023" : 7 Documents clear
Diagnosis of Heart Disease Using Optimized Naïve Bayes Algorithm with Particle Swarm Optimization and Gain Ratio Meidina, Anisa; Abidin, Zaenal
Recursive Journal of Informatics Vol. 1 No. 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/b1xr8v79

Abstract

Purpose: This study aims to apply feature selection particle swarm optimization (PSO) and gain ratio to the naïve Bayes algorithm and gauging the level of accuracy before and after applying PSO feature selection and gain ratio to the naïve Bayes algorithm in the diagnosis of heart disease.Methods/Study design/approach: Data collection is done by using taking the Cleveland dataset obtained from the UCI machine learning repository. The data used in this study were 303 samples. The data is processed using the preprocessing stage. The naïve Bayes algorithm is used for a classifier, while PSO and gain ratio for feature selection.Result/Findings: The results of the study revealed that the classification accuracy of the naïve Bayes algorithm without the application of feature selection in the Cleveland dataset is 86.88%, while the results of the classification accuracy of the naïve Bayes algorithm after applying PSO and gain ratio in the Cleveland dataset is 93.44%. Application of PSO and gain ratio as feature selection algorithms can improve classification accuracy by 6.56%.Novelty/Originality/Value: This study combines the PSO feature selection and gain ratio on the naïve Bayes algorithm using the Cleveland dataset. The research model that was carried out was enriched by carrying out the preprocessing stages, namely data cleaning, changing the number of class labels, data normalization, and data discretization. This study shows that using a combination of the PSO feature selection algorithm and the gain ratio gives better accuracy to the naïve Bayes algorithm in diagnosing heart disease.
Stock Return Prediction Using Voting Regressor Ensemble Learning Arrohman, Ramadhan Ridho; Arifudin, Riza
Recursive Journal of Informatics Vol. 1 No. 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ntg4dt04

Abstract

Abstract. The value of return on stock prices is often used in predicting profits in the process of buying and selling shares based on the calculation of the return on investment. The calculation of the value of return on stock prices can be predicted automatically at certain periods, both weekly and daily Purpose: The problem faced is determining a good algorithm for making predictions due to fluctuating data on stock prices making it difficult to predict. Methods: The stages carried out by the researcher include the data preprocessing stage and then proceed to the Exploratory Data Analysis (EDA) stage to get a pattern from the data, followed by the modeling stage on the data. This research was developed using the Python programming language where the models used to make predictions can be obtained in real-time. Result: The results obtained in this study show that the Voting Regressor has the best model with an error rate of 0.032523 using Root Mean Square Error (RMSE). The results of this study can be further developed to automatically predict stock return values in the future.
Machine Learning Model Using Extreme Gradient Boosting (XGBoost) Feature Importance and Light Gradient Boosting Machine (LightGBM) to Improve Accurate Prediction of Bankruptcy Syafei, Risma Moulidya; Efrilianda, Devi Ajeng
Recursive Journal of Informatics Vol. 1 No. 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edp9qp23

Abstract

Abstract. Humans have limitations in processing and analyzing large amounts of data in a short time, including in terms of analyzing bankruptcy data. Bankruptcy data is one of the data that has complex information, so it requires technology that can assist in the process of analyzing and processing data more quickly and efficiently. Data science technology enables data processing and analysis on a large scale, using parallel processing techniques. Parallel processing can be implemented in machine learning models. Purpose: Using parallel processing techniques, data science technologies enable data processing and analysis at scale. Parallel processing can be implemented in machine learning models. Therefore, this study aims to implement a machine learning model using the Light Gradient Boosting Machine (LightGBM) classification algorithm which is optimized using Extreme Gradient Boosting (XGBoost) Feature Importance to increase the accuracy of bankruptcy prediction. Methods/Study design/approach: Bankruptcy prediction is carried out by applying LightGBM as a classification model and optimized using the XGBoost algorithm as a Feature Importance technique to improve model accuracy. the dataset used is the Taiwanese Bankruptcy dataset collected from the Taiwan Economic Journal for 1999 to 2009 and has 6,819 data. Taiwanese Bankruptcy is unbalanced data, so this study applies random oversampling. Result/Findings: The results obtained after going through the model testing process using the confusion matrix obtained an accuracy of the performance of LightGBM+XGBoost Feature Importance of 99.227%. Novelty/Originality/Value: So it can be concluded that the implementation of XGBoost Feature Importance can be used to improve LightGBM's performance in bankruptcy prediction.
Comparison of Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) Algorithms for Diabetes Classification Azzahrah, Diah Siti Fatimah; Alamsyah, Alamsyah
Recursive Journal of Informatics Vol. 1 No. 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/c363d161

Abstract

Purpose: This study aims to compare algorithms to determine the accuracy of the algorithm and determine the speed of the algorithm used for diabetes classification. Methods: There are two algorithms used in this study, namely Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN). The data used is the Pima Indians Diabetes Database. The data contains 768 data with 8 attributes and 1 target class, namely 0 for no diabetes and 1 for diabetes. The dataset has been divided into 80% training data and 20% testing data. Result: Accuracy is obtained after implementing k-fold cross validation with a value of k = 4. The accuracy results show that the k-Nearest Neighbor algorithm is superior and has better quickness compared to the Probabilistic Neural Network. The k-Nearest Neighbor algorithm obtains an accuracy of 74.6% for all features and 78.1% for four features Novelty: The novelty of this paper is optimizing and improving accuracy which is implemented with by focusing on data preprocessing, feature selection and k-fold cross validation in the classification algorithm
Implementation of Convolutional Neural Network Algorithm Using Vgg-16 Architecture for Image Classification in Facial Images Hapsari, Renita Arianti; Purwinarko, Aji
Recursive Journal of Informatics Vol. 1 No. 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/4mqqh981

Abstract

Abstract: Face Recognition has now become a technology capable of recognizing facial patterns, facial image recognition is also used in various applications, for example in biological data recognition applications, digital image and video search, room security, and other applications. Purpose: This study aims to find out how the implementation of the CNN method with the VGG-16 architecture affects the classification of gender in facial images and how it affects the results. Methods/Study design/approach: In this study, we use the CNN method for data processing and build the program and use VGG-16 Architecture to build the model, then the tensorflow library for calling the required features such as when optimizing or for statistical plots and using the Confusion Matrix to determine the level of accuracy obtained. The desired results in this study are accuracy, precision, recall, and Fscore. Result/Findings: Classifying facial images using CNN with VGG-16 architecture provides an accuracy rate of 94%. From the results of this study it can be concluded that the model with the best accuracy is at epoch 20 compared to epoch 60, epoch 80, and epoch 100 which have previously been tested. Novelty/Originality/Value: The level of accuracy resulting from the implementation of the CNN method using the VGG-16 Architecture for image classification in facial images is quite good, resulting in an accuracy of 94%. Accuracy results were obtained from tests carried out by comparing several epoch values to produce the best accuracy of 94% using epoch 20.
Sentiment Analysist of the TPKS Law on Twitter Using InSet Lexicon with Multinomial Naïve Bayes and Support Vector Machine Based on Soft Voting Aisy, Salsabila Rahadatul; Prasetiyo, Budi
Recursive Journal of Informatics Vol. 1 No. 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/zbxqcc36

Abstract

Abstract. The Indonesian Sexual Violence Law (TPKS Law) is a law that regulates forms of sexual violence. The TPKS Law reaped pros and cons in the drafting process and was officially ratified on April 12th, 2022. However, after being ratified, pros and cons can still be found and supervision is needed over the implementation of the law. Purpose: This study was conducted to identify the application and accuracy of soft voting on multinomial naïve Bayes and support vector machine algorithm, also to find out public opinion on the TPKS Law as a support tool in evaluating the law. Methods/Study design/approach: The method used is InSet lexicon for labeling with the soft voting classification method on the multinomial naive Bayes and support vector machine algorithm. Result/Findings: The accuracy obtained by applying 10 k-fold cross validation in soft voting is 84.31%, which uses a weight of 1:3 for multinomial naive Bayes and support vector machines. Soft voting obtains better accuracy than its standalone predictor, and also works well for sentiment analysis of the TPKS Law. Novelty/Originality/Value: This study using two combined lexicons (Colloquial Indonesian lexicon and the InaNLP formalization dictionary) in normalization process and using InSet lexicon as automatic labeling for sentiment analysis on TPKS Law.
Image classification of Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning Adityatama, Resta; Putra, Anggyi Trisnawan
Recursive Journal of Informatics Vol. 1 No. 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/xnw7v590

Abstract

Abstract. The development of information technology in facial recognition is influenced by a faster and more accurate authentication system. This allows the computer system to identify a person's face. Purpose: Similar to fingerprints and the retina of the human eye, each person's face has a different shape and contour. Since it is known that the human face provides a lot of information, as well as topics that attract attention make it studied intensively. Methods/Study design/approach: Several studies examining information from human faces are facial recognition. One of the approaches used to recognize facial imagery is through the use of a Convolutional Neural Network (CNN). CNN is a method in the field of Deep Learning that can be used to recognize and classify objects in digital images. In this study, the method used to implement facial image classification is the Xception architecture CNN algorithm with a transfer learning approach. Result/Findings: The dataset used in this study was obtained from Kaggle, namely the Face Shape Dataset which contains 5000 data. After testing, an accuracy rate of 96.2% was obtained in the training process and 81.125% in the validation process. This study also uses new data to test the model that has been made, and the results show an accuracy rate of 85.1% in classifying facial imagery. Novelty/Originality/Value: Therefore, it can be said that the model created in this study has the ability to classify images of facial shapes Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning.

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