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Journal of Advances in Information Systems and Technology
Published by Universitas Negeri Semarang
ISSN : -     EISSN : 2715999X     DOI : https://doi.org/10.15294/jaist
Core Subject : Science,
Computer Science & IT
Journal of advances in Information Systems and Technology (JAIST) seeks to promote high quality research that is of interest to the international community.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 83 Documents
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User Experience Analysis of Satisfaction of Job Seekers (PENCAKER) in the City of Semarang Manpower Department in Using the SIKER Application Using the User Experience Questionnaire (UEQ) Method
Journal of Advances in Information Systems and Technology Vol 3 No 2 (2021): October
Publisher : Universitas Negeri Semarang

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Abstract

The Semarang City Manpower Service or also known as (DISNAKER) is a government agency that can foster and control the manpower sector. The Manpower Office has its website application, namely the work system (SIKER) which is an integrated employee recruitment information system from the Semarang city government, companies, and also the community or job seekers. This study aims to understand the level of comfort in terms of User Experience in using the SIKER application. Analysis of the SIKER website application by applying the User UEQ method approach given to 32 respondents who have used this website. The UEQ assessment focuses on six aspects, namely: efficiency, attractiveness, accuracy, clarity, novelty, and stimulation. The results obtained after conducting the analysis are that the average respondent gives below average results, at the User Experience level the efficiency item gets a sufficient value, on the clarity, accuracy, stimulation, attractiveness, and novelty scales it gets a value below the average. This tends to be the lack of seeker users with this SIKER application. In other words, the reason is that there are still many users who don't understand this application because this application is still relatively new.
Implementation of the FP-Growth Algorithm with RFID on the Monitoring System for Building Users in a Smart-Building Environment
Journal of Advances in Information Systems and Technology Vol 3 No 2 (2021): October
Publisher : Universitas Negeri Semarang

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Abstract

Internet of Things (IoT) technology is developing rapidly and is widely applied to assist all human needs. Radio Frequency Identification (RFID) is a very popular IoT device. One application is to limit employee access in the office. In this study, RFID devices are used to limit room access rights, after the building user performs an RFID scan, the data will be stored in the database. The stored data is then used to find patterns of employee activity using the FP-Growth algorithm. The FP-Growth algorithm runs by tracing the tree from bottom to top and getting frequent itemsets obtained from the FP Tree based on the stored data. This study indicates that the time required for the RFID device to scan to determine whether the user has access or not is 2000ms. The hardware communication time with the information system using the API is 262ms then proceeding from the software to the database is 239ms. In the implementation of the FP-Growth algorithm, the time required for the FP-Growth algorithm to process 100 activity data divided into 10 days is 324 ms. From the 100 activity data, it can be concluded that if the employee enters the server room, server room, guest room, and guest room, the probability of the employee entering the multimedia room is 1 (100%). Furthermore, if the employee enters the multimedia room, multimedia room, server room, employee room, and the guest room then the probability of the employee entering the data room is 0.1 (10%).
Multilayer Perceptron Optimization on Imbalanced Data Using SVM-SMOTE and One-Hot Encoding for Credit Card Default Prediction
Journal of Advances in Information Systems and Technology Vol 3 No 2 (2021): October
Publisher : Universitas Negeri Semarang

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Abstract

Credit risk assessment analysis by classifying potential users is an important process to reduce the occurrence of default users. The problems faced from the classification process using real-world datasets are imbalanced data that causes bias-to-majority in model training outcomes. These problems cause the algorithm to only focus on the majority class and ignore the minority class, even though both classes have the same important role. To overcome this problem, a combination of One-hot encoding (OHE) and SVM-Synthetic minority oversampling technique (SVM-SMOTE) techniques are used for the optimization process of the MLP classification algorithm. OHE is used to encode values categorical nominal and SVM-SMOTE for the oversampling. The results of the measurement of the ability of the model generated from the optimized MLP are then compared with the baseline using the AUC score. The data used is the default of credit card client dataset from Taiwan which has 30000 instances. The result of the highest AUC score of the MLP that has gone through optimization is 0.7184, an increase of 0.2179 compared to the baseline.
Optimization of Accuracy to Autism Spectrum Disorder Identification for Children Using Support Vector Machine and Correlation-based Feature Selection
Journal of Advances in Information Systems and Technology Vol 4 No 1 (2022): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i1.59453

Abstract

Autism is a developmental disorder that affects the function of the neurological system, which can have a negative impact on the sufferer’s quality of life. The ratio of people with autism is relatively high and tends to increase, based on WHO in 2013 the ratio of Indonesian children suffering from autism is 1:160 or more than 112.000. Data mining is the one of data processing techniques that works to find patterns and knowledge from big data. One of the data mining techniques is a classification that works to search for models that reveal and estimate previously unidentified classes. SVM is one of the classification algorithms that use data to find the optimal hyperplane. SVM has the advantage to work well on a dataset that cannot be linearly separated. The disadvantage is that it can be challenging to select parameters that are ideal and to determine which ones have an impact and which ones do not. To reduce attribute dimensions, CFS was provided as a feature selection to improve accuracy based on correlation values. The Autistic Spectrum Disorder Screening for Children Dataset from the UCI machine learning repository was used in this research to compare the accuracy of SVM and CFS. The result of this research is the SVM algorithm yields an accuracy rate of 94.91%. When the SVM algorithm is combined with CFS, the accuracy rate rises to 96.61%, representing an improvement in accuracy of 1.7% by using the 17 selected attributes.
Sentiment Analysis of Independent Campus Policy on Twitter Using Support Vector Machine and Naïve Bayes Classifier
Journal of Advances in Information Systems and Technology Vol 4 No 1 (2022): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i1.59501

Abstract

Merdeka Belajar Kampus Merdeka (MBKM) is a program that was inaugurated by the Ministry of Education and Culture in 2020 which emphasizes the independence and independence of learning. One of the social media that gives many opinions on this policy is Twitter. The sentiments written by the public about the independent campus policy can be analyzed and categorized as positive or negative sentiments as material for review. In this research, sentiment analysis on the independent campus policy was carried out with the support vector machine algorithm and naïve Bayes classifier. Sentiment analysis begins by crawling data on Twitter in the period from November 20, 2021 to December 19, 2021, with a total of 5980 data. Then preprocessing the data is carried out to normalize and clean the data before data classification is carried out. Data that has gone through preprocessing is then labeled using Vader. Furthermore, word vectorization was carried out with TF-IDF and data classification to test the accuracy of sentiment analysis with the support vector machine algorithm and naïve Bayes classifier. The test results for 20 times show that the highest level of accuracy is obtained by the support vector machine algorithm with an accuracy of 73.12%.
Detecting Hate Speech Tweets and Abusive Tweets In Indonesian Languange Using Random Forest and Support Vector Machine with Voting Classifier Technique
Journal of Advances in Information Systems and Technology Vol 4 No 1 (2022): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i1.59521

Abstract

The use of social media has become one of the main things in everyday life. This happens because the features provided make it easy for people to communicate and disseminate information. One of the social media used by many people is Twitter. the main feature of twitter is that its users can post posts that are termed tweets. There is a negative thing about the freedom to write a tweet, namely a tweet that does not contain things that harm other people or community. The problem that arises from this negative thing is to distinguish between hatespeech tweets and abusive tweets. Hate speech and abusive speech are often the same thing. These differences need to be considered because they can have a negative impact on social life. Sentiment analysis is used to distinguish the two things. Sentiment analysis is an implementation of natural language processing which is part of machine learning. The algorithms used in this research are Support Vector Machine, Random Forest, and Voting Classifier with soft voting type. The estimator for the Voting Classifier is the Support Vector Machine and Random Forest. TF-IDF and N-gram were used as feature extraction. The data used is a tweet dataset that has been labeled neutral, hate speech, and rude speech. Measurement of model accuracy is done by using confusion matrix. The highest accuracy was produced by a combination of Voting Classifier technique with TF-IDF feature extraction and the amount of N-gram was 1 gram, which was 82.57% accuracy.
Security Improvement of the 256-BIT AES Algorithm with Dynamic S-Box Based on Static Parameter as the Key for S-Box Formation
Journal of Advances in Information Systems and Technology Vol 4 No 1 (2022): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i1.59976

Abstract

Strong S-BOX is required for the usage of AES encryption and decryption process. Despite using strong S-BOX has better security, the usage of AES static S-BOX is something we can improve on. Symmetric encryption algorithm that uses block cipher is better if combined with make the S-BOX dynamic, but the problems occurred when we put totally random S-BOX into it because S-BOX has its own secure measurement. S-BOX that used in AES must have been tested having strong nonlinearity. In this research, we take collections of strong tested S-BOX to be used on AES encryption and decryption process. Because S-BOX that we used for encryption and decryption must be the same S-BOX, we added static parameters on both encryption and decryption process for the key in choosing which S-BOX we are used with pseudo random number generator (Pseudo RNG) for the algorithm. In practice, text input, hardware id’s, or other variables can be used as the static parameter we used on this process. Pseudo RNG will take the numbers of S-BOX-es we had for variable of maximum output with minimum is 1, so we can always get the index of the S-BOX chosen. The purpose of this research is to add complexity to both the algorithm and security with dynamize the S-BOX so we have more possibility output which is make it harder to be attacked. The test result in this research is also tested with SAC test showing the average of 0,499 which is better than the regular AES with 0,504 and the nonlinearity is the same as regular AES which is 112.
Increase Accuracy of Naïve Bayes Classifier Algorithm with K-Means Clustering for Prediction of Potential Blood Donors
Journal of Advances in Information Systems and Technology Vol 4 No 1 (2022): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i1.59977

Abstract

Branch of Computer Science knowledge is data mining. Data mining help people to processing a big and irregular data. In public health, data mining can be used to manage blood donors data. Blood donors is a proses to take some blood from volunteer then given to other people who need. One of the ways to fill up blood requirement in Indonesia is organize blood donors event regularly, but some people didn’t routine give they blood. Solution of that problems, a system needed to predict future blood donor behavior. Recency, Frequency, Monetary, Time, Churn Probability (RFMTC) is a modification from Recency of purchase, Frequency of purchase, and Monetary value of purchase (RFM) that used to predict a blood donors behavior. In this research, implemented a Naïve Bayes Classifier to blood donors classification. The classification result with 224 data from RFMTC dataset is 78.13% accuracy. Combination Naïve Bayes Classifier algorithm with K-Means Clustering increase accuracy to 80.80%.
Increasing Accuracy of Heart Disease Classification on C4.5 Algorithm Based on Information Gain Ratio and Particle Swarm Optimization Using Adaboost Ensemble
Journal of Advances in Information Systems and Technology Vol 4 No 1 (2022): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i1.60102

Abstract

The heart is a vital organ of the body that has an important role in the process of blood flow. Data mining is a process to get very useful information from a very large data warehouse to facilitate the decision-making process. In the data mining process, the first stage performs data processing called preprocessing by handling data formatting. Then, the feature selection stage is carried out using the Information Gain Ratio and Particle Swarm Optimization algorithms to find the best attributes. Then the Adaboost Ensemble was applied to optimize the accuracy results. Next, it is done by classifying the dataset. The algorithm used for classification is the C4.5 algorithm. Based on the research that has been done, using the k-fold = 5 model test with three trials, the best accuracy results are obtained for the C4.5 algorithm without feature selection and the Adaboost Ensemble produces an accuracy rate of 95.87%, while the C4.5 algorithm with Information Gain Ratio and Particle Swarm Optimization then applying the Adaboost Ensemble produces an accuracy rate of 96.68%. This shows that the feature selection algorithm, namely, Information Gain Ratio and Particle Swarm Optimization by applying the Adaboost Ensemble is considered to be able to improve the performance of the C4.5 classification algorithm.
Improving the Accuracy of Multinomial Naïve-Bayes Algorithm with Adaptive Boosting Using Information Gain for Classification of Movie Reviews Sentiment Analysis
Journal of Advances in Information Systems and Technology Vol 4 No 1 (2022): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v4i1.60267

Abstract

Movie is a means of delivering information as well as entertainment that can be enjoyed by all people through various platforms such as the internet, cinema, and television. Sentiment analysis is needed to analyze positive and negative comments from movie lovers, these comments come from many circles and from various sources, one of which is IMDb (Internet Movie Database). The naïve-Bayes multinomial classification algorithm has been proposed and used by many researchers in the case of sentiment analysis. The ensemble adaptive boosting algorithm is used as a boosting algorithm to improve accuracy in naïve-Bayes and information gain multinomial classification models. The accuracy test on the model is carried out using the python programming language. The accuracy results obtained when applying the naïve-Bayes multinomial classification algorithm is 84.82%, then an accuracy of 85.24% is obtained when implementing the informationgain feature selection on the naïve-Bayes multinomial classification algorithm. The highest accuracy result of 87.87% was obtained when implementing the naïve-Bayes multinomial classification algorithm with adaptive boosting and information gain selection features.

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