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JAIS (Journal of Applied Intelligent System)
Published by Universitas Dian Nuswantoro
ISSN : 25020493     EISSN : 25029401     DOI : -
Core Subject :
Journal of Applied Intelligent System (JAIS) is published by LPPM Universitas Dian Nuswantoro Semarang in collaboration with CORIS and IndoCEISS, that focuses on research in Intelligent System. Topics of interest include, but are not limited to: Biometric, image processing, computer vision, knowledge discovery in database, information retrieval, computational intelligence, fuzzy logic, signal processing, speech recognition, speech synthesis, natural language processing, data mining, adaptive game AI.
Arjuna Subject : -
Articles 6 Documents
 1 
Search results for , issue "Vol 6, No 1 (2021): Journal of Applied Intelligent System" : 6 Documents clear
Classification of Student Aspiration Using Naïve Bayes Classifier Ifan Rizqa; Christy Atika Sari; Mohamed Doheir
Journal of Applied Intelligent System Vol 6, No 1 (2021): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v6i1.4459

Abstract

Students aspiration are various demands from the student that packed in creative idea to propose changing process of a thing. Mostly, aspiration delivered in complaints and expectation. Aspiration is used for evaluating the laxity and early detection in university quality system for the better. This activity took place in Dian Nuswantoro University, and Student Representative Council (SRC) is the unit to manage the students aspiration. Aspiration is obtained through predetermined mechanism such as manual questionnaire distribution and or using google form. The provided questionnaire requires student to fill the content according to the provided aspiration categories. However, the problem is sometimes the student choose the wrong category according to the content. Therefore it is needed to create an application that can classified the students aspiration automatically. Document text classification become the best way to determine the category based on the content of the students aspiration. Naïve bayes classifier method is used because it is capable to produce high accuracy. With 1000 data training document of each category, "facilities and infrastructure" (facilities), "lecturers" (attitudes, teaching methods, material delivered), "staffing and the academic system"(attitudes, ways of working, providing information), and "suggestions and feedback". This experiment achieved 90.20% accuracy. It can be said that this method is worth to implement in this research.
Data Mining Applications for Violence Pattern Analysis with FP-Growth Algorithm Junta Zeniarja; Debrina Luna Arghata Mangkawa; Abu Salam
Journal of Applied Intelligent System Vol 6, No 1 (2021): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v6i1.4444

Abstract

Violence is a crime that is one of the problems the principal experienced by each country. Violence can be interpreted as a behavior that causes harm to someone. According to the results of DP3AKB research in Central Java Province in 2017, there are less many than 200 people in Central Java province experienced acts of violence. By because of the many acts of violence that occur in various forms of violence, it requires definite information about the form of violence that happens most often, in obtaining that information Data mining techniques are needed by using the FP-Growth algorithm. The application of the FP-Growth algorithm to produce form association patterns violence. Hardness data is 420 data, the best 7 rules have been obtained with min value support 50% and min value support 60%. On the best rule results have given a recommendation (solution) so that the DP3AKB can handle the problem of violence well and on target.
Sentiment Analysis on Indonesia Twitter Data Using Naïve Bayes and K-Means Method Ajib Susanto; Muhammad Atho’il Maula; Ibnu Utomo Wahyu Mulyono; Md Kamruzzaman Sarker
Journal of Applied Intelligent System Vol 6, No 1 (2021): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v6i1.4465

Abstract

This study focuses on the analysis of sentiments on Indonesian twitter data. Twitter data on Indonesian simultaneous pilkada used to get its sentiments using Naïve Bayes Classifier method as a method of classification and K-means method to get Label on the data train process. Combining the two methods is expected to get high accuracy results. The results obtained from the research shows a pretty good accuracy of 74.5%.
Comparation of Dice Similarity and Jaccard Coefficience Against Winnowing Algorithm For Similarity Detection of Indonesian Text Documents Santi Purwaningrum; Agus Susanto; Nur Wachid Adi Prasetya
Journal of Applied Intelligent System Vol 6, No 1 (2021): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v6i1.4453

Abstract

Plagiarism is the act of imitating and quoting and even copying or acknowledging other people's work as one's own work. Plagiarism is currently growing rapidly, especially in the world of education. So that plagiarism detection is needed to prevent plagiarism from growing rapidly. In response to this, this paper intends to conduct research that compares the dice similarity and the jaccard coefficient to find the best document similarity value level against the Winnowing algorithm which functions to find the fingerprint value of each document. The test results show that the winnowing algorithm is quite good at using the dice similarity level with the results of an average similarity value of 71.17615%  than testing using jaccard coefficient with the resulting value 35,58837%.
Keyphrase Extraction on Covid-19 Tweets Based on Doc2Vec and YAKE Fahri Firdausillah; Erika Devi Udayanti
Journal of Applied Intelligent System Vol 6, No 1 (2021): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v6i1.4454

Abstract

Keyword and keyphrase extraction are one of the initial foundations for performing several text processing operations such as summarization and document clustering. YAKE is one of the techniques used for unsupervised and independent keyphrase extraction, it does not require a corpus for linguistic tools such as NER and POS-tag. However, the use of YAKE in microblogging documents such as Twitter often results in a keyphrase that is less representative because of the lack of words used for ranking. This paper offers a solution to this problem by looking for similar tweets in the keyphrase extraction process using Doc2Vec so that the number of words used in the YAKE ranking process can be greater. Covid-19 tweets related are used as dataset as the topic is currently widely discussed on social media to prove that the proposed approach could improve keyphrase extraction performance
Application of the K-Nearest Neighbors (K-NN) Algorithm for Classification of Heart Failure Ryan Yunus; Uli Ulfa; Melinna Dwi Safitri
Journal of Applied Intelligent System Vol 6, No 1 (2021): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v6i1.4513

Abstract

Heart failure is a type of disease that has the largest number of patients in the world. Based on information from the data center, there were 229,696 people with heart failure in 2013. Lack of public knowledge about what indications of a person having heart failure make the main cause not handled properly by heart failure patients. In this study, data classification was carried out using KNN algorithm because it has a simple calculation and has a fast time. This study only uses 12 attributes, while the previous study compared 6 algorithms with 13 attributes from 299 data. The highest algorithm with 94.31% accuracy by Random Forest while KNN had an accuracy rate of 86.95% with the same data. In this study, the accuracy of the sample data was compared between 20 data and 299 total data. Both of them have different accuracy. 20 sample data has an accuracy rate of 89.29% while 299 data has an accuracy rate of 96.66%.

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