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All Journal IJISTECH (International Journal Of Information System & Technology) Journal of Research and Investigation in Education Journal of Economics and Management Scienties
Khairunnisa, K
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science Mathematics Social Sciences Other

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Diagnosis of Victims of Bullying Behaviour Using Bayes Method Hasugian, Abdul Halim; Furqan, Mhd.; Khairunnisa, K
IJISTECH (International Journal of Information System & Technology) Vol 3, No 2 (2020): May
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (889.527 KB) | DOI: 10.30645/ijistech.v3i2.61

Abstract

Victims of bullying behavior in the first high school students are still going on and unresolved. Victims of bullying behavior that is not easily visible to the naked eye and the lack of knowledge about bullying are problems in resolving the problem. This research is to create an expert system that can diagnose victims of bullying behavior based on the symptoms suffered by the victims of bullying to address the problems faced during this time. The Bayes method describes the relationship between the probability of A with event B has occurred. The probability of event B on the condition of event A has occurred. The occurrence of an event based on the influence gained from the observation result, Like bullying symptoms that occur in victims of bullying behavior, the Bayes method will calculate the probability and generated types of bullying experienced by students based on the knowledge that is in the can of an expert and made into an application.
Optimalisasi Penentuan Kriteria Siswa Kelas Unggul dengan Metode Simple Additive Weighting Khairunnisa, K
Journal of Research and Investigation in Education Volume 1, No. 1, April 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (206.03 KB) | DOI: 10.37034/residu.v1i1.9

Abstract

Education has an important role that is very decisive, not only for the development and realization of individuals but also for the development of the nation and state. This relates to the same quality of education and must be provided to Indonesian citizens, both normally and differently. The process of class division every new academic year is usually still carried out conventionally, namely by means of the sorting method and does not involve intelligent system technology. Therefore, not all students who enter the superior class have the desired criteria. The use of a Decision Support System (DSS) is expected to help the decisions made in selecting and determining who is the best student, considering that so far no particular method has been used in selecting students so that sometimes decisions are considered less objective and not on target. This decision support system using the simple additive weighting (SAW) method is processed with data sourced from student data at SMP Negeri 25 Padang. This research was conducted by finding the weight value for each attribute, then a ranking process was carried out that would determine the optimal alternative, namely the best students. Based on the analysis of the decision support system with this method is done by taking 3 criteria, namely the criteria for attitudes, criteria for value and criteria for ranking. Where the results of the researcher are the rankings that can support the best decisions for the criteria of superior class students. From the researcher, it can be concluded that the best students have high attitude criteria, high scoring criteria, and high ranking criteria. Based on the data that has been researched, there are students who score Exelia Monica with code A5 having a value of 3.
Implementation of Artificial Neural Network (ANN) to Predict Financial Distress (A Case Study on Metal and Mineral Industry Companies Listed on IDX 2019–2023 Period) Rahayu, Wardanianti Sari; Khairunnisa, K
Journal of Economics and Management Scienties Volume 7 No. 4, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jems.v7i4.184

Abstract

This research utilizes data mining techniques, specifically the Artificial Neural Network (ANN) model, to predict financial distress. In this ANN model, five financial ratios serve as the main input variables, namely Return on Assets (ROA), Debt to Assets Ratio (DAR), Current Ratio, Total Assets Turnover, and Operating Cash Flow Ratio. The selection of these ratios is based on evidence that they are effective in predicting financial distress. This study aims to develop a financial distress prediction model for metal and mineral industry companies listed on the Indonesia Stock Exchange during the 2019-2023 period, using a data mining approach with Artificial Neural Network (ANN). The study results show that the financial ratios of companies experiencing financial distress tend to be lower than companies that do not experience it, so these ratios are effective as input variables for the model. The best ANN architecture, found through training using a sample of 26 companies, has a configuration of 25 neurons in the input layer, 10 neurons in the hidden layer, and 1 neuron in the output layer. Further analysis revealed that 12 out of 26 energy companies were predicted to experience financial distress, with the model achieving the highest accuracy of 84.62%.
Co-Authors Abdul Halim Hasugian Mhd Furqan Rahayu, Wardanianti Sari