Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Indonesian Journal of Artificial Intelligence and Data Mining Indonesian Journal of Electrical Engineering and Computer Science
Riswan Efendi
Universitas Islam Negeri Sultan Syarif Kasim Riau
Computer Science & IT

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Association Between Spritual Intelligence, Social Support and Mental Health of University Student During Covid-19 Based on Dependency Degree and Ordinal Logistics Regression Reza Agustina; Reza Selfiana; Auzia N. Oktavani; Risa K. Sari; Veny Alvionita; Maulidya T. Putri; Yusnita Hasibuan; Riswan Efendi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 1 (2022): March 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i1.15276

Abstract

The modeling of mental health has been widely studied using statistical approaches such as the chi-square test, regression analysis, and ordinal logistics. However, not many studies apply ordinal logistic regression and dependency degree into this domain, where both of these approaches are good statistical and non-statistical approaches for investigating categorical data. In this study, the researcher is interested in combining the dependency degree and ordinal logistic regression and identifying the mental health factors of students in the Covid-19 era. The research sample was students majoring in Al-Qur'an and Tafsir Study at UIN Suska Riau. The results indicated that a significant relationship between the informative support dimension to the disorder, the instrumental dimension to avoidance and increased self-awareness with an average dependency value between dimensions of 4.13% and 4.53%. Through this relationship, it can be seen that students are more likely to experience stress disorder (PTSD) if they are reluctant to do good than the number of awards received by students.
Forecasting ASEAN countries exchange rates using auto regression model based on triangular fuzzy number Hamijah Mohd Rahman; Nureize Arbaiy; Riswan Efendi; Chuah Chai Wen
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i3.pp1525-1532

Abstract

Exchange rate forecasting is important to represent the expectation of exchange rates future values. The forecasting task is due to the economic factor and the historical data used to forecast are exposed to uncertainty and observational error during data collection. The existing auto regression model only deals with uncertainty exist in the model, not in the data preparation. Uncertainties may contained in the data input and should be treated during data preparation which is an early stage of forecasting process. To date, only few researches discuss intensely on a fuzzy data preparation. However, data treatment during data preparation is important to reduce model’s error due to uncertainty problem. Hence, this paper presents an approach to construct Triangular Fuzzy Number to handle uncertainty in data during data preparation. As the Triangular Fuzzy Number is often used to represent uncertain information in a form of interval, this study proposed a procedure to construct Triangular Fuzzy Number from single point data. In this study, the Triangular Fuzzy Number is built in a form of symmetric triangular with 1%, 3% and 5% spread value. Autoregressive model is then used to forecast the exchange rate of Association of South East Asian Nation (ASEAN) countries. The result in this study shows that the forecasting exchange rate is significantly important to trace the movement of ASEAN countries exchange rates and beneficial in forecasting planning.
Multi-label classification approach for quranic verses labeling Abdullahi Adeleke; Noor Azah Samsudin; Mohd Hisyam Abdul Rahim; Shamsul Kamal Ahmad Khalid; Riswan Efendi
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp484-490

Abstract

Machine learning involves the task of training systems to be able to make decisions without being explicitly programmed. Important among machine learning tasks is classification involving the process of training machines to make predictions from predefined labels. Classification is broadly categorized into three distinct groups: single-label (SL), multi-class, and multi-label (ML) classification. This research work presents an application of a multi-label classification (MLC) technique in automating Quranic verses labeling. MLC has been gaining attention in recent years. This is due to the increasing amount of works based on real-world classification problems of multi-label data. In traditional classification problems, patterns are associated with a single-label from a set of disjoint labels. However, in MLC, an instance of data is associated with a set of labels. In this paper, three standard MLC methods: binary relevance (BR), classifier chain (CC), and label powerset (LP) algorithms are implemented with four baseline classifiers: support vector machine (SVM), naïve Bayes (NB), k-nearest neighbors (k-NN), and J48. The research methodology adopts the multi-label problem transformation (PT) approach. The results are validated using six conventional performance metrics. These include: hamming loss, accuracy, one error, micro-F1, macro-F1, and avg. precision. From the results, the classifiers effectively achieved above 70% accuracy mark. Overall, SVM achieved the best results with CC and LP algorithms.
Co-Authors Abdullahi Adeleke Auzia N. Oktavani Chuah Chai Wen Hamijah Mohd Rahman Maulidya T. Putri Mohd Hisyam Abdul Rahim Noor Azah Samsudin Nureize Arbaiy Reza Agustina Reza Selfiana Risa K. Sari Shamsul Kamal Ahmad Khalid Veny Alvionita Yusnita Hasibuan