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All Journal Building of Informatics, Technology and Science
Kemas Muslim Lhaksamana
Telkom University, Bandung
Computer Science & IT

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A Multi-Label Classification of Al-Quran Verses Using Ensemble Method and Naïve Bayes Muhammad Rizqi Choirulfikri; Kemas Muslim Lhaksamana; Said Al Faraby
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): Maret 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (338.199 KB) | DOI: 10.47065/bits.v3i4.1287

Abstract

Al-Quran is the holy book as a guide and also a source of law for muslims. Thus, understanding and studying Al-Quran is very important for muslims. To make it easier for muslims to understand and study the Qur'an, it is necessary to classify the verses of the Al-Qur'an. This study built a system that can perform multi-label classification of Al-Quran verses. Multi-label means that the classification will divide each verse of the Al-Quran into more than 1 topic. The model is built using the ensemble method by combining several Naïve Bayes algorithms. The ensemble method was chosen because research with different datasets can obtain good performance. The naïve Bayes algorithm was also chosen because it has a simple calculation so it requires a fairly short computation time. The preprocessing step is also carried out to see the comparison of performance results. To measure the performance of the system that has been built, the calculation of hamming loss is used. Based on the experimental results with several testing scenarios, the best performance results are obtained by combining Multinomial NB and Bernoulli NB with a hamming loss value of 0.1167. Thus, the use of the ensemble method can improve performance compared to without the ensemble method. This research can also of course build a multi-label classification model for the verses of Al-Quran with the ensemble method
Classification Analysis of Waiting Period for Telkom University Alumni to Get Jobs Using Decision Tree and Support Vector Machine Annisa Miranda; Kemas Muslim Lhaksamana
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1963

Abstract

Tracer analysis is one of the ways to increase a university's accreditation. Tracer studies, also known as graduate surveys, are beneficial for enhancing learning and developing university curricula. The period it takes graduates to secure employment is a measure of their quality. The sooner graduates obtain a job, the higher their perceived quality. Conversely, if it takes graduates longer to find employment, their quality is deemed lower. To gain new knowledge from the tracer study dataset regarding the relationship between university contribution and alumni capability in the job market, in this study, data mining techniques are used to determine what factors influence the length of time it takes college graduates to find employment. This classification model contains a total of 2288 data instances from the categorical type of dataset. The features are selected using chi-square. Two classification algorithms, Decision Tree and Support Vector Machine, are compared for the best model. This study also used hyperparameter tuning to improve accuracy. The results show decision tree produces higher accuracy compared to the support vector machine. The accuracy obtained from the decision tree model is 55.02% and increased to 65.06% after hyperparameter tuning. Meanwhile, the support vector machine brought an accuracy of 60.40% and increased to 62.15% after hyperparameter tuning. Factors that affect the classification of the alumni waiting period in getting a job in this study are sex, faculty of the study field, department of the study field, study period, company specification, company category, and work location.
Co-Authors Al Faraby, Said Annisa Miranda Muhammad Rizqi Choirulfikri