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All Journal Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Sains dan Teknologi Informasi
Ilham, Safarul
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Pendukung Keputusan Dengan Perbandingan Metode WASPAS dan Metode MAUT pada Pemilihan Karyawan Baru Ilham, Safarul
Jurnal Sains dan Teknologi Informasi Vol 4 No 4 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jussi.v4i4.8470

Abstract

In searching for employee candidates who meet the desired criteria, a solution is needed to select prospective employees. The solution that can be done is to create a decision support system to simplify the process of selecting employee files that have been received. Decision Support Systems (DSS) that can help companies to select prospective employees who register using the comparison of the Weight Aggregated Sum Product Assessment (WASPAS) method is a combination of the WP and SAW methods, where the WP method is a method that uses multiplication in connecting the value of each attribute that must previously be raised to the power of the attribute's weighted value, while the SAW method is generally known as a weighted sum that focuses on calculating the sum of the weighted values ​​of alternative criteria, with the Multi-Utility Attribute Theory (MAUT) method being a quantitative method that is used as a basis for decision making through systematic procedures that identify and analyze several variables. In its application, the decision support system, comparing the WASPAS and MAUT methods in selecting prospective employees according to the given criteria, yielded alternative A5, but with different scores. The WASPAS method scored alternative A5 at 0.890, while the MAUT method scored 0.875.
SMOTE and BERT Approaches for Handling Class Imbalance in Sentiment Analysis of the CoreTax Application on Big Data Ginting, Meiliyani Br; Surbakti, Asprina Br; Ilham, Safarul; Utomo, Dito Putro; Ginting, Raheliya Br
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8310

Abstract

Coretax is a tax information system developed by the Directorate General of Taxes (DJP) to support digital and integrated tax administration processes, covering everything from taxpayer registration to reporting and auditing. Although it was designed to improve efficiency, transparency, and accuracy in tax management, its implementation has sparked mixed reactions among the public due to various technical challenges and the complexity of the annual tax reporting process. This situation highlights the need for a sentiment analysis that can objectively capture public perceptions of the system’s performance. In this study, Natural Language Processing (NLP) and Machine Learning techniques were applied to analyze 3,000 tweets from Twitter (X) related to Coretax. One of the main issues identified in the dataset is class imbalance, where positive sentiments significantly outnumber negative and neutral ones, leading to biased classification results. To address this issue, the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset by generating synthetic samples for the minority classes. The BERT model was then employed for sentiment classification because of its strong ability to understand contextual meaning through its transformer-based architecture. Experimental results show that before applying SMOTE, the BERT model achieved an accuracy of 77%, which increased to 80% after SMOTE was implemented, along with improvements in precision, recall, and F1-score, particularly for the minority classes. These findings demonstrate that the combination of SMOTE and BERT significantly enhances the performance of sentiment analysis in understanding public responses to Coretax. This approach can serve as a valuable reference for evaluating and improving tax digitalization policies, ensuring they are more effective, inclusive, and responsive to public needs.
Implementation of the Preference Selection Index Method in a Decision Support System for Determining Customer Loan Eligibility Ilham, Safarul
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8357

Abstract

This study aims to apply the Preferential Selection Index (PSI) method in evaluating the eligibility of loan applicants for the PNM Mekaar program. The research investigates the effectiveness of PSI in selecting the most eligible borrowers based on multiple criteria, such as Age, Owns a Business, Loan Capital, Income, Business Permit, Business Permit Level and Customer History. The analysis is conducted using a decision support system (DSS), where each alternative is evaluated against these criteria and weighted accordingly. The study finds that the alternative with the highest value, C8 (0.222403657), is followed closely by alternative A7 (0.207720657). The results of this study demonstrate that PSI offers a more structured, objective, and efficient approach to loan eligibility assessment compared to traditional methods. The integration of PSI within the DSS allows for faster decision-making, improved consistency, and a reduction in the risk of loan defaults. These findings contribute to enhancing the decision-making process in microfinance institutions, particularly in improving financial inclusion and supporting the growth of micro, small, and medium enterprises (MSMEs). The research concludes that PSI is a valuable tool for financial institutions seeking to adopt data-driven, transparent, and reliable loan approval procedures.
Co-Authors Asprina Br Surbakti, Asprina Br Dito Putro Utomo Meiliyani Br Ginting, Meiliyani Br Raheliya Br Ginting, Raheliya Br