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All Journal Dinamik Sistemasi: Jurnal Sistem Informasi Jurnal Ilmiah FIFO KOMPUTIKA - Jurnal Sistem Komputer Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Paradigma Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Ilmiah Informatika dan Ilmu Komputer CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science Bulletin of Artificial Intelligence Jurnal Informatika dan Rekayasa Perangkat Lunak
Aditia Yudhistira
Universitas Teknokrat Indonesia
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Hybrid Logarithmic Percentage Change-Driven Objective Weighting and Grey Relational Analysis Method in Employee Contract Renewal Setiawansyah, Setiawansyah; Rahmanto, Yuri; Aldino, Ahmad Ari; Yudhistira, Aditia; Palupiningsih, Pritasari; Sulistiyawati, Ari
TIN: Terapan Informatika Nusantara Vol 4 No 12 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i12.5121

Abstract

Contract employees are individuals who are hired for a specific period of time within a company or organization for a specific purpose. They usually do not have permanent employee status and are bound by work contracts that govern their tenure, salary, and other obligations. Despite not having long-term job security, contract employees often bring specialized skills or experience needed for specific projects. They are often instrumental in handling temporary projects, fulfilling temporary company needs, or filling temporary vacancies. One of the main problems in determining employee contract renewal is the lack of transparency and clear communication from the company. Employees often feel confused or uncertain about the criteria used by management in determining whether or not their contract will be renewed. Lack of clear information can cause anxiety and uncertainty among employees, and impair their performance and motivation. Hybrid Logarithmic Percentage Change-Driven Objective Weighting and Grey Relational Analysis (HLOPCOW-GRA) is an approach that combines two analysis methods, namely LOPCOW and GRA to improve accuracy and reliability in decision making. HLOPCOW-GRA provides an advantage in combining LOPCOW's advantage in handling dynamic data fluctuations with GRA's advantage in analyzing relative relationships between criteria, this approach allows decision makers to gain a deeper understanding of the factors that affect the final outcome. The results of alternative ranking showed that the first place with a GRA final value of 0.1406 was obtained by EM alternatives, second place with a GRA final value of 0.1366 was obtained by SVR alternatives, third place with a GRA final value of 0.1366 was obtained by SVR alternatives, third place with a GRA final value of 0.1406 was obtained by EM alternatives. The final GRA value of 0.1245 obtained alternative ASR.
COMBINATION OF LOGARITHMIC PERCENTAGE CHANGE-DRIVEN OBJECTIVE WEIGHTING AND MULTI-ATTRIBUTIVE IDEAL-REAL COMPARATIVE ANALYSIS IN DETERMINING THE BEST PRODUCTION EMPLOYEES Hadad, Sitna Hajar; Subhan, Subhan; Setiawansyah, Setiawansyah; Arshad, Muhammad Waqas; Yudhistira, Aditia; Rahmanto, Yuri
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2057

Abstract

The problem that occurs in the selection of the best production employees is the lack of transparency and objectivity in the selection process. Without clear procedures and well-defined criteria, employee selection decisions can be influenced by subjective preferences or irrelevant non-performance factors. This can result in injustice in employee selection and lower the morale and motivation of unselected employees. The purpose of the combination of LOPCOW and MAIRCA in determining the best production employees is to provide a holistic and adaptive framework in the employee performance evaluation process. LOPCOW allows decision makers to dynamically adjust the weight of criteria according to the level of volatility or change in the relevant environment or situation. LOPCOW offers an adaptive and responsive approach in determining the weight of criteria, enabling decision makers to respond quickly to changes occurring in the relevant environment or situation. MAIRCA is an analytical method used to assist decision makers in evaluating and selecting alternatives based on several relevant criteria or attributes. MAIRCA provides a strong framework for decision makers to make more informed and informed decisions. Combining these two methods results in a more comprehensive and accurate understanding of production employee performance, thus enabling managers to identify the most effective employees and provide rewards or development accordingly. The final results of the ranking of the best production employees obtained by JR employees get 1st place, YP employees get 2nd place, and AJL employees get 3rd place.
Optimizing Employee Admission Selection Using G2M Weighting and MOORA Method Rahmanto, Yuri; Wang, Junhai; Setiawansyah, Setiawansyah; Yudhistira, Aditia; Darwis, Dedi; Suryono, Ryan Randy
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i1.8224

Abstract

An objective and effective employee admission selection process is a crucial step for the success of the organization in achieving its goals. Problems in employee recruitment selection often arise due to a lack of good planning and system implementation, namely decisions are often influenced by personal preferences, stereotypes, or non-relevant factors, thus reducing objectivity in choosing the best candidates. Objective selection ensures that candidate assessments are conducted based on measurable, relevant, and bias-free criteria, so that only individuals who truly meet the company's needs and standards are accepted. The purpose of developing an optimal approach in employee admission selection using G2M weighting and MOORA is to create a more objective, efficient, and accurate selection process. This approach aims to integrate the calculation of criterion weights mathematically, such as those offered by G2M, in order to eliminate subjective bias in determining criterion prioritization. The MOORA method of evaluating alternative candidates is carried out through ratio analysis that takes into account various criteria simultaneously, resulting in a transparent and data-driven ranking. The results of the employee admission selection ranking based on the criteria that have been evaluated, Candidate 3 obtained the highest score of 0.4177, indicating that this candidate best meets the expected criteria. The second position was occupied by Candidate 6 with a score of 0.3886, followed by Candidate 9 with a score of 0.3528. This research contributes to the recruitment process, by providing a more reliable, transparent, and less subjective way of selecting the right candidates for the positions that companies need.
Sistem Pendukung Keputusan Rekrutmen Staff Marketing Menggunakan Metode Analytical Hierarchy Process Yudhistira, Aditia
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) Vol. 2 No. 2 (2024): Volume 2 Number 2 April 2024
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v2i2.110

Abstract

The company's marketing staff recruitment process focuses on finding individuals who have strong communication skills, high creativity, and a deep understanding of the market and industry trends. The ideal candidate should be able to develop innovative marketing strategies to increase brand visibility and expand the company's market share. The decision support system (DSS) for marketing staff recruitment using the analytical hierarchy process (AHP) method is an application designed to assist HR managers or recruitment teams in making effective and efficient decisions related to marketing staff recruitment. The AHP method is used to evaluate criteria that are important in the recruitment process, as well as to compare prospective employees based on these criteria. By using AHP, DSS can assign relative weight to each criterion and alternative, thus enabling managers to make decisions based on structured and mature analysis. This DSS will help minimize errors in employee selection and improve the match between prospective employees and company needs, so as to improve the performance of the marketing team and the company's overall contribution. The ranking results found that Budi became the 1st best in marketing staff recruitment with a value of 2.170024213.
Optimization of Production Operator Performance Assessment with Grey Geometric Mean Weighting and Combinative Distance-based Assessment Wang, Junhai; Setiawansyah, Setiawansyah; Ulum, Faruk; Yudhistira, Aditia; Wahyudi, Agung Deni
Komputika : Jurnal Sistem Komputer Vol. 14 No. 2 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i2.15977

Abstract

The performance of production operators plays a crucial role in determining the level of efficiency and effectiveness of the manufacturing process in a company that has a long-term impact on the company's competitiveness. Production operator performance appraisals often face a number of problems that can reduce the accuracy and fairness of evaluations. One of the main problems is the subjectivity of assessment, where evaluation is based more on the personal perception of the supervisor or assessor without a consistently measurable standard. The purpose of this study is to apply a more objective, structured, and accurate production operator performance evaluation model by integrating the grey geometric mean weighting (G2M Weighting) method as an uncertainty-based criterion weighting approach and combinative distance-based assessment (CODAS) as an alternative ranking method. The results of the production operator's performance ranking are that CR Operator ranks first with the highest performance score of 0.7737, GM Operator is ranked second with a score of 0.6187, followed by AN Operator in third place with a score of 0.5895. This research makes a significant contribution to the development of a performance evaluation system in the manufacturing industry environment by integrating the G2M Weighting and CODAS methods as an objective and systematic approach.
Decision Support System for Determining Promotion Using a Combination of Entropy and Weighted Aggregated Sum Product Assessment Yudhistira, Aditia; Rahmanto, Yuri; Pasaribu, A. Ferico Octaviansyah; Yasin, Ikbal; Aldino, Ahmad Ari; Setiawansyah, Setiawansyah
Jurnal Ilmiah FIFO Vol 17, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2025.v17i2.005

Abstract

Decision-making in determining employee promotions often faces challenges due to the subjectivity of assessments. To address this issue, this research develops a decision support system by combining the Entropy method and the Weighted Aggregated Sum Product Assessment (WASPAS). The Entropy method is used to objectively determine the weights of criteria based on data variation, while the WASPAS method is applied to comprehensively rank alternatives through the integration of the Weighted Sum Model (WSM) and Weighted Product Model (WPM). The test results on seven candidates showed that Candidate A-016 ranked first with a score of 0.9733, followed by Candidate A-013 with a score of 0.7454, and Candidate A-011 with a score of 0.5386. Meanwhile, the candidate with the lowest score was Candidate A-017 with a value of 0.3456. These findings prove that the combination of Entropy and WASPAS methods can produce a more objective, transparent, and solid basis for management to make fair and rational decisions in the promotion process.
Klasifikasi Tingkat Kemiskinan Kabupaten/Kota Di Indonesia Tahun 2023 Menggunakan Logistic Regression Hafizhah, Hafizhah; Yudhistira, Aditia
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.v7i2.8343

Abstract

Poverty remains a major challenge in Indonesia, with a national rate reaching 9.36 percent in 2023, despite significant disparities between rural (12.22 percent) and urban (7.29 percent) areas, as well as the influence of outlier that can distort classification analysis at the district/city level. This study aims to classify poverty levels in 514 districts/cities into high (above 9.36 percent) and low (below or equal to 9.36 percent) categories using logistic regression, and to compare the model performance on original data with outlier-adjusted data through Z-score and interquartile range (IQR) methods. The methods applied include the collection of secondary data from the Central Statistics Agency and the Ministry of Home Affairs, exploratory data analysis to identify patterns and correlations (such as the negative correlation between per capita expenditure and poverty), and pre-processing by capping outlier. logistic regression training with hyperparameter tuning through grid search and cross-validation, as well as evaluation using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC-AUC) metrics. The predictor variables include gross domestic product (GDP), life expectancy, average length of schooling, and per capita expenditure. The results show consistent performance across techniques, with test accuracy reaching 77.67 percent, ROC-AUC of 0.8566, macro precision of 77.90 percent, macro recall of 77.79 percent, and macro F1-score of 77.66 percent. Outlier handling reduced the poverty rate standard deviation from 6.45 to 5.99 (Z-score) and 5.57 (IQR), without changing the distribution of binary labels (266 low, 248 high). The model coefficients confirm the dominant negative influence of per capita expenditure (-1.067), supporting targeted policies to reduce regional disparities.
Penerapan Metode Decision Tree dan Naïve Bayes pada Kasus Kriminalitas di Lampung Khadafi, Muhammad; Yudhistira, Aditia
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10411

Abstract

Crime, an unlawful act that contradicts ethics and norms, has now become a primary factor for the police in Lampung province. This presents a challenge for the police institution in predicting high crime rates. However, there are still many crimes that have not become the main focus of problem-solving at the Lampung Regional Police.This research aims to identify the types and criminal acts of crime with the highest recorded incidence in a crime dataset by performing classification using the Naïve Bayes algorithm. The data was obtained from investigators at the Directorate of General Criminal Investigation of the Lampung Regional Police, with a total of 12,034 JTP (Total Criminal Acts) and 7,518 PTP (Crime Resolution) data points for each type of crime, distributed across the Regional Police, City Police, and District Police throughout Lampung province. The classification process using the Naïve Bayes algorithm reveals the relationship between the work unit (Satker) and the type of crime handled, thereby identifying crime patterns based on the location where they are handled. The results of the research, which involved converting numerical data into binomial (binary) form using the "Numerical to Binominal" feature in Rapid miner, show that the analysis and modeling process, especially in algorithms like Naïve Bayes or decision trees, is more effective when using data in a binary format. Thus, the initial dataset can be visualized in the form of a , with the size of the text varying according to the level of each high-incidence crime; the larger the text, the more frequently or significantly the crime occurred or was reported. The application of this method can help in identifying patterns, dominant trends, and areas of focus for more targeted law enforcement efforts or crime prevention policies.
Multi-Criteria Decision Model for Ranking the Best Marketplace Using CRISUS Weighting and OPARA Ranking Thalib, Akil; Asistyasari, Ayuni; Nuryaman, Yosep; Oprasto, Raditya Rimbawan; Yudhistira, Aditia
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1568

Abstract

The rapid growth of e-commerce marketplaces in Indonesia has increased competition among platforms and created challenges in identifying the most suitable marketplace for users and businesses. Previous studies commonly applied conventional Multi-Criteria Decision-Making (MCDM) approaches, yet many of these methods rely heavily on subjective weighting or limited data-based evaluation, which may lead to inconsistent ranking results. Therefore, this study aims to develop a more objective decision-making model for marketplace evaluation by integrating the CRISUS weighting method with the OPARA ranking approach. The dataset consists of quantitative marketplace performance indicators collected from public digital statistics, including monthly visits, annual visits, application ratings, number of downloads, and the number of active sellers for several major marketplaces operating in Indonesia. The CRISUS method is used to determine criterion weights based on actual data variation to reduce subjective bias, while OPARA evaluates the alternatives through an optimized pairwise ratio mechanism to obtain the final preference values. The experimental results indicate that Shopee achieves the highest score of 0.3078, followed by Lazada with 0.2476 and Tokopedia with 0.2327, demonstrating their stronger performance compared with other marketplace alternatives based on the evaluated criteria. These findings contribute both academically and practically by providing a transparent and data-driven MCDM framework that improves the reliability of marketplace ranking and can support stakeholders in making more informed platform selection decisions.
Perbandingan Naïve Bayes dan Support Vector Machine Berbasis Term Frequency−Inverse Document Frequency pada Analisis Sentimen Ulasan Produk Afiliasi Lintas Platform TikTok dan Shopee Putri, Clara Indriani; Yudhistira, Aditia
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9454

Abstract

The growth of affiliate marketing on digital platforms, particularly TikTok and Shopee, has led to a rapid increase in consumer reviews that can be leveraged as actionable insights for businesses. However, reviews across platforms exhibit different linguistic characteristics: Shopee reviews tend to be more repetitive and transactional, whereas TikTok reviews are more informal, rich in slang, and noisier. This difference creates a research gap because sentiment classification performance may vary across platforms, while comparative studies on cross-platform affiliate reviews remain limited. This study aims to analyze and compare the performance of Multinomial Naïve Bayes and Support Vector Machine in identifying positive and negative sentiment polarity in TikTok and Shopee affiliate product reviews. Data were collected via web scraping during December 2025–January 2026, yielding 5,502 raw reviews. After text preprocessing (case folding, regex-based cleaning, normalization, stopword removal, and stemming using Sastrawi), 4,593 clean reviews were obtained. Lexicon-based automatic labeling with negation handling produced a binary dataset of 3,314 reviews (2,729 positive and 585 negative), indicating class imbalance; therefore, no data balancing was applied and evaluation emphasized precision, recall, and F1-score in addition to accuracy. Feature representation used Term Frequency–Inverse Document Frequency, and the dataset was split using an 80:20 hold-out scheme (2,651 training and 663 testing instances). Experimental results show that the Support Vector Machine achieved higher performance (95.93% accuracy; 0.81 negative-class F1) than Multinomial Naïve Bayes (89.14% accuracy; 0.12 negative-class F1). This superiority is related to the ability of Support Vector Machine to learn a maximum-margin hyperplane in the high-dimensional and sparse Term Frequency–Inverse Document Frequency feature space, making it more robust to linguistic variation and noise than the probabilistic Naïve Bayes approach, which is more sensitive to majority-class dominance.
Co-Authors Adamansyah, Eka Putri Ahmad Ari Aldino Akil Thalib Anggoro, Bayu Ari Sulistiyawati Arshad, Muhammad Waqas Asistyasari, Ayuni Chandra, Iryanto Dedi Darwis Gustina, Intan Hafizhah, Hafizhah Handika, Tegar Dio KHADAFI, MUHAMMAD Luthfi Falih, Yudep Rafidal Ma'rufudin Mahmudah, Siti Anisa Makin, Maria Angelina Luisa Megawaty, Dyah Ayu Nasrul, Royhan niswatun umami, Nila Nuryaman, Yosep Oprasto, Raditya Rimbawan Palupiningsih, Pritasari Pasaribu, A. Ferico Octaviansyah Putri, Clara Indriani Ryan Randy Suryono Sanjaya, M. Alung Setiawansyah Setiawansyah Sitna Hajar Hadad Subhan Subhan Suci Fitrianti, Suci Sumanto, Sumanto Tri Widodo Ulum, Faruk Wahyudi, Agung Deni Wang, Junhai Yansyah, Dery yasin, ikbal Yuri Rahmanto