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INDONESIA
Jurnal Informatika dan Rekayasa Perangkat Lunak
Published by Universitas Teknokrat Indonesia
ISSN : 27973492     EISSN : 27972011     DOI : https://doi.org/10.33365/jatika
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering
Jurnal Informatika dan Rekayasa Perangkat Lunak (JATIKA), an Indonesian national journal, publishes high quality research papers in the broad field of Informatics and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 21 Documents
 1   2   3 
Optimizing E-Commerce Platform Selection Using Root Assessment Method and MEREC Weighting Wang, Junhai; Darwis, Dedi; Gunawan, Rakhmat Dedi; Ariany, Fenty
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.6

Abstract

The number of users of e-commerce platforms has increased significantly in recent years, and consumers are now more likely to shop online due to ease of access, diverse product choices, and flexibility in transaction times. The difficulty in determining the best e-commerce platform is often caused by subjectivity in the weighting of the criteria used for evaluation. The weighting process is carried out based on the preferences of certain individuals or groups, without considering objective data. This research aims to apply an objective, structured, and accurate approach in evaluating and ranking e-commerce platforms based on relevant multi-dimensional criteria. By using the root assessment method, the evaluation process can be carried out systematically through hierarchical analysis, while the MEREC weighting ensures that the weight of each criterion reflects its real impact on the outcome of the decision. Through the combination of these two methods, this research is expected to make a significant contribution to improving the quality of decision-making, especially in helping users or business people choose the e-commerce platform that best suits their needs. The results of the final score calculation Platform E was ranked first with the highest score of 4.87083, Platform A was ranked second with a score of 4.85162, and Platform B was ranked third with a score of 4.83842. Future research should address the identified limitations by exploring the integration of advanced predictive analytics and artificial intelligence techniques to improve the adaptability and resilience of models. In addition, sensitivity analysis of the MEREC Root Assessment and Weighting Methods should be performed to understand its performance under various data conditions.
Decision Support System for New Employee Admission Selection Using a Combination of LOPCOW and MARCOS Imron, Imron; Yulia, Eka Rini; Andriansah, Andriansah; Sefrika, Sefrika
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.9

Abstract

The selection of new hires is an important process in human resource management to ensure that the organization gets the individuals who best suit the company's needs and goals. The main problem in the selection of new employee admissions is often related to the difficulty of achieving objectivity and fairness in the assessment process. Reliance on subjective assessment, lack of structured selection methods or absence of valid and reliable measurement tools can result in inaccurate decisions. The ranking results in the selection of new employee admissions show the value generated from each candidate, Candidate AE is ranked first with the highest score of 24.48, followed by Candidate DS with a score of 22.95. JE Candidate was ranked third with a score of 21.36, followed by FY Candidate with a score of 21.3. These results reflect the performance of each candidate in meeting the selection criteria that have been determined. This research contributes to improving accuracy and fairness in selection decision-making, by reducing subjectivity bias in weighting and ranking candidates. With transparent and measurable results, this research helps companies in systematically selecting the best candidates, while improving the efficiency and effectiveness of the recruitment process. The combination of the LOPCOW and MARCOS methods offers the flexibility to be applied in a variety of selection contexts, not only in employee admissions, but also in other multi-criteria decision-making.
Modification of Multi-Attributive Border Approximation Area Comparison (MABAC) to Improve Multi-Criteria Assessment Hariyanto, Hariyanto; Christian, Ade; Nurhayati, M. Sinta; Sudarsono, Bibit
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.15

Abstract

Multi-criteria decision making (MCDM) is a field of study in decision-making that focuses on selecting or ranking alternatives based on several competing criteria. Multi-attributive border approximation area comparison (MABAC) is one of the methods in MCDM that is designed to evaluate and select the best alternative based on relevant criteria. The weakness of the MABAC method in the aspect of criterion weighting mainly lies in its dependence on the external weighting method. The data used in the Best Staff Selection case study included staff performance assessments based on several key criteria. The results of this data are then used in MCDM to determine the best staff based on the weight of objectively established criteria. The purpose of this study is to modify the MABAC method by integrating the geometric average method which aims to improve accuracy and objectivity in multi-criteria assessment. The results of the ranking with the MABAC-G method for the selection of the best employees show that employee 5 obtained the highest score of 0.2868 so that it is the best alternative in this assessment. The results of the comparison of the ranking of alternative selection of the best employees using the ranking from the company and the MABAC-G method obtained a Pearson correlation value of 0.9511 which shows that there is a very strong relationship between the two assessment systems. The application of research findings from MABAC-G in the future can be used in various fields that require multi-criteria decision-making with complex and uncertain data.
Implementation of the Objective Weighting and Grey Relational Analysis Method for the Promotion of the Position of Chief Financial Officer Abdullah, Muksin Hi; Abdurahman, Muhdar; A. Thais, Iswan; Hadad, Sitna Hajar
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.16

Abstract

The promotion of the Chief Financial Officer (CFO) position is a form of appreciation for performance, competence, and significant contribution to the organization's financial management. This promotion is expected to motivate individuals to continue to improve their professional competence and have a greater positive impact on the development of the organization in the future. Problems in the promotion of the CFO position often arise due to various factors, both from the internal side of the organization and individuals. One of the main problems is the lack of transparency in the performance appraisal process, where the criteria for promotion are not clear or not in accordance with the standards that have been set. Subjective factors in job appraisals can trigger employee dissatisfaction, especially if the decisions taken are more influenced by personal proximity than competence and achievements. This study aims to implement an objective and accurate decision support system in the promotion process for the position of CFO by applying the GRA method and objective weighting using the CRITIC method. The ranking results show the ranking of candidates in the promotion of the Chief Financial Officer position based on their respective evaluation scores. Candidate 7 took first place with the highest score of 0.1251, followed by Candidate 6 with a score of 0.1242, and candidate 4 was in third place with a score of 0.1101. This shows that Candidates 7 and 6 have a significant competitive advantage over other candidates for the position. Contribution to the promotion of the CFO position is crucial in ensuring that decisions made in the assessment of candidates are not only based on intuition or subjective considerations, but through a systematic, objective, and data-driven approach.
Comparison of Prediction Models: Decision Tree, Random Forest, and Support Vector Regression Putra, Kurnia Ramadhan
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.18

Abstract

The Information Technology (IT) industry continues to grow rapidly, creating challenges in determining fair and competitive salaries for professionals. Accurate salary predictions are essential for companies to attract and retain talent while providing insights for individual career planning. This research aims to compare the performance of three machine learning models, such as Decision Tree Regression, Random Forest Regression, and Support Vector Regression in predicting IT sector salaries using demographic and professional data, including age, gender, education level, job position, and work experience. The study uses a dataset of 6,704 entries from Kaggle, with relationships between variables analyzed through statistical techniques such as Pearson Correlation and ANOVA. Model performance was evaluated using the R² Score, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Among the models, Random Forest Regression demonstrated the best performance, achieving the highest R² of 91.49% and an RMSE of 0.058, indicating high predictive accuracy with low error rates. Scatter plot visualizations confirm a strong correlation between actual and predicted salaries, supported by error analysis identifying minimal overestimation and underestimation cases. The research concludes that Random Forest Regression is the most effective model for IT salary predictions. These findings provide practical insights for organizations and individuals, highlighting the potential of data-driven approaches in salary determination. Future studies may focus on hyperparameter optimization and incorporating additional features to improve model performance and generalizability further improve model performance and generalizability.
Optimalization of Grouping Models on Sales Transaction Data in the Josi.Id Store Using the K-Means Algorithm Dayanti, Resda; Kurniawan, Rudi; Suprapti, Tati
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.21

Abstract

This study aims to optimize the K-Means algorithm to improve the clustering model of fashion goods sales transaction data at the josi.id store over a period of seven months. One of the main challenges is the lack of understanding of the characteristics of sales transaction data at the josi.id store, as well as the difficulty in identifying products that cause spikes on big days. With the K-Means clustering method used to group data, the optimal K value, attributes that affect the Davies Bouldin index (DBI) value. The analysis of the results shows that the key attribute that affects the k value is the TYPE OF ITEM with K = 3 as the optimal value, has the lowest DBI value of 0.258 compared to other cluster configurations. With the characteristics of cluster 0 (429 items) showing dominant sales during the Eid season. Cluster 1 (343 items) shows high sales during the holiday period. Cluster 2 (309 items) has stable sales during weekdays. These results show good separation and uniformity of clusters in each cluster. The attribute of ITEM TYPE, based on the characteristics of each cluster is Bracket clothes products show the highest total sales of up to 7 million, supported by traffic (love feature) that is often viewed. Blouses have total sales of under 2 million, while dresses show great variation with total sales between 1 and more than 3 million. Skirts have a more diverse sales distribution, with transactions reaching 3 million. which includes categories such as Dresses, bracket clothes, Tops, and Skirts, plays an important role in grouping sales transaction data, especially for seasonal products such as during Eid.
Analysis and Design of Sales Website at Twins Petshop Using the Waterfall Method Pinasti, Rafa Hadiya; Fajri, Ika Nur
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.29

Abstract

The pet shop industry continues to grow as people's interest in pets increases. However, many petshops face challenges in managing products and transactions that are still done manually. This is also experienced by Twins petshop, which still uses manual methods in managing product and transaction data, thus hindering data operational efficiency and market reach that has not been maximized. To overcome this problem, this study was made with the aim of designing and developing a website-based petshop sales information system, thereby helping to improve the efficiency of product and transaction data management. The development method used is the waterfall method which consists of several stages that must be carried out in stages, namely needs analysis, design, implementation, and testing. The tests are carried out using the balck-box testing method to ensure that all features run according to user needs. The results of the balckbox test show that of the eight scenarios tested, all succeeded with a 100% success percentage. Scenarios include admin logins with valid and invalid data, data editing and deletion, and adding products with invalid forms. The results of this study show that the website developed is able to increase the efficiency of product recording, transactions, and provide more complete information than the previous manual system.
Implementation of VGG19 Model for Pest Detection on Mustard Leaves Pratomo Prawirodirjo, Raden Ronggo Bintang; Sahira, Putri; Aulia, Zahra Windi; Syakirin, Hirzan Fakhrusy; Febriansyah; Prayitno, Budi; Siregar, Riki Ruli Affandi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.33

Abstract

Green mustard is a leading agricultural commodity in Indonesia but often faces pest attacks such as Spodoptera litura, which can reduce productivity by up to 85%. This study aims to develop an automated pesticide sprayer prototype using Convolutional Neural Network (CNN) technology with the VGG19 architecture. The system utilizes Raspberry Pi, Arduino, ESP8266, and a camera to detect pests in real-time and accurately spray pesticides. The dataset used consists of 1,380 images, divided into 10% for testing, 25% for validation, and 75% for training. The model evaluation values for the ‘mustard with pests’ class achieved precision, recall, and F1-Score of 96% each, while for the ‘mustard without pests’ class they were 95% each. In addition, the MAPE (Mean Absolute Percentage Error) value of 4.61% shows that the percentage error of the model prediction is very small. The developed VGG19 model achieved an accuracy of 95% and high efficiency after conversion to the TFLite format, reducing model size by 75.57%. This tool is highly recommended to enhance farmers' work efficiency, reduce excessive pesticide use, and support sustainable agriculture. Its ability to operate autonomously and precisely makes it an ideal solution to assist farmers regarding pest problems.
Decision Support System for Providing Social Assistance for Poverty in Manyaran Village Semarang With AHP And Smart Methods Fauzhan, Yoga Agus; Supriyanto, Aji
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.35

Abstract

One of the problems still faced by the Indonesian state is the problem of poverty. The problem of poverty is a complex problem, especially those who often experience problems in the distribution of poverty social assistance is Manyaran village where the distribution of poverty social assistance is still not optimal. Using the Analytical Hierarchy Process (AHP) and Simple Multi Attribute Rating Technique (SMART) methods used for ranking social assistance in Manyaran Semarang village. The AHP method is used to weight poverty criteria according to the Central Bureau of Statistics (BPS), while the SMART method is used to rank 45 alternatives in Manyaran Semarang. The resul ts of the above ranking obtained the ranking order of alternatives, the first rank got a value of 0.9209 obtained by (A37), while the last rank got a value of 0.0732 obtained by (A15).
Website-Based Integrated One-Stop Service System of the Ministry of Religious Affairs Office Jayapura Regency Using Rational Unified Process Lolombulan, Chaelsie Ribka; Titaley, Jullia; Alfonsius, Eric
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.76

Abstract

The public service management system at the Ministry of Religious Affairs Office, Jayapura Regency, remains reliant on manual processes, resulting in inefficiencies such as prolonged processing times, data redundancy, and challenges in tracking service requests. This study aims to design and develop a web-based Integrated One-Stop Service (IOSS) system to enhance service efficiency, accuracy, and accessibility. The Rational Unified Process (RUP) methodology was employed as the system development framework due to its structured, iterative, and flexible approach to managing the software development lifecycle. The design phase commenced with a needs analysis conducted through in-depth interviews with employees and service users to identify system requirements and existing challenges. The system design encompassed the development of an intuitive user interface prototype and a comprehensive database to support IOSS operations. System evaluation through black box and usability testing methods yielded a 100% functionality success rate and a user satisfaction score of 4.46 on a scale of 1 to 5, indicating that the system operates effectively. The findings demonstrate that the developed system significantly improves public accessibility and satisfaction with government services. This study contributes to the advancement of public service digitalization, fostering a more efficient and responsive service delivery system.

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