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All Journal Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer J-Intech (Journal of Information and Technology) Jurnal Telematics and Information Technology (TELEFORTECH) Jurnal Media Borneo Journal of Information Technology, Software Engineering and Computer Science
Megawaty, Dyah Ayu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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DYNAMIC WEIGHT ALLOCATION IN MODIFIED MULTI-ATRIBUTIVE IDEAL-REAL COMPARATIVE ANALYSIS WITH SYMMETRY POINT FOR REAL-TIME DECISION SUPPORT Hadad, Sitna Hajar; Chandra, Iryanto; Wang, Junhai; Megawaty, Dyah Ayu; Setiawansyah, Setiawansyah; Yudhistira, Aditia
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4170

Abstract

Decision Support Systems (DSS) have a crucial role in real-time decision-making, especially in the digital era that demands high speed and accuracy. Managing criterion weights in a dynamic environment presents significant challenges due to rapid and unpredictable changes in conditions. However, determining an accurate weight becomes difficult due to uncertainty, incomplete data, and subjective factors from decision-makers. In addition, changes in the external environment, such as market trends, regulations, or customer preferences, can affect the relevance of each criterion, thus requiring a real-time weight adjustment mechanism. The purpose of this study is to develop and explore the dynamic weight allocation method in symmetry point- multi-attributive ideal-real comparative analysis (S-MAIRCA) to support more accurate and responsive real-time decision-making in a dynamic environment. This research contributes to the understanding of how the weights of criteria can be adjusted automatically and responsively to changing conditions or new data, which increases the relevance and accuracy of decisions in a dynamic environment. The urgency of S-MAIRCA research is important because it often involves real-time, dynamic, and complex data. This development not only improves the adaptability of the S-MAIRCA method, but also contributes significantly to creating computer science-based applications that are more intelligent, flexible, and relevant to the evolving needs of the system. The results of the alternative ranking comparison using the CRITIC-MAIRCA, LOPCOW-MAIRCA, ROC-MAIRCA, and S-MAIRCA methods showed variations in the ranking order generated for each alternative using spearman correlation. The results of the correlation value of CRITIC-MAIRCA and LOPCOW-MAIRCA have a very high correlation of 0.993, which shows that these two methods provide almost identical rankings in alternative evaluation. Likewise, CRITIC-MAIRCA and S-MAIRCA had a high correlation of 0.979, signaling a strong similarity in ranking results despite slight differences in the approaches used by the two methods. The results of the application of the MAIRCA-S method in the development of DSS based on real-time data have a significant impact on improving the speed, accuracy, and adaptability of decisions. MAIRCA-S strengthens the validity of decision results by considering a variety of attributes on a more comprehensive scale, providing added value in the development of DSS for various industrial sectors.
Combination of Response to Criteria Weighting Method and Multi-Attribute Utility Theory in the Decision Support System for the Best Supplier Selection Ulum, Faruk; Wang, Junhai; Megawaty, Dyah Ayu; Sulistiyawati, Ari; Aryanti, Riska; Sumanto, Sumanto; Setiawansyah, Setiawansyah
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1810

Abstract

Choosing the right supplier is a strategic factor in supporting operational efficiency and a company's competitive advantage. This process requires a decision support system that is able to assess various alternatives objectively and in a structured manner. This study aims to develop a decision support system in the selection of the best supplier by combining the Response to Criteria Weighting (RECA) and Multi-Attribute Utility Theory (MAUT) methods. The RECA method is used to objectively determine the weight of each criterion based on the variation of data between alternatives, so as to reduce subjectivity in the weighting process. Meanwhile, the MAUT method functions to calculate the total utility value of each supplier based on the normalization value and weight that has been obtained. The results of the RECA method show the objective weight of each criterion, which is then used in the MAUT calculation process. The results of the analysis, obtained in the best supplier selection based on the total score of each candidate, it can be seen that PT Global Niaga Mandiri ranks first with the highest score of 0.6512, this shows that this company is the best choice in the supplier selection process. In second place is UD Anugrah Bersama with a score of 0.399, followed by PT Indo Logistik Prima in third place with a score of 0.3451. The combination of the RECA and MAUT methods has been proven to be able to produce accurate, rational, and accountable decisions. This system provides a measurable approach in filtering supplier alternatives efficiently and is relevant to be applied to various other multi-criteria decision-making contexts.
Comparison of Logistic Regression, Random Forest, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) Algorithms in Diabetes Prediction Kurniawan, M. Fadli; Megawaty, Dyah Ayu
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.9815

Abstract

Diabetes mellitus is a prevalent chronic illness that continues to grow in incidence worldwide, placing significant strain on healthcare systems. The timely prediction of diabetes is crucial for early intervention and management. This study explores the comparative effectiveness of four machine learning algorithms Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in identifying diabetes cases using a large public dataset containing 100,000 patient records obtained from open source Kaggle. The dataset includes nine clinical variables, such as age, gender, body mass index (BMI), blood glucose level, and HbA1c levels, among others. To address class imbalance, which showed less than 10% positive (diabetic) cases initially, the Synthetic Minority Oversampling Technique (SMOTE) was applied exclusively to the training data after an 80:20 stratified split. All models were evaluated using 5-fold stratified cross-validation, measuring their performance through accuracy, precision, recall, F1-score, area under the ROC curve (AUC), and training time. Among the models, Random Forest achieved the highest classification accuracy (96.88%) and AUC (99.70%), indicating superior overall performance. Furthermore, McNemar statistical tests revealed that the differences in performance between Random Forest and the other models were statistically significant. An analysis of feature importance highlighted that HbA1c, glucose level, and BMI were the most influential predictors. These results demonstrate that Random Forest offers the most balanced combination of accuracy, interpretability, and robustness, making it highly suitable for real-world clinical screening scenarios where early detection of diabetes is critical.
SENTIMENT ANALYSIS OF COMMENTS ON TOURIST ATTRACTIONS IN LAMPUNG PROVINCE USING THE NAIVE BAYES METHOD Wirahudha, Muhammad Arif; Damayanti, Damayanti; Megawaty, Dyah Ayu
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4037

Abstract

Lampung Province is a province that has so much natural beauty, this also makes Lampung Province one of the tourist destinations that are visited by many domestic and foreign tourists so that there is a problem, namely the many negative comments that are not in accordance with reality affect the number of tourist visits to Lampung Province because they are not in accordance with reality so that they affect public opinion about tourism in Lampung Province which results in tourist attractions being deserted. The method used to analyze sentiment analysis is the naive bayes algorithm by crawling data using python. The stages of the naive bayes algorithm in the study using preprocessing consist of five processes, namely cleansing, tokenization, case folding, stopword removal, and stemming. Lampung Province tourist attractions are Pahawang, Way Kambas, Krui Beach / West Coast, Mutun Beach and Kiluan Bay. The results of a fairly high level of accuracy in positive comments on Pahawang Beach. In this study, it was concluded that the impact of comments can affect the number of visitors coming to tourist attractions.
Penerapan Kombinasi Metode Multi-Attribute Utility Theory (MAUT) dan Rank Sum Dalam Pemilihan Siswa Terbaik Saputra, Williyandi; Wardana, Suwarman Adi; Wahyuda, Hana; Megawaty, Dyah Ayu
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) Vol. 2 No. 1 (2024): Volume 2 Number 1 January 2024
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v2i1.89

Abstract

The selection of the best students can involve a variety of issues that need to be considered. Some of the problems that arise in the process of selecting the best students include the determination of clear and objective criteria to select the best students, the availability of accurate data and information to assess student achievement and qualifications, and uncertainty or controversy related to the decisions taken. This study aims to apply a combination of Multi-Attribute Utility Theory (MAUT) and Rank Sum methods in selecting the best students so as to make it easier for schools, especially the student affairs department, to determine the best students at the end of each semester. The ranking results showed that rank 1 was obtained with a final grade of 0.5327 on behalf of student Nadia Kadhita Andriane, rank 2 was obtained with a final grade of 0.5327 on behalf of student Fio Abiyu Yahara, and rank 3 was obtained with a final grade of 0.5300 on behalf of student Robinson Pasaribu.
Komparasi Performa Klasifikasi Sentimen Masyarakat Terhadap Kurikulum Merdeka di Sekolah Menggunakan SVM dan KNN Apriyani, Risa Fitria; Megawaty, Dyah Ayu
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6877

Abstract

The Independent Curriculum is a strategic education policy that aims to increase learning flexibility and develop student competencies in the 21st century. This research focuses on analyzing public sentiment towards the implementation of the Independent Curriculum using two machine learning algorithms, namely Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). One of the main challenges in this study is the imbalance of sentiment data that includes negative, neutral, and positive classes. To overcome this, the Synthetic Minority Oversampling Technique (SMOTE) technique was applied to balance the distribution of data between classes. The results show that the SVM method is superior to KNN with an overall accuracy of 92% and a high F1-score in the majority class (Neutral: 96%), although the performance in the minority class (Negative: 43% and Positive: 40%) still needs improvement. In contrast, the KNN method recorded a lower overall accuracy of 31% but had a more even distribution of errors. After the implementation of SMOTE, there was a significant improvement in both methods, especially in recognizing minority classes. This study concludes that SVM is more effective for sentiment classification tasks on datasets with class imbalances, and recommends further exploration of ensemble methods to improve the quality of prediction and model generalization.
Perbandingan Algoritma K-Nearest Neighbor dan Support Vector Machine Pada Pengenalan Pola Tanda Tangan Digital Yadin, Yuli; Megawaty, Dyah Ayu
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6982

Abstract

In the fast-paced digital era, identity security has become crucial, and digital signatures play an important role in verification and authentication. This study focuses on the analysis and comparison of the performance of the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms in digital signature pattern recognition. Both algorithms are widely used in classification tasks, and this study aims to identify which algorithm is most effective in recognizing and classifying digital signatures with the highest accuracy. Digital signature data was collected from various sources, including public datasets and directly collected data. Key features were extracted using the Gray-Level Co-occurrence Matrix (GLCM) method, which is effective in describing the texture and pattern of the signature. These features were used to train the KNN and SVM classification models. The performance of both algorithms was evaluated based on accuracy, precision, and recall metrics. The results showed that KNN with a value of k = 3 achieved an accuracy of 91.42%, while SVM with a linear kernel excelled with an accuracy of 97.06%. In addition, SVM is also more stable in handling complex signatures and has higher precision and recall than KNN, at 97.52% and 97.06%, respectively. On the other hand, KNN is faster in the training process and has a simpler implementation. This study provides valuable insights into the selection of optimal classification algorithms for digital signature recognition applications. The results of this study can be a guide for security and authentication system developers in choosing the most effective method to protect identity and prevent signature forgery.
Co-Authors - Damayanti Aditia Yudhistira Agustina, Intan Ahmad Ari Aldino Akmal Junaidi Andi Nurkholis Apriyani, Risa Fitria Ari Sulistiyawati Chandra, Iryanto Damayanti Damayanti Dedi Darwis Erwanto Favorisen R. Lumbanraja Gibtha Fitri Laxmi Heni Sulistiani Irsan, Aqilla Hattami Kurniawan, M. Fadli Palupiningsih, Pritasari Parjito, Parjito Permata, Permata Riska Aryanti Ryan Randy Suryono Saputra, Williyandi Setiawan, Dandi Setiawansyah Setiawansyah Sitna Hajar Hadad Sumanto, Sumanto Susanto, Gregorius Nugroho Sutyarso, - Ulum, Faruk Wahyuda, Hana Wang, Junhai Waqas Arshad, Muhammad Wardana, Suwarman Adi Widiyanti, Adella Wirahudha, Muhammad Arif Yadin, Yuli Yuri Rahmanto