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Implementation of EDAS Method in the Selection of the Best Students with ROC Weighting Darwis, Dedi; Sulistiani, Heni; Megawaty, Dyah Ayu; Setiawansyah, Setiawansyah; Agustina, Intan
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v20i2.7904

This study aims to provide recommendations for the best students to be selected using the EDAS method and ROC weighting, so as to help schools in decision making. The EDAS method requires a lot of input, and preference must be precise in the determination of the weight of the criteria. To fix the problem of weighting criteria in the EDAS method, the Centroid Rank Order (ROC) method is used. ROC is a simple method used to assign weight values to each criterion used. The results of this study provide recommendations for the best students to be selected using the EDAS method and ROC weighting, so as to help schools in decision making. The application of the EDAS method in the selection of exemplary student candidates resulted in exemplary prospective students obtained on behalf of Hadi Santoso with a final score of 0.70885 and obtained 1st rank. The results of these recommendations can help the school determine the selection of the best students by applying the EDAS method and ROC weighting.

Sistem Pendukung Keputusan Pemilihan Siswa Praktik Kerja Lapangan Terbaik Menggunakan Metode A New Additive Ratio Assessment Megawaty, Dyah Ayu
Jurnal Media Borneo Vol. 1 No. 3 (2024): Volume 1 Number 3 April 2024
Publisher : CV. Keranjang Teknologi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/mediaborneo.v1i3.61

Student Field Work Practice (PKL) is an educational program that provides students with opportunities to gain work experience in an industrial or corporate environment. Through PKL, students can apply the knowledge they have learned in school in a real-world context, as well as gain an understanding of the work process and demands of a particular profession. The problem in this study is the difficulty in assessing the quality and potential of students holistically. Student performance evaluations are often based solely on academic achievement or previous work experience, which may not fully reflect the student's abilities and personality in the actual work environment. The purpose of this study is to implement the A New Additive Ratio Assessment method in the development of a decision support system to select the best PKL students. Using the A New Additive Ratio Assessment method, this study aims to identify and obtain optimal ratings from student candidates based on a number of predetermined criteria. Through the application of the A New Additive Ratio Assessment method provides an effective tool for decision makers, in selecting PKL students who best suit the needs and requirements set. The recommendation results show the rank of 1 best PKL student with an optimization function value of 0.9695, namely IS Students. These results can be a recommendation for companies in determining the Best PKL Students by applying the A New Additive Ratio Assessment method.

APLIKASI SMART VILLAGE DALAM PENERAPAN GOVERMENT TO CITIZEN BERBASIS MOBILE PADA KELURAHAN CANDIMAS NATAR Erwanto, Erwanto; Megawaty, Dyah Ayu; Parjito, Parjito
TELEFORTECH : Journal of Telematics and Information Technology Vol 2, No 2 (2021): TELEFORTECH VOL 2 No. 2 (JANUARI 2022)
Publisher : Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/tft.v2i2.3704

Penerapan smart village saat ini masih belum banyak diterapkan seperti kelurahanCandimas Kecamatan Natar yang berlokasi di Kabupaten Lampung Selatan dan memiliki jumlah penduduk 10470 Jiwa dengan 226 Kepala Keluarga. Berdasarkan jumlah penduduk tersebut tentunya pihak desa perlu meningkatan layanan kepada masyarakat sebagai bentuk inovasi berupa desa pintar dengan memanfatkan teknologi informasi.Berdasarkan hasil wawancara yang dilakukan kepada pihak kelurahan diperoleh permasalahan seperti proses pengolahan data yang dilakukan secara keseluruhan masih manual yaitu dengan pencatatan pada buku maupun media cetak melalui media office, hal tersebut berdampak pada proses pengolahan data yang lambat, kerusakan data akibat data arsip berupa media kertas hingga kehilangan dan manipulasi data. Permasalahan berikutnya yaitu penyampaian informasi kepada masyarakat berupa kegiatan maupun pengumuman masih dilakukan menggunakan papan pengumuman ataupun menggunakan pamflet, sehingga dampak yang timbul yaitu tingginya biaya operasional dan cakupan informasi yang terbatas.Metode yang digunakan yaitu extreme programming dengan pembentukan sistem berorientasi objek serta media penyimpanan menggunakan Mysql. Tujuan penelitian yang dilakukan untuk menghasilkan media informasi bagi masyarakat kepada keluarahan untuk memperoleh layanan. Hasil penelitian berupa aplikasi mobile yang diakses oleh masyarakat untuk mempermudah melakukan permohonan surat, pengaduan, kritik dan info pajak serta informasi. Kata Kunci: Smart Village, Government To Citizen, Kelurahan Candimas, Natar

Employing PIPRECIA-S weighting with MABAC: a strategy for identifying organizational leadership elections Setiawansyah, Setiawansyah; Hadad, Sitna Hajar; Aldino, Ahmad Ari; Palupiningsih, Pritasari; Fitri Laxmi, Gibtha; Megawaty, Dyah Ayu
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7713

The election of organizational leaders, especially in organizations whose members have diverse backgrounds and interests, can cause various problems. Problems in the selection of school organization leaders include the absence of an objective selection of organizational leadership candidates because they are selected based on comparisons between candidates without considering the criteria in the selection of organizational leadership candidates. Research related to the multi-attributive border approximation area comparison (MABAC) and simplified pivot pairwise relative criteria importance assessment (PIPRECIA-S) methods has never been conducted so far, so it is a reference in conducting this research using the MABAC and PIPRECIA-S methods. This study aims to select the head of the school organization using the MABAC method and PIPRECIA-S weighting can increase the objectivity of the criteria assessment results by relying on calculations from the PIPRECIA-S weighting method. Based on the selection results using the MABAC method and PIPRECIA-S weighting, candidate 1 was recommended as the leader of the school organization because it achieved rank 1 with a total score of 0.293. The contribution of this research is to help in the selection of the head of the organization using the PIPRECIA-S and MABAC methods as a decision-making solution.

Perbandingan Algoritma K-Nearest Neighbor dan Support Vector Machine Pada Pengenalan Pola Tulisan Tangan Widiyanti, Adella; Megawaty, Dyah Ayu
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7757

Handwriting is a biometric characteristic because each person has a unique handwriting pattern. This uniqueness can be utilized as a biometric identity. Handwriting pattern recognition is one of the important fields in document analysis to biometric authentication. This research explores the implementation of K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) algorithms in the context of handwriting pattern recognition. In addition, this research incorporates digital image processing technology by utilizing feature extraction using Gray-Level Co-occurrence Matrix (GLCM). This process involves taking handwriting samples, digitizing them into digital images, and utilizing GLCM to extract texture features. These features play an important role in capturing the unique characteristics of each handwriting pattern. This research was conducted because handwriting has a wide implementation in various fields. In the field of data security, handwriting recognition can be used for data verification in financial transactions and official documents. A comparison of the K-NN and SVM algorithms was conducted to determine the most effective and efficient algorithm in handwriting pattern recognition. These two algorithms are very popular and often used in classification. By comparing these two algorithms, this research aims to evaluate and compare the performance of two classification algorithms in handwriting pattern recognition so as to provide recommendations for implementation in handwriting pattern recognition. The main focus of this research is to investigate the effectiveness and accuracy of the K-NN and SVM algorithms in recognizing and classifying handwriting. K-NN algorithm produces the highest accuracy value of 82.11%, while the SVM algorithm produces the highest accuracy value of 83.87%, so that the SVM algorithm becomes the best algorithm in the classification of handwriting pattern recognition.

Combination of Weighted Product Method and Entropy Weighting in the Best Warehouse Employee Recommendation Waqas Arshad, Muhammad; Darwis, Dedi; Sulistiani, Heni; Suryono, Ryan Randy; Rahmanto, Yuri; Megawaty, Dyah Ayu; Setiawansyah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2095

The best warehouse employees are individuals who show exceptional dedication and precision in carrying out their duties. Not only do they ensure that every process, from receipt to delivery, is carried out with high accuracy, but they are also proactive in finding ways to improve operational efficiency. The main problem lies in the proper assessment of employees' technical skills and soft skills, such as rigor, time management ability, and teamwork. Additionally, the selection process can be complicated when it comes to balancing previous work experience and adaptability to new technologies. Without effective assessment methods, the risk of selecting the wrong employee can negatively impact the overall productivity and operational efficiency of the warehouse. The purpose of the study, which combines entropy weighting method with the WP method is an approach that can increase objectivity and accuracy in multi-criteria decision-making. In this combination, the Entropy method is used first to objectively determine the weight of the criteria based on the degree of variation or information contained in the data of each criterion. The weights generated by the Entropy method reflect the importance of criteria based on how much information is provided, assuming that criteria with more variety have more information. Once the weights are determined, the Weighted Product method is used to evaluate and rank alternatives. Based on the results of the recommendation for the selection of the best warehouse employee Hadi occupies the top position in the selection of the best warehouse employee with a score of 0.07194. His position was followed by Putri who got a score of 0.07082, showing a performance that was also very good and only slightly below Hadi. Budi is ranked third with a score of 0.06621, while Deni is ranked fourth and Kiki is fifth with a score of 0.06544 and 0.06524, respectively. The score obtained by each employee shows a relatively small difference, reflecting the fierce competition and high quality of performance among the employees

APLIKASI AUGMENTED REALITY MENGGUNAKAN METODE MARKERLESS UNTUK PROMOSI UNDANGAN BERBASIS MOBILE Nurkholis, Andi; Megawaty, Dyah Ayu; Irsan, Aqilla Hattami
Jurnal Teknoinfo Vol 19, No 1 (2025): January 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v19i1.4597

Pelayanan dan pengelolaan informasi pada percetakan CV. Jastra card sejauh ini masih menggunakan cara konvensional untuk mempromosikan percetakan undangan pelanggan datang ke percetakan untuk melihat undangan yang tersedia. Promosi konvensional tersebut menghadapi beberapa tantangan, seperti halnya biaya yang tinggi, terbatasnya jangkauan, serta perkembangan tren digital. Dengan semakin berkembangnya teknologi dan penetrasi internet, banyak orang beralih ke media digital untuk mencari informasi dan produk. Promosi konvensional kurang efektif dalam menjangkau target pasar yang lebih muda atau lebih terhubung secara digital. Untuk mengatasi permasalahan ini, bisnis undangan dapat mempertimbangkan strategi promosi yang lebih terarah dan efektif. Beberapa contoh strategi yang bisa diterapkan adalah memanfaatkan pemasaran digital, beriklan secara online melalui suatu platform yang interaktif, seperti halnya teknologi augmented reality. Penelitian ini bertujuan membangun Aplikasi Augmanted reality berbasis mobile menggunakan metode markerless sebagai media promosi, mempermudah pelanggan, menghemat waktu dan biaya. Sistem dibangun menggunakan metode pengembangan waterfall dengan memanfaatkan beberapa perangkat lunak tambahan, yakni Unity 3D, Sketchup dan Blender. Kelayakan sistem berhasil memperoleh penilaian dengan predikat Sangat Baik yang merupakan representasi dari Skala Likert dari pengujian ISO 25010 yang mencakup aspek functional suitability mencapai 100% dan aspek usability mencapai 91.2%.

A New Feature Extraction Approach in Classification for Improving the Accuracy of Proteins Damayanti, -; Lumbanraja, Favorisen Rosyking; Junaidi, Akmal; Sutyarso, -; Susanto, Gregorius Nugroho; Megawaty, Dyah Ayu
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2589

Proteins play a vital role in life as essential macromolecules, consisting of linear heteromeric biopolymers formed by amino acids covalently bonded through peptide bonds. They contribute to cell development and bolster the body's defense mechanisms. Post-translational modification processes, such as glycosylation, are necessary for proteins to function optimally. Glycosylation involves adding sugar groups to proteins, playing a critical role in various protein folding processes. Dysregulation of protein glycosylation can lead to diseases like Alzheimer's and cancer. Manual classification of glycosylated proteins is time-consuming, necessitating a faster approach. This study aims to expedite glycosylated protein classification using novel methods like AAindex, CTD, SABLE, hydrophobicity, and PseAAC for increased accuracy, comparing them with existing approaches. The dataset comprises protein sequences sourced from the openly accessible UniProt database. Results demonstrate that glycosylated protein prediction achieved 100% accuracy, surpassing previous approaches. Several features contributed to this improvement, with Hydrophobicity making a significant contribution at 24%, and PseAAC making the most significant contribution at 40% among the five extraction methods developed.

Development of a Decision Support System Based on New Approach Respond to Criteria Weighting Method and Grey Relational Analysis: Case Study of Employee Recruitment Selection Megawaty, Dyah Ayu; Damayanti, Damayanti; Sumanto, Sumanto; Permata, Permata; Setiawan, Dandi; Setiawansyah, Setiawansyah
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2744

The purpose of this research is to propose a new approach in the criteria weighting method using the RECA method, the RECA method can help provide a systematic and structured framework for determining criteria weights in multi-criteria decision making. The determination of weights using the RECA method is to increase objectivity and accuracy in the candidate assessment and selection process by determining the appropriate weight for each criterion based on responses and assessments from experts or stakeholders. Testing the RECA Method with Multi Attribute Decision Making (MADM) techniques is an important step in measuring the effectiveness of the RECA Method in the context of multi-criteria decision making. Ranking tests using Spearman correlation between the RECA method and other methods such as SAW with a correlation value of 1, MOORA with a correlation value of 0.9636, MAUT with a correlation value of 0.9515, WP with a correlation value of 0.891, SMART with a correlation value of 0.9636, and TOPSIS with a correlation value of 0.8788 show a high level of rank consistency between the RECA method and these methods. This indicates that the RECA Method has a strong ability to generate similar candidate rankings with other methods, validating its reliability and consistency in the context of multi-criteria decision making. Implications for further research include exploring the application of the RECA method in different decision-making contexts other than recruitment, such as performance evaluation, project selection, or supplier selection. Further research could investigate the integration of the RECA method with other decision-making methods or algorithms to improve its performance and applicability in complex decision environments. Comparative studies with larger sample sizes and diverse datasets can provide deeper insights into the effectiveness and reliability of the RECA method compared to other methods.

Perbandingan Algoritma K-Nearest Neighbor dan Support Vector Machine Pada Pengenalan Pola Tulisan Tangan Widiyanti, Adella; Megawaty, Dyah Ayu
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7757

Handwriting is a biometric characteristic because each person has a unique handwriting pattern. This uniqueness can be utilized as a biometric identity. Handwriting pattern recognition is one of the important fields in document analysis to biometric authentication. This research explores the implementation of K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) algorithms in the context of handwriting pattern recognition. In addition, this research incorporates digital image processing technology by utilizing feature extraction using Gray-Level Co-occurrence Matrix (GLCM). This process involves taking handwriting samples, digitizing them into digital images, and utilizing GLCM to extract texture features. These features play an important role in capturing the unique characteristics of each handwriting pattern. This research was conducted because handwriting has a wide implementation in various fields. In the field of data security, handwriting recognition can be used for data verification in financial transactions and official documents. A comparison of the K-NN and SVM algorithms was conducted to determine the most effective and efficient algorithm in handwriting pattern recognition. These two algorithms are very popular and often used in classification. By comparing these two algorithms, this research aims to evaluate and compare the performance of two classification algorithms in handwriting pattern recognition so as to provide recommendations for implementation in handwriting pattern recognition. The main focus of this research is to investigate the effectiveness and accuracy of the K-NN and SVM algorithms in recognizing and classifying handwriting. K-NN algorithm produces the highest accuracy value of 82.11%, while the SVM algorithm produces the highest accuracy value of 83.87%, so that the SVM algorithm becomes the best algorithm in the classification of handwriting pattern recognition.

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