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Analisis dan Perancangan Perangkat Lunak Kompresi Citra Menggunakan Algoritma Fast Fourier Transform (FFT) Rima Lestari; Marihat Situmorang; Maya Silvi Lydia
Alkhawarizmi Vol 1, No 1 (2012): Jurnal Alkhawarizimi
Publisher : Alkhawarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (458.15 KB)

Kecepatan pengiriman informasi dalam bentuk perpaduan data teks, audio maupun citra secara nyata merupakan aspek penting dalam pertukaran informasi. Kecepatan pengiriman ini sangat bergantung kepada ukuran dari informasi tersebut. Pada umumnya informasi yang berupa citra akan membuat file menjadi lebih besar sehingga mempengaruhi kecepatan proses pengiriman informasi. Salah satu solusi untuk masalah di atas adalah dengan melakukan kompresi data citra sebelum ditransmisikan. Kompresi data adalah mengurangi ukuran file atau meminimalkan kebutuhan memori untuk merepresentasikan sebuah file digital. Pada penelitian ini dilakukan kompresi file citra dengan teknik lossy menghasilkan file citra hasil kompresi lebih kecil dari file semula karena adanya data yang dihilangkan. Data yang dihilangkan tersebut tidak akan terlihat oleh kasat mata manusia secara kualitas. Teknik kompresi yang tepat untuk keperluan diatas adalah transformasi citra, dimana yang digunakan adalah Tranformasi Fast Fourier. Hasil kompresi dengan perangkat lunak yang dibangun menunjukkan ukuran file citra yang dihasilkan berkurang rata-rata untuk format BMP sebesar 87,02 % dan format JPG sebesar 41,11 %.

Peningkatan Akurasi Metode K-Nearest Neighbor dengan Seleksi Fitur Symmetrical Uncertainty Anirma Kandida Br Ginting; Maya Silvi Lydia; Elviawaty Muisa Zamzami
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3254

Accuracy of K-Nearest Neighbor (KNN) tends to be lower than other classification methods. The cause of this is related to the attributes used and the percentage of the influence of these attributes on the classification process in a data. And also attributes with less relevant influence can be a problem in determining the new class. One way that can be done to overcome this is by doing Feature Selection. In this research, the author selects features on K-Nearest Neighbor by using Symmetrical Uncertainty to remove attributes that have an unfavorable effect from the data set. Testing of the proposed method uses data sets obtained from the UCI Machine Learning Repository. The results obtained from testing the proposed method using feature selection with Symmetrical Uncertainty are able to increase the classification accuracy of KNN, with an increase in accuracy obtained after feature selection is 3.00 %.

Sistem Pendukung Keputusan Penilaian Kinerja Guru Selama Pembelajaran Daring menggunakan Metode Vikor Sedihati Kayan Lumbangaol; Erna Budhiarti Nababan; Maya Silvi Lydia
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.3798

Teacher is a profession that has important role for the progress of education literacy, primarily in this current digitalization era that implements the online learning system. Therefore, the assesment of teacher’s performance during online learning is needed to find the advantages and disadvantages of each teacher, with an aim to get an evaluation that can be utilized to fix or improve the teacher’s performance. This study proposes a decision support system that applies the Vikor method as a solution to get the result of teacher’s performance assessment during online learning and make it easier for the decision makers. By using 4 research criteria and 5 alternatives, this research shows that A5 on behalf of Kayan Marbun with a value of 0.5025 is chosen as the teacher with the best performance.

PEMODELAN PERENCANAAN TERINTEGRASI UNTUK RANTAI SUPLAI DAN STOK PENGAMAN MULTI ESELON Irwitadia Hasibuan; Opim Salim Sitompul; Maya Silvi Lidya
JISTech (Journal of Islamic Science and Technology) Vol 4, No 1 (2019)
Publisher : UIN Sumatera Utara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/jistech.v4i1.5344

Business environment has strong competition from year to year. This is because various changes and uncertainties fill the competition. Most of the changes or uncertainties in the business world are caused by the increasing of consumer bargaining power in business practices. Consumers have high power in determining their requests that must be fulfilled by business people. Changes or uncertainties are most of the main factors that cannot be anticipated when the business world has strong and uncertain competition. These uncertainties require business people to design an appropriate plan in order to minimize costs, especially inventory costs with consumer demand are still fullfilled. In that design plan, business people must be able to optimize the supply chain. In industrial systems, supply chain optimization and its response are strongly influenced by inventories. Inventories and its numbers are important issues in the supply chain that must be integrated with the optimization of the supply chain to manage demand uncertainty and to maintain customer service levels. This study designs an integrated planning model for supply chain and multi-echelon inventory in determining the location and the numbers of inventory in a supply chain with a general configuration with considering the uncertainty of consumer demand or all things coming from production time

Model Dynamic Facility Location in Post-Disaster Areas in Uncertainty lili Tanti; Syahril Efendi; Maya Silvi Lydia; Herman Mawengkang
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.2095

Indonesia has many disaster-prone areas, natural disasters that occur in Indonesia in 2021 are 5,402 disasters. For disaster management in post-disaster areas, logistical planning is needed in the distribution of logistical assistance, it is estimated that the logistics costs of disaster assistance reach approximately 80% of the total costs in disaster management so that logistical assistance is an expensive activity of disaster relief. However, so far the process of distributing logistical assistance to disaster posts has not been evenly distributed. One of the causes of the unequal distribution is the inappropriate selection of distribution post locations. The facility location model is dynamic and has the objective function of minimizing the distance between emergency posts and refugee posts in terms of distribution of disaster relief goods in one cluster group. For grouping unsupervised learning data using a machine learning clustering algorithm, k-means. Model validation has been carried out using max run and max optimization 1000 times with results reaching 90%. This proves that the emergency facility location model can be used to determine the location of the emergency center, where the determination of the location of the emergency center has the closest distance to the request point/post shelter for disaster victims

Optimization of Support Vector Machine Algorithm Using Stunting Data Classification Saraswati Yoga Andriyani; Maya Silvi Lydia; Syahril Efendi
Prisma Sains : Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Vol 11, No 1: January 2023
Publisher : IKIP Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/j-ps.v11i1.6619

Several studies from Indonesia reveal that malnutrition and stunting are still severe concerns to be addressed in the future. The complexity of the problem of stunting or nutritional status requires the responsibility of all parties, including science and technology. The issue of monitoring and data collection related to stunting or the nutritional status of children in Indonesia, especially Medan City, North Sumatra Province, is an essential factor in determining the calculations carried out by each Community Health Center with many attributes. Currently, the Support Vector Machine method is a solution to increase government intervention's effectiveness in classifying malnutrition and stunting. However, the Support Vector Machine algorithm still needs to improve, namely the difficulty of selecting the right and optimal features for the attribute weights, causing a low prediction accuracy. Therefore, researchers aim to optimize the Support Vector Machine Algorithm with Particle Swarm Optimization using Linear, Polynomial, Sigmoid, and Radial Basic Function kernels. The results were obtained from research utilizing nutritional status data, that performance in improving the Support Vector Machine algorithm based on Particle Swarm Optimization using four kernel tests, namely Linear, Polynomial, Sigmoid, and Radial Basic Function obtained different results, not all kernels in this study can improve accuracy well. The best performance is using the Radial Basic Function kernel with an Accuracy value of 78%, Precision of 89%, Recall of 66%, and F1-Score of 72%, so it is feasible for accurate information regarding the classification of nutritional status.

Pengamanan Pada Citra Digital dengan Menggunakan Modifikasi Blok Data Algoritma AES - Rijndael Muhammad Haris; Maya Silvi Lydia; Sutarman Sutarman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5458

Digital Image Security is one of the most important information security topics today. Along with the increasing use of digital images both for communication and documentation purposes, the security of information contained in digital images needs serious attention. Rijndael is a cryptographic method that is not only used for text encryption but also for digital image encryption. Just like block-based cryptographic methods in general, encoding data bytes only has an effect on the internal environment of the block, because the transformation process in Rijndael is done separately for each input block. In digital images, this can result in visible patterns or shapes of objects contained in the image, especially when using the Rijndael standard block size of 4x4. To improve the quality of digital image encryption on Rijndael, several studies have made modifications, especially at the transformation stage. In terms of data, the statistical values obtained from the encryption results such as the correlation coefficient do show an increase, but visually the pattern of objects is still visible and modifications tend to be high. This research proposes a modification of Rijndael which focuses on increasing the input block size from 4x4 to 8x8 with minimal changes to the transformation function. The results showed that the value of the correlation coefficient was better and the results of the encryption visually disguised the shape of the object more than the usual Rijndael, especially in text images, logos and caricatures. From the process carried out there is an increase in the quality of security for the encryption process by 13.22% to 91.48%.

Reduksi Atribut Menggunakan Chi Square untuk Optimasi Kinerja Metode Decision Tree C4.5 Anirma Kandida Br Ginting; Maya Silvi Lydia; Elviawaty Muisa Zamzami
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 1 (2023): Volume 9 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i1.56542

Pada metode decision tree C4.5, proses split atribut masih belum dapat secara maksimal mengoptimalkan kinerja akurasi pada decision tree yang disebabkan oleh noisy pada atribut yang kurang relevan. Hal tersebut berimplikasi terhadap ukuran dari pohon keputusan menjadi over-fitting sehingga perolehan akurasi pengujian menjadi kurang maksimal. Reduksi atribut merupakan salah satu cara yang dapat dilakukan dalam melakukan seleksi terhadap atribut data yang memiliki persentase pengaruh cenderung kecil sehingga diharapkan mampu dalam meningkatkan akurasi pada metode klasifikasi data. Adapun metode yang diusulkan pada penelitian ini yang digunakan untuk mereduksi atribut yang kurang relevan dari dataset yaitu dengan metode Chi Square sehingga menghasilkan atribut yang mempunyai pengaruh besar terhadap data dan kemudian diklasifikasikan menggunakan decision tree C4.5. Untuk melakukan pengujian terhadap model yang diusulkan, maka penelitian ini menggunakan dataset dari kaggle.com yaitu South Germany Credit yang terdiri dari 1000 records data dengan 20 atribut. Evaluasi kinerja klasikasi yang diusulkan yaitu berdasarkan Confusion Matrix. Dari hasil uji metode yang diusulkan, didapatkan kesimpulan bahwa metode yang diusulkan mampu meningkatkan akurasi decision tree c4.5 dengan rata-rata peningkatan akurasi sebesar 2.5%.

Perbandingan Metode Klaster dan Preprocessing Untuk Dokumen Berbahasa Indonesia Amalia Amalia; Maya Silvi Lydia; Siti Dara Fadilla; Miftahul Huda
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

Clustering is an unsupervised method to group multiple objects based on the similarity automatically. The quality of clustering accuracy is determined by the number of similar objects in a correct cluster group. The robust preprocessing process and the choice of cluster algorithm can increase the efficiency of clustering. The objective of this study is to observe the most suitable method to cluster document in Bahasa Indonesia. We performed tests on several cluster algorithms such as K-Means, K-Means++ and Agglomerative with various preprocessing stages and collected the accuracy of each algorithm. Clustering experiments were conducted on a corpus containing 100 documents in Bahasa Indonesia with a commonly used preprocessing scenario. Additionally, we also attach our preprocessing stages such as LSA function, TF-IDF function, and LSA / TF-IDF function. We tested various LSA dimension reductions values from 10% to 90%, and the result shows that the best percentage of reduction rates between 50%-80%. The result also indicates that K-Means++ algorithm produces better purity values than other algorithms.

Perbandingan Metode Klaster dan Preprocessing Untuk Dokumen Berbahasa Indonesia Amalia Amalia; Maya Silvi Lydia; Siti Dara Fadilla; Miftahul Huda
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v14i1.9027

Clustering is an unsupervised method to group multiple objects based on the similarity automatically. The quality of clustering accuracy is determined by the number of similar objects in a correct cluster group. The robust preprocessing process and the choice of cluster algorithm can increase the efficiency of clustering. The objective of this study is to observe the most suitable method to cluster document in Bahasa Indonesia. We performed tests on several cluster algorithms such as K-Means, K-Means++ and Agglomerative with various preprocessing stages and collected the accuracy of each algorithm. Clustering experiments were conducted on a corpus containing 100 documents in Bahasa Indonesia with a commonly used preprocessing scenario. Additionally, we also attach our preprocessing stages such as LSA function, TF-IDF function, and LSA / TF-IDF function. We tested various LSA dimension reductions values from 10% to 90%, and the result shows that the best percentage of reduction rates between 50%-80%. The result also indicates that K-Means++ algorithm produces better purity values than other algorithms.

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