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Comparison of Distance Models on K-Nearest Neighbor Algorithm in Stroke Disease Detection Iswanto Iswanto; Tulus Tulus; Poltak Sihombing
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 4 No 1 (2021): June
Publisher : Unusa Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v4i1.2097

Stroke is a cardiovascular (CVD) disease caused by the failure of brain cells to get oxygen supply to pose a risk of ischemic damage and result in death. This Disease can detect based on the similarity of symptoms experienced by the sufferer so that early steps can be taking with appropriate counseling and treatment. Stroke detecting requires a machine learning method. In this research, the author used one of the supervised learning classification methods, namely K-Nearest Neighbor (K-NN). K-NN is a classification method based on calculating the distance to training data. This research compares the Euclidean, Minkowski, Manhattan, Chebyshev distance models to obtain optimal results. The distance models have been tested using the stroke dataset sourced from the Kaggle repository. Based on the test results, the Chebyshev model has the highest levels of accuracy compared to the other three distance models with an average accuracy value of 95.49%, the highest accuracy of 96.03%, at K = 10. The Euclidean and Minkowski distance models have the same level of accuracy at each K value with an average accuracy value of 95.45%, the highest accuracy of 95.93% at K = 10. Meanwhile, Manhattan has the lowest average compared to the other distance models, which is 95.42% but has the highest accuracy of 96.03% at the value of K = 6

Weighting Comparative Analysis Using Fuzzy Logic and Rank Order Centroid (ROC) in the Simple Additive Weighting (SAW) Method Alfin Ghazali; Poltak Sihombing; Muhammad Zarlis
CESS (Journal of Computer Engineering, System and Science) Vol 7, No 1 (2022): January 2022
Publisher : Universitas Negeri Medan

Decision Support System Method which is often referred to as the weighted addition method, one of which is Simple Additive Weighting. But the value of the weights in this system is not officially the calculation used. Therefore, usually a number of researchers combine this method with other methods to be more precise and accurate in supporting their decisions. In this study, the authors compare the results of the SAW method between the weighting based on the Fuzzy Logic method and the weighting based on the Rank Order Centroid (ROC) method. The case studied was the number of student satisfaction with learning outcomes during the Covid-19 pandemic. The results obtained are the number of students who are declared satisfied with learning during the Covid-19 pandemic as many as 6 students for the weighting of the Fuzzy Logic method and 5 students for the weighting of the Rank Order Centroid (ROC) method.

Peningkatan Nilai Keanggotaan Model Segitiga Samakaki Pada Metode Mamdani Dengan Menggunakan Defuzzyfication Mean Of Maximum (MOM) Peniel Sam Putra Sitorus; Poltak Sihombing; Sawaluddin Sawaluddin
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 6, No 1 (2021): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v6i1.3911

Di dalam paper ini membahas serta menguji mengenai membandingkan dua model segitiga untuk menaikkan fungsi keanggotaannya. Pada model membership function segitiga samakaki terdapat nilai yang menurun dan sedangkan di dalam segitiga siku-siku ternyata tidak terdapat nilai yang turun. Berhitung dengan metode mamdani, hasil input output dengan model segitiga samakaki lebih rendah dari pada model segitiga siku-siku. Ini menyebabkan bahwa model segitiga siku-siku ternyata tak termiliki belahan sisi turun dan titik atas nilai keanggotaannya satu, serta batas pendekatan variabel setelahnya tak termiliki. Di defuzzyfication mean of maximum ternyata model segitiga siku-siku tak meningkatkan membership function.

COMBINATION OF AES ALGORITHM WITH BLOWFISH ALGORITHM FOR FILE ATTACHMENT AT E-MAIL SENDING Yongki Iswo; Poltak Sihombing
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 2 No. 1 (2016): Maret 2016
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v2i1.21

Data security in the delivery of online file becomes very important in the world of information itself. One way that can be done for the security of the data is to perform encryption before the data is sent. Cryptographic message encoding divided into two symmetric and asymmetric. Kriptogarfi AES (Advanced Encryption Standard) is a symmetric cryptographic algorithms means that the key used in the encryption process is the same as the key to the decryption process. The analysis concludes theory, AES encryption process is designed to make the process of encoding in secret with no security level of complexity linear with time as efficiently as possible through the use of processes of transformation of light in the implementation. Aside from the AES algorithm, Blowfish algorithm is also a symmetric cryptographic algorithm. Theory analysis shows that Blowfih a cryptographic algorithm that uses a key with variable length provided that no more than 448-bit. Blowfish also combine non-reverse function f, keydependent S-Box, dun Feistel network. The process of encryption and decryption using the ECB and CBC operation has the same worst case is O (n). In this study the authors combine these two algorithms in the security of a file attachment in an email that is expected to increase the security file.

ANALISIS CONTRAST STRETCHING MENGGUNAKAN ALGORITMA EUCLIDEAN UNTUK MENINGKATKAN KONTRAS PADA CITRA BERWARNA Nurliadi Nurliadi; Poltak Sihombing; Marwan Ramli
Jurnal Teknovasi : Jurnal Teknik dan Inovasi Vol 3, No 1 (2016): Teknovasi April 2016
Publisher : LPPM Politeknik LP3I Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55445/teknovasi.v3i1.76

Contrast stretching merupakan metode peningkatan kontras pada citra, pada umumnya Contrast Stretching banyak digunakan untuk citra hitam putih (grayscale), penggunaan metode contrast stretching sering juga disandingkan dengan Histogram Equalization untuk melihat diagram dari hasil citra yang sudah diproses. Dari salah satu jurnal tentang Contrast Stretching membuktikan bahwa Contrast Stretching dapat juga diproses untuk citra berwarna, namun dalam prosesnya masih menggunakan cara manual atau melalui pergeseran control transformasi sebagai input peningkatan kontras, pada penelitian ini peneliti mencoba menerapkan salah satu Algoritma sebagai penentu titik transformasi antara R1,S1 dan R2,S2 dalam Contrast Stretching yaitu dengan menggunakan Algoritma Euclidean. Dengan mengambil jumlah nilai Contrast Stretching maka akan menghasilkan peningkatan kontras serta mendapatkan jarak Euclidean dari transformasi R1, S1 dan R2, S2.

ANALISIS CONTRAST STRETCHING MENGGUNAKAN ALGORITMA EUCLIDEAN UNTUK MENINGKATKAN KONTRAS PADA CITRA BERWARNA Nurliadi Nurliadi; Poltak Sihombing; Marwan Ramli
Jurnal Teknovasi : Jurnal Teknik dan Inovasi Vol 3, No 1 (2016): Teknovasi April 2016
Publisher : LPPM Politeknik LP3I Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55445/teknovasi.v3i1.75

Contrast stretching merupakan metode peningkatan kontras pada citra, pada umumnya Contrast Stretching banyak digunakan untuk citra hitam putih (grayscale), penggunaan metode contrast stretching sering juga disandingkan dengan Histogram Equalization untuk melihat diagram dari hasil citra yang sudah diproses. Dari salah satu jurnal tentang Contrast Stretching membuktikan bahwa Contrast Stretching dapat juga diproses untuk citra berwarna, namun dalam prosesnya masih menggunakan cara manual atau melalui pergeseran control transformasi sebagai input peningkatan kontras, pada penelitian ini peneliti mencoba menerapkan salah satu Algoritma sebagai penentu titik transformasi antara R1,S1 dan R2,S2 dalam Contrast Stretching yaitu dengan menggunakan Algoritma Euclidean. Dengan mengambil jumlah nilai Contrast Stretching maka akan menghasilkan peningkatan kontras serta mendapatkan jarak Euclidean dari transformasi R1, S1 dan R2, S2.

ANALISIS SUBSPACE CLUSTERING MENGGUNAKAN DBSCAN DAN SUBCLU UNTUK PROYEKSI PEKERJAAN ALUMNI PERGURUAN TINGGI Anni Rotua Aritonang; Sutarman Sutarman; Poltak Sihombing
Jurnal Teknovasi : Jurnal Teknik dan Inovasi Vol 2, No 1 (2015): Teknovasi April 2015
Publisher : LPPM Politeknik LP3I Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55445/teknovasi.v2i1.42

Subspace clustering diproyeksikan sebagai teknik pencarian untuk mengelompokkan data atau atribut pada klaster yang berbeda, Pengelompokan dilakukan dengan menentukan tingkat kerapatan data dan juga mengidentifikasi outlier atau data yang tidak relevan, sehingga masing-masing cluster ada dalam subset tersendiri. Tesis ini mengusulkan inovasi algoritma subspace clustering based on density connection. Pada tahap awal akan dihitung kerapatan dimensi, hasil kerapatan dimensi akan dijadikan data masukan untuk menentukan klaster awal yang berdasarkan kerapatan dimensi, yakni dengan menggunakan Algoritma DBSCAN. Data pada setiap klaster kemudian akan diuji apakah memiliki hubungan dengan data pada klaster yang lain, yakni dengan menggunakan Algoritma SUBCLU. Hasil dari penelitian ini ditemukan bahawa SUBCLU tidak memiliki un-cluster dataset nyata, sehingga persepsi hasil cluster akan menghasilkan informasi yang lebih akurat sedangkan untuk kepuasan kerja dataset DBSCAN membutuhkan waktu lebih lama daripada metode SUBCLU. Untuk lebih besar dan lebih kompleks data, kinerja SUBCLU terlihat lebih efisien daripada DBSCAN.

Blockchain in Land Registry Fauzi Amri; Poltak Sihombing; 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.v11i1.6537

Blockchain technology that is increasingly developing can be a solution to land disputes. By designing a land title certificate system based on Blockchain technology, which has complete verification and recording of data history. So that it can help the government's efforts in Agrarian Reform. This research resulted in a system of recording of data history of Land Certificate. that can prove the Blockchain concept where every change that occurs in land title certificate data can be recorded, and distributed to all participants involved in the system.

Performance Analysis Of Entropy Method In Determining Influence Of Self Organizing Map In Classification Process Victor Tarigan, S.Kom, M.Kom; Poltak Sihombing; Pahala Sirait
Jurnal Sains dan Teknologi ISTP Vol. 12 No. 1 (2019): Desember
Publisher : LPPM ISTP

Self Organizing Map (SOM) is the method to grouping topography shape of two dimension as a map that to get easy monitoring the result of grouping distribution. The process of SOM consist of 4 component, there are : inisialitation, competition, team work, and adaptation. From the fourth component, at the first initialitaion process, in initialitaion value quality beginning vector is according to randomly. The concequency from disseminating randomly is to sensitive forward accuration level because of unexacly in choosing quality beginning with the result that get bad enough of accuration to get better of accuration, we can choose one of method are entropy method. Entropy method is using for qualities or to get level of criteria importance based on atribut of dataset. At this research, entropy method is using to get beginning of qualities to algorithm SOM and to computing the accuration level with qualities of randomly scale. After the test with 3 dataset with total of class and the difference attribute then mean level of accuration to SOM method with entrophy is 67.8401% and with randomly is 51.1878%. The result is proving that the beginning quality with entropy is better with quality method beginning as randomly

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