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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF SCIENCE AND SOCIAL RESEARCH KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Varian KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JUKI : Jurnal Komputer dan Informatika MEANS (Media Informasi Analisa dan Sistem) Jurnal Ipteks Terapan : research of applied science and education Data Sciences Indonesia (DSI) International Journal of Economic, Technology and Social Sciences (Injects)

Zakarias Situmorang

Universitas Katolik Santo Thomas

Author-ID : 3488175

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PERBANDINGAN AKURASI ALGORITMA NAÏVE BAYES, K-NN DAN SVM DALAM MEMPREDIKSI PENERIMAAN PEGAWAI Novendra Adisaputra Sinaga; B Herawan Hayadi; Zakarias Situmorang
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 5 No 1 (2022)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v5i1.446

To supporting academic and non-academic activities, the Polytechnic Business Indonesian (PBI) must be supported by employees with reliable Human Resources (HRD) who have good behavior, good abilities and can complete work professionally and responsibly. Conventional techniques for analyzing existing large amounts of data cannot be handled which is the background for the emergence of a new branch of science to overcome the problem of extracting important information from data sets, which is called Data Mining. Utilizing methods to classify data by utilizing methods including: Naïve Bayes method, K-Nearest Neighbor (K-NN) and Supervise Vector Machine (SVM). From this research, in Predicting Applicants Graduation at PBI, the SVM method is better than Naïve Bayes and K-NN. With 33 test data used, SVM has 84.9% accuracy, 85.1% precision while K-NN has 81.8% accuracy, 84.1% precision and Naïve Bayes has 78.8% accuracy and 80.1% precision.

ANALISIS VARIATION K-FOLD CROSS VALIDATION ON CLASSIFICATION DATA METHOD K-NEAREST NEIGHBOR Ridha Maya Faza Lubis; Zakarias Situmorang; Rika Rosnelly
Jurnal Ipteks Terapan (Research Of Applied Science And Education ) Vol. 14 No. 3 (2020): Re Publish Issue
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah X

To produce a data classification that has data accuracy or similarity in proximity of a measurement result to the actual numbers or data, testing can be done based on accuracy with test data parameters and training data determined by Cross Validation. Therefore data accuracy is very influential on the final result of data classification because when data accuracy is inaccurate it will affect the percentage of test data grouping and training data. Whereas in the K-Nearest Neighbor method there is no division of training data and test data. For this reason, researchers analyzed the determination of training data and test data using the Cross validation algorithm and K-Nearest Neighbor in data classification. The results of the study are based on the results of the evaluation of the Cross Validation algorithm on the effect of the number of K in the K-nearest Neighbor classification of data. The author tests using variations in the value of K K-Nearest Neighbor 3,4,5,6,7,8,9. While the training and test data distribution using Cross validation uses variations in the number of K-Fold 1,2,3,4,5,6,7,8,9,10

COMPARATIVE OF ID3 AND NAIVE BAYES IN PREDICTID INDICATORS OF HOUSE WORTHINESS Ade Clinton Sitepu; Wanayumini -; Zakarias Situmorang
Jurnal Ipteks Terapan (Research Of Applied Science And Education ) Vol. 14 No. 3 (2020): Re Publish Issue
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah X

Decision making is method of solving problems using certain way / techniques so that can beaccepted. After making some calculations and considerations through several stages, the decisionhave taken that decision maker goes through. This stage will be selected until the best decision hasmade. Decision-making aims to solve problems that solve problems so that decisions with finalgoals can be implemented properly and effectively. This study uses a simulation of decision makingfrom seven attributes to the proportion of the feasibility of a house based on data from CentralStatistics Agency (BPS). There are several techniques for presenting decision making including: ID3(decision tree) algorithm concept and Naïve Bayes algorithm. Both classification are learningsuperviseddata grouping. ID3 algorithm depicts the relationship in the form of a tree diagramwhereas Naïve Bayes makes use of probability calculations and statistics. As a result, in datatraining, decision trees are able to model decision making more accurately. The prediction resultsusing the decision tree model = 90.90%, while Naïve Bayes = 72.73%. Meanwhile, the speed of theNaive Bayes algorithm is better

ALGORITMA C45 DALAM MEMPREDIKSI MINAT CALON MAHASISWA Zakarias Situmorang; Sartika Mandasari; Yuni Franciska; Karina Andriyani; Puji Sari Ramadhan
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 5, No 1 (2022): February 2022
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v5i1.809

Penelitian ini membahas tentang memprediksi minat calon mahasiswa sebelum mendaftar di program studi yang dituju. Dalam memprediksi minat mahasiswa STMIK Triguna Dharma belum memiliki sebuah sistem yang mampu melakukan prediksi dalam mengetahui minat calon mahasiswa yang akan mendaftar(Haryoto et al., 2021). Untuk menyelesaikan permasalahan tersebut maka dibutuhkan sebuah algoritma yang mampu menghasilkan keputusan tentang minat calon mahasiswa yang akan mendaftar. Berdasarkan penelitian yang telah dilakukan maka diperoleh hasil 4 aturan baru dengan menggunakan kriteria jenis kelamin, minat, jurusan asal sekolah dan hobi. Dengan hasil ini dapat diketahui bahwa algoritma C45 telah terbukti berhasil melakukan analisis terhadap minat calon mahasiswa baru diÂ STMIK Triguna Dharma.

Perancangan Pemesanan Jasa Bengkel Mobil Kota Medan Berbasis Web Menggunakan Metode Hill Climbing Search Jupri Manungkalit; Zakarias Situmorang
KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer Vol 2 Nomor 2
Publisher : UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/kakifikom.v2i2.939

Aturan pelayanan servis mobil diawali dengan pemesanan jasa teknisi mobil, kemudian konsumen menunggu hingga pesanan diterima. Permasalahan yang terjadi di bengkel ZEE adalah Sistem Bengkel yang dilakukan belom bisa melakukan pemesan jika ada konsumen kerusakan mobil dijalan. Maka perlunya dibuat sistem peracangan pemesanan jasa teknisi bengkel mobil. Untuk dapat memudakan masysarakat dalam pencarian jasa teknisi mobil. keuntungan dalam mengguunakan pemesanan jasa teknisi mobil, masyarakat tidak perlu mendatangi teknisi atau toko servis mobil, masyarakat serta mempermudah dalam pencarian jasa teknisi mobil, dan lebih efesien dibandingkan harus mencari-cari seorang teknisi maupun toko teknisi mobil.

APLIKASI TANDA TANGAN DIGITAL DENGAN ALGORITMA GOST UNTUK KEAMANAN PENGIRIMAN FILE DOKUMEN Edunal Yoppi; Zakarias Situmorang
KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer Vol 3 Nomor 1
Publisher : UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/kakifikom.v3i1.1196

Tanda tangan digital adalah sebuah tanda tangan yang berbasiskan skema kriptografi. Tanda tangan digital dibuat dengan memanfaatkan kriptografi kunci publik. Algoritma GOST adalah salah satu algoritma kunci publik yang dapat digunakan untuk sistem tanda tangan digital. Mekanisme kerja algoritma GOST cukup sederhana dan mudah dimengerti tetapi kokoh. Keamanan GOST terletak pada sulitnya memfaktorkan bilangan yang besar menjadi faktor-faktor prima. Perangkat lunak untuk simulasi sistem tanda tangan digital akan dibangun dengan menggunakan bahasa pemrograman Java. Perangkat lunak yang dibangun akan menjelaskan proses tanda tangan digital yang meliputi proses pembentukan kunci, pembentukan tanda tangan dan proses verifikasi.

Analisa Distance Metric Algoritma K-Nearest Neighbor Pada Klasifikasi Kredit Macet Khairul Fadhli Margolang; Muhammad Mizan Siregar; Sugeng Riyadi; Zakarias Situmorang
Journal of Information System Research (JOSH) Vol 3 No 2 (2022): Januari 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Data mining is a method that can classify data into different classes based on the features in the data. With data mining, non-performance loan categories can be classified based on data on lending from cooperatives to their members. This study uses K-Nearest Neighbor to classify non-performance loan categories with various distance metric variations such as Chebyshev, Euclidean, Mahalanobis, and Manhattan. The evaluation results using 10-fold cross-validation show that the Euclidean distance has the highest accuracy, precision, F1, and sensitivity values compared to other distance metrics. Chebyshev distance has the lowest accuracy, precision, sensitivity, while Mahalanobis distance has the lowest F1 value. Euclidean and Manhattan distances have the highest reliability values for true-positive and true-negative class classifications. Mahalanobis distance has the lowest reliability value for false-positive class classification, while Chebyshev distance has the lowest value for false-negative class classification

Analisis Pemasaran Bisnis dengan Data Science : Segmentasi Kepribadian Pelanggan berdasarkan Algoritma K-Means Clustering Mawaddah Harahap; Yusniar Lubis; Zakarias Situmorang
Data Sciences Indonesia (DSI) Vol. 1 No. 2 (2021): Article Research Volume 1 Number 2, Desember 2021
Publisher : ITScience (Information Technology and Science)

Dalam makalah ini kami menyajikan analisis kepribadian pelanggan dalam membantu bisnis untuk memodifikasi produknya berdasarkan target pelanggan dari berbagai jenis segmen pelanggan sehingga menemukan pelanggan yang potensial, membuat pemasaran agar lebih efektif, melihat tren dalam perilaku pembelian pelanggan dan membuat penawaran produk yang relevan kepada pelanggan. Kerangka kerja Data Science (ilmu data) dengan metodologi CRIS-DM diterapkan untuk memberikan pemahaman bisnis, pemahaman data, analisis data dan pemodelan. Pada tahapan pemodelan diusulkan Principal component analysis (PCA) untuk pengurangan dimensial fitur, kemudian algoritma K-Means untuk segmentasi pelanggan dengan menggunakan metode ellow dan silhouette yang menghasilkan nilai k=4 yang paling optimal. Terakhir, hasil 4 cluster di analisis berdasarkan proposi, belanja, pendidikan dan tingkat keberhasilan kampanye yang disajikan secara visualisasi.

Artificial Neural Network Backpropagation Method to Predict Tuberculosis Cases Valencya Lestari; Herman Mawengkang; Zakarias Situmorang
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2023): Articles Research Volume 8 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11998

Artificial neural networks are information processing systems that have certain performance characteristics in common with biological neural networks. Backpropagation is a method in artificial neural networks that uses supervised learning. Backpropagation has a weakness in reaching the convergence level. The convergence rate is the difference from the mean square error value. The mean square error is the difference between the target value and the actual value. One way to increase the convergence rate is to provide input values. in this study using the nguyen widrow backpropagation method. The network will predict Tuberculosis cases. Data sourced from the North Sumatra Provincial Health Office from 2019 to 2021. architectural testing with a learning rate ranging from -0.5 to 0.5 and momentum ranging from 0 to 1 obtained a learning rate of 0.5, the epoch process stops at the 172nd iteration with an achievement gradient of 0.0001598 and the R value for training data is 0.99841 which means it is very good because it is close to 1 with an accuracy rate of 81.82%.

Improvement Ranking Accuracy of Weighted Aggregated Sum Product Assessment With Lambda Variable Muhadi M. Ilyas Gultom; Erna Budhiarti Nababan; Zakarias Situmorang
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.v7i1.5280

Conventional methods are still used in selecting the best students in the various institutions depending on the subjectivity of each member of the assigned committee. In order to make an objective decision, it is necessary to have a method that can consider the criteria used to select the candidates to be elected. The decision-making method used in this study is Weighted Aggregated Sum Product Assessment(WASPAS). This study aims to analyze the increase in accuracy of the WASPAS method that occurs in the implementation of the lambda variable in the process of combining the Weight Product Method(WPM) and Weight Sum Method(WSM). This method is use because it is suitable for the case studied where the application of this method focuses on weighting criteria with a dynamic number of alternatives and low computational complexity providing good performance in handling large amounts of data.The application of this method uses data from students from Engineering Faculty of Universitas Islam Sumatera Utara which is tested on 10 students with criteria adapted from student data attributes that can be used as parameters for decision making. The results of this study show an increase for each alternative with an average value of 23.6% for each alternative. From this study it can be concluded that accuracy is highly dependent on variations in lambda values which are affected by the determinant operator in the equation used. Therefore it is possible to find an absolute equation to give optimal effect on a single value without variation by considering the bias of the effect of the WASPAS method on the lambda variable in future research.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//

Co-Authors Abwabul Jinan Ade Clinton Sitepu Ade Clinton Sitepu Aditia Rangga Agus Fahmi Limas Putra Asyahri Hadi Nasyuha B Herawan Hayadi Daim Azhari Parinduri Edunal Yoppi Erna Budhiarti Nababan Herman Mawengkang Ita Juwita Saragih Jaka Kusuma Junaidi Junaidi Jupri Manungkalit Karina Andriyani Khairul Fadhli Margolang Khoirunsyah Dalimunthe Masri Wahyuni Mawaddah Harahap Muhadi M. Ilyas Gultom Muhammad Mizan Siregar Novendra Adisaputra Sinaga P.P.P.A.N.W. Fikrul Ilmi R.H. Zer Puji Sari Ramadhan Raden Aris Sugianto Ridha Maya Faza Lubis Ridha Maya Faza Lubis Rika Rosnelly Rika Rosnelly Rimbun Siringoringo, Rimbun Roslina, Roslina Sartika Mandasari Sugeng Riyadi Valencya Lestari Wanayumini Yuni Franciska Yusniar Lubis

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