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All Journal Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Mantik Jurnal Teknik Informatika C.I.T. Medicom Journal of Computer Networks, Architecture and High Performance Computing Data Sciences Indonesia (DSI) Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat
Yennimar Yennimar
Universitas Prima Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Physics Social Sciences

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ANALISIS PERFORMA ALGORITMA BLOWFISH DAN TEA DALAM PENGAMANAN DATA: ANALISIS PERFORMA ALGORITMA BLOWFISH DAN TEA DALAM PENGAMANAN DATA Yennimar Yennimar; Annisa Fadhlila; Pratiwi Pratiwi; Anita Yose Fanny Manurung; Risda Amelia Amanta Br Ginting
Jurnal Mantik Vol. 3 No. 2 (2019): Augustus: Manajemen, Teknologi Informatiak dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Keamanan maupun kerahasiaan data menjadi hal penting untuk diperhatikan dikarenakan keamanan data berhubungan dengan pencegahan dari pencurian data oleh pihak yang tidak bertanggung jawab. Teknik yang dapat digunakan untuk mengamankan data yaitu dengan kriptografi. Beberapa metode kriptografi memiliki performa yang baik dan buruk tergantung dengan tipe kuncinya. Maka dari itu, tujuan dari penelitian ini adalah mengukur tingkat kecepatan dari algoritm Blowfish dan algoritma TEA, dengan tipe data text dan gambar. Hasil dari penelitian ini Algoritma Blowfish dalam proses enkripsi dan dekripsi data lebih aman dan lebih unggul dari algoritma TEA. Rata-rata kecepatan enkripsi algoritma Blowfish untuk file gambar 650ms dan deskripsi 637ms sedangkan TEA waktu kecepatan enkripsi 685ms dan deskripsi 699ms. Pada file docx algoritma Blowfish memiliki kecepatan proses enkripsi 31ms dan deskripsi 94ms dan Algoritma TEA waktu kecepatan enkripsi 157ms dan deskripsi 141ms.
Comparison Of Jenkins Box Method And Multiple Linier Regression In Predicting The Noble Metal Price: Comparison Of Jenkins Box Method And Multiple Linier Regression In Predicting The Noble Metal Price Bayu Gunara; Yennimar Yennimar
Jurnal Mantik Vol. 3 No. 4 (2020): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Nowadays, gold investment practitioners generally use instinct and guess in investing in gold. This is certainly a problem because it has a high error margin. To solve these problems, the forecasting process can be carried out.To be able to forecast gold prices with low error rates, various studies have been conducted. The Box-Jenkins method performs better than other methods in predicting the price of gold, because the Box-Jenkins method applies forecasting by relying on the historical statistics of gold prices beforehand. The Box-Jenkins method is an iterative of choosing the best model for the stationary series of a group of linear time series models called the ARIMA (Autoregressive Integrated Moving Average) model. However, the ARIMA method is a complex method and is not easy to use and requires a long execution time to obtain forecasting results with a high degree of accuracy. To improve the accuracy of prediction results from ARIMA, the ARIMA method can be combined with the multiple regression method into a hybrid method. The Multiple Linear Regression Method is a mathematical technique that minimizes the difference between the actual value and the predicted value.The results of this study are an application of forecasting the price of gold using the ARIMA method and Multiple Linear Regression. The application also provides a facility to test the results of the methods used. Based on the results of testing the accuracy of the prediction results from the hybrid method with 30 data = 48%, 60 data = 40%, and 118 data = 40.81%.
Implementation Of Extreme Learning Machine Method With GVF Snake For Character Recognition: Implementation Of Extreme Learning Machine Method With GVF Snake For Character Recognition Pradana Yusna; Yennimar Yennimar
Jurnal Mantik Vol. 4 No. 1 (2020): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

In everyday life, sometimes it is necessary to change the contents of the printouts of certain documents, but the digital files from the printed documents have been lost. Retype the document manually will certainly spend a lot of time making it inefficient. To solve this problem, the printed document can be scanned into a digital image file and a character recognition system is implemented to recognize the characters contained therein. In this study, the Gradient Vector Flow Snake (GVF Snake) method is used to determine the boundaries of an object based on the computed vector gradient in the form of binary or gray-level values ??obtained from an image with several frameworks. After that, the Extreme Learning Machine (ELM) method will be used to predict the characters that have been broken up by the GVF Snake method. The results of this study are software that applies the GVF Snake and ELM methods to perform the character recognition process of an image printed by a document.
Comparison of Spread Spectrum with Redundant Pattern Coding In Securing Text Messages Into Audio: Comparison of Spread Spectrum with Redundant Pattern Coding In Securing Text Messages Into Audio Ray Sanjaya Gulo; Reni Lusiana Simangunsong; Stephen Boy Kristian Tafonao; Muhammad Yusuf; Yennimar Yennimar
Jurnal Mantik Vol. 4 No. 2 (2020): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.928.pp1237-1242

Abstract

The transmission of information from one place to another is mostly constrained by the security problems of the information itself. Moreover, this information is very confidential, so not just anyone can open it. One of the ways that can be done to hide confidential information is by using cryptographic techniques, namely by encoding the information using certain algorithms. The second way is to insert the information into certain media, such as digital images or audio, so that the information will be hidden and what will appear is the media only, while the information is disguised. In this study, the comparison of the Spread Spectrum steganography algorithm with the Redundant Pattern Coding algorithm to secure text messages in audio files. The data used are audio files with a size between 200 Kb to 265 Kb and text with a size of 50 bytes to 275 bytes. The experimental results obtained stego audio files from the insertion of the Spread Spectrum algorithm with an average PSNR value of 41,138 db and for the Redundant Pattern Coding algorithm the average PSNR value was 29,885 db.
Customer Churn’s Analysis In Telecomunications Company Using Fp-Growth Algorithm: Customer Churn’s Analysis In Telecomunications Company Using Fp-Growth Algorithm Kelvin Kelvin; Cindy Cindy; Charles Charles; Denny Peter Leonardo; Yennimar Yennimar
Jurnal Mantik Vol. 4 No. 2 (2020): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.933.pp1285-1290

Abstract

Nowadays the competition between companies is increasing. Companies need to predict their customers to find out the level of customer loyalty. One way is to analyze customer data by doing Customer Churn Prediction. In this study the method used is the FP-Growth Algorithm. The FP-Growth algorithm is an algorithm that uses the association rules technique to determine the data that appears most frequently. The data used in this study are secondary data and have 7,403 data from customers. The data has 21 variables. By using a minimum support of 1.2% and confidence at 80%, the associative rules generated are 60. The variable of the type of internet the customer has is strong enough to predict churn. It can be seen that of the 60 associative rules, there are 36 associative rules that have this variable. Testing associative rules on test data yields an accuracy of 71%.
Comparative Analysis of Five Modulus an Pictorial Block Algorithms For Data Hiding in Digital Images Syandi Nainggolan; Yennimar Yennimar
Jurnal Mantik Vol. 4 No. 3 (2020): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.976.pp1663-1670

Abstract

Steganography is an art and science that studies invisible communication from confidential data on a multimedia carrier such as image, audio and video files. The most popular steganography method is the LSB (Least Significant Bit) method. However, the LSB method is very vulnerable to attack by using basic image processing operations. In 2013, Jassim applied the Five Modulus method in the steganography process. Five Modulus method will solve a digital image into a set of image sub-blocks called windows with size n x n. A secret message will be inserted in that window. According to Jassim, the smaller the window size, the more secret messages that can be inserted into the image. Meanwhile, in the Pictorial Block steganography algorithm, the secret information will be stored in a grayscale digital image file and converted to ASCII values ??and the length of the information will be calculated. After that, the digital image will be divided into 2n x 2n blocks using the block truncation coding (BTC) algorithm. Then, the block will be converted to binary format and incorporate the original information on its decomposition matrix. The pictorial block steganography algorithm uses the BTC algorithm to convert the grayscale input image block into a binary image block so that the secret information bit insertion operation can be performed. The resulting steganography software can hide confidential data into a digital image. The secret data stored in the stego image can be extracted out in the extraction process.
Implementation of Clustering Methods to Know Electricity Energy Sold Average Customer Type Reinhard Oktaveri Gultom; Estomih Wau; Jodi Alexsander Laia; Ivan Leonardo Silaban; Yennimar Yennimar
Jurnal Mantik Vol. 5 No. 2 (2021): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jurnalmantik.Vol5.2021.1475.pp1119-1126

Abstract

The need for electrical energy in Indonesia continues to increase from time to time. In realizing the need for electrical energy, it is important to order electrical energy based on the type of customer. In collecting this electrical energy, grouping is needed. The results of the gathering can be utilized by PT. PLN to anticipate disruptions in the supply of electrical energy. This investigation plans to determine the electrical energy sold by type of customer (KWH) in 2016 to 2019 in Indonesia and dissect the power requirements in 2019. The strategy used is group development using rapidminner. The variables that cause the utilization of the tourism industry in Indonesia are Population, Economic and Industrial Developments. To meet the need for the use of electrical energy which continues to increase from year to year, the matters and groupings of the use of electrical energy for the use of electrical energy were completed in 2014 to 2019. The information and targets used in the meeting were gross domestic electricity, group information and power information. (per customer type and power utilization). from 2011 to 2015. The side effect of ordering electrical energy in Indonesia using the fuzzy bunching strategy was 272,630 KWH in 2019, an increase of 85,089 KWH with an annual normal increase of 6.96%. the result group of the strategy has a normal of 18,730 KWH against RUPTL.
Analysis of Sales Vitamins Drugs Covid-19 Pandemic with the Random Forest Method Yennimar Yennimar; Miko Pasaribu; Laila Hendrina; Ayu Widila; Dono Sitinjak
Jurnal Mantik Vol. 5 No. 3 (2021): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

During the covid-19 pandemic that occurred in Indonesia until the new normal period, consumer demand for a vitamin drug was fast and unpredictable, thus making pharmacies must be able to plan the optimal supply of vitamin drugs based on the graph of public demand during the pandemic and new normal. This means that the pharmacy does not experience a shortage of stock or excess stock. Therefore, all systems that can predict sales of vitamin drugs are needed during the new normal pandemic. To be able to make predictions, an appropriate method is needed to get accurate results. One of them is the Random Forest method. With Random Forest, the data is predicted based on attribute data so as to produce a conclusion about the value of the desired attribute. Based on this study, 200 sales data of vitamin drugs were taken during the pandemic and new normal with categories of vitamin B complex drugs, vitamins C, D, E, K and Multivitamins and the attributes used were vitamin brand, category, sales conditions, price and sales category. and doesn't work. The test results from this study, it was found that the prediction of vitamin drug sales during the new normal pandemic using the Random Forest method obtained 100% accuracy for testing data with a composition of 80:20, 70:30 and 60:40, so that vitamin drugs would still be sold in new normal.
Comparison Analysis of SVM Algorithm with Linear Regression in Predicting used Car Prices Yennimar Yennimar; Kelvin Kelvin; Suwandi Suwandi; Amir Amir
Jurnal Mantik Vol. 5 No. 4 (2022): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

During the high activity , car has become a basic need. On the other hand, the price of new car is getting higher. To meet these needs, people are looking for alternatives by buying used cars. One of the factors to consider when looking for a used car is price. In this study, two algorithms that are quite popular in terms of prediction will be tested, namely the Support Vector Machine algorithm and the Linear Regression algorithm in predicting used car prices. Support Vector Machine is a supervised learning method that analyzes data and recognizes patterns for regression. Support Vector Machine has the ability to solve linear and nonlinear problems. Linear Regression Algorithm is a modeling and analysis of numerical data consisting of one or more independent variables and the value of the dependent variable, with the aim of using regression analysis to estimate the value of the dependent variable based on the value of the independent variable. The result of this research is that the SVM method can perform better than linear regression. SVM can perform kernel-tricks that can handle non-linear data, thus making the non-linear data appear to be linear. but this cannot be done by Linear regression.
Estimation Of Drug Stocks In Pharmacies In The Covid-19 Era Using The Fp-Growth Algorithm Yennimar; Johanes T. Gultom; William S. Purba; Dewi S. Sihotang
Jurnal Mantik Vol. 6 No. 2 (2022): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i2.2823

Abstract

In the era of covid-19, the response in controlling covid-19 requires an adequate supply of medicines in hospitals and pharmacies. The supply of these drugs is felt to be very important because seeing the impact of the covid-19 virus is very dangerous for human health. Therefore, the existence of the supply of medicines in pharmacies during this Covid era really needs to be considered. Every transaction of the sale of the drug in pharmacies is always recorded. This sales transaction data can be processed to find certain patterns in selling drugs in a certain period of having so many drug sales transaction activities. If the sales transaction data is analyzed, a pattern can be found out that is very helpful in estimating drug stocks from the drug sales data. To be able to estimate drugs, the right method is needed in order to get accurate results. One of them is the Fp-Growth method. With Fp-Growth, data is entered for calculations based on drug sales data so as to produce conclusions of minimum support and minimum confidence values. Based on this study, 35 data on drug sales transactions were taken. The test results of this study obtained calculation results from simultaneous drug sales data that are often purchased by consumers with the highest accuracy are 17.14% and 50%, namely Paracetamol and CTM.
Co-Authors Amir Amir Anita Christine Sembiring Anita Yose Fanny Manurung Annisa Fadhlila Antonius Nyomadi Ayu Widila Bayu Gunara Charles Charles Christin Erniati Panjaitan Cindy Cindy Denny Peter Leonardo Dewi S. Sihotang Dini M. Hutagalung Dono Sitinjak Estomih Wau Evan Chandra Sibarani Fachrul Rozi Gerson Tandiono Irwansyah Irwansyah Ivan Leonardo Silaban Jodi Alexsander Laia Johanes T. Gultom Julfan Farman Zebua kelvin kelvin Kristina Ginting Laila Hendrina Landong Hutasoit Lena LH Hasibuan M. Anhar Guci M. Rafi Faturrahman Mawaddah Harahap Miko Pasaribu Muhammad Iqbal Tri Rahmadi Muhammad Yusuf Pradana Yusna Pratiwi Pratiwi Ray Sanjaya Gulo Reinhard Oktaveri Gultom Reni Lusiana Simangunsong Reza Syahputra Risda Amelia Amanta Br Ginting Samuel Rifaldi Pasaribu Siregar, Saut Dohot Siwa Nesen Sri Wahyuni Tarigan Standley Malvin Japardy Stephen Boy Kristian Tafonao Steven Steven Sunarly Wijaya Suwandi Suwandi Syandi Nainggolan Toppan Sintio Uni Pratama Tarigan Widya Fernanda Putri Wikesyah Putri Sitorus William S. Purba Yans Y.M.P. Situmorang