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All Journal Prosiding Seminas Competitive Advantage Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Optimasi Sistem Industri Jurnal Edukasi dan Penelitian Informatika (JEPIN) Prosiding Semnastek Jurnal Ilmiah Teknik Industri Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Teknologi Informasi Komunikasi dan Industri Prosiding Seminar Nasional Pakar Jurnal ULTIMATICS Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Ilmiah Teknik Industri Abdimas Universal Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Proceeding ISETH (International Summit on Science, Technology, and Humanity) Jurnal AKAL: Abdimas dan Kearifan Lokal JURNAL TEKNIK INDUSTRI JURNAL ABDI MASYARAKAT INDONESIA (JAMIN) Literasi: Jurnal Pengabdian Masyarakat dan Inovasi

Dedy Sugiarto

Universitas Trisakti

Author-ID : 388741

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Peramalan harga beras IR64 kualitas III menggunakan metode Multi Layer Perceptron, Holt-Winters dan Auto Regressive Integrated Moving Average Anung B. Aribowo; Dedy Sugiarto; Iveline Anne Marie; Jeany Fadhilah Agatha Siahaan
Ultimatics : Jurnal Teknik Informatika Vol 11 No 2 (2019): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

This paper aims to present the analysis of price movements of IR64 quality III at the Cipinang Rice Main Market (PIBC) and the accuracy comparison of forecasting using Multi Layer Perceptron (MLP), Holt-Winters, and Auto Reggressive Integrated Moving Average (ARIMA) method. The data are daily price from 1 January 2016 to 31 May 2018 sourced from PT. Food Station. The analysis shows that the price of IR64 quality III rice tends to rise towards the end of 2016 and 2017. This is related to the decrease in the level of rice supply by January each year which encourages PT Food Station to conduct market operations to control the price of rice in the market. The results of accuracy comparison show that the MLP produces a value of Root Mean Square Error (RMSE) of 5,67, Holt-Winters exponential smoothing with trend and additive seasonal component produces a value RMSE of 70.71 and ARIMA method with parameters (1,1,2) resulted in RMSE values of 58.71. The RMSE values of the MLP method have smaller values than the Holt Winter and ARIMA methods which indicate that the MLP method is more accurate

Komparasi Metode Multilayer Perceptron (MLP) dan Long Short Term Memory (LSTM) dalam Peramalan Harga Beras Steven Sen; Dedy Sugiarto; Abdul Rochman
Ultimatics : Jurnal Teknik Informatika Vol 12 No 1 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Rice is one of the main commodities in Indonesian society. The main problem with rice nationally is inflation of rice prices. Therefore, this research predicts the price of rice using Multilayer Perceptron (MLP) artificial neural network architecture and deep learning: Long Short Term Memory (LSTM) to anticipate these problems. The data used in this study are real data on rice prices during 2016 - 2019 obtained from PT. Food Station. The total dataset is 1307 with the distribution of 1123 as data train and 184 as test data. The final results obtained in this study are LSTM superior to MLP, with the value of Root Mean Square Error (RMSE) training data: 0.49 RMSE loss value of test data is 0.27. The most optimal LSTM model from 3 tests was carried out, namely the number of hidden layers = 16 and epochs = 150 times.

Peramalan Utilisasi Perangkat Jaringan dan Bandwidth Dengan Metode Holt-Winters dan Multi Layer Perceptron Muhammad Taufiq; Dedy Sugiarto; Abdul Rochman
Ultimatics : Jurnal Teknik Informatika Vol 12 No 1 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Network devices become an important medium for transferring data from one node to another node in the form of switches, routers or network security devices. The reliability of network devices must be maintained both in terms of device resources and bandwidth. The study was conducted by applying the Holt-Winters and Multi Layer Perceptron (MLP) method to network device and bandwidth data utilization. The two methods are compared to assess which accuracy is better when applied to network device and bandwidth utilization data by calculating Root Mean Squared Error (RMSE) and Mean Absolute Percentage (MAPE). The results of the measurement of accuracy in the network device testing data, MLP produces a value of RMSE of 5,67 and MAPE of 2.34, and Holt-Winters produces a value of RMSE of 14.56 and MAPE of 2.95. For the results of the measurement of accuracy in the bandwidth testing data with MLP produces a value of RMSE of 0.13 and MAPE of 7.27, and Holt-Winters produces RMSE values of 2.59 and MAPE of 134.31. Based on the results of these measurements it is concluded that the MLP method has a smaller error value compared to the Holt-Winters method applied to network device and bandwidth utilization data with a span of 3 years historical data.

The Elastic Stack Ability Test To Monitor Slowloris Attack on Digital Ocean Server Is Mardianto; Dedy Sugiarto; Krisna Aditama Ashari
Ultimatics : Jurnal Teknik Informatika Vol 13 No 2 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i2.2209

Servers have a central role in computer network. The server is in charge of serving user requests with various types of services. Every server activity in handling these things will generate different types of logs. Information from this large amount of logs is often ignored and has not been widely used as material for analyzing the performance of the server itself. In this study, Elastic Stack is functioned as a system that handles upstream to downstream processes starting from collection, transformation, and storage as well as graphical visualization of the Nginx web server given an attack scenario in the form of massive incoming connection requests and server login access attempts. The Elastic Stack components used as log collectors are Filebeat and Metricbeat for system metric data. For testing attacks using the Slowloris tool which will consume web server resources. The results of the research that have been carried out are when there are 500 incoming connections, the web server can serve requests normally, at 1000 connections there are some packets that are not served, the server becomes unable to access when it reaches a total of 2000 incoming connections. Metric data in the form of CPU Usage and Memory Usage are affected, although not significantly. Identification of IP Address shows the source of the attack comes from Singapore, according to the domicile of the attacker's computer. All access data in the form of username, time, origin of region trying to enter the server are recorded by the system.

Sentiment Analysis of An Internet Provider Company Based on Twitter Using Support Vector Machine and Naïve Bayes Method Farhan Hashfi; Dedy Sugiarto; Is Mardianto
ULTIMATICS Vol 14 No 1 (2022): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v14i1.2384

Tweets from users in the form of opinions about a product can be used as a company evaluation of the product. To obtain this evaluation, the method that can be used is sentiment analysis to divide opinions into positive and negative opinions. This study uses 1000 data from Twitter related to an internet service provider company where the data is divided into two classes, namely 692 positive classes and 308 negative classes. In the Tweet there are still many words that are not standard. Therefore, previously carried out the initial process or preprocessing to filter out non-standard words. Before doing the classification, the data needs to be divided into training data and test data with a ratio of 90:10, then processed using the Support Vector Machine and Naïve Bayes techniques to get the results of the classification of positive opinions and negative opinions. The level of accuracy in the classification using the Support Vector Machine is 84% and using Naïve Bayes is 82%.

Co-Authors A.A. Ketut Agung Cahyawan W Abdul Rochman Abdul Rochman Ahmad Zuhdi AINUL YAQIN Alya Shafa Nadia Annisa Dewi Akbari Anung B Ariwibowo Anung B. Aribowo Arviandri Naufal Zaki Ashari, Krisna Aditama Azhar Rizki Zulma Betha Ariandini Binti Solihah Chani Anugerah Cicilia Puji Rahayu Dadan Umar Daihani Dadan Umar Daihani Dadang Surjasa, Dadang Dara Mustika Dimmas Mulya Dita Mayasai Dorina Hetharia Dorina Hetharia Dorina Hetharia Elfira Febriani Elita Wahyu Firdasari Ema Utami Emelia Sari Farhan Hashfi Febriana Lestari Fitria Nabilah Putri Gatot Budi Santoso Grace Listiani Gunawan, Muhamad Ichsan Habyba, Anik Nur Ida Jubaedah Ida Jubaidah Idriwal Mayusda Illah Sailah Indah Permata Sari Is Mardianto Is Mardianto Is Mardianto Is Mardianto, Is Iveline Anne Marie Iveline Anne Marie Iwan Purwanto Jeany Fadhilah Agatha Siahaan Johnson Saragih Khoirun nisa Krisna Aditama Ashari Lukmanul Hakim Lukmanul Hakim M Syamsul Ma’arif Marie, Iveline Anne Marimin . Marimin Marimin Muhamad Ichsan Gunawan Muhamad Ichsan Gunawan Muhammad Hidayat Tullah Muhammad Ichsan Gunawan Muhammad Taufiq Noufal Zhafira Nurlailah Badariah PUDJI ASTUTI Putri Shan ASP Randy Andy Ratna Mira Yojana Ratna Mira Yojana Ratna Shofiati Ratna Shofiati Ratna Shofiati Rianti Dewi Sulamet-Ariobimo Ricky Saputera Ridho Rachmat Giffary S. Dewayana, Triwulandari Steven Sen Suharto Honggokusumo Sukardi Sukardi Syandra Sari Syandra Sari Syandra Sari Tasya Aulia Teddy Siswanto Teddy Siswanto Tiena Gustina Amran Tiena Gustina Amran Titik Susilowati Titik Yusrini Triwulandari S Dewayana Triwulandari S. Dewayana Triwulandari Satitidjati Dewayana Viera Astry Wawan Kurniawan Winnie Septiani Winnie Septiani Winnie Septiani Yuli Kurnia Ningsih

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