Article Per Year (5 Year)
p-Index From 2021 - 2026
2.148
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Adinugroho, Sigit
Brawijaya University, Malang
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 79 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 79 Documents
Search

 1   2   3   4   5 
Implementasi Metode Text Mining dan K-Means Clustering untuk Pengelompokan Dokumen Skripsi (Studi Kasus: Universitas Brawijaya) Muhammad Sholeh Hudin; Mochammad Ali Fauzi; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1213.336 KB)

Abstract

Research or final assignment is a requirement of graduation students. Every year the research becomes increasing and allows the students to take the same or similar topics. Through this research developed an application to classify student thesis reports. The results of this grouping also indicate that the themes are varied and when the themes becomes non-varied. Student research reports or commonly called a thesis report can be grouped by theme, object or method of the research. The process of extracting this thesis is done by using text mining technology. Then the process of grouping thesis document can be done by using k-means clustering method on a set of thesis documents by taking abstract, keywords and table of contents as an important information that represents the content of the document. Then the document will be done preprocessing first by using text mining method. To process the preprocessing is divided into several parts, namely tokenisasi, filtering, stemming and term weighting. After the document passes through the preprocessing process, then the document can be grouped by using the method of k-means clustering. In this experiment, trials are conducted by entering the number of clusters that vary. From the results of the analysis by entering the different cluster values have obtained the optimal value by entering the number of with the resulting silhouette value 0,483695522.
Clustering Dokumen Skripsi Dengan Menggunakan Hierarchical Agglomerative Clustering Danang Aditya Wicaksana; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (323.076 KB)

Abstract

A minor thesis is a document of a scientific work compiled by a student at the level of stratum 1 which discusses a particular topic or field of research or development results that the student has undertaken in order to take the final examination to obtain a degree. In the Reading Room of the Faculty of Computer Science and the Central Library of Brawijaya University there is a problem that arises that there is no categorization of all minor thesis documents stored. Hierarchical Agglomerative Clustering (HAC) method is implemented for clustering minor thesis documents based on minor thesis title. HAC classifies iterative documents from the smallest cluster to the largest 1 cluster. Input data that is in the form of title of minor thesis document of Informatics Engineering Brawijaya University. The preprocessing stage is performed on the minor thesis title data to get the term feature. All the terms obtained are processed to get the weight of TF-IDF. The value of similarity between documents obtained from the value of cosine distance. The clustering process uses 3 distance options as the single linkage, complete linkage and average linkage parameters. The clustering results of each distance parameter are displayed on the label of each cluster generated and each cluster generated is evaluated using silhouette coefficient. From the test result on 100 minor thesis documents obtained the value of Silhouette Coefficient from single linkage is 0,10125, complete linkage is 0,155733 and average linkage is 0,160428. Average linkage is better in grouping documents than single linkage and complete linkage.
Analisis Sentimen Twitter Menggunakan Ensemble Feature dan Metode Extreme Learning Machine (ELM) (Studi Kasus: Samsung Indonesia) Alqis Rausanfita; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (428.668 KB)

Abstract

Business activity is very crucial and has a real impact on organizational growth and ROI (Return Of Investment) is to understand and respond appropriately sentiment from customers by conducting a sentiment analysis. The sentiment analysis can be a guide to evaluate a company's product, service, reputation, brand reputation, and the company can be a market leader supported by a very emotional customer condition so that disappointing products / services will lose the customer's commitment even customers will find it difficult to recover customer experience if a company does not care about customer sentiment. Based on the explanation, this research is done using ensemble feature and Extreme Learning Machine for Twitter sentiment analysis. The data used in this research is 72 tweets with the ratio of the amount of training and testing data 70:30 where the amount of data per class is balanced. Prior to the classification of data is done preprocessing, weighting ensemble feature, and weighting the word. The result of this research is get the best hidden neuron number as much as 5000, best activation function is sigmoid bipolar, and ensemble feature influence to accuracy result. Twitter sentiment analysis using ensemble feature and Extreme Learning Machine method in Samsung Indonesia case study did not get high accuracy. Accuracy in getting only amounted to 42.857 percent. The low accuracy caused by sparse data matrix resulting in overfitting which then resulted in low classification results.
Sistem Pendukung Keputusan Pemilihan Skuad Utama Tim Bola Voli Menggunakan Metode AHP-TOPSIS Hangga Eka Febrianto; Muhammad Tanzil Furqon; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (391.552 KB)

Abstract

Exercise is one of the physical activities that a person does to maintain and mprove the quality of health. One of the most widely played sports is volleyball. Brawijaya University, which is one of the major universities in Malang, currently also has several volleyball teams organized by UB Volleyball Activity Unit (UABV-UB). In every year UABV-UB always receives new member registration for students who want to join. Seeing the development and the amount of nterest then this makes UABV-UB having difficulty in choosing the players. So in this to solve the problem is used method AHP-TOPSIS. The AHP method is used for weighting which consists of making matched pair matrices, calculating matrix normalization, computing consistency test and producing krteria weight. While Topsis consists of paired normalization process of alternative data, after calculating the weighted normalization value of AHP and paired normalization process TOPSIS. The weighted normalization value will be used to find the positive and negative deal solution value as well as the distance between positive and negative deal solutions. The value is used to calculate the preference value of each alternative. Then do a ranking against the preference value. The result of system accuracy obtained from the test result is 85.7%.
Analisis Sentimen Konten Radikal Melalui Dokumen Twitter Menggunakan Metode Backpropagation Brian Andrianto; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (315.327 KB)

Abstract

Twitter is a social networking service where users can post and interact with messages, known as "tweets". Twitter is also used by some people to give their opinions on something but sometimes too excessive even sometimes found a tweet that smells radical. The radical actions that exist in social media are usually referred to as radical content. The radical content available on social media can certainly harm some parties. There are also certain parties who utilize radical content to achieve certain goals. Therefore, in this study try to analyze the Indonesian tweet that contains the word radical, including in the content of radical positive or negative radical. Tweet can be from twitter that contains public opinion that leads to radical content will be classified. Tweet can be called a document or data will first go through the preprocessing process. Then the document was broken into 6 types of words, including the nouns, verbs and adjectives where each type of word will be divided again into positive and negative. After the break will be calculated how many the number of types of words in each document so that it can be converted into numbers that can then be incorporated into the algorithm formula.
Klon Perilaku Menggunakan Jaringan Saraf Tiruan Konvolusional Dalam Game SuperTuxKart Arrizal Amin; Yuita Arum Sari; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (597.803 KB)

Abstract

One of the important component of the video game is an artificial intelligence to make the game more competitive. Artificial intelligence used to decide action to reach goal in the game and challenge game player. In the process of developing artificial intelligence, developer needs to program an aritficial intelligence to make a decision for action for each states possible in the game. In this research, artifical neural network will be used as an artificial intelligence inside video game. Neural network will simplify process of developing artificial intelligence because developer does not have to program an algorithm to decide each action for each possible states in the game. Furthermore, neural network can learn or clone gamer's behavior while playing the game. In this research, SuperTuxKart will be used for an example to develop artificial intelligence inside video game. Artificial Intelligence with learning rate 0.0001, momentum 0.3 and epoch 100 reaches accuracy 86.72% for cloning game's behavior while playing video game. So this research concluded that neural network can be used as an artificial intelligence inside game.
Implementasi Metode Backpropagation Untuk Peramalan Luas Area Terbakar di Hutan dengan Inisialisasi Bobot Nguyen-Widrow Afrizal Aminulloh; Sigit Adinugroho; Ahmad Afif Supianto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (346.969 KB)

Abstract

Forest fires are a serious event that must be watched out for areas dominated by forest areas. In forest fires, there are several factors that can affect the occurrence of fires such temperature, humidity, rain, wind, and others. This paper implements the backpropagation method to predict the area of the fire. The input used is a factor that influences the occurrence of 7 forest fires. The process of backpropagation method begins with normalizing input data with a range based on the activation function used, after that initialization is weighted and can use the Nguyen-Widrow algorithm, feeds the feedforward and continues to the next process, feedbackward with the MSE requirement less than the error or iteration limit. less than the same as the maximum iteration, if the requirements have been met the output will be normalized, will get a forecasting value, and the last process calculates the results of MSE and SMAPE as a result of the success of the forecasting process. Based on the results of the tests that have been done, it is obtained that the optimal parameters are 5 hidden layer neurons, 0.1 learning rate, and maximum 1500 iterations. The highest average SMAPE result from this study is 49,1796 and the lowest SMAPE average is 31,4492 which shows that the backpropagation method can be used to forecast burn areas in the forest.
Implementasi Metode Template Matching untuk Mengenali Nilai Angka pada Citra Uang Kertas yang Dipindai Muria Naharul Hudan Najihul Ulum; Tibyani Tibyani; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (780.43 KB)

Abstract

Money is a valuable tools that are needed by all of the people for payment. Moreover, when people knowing about the money, the computer have limited ability to read the image of money. Computer is an electronic tool that used to receive and store the data, processing that and produce the output that already saved in the memory. The case of computer or robot that cannot notes the value of money because of the limited access in introducing the data, made the researcher provide a solutions from that problem to support the application of money which called template matching. A matching template is an input image that matches the linkeness of the test image. Based on that template matching method is designed for the data training and introduction of the data. A series of test was used in diagnostic to calculate the accuracy. So the average result will be 91% from all of the calculation of diagnostic test. Therefore, the error will state in front of money.
Pencarian Resep Makanan Berdasarkan Citra Makanan Menggunakan Ekstraksi Fitur Simple Morphological Shape Descriptors dan Color Moment Tri Rahayuni; Yuita Arum Sari; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1250.174 KB)

Abstract

The existing food recipe search application only uses text queries. Text queries often does not represent everything the user wants and cannot be done if user only knows food images. Solution offered to overcome this problem is make food recipe search using food image. Image search is done by measuring similarity between query image features and corpus image features. Features image are obtained by extracting Simple Morphological Shape Descriptors and Color Moment features. After feature extraction, similarity measurements are carried out using Euclidean Distance. Then system display search results which are as many as n images that have the greatest degree of similarity. The results of this study indicate the highest MAP value at k-rank 10 is 95.713% and the lowest MAP value is at k-rank 100 is 76.108%. Color Moment feature is better than Simple Morphological Shape Descriptors because MAP Color Moment value is higher at 93.32% than the Simple Morphological Shape Descriptors is 89.8%. Merging of the two features proved to be able to increase MAP value. It can be concluded that at k-rank 10 system returns good results according to user requirements and the use of the two merged features can overcome disadvantages of using each feature.
Klasifikasi Pengidap Kanker Payudara Menggunakan Metode Voting Based Extreme Learning Machine (V-ELM) Dheby Tata Artha; Sigit Adinugroho; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (962.841 KB)

Abstract

Breast cancer is a malignant tumor that formed by the abnormal growth of breast cells. Every year, breast cancer causes about 2,1 million women to die. To reduce the number of deaths caused by breast cancer, screening can be chosen for prevention efforts. The development of medical technology and information technology, in the medical world, can be used by researchers in their fields to develop early detection models, from routine consultation data and blood analysis. In this study, breast cancer data will be classified using the Voting Based Extreme Learning Machine (V-ELM). This study using Coimbra Dataset Breast Cancer which published on UCI Machine Learning in 2018. It consists of 116 data, 9 features and 2 classes (Healthy Control and Patient). Firstly, the dataset would be normalized, then began the training process of V-ELM with data train. After that, began the testing process of V-ELM with input values from the training process and data test. The ratio between training data and testing data in this study is 80:20. This study tested several parameters and obtained optimal results, including 20 hidden neurons, the value of k for V-ELM is 35 and the activation function with optimal results is the Sigmoid function. By using those optimal parameters, gives accuracy of 89.56%, sensitivity of 96.924% and specificity of 80%.
Co-Authors Afif Musyayyidin Afrizal Aminulloh Afrizal Rivaldi Agus Wahyu Widodo Ahmad Afif Supianto Akhmad Muzanni Safi'i Alan Primandana Albert Bill Alroy Alimah Nur Laili Allysa Apsarini Shafhah Alqis Rausanfita Ananda Fitri Niasita Arifin Kurniawan Arrizal Amin Arrofi Reza Satria Aulia Rahma Hidayat Ayustina Giusti Bayu Rahayudi Brian Andrianto Budi Darma Setiawan Candra Dewi Cornelius Bagus Purnama Putra Dahnial Syauqy Danang Aditya Wicaksana Daris Hadyan Tisantri Dayinta Warih Wulandari Dese Narfa Firmansyah Dewan Rizky Bahari Dheby Tata Artha Diajeng Ninda Armianti Dwi Novi Setiawan Edy Santoso Eky Cahya Pratama Faizatul Amalia Felicia Marvela Evanita Fitra Abdurrachman Bachtiar Gessia Faradiksi Putri Gilang Pratama Hangga Eka Febrianto Hanson Siagian Humam Aziz Romdhoni Husein Abdulbar Ilham Firmansyah Imam Cholissodin Inas Hakimah Kurniasih Indah Wahyuning Ati Indriati Indriati Inosensius Karelo Hesay Irwin Deriyan Ferdiansyah Iskarimah Hidayatin Kenza Dwi Anggita Khairul Rizal Krishnanti Dewi Lailil Muflikhah Listiya Surtiningsih M. Ali Fauzi Mahendra Okza Pradhana Mayang Panca Rini Melati Ayuning Lestari Moch. Yugas Ardiansyah Mohammad Angga Prasetya Askin Muhammad Alif Fahrizal Muhammad Dio Reyhans Muhammad Dzulhilmi Rifqi Bassya Muhammad Iqbal Pratama Muhammad Mauludin Rohman Muhammad Reza Ravi Muhammad Sholeh Hudin Muhammad Tanzil Furqon Muhammad Yudho Ardianto Muria Naharul Hudan Najihul Ulum Naziha Azhar Nendiana Putri Nurhana Rahmadani Putra Pandu Adhikara Putra Pandu Adikara Rahman Syarif Randy Cahya Wihandika Randy Cahya Wihandika Ratna Ayu Wijayanti Regina Anky Chandra Ridho Ghiffary Muhammad Rizal Maulana Rizky Adinda Azizah Salsabila Insani Salsabila Multazam Sarah Yuli Evangelista Simarmata Shima Fanissa Sukma Fardhia Anggraini Sulaiman Triarjo Supraptoa Supraptoa Sutrisno Sutrisno Tibyani Tibyani Tri Kurniawan Putra Tri Rahayuni Utaminingrum, Fitri Wahyu Rizki Ferdiansyah Yohana Yunita Putri Yose Parman Putra Sinamo Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari