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

 2   3   4   5   6 
Penentuan Waktu Terakhir Penggunaan Ganja dengan Metode Radial Basis Function Neural Network (RBFNN) Sukma Fardhia Anggraini; Sigit Adinugroho; Randy Cahya Wihandika
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 (404.692 KB)

Abstract

In 2017, there are 1,742,285 cannabis (popular as marijuana) abusers in Indonesia. If a marijuana addict suddenly wants to stop using marijuana, it can cause symptoms of “sakau”. To anticipate the symptoms of “sakau”, rehabilitation treatment can be taken, so that marijuana addicts can get comprehensive treatment. Determining the appropriate type of rehabilitation, can make it useful. Then knowing the last time abusers had consumption the marijuana, be expected to provide supporting information to determine the appropriate rehabilitation program for marijuana addicts. One technique in data mining that can be used to solve this problem is classification techniques. In this study using Radial Radial Basis Function Neural Network (RBFNN) with K-Means as the classification method. The steps taken included data normalization, K-Means to found the value of centers and spread for Gaussian activation function, training and testing RBFNN. This study using 627 marijuana abuser data which was published on the UCI Machine Learning in 2016. The results of the research showed the optimal parameters involves 7 hidden neurons and 100 as the maximum limit of K-Means iterations. By using these parameters, the classification result achieved accuracy of 35,908%.
Klasifikasi Citra Makanan Menggunakan Algoritme Learning Vector Quantization Berdasarkan Ekstraksi Fitur Color Histogram dan Gray Level Co-occurrence Matrix Sarah Yuli Evangelista Simarmata; Yuita Arum Sari; Sigit Adinugroho
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 (651.157 KB)

Abstract

Many photos of food we see on social media, but we forget and don't even know the name of the food. Humans ability to recognize and identify is also subjective to external such as fatigue, prejudice and etc. Computers can help by build a system that can recognize and identify food through images. Researches have been conducted that the process of automatically identifying and classifying using computer can save more time compared to identify manually. Food image has different colors and textures. The color feature extraction method used in this research is Color Histogram and for the texture feature extraction is Gray level co-occurrence matrix (GLCM). The classification algorithm used is Learning Vector Quantization (LVQ) with the best parameters that can be used are learning rate (α) 0.1, decreament learning rate 0.1, maximum epoch 2, minimum learning rate 0.01 and gives accuracy that is equal to 53,33%. The test gives 53.33% accuracy for using color and texture extraction. The use of color feature extraction only gives the highest accuracy that is equal to 67.00%, and the use of texture feature extraction only gives accuracy that is equal to 53.33%. From the results, concluded that LVQ algorithm based on Color Histogram feature extraction and GLCM can be used to classify food image but can not give a perfect accuracy.
Seleksi Fitur Information Gain Pada Temu Kembali Citra Jenis Makanan Menggunakan Dominant Color Descriptor Dan Gray Level Co-occurence Matrix Sulaiman Triarjo; Yuita Arum Sari; Sigit Adinugroho
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 (511.887 KB)

Abstract

Nutritional information on social media is supported by image of food being reviewed. It requires hard work to explore similar foods that have almost same nutrition. Therefore an information search system is needed to speed up the information search process. This research has been conducted to be able to search for similar informations based on a query in form of image. It uses Dominant Color Descriptor method for color feature extraction and Gray Level Co-occurence Matrix method for texture feature extraction and information gain selection feature to select texture features. The data used were 29 types of food imaged with total is 435 images which each type has 15 images. Testing is done by comparing the performance of calculation of Euclidean distance, Chebyshev distance, and Manhattan distance for texture feature and Quadratic distance and Yang distance for color feature. The evaluation uses MAP value, test result using only the texture feature obtained MAP value of 0,5542 using Euclidean distance and without feature selection. The test result using only color feature obtained MAP value of 0,7488 when using Yang distance. And testing using color feature and texture feature obtained a value of 0,7118 by using Manhattan distance and Yang distance with 10 features. In this research, the use of DCD was more effective than GLCM by producing higher MAP value.
Klasifikasi Jenis Makanan dari Citra Smartphone Berdasarkan Ekstraksi Fitur Haralick dan CIE Lab Color Moment Menggunakan Learning Vector Quantization Akhmad Muzanni Safi'i; Yuita Arum Sari; Sigit Adinugroho
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 (1388.279 KB)

Abstract

Choosing a food becomes something important for sufferers of certain diseases. However, choosing a food is a problem for people who taste a food for first time or tourists who are visiting a country for first time. To overcome these problems, research needs to be done to identify / classify a food image. The Haralick and CIE Lab Color Moments features are proven to produce good features for classification cases. The Learning Vector Quantization method is also an alternative for classification process. Based on the k-fold cross validation with k = 10 and accuracy as evaluation method, the maximum accuracy is 0.642051 with learning rate parameter value is 0.2, the learning rate multiplier is 0.8, the m value is 0.1, the epsilon value is 0.4, maximum iteration is 10 and minimum learning rate is 0.000001. This result shows that food image classification based on Haralick feature extraction and CIE Lab Color Moment using Learning Vector Quantization produces fairly good accuracy. In addition, the use of both texture (Haralick) and color features (CIE Lab Color Moments) has an effect on the results of accuracy. This is indicated by all the test results which show that the highest accuracy results are achieved using texture and color features.
Implementasi Algoritme Modified K-Nearest Neighbor (MKNN) Untuk Mengidentifikasi Penyakit Gigi Dan Mulut Muhammad Reza Ravi; Indriati Indriati; Sigit Adinugroho
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 (373.657 KB)

Abstract

Teeth and mouth are the most important parts of the human body that must be maintained and cared for. But the problem of dental and oral diseases in Indonesia still needs attention. There are several types of dental and oral diseases. The most common diseases suffered by the people of Indonesia are cavities, dental caries and periodontitis. The causes of dental and oral disease are poor hygiene of the teeth, eating foods and drinks that contain high carbohydrates, smoking, consuming alcoholic beverages, brushing teeth incorrectly and also growing imperfect gums. It has symptoms, among others, the teeth become more sensitive, the emergence of an erratic pain, and often feel pain or pain when biting something. In this study, identification of the types of dental and oral diseases determined from symptoms experienced using the classification method Modified K-Nearest Neighbor (MKNN). The MKNN method is the development method of the NNC, there are differences from MKNN and KNN namely MKNN there is a process of calculating validity and Weight Voting. This study used 6 classes which included Pulpitis, Gingivitis, Dental Caries, Periodontitis, Deposits, and Pulp Necrosis. This study proves that in the training data as many as 70 and 30 test data and K = 60, the MKNN method can identify the types of dental and oral diseases by reaching 86.6%. This study also proves that the MKNN method tends to be more accurate compared to the KNN method where the MKNN method has an accuracy rate of 76.66% while the KNN is 43.33%. this is caused by the calculation of the validity value which will affect the Weight Voting and also the accuracy.
Analisis Sentimen Pembangunan Infrastruktur di Indonesia dengan Automated Lexicon Word2Vec dan Naive-Bayes Ananda Fitri Niasita; Putra Pandu Adikara; Sigit Adinugroho
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 (928.892 KB)

Abstract

Infrastructure development is a project that being intensively carried out by the current government. With the existence of good infrastructure, the government hopes that in the future the economy and the level of Indonesian welfare will increase. Infrastructure development attracts the community attention. Various comments regarding this project were mentioned through social media, for example Twitter. The number of pros and cons community comparisons known by using sentiment analysis. In this case, sentiment analysis uses a lexicon dictionary to determine whether the data is positive or negative. The lexicon dictionary created automatically using the Word2Vec method. Word2Vec method is used to find closeness between words.. Then, the sentiment class is determine using the Naive-Bayes method. This study uses 100 training data and 50 testing data divided into positive and negative sentiments. The highest accuracy value are 64% with precision of 0.36, re-call of 0.818 and f-measure of 0.5.
Prediksi Suku Bunga Acuan (BI 7-Day Repo Rate) Menggunakan Metode Extreme Learning Machine (ELM) Yohana Yunita Putri; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1122.171 KB)

Abstract

Reference interest rates or often referred to as BI 7-Day Repo Rate is a policy interest rate that describes the establishment or view of monetary policy whose determination is made by Bank Indonesia which is then notified to the public.. BI 7-Day Repo Rate has an influence on economic activities, such as investment, inflation and currency changes. Investors and market players in making economic decisions will refer to the fluctuation of interest rates set by the central bank. Therefore, the prediction of the benchmark interest rate (BI 7-Day Repo Rate) is important. The purpose of the BI 7-Day Repo Rate prediction is to facilitate and assist investors and market players to make estimates of the decisions to be taken according to the prediction of the benchmark interest rate. This study uses the Extreme Learning Machine (ELM) method to predict the reference interest rate (BI 7-Day Repo Rate). The process of the first ELM algorithm is to normalize, then initialize the input and bias weights, then continue to carry out the training process and proceed with the testing process, then do the normalization to obtain the actual value. Based on the Extreme Learning Machine (ELM) algorithm that has been conducted, it produces the best Mean Absolute Percentage Error (MAPE) of 1,1% and the fastest processing time is 0.125 seconds using 50 hidden neurons, sigmoid activation function and 96 data counts.
Penerapan Term Frequency - Modified Inverse Document Frequency pada Analisis Sentimen Ulasan Barang menggunakan Metode Learning Vector Quantization Moch. Yugas Ardiansyah; Mochammad Ali Fauzi; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (456.314 KB)

Abstract

In online stores there are reviews of items that contain comments about feedback from previous buyers that are useful for subsequent buyers as well as sellers at online stores. Reviews Usually consist of negative comments or positive comments. The number of reviews is very much. In overcoming this problem, sentiment analysis is needed. This study uses the Learning Quantization Vector and Term Frequency-Modified Inverse Document Frequency methods. The LVQ method was chosen because it has the advantage of being able to summarize the dataset into a codebook vector. The data used consisted of 250 positive comments and 250 negative comments. The data will be preprocessing, weighting the word using TF-mIDF and consequently using the LVQ method. The results of testing the LVQ parameters obtained an accuracy value of 75.11%, recall of 75.11% precision of 77,80%, f-measure of 76.43% with parameter values ​​of learning rate 10-3, dec α 10-6, and values maximum epoch 19. Based on the final test results, obtained the value of the Learning Vector Quantization method with TF-mIDF resulted in an average accuracy of 72.47%, recall of 72.47%, precision of 76.39%, and f-measure of 74.33 % and using the Learning Vector Quantization method with TF-IDF resulted in an average accuracy of 54.80%, recall of 54.80%, precision of 54.30%, and f-measure of 52.61%.
Rekomendasi Film Berdasarkan Sinopsis Menggunakan Metode Word2Vec Alimah Nur Laili; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (695.813 KB)

Abstract

The number of movie production have increased each year. This shows that the society interest in the film industry is getting higher. It's difficult to get the appropriate result of what desired by searching for data with certain parameters on the internet because of the large amount of data exists but there is limited adequate tools. The screening of the excess data can be done using recommendation process. There are several stages in movie recommendation process. Those are Pre-processing to process film synopsis documents, TF-IDF method to obtain the highest value as much as the amount determined based on the query result on the document. Word2vec as a method to get the query expansion from the top word result that taken from TF-IDF process and Cosine Similarity is used to get the similarity between document and query. The Word2Vec method plays role to find the proximity value between words to one another in order to get the words that will be added to the initial query. The training data are 150 movies title with English synopsis. The evaluation process took 30 data of movie title and synopsis from the training data based on the movies selected by the examiners. The highest Precision@k value is 0,47 and the highest Mean Average Precision (MAP) value is 0.709603374.
Prediksi Ketinggian Gelombang Laut Menggunakan Metode Jaringan Saraf Tiruan Backpropagation Nurhana Rahmadani; Budi Darma Setiawan; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1250.274 KB)

Abstract

Sea wave height prediction is difficult thing to do. One factors become wave generator is wind that influenced by wind direction and wind speed. These factors are difficult to calculate and predict manually, because wind conditions change any time. Wave height prediction is important because useful for shipping safety. Many prediction methods can used to make predictions, one of them is ANN Backpropagation used in this study to predict wave height in the next hour. Time-series data used in this study is wave height, wind direction, and wind speed data every one hour in East Java Sea from 2013 to 2014. The application of ANN Backpropagation in prediction of wave height is through by several phases, there are data normalization, weight initialization using Nguyen-Widrow, training, testing, and forecasting. The training data used is wave height, wind direction, and wind speed data every one hour from January to December 2013 and the test data used is data from January to June 2014. The training process used learning rate 0.5 ,4 neurons input layer,3 neurons hidden layer,1 neuron output layer, error limit MAPE training of 13,2% and maximum of 30000 iterations.The combination of these parameters produces average MAPE test value of 17.53182%.
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