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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Artificial Intelligence Research Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Adinugroho, Sigit
Brawijaya University, Malang
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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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

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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

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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

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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

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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%.
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

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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

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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

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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

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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

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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

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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.
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 Anggi Gustiningsih Hapsani 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 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 Lukman Hakim 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 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 Siti Mutrofin 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