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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Community Empowerment
Randy Cahya Wihandika
Fakultas Ilmu Komputer , Universitas Brawijaya
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

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Klasifikasi Penurunan Fungsi Kognitif Pasien Stroke Menggunakan Metode Klasifikasi Random Forest Muhammad Shidqi Fadlilah; Randy Cahya Wihandika; Bayu Rahayudi
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

Stroke is a disease that attacks all human, regardless of race, gender, and age. One of the effects of stroke is a decreased cognitive function. A human brain has many nerves, one of them is regulating the work of the human's cognitive function. Based on research by Wardhani (2015), factors that decreasing cognitive function consist of thirteen factors. So, a system that can detect a decreased cognitive function on a stroke patient is needed. So in this research, we make a system that could be used to classify the decreasing cognitive function using a random forest method. the random forest was chosen because this method is good for categorical data. Based on the testing result, the best tree that builds in this system was 100 trees. The average result of the accuracy obtained from all experiments were 53.094%. That number means that the system is still far from perfect. One of the factors that caused this system's imperfection was the distribution of training classes were not evenly distributed.
Implementasi Metode Jaringan Saraf Tiruan Backpropagation Pada Prediksi Payload 4G di Telkomsel Jember M. Rikzal Humam Al Kholili; Budi Darma Setiawan; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

PT. Telkomsel is one of the largest telecommunication providers in Indonesia which also has the most customers spread throughout Indonesia. Customers of PT. Telkomsel from year to year has experienced an increase and this will result in the use of an increasing number of payloads because the payload is all packages that are received and sent by mobile to a receiver (signal receiver) and if the amount of payload usage is smaller than the number of users it will occur over lagging and users will feel uncomfortable. With these problems, it requires an implementation of several methods to predict the amount of 4G payload usage so that PT. Telkomsel can find out the number of 4G payload usage in the next day or month so that it can anticipate losses or complaints from customers. Of the many prediction methods available, the authors use the backpropagation neural network method to perform a prediction process using artificial neural network architecture 4 input node neurons, 6 hidden node neurons and 1 node output neuron. By using the MAPE calculation (Mean Absolute Precentage Error) the most optimal value is 6.0154830745999%.
Peramalan Harga Cabai Merah Besar Wilayah Jawa Timur Menggunakan Metode Extreme Learning Machine Pindo Bagus Adiatmaja; Budi Darma Setiawan; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In fulfilling economic needs in Indonesia the agricultural commodity sector has a very important role. Due to agricultural commodities are the livelihoods and basic consumption of the people in Indonesia. Daily people's needs cannot be separated from agricultural commodities, one of which is large red chili. This is due to the level of consumption of large red chili used for kitchen spices and the ingredients are quite high. Therefore large red chili which is included in agricultural commodities can be categorized as the primary food needs in people's lives. The prices of large red chili which are erratic and tend to rise can cause losses to the state and society. To overcome this problem, one solution is to forecast prices that can be used to predict the possibility of chili price increases quickly and accurately. This study aims to forecast the price of large red chili using the ELM method. Based on the results of the implementation and analysis that has been carried out using the data of large red chili prices from July 18, 2016 to December 28, 2018 the smallest error was obtained using Mean Absolute Error (MAPE) of 3 % using 2 feature, the number of neurons as many as 3 and the range of weight values ​​[-1,8, 1,8].
Algoritme K-Nearest Neighbors Untuk Klasifikasi Jenis Makanan Dari Citra Digital Dengan Local Binary Patterns Dan Color Moments Gregorius Ivan Sebastian; Yuita Arum Sari; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Food is a primary need to help individuals in executing their daily activities. The nutritional value provided by certain food items affects one's performance in executing their daily activities. Individuals need to be assisted in identifying what food items are nutritious and those that are not, hence a classification algorithm is made for this task. Computer vision can be utilized to classify food items based on analyzing certain features. This research uses color and texture features to classify food items that are in images. Color feature extraction utilizes Color Moments (CM) using a Red, Green, and Blue (RGB) color channel, while Local Binary Patterns (LBP) is utilized for texture feature extraction. The k-Nearest Neighbors (k-NN) is used for the classification process. The digital images, from both the testing and training groups, will be preprocessed whose color features will be extracted with CM and the texture features with LBP. The extracted features will then be saved in a database, which will decrease computing time during the classification time. Varying the values of k in the k-nearest neighbors algorithm during testing and combinatios of features used, showed that the highest value for f1-score during evaluations was 0,89 when the value of k=1 and when only the color features from using color moments were used. Therefore the classification algorithm works efficiently on the dataset used in the research if only color features were used using k-NN as the classification algorithm.
Deteksi Emosi Pada Twitter Menggunakan Metode Naive Bayes Dan Kombinasi Fitur Fera Fanesya; Randy Cahya Wihandika; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Emotion shapes human behavior in general and very important in life. Detecting emotions provides an important role in various aspects because it can be applied in various fields such as decision-making, predicting human emotions conditions, providing a review product quality, tracking support for political problems, and recognizing depression disorders. Identifying emotions can use textual data that is text, text can be used to communicate and declare information. The social media that used to exchange information is Twitter. Twitter contains information about human attitude and human emotions. Therefore, emotional detection is needed to determine human emotions using Naive Bayes method and feature combinations. This research using several Naive Bayes classification models namely Bernoulli Naive Bayes for binary data types and Multinomial Naive Bayes for discrete data types. Feature Combination used in this research is as follows: linguistic features, orthographic features, and N-gram feature combinations. The best accuracy result obtained a value of 0.555 that is in testing N-gram feature combinations. While the combination of features including linguistic features, orthographic features, and N-gram features produced an accuracy value of 0.5317 which means this value was better than testing with a single feature and lower than testing the N-gram feature combinations. This is due to the influence of linguistic features, orthographic features, and N-gram features. Based on these results it can be concluded that by using combination features can cover the weaknesses of each feature that can improve the performance of accuracy even though the increase is not too significant.
Algoritme Enhanced K-Means dengan Ekstraksi Fitur Local Binary Pattern dan Color Moment untuk Pengelompokan Citra Makanan Mohammad Rizky Hidayatullah; Yuita Arum Sari; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Food is a source of our energy for doing our daily activities. Food have each color and texture for their identity. Using color and texture from the food, we can feel the taste in our mind while we see that food. In this paper, we want to know about what information we can get with color and texture of food. To do that, we use clustering to see how color and texture can show any information like nutrition inside the food. We used Enhanced K-means for grouping food image because we want to get a consistent results cause in Enhanced K-means, the initialization didn't use random data. The food image is grouping by color and texture cause they are two thing who can increase someones appetite. To get the color feature we used Color Moment and for texture feature we used Local Binary Pattern. For the result of evaluation using Coefficient Silhouette (CS) and Davies-Boulding Index (DBI), clustering using color texture get best result with DBI score is 0.957 and Silhouette score is 0.399 whereas when we use color and texture, result for DBI score is just 1.058 and Silhouette score is 0.31.
Segmentasi Citra Kue Tradisional menggunakan Ruang Warna Hue Saturation Value dan Otsu Thresholding Ani Enggarwati; Yuita Arum Sari; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

There are some problems regarding food that occur. One of them is nutrition and the quality of food that still needs attention. To find out the nutrients content in food we can use food classification using digital images. Classification requires an initial process called segmentation. In this case, the color space that used is Hue Saturation Value (HSV) and Otsu thresholding. Segmentation in this thesis uses 50 traditional cake images, begins with converting RGB images to HSV images. The Otsu thresholding is performed on each color component. Based on the results of these studies, the Value component of color gives the opposite result, the background is white and the foreground is black. Therefore, invert is applied to it. After thresholding on each color component, accuracy, specificity and sensitivity are obtained. Hue color component has an average accuracy rate of 42.64%, Saturation color component has an average accuracy rate of 94.34%, Value color component has an average accuracy rate of 70.68%. Tests for specificity and sensitivity show that Saturation color component has a higher value than other color components, with values 82.08% and 91.30%. Thus the Saturation color component is best used for segmentation using Otsu Thresholding.
Klasifikasi Gender berbasis Wajah menggunakan Metode Local Binary Pattern dan Random KNN Ruri Armandhani; Randy Cahya Wihandika; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Automatic gender classification based on facial image is one of the interesting research topics in the world of computer vision. The automatic gender classification system has an important role in developing applications such as surveillance system and monitoring system. However, computers find it difficult to find a special characteristics that can distinguish someone's gender so that a feature extraction is needed. In addition, the selection of classification method is also important to get a better accuracy. The initial stage in this research is to do face detection. After that, pre-processing is done to get the face image only and the size of the image is normalized to 100x100 pixels. Then, the feature extraction process with Local Binary Pattern (LBP) method is done on the pre-processing image. Then, the texture image produced by LBP is divided into several small parts called region. The 32-bin histogram is extracted from each region. All of the histograms from each region are concatenated into a single vector which become the histogram feature used to classify gender. The classification was performed by Random KNN method. Based on the results of testing in this research, the best features produced from the LBP feature extraction which has 7x6 regions. The highest average accuracy produced by Random KNN is 72.5%. The optimal parameter value used for Random KNN in this research is k = 11 and r = 29.
Ekstraksi Ciri Untuk Klasifikasi Gender Berbasis Citra Wajah Menggunakan Metode Histogram of Oriented Gradients Dani Devito; Randy Cahya Wihandika; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Human gender could be recognized from his or her face. Males and females have many different features, such as face shape, eyebrows, mouth, chin, nose, eyes, including facial hairs. Many fields aided by such system which could be developed to automatically recognize human gender, especially for demographic analysis by Indonesian Government. Such gender classification system will rapidly help decision maker that need gender recognition ability. A classifier model could be built to distinguish males from females from its facial features by learning a collections of male and female images data. One method for shape feature extraction is Histogram of Oriented Gradients (HOG). Results shows that classifier ability could be improved by tuning HOG parameter like size for dividing to local image regions, orientation histogram bin size and how each histogram relate to another. This research discussing case of subjects wearing glasses and not. This research explains how to build classification model from Histogram of Oriented Gradients based on face images. Built model able to classify men and women up to 97,83% and 95,92% each. Best parameter for Histogram of Oriented Gradients to classify gender is using (8,8) pixels per cell, 9 bin histogram each cell, (2,2) cell per blocks from (128,128) face image. It could be concluded too that glasses shape affects classification model ability.
Rekomendasi Lagu Cross Language Berdasarkan Lirik Menggunakan Word2VEC: ` Gilang Widianto Aldiansyah; Putra Pandu Adikara; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The song is a rhythmic variety of sounds and is one of the entertainment facilities that is popular among today's modern society. The desire of a society to listen to a song can be based on various things such as music (music genres), singers, years of songs to the lyrics of a song, singers, song years to song lyrics. In considering this matter, a system is needed that can facilitate the community in discussing a song based on the basics, one of which is a system of song contributions based on the lyrics. One method that can be used in building a song contribution system is Word Embedding which in this study uses Word2Vec. The stages of the system discussion of songs that contain data sets that produce a collection of words Word2Vec and TF-IDF, then the system will perform the processes of the lyrics of input songs by making expansion requests in accordance with the words words from TF-IDF close words the results of Word2Vec training. The process carried out by the system will produce 10 song titles that have similar lyrics to the input song. Results The aiming of the word parameters with TF-IDF taken and the close word test taken is the highest precision @ 10 value for the highest TF-IDF word taken = 25 and for the close word taken = 3.
Co-Authors Achmad Arwan Achmad Ridok Achmad Yusuf Adam Hendra Brata Adam Sulthoni Akbar Adinugroho, Sigit Aditya Putra Pratama Agi Putra Kharisma Agung Nurjaya Megantara Agus Wahyu Widodo Akhmad Sa'rony Amar Ikhbat Nurulrachman Anang Hanafi Angky Christiawan Rongre Ani Enggarwati Ardisa Tamara Putri Ardiza Dwi Septian Arif Pratama Arynda Kusuma Dewi Barlian Henryranu Prasetio Bayu Kusuma Pradana Bayu Laksana Yudha Bayu Rahayudi Budi Darma Setiawan Budi Dharma Setiawan Candra Dewi Chandra Tio Pasaribu Cindy Cunday Cicimby Cornelius Bagus Purnama Putra Cusen Mosabeth Dani Devito Daris Hadyan Tisantri Denny Sagita Rusdianto Devinta Setyaningtyas Atmaja Dhan Adhillah Mardhika Dhanika Jeihan Aguinta Diajeng Sekar Seruni Dian Eka Ratnawati Dimi Karillah Putra Dito Rizki Pramudeka Dizka Maryam Febri Shanti Dwi Rahayu Eka Putri Nirwandani Emma Wahyu Sulistianingrum Ersya Nadia Candra Fachril Rachma Zulfidar Fachrur Rozy Faizatul Amalia Fajri Eka Saputra Fanny Aulia Dewi Fera Fanesya Fida Dwi Febriani Fikri Hilman Firda Oktaviani Putri Fitra Abdurrachman Bachtiar Frisma Yessy Nabella Gilang Widianto Aldiansyah Glenn Jonathan Satria Gregorius Ivan Sebastian Hafiz Ari Putra Hamim Fathul Aziz Heykhal Hafiddhan Rachman I Gusti Ngurah Ersania Susena Imam Cholissodin Indriati Indriati Irnayanti Dwi Kusuma Jonathan Reynaldo Kevin Haidar Kevin Nastatur Chatriavandi Koko Pradityo Lailil Muflikhah Lalu Muhammad Ivan Natania Latifa Nabila Harfiya M. Rikzal Humam Al Kholili Moh. Dafa Wardana Mohammad Rizky Hidayatullah Muchlas Mughniy Muh. Arif Rahman Muhamad Ilham Dian Putra Muhamad Wahyu Budi Santoso Muhammad Alif Fahrizal Muhammad Amin Nurdin Muhammad Faiz Abdul Hamif Muhammad Ihsan Diputra Muhammad Shidqi Fadlilah Muhammad Tanzil Furqon Muhammad Tegar Kanugroho Naufal Akbar Eginda Nindy Deka Nivani Nova Amynarto Novanto Yudistira Nur Wahyu Ningtyas Nurul Hidayat Nurul Muslimah Pindo Bagus Adiatmaja Pupung Adi Prasetyo Puspita Sari Putra Pandu Adikara Putu Gede Pakusadewa Qurrata Ayuni Raden Rafika Anugrahning Putri Ratih Kartika Dewi Rayindita Siwie Mazayantri Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Rizal Setya Perdana Rizky Nur Ariyanti Ruri Armandhani Sarah Najla Adha Satria Dwi Nugraha Satyawan Agung Nugroho Sema Yuni Fraticasari Sevtyan Eko Pambudi Sigit Adinugroho Siti Robbana Sukma Fardhia Anggraini Supraptoa Supraptoa Sutrisno Sutrisno Tahajuda Mandariansah Threecia Agil Regitasari Tifo Audi Alif Putra Tri Kurniawan Putra Utaminingrum, Fitri Valen Novandi Kanasya Vandi Cahya Rachmandika Winda Cahyaningrum Yosendra Evriyantino Yosua Christopher Sitanggang Yudha Prasetya Anza Yuita Arum Sari Yurdha Fadhila Hernawan