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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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Perolehan Informasi Rating Buku Berdasarkan Gambar Sampul Buku Menggunakan Metode Scale-Invariant Feature Transform Hamim Fathul Aziz; Yuita Arum Sari; Randy Cahya Wihandika
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

Along with the development of technology, almost kinds of all information are available on media online. The expanding technology are expected to make it easier to access all kinds of information in media online. As an example by searching book's rating automatically in media online by utilizing book cover. With the existence of a system that can find the book's rating by using the image on the cover getting from camera phone hopefully can make it easier and make it more fast to get rating information from the book, so it can make the customer do less mistaken when buying a book. Based on explanation above this research will uses scale-invariant feature transform to recognize the book's object in an image. Before find the appropriate image of the book; First, preprocessing will be doing on the image by searching for scale space; Then, find the key point or key point localization; Next, by calculation of pixel's angle or orientation assignment; Finally, transforming of the image descriptor or key point descriptor. On this research the image will be tested of this effect on light intensity, image rotation, and scaling. The result by matching test the image of book's cover using scale-invariant feature transform method has high accuracy in condition of bright light intensity and it has low accuracy when using image rotation and image scaling. The average accuracy can obtain in bright light conditions, rotation, and scaling are 90%, 57.5%, and 46.6% respectively.
Klasifikasi Film Berdasarkan Sinopsis dengan Menggunakan Improved K-Nearest Neighbor (K-NN) Nurul Muslimah; Indriati Indriati; Randy Cahya Wihandika
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

Movie is audio visual communication media, which imply the message the movie creator wants to convey. Movie has several genres namely romantic, horror, thriller, comedy, fantasy and so on. Not a few movie connoisseurs are still confused about the differences in these genres. This resulted in many movie lovers who were difficult to distinguish the genre of movie so that the message in the movie could not be fully conveyed to the audience of the movie. Therefore, the classification of movies based on the synopsis of the movie can be one of the solutions to the problem. Classification in the movie synopsis will help in grouping movies with the appropriate genre. The genre genre classification process based on the synopsis begins with preprocessing, then weighting the term to classification with the Improved K-NN method. Based on the implementation and testing conducted in the movie classification research based on the synopsis using Improved K-NN which uses 250 documents as training data and 50 documents as the test data the best results are precision = 1, recall = 0.88, f-measure = 0.936170213, and an accuracy rate of 88%. As well as comparison with K-NN, it was proven that classification using the Improved K-NN method was better than the K-NN method.
Implementasi Metode Particle Swarm Optimization - Certainty Factor Untuk Pengenalan Kondisi Ikan Lele Sevtyan Eko Pambudi; Randy Cahya Wihandika; Rekyan Regasari Mardi Putri
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

Failure in catfish farming is often caused by not finding the best composition combination when you want to start cultivation such as, types of ponds, antiseptic administration, etc. Therefore we need a system that aims to find the best combination of parameters while predicting the condition of the fish before it is implemented in the real world. One method that can be applied to predict fish conditions is certainty factor. However, the performance of certainty factor is highly dependent on experts related to the problem so that the resulting solution is vulnerable to being trapped in the local optimum area. One approach that can be used to overcome this problem is to apply optimization algorithms, namely Particle Swarm Optimization (PSO). PSO explores the search space to find the value of the expert cf based on the value of the particle cost. The value of the cost is designed to minimize the distance between random values ​​and the weight value so that the smaller is close to 0 (zero) the greater the chance of a particle being selected as a solution. This study uses hybrid Particle Swarm Optimization-Certainty Factor to identify the condition of catfish. The quality of Certainty Factor is evaluated using test data from experts by comparing system output. The experimental results show that the PSO-Certainty Factor hybrid algorithm produces better predictive results compared to the Certainty Factor algorithm which is 90%.
Klasifikasi Penyakit Kambing Dengan Menggunakan Algoritme Support Vector Machine (SVM) Ardiza Dwi Septian; Lailil Muflikhah; Randy Cahya Wihandika
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

Indonesian's rural communities really familiar with goat cattling, that's because the fund for it's nurturing more cheaper and they breed faster.The main factor to nurturing is the health of the goat itself if the goat get sick, it will become disadvantage for them. So that's why health issues become the main factor.If there's disease indications exist, the early handling must be done soon. A disease diagnose is first thing to do.But, the awareness to diagnose the disease are still unknown. That's make the cattleman feel uneasy to handle it.Therefore, it need a system to help them to clasify the disease.This goat disease's research used algoritme support vector machine with one againts all strategy. The data that used are 148 datas with 11 disease classes, there are wormy, endometritis, paralizing, bloated, poisoning, Masistis, Myasis, Orf, Pink Eye, Pneumonia and Scabies.The accuracy result that get from this system is 90% with using the best parameter that called k-fold cross validation 10 , λ= 0.1, C = 0,1, iterasi = 500 and σ = 1.
Identifikasi Jenis Attention Deficit Hyperactivity Disorder (ADHD) Pada Anak Usia Dini Menggunakan Metode Modified K-Nearest Neighbor (MKNN) Rizky Nur Ariyanti; Indriati Indriati; Randy Cahya Wihandika
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

Growth and development in early childhood certainly affects how a child is when reaching adulthood both in terms of mental, physical, and intelectual. In the development phase of course not all children experience normal development, there may be a developmental disorder. One developmental disorder that is often experienced in early childhood is ADHD (Attention Deficit Hyperactivity Disorder). For ADHD itself there are three types, among others Inattention, Impulsive, and Hyperactivity. In this research will be identification type of ADHD based on symptoms that appear by using method of classification of Modified K-Nearest Neighbor (MKNN). MKNN method is one method of development of the KNN method, which distinguishes the MKNN there is a validity process and also weight voting of each type to be classified. In this study will be done type identification consisting of 4 types include Inattention, Impulsive, Hyperactivity, and Not ADHD. The results of this study indicate that MKNN method can identify ADHD type well when the data used is 80 data with 20 test data, K = 3 with 90% accuracy. In this study also proves that MKNN method tends to be lower accuracy than KNN method, it is caused by low validity value which will affect weight voting and also accuracy.
Diagnosis Penyakit Sapi Menggunakan Metode Promethee Bayu Kusuma Pradana; Nurul Hidayat; Randy Cahya Wihandika
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

Indonesia has the potential of a large farm with superior products such as dairy cattle and beef cattle, the superior products of these farms are growing and concentrated in the development area of ​​the production center. With large amounts of production, the need for animal protein in Indonesia is increasing with increasing public awareness of the importance of nutritional intake. Therefore, the health of livestock raised by farmers is important to meet the nutritional needs and in addition to income for the livestock owners themselves. Promethee is a method of determining the order (priority) in multicriteria analysis. The key issues are simplicity, clarity, and stability. Promethee method is well used for the diagnosis of disease in cattle because it produces an accuracy of 92.73%.
Seleksi Fitur Information Gain Pada Klasifikasi Citra Makanan Menggunakan Hue Saturation Value dan Gray Level Co-Occurrence Matrix Frisma Yessy Nabella; Yuita Arum Sari; Randy Cahya Wihandika
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

Choosing food has become a challenge for those who are presented with new food choices. Classification is important for those who have a strict diet regarding food that they consume. Food selection is essential for those who are visually impaired to identify food items. The classification process in this research is initiated with the pre-processing of the image, resulting in a segmented image which is then continued with feature extraction where Hue Saturation Value (HSV) for color extraction and Gray Level Co-Occurence Matrix (GLCM) for texture features. Based on features that have been extracted the next step is to gather relevant features using Information gain to reduce the workload. The last process is classification, using K-Nearest Neighbor. Accuracy results are 95,24% at best using only HSV with k=1 for feature selection. A combination of HSV and GLCM using Information gain results in a accuracy from 57,14% to 87,61%. This also applies to only using GLCM with information gain that raises the accuracy from 57,14% to. 74,28%. With the previous statement taken into consideration, Information Gain as a feature selection method increases accuracy with a significant amount and is able to obtain relevant feature if the list of features is abundant. If there are only a few features used, the accuracy increment is not that significant but it decreases the workload of the system.
Seleksi Fitur Information Gain pada Klasifikasi Citra Makanan Menggunakan Ekstraksi Fitur Haralick dan YUV Color Moment Devinta Setyaningtyas Atmaja; Yuita Arum Sari; Randy Cahya Wihandika
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

Food classification is a classic problem that still becomes an interest for many researchers. Several studies have been conducted using only one type of food, which is fruit as the object of the classification. This research was conducted to improve the previous ones. This study uses five types of single food as its object. The method used is color feature extraction using YUV Color Moment, texture feature extraction using Haralick, and feature selection using Information Gain. The classification algorithm is K-Nearest Neighbor (KNN). The highest accuracy obtained is 94.26% obtained from the combined features of the two selected feature extraction methods. From these results, it can be concluded that the application of a combination of feature extraction methods, namely color and texture, and feature selection method greatly influence the food image classification process.
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 Jenis Citra Makanan Tunggal Berdasarkan Fitur Local Binary Patterns dan Hue Saturation Value Menggunakan Improved K-Nearest Neighbor Sarah Najla Adha; Yuita Arum Sari; 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

To fulfill their basic needs, living things need foods. Foods that have poor quality can cause disease. To avoid this, digital image processing can be used to create a food classification system. Digital image processing is used to analyze features contained in food images. In this study, the feature used to classify the types of food images is a feature of color and texture. Color feature extraction is done by Hue Saturation Value (HSV) color space and texture features using the Local Binary Patterns (LBP) method. Classification is done by the Improved K-Nearest Neighbor (Improved K-NN) method. The test results for the k value indicate that the highest accuracy is obtained at 90.476% with the value of k = 1. When the feature used is only a color feature, the highest accuracy value is obtained at 90.476% with a value of k = 1. When the feature used is only a texture feature, the highest accuracy value is obtained 85.714% with a value of k = 1. The results of testing the classification method showed that the Improved K-NN method produced higher accuracy than the K-NN method with an average accuracy of 80.306%. So the best classification results are obtained by using a combination of color and texture features with the Improved K-NN classification method.
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