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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Achmad Ridok
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Prediksi Harga Saham Menggunakan Metode Extreme Learning Machine (ELM) (Studi Kasus: Saham PT Bank Rakyat Indonesia) Yunita Dwi Lestari; Edy Santoso; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Shares are a sign of ownership or membership of an entity or individual. The profit from the purchase of shares is derived from the acquisition of dividends and capital gains. The existence of stock trading in the secondary market, making the ups and downs of the share price so that there is a capital gain. A person who becomes a stock investor and takes advantage by selling shares he owns when the share price is higher than his previous purchase price is referred to as a trader. Due to fluctuations in the stock price, a trader needs analysis before making a stock purchase in order to avoid the risk of losses. In order to avoid losses of traders in the stock market, a stock price prediction system was created using the Extreme Learning Machine (ELM) method. After the prediction test with ELM obtained the most optimal parameters to make stock predictions, namely by using the number of data inputs 3, the number of hidden nodes 5, the type of activation function is binary sigmoid, and the ratio of training data and test data by 90% : 10% so that the average value of MAPE is obtained by 1.59722%.
Peramalan Debit Inflow Waduk Gajah Mungkur menggunakan Metode Extreme Learning Machine Yudha Irwan Syahputra; Indriati Indriati; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 8 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The Gajah Mungkur Reservoir is one in all the most largest dams or reservoirs in Java which is categorized as a multipurpose reservoir. With various benefits, it is necessary to forecast the inflow discharge to avoid excess or shortage of water in the reservoir as well as errors in water disposal. A common mistake is the release of water which can cause flooding in areas lower than the reservoir. Discharge forecasting can also be used to plan water allocations such as power generation and irrigation. Changes in inflow discharge that occur are always fluctuating, so from these problems forecasting inflow discharge is needed to overcome the large amount of water discharge that comes out. The data used is inflow discharge from January 2009 to December 2019 and the method used is Extreme Learning Machine (ELM) because it has fast learning speed and good generalization. The test results obtained are the optimal number of features as many as 7, the optimal number of hidden neurons as many as 9, with the percentage of training data 80% and 20% of the test data producing RMSE 28.7303, MAD 21.8002 with a runtime of 0.0272s. With an RMSE value that is far from zero, the error rate obtained is high and bad. And also with a runtime that is in seconds or less than seconds, this research also confirms that ELM has advantages in fast learning speed.
Klasifikasi Artikel Publikasi berdasarkan Judul pada Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) Universitas Brawijaya dengan menggunakan Metode Improved K-Nearest Neighbor Tanica Rakasiwi; Bayu Rahayudi; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 10 (2021): Oktober 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Publication articles are contained in journals published by universities or colleges and many are also published by institutions providing national and international journals. Journal of Information Technology and Computer Science (JTIIK) is one of several journals that accept publication articles based on the principle of open dissemination of publications to advance science, especially in the IT field. JTIIK itself is managed by the Faculty of Computer Science, Universitas Brawijaya and started publishing journals in 2014. At JTIIK, the author categorises Publication Articles manually by the author when submitting the manuscript or by the editor during the review and editing process. Therefore, there is a need for automatic grouping of Published Articles documents. The focus of this study is to determine the application of the Improved K-Nearest Neighbor method in the classification of the title of JTIIK Publication Articles. This research was carried out in several processes: Preprocessing, Word Weighting, Feature Selection, Cosine Similarity, and classification with Improved K-Nearest Neighbor. Testing the value of k and using feature selection with K-Fold Cross Validation, the results of changing the value of k and the application of feature selection affect the evaluation results of Improved K-Nearest Neighbor, with a value of k = 5 and feature selection with a threshold DF = 1 as the optimal parameter. Tests with all data resulted in Accuracy of 90.00%, F-measure of 85.78%, Recall of 87.58% and Precision of 84.40%.
Pengelompokan Topik Skripsi Mahasiswa Fakultas Ilmu Komputer Universitas Brawijaya berdasarkan Judul pada Periode 2015-2019 menggunakan Metode Semi Supervised K-Means Mochammad Ilman Asnada; Bayu Rahayudi; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 1 (2022): Januari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The title of the thesis is a sentence that briefly conveys some of the contents of the thesis itself. Every year the research or final project is always increasing, from the many titles used as the thesis it is possible that the topics discussed are almost the same or even the same. Based on this, in this study grouping the title of the thesis which is implemented in a program. The results of title grouping are displayed annually (2015 to 2019) in the form of a bar chart and then the number of data groups based on a predetermined topic or category will be seen. Extracting a collection of thesis titles using the flow of text mining which will be used as a dataset. Then the datasets are grouped using the semi-supervised k-means method, the method is the development of k-means. After that, the collection of thesis titles is preprocessed with the text mining method in which there are several stages, namely tokenization, filtering, stemming, term weighting. The initial stage of the semi-supervised k-means method is to label several datasets to determine the initial centroid, after which the data grouping process is carried out. Based on the results of tests carried out using the amount of test data that varies each year. From the test results every year (2015 to 2019) the silhoutte value is different and the largest silhoutte is in 2016 using the amount of 30% test data with a silhoutte of 0.0274024334, while the Davies Bouldin Index (DBI) value is optimal for testing 30% of the data. test in 2015 was 0.345362812. The results of grouping with the same amount of training data on each label also have a better silhouette value than the number of training data on each label that is not the same.
Segmentasi Citra Makanan pada Tray Box menggunakan Metode Otsu Thresholding dengan Ruang Warna Griselda Anjeli Sirait; Novanto Yudistira; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 2 (2022): Februari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Food is one of the basic needs needed by humans to fulfill the process of developing and growing needs. Serving food can be placed in containers such as plates, bowls, or lunch boxes. Tray box is a type of food lunch box consisting of 4 compartments. Rice, side dishes, vegetables are placed in each compartment so there aren't mix with each other and notice to the nutritional value of the food. An alternative way to know the nutritional content in food is digital image processing technology, by segmenting the image as the first step. In this research, the data used were 31 tray box images (full) consisting of 124 compartment images. The Otsu Thresholding method is used as a method for segmenting food images on a tray box with a color space. Each HSV channel is selected as color feature extraction for the compartment image segmentation process, the average of HSV and RGB are used for the full image segmentation process. The IoU accuracy results for compartmentalized image segmentation on each HSV channel are 0,6058237; 0,9006499, and 0,7726735. The results of IoU accuracy and MSE error for full image segmentation on the HSV average are 0,3069244 and 0,8644671, while the average RGB are 0,2761036 and 0,0267637. Based on the results, the Otsu Thresholding method with color space has good accuracy and provides a small error rate.
Pengelompokan Tweets mengenai Covid-19 dengan Metode BM25 dan K-Means Clustering Kornelius Putra Aditama; Indriati Indriati; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 2 (2022): Februari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The COVID-19 pandemic has hit Indonesia, many community activities are carried out at home. At that time, people often expressed their concerns through social media. One of the popular social media is Twitter which has a Tweets feature. Indonesian people who use Twitter use Tweets to write various opinions on the situation caused by COVID-19, be it government policies, vaccines, new variants of COVID-19 and so on. The diversity of these Tweets can be reflected in a section or group based on the context of the Tweets. The results of grouping Tweets can get opinions that are often expressed by the public about COVID-19. Where the results of the analysis can be used as reference material for the government in making policies during the COVID-19 pandemic. In this grouping using the BM25 method as a weighting and measuring Tweets. And K-Means Clustering where this algorithm is used. The results of the analysis and testing show that the number of terms must be reduced because the number of terms is a description of the many features used. a major feature that causes the BM25 method to be unable to distinguish the data. With the number of terms 20, parameters BM25 k1 = 1.2 and b = 0.5 and with a value of K = 3 will get the highest Silhouette Coefficient value, which is 0.3003
Klasifikasi Topik Skripsi pada Skripsi Mahasiswa FILKOM Universitas Brawijaya Periode 2015-2019 menggunakan Algoritme Support Vector Machine Dimas Adi Syahbani Achmad Putra; Bayu Rahayudi; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 3 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

A thesis is a scientific paper that discusses a particular topic or field, written and designed by a student with the guidance of a supervisor as a condition for obtaining a bachelor's degree. In the last five years, various titles and thesis topics have been designed by students of the Faculty of Computer Science, Universitas Brawijaya. The research data used is the title of the thesis with labeling based on the thesis topic of FILKOM UB students on the UB repository website. The topic class of the title of this classification thesis consists of areas of interest in FILKOM, namely Image Processing, Software Engineering, Decision Support Systems. The data used are 225 data with each topic totaling 75 data. This study aims to determine the classification of the thesis title data that has been labeled with the topic with the Support Vector Machine Algorithm using the Radial Basis Function kernel and the selection of Chi-Square features. The results of the system evaluation obtained by testing applying 10-fold cross-validation and 50% of the features used with Chi-Square are accuracy = 0.88, recall = 0.9063, precision = 0.881, f1-score = 0.8789 with the best parameter values ​​from the Support Vector Machine namely parameter (sigma) RBF kernel = 2.5, parameter (lambda) = 0.3, parameter (gamma) = 0.001, parameter C (complexity) = 0.1, parameter (epsilon) = 0.00001, and iteration = 75.
Ekstraksi Ciri Corner Triangle Similarity dan Eye Aspect Ratio untuk Deteksi Tatapan Mata Delapan Arah Dimi Karillah Putra; Randy Cahya Wihandika; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 4 (2022): April 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Someone with a disability and cannot move their body parts is having a harder time when operating a computer system. This is becoming a problem because the computer itself has become one of the technologies used to find information in this information technology era. Someone with a heavy case of disability can operate computers using an eye tracker system. In this research, corner triangle similarity and eye aspect ratio method are used to extract features from facial image data so the eye gaze direction can be classified using random forest classifier. The research is conducted using facial data images with 270 images divided into nine classes. According to the testing that has been done, the accuracy of the scenario where the image is used, the facial image without turning the head has better accuracy than the image where the head is turned. The accuracy that has been obtained is 88% on the train data and 50% on the test data. While doing analysis of the test result, it was revealed that the feature extraction method can be implemented but didn't give the best result like didn't detect the pupil at the eyes or wrongly detected circle in the image with the center of the circle located on the sclera of the eyes or the skin around the eyes. Besides that, with the existence of the turned head image in the dataset without the turning direction feature in the dataset made the similar and almost the same data but have different class. These things impacted on the classification result that in the end didn't produce a really accurate result.
Analisis Sentimen Data Tweets terhadap Penanganan Covid-19 di Indonesia menggunakan Metode Naive Bayes dan Pemilihan Kata Bersentimen menggunakan Lexicon Based Abdul Azis Adjie Sumanjaya; Indriati Indriati; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 4 (2022): April 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is a very popular social media platform in the current millennial era. Twitter is widely used as a means to express opinions and criticisms on issues that are currently being discussed. At the beginning of July the government had made efforts to handle COVID-19 in Indonesia by establishing a policy Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) Darurat. Such a policy is very necessary considering the spread of the COVID-19 virus is still high, especially in big cities. But on the other hand, the limitation of activities as part of the policy has a very large impact on the community, especially with the addition of the extension of the policy which makes people bored because they find it difficult to carry out activities. For this reason, this research conducted a sentiment analysis to see the tendency of public sentiment during the implementation Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) Darurat policy in Indonesia using the Naive Bayes classification method and the addition of the Lexicon Based feature. The provision of the Lexicon Based feature aims to filter sentimental words, so that data processing becomes faster. Based on the test results obtained, through the division of cross validation with the confusion matrix test, the accuracy is 0.75, precision is 0.76, recall is 0.76, and f-measure is 0.75. The use of the stopword feature has an influence on the classification results, because the use of the stopword feature can eliminate some of the terms resulting from the Lexicon Based feature which causes a reduction in term variations so that the accuracy results obtained are lower than without using the stopword feature.
Analisis Sentimen Opini Masyarakat terhadap Pelayanan Rumah Sakit Umum Daerah menggunakan Metode Support Vector Machine dan Term Frequency - Inverse Document Frequency Jasico Da Comoro Aruan; Bayu Rahayudi; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 5 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Regional General Hospital is a health service institution owned by the local government. The services provided by hospitals are required to always make changes, so that the services can be in accordance with the expectations and needs of the community. Hospitals as one of the institutions that have the function of providing health services must of course be in accordance with predetermined standards. Regarding improving the quality of hospital services, the government as the main actor who plays a direct role both to be responsible and to plan, regulate, organize, foster, and supervise the implementation of improving the quality of health in this case through several public policy products has explicitly discussed and regulated everything related to achieving this. Based on the needs of hospitals in assessing public sentiment as an important point in the accreditation process of hospitals, it is necessary to analyze the sentiments of public opinion towards hospitals. To perform sentiment analysis, the method used in this research is Support Vector Machine and word weighting uses Term Frequency-Inverse Document Frequency. Testing using Cross Validation with 132 training data and 33 test data resulted in an accuracy value of 88% and recall of 87.5%, precision of 90% and f-measure of 87.5%. This value is obtained when using the parameters C=1 and Itermax=1000.
Co-Authors Abdul Azis Adjie Sumanjaya Anindya Celena Khansa Kirana Barlian Henryranu Prasetio Bayu Rahayudi Dian Eka Ratnawati Dimas Adi Syahbani Achmad Putra Dimi Karillah Putra Edy Santoso Fitra Abdurrachman Bachtiar Griselda Anjeli Sirait Hasyir Daffa Ibrahim Ikhsan Putra Arisandi Indriati Indriati Iqbal Maulana Susanto Jasico Da Comoro Aruan Kornelius Putra Aditama Manat Hendry Fernando Sianturi Mochammad Ilman Asnada Muhammad Tanzil Furqon Novanto Yudistira Rama Humam Syarokha Randy Cahya Wihandika Tanica Rakasiwi Yudha Irwan Syahputra Yunita Dwi Lestari