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Jurnal ULTIMATICS
Published by Universitas Multimedia Nusantara
ISSN : 20854552     EISSN : 2581186X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Jurnal ULTIMATICS merupakan Jurnal Program Studi Teknik Informatika Universitas Multimedia Nusantara yang menyajikan artikel-artikel penelitian ilmiah dalam bidang analisis dan desain sistem, programming, algoritma, rekayasa perangkat lunak, serta isu-isu teoritis dan praktis yang terkini, mencakup komputasi, kecerdasan buatan, pemrograman sistem mobile, serta topik lainnya di bidang Teknik Informatika. Jurnal ULTIMATICS terbit secara berkala dua kali dalam setahun (Juni dan Desember) dan dikelola oleh Program Studi Teknik Informatika Universitas Multimedia Nusantara bekerjasama dengan UMN Press.
Arjuna Subject : -
Articles 275 Documents
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Analisis Clustering Pengelompokan Penjualan Paket Data Menggunakan Metode K-Means Dimas Galang Ramadhan; Indri Prihatini; Febri Liantoni
Ultimatics : Jurnal Teknik Informatika Vol 13 No 1 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i1.1981

Abstract

At present with the COVID-19 pandemic situation that requires all activities based in the network, starting from work, college, school, everything is based on the network. Certain provider users will experience excessive data plan usage. This also has an effect on a counter that sells data packages, which must provide several data package services in accordance with current conditions. This research was conducted to analyze the grouping of sales of data packages that are often purchased by customers in a counter by using the K-Means method. The K-Means method is used because the K-Means algorithm is not influenced by the order of the objects used, this is proven when the writer tries to determine the initial cluster center randomly from one of the objects in the first calculation. sales of data packages at a counter. Variables used include Price, Active period, and number of data packages. The K-Means Cluster Analysis algorithm is basically applied to the problem of understanding consumer needs, identifying the types of data package products that are often purchased. The K-Means algorithm can be used to describe the characteristics of each group by summarizing a large number of objects so that it is easier.
Sistem Informasi Klinik Hewan Untuk Meningkatkan Kinerja Keuangan Menggunakan Metode Rasio Profitabilitas Taufik Kurnialensya
Ultimatics : Jurnal Teknik Informatika Vol 13 No 1 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i1.2006

Abstract

Veterinary Clinic which is located at Jalan Brigjend. S. Sudiarto No. 134 Semarang is an agency engaged in serving its clients in caring for the health of livestock and pets. In recording and processing data using an organized system and not yet well integrated. In financial management and also in making financial reports, it is not optimal in evaluating financial performance, so that they cannot see the ability to generate operating profit from the income generated. There is a need for a system to be used to increase activity at the veterinary clinic, so that it is much more effective and efficient in the provision of equipment, equipment, and data management including recording, making financial reports, and assessing financial performance, so that it can show the time used and the system as well. can be accessed simultaneously by interested parties. In the implementation of a new system, namely the Animal Clinic Information System to Improve Financial Performance with the Web-Based Ratio method. From these problems, an application program can be made using PHP and MySQL as the database. With the above design, it provides benefits for veterinary clinics that provide benefits in improving their financial performance, can be accessed simultaneously, and provides services in the provision of equipment and equipment needed.
Digital Image Processing using Texture Features Extraction of Local Seeds in Nekbaun Village with Color Moment, Gray Level Co Occurance Matrix, and k-Nearest Neighbor Yampi R Kaesmetan; Marlinda Vasty Overbeek
Ultimatics : Jurnal Teknik Informatika Vol 13 No 2 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i2.2038

Abstract

The problem in determining the selection of corn seeds for replanting, especially maize in East Nusa Tenggara is still an important issue. Things that affect the quality of corn seeds are damaged seeds, dull seeds, dirty seeds, and broken seeds due to the drying and shelling process, which during the process of shelling corn with a machine, many damaged and broken seeds are found. So far, quality evaluation in the process of classification of the quality of corn seeds is still done manually through visible observations. Manual systems take a long time and produce products of inconsistent quality due to visual limitations, fatigue, and differences in the perceptions of each observer. The selection of local maize seeds in Timor Island, East Nusa Tenggara Province, especially in Nekbaun Village, West Amarasi District with feature extraction with a color moment shows that the mean, standard deviation and skewness features have an average validation of 88% and use the GLCM method which shows the neighbor relationship. Between the two pixels that form a co-occurrence matrix of the image data, namely GLCM, it shows that the features of homogeneity, correlation, contrast and energy have an average validation of 70.93%. The k-Nearest Neighbor (k-NN) algorithm is used in research to classify the image object to be studied. The results of this study were successfully carried out using k-Nearest Neighbor (k-NN) with the euclidean distance and k = 1 with the highest extraction yield of 88% and the results of GLCM feature extraction for homogeneity of 75.5%, correlation of 78.67%, contrast of 65.75 % and energy of 63.83% with an average accuracy of 70.93%.
Perbandingan Convolutional Neural Network pada Transfer Learning Method untuk Mengklasifikasikan Sel Darah Putih Daniel Martomanggolo Wonohadidjojo
Ultimatics : Jurnal Teknik Informatika Vol 13 No 1 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i1.2040

Abstract

Analysis of WBC structure from microscopic images and classification of cells into types is challenging. Although white blood cells can be differentiated based on their shape, color and size, one challenging aspect is that they are surrounded by other blood components such as red blood cells and platelets. In this study, transfer learning method using four network architectures that have been trained in advance is applied to classify the white blood cell images. The network architectures used are AlexNet, GoogleNet, ResNet-50 and VGG-16. A comparative analysis of the performance of these architectures was carried out in classifying the images. The evaluation method was undertaken using Confusion Matrix. The performance metrics measured in the evaluation are Accuracy, Precision, Recall and Fmeasure. The results showed that all architectures succeeded in classifying white blood cells using the transfer learning method. ResNet-50 is the network architecture that shows the highest performance in classifying white blood cell images.
Pengenalan Aktivitas Manusia Melalui Analisis Data Gerakan Smartphone Eunike Endariahna Surbakti; Andes Suciani; Philipus Silaen; Septian Adibowo
Ultimatics : Jurnal Teknik Informatika Vol 13 No 1 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i1.2044

Abstract

Abstract− Today's smartphones are not only a means of communication but now offer many features and deployment of sensors. Smartphones are also designed to track the user's daily activities, learn and then help the user to make better decisions about what the user will take in the future. Applications that utilize the movement of a smartphone to analyze human activity are used by the Moves app, Fitbit Charge, Nike Fuelband, Apple Watch Health app. To perform human motion recognition activities, data is generated and collected from smartphones such as iPhones and Androids, or wearables such as the Apple Watch smartwatch, Nike Fuelband, and Fitbit Charge. Sensors commonly used to collect data include an accelerometer, gyroscope, heart rate monitor, and thermometer. Another method also combines these sensors with a magnetometer and GPS. This study compares previous research to seek opportunities from the resulting benefits such as monitoring city prisoners, community grouping, city density detection and distribution maps whose data can be used by business opportunities. Keywords: Accelerometer, Human movement recognition activity, Gyroscope, Heart rate monitor, Thermometer.
Prediksi Akurasi Kemenangan Pada Permainan Poker Menggunakan Algortima C5.0 Dan WIPSO M. Fariz Januarsyah; Ermatita Ermatita
Ultimatics : Jurnal Teknik Informatika Vol 13 No 1 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i1.2070

Abstract

In the era of information technology, a lot of data can be taken from human activities based on computer systems. But the system is not only found on computers, but in all areas of human life, be it in terms of health, security, even in games where the data set from these activities becomes a database that can be used to find a new knowledge. This study aims to predict the accuracy of poker games using the Weight Improved Particle Swarm Optimization (WIPSO) algorithm for attribute selection which then uses the C5.0 algorithm to predict accuracy. Before being processed, the dataset will be changed from 11 attributes to 6 attributes. The results of this study indicate that the accuracy of the poker card will increase, when using the C5.0 algorithm the accuracy obtained is 49.952% while the accuracy obtained by the C5.0 + WIPSO algorithm is 51.2%.
The Implementation of the Weight Product (WP) Method on the Best Employee Selection Komang Redy Winatha; I Nyoman Tri Anindia Putra; Naufal Akbar Ihsan Baedlawi
Ultimatics : Jurnal Teknik Informatika Vol 13 No 2 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i2.2092

Abstract

PT. Autogrill Services Indonesia is a private company engaged in the food and beverages selling. There are 8 outlets and 379 employees. To achieve maximum performance within the company environment, PT Autogrill Services Indonesia gives an appreciation to employees in the form of the best rewards every month and year calculated based on certain criteria. PT AutoGrill Services Indonesia needs to have a decision support system to simplify the decision-making process. To meet these needs, a web-based decision support system for selecting the best employees was designed using the weight product (WP) method at PT Autogrill Services Indonesia. The design stage includes needs analysis, context diagrams, data flow diagrams, and designing database tables. This system is web-based, using the programming language PHP and MySQL as database storage. The main features contained in this system include processing user data, outlets, employees, criteria, periods, alternatives, scores, and the calculation of monthly and annual winners. Based on the test results, all system functionality components can run well and by expectations.
FastText Word Embedding and Random Forest Classifier for User Feedback Sentiment Classification in Bahasa Indonesia Yehezkiel Gunawan; Julio Christian Young; Andre Rusli
Ultimatics : Jurnal Teknik Informatika Vol 13 No 2 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i2.2124

Abstract

User feedback nowadays become a platform for software developer to identify and understand user requirements, preferences, and user’s complaints. It is important for the developer to identify the problem that exist in user feedback. According to software growth, user amount also growth. Read and classify one by one manually are wasting time and energy. As the solution for the problem, sentiment analysis system using Random Forest Classifier which use word embedding as the feature extraction is made to help to classify which feedback is positive, neutral, or negative. Random Forest Algorithm is chosen because it gives the best performance, even its need the larger resources. Furthermore, with word embedding, the words which has semantic or syntactic similarities will be detected. Word embedding does not need stemming and stop word removal, so the context of the sentences keep remains. This research is made to implement word embedding to classify sentiment of user feedbacks using Random Forest Classifier. 70.27% accuracy, 80% precision, 54 recall and 54% F1 score is reached when BYU dataset (200 dimension) as embedding dataset with the train and test ratio 80:20.
Spam Filtering On User Feedback Via Text Classification Using Multinomial Naïve Bayes And TF-IDF Septiyan Mudhiya Sadid; Julio Christian Young; Andre Rusli
Ultimatics : Jurnal Teknik Informatika Vol 13 No 2 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i2.2149

Abstract

User feedback could give developer an information on what should be fixed or should be improved. But there are many user feedback that are actually spam. In user feedback, spam contents are more likely to be an inappropriate feedback, a feedback that is not actually a feedback, just some random comment or even a question. Reading and choosing feedback manually could be costly, especially in terms of time and energy. Therefore, this research focuses in building a spam filtering model using Multinomial Naïve Bayes that implement a TF/IDF approach to detect spam automatically. For text classification, Multinomial Naïve Bayes proved on having better speed and having good performance. With TF/IDF, word that highly occurred in many documents has less impact than other so it could help increasing performance from imbalanced dataset. This research aims to implement Multinomial Naïve Bayes for spam filtering in user feedback and to measure performance of the model. Best performance of this classifier was obtained when using up-sampling method and typo corrector with 70:30 ratio of train and test set resulting in 89.25% for accuracy, 45% for precision, 56% for recall, and 50% for F1-Score.
Classification of Metagenome Fragments With Agglomerative Hierarchical Clustering Alex Kurniadi; Marlinda Vasty Overbeek
Ultimatics : Jurnal Teknik Informatika Vol 13 No 2 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i2.2180

Abstract

Unlike genomics which study specifically culturable microorganisms, metagenomics is a field that studies microorganic samples retrieved directly from the environment. Such samples produce widely varying fragments when sequenced, many of which are still unidentified or unknown. Assembly of these fragments in the goals of identifying the species contained among them are thus prone to make said goals more difficult, so it becomes necessary for binning techniques to come in handy while trying to classify these mixed fragments onto certain levels in the phylogenetic tree. This research attempts to implement algorithms and methods such as k-mers to use for feature extraction, linear discriminant analysis (LDA) for dimensionality reduction, and agglomerative hierarchical clustering (AGNES) for taxonomic classification to the genus level. Experimentation is done across different objective measurements, including the length of the observed metagenome fragment that spans from 0,5 Kbp up to 10 Kbp for both the 3-mer and 4-mer contexts (k = 3 and k = 4). The averaged validity scores of the resulting data clusters generated from both the training and test sets, computed with the silhouette index metric, are 0.6945 and 0.0879 for the 3-mer context, along with 0.5219 and 0.1884 for the 4-mer context.

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