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All Journal Jupiter Jurnal Teknologi Informasi dan Ilmu Komputer Computatio : Journal of Computer Science and Information Systems Jurnal Informatika Global Jurnal Abdimas Mandiri

Lastri Widya Astuti

Universitas Indo Global Mandiri

Author-ID : 2742645

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences

Published : 12 Documents Claim Missing Document

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Optimalisasi Klasifikasi Kanker Payudara Menggunakan Forward Selection pada Naive Bayes Lastri Widya Astuti; Imelda Saluza; Faradilla Faradilla; M. Fadhiel Alie
Jurnal Informatika Global Vol 11, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v11i2.1235

Breast cancer is a type of malignant tumor which is still the number one killer where the process of spread or metastasis takes a long time. The number of breast cancer sufferers increases every year so that if detected or caught early, prevention can be done early so as to reduce the number of breast cancer sufferers. To reduce the risk of increasing the number of cancer patients, it is necessary to do early detection, several methods can be used to assist the early detection process such as cancer screening, or computational methods. Several machine learning methods that have been chosen to solve cases of breast cancer prediction, especially the classification algorithm, including Naive Bayes have the advantage of being simple but having high accuracy even though they use little data. Weaknesses in Naive Bayes, namely the prediction of the probability result is not running optimally and the lack of selection of relevant features to the classification so that the accuracy is low. This research is intended to build a classification system for detecting breast cancer using the Naive Bayes method, by adding a forward selection method for feature selection from the many features that exist in breast cancer data, because not all features are features that can be used in the classification process. The result of combining the Naive Bayes method and the forward selection method in feature selection can increase the accuracy value of 96.49% detection of breast cancer patients.

Ekstrasi Fitur Citra MRI Otak Menggunakan Data Wavelet Transform (DWT) untuk Klasifikasi Penyakit Tumor Otak Lastri Widya Astuti
Jurnal Informatika Global Vol 10, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

ABSTRACTThe brain is formed from two types of cells: glia and neurons. Glia functions to support and protect neurons, while neurons carry information in the form of electrical pulses known as potential action. The brain regulates and coordinates most of the body's movements, behavior, and homeostasis functions such as heart rate, blood pressure, body fluid balance and body temperature. A brain tumor is a mass of abnormally growing brain cells. Most brain tumors can spread through brain tissue, but rarely spread to other areas of the body. But in the case of benign brain tumors, as they grow they can destroy and suppress other normal brain tissue, which can result in paralysis. Several methods are used to detect disorders of the brain nerve tissue, including: Magnetic Resonance Imaging (MRI). This research is intended to build a classification system for brain image data using Magnetic Resonance Imaging (MRI) with the category, normal, Glioma, metastatic bronchogenic carcinoma or Alzheimer's using Magnetic Resonance Imaging (MRI) so that it can assist in decision making for medical experts. While the method used in this research is Discrete Wavelet Transformation (DWT) for the feature extraction process so that the unique characteristics of an object can be recognized, as well as the classification process using the adaptive neighborhood neural network method. This research is able to integrate the two methods with the acquisition of significant accuracy.Keywords : feature extraction, classification, MRI, BrainABSTRAKOtak terbentuk dari dua jenis sel: glia dan neuron. Glia berfungsi untuk menunjang dan melindungi neuron, sedangkan neuron membawa informasi dalam bentuk pulsa listrik yang di kenal sebagai potensi aksi. Otak mengatur dan mengkordinir sebagian besar,gerakan, perilaku dan fungsi tubuh homeostasis seperti detak jantung, tekanan darah, keseimbangan cairan tubuh dan suhu tubuh. Tumor otak adalah sekumpulan massa sel-sel otak yang tumbuh abnormal. Sebagian besar tumor otak dapat menyebar melalui jaringan otak, tetapi jarang sekali menyebar ke area lain dari tubuh. Namun pada kasus tumor otak yang jinak, saat mereka tumbuh dapat menghancurkan dan menekan jaringan otak normal lainnya, yang dapat berakibat pada kelumpuhan. Beberapa metode dipergunakan untuk mendeteksi gangguan pada jaringan syaraf otak, diantaranya: Magnetic Resonance Imaging (MRI). Penelitian ini dimaksudkan untuk membangun sistem klasifikasi untuk data citra otak menggunakan Magnetic Resonance Imaging (MRI) dengan kategori, normal, Glioma, metastatic bronchogenic carcinoma atau Alzheimer menggunakan Magnetic Resonance Imaging (MRI) sehingga dapat membantu dalam pengambilan keputusan bagi tenaga ahli dibidang kedokteran. Sedangkan metode yang digunakan dalam penelitian adalah Discrete Wavelet Transformation (DWT) untuk proses ekstrasi fitur (feature extraction) agar karakteristik unik dari suatu objek dapat dikenali, serta proses klasifikasi menggunakan metode adaptive neighborhood neural network. Penelitian ini mampu mengintegrasikan kedua metoda dengan perolehan hasil akurasi yang signifikan.Kata kunci : ekstrasi fitur, klasifikasi, MRI, Otak

Feature Selection Menggunakan Binary Wheal Optimizaton Algorithm (BWOA) pada Klasifikasi Penyakit Diabetes Lastri Widya Astuti; Imelda Saluza; Evi Yulianti; Dhamayanti Dhamayanti
Jurnal Informatika Global Vol 13, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v13i1.2057

Diabetes Mellitus (DM) is a chronic disease characterized by blood glucose (blood sugar) levels exceeding normal, i.e. blood sugar levels being equal to or more than 200 mg/dl, and fasting blood sugar levels being above or equal to 126 mg/dl. The increase in the number of people with diabetes is due to delays in detection. Utilization of machine learning in helping to establish a fast and accurate diagnosis is one of the efforts made in the health sector. One of the important steps to produce high classification accuracy is through the selection of relevant features. The problem in feature selection is dimensionality reduction, where initially all attributes are required to obtain maximum accuracy while not all features are used in the classification process. This study uses the Binary wheal Optimization Algorithm (BWOA) as a feature selection method to increase accuracy in the classification of diabetes mellitus. The use of metaheuristic algorithms is an alternative to increase computational efficiency and avoid local minimums. The BWOA algorithm reduces the 8 attributes in the dataset to the 3 best attributes that are able to represent the original dataset. The results showed that from the six classification methods tested, namely: K-NN, Naïve Bayes, Random Forest, Logistics Regression, Decision Tree, Neural Network. then the three logistic regression methods, naive Bayes and neural network are in good classification criteria based on Area Under Curve (AUC) while the calculation of the accuracy value shows an average of above 70%. Keywords : Feature Selection, Classification, Diabetes Mellitus, Accuracy, Area Under Curve (AUC)

Prediksi Data Time Series Harga Penutupan Saham Menggunakan Model Box Jenkins ARIMA Imelda Saluza; Dewi Sartika; Lastri Widya Astuti; Faradillah Faradillah; Leriza Desitama; Endah Dewi Purnamasari
Jurnal Informatika Global Vol 12, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v12i2.1940

The ability to predict time series data on closing market prices is critical in determining a company's stock results. The development of an efficient stock market has a positive correlation with economic growth, in a country both in the short and long term. In practice, investors tend to invest in countries that have a stable economy, low crime. The rise and fall of stock prices has made many investors develop various effective strategies in predicting stock prices in the future with the aim of making investment decisions so that investors can guarantee their profits and minimize risk.As a result, the researchers developed a model that could accurately estimate precision. Time series data models are one of the most powerful methods to render assumptions in decisions containing uncertainty. The AutoRegressive Integrated Moving Average (ARIMA) model with the Box Jenskins time series procedure is one of the most commonly used prediction models for time series results. The steps for using the Box Jenskins ARIMA model for historical details of expected stock closing prices are outlined in this paper. BBYB and YELO stock data from yahoo.finance were used as historical data. The Aikake Information Criterion (AIC), Bayesian Information Criterion (BIC) / Schawrz Bayesia Criterion (SBC), Log Probability, and Root Mean Square Error (RMSE) are used to choose an effective model, and the model chosen is ARIMA (1 , 1,2). The findings suggest that the Jenkins ARIMA box model has a lot of scope for short-term forecasting, which may help investors make better decisions. Keywords: prediction, the stock's current closing price, Box Jenskins ARIMA model

APLIKASI ISC (INFORMATICS STUDENT CENTER) MENGGUNAKAN METODE PERSONAL EXTREME PROGRAMMING BERBASIS ANDROID Rizka Anjuliani; Lastri Widya Astuti; Hartini Hartini
Jurnal Informatika Global Vol 6, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Gathering for informations today can be classified as a primary need. Various kinds of activities can be done using technology, especially smartphones, for example, learning, interacting with other people, searching for information, or just for entertainment. There are many kinds of activities and many variety to access them. So it cannot be done all at once in a practical way, we need a solution to be able to carry out these activities at once with easy access. The above issue is one of the problems which is faced by the University of Indo Global Mandiri (UIGM). In order for learning and getting information can be done at once and easily accessible, then the researcher do research that aims to build applications Informatics Student Center (ISC) which is developed by using the Personal Extreme Programming (PXP) consisting of several phases, such as requirements, planning, iteration initialization, design, implementation, and system testing. ISC applications using the Android operating system, built with the collaboration of the Java language, PHP, and MySQL database. In addition to ease of access, the ISC has some features that academic information, discussion forums, entertainment, and job opportunity. Based on test results, it was concluded that the ISC application can run on Android mobile devices which are tested. Result from this study is the availability of applications supporting learning activities and access information that can be accessed online through the Android mobile devices

GAME EDUKASI TEBAK GAMBAR BENDERA NEGARA MENGGUNAKAN METODE LINEAR CONGRUENTIAL GENERATOR (LCG) BERBASIS ANDROID Karli Ramadhan; Lastri Widya Astuti; Dwi Asa Verano
Jurnal Informatika Global Vol 6, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

The game is one of the entertainment media is the choice of children to relieve boredom or just for leisure. This game is not a means of entertainment, but it is a lesson to improve child development. This guessing game played in the form of image display using the following image is not moving so that the game felt static and monotonous. The development of the view of the games that begin to move with color graphics from the original form of dimensional approach. The games that will be made an interactive game. This is an interactive multimedia game with a controller that can be managed by the user, so the user can select any subsequent process of desired remedy, not menoton accompanied by images, sounds and features that attract so this game will be of interest to the game players have a number of criteria one of which is the educational game. Educational game that digital games designed for educational enrichment (support teaching and learning), using interactive multimedia technology and is expected to enhance learning to identify different countries flags. Applications developed flag game that has 10 levels of difficulty depending on each level, using a system based on Android Eclipse indigo are guessing. In the method of randomization flag was linear congruential generator (LCG). Linear congruential generator (LCG) is used to generate random numbers with uniform distribution, in the form of randomization in the state flag, the game design is intended to recognize the various state flag and its benefits for children to practice the logic ( analysis), training capacity spancial (intelligence picture), and the ability to read the game guess the state flag image, for parents like to give attention, accompanying them and monitor the child's development in the game

Co-Authors Anggraini, Leriza Desitama Dhamayanti Dhamayanti Dhamayanti Dwi Asa Verano Endah Dewi Purnamasari Faradilla Faradilla Faradillah Faradillah Hartati Hartini Hartini Herri Setiawan Imelda Saluza, Imelda Karli Ramadhan Leriza Desithama M Fadhiel Alie M. Agus Munandar M. Fadhiel Alie Mohammad Taufikurrahman Muhammad Fadhiel Alie Nurlia Apriani Rita Wiryasaputra Rizka Anjuliani Sartika, Dewi Siti Muyaroah Yulianti, Evi

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