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All Journal Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Journal of Electronics, Electromedical Engineering, and Medical Informatics Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP)
Dwi Kartini
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health

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IMPLEMENTASI ALGORITMA C5.0 UNTUK MEMBENTUK POLA POHON KEPUTUSAN DIAGNOSA PENYAKIT DIABETES MELLITUS Muhammad Latief Saputra; Irwan Budiman; Radityo Adi Nugroho; Dwi Kartini; Muliadi
Journal of Data Science and Software Engineering Vol 1 No 02 (2020)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

This study applies the C5.0 algorithm to form a decision tree pattern for diagnosing diabetes mellitus. C5.0 algorithm is a decision tree based classification algorithm. This algorithm focuses on the acquisition of information gain on all attributes. The data used is a diabetes mellitus dataset obtained from the Kaggle database website. Data preprocessing is done and data sharing is done 4 times with the distribution of training data 60% 70% 80% and 90%. Data sharing uses stratafied random sampling methods so that the distribution of training and testing data is in accordance with its portion. Calculation of accuracy performance using confusion matrix. Classification performance using C5.0 algorithm. With 90% training data get 72.73% accuracy of rules generated as many as 70 rules. With 80% training data the accuracy value is 74.03%. The rule is 64 rules. With 70% training data get an accuracy value of 76.52% of the rules generated 59 rules. With 60% training data get an accuracy value of 74.59% of the rules generated as many as 53 rules. From all the experiments that have been done, the best accuracy is found in experiments with 70% training data.
IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK UNTUK PREDIKSI HARGA SAHAM LQ45 Aris Pratama; Dwi Kartini; Akhmad Yusuf; Andi Farmadi; Irwan Budiman
Journal of Data Science and Software Engineering Vol 1 No 02 (2020)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Stock are securities of ownership of a company. Investments in the stock market on average can produce a return rate of 10-30% per year, this amount is about two to three times higher than the rate of return on deposits or savings in banks which are only 5-10 % every year. One problem is the stock price is fluctuating or changing due to certain factors. This study compares several window size data with different amounts of data, aiming to find window size data with a more accurate amount of data for stock price predictions. Convolutional neural network algorithm with window size data of 7 days, 14 days, 21 days and 28 days in the amount of data 1 year and 2 years for stock price predictions. The results of this study are the convolutional neural network algorithm with a data window size of 7 days at the amount of data 2 years is more accurate than the window size data and the amount of other data. Because the smallest error result is 0.000201587.
IMPLEMENTASI ALGORITMA GENETIKA DENGAN TEKNIK SELEKSI TOURNAMENT UNTUK PENYUSUNAN JADWAL KULIAH Faisal Murtadho; Andi Farmadi; Dodon Turianto Nugrahadi; Irwan Budiman; Dwi Kartini
Journal of Data Science and Software Engineering Vol 2 No 01 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Genetic Algorithms can help human work, one of which is compiling course schedules. Preparation of course schedules, if done manually, will take a long time because you have to make a schedule where there are no schedule conflicts between one course and another. Therefore, this study will implement a Genetic Algorithm for the preparation of course schedules, so that it will speed up the preparation of course schedules compared to manual scheduling. In this study, the Genetic Algorithm with Tournament Selection was carried out with the input of control parameters, namely Population Size = 10, Crossover Rate (CR) = 0.75, and Mutation Rate (MR) = 0.01. In this study, the Genetic Algorithm has succeeded in obtaining the desired solution, namely scheduling courses where there are no schedule conflicts between one course and another. This search process took 88 generations to find the best solution.
PENGARUH OPTIMASI BOBOT MENGGUNAKAN ALGORITMA GENETIKA PADA KLASIFIKASI TINGKAT KERAWANAN DBD Bayu Hadi Sudrajat; Muliadi; Muhamad Reza Faisal; Radityo Adi Nugroho; Dwi Kartini
Journal of Data Science and Software Engineering Vol 2 No 02 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Dengue Hemorrhagic Fever (DHF) is a disease transmitted by the Aedes Ageypti mosquito. In South Kalimantan, especially in the city of Banjarbaru, the number of cases tends to increase every year. Existing research has identified the level of dengue susceptibility by using computational methods, one of which is classification. The method used in this research is Neural Network Backpropagation with weight optimization using Genetic Algorithms for data classification of dengue disease in Banjarbaru City. The purpose of this study was to determine the performance of the classification of dengue susceptibility levels using Neural Network Backpropagation and weighting using Genetic Algorithms. The results showed that the performance obtained for the classification of the level of dengue susceptibility using the Neural Network Backpropagation Algorithm was 83.33% in the accuracy, 96.51% precision, and 84.69% recall, whereas when using the Neural Network Backpropagation Algorithm based on Genetic Algorithm for weight optimization, obtained an accuracy value of 96.29%, a precision of 98.97%, and a recall of 97%.
PERFORMANCE COMPARISON OF ADAPTIVE NEURO FUZZY INFERENCE SYSTEM AND SUPPORT VECTOR MACHINE ALGORITHM IN BALANCED AND UNBALANCED MULTICLASS DATA CLASSIFICATION Muhammad Irfan Saputra; Irwan Budiman; Dwi Kartini; Dodon Turianto Nugrahadi; Mohammad Reza Faisal
Journal of Data Science and Software Engineering Vol 2 No 03 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Data is a record collection of facts. At first the data in the real world were largely unbalanced. Although, the existence of data on fewer categories is much more important to know data on more categories. However, there are some balanced data. This balanced data is the possibility of a ratio of 1:1 in which, the data in the dataset is the same. In this study, using the ANFIS algorithm and SVM to see affected performance on balanced and imbalanced data with multiclass. Data is taken from the UCI Machine Learning. From the research conducted, it is known that the SVM method on the Wine dataset has an accuracy of 96.6% and the ANFIS method on the Iris dataset has an accuracy of 94.7%.
HYPERPARAMETER TUNING METHOD OF EXTREME LEARNING MACHINE (ELM) USING GRIDSEARCHCV IN CLASSIFICATION OF PNEUMONIA IN TODDLERS Pirjatullah; Dwi Kartini; Dodon Turianto Nugrahadi; Muliadi; Andi Farmadi
Journal of Data Science and Software Engineering Vol 2 No 03 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Pneumonia is a disease that is susceptible to attack toddlers. According to data from the Ministry of Health, the cause of under-five mortality due to pneumonia is number 2 of all under-five deaths. The dataset used is pneumonia disease data at the MTBS Health Center of East Martapura Health Center. The classification method in this study uses the Extreme Learning Machine (ELM) method. The classification process starts from SMOTE upsampling to balance the class, then parameter tunning is performed using GridsearchCV on the hidden layer neurons, then classification is carried out using the ELM method using the Triangular Basis activation function by comparing the test datasets 90:10, 80:20, 70:30, 60:40 and 50:50. This study provides the best performance results with an accuracy of 86.36%, the ratio of training and test data is 90:10 and 3 neurons hidden layer.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN POHON UNTUK RESTORASI LAHAN BEKAS KEBAKARAN DENGAN METODE ANALYTIC HIERARCHY PROCESS (AHP) DAN SIMPLE MULTI ATTRIBUTE RATING TECHNIQUE EXPLOITING RANKS (SMARTER) Muhammad Denny Ersyadi Rahman; Muliadi; Rudy Herteno; Dwi Kartini; Friska Abadi
Journal of Data Science and Software Engineering Vol 2 No 03 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Utilization or use of forest and land areas that are not in accordance with conservation principles can cause critical land to occur. Critical land is land inside or outside the forest area that has been damaged, so that it can cause loss or decrease in its function. The lack of knowledge of some people on critical land and the selection of inappropriate plant types sometimes makes the condition of burnt land increasingly become one of the obstacles for the Forest and Land Rehabilitation Program (RHL). Statistical data analysis can be used in the data processing process to become valuable information for the system. Applying statistical analysis methods in making decisions in selecting statistical data that has several criteria. This research is focused on the application of the Analytical Hierarchy Process (AHP) method to see a comparison of criteria. The SMARTER (Simple Multi Attribute Rating Technique Exploiting Rank) method is very suitable to be used to overcome the many alternatives that will be given to different soil samples later. In short, each final weight that affects the alternative is calculated with the results of the alternative assessment, so that the utility value of each alternative is obtained. From the research of the Analytical Hierarchy Process (AHP) and Simple Multi Attribute Rating Technique Exploiting Rank (SMARTER) method, the results of the Balangeran vegetation are obtained as the main recommendation with the greatest utility value, namely 1.321668.
Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Gambar X-Ray Penyakit Covid-19 dan Pneumonia Fitria Agustina fitria; Andi Farmadi; Dwi Kartini; Dodon Turianto Nugrahadi; Ando Hamonangan Saragih
Journal of Data Science and Software Engineering Vol 3 No 01 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Abstrak Pneumonia caused by the corona virus is different from ordinary pneumonia. One way to find out which pneumonia is caused by the corona virus is to do an X-ray. The disadvantage of this examination is that it requires a radiologist and the analysis time is relatively long. Therefore, to overcome this problem, deep learning methods can be used by implementing the Convolutional Neural Network (CNN) Algorithm method for X-ray image classification. The implementation of the Convolutional Neural Network (CNN) Algorithm is done by using training data of 4800 images which are trained using batch size values ​​of 16, 32, and 64. The train process with batch size values ​​of 16, 32 and 64 produces an average accuracy of 90%, 91% and 92%, while the loss values ​​are 0.22, 0.16 and 0.25. From this process it was found that batch 64 was the best loss and accuracy result for training data. The test data with batch values ​​of 16, 32, and 64 resulted in an accuracy of 76%, 82% and 76%, while the loss values ​​were 0.79, 0.53 and 0.63. The results of this manual testing of 30 photos contained 7 images that are not recognized by the model because of the images look similar to each other with an accuracy of 76%. From this process it was found that batch 32 was the best loss and accuracy result for testing data.
Text Mining Untuk Mengklasifikasi Judul Berita Online Studi Kasus Radar Banjarmasin Menggunakan Metode TF-IDF dan K-NN Salsabila Anjani; Andi Farmadi; Dwi Kartini; Irwan Budiman; Mohammad Reza Faisal
Journal of Data Science and Software Engineering Vol 3 No 01 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

ABSTRACT The news media that used to be commonly used were newspapers. However, with the development of the times, the news media is now entering the digital era. Many online news media spread on the internet. The sophistication of the internet makes it easier for readers to choose which news they want to read. Unlike newspapers, online news media have categories where readers can choose. In general, the categorization of a news in online media is determined by the editor. Given the number of news published in a day, of course, makes the editor's job difficult. A category in the news is usually not appropriate because usually the headline is made as attractive as possible to attract the interest of the reader. So there are times when the news title does not match the category that has been entered by the editor. The use of the K-Nearest Neighbor (K-NN) method can be used in determining the categorization of a news. By using a case study of the online media Radar Banjarmasin, a research was conducted to find out how well the Canberra and Euclidean classification methods were using news headline data for categorization. The results obtained in this study are the better classification method is Euclidean and with an accuracy value of 65.00%. Improvements that should be made for further research is to use other methods for comparison.
PENERAPAN METODE FUZZY NEUTROSOPHIC SOFT SETS UNTUK PREDIKSI STATUS PENGAWASAN COVID-19 Lisnawati; Andi Farmadi; Dwi Kartini; Mohammad Reza Faisal; Rudy Herteno
Journal of Data Science and Software Engineering Vol 3 No 03 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Coronavirus Disease 2019 (COVID-19) is a new type of disease that has never been previously identified in humans and has been declared a pandemic. The diagnosis of the disease is complicated by the variety of symptoms and imaging findings and the severity of the disease at the time of presentation. Fuzzy Neutrosophic Soft Sets are able to handle many types of uncertainty data such as ambiguity, inaccuracy, ambiguity, and inconsistency. Therefore, Fuzzy Neutrosophic Soft Sets can be applied to overcome the uncertainty of symptoms in COVID-19 surveillance. This research was conducted by collecting and presenting the respondent's Neutrosophic value and Neutrosophic value as a knowledge base, then performed Fuzzy Neutrosophic Soft Sets operations (composition, complement, value function, and score function) to obtain the monitoring status of the predicted results. Furthermore, the monitoring status of the predicted results is compared with the actual monitoring status of the respondents to obtain the accuracy level of Fuzzy Neutrosophic Soft Sets. Based on testing of 12 respondents, with 7 respondents as training data and 5 respondents as testing data, the accuracy of the Fuzzy Neutrosophic Soft Sets method in the diagnosis of COVID-19 surveillance status was 80%.
Co-Authors Ade Saputra Akhmad Yusuf Andi Farmadi Andi Farmadi Ando Hamonangan Saragih Aris Pratama Bayu Hadi Sudrajat Dodon Turianto Nugrahadi Faisal Murtadho Fathul Hadi Fatma Indriani Fitria Agustina fitria Friska Abadi Halimah Hendy Setyoputro Ichsan Ridwan Irwan Budiman Ivan Sitohang Lisnawati MARIA ULFAH Megi Adhiyani Muhammad Denny Ersyadi Rahman Muhammad Irfan Saputra Muhammad Itqan Mazdadi Muhammad Latief Saputra Muhammad Mirza Hafiz Yudianto Muhammad Reza Faisal, Muhammad Reza Muhammad Ridho Ansyari Muliadi Muliadi Aziz Oni Soesanto Pirjatullah Radityo Adi Nugroho Regina Risky Meliawati Rudy Herteno SALLY LUTFIANI Salsabila Anjani Saragih, Triando Hamonangan Siti Hatimah Rahmadaniah