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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics jeti Jurnal Begawe Teknologi Informasi Journal of Data Science and Software Engineering

Muliadi

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Author-ID : 1766578

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Articles

Sistem Informasi Pendaftaran Beasiswa Tugas Akhir pada Majelis Pendidikan Daerah Aceh Utara Berbasis Web Zulfikar; Muliadi
Jurnal Elektro dan Informatika Vol 1 No 1 (2020): Maret 2020
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (807.952 KB)

Proses pendaftaran, seleksi, dan penerimaan beasiswa pada Kantor MPD Aceh Utara saat ini masih menggunakan prosesmanual. Proses ini membuat kinerja penerimaan beasiswa sangat lama, ditambah pengarsipan berkas yang masihmenggunakan lemari. Sistem penerimaan beasiswa masih menggunakan excel belum lagi pendaftar yang harus bolakbalik untuk mengumpulkan berkas dan menanyakan informasi mendapatkan atau tidaknya beasiswa dari MPD AcehUtara. Solusi yang akan diberikan adalah dibuatkan sistem informasi berbasis web yang dapat membantu pendaftaransecara online, pengumuman secara online, penyeleksi secara online. Dari sistem ini akan menyerap mahasiswa yangkurang mampu untuk pembiayaan Tugas Akhir. Hasil uji coba yang sudah dilakukan bahwa sistem ini membantu dalamproses pendaftaran, penyeleksian, dan penerimaan beasiswa. Petugas juga membantu proses penyeleksian dengan metodeAHP yang tersistem. Kepala MPD dengan mudah mengetahui laporan penerimaan dan pendaftar beasiswa secara realtime.Kata Kunci: Sistem Informasi, AHP, Beasiswa, Penerimaan Online, Website

Perancangan Dan Pembuatan Sistem Informasi Perpustakaan Pada SDN 26 Mataram Nanda Wafiya; Nadiyasari Agitha; Muliadi
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 2 No. 1 (2021): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Manajemen perpustakaan yang terkomputerisasi menggunakan Microsoft Excel saat ini sudah banyak digunakan. Namun Microsoft excel masih memiliki banyak kekurangan dalam penggunaanya seperti, data dapat hilang sewaktu-waktu, human error, dan untuk penggunaan bersama masih kurang tepat digunakan karena user yang lain akan kesulitan jika ingin melakukan update data karena data tidak tersimpan pada database terpusat. Kekurangan tersebut dapat diatasi dengan membuat sebuah sistem informasi perpustakaan berbasis website menggunakan bahasa pemrograman PHP dan database MySQL.Metode perancangan sistem yang digunakan yaitu menggunakan metode waterfall. Dalam pengembangannya metode waterfall memiliki beberapa tahapan yang runtut: requirement (analisis kebutuhan), design sistem, coding & testing, penerapan program dan pemeliharaan. Pengujian sistem dilakukan dengan menggunakan user acceptance testing dengan melakukan uji black box dan kuisioner. Dari hasil kuisioner didapat hasil bahwa seluruh responden di SDN 26 Mataram setuju dengan sistem yang dibuat.

Penyeleksian Calon Karyawan Menggunakan Metode Pembobotan Shannon Entropy dan Metode ARAS Halimah; Dwi Kartini; Friska Abadi; Irwan Budiman; Muliadi
Journal of Data Science and Software Engineering Vol 1 No 01 (2020)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

This study discusses the selection of prospective employees using the Shannon Entropy weighting method and the Additive Ratio Assessment (ARAS) method which aims to determine the accuracy of the results obtained from the method. The Shannon Entropy method is a weighting method that assigns criteria weights based on the calculation of alternative employee selection data and the Additive Ratio Assessment (ARAS) method is a ranking method that has a utility function. Testing the data in this study using the Mean Absolute Error (MAE) method to get system accuracy results. Based on testing conducted using 6 criteria and 56 alternative data for prospective employees, the accuracy of the method used was 85.34%.

SENSITIVITY TEST FUZZY TOPSIS AND FUZZY TOPSIS ROC METHODS FOR THE SELECTION OF THE SASIRANGAN BANJAR FABRIC MOTIFS Nafis Satul Khasanah; Andi Farmadi; Dodon Turianto Nugrahadi; Muliadi; Rudy Herteno
Journal of Data Science and Software Engineering Vol 1 No 01 (2020)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Decision making is one of problem that we often find in society, as well as choosing the fabric of the motif of Sasirangan Banjar that currently there are many kinds of it. So, to find out the ideal solution need the various of considerations that will make hesitation. And it will influence in time of accuracy of decision-making. Multi Atribut Decision Making (MADM) is a part of decision-making with various criteria that have a weight. The objective is to find out and ideal solution that can be optimal in implementation. The used Fuzzy TOPSIS and Fuzzy TOPSIS ROC methods an important to make an assesment with a simple system and calculation of priority weights to produces various motives. The result of two methods that have been test sensitivity are the best decision with the result 7,16% for weight Rank Order Centroid (ROC) and 0,6% for weight TOPSIS. So, Fuzzy TOPSIS ROC is better in values weight because it has a higher sensitivity than the Fuzzy TOPSIS.

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.45 KB)

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.

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (271.92 KB)

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%.

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.07 KB)

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.

OPTIMASI NILAI N PADA SINGLE MOVING AVERAGE (SMA) DENGAN PARTICLE SWARM OPTIMIZATION (PSO) STUDI KASUS SAHAM BRI Rahman Hadi Rahman; Irwan Budiman; Friska Abadi; Andi Farmandi; Muliadi
Journal of Data Science and Software Engineering Vol 2 No 03 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (456.928 KB)

The stock market is a promising business area. The potential to obtain high returns in a fairly short time is one of the main attractions of this business. Prediction of stock prices has become a very interesting and challenging thing for researchers and academics, recently it was found that stock prices can be predicted with a certain degree of accuracy. Single Moving Average (SMA) is one method for predicting time series data. However, the N value in SMA needs to be optimized in order to get the N value with optimal results at the SMA and get accurate results. The Particle Swarm Optimization Algorithm is implemented to find out the best N value in the Single Moving Average methodwhich is more optimal. SMA+PSO and SMA are calculated using the initial N values of 3,5,7,9,11. So the results of this study are SMA with an accuracy of 97.98464% and for SMA+PSO with an accuracy of 98.15442% . The test results from this study are the influence of PSO on SMA in increasing accuracy in determining the best N value.

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (449.162 KB)

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.

Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine Muhamad Fawwaz Akbar; Muhammad Itqan Mazdadi; Muliadi; Triando Hamonangan Saragih; Friska Abadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.328

In the current digital era, sentiment analysis has become an effective method for identifying and interpreting public opinions on various topics, including public health issues such as COVID-19 vaccination. Vaccination is a crucial measure in tackling this pandemic, but there are still a number of people who are skeptical and reluctant to receive the COVID-19 vaccine. This public perception is largely influenced by, including information received from social media and online platforms. Therefore, sentiment analysis of the COVID-19 vaccine is one way to understand the public's perception of the COVID-19 vaccine. This research has the purpose to enhance the classification performance in sentiment analysis of COVID-19 vaccines by implementing Information Gain Ratio (IGR) and Particle Swarm Optimization (PSO) on the Support Vector Machine (SVM). With a dataset of 2000 entries consisting of 1000 positive labels and 1000 negative labels, validation was performed through a combination of data splitting with an 80:20 ratio and stratified 10-Fold cross-validation. Applying the basic SVM, an accuracy of 0.794 and an AUC value of 0.890 were obtained. Integration with Information Gain Ratio (IGR) feature selection improved the accuracy to 0.814 and an AUC of 0.907. Furthermore, through the combination of SVM based on PSO and IGR, the accuracy significantly improved to 0.837 with an AUC of 0.913. These results demonstrate that the combination of feature selection techniques and parameter optimization can enhance the performance of sentiment classification towards COVID-19 vaccines. The conclusions drawn from this research indicate that the integration of IGR and PSO positively contributes to the effectiveness and predictive capability of the SVM model in sentiment classification tasks.

Co-Authors A.A. Ketut Agung Cahyawan W Agitha, Nadiyasari Andi Farmadi Andi Farmandi Bayu Hadi Sudrajat Dodon Turianto Nugrahadi Dwi Kartini Friska Abadi Halimah Irwan Budiman Muhamad Fawwaz Akbar Muhammad Denny Ersyadi Rahman Muhammad Itqan Mazdadi Muhammad Latief Saputra Muhammad Reza Faisal, Muhammad Reza Nafis Satul Khasanah Nanda Wafiya Pirjatullah Radityo Adi Nugroho Rahman Hadi Rahman Rudy Herteno Saragih, Triando Hamonangan

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