VEKTOR PRIORITAS DALAM ANALYTICAL HIERARCHY PROCESS (AHP) DENGAN METODE NILAI EIGEN

Pada Penelitian ini dikaji metode nilai eigen yang digunakan untuk mengkonstruksivektor prioritas model pengambilan keputusan yang dikenal dengan Analytical Hierarchy Process (AHP). AHP merupakan suatu metode pengambilan keputusan yang berdasarkan pada keragaman kriteria. Melalui metode nilai eigen ini diperoleh λ_mak≥n, dengan λ_mak adalah nilai eigen maksimum dan n adalah ukuran matriks. Untuk membatasi apakah suatu keputusan yang telah diambil dengan AHP sudah valid atau belum, bisa diverifikasi dengan menggunakan indeks konsistensi

Detection of potential errors in measurement results of madrasa admission instruments in Indonesia

Madrasa (Islamic boarding school) in Indonesia have a strategic role in character building. At present madrasa education is still considered second class education. Besides, to improve the quality of madrasas can be started by improving the quality of the student national admission to all madrasas in Indonesia. This study aimed to trace the potential errors in the measurement results of Students National Admission of Madrasah Aliyah Negeri (SNPDB MAN-IC) 2020. Tracing was carried out on two aspects: i) Equality between test sets used based on evidence of test responses; and ii) Further tests on equality between question sets based on evidence of relationship between variables, taking into account the origin of the participating schools (MTs/JHS) and the origin of the participating regions (West, Central and East of Indonesia). This study involved 13,115 participants in 23 MAN-ICs throughout Indonesia in 2020. The materials tested comprised learning potential and academic ability (Mathematics, Natural Sciences, Social Studies, English, Arabic, and Islamic Religious Education). The study used achievement test with mathematics as a sample of test subjects. Based on the test response evidence, it was found that seven of the 15 questions were thought to have an indication of inequality between item sets. The results of tracing the evidence between variables indicated that it was the participants' origin of institutions that influenced the inequality between item sets. On the other hand, regional origin did not affect the inequality between item sets because the majority of participants came from the western region of Indonesia

Automated Diagnosis System of Diabetic Retinopathy Using GLCM Method and SVM Classifier

Diabetic Retinopathy (DR) is the cause of blindness. Early identification needed for prevent the DR. However, High hospital cost for eye examination makes many patients allow the DR to spread and lead to blindness. This study identifies DR patients by using color fundus image with SVM classification method. The purpose of this study is to minimize the funds spent or can also be a breakthrough for people with DR who lack the funds for diagnosis in the hospital. Pre-processing process have a several steps such as green channel extraction, histogram equalization, filtering, optic disk removal with structuring elements on morphological operation, and contrast enhancement. Feature extraction of preprocessing result using GLCM and the data taken consists of contrast, correlation, energy, and homogeneity. The detected components in this study are blood vessels, microaneurysms, and hemorrhages. This study results what the accuracy of classification using SVM and feature from GLCM method is 82.35% for normal eye and DR, 100% for NPDR and PDR. So, this program can be used for diagnosing DR accurately

APLIKASI METODE NILAI EIGEN DALAM ANALYTICAL HIERARCHY PROCESS UNTUK MEMILIH TEMPAT KERJA

Dalam fakta kehidupan, kita seringkali dihadapkan pada suatu permasalahan yang cukup kompleks sehingga diperlukan banyak kriteria sebagai bahan pertimbangan dalam menentukan pilihan atau keputusan. Dalam kondisi semacam itu, adanya berbagai macam kriteria, ditambah lagi dengan ketidaksempurnaan informasi seringkali menyulitkan dalam membuat keputusan.Salah satu solusi yang memungkinkan adalah dengan Analytical Hierarchy Process (AHP). Pada penelitian ini dikaji salah satu metode untuk menentukan vector prioritas dalam AHP dengan menggunakan nilai eigen. Selain itu terapan metode nilai eigen juga dibahas untuk memberikan alternatif pilihan tempat bekerja bagi siswa SMK Negeri I Jombang yang telah memiliki kerjasama dengan berbagai badan usaha diantaranya PT. SAI Mojokerto, PT. JAI Pasuruan, PT HWT Surabaya, BPR Surasari Hutama Bangil dan Western Digital Malaysi

PERBANDINGAN PENGKLUSTERAN DATA IRIS MENGGUNAKAN METODE K-MEANS DAN FUZZY C-MEANS

Indonesia with abundant natural resources, certainly have a lot of plants are innumerable. To clasify the plants into different clusters can use several methods. Methods used are K-Means and Fuzzy C-Means. However, this methods have difference. Not only in terms of algorithms, but in terms of value calculation on the root mean square error (RMSE) also different. To calculate the value of RMSE there are two indicators are required, namelt the training data and the checking data. Of discussion, the Fuzzy C-Means method has RMSE values smaller than the K-Means method, namely on 80 training data and 70 checking data with RMSE value 2,2122E-14. This indicates that the Fuzzy C-Means method has a higher level of accuracy than the K-Means metho

PERBANDINGAN PENGKLUSTERAN DATA IRIS MENGGUNAKAN METODE K-MEANS DAN FUZZY C-MEANS

Indonesia with abundant natural resources, certainly have a lot of plants are innumerable. To clasify the plants into different clusters can use several methods. Methods used are K-Means and Fuzzy C-Means. However, this methods have difference. Not only in terms of algorithms, but in terms of value calculation on the root mean square error (RMSE) also different. To calculate the value of RMSE there are two indicators are required, namelt the training data and the checking data. Of discussion, the Fuzzy C-Means method has RMSE values smaller than the K-Means method, namely on 80 training data and 70 checking data with RMSE value 2,2122E-14. This indicates that the Fuzzy C-Means method has a higher level of accuracy than the K-Means metho

APLIKASI METODE NILAI EIGEN DALAM ANALYTICAL HIERARCHY PROCESS UNTUK MEMILIH TEMPAT KERJA

Dalam fakta kehidupan, kita seringkali dihadapkan pada suatu permasalahan yang cukup kompleks sehingga diperlukan banyak kriteria sebagai bahan pertimbangan dalam menentukan pilihan atau keputusan. Dalam kondisi semacam itu, adanya berbagai macam kriteria, ditambah lagi dengan ketidaksempurnaan informasi seringkali menyulitkan dalam membuat keputusan.Salah satu solusi yang memungkinkan adalah dengan Analytical Hierarchy Process (AHP). Pada penelitian ini dikaji salah satu metode untuk menentukan vector prioritas dalam AHP dengan menggunakan nilai eigen. Selain itu terapan metode nilai eigen juga dibahas untuk memberikan alternatif pilihan tempat bekerja bagi siswa SMK Negeri I Jombang yang telah memiliki kerjasama dengan berbagai badan usaha diantaranya PT. SAI Mojokerto, PT. JAI Pasuruan, PT HWT Surabaya, BPR Surasari Hutama Bangil dan Western Digital Malaysi

Analisis Sentimen Ulasan Aplikasi Jamsostek Mobile Menggunakan Metode Support Vector Machine

Sentiment Analysis of Jamsostek Mobile Application Reviews Using the Support Vector Machine Method. Today's technology is evolving quickly, leading to new developments that have helped produce JMO and other mobile applications that can be useful to Indonesians. The reviews or comments in the JMO can be used as a gauge for quality and user satisfaction. This study aims to analyze the quality of JMO applications and classify reviews or opinions into positive, negative, and neutral categories through sentiment analysis. The Support Vector Machine method is used in this analysis process with a linear kernel approach to determine the level of accuracy of classifying JMO application reviews. Research shows that classifying the SVM method against sentiment analysis of reviews or JMO application reviews produces the best accuracy scores, obtaining results with accuracy of 96%, precision of 92%, recall of 96%, and f1-score of 94%, while for the results of most reviews are positive category reviews with a total of 17.571.Keywords: sentiment analysis, JMO, SVM, linear kernel   Perkembangan pesat teknologi saat ini memunculkan inovasi baru untuk menciptakan berbagai aplikasi mobile yang dapat memberi kemudahan bagi masyarakat Indonesia, salah satunya yaitu JMO. Penelitian ini bertujuan untuk menganalisis kualitas aplikasi JMO dan mengklasifikasikan ulasan atau opini kedalam kategori positif, negatif dan netral melalui analisis sentimen. Metode Support Vector Machine digunakan pada proses analisis ini dengan pendekatan kernel linear untuk mengetahui tingkat akurasi dari pengklasifikasian ulasan aplikasi JMO tersebut. Penelitian menunjukkan bahwa pengklasifikasian metode SVM terhadap analisis sentimen ulasan atau review aplikasi JMO menghasilkan nilai akurasi terbaik, didapatkan hasil dengan accuracy 96%, precision 92%, recall 96%, dan f1-score 94%, sedangkan untuk hasil ulasan terbanyak adalah ulasan berkategori positif dengan jumlah 17.571.Kata Kunci: analisis sentimen, JMO, SVM, kernel linea

Peramalan Kecepatan Angin yang Direkam oleh Sistem AWS dengan Analisis Fuzzy Time Series

Kehidupan manusia tidak bisa dipisahkan dari faktor alam yang bernama cuaca. Salah satunya yaitu kecepatan angin. Angin mempunyai banyak manfaat bagi kehidupan manusia. Tetapi, angin juga bisa mempunyai dampak buruk bagi manusia. Untuk mengantisipasi dampak buruk yang ditimbulkan oleh angin, maka diperlukan peramalan kecepatan angin. Selain itu, adanya krisis energi global juga menyebabkan pengembangan energi terbarukan yang salah satunya adalah energi angin. Jurnal ini berisi tentang peramalan data kecepatan angin dengan analisis fuzzy time series. Hasil error peramalan dihitung dengan metode MSE dan didapat error sebesar 1,0909.

Optimal ANFIS Model for Forecasting System Using Different FIS

Adaptive Network Based Fuzzy Inference System (ANFIS) using time series analize is one of intelligent systems that can be used to predict with good accuracy in all fields like in meteorology. However, some research about forecasting has less emphasis on the structure of the FIS ANFIS. Thus, in this paper, the optimization of the ANFIS model for predicting maritime weather is carried out by analyzing the appropriate initialization determinations of the three fuzzy Inference structures ANFIS which includes FIS structure 1 (grid partition), FIS structure 2 (subtractive clustering) and FIS structure 3 (fuzzy c-means clustering). In this paper, the variable input used are two hours (t-2) and one hour (t-1) before, and data at that time (t), and the output of this system is the prediction of next hour, six hours, twelve hours and next day of variable ocean currents velocity (cm/s) and wave height (m) using the three FIS ANFIS approaches. Based on the smallest goal error (RMSE and MSE) of the three FIS ANFIS approaches used to predict the ocean currents speed (velocity) and wave height, the model is best generated by subtractive clustering. It can be seen that subtractive clustering produces the smallest RMSE and MSE error values of other FIS structure