Perbandingan Metode-Metode Klasifikasi Untuk Indoor Positioning System

Indoor Positioning System can provide position and navigation guidances inside a building. This paper discusses about systematic comparison between K-Nearest Neigbors and Naïve Bayes Classifier over WiFi-based Indoor Position System dataset. The dataset is collected using a custom Android Application, which able to receive and record WiFi signal strengths from the surrounding WiFi hotspots in UKDW campus. The dataset consists of 11658 Received Signal Strength (RSS) data from 41 public locations in UKDW campus. We use 10-folds cross validation and T-Test with 0.05 significance level to compare classification accuracy between K-Nearest Neigbors and Naïve Bayes classifier. Based on the experiment result, we can conclude that K-Nearest Neighbors classifier produces better classification accuracy (83.58%) than Naïve Bayes (61.52%)

Penerapan Sentiment Analysis Pada Hasil Evaluasi Dosen Dengan Metode Support Vector Machine

The quality of lectures can be determined by some feedbacks from students. From the feedbacks, we can give appreciations for those lectures who get good feedback from students, and evaluations for those who get bad feedback. The problem is classifying large size of feedbacks manually isn\u27t effective and took a lot of time. Therefore, we need a system that can classify feedbacks automatically. These feedbacks will be classified into positive, negative, and neutral, usually called as sentiment analysis. Sentiment analysis implementation can be done by several methods, one of them that has a good accuracy is Support Vector Machine (SVM). SVM performance in this research is measured with the level of accuracy. The number of accuracy indicate the success level of system. The conclusion of this research is factors that affects the accuracy. The factors are the range of each classes and number of unique words in the training document

Implementasi Algoritma Rijndael 128 Pada Aplikasi Chatting Berbasis Html5 Websocket

In the past, web-based chat application didn\u27t consider security as part of must-have requirement, thus many insecure examples were broken in short time after it was released. Data sniffing is one common attack that could be used to attack insecure applications because the data was transferred using an insecure medium, which is HTTP. We propose a new web-based chat application that is built based on HTML5 WebSocket technology using Socket.IO library to improve confidentiality of the messages sent between two or multiple parties. We combine it with NodeJS and Express to facilitate real-time discussion between client and server and vice versa. We also use Rijndael (known as AES - Advanced Encryption Standard) to make sure that the message stays confidential and only known by sender and receiver. To satisfy the integrity property, we apply SHA-3 hash function. By combining SSL/TLS, AES, and SHA-3 hash function, we have added multiple layer of security inside this application and no additional effort needed by the user. Based on conducted experiments, we can conclude that this application could satisfy security requirements (confidentiality and integrity), either on the client or server side

Implementasi Dynamic Programming Pada Penentuan Jenis Material Utama Bangunan Arena Futsal

Building futsal court need some planning, especially about the materials needed to build a futsal court such as synthetic grass, roofs, walls, benches, and some other materials. Each of materials have many choices usually based on quality and price. Thus it needs a system to assist on calculating the optimum materials combination based on a specified budget. Minimax Route method are used with dynamic programming techniques to maximize the quality of materials while minimizing the price of materials chosen. Based on system testing conducted to futsal court owners in Yogyakarta, the implementation are helpful and have many useful information for someone who want to build futsal court

Verifikasi Akun Database Dengan Penerapan Metode Template Matching Pada Karateristik Wajah Personal

The identification of account is a step to keep the important data secure. Nowadays, it can be done by using username and password, but, after see the reality, the using of username and password can\u27t keep the data secure from the thief. Because of that, the verification of the characteristic of personal face can be a solution to change the using username and password. The method that can be used for verification is template matching.It is implemented in four features of personal face, such as left eye, right eye, nose and lips. The four images of each feature will be extracted with wavelet haar method. The feature extraction will be done during template taking process and verification. The result of this research, the result of verification is determined by two factors, such as the distance between face and web camera is different when the template taking process and the verification process and the diferrent brightness condition when the template taking process and the verification proccess. The threshold value that has been decided is not really able to block the unregistered data. Then the accuracy of the verification activity is still low and it is still not able yet to identify an account well

Implementasi Algoritma Naïve Bayes Menggunakan Isear Untuk Klasifikasi Emosi Lirik Lagu Berbahasa Inggris

Lirik lagu merupakan suatu ungkapan perasaan seseorang terhadap sesuatu hal yang sudah dilihat, didengar maupun dialaminya sehingga tidak jarang emosi menjadi salah satu kriteria user dalam melakukan pencarian. Pencarian lirik melekat pada kategori yang tidak hanya terbatas berdasarkan genre atau judul lagu, namun juga melalui emosi dari lirik lagu yang diungkapkan. Agar dapat mencapai tujuan tersebut, diperlukan suatu sistem pengkategori yang mengenali lirik lagu secara otomatis dengan salah satu metode klasifikasi yaitu Naïve Bayes. Faktor yang mendorong tingginya tingkat akurasi bukan hanya terletak pada metode klasifikasi saja, namun proses sebelum menuju tahap klasifikasi juga berpengaruh pada hasil yang didapatkan. Maka dari itu, penulis melakukan penelitian melalui beberapa tahap yaitu preprocessing berupa tokenisasi, stopword dan stemming, kemudian feature selection yang digunakan adalah TF-IDF dengan bantuan ISEAR karena mengandung 7 emosi dasar. Dari ketujuh emosi dasar tersebut, tiga diantaranya merupkan emosi yang akan digunakan dalam penelitian ini yaitu anger, sadness dan joy. Hasil dari penelitian ini menunjukkan dengan menggunakan ISEAR akurasi tertinggi terdapat pada feature set 60% dan 100% yaitu sebesar 82,2%. Perbedaan signifikan dihasilkan pada penggunaan ISEAR dengan akurasi rata-rata keseluruhan porsi featureset sebesar 77% sedangkan tanpa menggunakan ISEAR rata-rata akurasi sebesar 53%. Dokumen paling relevan untuk pengujian menggunakan ISEAR terdapat pada kategori angry dengan rata-rata f-measure sebesar 0.7267