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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Mardiani, Eri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Implementasi Metode Penalaran CBR dalam Mengidentifikasi Gejala Awal Penyakit Jantung menggunakan Algoritma Sorensen Coeffient Arundy, Vicky Agnes; Fitri, Iskandar; Mardiani, Eri
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 5, No 3 (2021): JTIK
Publisher : KITA Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v5i3.220

Abstract

Heart disease is a condition when the heart is experiencing a disorder. The forms of disturbance that are experienced are usually various. Usually there is a disturbance in the blood vessels of the heart, heart rate, heart cover, or congenital problems. The heart itself is a muscle consisting of four chambers. That is, the first two rooms are located at the top, the atrium (foyer) to the left and right. Then the other two rooms are at the bottom, namely the right and left ventricles. To provide information on how to diagnose the type of disease and how to control heart disease, an application of an expert system that can represent someone who is an expert in their field is needed to provide solutions to this disease problem using the Case-Based Reasoning method with the Sorensen Coeffient approach. The result of this research is the creation of an expert system for diagnosing heart disease using the Case-Based Reasoning method with the Sorensen Coeffient approach which is able to provide solutions to heart disease.Keywords:CBR, Expert system, Heart Disease, Method Sorensen Coeffient.
Penerapan Face Recognition pada Aplikasi Akademik Online Utomo, Budi Tri; Fitri, Iskandar; Mardiani, Eri
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 5, No 4 (2021): JTIK
Publisher : KITA Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v5i4.244

Abstract

In the era of big data, the biometric identification process is growing very fast and is increasingly being implemented in many applications. Face recognition technology utilizes artificial intelligence (AI) to recognize faces that are already stored in the database. In this research, it is proposed to design an online academic login system at the National University using real time face recognition used OpenCV with the Local Binary Pattern Histogram algorithm and the Haar Cassade Classification method. The system will detect, recognize and compare faces with the stored face database. The image used is 480 x 680 pixels with a .jpg extension in the form of an RGB image which will be converted into a Grayscale image., to make it easier to calculate the histogram value of each face that will be recognized. With a modeling system like this it is hope to make it easy for user to log into online academics.Keywords:Face Recognition, Haar Cascade Clasifier, Local Binary Pattern Histogram, Online Akademic, OpenCV. 
Analisis Sentimen Tweet KRI Nanggala 402 di Twitter menggunakan Metode Naïve Bayes Classifier Djamaludin, Muhammad Ariel; Triayudi, Agung; Mardiani, Eri
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 6, No 2 (2022): April-June
Publisher : KITA Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v6i2.398

Abstract

Social media is one of the technological developments that has contributed greatly in making it easy for us to communicate and socialize, one of which is using Twitter social media. Twitter in this study is used as a data source to analyze tweets discussing KRI Nanggala 402. Analysis of KRI Nanggala 402 twitter sentiment is used to see the tendency of public responses to the sinking of the KRI Nanggala 402 submarine whether to give positive or negative opinions. This Sentiment analysis uses the Naïve Bayes Classifier method, which is a classification method. The first research stage is crawling, processing, classification, and evaluation. The classification stage is carried out after the processing phase, where the classification results tend to be positive or negative, using the Naïve Bayes Classifier method. The accuracy of the system in the Sentiment analysis of the KRI Nanggala 402 tweet is 73.00%.
Co-Authors Agung Triayudi Arundy, Vicky Agnes Djamaludin, Muhammad Ariel Iskandar Fitri, Iskandar Utomo, Budi Tri