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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika: Jurnal Pengembangan IT
Fidelson Tanzil
Bina Nusantara University
Computer Science & IT

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A Development of Android-based Mobile Application for Getting Ideal Weight Alvina Aulia; Fidelson Tanzil; Irma Kartika Wairooy; Leonardus Kristian Gunawan; Alvin Cunwinata; Albert Albert
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.8342

Abstract

The development of this application has a goal to give people who don’t have any idea of how to keep an ideal body weight and to maintain it by having controlled food intakes and exercises. This application is designed by using a Waterfall Model, a 5-phase model that is Communication, Planning, Modeling, Construction, and Deployment starting from as the aforementioned steps. Based on the responds we get from distributing questionnaire, many people didn’t know how to exercise properly and didn’t bother to check the calories of their food. Their weight is also a problem because most of them still have an Overweight or Underweight status. From those responds, we can conclude that our application will indeed help people to get and maintain their ideal body weight.
A Comparison of Text Classification Methods k-NN, Naïve Bayes, and Support Vector Machine for News Classification Fanny Fanny; Yohan Muliono; Fidelson Tanzil
Jurnal Informatika: Jurnal Pengembangan IT Vol 3, No 2 (2018): JPIT, Mei 2018
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v3i2.828

Abstract

In this era, a rapid thriving Internet occasionally complicates users to retrieve news category furthermore if there are plentiful of news to be categorized. News categorization is a technique can be used to retrieve a category of news which gives easiness for users. Internet has vast amounts of information especially at news. Therefore, accurate and speedy access is becoming ever more difficult. This paper compares a news categorization using k-Nearest Neighbor, Naive Bayes and Support Vector Machine. Using vary of variables and through a several steps of preprocessing which proving k-Nearest Neighbor is producing a capable accuracy competes with Support Vector Machine whereas Naive Bayes producing just an average result, not as good as k-Nearest Neighbor and Support Vector Machine yet as bad as k-Nearest Neighbor and Support Vector Machine ever reach. As the results, k-Nearest Neighbor using correlation measurement type produces the best result of this experiment. 
Co-Authors Albert Albert Alvin Cunwinata Alvina Aulia Fanny Fanny Irma Kartika Wairooy Leonardus Kristian Gunawan Yohan Muliono