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All Journal JURNAL ILMIAH INFORMATIKA IT (INFORMATIC TECHNIQUE) JOURNAL Jurnal Pengabdian Masyarakat IPTEK Ardhi: Jurnal Pengabdian dalam Negri Jurnal Sistem Informasi dan Teknologi Informasi Ekspresi: Publikasi Kegiatan Pengabdian Indonesia
Nidia Enjelita Saragih
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : JURNAL ILMIAH INFORMATIKA

 1 
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN SEKOLAH TERBAIK DENGAN METODE MOORA Ermayanti Astuti; Nidia Enjelita Saragih
JURNAL ILMIAH INFORMATIKA Vol 8 No 02 (2020): Jurnal Ilmiah Informatika (JIF)
Publisher : Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v8i02.1984

Abstract

Many schools need the implementation of articles about the students selection of best transfer Junior High school. In order of fulfil this necessity, we perform a research that giving recommendation of best transfer school for students as a solution by using scientific articles.  The selection need some criterias such as the economics value,study ability, time, and facilities. This research implement Multi-Objective Optimization On The Basic Of Ratio  Analysist (MOORA).The final result of this research is final decision that help selecting best junior high school for students because it give recomendation output from the highest to lowest value of alternative with each criteria in Moora. This research will give best decision in selecting best junior high school transfer as the students expected.
RANCANG BANGUN SISTEM PAKAR MENDIAGNOSA PENYAKIT OBSESSIVE COMPULSIVE DISORDER DENGAN METODE DEMPSTER SHAFER Nidia Enjelita Saragih; Robiatul Adawiyah
JURNAL ILMIAH INFORMATIKA Vol 8 No 02 (2020): Jurnal Ilmiah Informatika (JIF)
Publisher : Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v8i02.2478

Abstract

Anxiety is a normal reaction to stress and can be beneficial in some situations. It can alert us to dangers and help us prepare and pay attention. Anxiety disorders differ from normal feelings of nervousness or anxiousness, and involve excessive fear or anxiety. Anxiety disorders can cause people into try to avoid situations that trigger or worsen their symptoms. There are several types of anxiety disorders, including Obsessive Compulsive Disorder. Obsessive Compulsive Disorder (OCD) is a disorder in which people have recurring, unwanted thoughts, ideas or sensations (obsessions) that make them feel driven to do something repetitively (compulsions). The repetitive behaviors, such as hand washing, checking on things or cleaning, can significantly interfere with a person’s daily activities and social interactions. This Expert system which implement Dempster Shafer Method can help anyone to make sure that they have OCD and need psichologist help to prevent this distress becoming worst.
PENERAPAN DATA MINING DENGAN ALGORITMA NAIVE BAYES CLASSIFIER DALAM MEMPREDIKSI PEMBELIAN CAT Fitriana Harahap; Nidia Enjelita Saragih; Elida Tuti Siregar; Husin Sariangsah
JURNAL ILMIAH INFORMATIKA Vol 9 No 01 (2021): Jurnal Ilmiah Informatika (JIF)
Publisher : Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v9i01.3702

Abstract

Companies need several types of communication technology that can predict customer purchase interest, the goal is that the company can properly consider product sales and determine the company's paint product supply. So far, the decision of the Home Smart sales manager has been made by looking at the closeness of the supplier relationship and how many sponsors are funding the company. So that sometimes the product cannot compete with other companies. The Naive Bayes classifier algorithm is one of the algorithms included in the classification technology. The application of the Naive Bayes method is expected to predict paint purchases from suppliers. From 60 paint purchase data tested with the Naive Bayes method, the results reached 80% of the accuracy of the predictions. Of the 60 tested paint purchase data, 48 paint purchase data were successfully classified correctly.
Co-Authors Amalia Diah Syah Putri Elida Tuti Siregar Ermayanti Astuti Fildza Hafazana Fitriana Harahap Henry Aditya Syahputra Henry Aditya Syahputra Husin Sariangsah Jamhari Jamhari Muhammad Faris Robiatul Adawiyah Siti Nurhidayah