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JIKO (Jurnal Informatika dan Komputer)
Published by Universitas Teknologi Digital Indonesia
ISSN : 24774413     EISSN : 24773964     DOI : https://doi.org/10.26798/jiko
Core Subject : Science,
Computer Science & IT
JIKO (Jurnal Informatika dan Komputer) is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat of Universitas Teknologi Digital Indonesia (d.h STMIK AKAKOM) Yogyakarta, Indonesia. First published in 2016 for a printed and online version. We receive original research articles and any review papers. The aims of JIKO are to disseminate research results and to improve the productivity of scientific publications. JIKO is published in February and September with the scopes and focus of the research areas that are: Software Engineering, Information Systems, Computer Science Applications, Computer Networks and Communications, and Artificial Intelligence.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 3, No 2 (2018): SEPTEMBER - JANUARI 2019" : 5 Documents clear
IDENTIFIKASI CITRA DAUN TANAMAN JERUK DENGAN LOCAL BINARY PATTERN DAN MOMENT INVARIANT Ayu Novitasari; Endina Putri Purwandari; Funny Farady Coastera
JURNAL INFORMATIKA DAN KOMPUTER Vol 3, No 2 (2018): SEPTEMBER - JANUARI 2019
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (911.256 KB) | DOI: 10.26798/jiko.v3i2.141

Abstract

Citrus species identification can use from citrus leaf image. The research purposes build the identificate citrus species based on leaf texture and shape by using Local Binary Pattern as texture feature and Moment Invariant as shape feature, and Euclidean Distance as image distance measurement. The research data use citrus leaf image consist of Citrus aurantifolia, Citrus sinesis, Citrus hystrix, Citrus limon, Citrus maxima, Citrus amblycarpa, and Citrus microcarpa. Based on experiment tests, we can conclude that (1) 100% accuracy for citrus leaf from a smartphone, (2) 100% accuracy for citrus leaf from a smartphone with red background, (3) 85,71% accuracy for citrus leaf from a smartphone with green background, (4) 100% accuracy for citrus leaf from a smartphone with blue background, (5) 85,71% accuracy for citrus leaf from a smartphone with black background, (6) 85,71% for images from the internet.
DYNAMIC SYSTEMS DEVELOPMENT METHOD DALAM PERANCANGAN SISTEM DARING KERAJINAN KHAS DAERAH M Marfuah; I Irfan
JURNAL INFORMATIKA DAN KOMPUTER Vol 3, No 2 (2018): SEPTEMBER - JANUARI 2019
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (204.276 KB) | DOI: 10.26798/jiko.v3i2.129

Abstract

Various kinds of potential are owned by a community, especially Batam as a target of tourist destination. One potential that continues to be fostered is regional crafts that realize the economic independence of the community itself. The Government partner that have some programs to foster and develop regional handicraft products is National Craft Council of Batam under the guidance of the Industry and Trade Office. There are some obstacles faced by the management of traditional handicrafts, in term of promotion since it has been carried out in the exhibitions that are limited by time and place. It makes the development of the promotion and their potential run slowly in terms of disseminating information. It becomes the basic consideration for an online system of traditional crafts that prioritizes speed, easiness, flexibility and interactivity for some system users. Dynamic Systems Development Method (DSDM) is part of the agile software method. This method helps users to define clearly the system needs through early and continuous software delivery. The iteration in the design process makes it easier to adjust the needs of system users. The results of the system design are adjustments to the functional requirements of the system in accordance with the user needs.
KLASIFIKASI KOMENTAR SPAM PADA YOUTUBE MENGGUNAKAN METODE NAÏVE BAYES, SUPPORT VECTOR MACHINE, DAN K-NEAREST NEIGHBORS . Burhanudin; Yunarti Musa'adah; Yaya Wihardi
JURNAL INFORMATIKA DAN KOMPUTER Vol 3, No 2 (2018): SEPTEMBER - JANUARI 2019
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (653.608 KB) | DOI: 10.26798/jiko.v3i2.139

Abstract

Social mediabecome popular in this day. Sharing the daily moments in social media has become a daily routine. People can also discuss about the post in the existing comment field. For example a comment on Youtube video. But the popularity of social media bring some problems with attracting users who spread spam content on comments. In this research, will be discussed about the classification of spam comments on Youtube with several methods tested. The dataset contains 1956 data,that used to train data. The result of model evaluation using cross validation resulted Support Vector Machine method with Linear approach has highest accuracy equal to 91,92%. Expectedby this research can provide solutions as an effort to prevent spam content in social media comment field..
PENGENALAN VARIETAS KOPI ARABIKA BERDASARKAN FITUR BENTUK Maria Mediatrix Sebatubun; Erna Hudianti Pujiarini
JURNAL INFORMATIKA DAN KOMPUTER Vol 3, No 2 (2018): SEPTEMBER - JANUARI 2019
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (393.646 KB) | DOI: 10.26798/jiko.v3i2.117

Abstract

Coffee is one of the most popular beverages in the world and has a different taste. One of the most famous types of coffee is arabica. This coffee has many varieties depend on the area of planting. Therefore, sometimes though both arabica coffee varieties, but may have different features such as differences in color, shape, or texture. So that sometimes farmers or coffee shop owners even people can make mistakes in recognizing arabica coffee varieties. This problem will affect the price determination of coffee, because each varieties have different prices. Therefore, needed a system that is able to recognize arabica coffee varieties accurately so it can be used as a second opinion in recognizing arabica coffee varieties. One of the technique that can be used is by imaging. This research begins with the pre-processing stage that is cropping the image manually. The next stage is feature extraction using the solidity features associated with convex hull. The last stage is classification using MultiLayer Perceptron and obtained 80% accuracy, 80% sensitivity, and 80% specificity. 
IMPLEMENTASI PARTICLE SWARM OPTIMIZATION UNTUK OPTIMALISASI DATA MINING DALAM EVALUASI KINERJA ASISTEN DOSEN Indah Ariyati; . Ridwansyah; . Suhardjono
JURNAL INFORMATIKA DAN KOMPUTER Vol 3, No 2 (2018): SEPTEMBER - JANUARI 2019
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.082 KB) | DOI: 10.26798/jiko.v3i2.127

Abstract

The existing complaints on the performance of assistant lecturers show the impact of the absence of better competence, so that an accurate evaluation process on the performance of lecturer assistants based on their duties and obligations in a certain period of time. The evaluation process required an improved model of accuracy which was a formidable challenge in the selection of more efficiency and effectiveness features, in which case we proposed a method of particle swarm optimization to improve the accuracy of neural network methods that experienced problems in the selection of features that were weighted in detailed analysis by particle swarm optimization with neural network learning performance. This study aims to find a complex alternative solution in the evaluation of lecturer's assistant where research is based on parameters obtained from UCI Machine Repository. The final research shows that particle swarm optimization method can in-crease the accuracy of 75.56% from the previous value of 51.75% and increase the kappa value of 0,632 from the previous kappa value 0,276. The result of developing particle swarm optimization toward neural network by increasing the accuracy and kappa value can be used as controlling periodically in evaluating the performance of assistant lecturer.

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