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Program Studi Ilmu Komputer Universitas Nusa Cendana Jl. Adisucipto - Penfui - Kupang - NTT -Indonesia
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J-Icon : Jurnal Komputer dan Informatika
Published by Universitas Nusa Cendana
ISSN : 23377631     EISSN : 26544091     DOI : -
Core Subject : Science,
Computer Science & IT
J-ICON : Jurnal Komputer dan Informatika focuses on the areas of computer sciences, artificial intelligence and expert systems, machine learning, information technology and computation, internet of things, mobile e-business, e-commerce, business intelligence, intelligent decision support systems, information systems, enterprise systems, management information systems and strategic information systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 205 Documents
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KAJIAN MACHINE LEARNING DENGAN KOMPARASI KLASIFIKASI PREDIKSI DATASET TENAGA KERJA NON-AKTIF Derwin Rony Sina; NEUTRINO KUSRORONG SAE BELAUDIN KUSRORONG; Nelci Dessy Rumlaklak
J-Icon : Jurnal Komputer dan Informatika Vol 7 No 1 (2019): Maret 2019
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v7i1.880

Abstract

Comparative studies of machine learning are carried out with the aim of determining the best method base based on the ability to predict with true data. The study carried out on the labor dataset aims to extract information on the choice of agency employees to exit or not. The method used in the comparative study is K-Nearest Neighbors (KNN) from the basis of similarity, Naïve Bayes (NB) from the probability base, and C4.5 from the basis of the decision tree. Application design and construction is done by receiving input labor data, the dataset is divided into training data and test data, training data for training and models while the test data is used when classifying by model. The classification process is carried out using supply training scenarios and cross validation of 14,999 data. The initial hypothesis C4.5 is the best method with an accuracy measure. Proof of the initial hypothesis will be true if the best accuracy majority is owned by the C4.5 method with supply trainning scenarios and cross validation. The results of the classification data analysis found that the C4.5 accuracy was superior in each parameter of the inventory training scenario data distribution and the k-fold parameter was 3. 5. 7, and 9 of the cross validation scenario so that the best method of non-active labor classification was C4.5.
ANALISIS METODE CYCLE CROSSOVER (CX) DAN METODE PARTIAL MAPPED CROSSOVER (PMX) PADA PENYELESAIAN KASUS TRAVELING SALESMAN PROBLEM (TSP) Theresia Kolo; Adriana Fanggidae; yelly y nabuasa
J-Icon : Jurnal Komputer dan Informatika Vol 7 No 1 (2019): Maret 2019
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v7i1.882

Abstract

Travelling Salesman Problem (TSP) is a form of a problem in optimizing the search for the shortest route by passing through every city in exactly one time. The problem of searching the shortest route of a location can be solved by using many other optimizing algorithms. In this research, genetics algorithm was used by using two crossover methods namely cycle crossover and partial-mapped crossover. The parameters used were crossover probability and mutation probability, the sum of the city, maximum generation, the sum of the population and also threshold. In this research two testing models were used. In the first one, in order to get the generation and the best fitness it used the 80% consistency stopping criteria, and in the second one, in order to get the best testing time, it used the 100 and 500 maximum generation stopping criteria. The result of the first test showed that PMX method is better than the CX one. This was shown through the 8 times of testing which the result was the best PMX generation was 104,0469 and the CX was 350,4563. The second test resulted that the best testing time of the PMX time was 1,1035 second and the CX method was 2,2374 second, thus, it can be concluded that the solution brought by the PMX method is considered better than the CX.
IMPLEMENTASI SISTEM INFORMASI GEOGRAFIS DALAM PENENTUAN INDEKS KESESUAIAN LAHAN TANAMAN PADI DI KOTA KUPANG MENGGUNAKAN METODE SKORING Tiwuk Widiastuti
J-Icon : Jurnal Komputer dan Informatika Vol 7 No 1 (2019): Maret 2019
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v7i1.883

Abstract

Geographic Information System (GIS) is a special information system for managing data that has spatial information. In GIS is often used overlay techniques in merging 2 or more maps in to a unit ofland. In this research, the authors use the scoring method in determining the land suitability index. To get a unit of land done overlay on land type map, slope map, district boundary map, geological map and land cover or land use map. Scoring is done on temperature parameters, rainfall, dry moon, drainage, texture, effective depth, KTK, ground ph, n total, P2O5, K2O, slope, numbers of rock, and rock out crop. The calculationis done by scoring for each parameter based on the predetermined score class, the total of each unit of land will be used to determine the Land Suitability Index based on the classification class. The calculation result is done on 76 land (rice field and rainfed rice field). This test use Simple Linear Regression to find the closeness value between Land Suitability Index and rice produktivity. Linear correlation coeficient (r) obtained value r = 0,77 (positive correlation). The value of this correlation coefficient indicates the degree of closeness of the relationship between the Land Suitability Index and the productivity of the paddy. The value of coefficient of determination R = 0,59 = 59% indicates that 59% of the proposed variation of Y variable (paddy productivity) can be explained by X variable (Land Suitability Index) through linear relationship.
CASE BASED REASONINGUNTUK MENDIAGNOSIS GIZI BURUK PADA ANAKUSIA 0-5 TAHUN MENGGUNAKAN METODE COSINE SIMILARITY Derwin Rony Sina; Meiton Boru; Marselina Elisabeth Soinbala
J-Icon : Jurnal Komputer dan Informatika Vol 7 No 1 (2019): Maret 2019
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v7i1.885

Abstract

Case Based Reasoning (CBR) method is one method to build a system with new case decision based on solution from previous cases by calculating similarity level. The calculation of similarity values ​​using the Cosinr Similarity with threshold 80%. This system can diagnose 3 diseases based on 23 existing symptoms.Based on the results of the test case obtained the results: the system can take back the old case is appropriate and has used the formulation of Naïve Bayes method to distribution of disease class and used the formulation of Cosine Similarity method to calculation correctly indicated with 100% accuracy, and use 122 cases. . Based on the test of 40 new cases obtained system accuracy of 80%.
IMPLEMENTASI PENETAPAN PAJAK KENDARAAN BERMOTOR UBAH BENTUK PADA SAMSAT KABUPATEN TIMOR TENGAH SELATAN Emerensye Sofia Yublina Pandie; Febryan Cornelis Lomi; Sebastianus Adi Santoso Mola
J-Icon : Jurnal Komputer dan Informatika Vol 7 No 1 (2019): Maret 2019
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v7i1.886

Abstract

The problem in doing the majoring in SAMSAT of sub-province of Timor Tengah Selatan can be solved by using information system. In a month SAMSAT of sub-province of Timor Tengah have served 3.131 taxpayer, that means for a day SAMSAT of sub-province of Timor Tengah served approximately reach 150 taxpayer and total vehicles in registration is 39.492 vehicles. The system that is developed to maintain vehicles data, types of vehicle data, vehicles brands data, dumps data, vehicles price data, registration data, and to count vehicles tax determining rightly dan quickly and can gain the report of all registration data, specific report of the vehicles, specific report of vehicles tax determining and receipt tax payments of vehicles transform. This system capable to answered the hypothesis H0 about contentment of service with satisfaction level more than 70% viz 78%.
PENERAPAN METODE COLOR FILTERING DAN LEARNING VECTOR QUANTIZATION DALAM PENENTUAN TINGKAT KEMATANGAN CAKE DASAR PUTIH Daniel Boys; Arfan Y Mauko; Kornelis Letelay
J-Icon : Jurnal Komputer dan Informatika Vol 7 No 1 (2019): Maret 2019
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v7i1.888

Abstract

Cake merupakan panganan yang terbuat dari campuran bahan-bahan seperti tepung, gula, telur, garam, susu, aroma dan lemak yang dikembangkan dengan atau tanpa bahan pengembang. Penentuan tingkat kematangan cake dasar putih dilakukan berdasarkan grade warna permukaan pada saat proses pemanggangan. Namun hal ini sering menjadi kendala karena faktor persepsi komposisi warna setiap orang berbeda-beda. Pengambilan data citra menggunakan kamera 3.2 mp dan 13 mp, setelah itu citra disegmentasi dengan color filtering untuk membuang pixels yang mengandung efek lighting. Tahap selanjutnya yaitu ekstraksi ciri warna RGB kemudian dilakukan pelatihan dengan metode Learning Vector Quantization (LVQ). Hasilnya aplikasi mampu menentukan tingkat kematangan kue cake dasar putih dengan rata-rata akurasi 65,19% dan cake dasar cokelat sebagai kelas validasi 96,88% untuk kamera 3.2 mp sementara pada kamera 13 mp rata-rata akurasi 64,93% dan cake dasar cokelat sebagai kelas validasi yaitu 93,75%. Keberhasilan identifikasi dipengaruhi oleh faktor pencahayaan dalam ruangan, jarak pengambilan dan wadah penampung.
PENGOLAHAN CITRA DIGITAL PERBANDINGAN METODE HISTOGRAM EQUALIZATION DAN SPESIFICATION PADA CITRA ABU-ABU yelly y nabuasa
J-Icon : Jurnal Komputer dan Informatika Vol 7 No 1 (2019): Maret 2019
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v7i1.889

Abstract

A digital image processing software has been successfully constructed. The software can increase image contrast using the histogram equalization method. The results given by the equalizaton histogram method can improve image quality, so that the information in the image is more clearly seen. But not all digital images have a visual display that satisfies the human eye. Dissatisfaction can arise due to noise, the lighting quality in digital images that are too dark or too bright. So that a method is needed to improve the quality of the digital image. To improve image quality in terms of color contrast, we can give treatment to the histogram. The treatment referred to in this article is an equalization histogram on grayscale images. Image histogram is said to be good if it is able to involve all possible levels or levels at the gray level. Of course the goal is to be able to display details on the image so that it is easy to observe. The process of segmenting and repairing digital images is done using MATLAB.
Implementasi Metode Backpropagation Untuk Memprediksi Beban Listrik Di Kabupaten Sikka Maria Olinda Harun; Sebastianus A.S Mola; Emerensye S.Y. Pandie
J-Icon : Jurnal Komputer dan Informatika Vol 4 No 1 (2016): Maret 2016
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v4i1.1397

Abstract

Electric energy is one of the tools to support the welfare of society. Their population grow and their activities increase, therefore, their need of electricity is increasing too. The electricity almost cover up the area of Sikka Region. Based on the real data, the electrical load is increased every month. In order to reach the balance between the productivity of electric energy and the consumption of electric energy, then, the electrical provider should know the electrical load for the future.Backpropagation method is one of the methods at artificial neural network which can be used in this research to predict the electrical load for the next one month. The backpropagation method consists of some steps such as training, data testing and prediction.The data which used as a paramater in this research is the data of electrical load (KWH carry), numbers of consumers and the data of connected electrical power. The researcher used the data January 2007 until December 2012 as learning process, and for the testing, the researcher used data from January 2013 until December 2013.The result of this application is the load power consumption for the next one montj in Sikka Regency. The presentation of final result of this testing for the data that has been trained is 85% and the data which never been trained is 80%. Because of the presentation is above 50% the backpropagation can be used to predict the electrical load for the next one month.
PENGENALAN POLA SIDIK JARI DENGAN METODE LOCAL BINARY PATTERN DAN LEARNING VECTOR QUANTIZATION Adriana Fanggidae; Dony M Sihotang; Adnan Putra Rihi Pati
J-Icon : Jurnal Komputer dan Informatika Vol 7 No 2 (2019): Oktober 2019
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v7i2.1635

Abstract

Fingerprint is the generic structure in the form of a very detailed pattern and a sign that inherent in human beings. Many biometric systems using fingerprint as input data, because the nature of each individual is different although identical twins and do not change unless got a accident. The method used in this research is image segmentation using Otsu thresholding algorithm, feature extraction using Local Binary Pattern (LBP) algorithm and the learning method using Learning Vector Quantization (LVQ) algorithm. The used data is grayscale fingerprint image with 200x300 pixel and *.jpg extension format. The fingerprint image is composed of 25 people, each person has 6 training data and 2 test data. Experiment of training data and test data conducted for four systems, namely the system with characteristics of LBP = 8, 64, 128 and 256 and their respective uses 2 pieces of data set where data set 1 amounted to 15 people and data set 2 amounted to 25 people. The fourth experiment results show that the system is a system with a number of LBP characteristics = 128 is a system with the best combination of high system accuracy and fast learning time.
PENERAPAN LOGIKA FUZZY MENGGUNAKAN METODE MAMDANI DALAM OPTIMASI PERMINTAAN OBAT Inggrid Raga Djara; Tiwuk Widiastuti; Dony M Sihotang
J-Icon : Jurnal Komputer dan Informatika Vol 7 No 2 (2019): Oktober 2019
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v7i2.1645

Abstract

Planning a good drug supply at the puskesmas is needed to support health services provided by the puskesmas, in addressing the problem of planning drug requests to suit the needs that exist, the researcher uses Mamdani method in fuzzy form that are making fuzzy set, application of rule function, composition rule, affirmation (defuzzy) using method of MOM (Mean of Maximum). Parameters used are initial stock, receipt, preparation, use, ending and demand stock. The calculation was performed using data for 2 years, and it was done 1 year to compare the results of the Health Centre request and the system request. From the test results, the total system demand is smaller than the total demand for Puskesmas, so the system optimization is obtained at 7.623% for 3 drug data so that it can increase the efficiency of the budget funds of Rp. 3,168,223, so it can be concluded that the Fuzzy Mamdani method is a method that provides optimal solutions

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