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Sebastianus Adi Santoso Mola
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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
 1   2   3   4   5 
DETEKSI DAN IDENTIFIKASI BARCODE 2D MENGGUNAKAN METODE EKSTRAKSI CIRI GABOR FILTER DAN IDENTIFIKASI CIRI BACKPROPAGATION NEURAL NETWORK Hepiyana V Runesi; Adriana Fanggidae; Meiton Boru
J-Icon : Jurnal Komputer dan Informatika Vol 6 No 2 (2018): Oktober 2018
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v6i2.511

Abstract

Barcode is a device in the form of a black and white matrix to represent 1 and 0, which aims in storing information. It is divided into two types, namely 1D and 2D barcodes. The different between them is 1D barcode has black and white bars, while 2D barcode has square shape. The method used in this research is grayscaling, floating and screening comprehensive using flood fill pixel reduction algorithm, the perimeter of objects, extraction feature using gabor filter algorithm, the learning method uses backpropagation neural network algorythm, and the identification process using the feedforward method to backpropagation neural network algorythm. The data used in this research is a data of 2D barcode on each of it amounted to 20 users who are taken from the BBM (Blackberry Messenger) contact, due to the lack of data thus a data of the 2D barcode is cropped for 8 times to be the training data and twice to be the test data. The test is done in three stages which the first data set consists of 10 users, the second one consists of 15 users and the last one consists of 20 users. The result of the testing system for those data sets show that the first data set obtains an accuracy of 100%, the second one obtains 93,33% and the last one obtains 66%.
MULTINOMIAL NAIVE BAYES UNTUK KLASIFIKASI STATUS KREDIT MITRA BINAAN DI PT. ANGKASA PURA I PROGRAM KEMITRAAN Meilani T Bunga; Bertha S Djahi; Yelly Y Nabuasa
J-Icon : Jurnal Komputer dan Informatika Vol 6 No 2 (2018): Oktober 2018
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v6i2.512

Abstract

Status classification of partner acordiing to sector parimeter, loan disbursement, loan reimbursment, loan arrears, remaining loan and grace period is very important in anticipating the case in PT. Angkasa Pura I. Problematic credit is very unbeneficial for the PT. Angkasa Pura I because it will disturb the economy condition of a company and will affect the next small and medium enerprises (SME). To solve this, the reserch uses Multinominal Naive Bayes to method to classify the partners status in the PT. Angkasa Pura I according to the parimeter that is divided into 4 clases namely smooth class, less smooth class, doubted and jammed class. The process used was classification process where it calculated probability value and the atribute of the partner. The data used in this research is consisted of 148 that taken from 2012-2015. The final result, after the classification is done, the class probability value that was taken randomly is gained, with the resuld to system test with 5 times of testing data division that is taken randomly, it is gained the accuracy as big as 86,56%, precision is as big as 73%, recall is as big as 73% and F-1 Measure is as big as 73%.
PENERAPAN METODE MULTI FACTOR EVALUATION PROCESS PADA APLIKASI SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PERMOHONAN PINJAMAN NASABAH PADA KOPERASI SIMPAN PINJAM GLORIA Redian A Sina; Kornelis Letelay; Dony M Sihotang
J-Icon : Jurnal Komputer dan Informatika Vol 6 No 2 (2018): Oktober 2018
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v6i2.513

Abstract

Lending to customers is one of the services provided by Koperasi Simpan Pinjam Gloria. Bad loans caused by the process of loan transactions manually causing harm to the cooperative. In this study, designed and constructed a decision support system application using MFEP (Multi Factor Evaluation Process) where the process begins with entering customer data, the criteria data including asset, utility loans, loan size, duration, guarantees, profit / loss of the principal efforts per month and profit / loss of the financial per month, the data classification criteria, the data assessment for further calculated and sorted based on the highest value. System testing by comparing the manual calculation results with the results of the system on 3 customer data as test data obtained similar results which showed that the system has accuracy of 100% and system testing using blackbox method showed compliance with the system design. The end result of this research is the determination of the decision support system application for a loan customer with MFEP method. The output of this system is the result of several alternative rank that become the managers reference in decision-making.
SISTEM PENDUKUNG KEPUTUSAN CALON PENERIMA RASKIN DENGAN METODE POLYGONS AREA METHOD (PAM) DI KELURAHAN AIRNONA-KOTA KUPANG Reza S Baliara; Dony M Sihotang; Arfan Y Mauko
J-Icon : Jurnal Komputer dan Informatika Vol 6 No 2 (2018): Oktober 2018
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v6i2.514

Abstract

Raskin (Beras Miskin) is one of the Indonesian government programs to help reduce the expenditure of the poor people. This program is conducted by Bulog and Local Government. Raskin distribution procedure at Airnona sub-district is still using manual method, that those who will receive Raskin is submitted by RT, so that a Decision Support System (DSS) is needed to help handle the problem. The PAM (Polygons Area Method) method is one of the methods in DSS which can help solve unstructured problems. This study uses 8 criteria namely, monthly income, quantity of dependents, floor area of the house, the type of house floor, type of the house wall, assets, lighting source, and drinking water source. System test is done by comparing the ranking system with the name issued by Dinas Sosial. This test uses 66 interview data with 2016 recipient data resulting in similarity rate of 43% and unsimilarity rate is 57%. During then analysis on several data the conclusion is system able to provide good result.
IMPLEMENTASI CASE BASE REASONING MENGGUNAKAN METODE COSINE SIMILARITY UNTUK MENDIAGNOSA PENYAKIT PADA SAPI Ssainah P Faransyah; Sebastianus Adi Santoso Mola; Yelly Y Nabuasa
J-Icon : Jurnal Komputer dan Informatika Vol 6 No 2 (2018): Oktober 2018
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v6i2.515

Abstract

Case Based Reasoning (CBR) is a case-breaking technique based on experience in cases that have previously occurred with the highest similarity value. In this study, the authors apply CBR to diagnose cow disease. Sources of system knowledge are obtained by collecting cases from medical records on 2014, 2016, and 2017. The system uses the Rough Set method for indexing and the calculation of similarity values ​​using the Cosine Similarity method with threshold 70%. This system is able to diagnose 15 diseases based on 29 existing symptoms. The output of the system in the form of the illness experienced, the solution and the presentation of similarities with the previous case to show the truth level of possible diagnose. Based on the test of 30 cases on casebase obtained system accuracy at second part is 27% and at third part the system gets the best result using 3 fold by 33,33%. The system produces low accuracy due to the small number of cases and the scattered data in the case.
ANALISIS METODE SINGLE-POINT CROSSOVER (SPX), TWO-POINT CROSSOVER (TPX) DAN MULTI-POINT CROSSOVER (MPX) PADA FUNGSI NONLINEAR DUA PEUBAH DENGAN BINARY CODING Adriana Fanggidae
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.v7i1.872

Abstract

Algoritma genetika merupakan salah satu algoritma evolusioner yang memiliki 4 tahapan penting yaitu pengkodean, seleksi, crossover dan mutasi. Pada tulisan ini, kinerja dari binary coding pada 3 metode crossover SPX, TPX, dan MPX diuji pada 5 fungsi nonlinear dua peubah. Hasil yang diperoleh menunjukkan metode crossover TPX memberikan kinerja yang lebih baik daripada SPX dan MPX.
Analisis Model Verhults kaitannya dengan Ketersediaan Dokter Umum di Kabupaten Timor Tengah Selatan (TTS) Ariyanto Ariyanto
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.v7i1.874

Abstract

TTS Regency is the second largest regency with the highest population in East Nusa Tenggara. The ratio of general practitioners from 2015 to 2017 is below national standard. Therefore, the TTS regency has experienced a crisis of general practitioners. The research was conducted by taking the number of TTS population of the last eight years. We use the Verhulst model to predict the number of population and propose the ideal number of general practitioners. We found that the number of TTS population is predicted to be 647.815 in 2027. Furthermore, the ideal number of general practitioners in 2027 is 259 people.
PERBANDINGAN KINERJA METODE DETEKSI TEPI PADA CITRA 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.v7i1.875

Abstract

The results of the above analysis it is concluded that edge detection is best resulted from the user of the canny method. Edge detection using the canny method is the best edge detection because the line morphology generated by edge detection is better visible on the border of the image both on the inside and the edges of the image appear thick, vertical or horizontal lines on the front of the house is very clear when compared with the two methods above.
KLASIFIKASI SPAM E-MAIL MENGGUNAKAN METODE TRANSFORMED COMPLEMENT NAÏVE BAYES (TCNB) Hanna Florenci Tapikap; Bertha Selviana Djahi; 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.v7i1.878

Abstract

Classification is one of the ways to organize text so that the texts with the same contents can be grouped in the same category. One of the famous text classification methods is the Naïve Bayes Method. Naïve Bayes has efficient computation and good prediction result however the performance of Naïve Bayes is not really good in classifying unbalanced dataset. This Naïve Bayes method is then modified to overcome the weakness, this modified method is then known as Transformed Complement Naïve Bayes (TCNB) method. In this research, TCNB method was used to the spam e-mails whose dataset were unbalanced and were consisted of 481 dataset in spam e-mail class, and 2412 dataset in legitimate e-mail class (in total, there are 2893 dataset). The classification was done with and without cross validation. The classification with cross validation was done starting from k=2 until k=10. The classification without cross validation was done by dividing the training data by 80% and testing data by 20%. The result showed that the classification by using TCNB with cross validation had its best accuracy level on k=10 by 93,917% and the classification without cross validation had its best accuracy by 92,760%. Thus it can be concluded that TCNB can handle unbalanced dataset with good prediction accuracy.
PENERAPAN METODE TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS) DALAM PENENTUAN UANG KULIAH TUNGGAL DI UNIVERSITAS NUSA CENDANA Benyamin Libing; Dony M Sihotang; Meiton Boru
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.v7i1.879

Abstract

Uang Kuliah Tunggal (UKT) merupakan kebijakan pemerintah untuk membantu masyarakat kurang mampu memperoleh pendidikan sampai ke perguruan tinggi. Dalam penentuan UKT, Universitas Nusa Cendana menggunakan metode wawancara. Banyaknya jumlah mahasiswa baru yang diwawancarai untuk menetapkan UKT maka mempengaruhi tingkat keletihan dari pewawancara dan juga mempengaruhi keputusan yang diambil tidak lagi bersifat objektif, sehingga perlu sebuah Sistem Pendukung Keputusan (SPK) untuk membantu menangani masalah tersebut. Metode TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) merupakan salah satu metode dalam SPK yang dapat membantu menyelesaikan masalah tidak terstruktur. Sistem akan menyeleksi setiap alternatif menggunakan lima kriteria yaitu pendapatan orang tua, rekening air dan listrik, aset, jumlah tanggungan dan pekerjaan. Hasil dari pengujian senstifitas perubahan bobot, yang paling besar yaitu pada rekening air dan listrik dengan 91.66% dan yang paling sedikit yaitu pada pekerjaan dengan 35%. Sedangkan pengujian akurasi standar memperoleh hasil 26.66%.

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