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All Journal J-Icon : Jurnal Komputer dan Informatika
Bertha S Djahi
Universitas Nusa Cendana
Computer Science & IT

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SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN PINJAMAN MENGGUNAKAN APLIKASI FUZZY SIMPLE ADDITIVE WEIGHTING Lorenso Kanuru; Dony M Sihotang; Bertha S Djahi
J-Icon : Jurnal Komputer dan Informatika Vol 6 No 1 (2018): Maret 2018
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v6i1.352

Abstract

The loan service process is one of many routines applied to improve the welfare of either members or the community in cooperative. This process requires a high accuracy in selecting the eligible loans. Bad credits, that oftenly occurred in many cooperative membership, mainly caused by the lack of accuracy of the cooperative itself in selecting eligible loans based on the specific criterias. Implements and development for loan decision support system using Fuzzy Simple Additive Weighting (F-SAW) method. This method is able to accommodate the deficiancy of SAW in terms of providing linguistic assessments. The system is tested by comparing the system decision to the cooperative decision. According to 7 test data with loan amount below Rp 10,000,000 and 5 test data with loan amount between Rp 15,000,000 – Rp 20,000,000, it appears that 9 of them provide the same decision as what the committee decided (75%), while 3 of them do not (25%).
KLASIFIKASI JURUSAN MENGGUNAKAN METODE NAÏVE BAYES PADA SEKOLAH MENENGAH ATAS NEGERI (SMAN) 1 FATULEU TENGAH Arrdy Hailitik; Bertha S Djahi; Yelly Y Nabuasa
J-Icon : Jurnal Komputer dan Informatika Vol 5 No 2 (2017): Oktober 2017
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v5i2.361

Abstract

Naïve bayes is the classification method which utilizes the both probabilities and statistics to predict the future opportunity by using the last experiance. The system of major in the senior high school is the means of students directing to be more based on their interest and academic competence. The major in East SMAN 1 Fatuleu consists of the Science and Social majors. This research is using the Method of Naïve bayesto classify the student major. The data of student that is used here is the grade XI for second semester in the years of 2011 to 2015 with the 470 for the total data. For the testing proces is used 420 data (89%) as trains data and 50 data (11%) as tests data. The result of this research shows the amount of 99.31% accuracy in the process of major classification.
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 SISTEM PAKAR DALAM TES KEPRIBADIAN MYERS BRIGGS TYPE INDICATOR (MBTI) MENGGUNAKAN METODE FORWARD CHAINING Arlin Ardianensi; Sebastianus A S Mola; Bertha S Djahi
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.v4i1.5192

Abstract

Psychology focuses on learning individual characteristic. Individual characteristic are one factor determining the success of individuals. Most people use IQ as measure of success. However, it has beenfound that EQ (Emotional Quotient) is the most important factor determining the success of individuals.The levels of EQ can be determined using psikotest, which measure individual’s cognitive ability,emotions, inclination of act and things influence that. Myers Briggs type Indicator or MBTI is one of alltest of written test. MBTI have four of dimension that opponent. Although, it is truly everyone have all ofthat character. Therefore, we design an expert system using forward chaining method. This system has100% accurate.
APLIKASI KEAMANAN PESAN (.TXT) MENGGUNAKAN METODE TRIPLE DES DAN METODE KOMBINASI LSB DAN BLUM-BLUM-SHUB Derwin R Sina; Guido A Kiu; Bertha S Djahi; Emerensye S Y Pandie
J-ICON : Jurnal Komputer dan Informatika Vol 10 No 2 (2022): Oktober 2022
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v10i2.8465

Abstract

Security is one of the important aspects of the process of exchanging information (messages). To prevent misuse or attacks by unauthorized parties (attackers) on private messages, the message must be secured. Several methods can be used to secure messages, one of which is by combining the Triple Data Encryption Standard (DES) cryptography method, Blum-Blum Shub (BBS) random number generator, and Least Significant Bit (LSB) steganography. In this study, the Triple DES cryptographic method is used to encrypt messages (embedded-message) with the extension .txt and the BBS random number generator method is used to determine the position of a random pixel to be inserted in the cover-image message. The LSB steganography method is used to perform the embedding process of the encrypted embedded-message at the pixel position resulting from the BBS random number generation process. The test results show that the system can extract embedded messages hidden in a stego-image with 100% accuracy. The maximum number of embedded-message characters that can be used in the test is 150 characters. The test also produces a stego-image that has an average Peak Signal to Noise Ratio (PSNR) value of 88.61, which means that the resulting stego-image has high quality (no significant quality degradation) and the presence of messages in the stego-image is getting harder to detect (imperceptibility).
Co-Authors Arlin Ardianensi Arrdy Hailitik Derwin R Sina Dony M Sihotang Emerensye S Y Pandie Guido A Kiu Lorenso Kanuru Meilani T Bunga Sebastianus A S Mola Yelly Y Nabuasa