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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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INDONESIA
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 170 Documents
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KOMPUTERISASI PENGOLAHAN DATA AKADEMIK PADA SD INPRES OEBOBO 1 KUPANG NTT Dendrianto U Lakimbali; Yulianto T Polly; Yelly Y Nabuasa
J-Icon : Jurnal Komputer dan Informatika Vol 1 No 1 (2013): Maret 2013
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v1i1.146

Abstract

The role of computerized information systems in education, especially for academic data processing is needed in order to provide convenience for the user, processing the data using a computer will be faster and easier and it can be stored in a long time. SD Inpres Oebobo 1 is one of the primary schools in Kupang city. Currently, school is still using manual systems which means entire academic data is written on the books, so many mistakes and takes a long time to obtain information. Leave of about problem that therefore writer builds a system be computerized that can made process of academic data as students data, teachers data, class data, subjects data, the school year data, religious students and teachers data, parents jobs data, detailed data subjects, homeroom data, instructor data, headmaster data, student value data, dislocated student value data, student cards reports and will produce a system output as student data reports, teacher data reports, class data reports, detailed subjects data reports, attendance student data reports, homeroom data reports, instructor data reports, headmaster data reports, student value data reports, dislocated student value data reports, student cards reports, class increase graphs and students graduation graphs. ABSTRAKPeran sistem informasi yang terkomputerisasi dalam dunia pendidikan khususnya untuk mengolah data-data akademik sangat dibutuhkan sehingga dapat memberikan kemudahan-kemudahan bagi pengguna, pengolahan data dengan menggunakan komputer akan lebih cepat dan mudah serta dapat disimpan dalam jangka waktu yang lama. SD Inpres Oebobo 1 merupakan salah satu sekolah dasar yang berada di kota Kupang. Saat ini, sekolah tersebut masih menggunakan sistem yang masih manual yakni mencatat seluruh data akademik pada buku, sehingga banyak sekali terjadi kesalahan dan membutuhkan waktu yang lama untuk memperoleh informasi. Bertolak dari permasalahan tersebut maka penulis membangun suatu sistem terkomputerisasi yang dapat mengolah data akademik seperti data siswa, data guru, data kelas, data mata pelajaran, data tahun ajaran, data agama siswa dan guru, data pekerjaan orang tua siswa, data detail mata pelajaran, data wali kelas, data pengajar, data kepala sekolah, data nilai siswa, data nilai siswa pindahan dan data rapor siswa serta akan menghasilkan keluaran sistem berupa laporan data siswa, laporan data guru, laporan data kelas, laporan data detail mata pelajaran, laporan daftar hadir siswa, laporan data wali kelas, laporan data pengajar, laporan data kepala sekolah, laporan data nilai siswa, laporan data nilai siswa pindahan dan laporan data rapor siswa serta grafik kenaikkan kelas dan grafik kelulusan siswa.
DATA MINING UNTUK KLASIFIKASI STATUS GIZI DESA DI KABUPATEN MALAKA MENGGUNAKAN METODE K-NEAREST NEIGHBOR Brigita Fahik; Bertha S Djahi; Nelci D Rumlaklak
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.v6i1.348

Abstract

Classification of village status according to the number of malnourished patients is very important in anticipating malnutrition cases in a region, especially for the areas in the district of Malaka. Cases of malnutrition recorded quite a lot in the District of Malaka demanded the district government of Malaka to immediately anticipate the problem. To overcome this problem, we used k-Nearest Neighbor method to classify the status of villages in Malaka District based on the level of under-five children under the red line into three target classes: low, medium, and high. Prior to the classification process, clustering process is done using K-Means method so that all data can be divided into classes that have been determined. The data used in this study as many as 174 data taken from the year 2013-2015. The final result, after validation of clustering data obtained resemblance to the original data of 98.25%, and the results of system testing of 93.10%. Determination of the best value of k with the test data of 34 pieces and the training data of 140 pieces is at k = 7 with the average percentage of similarity of 95.53%.
APLIKASI PENENTUAN GOLONGAN DARAH MANUSIA DENGAN METODE SEED REGION GROWING DAN SELF ORGANIZING MAPS David Wewo; Adriana Fanggidae; Kornelis Letelay
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.v6i1.349

Abstract

The blood type of human are divided by four group wich is blood type A, B, O & AB. Artificial Neuron Network can help the identify process for blood type. Self organizing maps is a part of arrtificial neuron network who has function for data training and data clasification. The image data are using by blood clotting and obtained after spilled blood sample with the reagent. The real data image are converted into grayscale image, For taking the characteristic are doing by converted real image to image biner with the treshold more than 80 and smaller than 150, image are taken as much as 12 image of clotted blood and 12 image blood wich does not clot, and the next step will do the training process using self organizing maps. The first testing data are doing by the same test data and same with training data too and the result 100%. The second testing data is doing by 12 blood image test data wich is not the same as data training and the result 83.33%.
IMPLEMENTASI ALGORITMA DATA MINING NAIVE BAYES PADA KOPERASI Emerensye S. Y. Pandie
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.v6i1.350

Abstract

One of the factors of failure in the field of credit business is the lack of accurate assessment of the ability of the debtor, thus resulting in errors in credit decisions that culminate in credit congestion. Data mining techniques can be used to assess customer ability based on past data. Debtor data that has been through the stages of data mining is then processed using Naive Bayes data mining algorithm. Naive Bayes is a simple probabilistic based prediction technique based on the application of bayes rules. Implementation using Weka 3.8 with a total of 3018 records yields a truth level of 94%.
KOMBINASI STEGANOGRAFI BIT PLANE COMPLEXITY SEGMENTATION (BPCS) DAN KRIPTOGRAFI DATA ENCRYPTION STANDARD (DES) UNTUK PENYISIPAN PESAN TEKS PADA CITRA BITMAP GRAYSCALE 8 BIT Paulus Klau; Derwin R Sin; Yelly Y Nabuasa
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.v6i1.351

Abstract

Bit Plane Complexity Segmentation (BPCS) is steganography method that using uncapability of human’s vision in interpreting difficult biner form. Data Encryption Standard (DES) is cryptography algorhytm that is chiper block and changing data become blocks 64 bit and then using encryption key amount 56 bit. By combining steganography algorhytm and cryptography will increase quality of data security. In this research combination of BPCS and DES done by inserting text message into bitmap image, the increating text message restricted maximum 248 characteristic with the long of the key must 16 characteristic in hexsadecimal format. The result obtained by this system testing with image test about 30 images is the inserting text can be read again with the provision of using the same key for inserting process and reading text. This image of insertion result can’t stand to adding contrast operation (25%) and rotation (90 to the right, 90 to the left, 180) and cutting operation on the upper side dan left image, but if cutting on the lower side and right (image resolution > 100 piksel) the inserting text can be read again correctly. In image inserting result, will be found noise of the upper left side from image because these region is the initial region is inserted.
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%).
PENERAPAN METODE HEURISTIK (ALGORITMA IDA* DAN B&B) DALAM PEMECAHAN N-Queen Problem Novi Penna; Sebastianus Adi Santoso Mola; Meiton Boru
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.v6i1.353

Abstract

N-Queen problem is a problem which a N-Queen pawn in is place chess with n x n size. N-Queen pawn is a put in such away in chess board with under condition that the queen pawns do not attack each other. The attacking movement of N-Queen problem is similar to the way of the queen pawn attacking in chess. Commonly the queen pawn moves horizontally to left and right, forward and backward vertically and also diagonally, so there are no queen pawns in a line of horizontal, vertical and diagonal.Heuristical searching is one of the method which can be used to solve the game of N-Queen problem selectively, by giving solution of the shortest time channel efficiently in order to able the user to solve this game well, fast and relevantly. Some algorithms that use heuristic is Iterative Deepening algorithm A* (IDA*) and Branch and Bound (B&B) algorithm. The used heuristic function is by seeing the numbers of boxes which are empty and the number of queen which is not be put in board yet.The aim of making this final project to implement the solving of N-Queen problem using heuristic searching (B&B and IDA*). From this implementation could be seen that IDA* and B&B algorithm is able to give channel in solving N- Queen problem. After a repetition of test by using 19 sheet of data, it is shown the comparison of result between IDA* algorithm and B&B algorithm which IDA* algorithm result the shorter channel in solving N-Queen problem based on the node 61%, and time 41% which better than B&B algorithm.
PENERAPAN MODIFIED CERTAINTY FACTOR DALAM SISTEM PAKAR TES KEPRIBADIAN FLAG Romy O. D. Djami; Sebastianus Adi Santoso Mola; Tiwuk Widiastuti
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.v6i1.354

Abstract

Expert system is one of artificial intelligence engines that is using specific knowledge of an expert to solve a specific problem. In this study, the expert system is built to implement FLAG personality test using Modified Certainty Factor method in order to help counselee knowing his personality type and the careers suitable for him. Knowledge source for this system is obtained from the book Tes Bakat Anda (Test Your Own Aptitude) by Jim Barrett and Geoff Williams (2002) along with several consultations with Irianti Agustina, S.Pd., M.Pd. and Dra. Sri Rahayu Djami. This system is able to provide the output in the form of personality type of the counselee as well as career recommendations suitable for him. Based on study on 141 data of counselees, the results are: By using Modified Certainty Factor, this expert system has accuracy of 83.69%, and provides more certain output than the output provided by the conventional FLAG. Therefore, researcher recommends the using of Modified Certainty Factor method to improve any other personality test which still has not given certain output.
IMPLEMENTASI METODE BACKPROPAGATION UNTUK MEMPREDIKSI PEMAKAIAN OBAT DI PUSKESMAS OESAPA Rowin Djuli; Arfan Y Mauko; Meiton Boru
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.v6i1.355

Abstract

The medicine management incommunity health clinic is one of important aspect, because the health community clinic will have negative impact in costs if there is inefficiency in managing the medicine, the most used medicine will be out of stock before the re-order due date. In artificial neural network backpropagation method is classified as algorithm learning or traning tend to supervised and using rules of quality correction. Backpropagation is using error output to change the value of qualities in two ways, in backward and forward propagation. In this research, writer applying backpropagation method to predicting the medicine usage in oesapa community health clinic. The data was taken from usage report and medicine order receipt in 2014-2016. Where at 2014-2015 was data training and 2016 was data test. Which is the result of data training has ±99% accuracy and the data test has 70,66% accuracy.
RANCANG BANGUN APLIKASI PREDIKSI CALON KREDITUR PADA BANK MUAMALAT KUPANG Abdul G Farid; Sebastianus Adi Santoso Mola; Dony M Sihotang
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.v5i2.358

Abstract

The implementation of stored-transaction data can provide a lot of useful knowledge to createbusinesses intelligence in Muamalat Bank. But Muamalat Bank has not done it yet; so, it will be difficultto give credits to the creditors. This study aimed to create business intelligence in terms of prospectivecreditors prediction. It was expected that it could predict creditors in making payments using old existingcreditors forms data. The research applied the K-Nearest Neighbor algorithm (K-NN) where thisalgorithm looking for similarly between render candidates and old creditors as much as k values thatstill or have done their lends to Muamalat Bank Kupang. The result of this research shows that with KNNalgorithm, a creditor can be predict using data comparism. Highest accuracy can be reach when kvalue=5, with accuracy level up to 80%.

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