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All Journal JIKO (Jurnal Informatika dan Komputer) TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Jurnal Digit : Digital of Information Technology Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal Dialektika Informatika (Detika) Jurnal Pengabdian Kepada Masyarakat (JPKM) J-Icon : Jurnal Komputer dan Informatika Jurnal Inovatif Wira Wacana Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia
Arfan Y Mauko
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Public Health Social Sciences Other

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PERANCANGAN SISTEM INFORMASI GEOGRAFIS PEMETAAN WILAYAH RAWAN BENCANA ALAM Dwi Prasetyo; Arfan Yeheskiel Mauko; Axel Christian Kolokota
Jurnal Digit Vol 12, No 2 (2022)
Publisher : Universitas Catur Insan Cendekia (CIC) Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51920/jd.v12i2.289

Abstract

ABSTRAKKota Kupang merupakan salah satu wilayah di Provinsi Nusa Tenggara Timur yang rawan terdampak bencana alam selain itu wilayah ini juga tercatat pernah dilanda oleh beberapa bencana alam. Kehadiran sistem informasi geografis pemetaan daerah rawan bencana di Kota Kupang dapat membantu masyarakat untuk melihat informasi sebaran wilayah di Kota Kupang yang rawan terdampak bencana alam seperti nama bencana, nama lokasi, foto lokasi serta potensi dampak dari bencana tersebut. SIG ini dirancang secara terstruktur dan juga sistematis menggunakan metode pengembangan waterfall. dengan metode pengujian sistem yang digunakan yaitu menggunakan metode black box, yang dimana menunjukan bahwa hasil pengujian yang dilakukan dari berbagai komponen sistem seperti input, read, delete, update dan modul lainnya dapat berjalan dengan baik. Kesimpulan yang didapat bahwa Sistem Informasi Geografis pemetaan daerah rawan bencana alam di Kota Kupang dapat berjalan dengan baik dan bisa dioperasikan dengan baik oleh para pengguna.Kata kunci: Sistem Informasi Geografis, SIG, Pemetaan Daerah Rawan Bencana Alam, Waterfall, Black Box.
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.
CASE BASED REASONING UNTUK MENDIAGNOSA PENYAKIT GIGI DAN MULUT MENGGUNAKAN METODE BLOCK CITY Emanuel Fahik; Derwin R Sina; Arfan Y Mauko
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.v5i2.362

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. In this study, the authors apply CBR to diagnose dental and mouth disease in humans. Sources of system knowledge are obtained by collecting cases that have occurred before. The calculation of similarity values ​​using the Block City Gower method with threshold 60%. This system can diagnose 5 diseases based on 26 existing symptoms. The output of the system in the form of the illness experienced by the patient based on the symptoms entered by non-physician medical personnel, as well as the treatment solution which accompanied the presentation of similarities with the previous case to show the truth level of possible diagnosis. 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 Block City method correctly indicated with 100% accuracy, and use 122 cases is optimal enough to diagnose 5 diseases indicated by the average similarity to 20 cases for milk teeth growth phase of 80% and 30 cases for adult tooth growth phase of 90%.
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.
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.
Implementasi Metode Analisis Gap Dan Profile Matching Untuk Kelayakan Calon Debitur Di Koperasi Simpan Pinjam (Ksp) Kopdit Solidaritas Santa Maria Assumpta yelly y nabuasa; Adriana Fanggidae; Derwin R Sina; Arfan Y Mauko
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.v7i2.1652

Abstract

In granting credit to debtors, it must go through an assessment of whether the debtor is appropriate or not feasible. KSP Koprit Solidaritas has set policy standards in granting credit to accept or reject the risk of bad credit, namely assessing prospective borrowers who meet the conditions of character rating, ability to pay off credit, capital owned, collateral owned and socioeconomic conditions. In this study, the design and manufacture of decision support system applications were carried out using profile matching methods to assess the eligibility of prospective debtors. Profile Matching is used to determine the priority with the highest ranking, which is used as a suggestion from the right system in determining the best alternative. The test results using 60 data obtained an accuracy of 81.667% with an error rate of 18.333% which indicates that the decision support system is functioning optimally following the Profile Matching method.
IMPLEMENTASI METODE FUZY-SIMPLE ADDITIVE WEIGHTNG (F-SAW) UNTUK SISTEM PENERIMA BANTUAN RUMAH DI KECAMATAN AMARASI KABUPATEN KUPANG Yuyun Saudale; Kornelis Letelay; Arfan Y Mauko
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.v7i2.1655

Abstract

Recipients of housing assistance for poor people are government assistance programs that have a limited budget so that not all people receive housing assistance. Criteria for recipients of housing assistance can be seen from 13 criteria such as age, education, occupations, income, land ownership, home ownership, number of occupants, roof conditions, wall conditions, floor conditions, ownership of bathrooms, water sources, electricity sources. To get a decent recipient, a Decision Support System (DSS) is needed to make it easier for the government to provide housing assistance for poor people. Fuzzy Method Simple Additive Weighting (F-SAW) is one method in DSS that can help resolve unstructured problems and can accommodate weakness of SAW method in linguistic and numerical assessments. System testing conducted use sensitivity testing that is with change value weight each criteria in a manner gradually. From sensitivity test above that has been done, the most sensitive is education criteria because when tested three times of high weight is changed to very high has experienced one change, low has 4 times changed and very low has 4 times the change, with these changes the results got 90% presentation for education criteria. From the results of comparisons that have been made between Dinas Sosial and system, data quota that deserves to receive housing assistance as much as 30 data from dinas sosial showed that total data quota is 20 which is same with system and 10 data is not same with system.
EKSTRASI CIRI WARNA HSV DAN CIRI BENTUK MOMENT INVARIANT UNTUK KLASIFIKASI BUAH APEL MERAH Nikotesa Eko Rianto Pah; Sebastianus A S Mola; Arfan Y Mauko
J-Icon : Jurnal Komputer dan Informatika Vol 9 No 2 (2021): Oktober 2021
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v9i2.5043

Abstract

Red apple is one of the fruit plants with a lot of enthusiasts so it is very popular in the market. Red apples also have several types that at first glance look similar to one another. This is what makes it difficult for people to distinguish between red apples that are consumed, especially since there is no information label to explain these apples. Therefore, in this study a classification of red apples was carried out based on their shape and color characteristics. Image data used is secondary data in * JPG format with a size of 100 x 100 pixels. The method used is the extraction of the Mean HSV color feature (the output value is 3) and the Moment Invariant form feature (the output value is 7) so that each image has 10 values. Image classification results were obtained using Euclidean Distance. Meanwhile, the test scenario used K-Fold Cross Validation where 1,710 image data were divided into 10-folds with 171 images in each subset. From 10-fold tested 50 times, so that an average accuracy of 98.82% was obtained. The highest accuracy was obtained in the 46th test of 99.12% and the lowest accuracy was in the 48th test of 98.54%.
PREDIKSI MASA TUNGGU KERJA ALUMNI MENGGUNAKAN NAÏVE BAYES CLASSIFIER PADA PROGRAM STUDI ILMU KOMPUTER UNIVERSITAS NUSA CENDANA Rachmadiansyah Rachmadiansyah; Nelci Dessy Rumlaklak; Arfan Yeheskiel Mauko
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.v10i2.7426

Abstract

In a global era that is full of challenges, universities are expected to produce quality graduates in order to compete in the world of work. One indicator that can be used to assess the quality of graduates is the job waiting period. In this research, the researcher implements Naïve Bayes Classifier method using the RapidMiner 7.3 app to generate predictions for the job waiting period and the accuracy rate of the prediction results obtained. The data in this research were obtained from the results of the Tracer Study questionnaire distributed by Computer Science Study Program at The University of Nusa Cendana to determine the career achievements of alumni. The attributes used in this research are Study Period, Grade Point Average (GPA), Organizational Participation, and Competency Mastery with Waiting Period classes which are divided into 4, namely ≤ 10 months, 11 months - 2 years 1 month, 2 years 2 months - 3 years 4 months, and > 3 years 4 months. The prediction results of the job waiting period obtained are presented in the form of a confusion matrix with an accuracy rate of 81.82%.
ANALISIS ELITISME PADA ALGORITMA GENETIKA MENGGUNAKAN PENGKODEAN ORDINAL REPRESENTATION DALAM TRAVELLING SALESMAN PROBLEM Arfan Yehezkiel Mauko; Adriana Fanggidae; Yulianto Triwahyuadi Polly
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.v10i2.8473

Abstract

Traveling salesman problem (TSP) is one of the optimization problems to find the shortest route, where each city is only allowed to visit exactly once. The search for the shortest route can be completed by several algorithms, one of which is the genetic algorithm. Genetic algorithm is an optimization algorithm that works by imitating the evolutionary process in nature. During the evolution process, individuals with the best fitness may undergo changes that result in a decrease in fitness. Therefore, in order to keep individuals with the best fitness from becoming extinct during the evolutionary process, it is necessary to make copies of these individuals which is called elitism. There are three models of elitism, namely, Model 1: the best individuals are copied as many as m replacing the worst m individuals, Model 2: the best m individuals replace the worst m individuals, and Model 3: the best m individuals replace the worst m individuals selected randomly from 100%-m worst individual. The values ​​of the m parameters are 10%, 20%, 30%, and 40%. The tests were carried out with elitism and without elitism on different populations and cities. The test results show that Model 2 with m = 10% and population = 20 is the ideal parameter in solving TSP.
Co-Authors Adriana Fanggidae Adriana Fanggidae Axel Christian Kolokota Bolle, Apriando Sabat Sajati Boru, Meiton Clarissa Elfira Amos Pah Daniel Boys Derwin R Sina Derwin R Sina Didok, Juniardi Christian Dwi Prasetyo Emanuel Fahik Fernando, Oswaldus P Juan Rizky Mannuel Ledoh Kalvein Rantelobo Ketti, Kevin Junus Kornelis Letelay Meiton Boru Nelci Dessy Rumlaklak Nelci Dessy Rumlaklak Nikotesa Eko Rianto Pah Rachmadiansyah Rachmadiansyah Reza S Baliara Rowin Djuli Rumlaklak, Nelci Dessy Saragih, Alexandro Eurico Yosafat Sebastianus A S Mola Sebastianus Adi Santoso Mola Sihotang, Dony Martinus Sonlay, Febronia B Sultani, Akhzan Akbar Taklal, Adi J Tiwuk Widiastuti Umbu Ngedo, Raymond Kristian Bili Veryani Sumaa Wangge, Fajar A R yelly y nabuasa Yulianto Triwahyuadi Polly Yuyun Saudale