Article Per Year (5 Year)
p-Index From 2020 - 2025
2.17
P-Index
This Author published in this journals
All Journal J-Icon : Jurnal Komputer dan Informatika
Nelci Dessy Rumlaklak
Unknown Affiliation
Computer Science & IT

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Prediksi Masa Studi Mahasiswa Menggunakan Metode Topsis Pada Program Studi Ilmu Komputer Universitas Nusa Cendana Emerensye Sofia Yublina Pandie; Rheza A Costa; Nelci Dessy Rumlaklak
J-Icon : Jurnal Komputer dan Informatika Vol 8 No 1 (2020): Maret 2020
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v8i1.2397

Abstract

Graduation rates are considered as one of the parameters of the effectiveness of educational institutions. The period of student studies is an important issue that needs to be handled wisely by the Undana Computer Science department. Because graduation rates on time and not on time will affect the accreditation of majors, so we need a solution to be able to improve the problem of graduation on time. The solution that can help supervise and pay attention to active students in the 5th semester in order to graduate on time is to use a Decision Support System. Decision Support System can help predict the period of study of students with the TOPSIS method. The criteria used are Gender, Number of SKS that have been taken, Semester GPA, Cumulative Achievement Index and subsequent SKS Load. Sensitivity testing is done by adding a weight value of 0.5 and 1 to each criterion to get the results of criteria that are sensitive to changes in weights namely the GPA criterion of 3.92% change and subsequent SKS Load criteria of 3.92% change. The results of testing the sensitivity of criteria weights have an average percentage change in yield of 0.78%.
Sistem Peramalan Cuaca dengan Fuzzy Mamdani (Studi Kasus: BMKG Lasiana) Imanuel Here Wele; Nelci Dessy Rumlaklak; Meiton Boru
J-Icon : Jurnal Komputer dan Informatika Vol 8 No 2 (2020): Oktober 2020
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v8i2.2883

Abstract

The weather is one part of human daily life. Many people who depend their lives on the weather to do every activity. Therefore, knowing the weather forecasting will give consideration to the community to be able to carry out various activities of human life such as in the field of aviation, shipping, agriculture, processed industries and others that depend on weather conditions. For this reason, the Indonesian BMKG has the duty to provide weather forecast information based on existing meteorological data using complex calculations. This study aims to build a system that will be an alternative for BMKG in forecasting weather using fuzzy based on four supporting criteria, namely air temperature, humidity, wind speed and air pressure. In doing weather forecasts using mamdani fuzzy there are several steps, namely determining the fuzzy set, the application of the implication function using the MIN function, the composition of the rules using the MAX function, and finally the Defuzzification process using the MOM method. This system will produce weather forecast results based on data on air temperature, air humidity, wind speed and air pressure that have been entered by the system user by showing the membership level of the predicted results. Based on testing that has been done, it is concluded that the system built using mamdani fuzzy can do a good weather forecast with a system accuracy rate of 61,062% using daily weather data as many as 1826 data in 2013-2017, with the lowest accuracy level found in 2015 with an accuracy rate of 54,247 % and highest accuracy in 2017 amounted to 65.207%.
PENENTUAN KESESUAIAN LAHAN PERTANIAN TANAMAN CABAI MENGGUNAKAN METODE NAÏVE BAYES DI KABUPATEN KUPANG Welmy Sinlae; Sebastianus A. S. Mola; Nelci Dessy Rumlaklak
J-Icon : Jurnal Komputer dan Informatika Vol 9 No 1 (2021): Maret 2021
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v9i1.3848

Abstract

The chili plant is one of the plants cultivated in East Nusa Tenggara (NTT). Kupang Regency is one of the chili producing areas in NTT. Overall chili production in Kupang Regency from 2019 to 2020 has increased. However, the increase in production has not been maximized considering the large amount of land that has not been used as agricultural land. Therefore we need a system that helps in determining the suitability of agricultural land for planting chilies. In this research case-based reasoning (CBR) in determining the suitability of agricultural land for chili plants. The method used in this research is Naïve Bayes with 7 criteria, namely, rainfall, drainage, soil texture, soil depth, C-organic, land slope and the danger of a disaster. The process of finding a solution begins by eliminating irrelevant data using the Naive Bayes method and continues with ranking the best similarity values ​​using KNN. Based on the test results with 110 cases of chili fields, the highest accuracy result is 92.2%, and the average accuracy result of the entire fold is 89.1%.
IMPLEMENTASI ALGORITMA APRIORI UNTUK ANALISA DATA PENJUALAN (STUDI KASUS: TOKO UD. SURYANI) Ahmad Adri; Nelci Dessy Rumlaklak; Derwin Roni Sina
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.v9i2.5132

Abstract

Transaction data owned by a store or supermarket every day is sure to increase, but it is often found that the transaction data is just stored and notused. This is what happened at the UD. Suryani store, where the existing transaction data has not been used properly, even though the collection of transaction data has the potential for information that can be processed to produce useful new knowledge. This transaction data processing can be done with data mining techniques. One of the data-mining techniques that can be used is the association rule method. One of the data retrievalalgorithms with association rules is the Apriori algorithm. This algorithm serves to determine the association relationship of a combination of itemsand is suitable to be applied when there are several item relationships to be analyzed. The purpose of this research is to apply data mining to the transaction data for the last one year in the UD. Suryani store. The data mining processing process is carried out with the rapidminer application and from nine trials with different combinations of minimum support and minimum confidence values for 13,490 transaction data, the results obtained are that the item most purchased by consumers is the Masako Sapi Renteng 10g with a support value of 14,5% and for items that are often purchasedtogether, if you buy Eggs and Blue Band 200g, you will buy Kompas Kemasan 1kg, with the highest confidence value of 66.5%.
Klasifikasi Penentuan Status Zona di Kota Kupang Menggunakan Aalgoritma Naive Bayes Classifier Nelci Dessy Rumlaklak; Adriana Fanggidae; Yulianto Triwahyuadi Polly
J-ICON : Jurnal Komputer dan Informatika Vol 10 No 1 (2022): Maret 2022
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v10i1.6458

Abstract

The World Health Organization (WHO) made the corona virus a pandemic in 2020. This virus has hit the whole world, including Indonesia. East Nusa Tenggara (NTT) as of June 2021 recorded 18,741 positive cases of Covid-19 and the City of Kupang was the area that contributed the most positive cases. The daily increase in Covid-19 cases in Kupang City shows a fairly high increase. The purpose of this study is to build a classification system to determine the status of the Covid-19 zone in the city of Kupang. The system design using the waterfall model is used to design and build the system while the Naïve Bayes Classifier algorithm is used for classification. The criteria as input in the system for the classification process are positive confirmed data, recovered patient data and death data. The results of the classification process consist of 2 classes, namely the Green Zone and Red Zone. Kupang City's daily Covid-19 case data for January-June 2021 with a total of 181 as training data. 31 test data entered into the system were analysed using the Naïve Bayes Classifier method and succeeded in obtaining classification results as system output. Tests in the study were carried out on systems built using Blackbox testing to test the functionality of the system with the expected results. The confusion matrix is ​​used to test the performance of the classification method and the results have an accuracy rate of 77.91% and a precision value of 73.91%.
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%.
PENERAPAN METODE FUZZY C-MEANS DALAM PENENTUAN PENERIMA BEASISWA PROGRAM INDONESIA PINTAR (PIP) Mardiani Thiaralivta Geraldine Kadja; Nelci Dessy Rumlaklak; Bertha S Djahi
J-ICON : Jurnal Komputer dan Informatika Vol 11 No 1 (2023): Maret 2023
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v11i1.9846

Abstract

The process of selecting scholarship recipients for Program Indonesia Pintar (PIP) at SMA Negeri 2 Kupang is still done manually by comparing student data. That process can cause the emergence of a reasonably high level of complexity and requires a relatively long time to get the results. Therefore, a Decision Support System (DSS) was built using the Fuzzy C-Means (FCM) method in this study. The FCM method groups data on prospective scholarship recipients with almost the exact resemblance into one cluster. Five (5) criteria are used in selecting PIP scholarships: the number of dependents, parents’ income, water bills, electricity bills and the value of the latest report card. The data is from class XI (eleven) students in 2019 at SMA Negeri 2 Kupang, totaling 422 students. The results of the FCM calculation with a maximum iteration of 100 and an error value of 0.00001 get 240 students entering cluster 1, namely eligible to receive scholarships and as many as 182 students entering cluster 2, namely not eligible to receive scholarships. The testing method used in this study is blackbox testing which is divided into 8 (eight) test scenarios and obtains valid results for all of them. The DSS for determining PIP scholarship recipients using the FCM method is more effective and efficient because it can save time, and scholarships can be awarded to the right students.
Co-Authors Adriana Fanggidae Ahmad Adri Arfan Y Mauko Arfan Yeheskiel Mauko Bertha S Djahi Derwin Roni Sina Emerensye Sofia Yublina Pandie Imanuel Here Wele Mardiani Thiaralivta Geraldine Kadja Meiton Boru Rachmadiansyah Rachmadiansyah Rheza A Costa Sebastianus A. S. Mola Welmy Sinlae Yulianto Triwahyuadi Polly Yulianto Triwahyuadi Polly