Article Per Year (5 Year)
p-Index From 2019 - 2024
1.006
P-Index
This Author published in this journals
All Journal Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Computer Networks, Architecture and High Performance Computing
Marlince Nababan
Universitas Prima Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education

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

Found 7 Documents
Search

 1 
Aplikasi Penentuan Karyawan Terbaik dengan Metode AHP dan Metode Promethee Marlince Nababan
Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA) Vol. 1 No. 2 (2018)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (705.059 KB)

Abstract

Sistem Pendukung Keputusan di defenisikan sebuah sistem yang mampu menghasilkan pemecahan masalah maupun penanganan masalah, salah satu dari pendukung keputusan yaitu metode Analytical Hierarchy Process (AHP) dan Promethee. Proses penilaian penerimaan karyawan menghabiskan banyak waktu dan penilaian dilakukan masih secara subjektif. Dengan penerapan metode AHP dan Promethee dalam hal pengangkatan karyawan tetap dan yang layak, ada beberapa variabel atau kriteria penetapan pengangkatan karyawan tetap yaitu umur, pendidikan, kehadiran, pengalaman kerja dan loyalitas sebagai tolak ukur pengangkatan karywan teta dengan nilai maksimum 0.411 dan nilai minimum -0.341 sehingga metode AHP dan Promethee dapat membantu pengambilan keputusan.
Comparison of Decision Tree and Linear Regression Algorithms in the Case of Spread Prediction of COVID-19 in Indonesia Darwin Darwin; Dwiky Christian; Wilson Chandra; Marlince Nababan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 1 (2022): Article Research Volume 4 Number 1, Januay 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i1.1234

Abstract

COVID-19 is a disease that was first discovered in Wuhan, China and caused the 2019-2020 coronavirus pandemic. This virus can cause respiratory tract infections such as flu when infecting humans. According to Ministry of Health of the Republic of Indonesia, the number of confirmed cases of COVID-19 in Indonesia at March 2021 is 1,511,712 with 40,858 deaths and 1,348,330 recovered. For that, Indonesia is declared to have the highest confirmed cases in ASEAN. Several studies have been carried out to handle some cases by using the data mining techniques such as Decision Tree or Linear Regression algorithm, as example to classify the respiratory diseases and predict pregnancy hypertension. In this study, we tried to analyze COVID-19 cases in Indonesia and conducted an experiment of predicting COVID-19 new cases with the Decision Tree (CART) and Linear Regression algorithms. Then we will compare the values of these two algorithms by using R2 Score to evaluate the prediction performance. The results of this analysis state that DKI Jakarta province has the highest number of positive cases, cures and deaths in Indonesia. The value of the comparison results from the R2 Score obtained in the Decision Tree algorithm reached 95.69% (training) and 92.15% (testing) while the Linear Regression algorithm reached 79.93% (training) and 77.25% (testing).
COMPARISON OF SINGLE EXPONENTIAL SMOOTHING METHOD WITH DOUBLE EXPONENTIAL SMOOTHING METHOD PREDICTION OF SALT SALES Jesslyn Harly; Marlince Nababan; Lidya Haryati Bintang; Reyhan Achmad Rizal; Aisyah -
Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA) Vol. 6 No. 2 (2023): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM Prima)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3366

Abstract

Predicting the quantity of product sales in the future aims to control the amount of existing product stock, so that the shortage or excess of product stock can be minimized. When the quantity of sales can be predicted accurately, the fulfillment of consumer demand can be managed in a timely manner and the company's cooperation with consumers is maintained properly so that the company can avoid losing sales and consumers. This study aims to analyze the accuracy of predicting the quantity of sales of salt using the Single Exponential Smoothing (SES) method compared to using the Double Exponential Smoothing (DES) method, so that a more accurate method will be obtained for predicting the quantity of sales. The results of testing the comparison of the level of accuracy can be done by evaluating the error value of the forecasting results with the Mean Absolute Percentage Error (MAPE). The lowest MAPE result obtained is in the SES method when the parameter α = 0.054 with a MAPE result of 7.932% which means the accuracy value is very accurate. Whereas with the DES method the MAPE value is 28.145% while the parameter α = 0.845 β = 0.214 which means the value of accuracy is reasonable. Based on the MAPE results obtained using the two methods above, the Single Exponential Smoothing method is more accurate for use in predicting salt sales. Whereas with the DES method the MAPE value is 28.145% while the parameter α = 0.845 β = 0.214 which means the value of accuracy is reasonable. Based on the MAPE results obtained using the two methods above, the Single Exponential Smoothing method is more accurate for use in predicting salt sales. Whereas with the DES method the MAPE value is 28.145% while the parameter α = 0.845 β = 0.214 which means the value of accuracy is reasonable. Based on the MAPE results obtained using the two methods above, the Single Exponential Smoothing method is more accurate for use in predicting salt sales
PENDUKUNG KEPUTUSAN PENILAIAN KINERJA DOSEN MENGGUNAKAN TEKNIK PRINCIPLE COMPONENT ANALYSIS (PCA) Edgar Bagus Adytia Sianipar; Roy Wahyudi Hutasoit; Imanuel Eastherio L Wokamaw; Ibnu Iqrom; Marlince Nababan
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.877

Abstract

Lecturer performance learning requires knowledge, namely a process to evaluate lecturer performance during the learning process, learning is carried out every semester using 20 variables. The research objective is to analyze the most influential lecturer performance variables with the concept of Principle Component Analysis (PCA) technique with the help of Minitab software. Where is the principle of PCA namely reducing data to make it easier to get information and evaluation for lecturers by using 20 variables to be analyzed in the Principle Component Analysis (PCA) technique, of the 20 variables there are 4 PC variables (Principle Components) to be analyzed namely PC1, PC9 , PC11 and PC16, namely PC1(0.24), PC9(0.5), PC11(0.35) and PC16(0.4) with maximum eigenvalue (1.6978), from the results of 4 PC variables it turns out that PC9 and PC11 are the most related or influential variables of all variables or components
APPLICATION OF THE K-MEANS CLUSTERING METHOD FOR PERFORMANCE ASSESSMENT BASED ON EDUCATOR COMPETENCE Paul Erikson; Bobby Rahman Angkat; Eliza Christovel Yosua; Mutiara Sembiring; Marlince Nababan
Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA) Vol. 7 No. 1 (2023): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM Prima)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.3869

Abstract

Performance appraisal is one thing to respect someone while working in an institution, one of which is a private higher education institution. To respect the performance of resources, there needs to be a value assigned to someone. Assessments carried out for one semester need to be reviewed again because during filling in the student assessments do not fill in according to their understanding so that a review needs to be carried out again. The assessment was carried out using the K-Means method by applying the concept of the centroid value. There are 4 (four) variables used, namely pedagogic competence, personal competence, social and professional competence with a value of K = 3. The maximum number of observations for cluster 3 is 368 while the value of Distances Between Cluster Centroids shows 2 suitable clusters, namely cluster 1 and cluster 2, which is 1.7020. The author gives suggestions to remove outlier data before entering the data to be trained into the algorithm to improve visualization if the dataset is large. Key Word: Performance Appraisal, Data Mining, K-Means
APPLICATION OF THE K-MEANS CLUSTERING METHOD FOR PERFORMANCE ASSESSMENT BASED ON THE COMPETENCY OF EDUCATORS Marlince Nababan; Cristian Vieri Sinaga; Indra Wirayudha Napitupulu; Adzisyah Rahman; Steven -
Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA) Vol. 7 No. 1 (2023): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM Prima)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.3951

Abstract

The ability of lecturers to teach for 1 (one) semester needs to be evaluated. Evaluation is a student assessment through a student academic information system. Review using the system is separate because many students need to care about the filled things. There the author tries to analyze the values ​​from the questionnaire results distributed to students using the Principal Component Analysis (PCA) technique and the K-Means method, where PCA reduces data. At the same time, K-Means assumed the values ​​closest to the study's results and concluded that of the 3 (three) clusters, the maximum distance was 0.5475 in cluster 1 and cluster 3.
LIVER DISEASE CLASSIFICATION ANALYSIS USING THE XGBOOST METHOD Yadi Sitinjak; Muhaymin -; Marlince Nababan
Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA) Vol. 7 No. 1 (2023): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM Prima)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4130

Abstract

Liver disease is a severe pathological condition that can cause liver inflammation due to viral infection, toxic agents, or bacterial invasion, interfering with normal liver function. The death rate from this disease reaches 1.2 million people annually in Southeast Asia and Africa. Liver disease can cause damage to the liver and negatively affect overall body function. To reduce disease progression, it is critical to facilitate early diagnosis, thereby enabling rapid initiation of treatment for affected individuals. Classification methods are widely used to make decisions based on new information from previous data processing through calculation algorithms. This study uses the XGBoost classification method to build a predictive model for liver disease. The results of this study confirm that the XGBoost model is a robust and efficient choice for liver disease classification based on patient data. The use of the XGBoost approach has proven its success in the category of liver disease with an accuracy of up to 95% and an accuracy balance of 95%, demonstrating the effectiveness and efficiency of this method in overcoming class imbalances in liver disease classification data.   Keywords: Xgboost, Liver, Classification, Disease
Co-Authors Adzisyah Rahman Aisyah - Bobby Rahman Angkat Cristian Vieri Sinaga Darwin Darwin Dwiky Christian Edgar Bagus Adytia Sianipar Eliza Christovel Yosua Ibnu Iqrom Imanuel Eastherio L Wokamaw Indra Wirayudha Napitupulu Jesslyn Harly Lidya Haryati Bintang Muhaymin - Mutiara Sembiring Paul Erikson Reyhan Achmad Rizal Roy Wahyudi Hutasoit Steven - Wilson Chandra Yadi Sitinjak