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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) IJISTECH (International Journal Of Information System & Technology) Jurnal Mantik IJISTECH International Journal of Basic and Applied Science
Lise Pujiastuti
STMIK Antar Bangsa
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Physics Social Sciences

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Comparison of Euclidean with Manhattan in K-Means Clustering for Grouping Palm Oil Production in the Province North Sumatra S Solikhun; Lise Pujiastuti
IJISTECH (International Journal of Information System and Technology) Vol 5, No 6 (2022): April
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i6.197

Abstract

North Sumatra is the largest palm oil-producing province in Indonesia. The region of North Sumatra has an extensive area of oil palm plantations compared to other provinces in Indonesia. To produce a good clustering of oil palm production using the K-Means Clustering method, it is necessary to compare several calculation methods to find the shortest distance in K-Means Clustering. This study focuses on comparing Euclidean Distance with Manhattan Distance on K-Means Clustering. To determine the best method of calculating the shortest distance, the researchers looked for the smallest Davies Bouldin Index (DBI). The smallest DBI value is at k=2 0.145. The result of grouping oil palm production in Sumatra province with k=2 is the high group being Asahan, Langkat and North Labuhanbatu regencies, while 30 other regencies/cities are in the low group
Method Implementation Multifactor Evaluation Process (MFEP) in Recommending the Best Types of Cattle for Beef Cattle Farming Lise Pujiastuti; Mochamad Wahyudi; Freshtiya Beby Larasati; Solikhun
Jurnal Mantik Vol. 5 No. 1 (2021): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol5.2021.1292.pp147-152

Abstract

This study aims to implement a decision support system in recommending the best type of cow. The research data comes from interviews and field observations. Based on these observations and interviews, 5 alternatives were obtained Lemosin (A1), Simental (A2), Bali (A3), Dairy (A4), Brahma (A5), as the selected type of cow and 5 criteria for selecting cattle, namely Origin (A), Price (B), Age (C), Weight (D), and Size (E). This research uses the MFEP (Multi Factor Evaluation Process) method. From the results of research and calculations using the MFEP method, it is found that Lemosin (A1) is the best type of cow with a total weight value of 0.6875.This study aims to implement a decision support system in recommending the best type of cow. The research data comes from interviews and field observations. Based on these observations and interviews, 5 alternatives were obtained Lemosin (A1), Simental (A2), Bali (A3), Dairy (A4), Brahma (A5), as the selected type of cow and 5 criteria for selecting cattle, namely Origin (A), Price (B), Age (C), Weight (D), and Size (E). This research uses the MFEP (Multi Factor Evaluation Process) method. From the results of research and calculations using the MFEP method, it is found that Lemosin (A1) is the best type of cow with a total weight value of 0.6875.
Online Exam Application Study Using the Pieces Framework Method Freshtiya Beby Larasati; Lise Pujiastuti; Solikhun Solikhun
Jurnal Mantik Vol. 5 No. 3 (2021): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

To find out the level of success in web-based or online implementation and have to do an evaluation. Evaluation is in the form of a reference or benchmark used in relation to the audit of the information system analysis method itself, by looking at the performance of a system quantitatively and qualitatively. The purpose of this study was to evaluate the level of success, efficiency, effectiveness, and company profits in implementing an online-based exam system conducted at the AMIK Tunas Bangsa Pematangsiantar campus. In providing analysis or evaluation of a system, it can be done with several analytical models. In this study, a model analysis framework will be used, namely by using the Pieces Framework method.
Application of K-Means Algorithm Data Mining in Goat Meat Production Data Grouping in Indonesia Mochamad Wahyudi; Solikhun Solikhun; Lise Pujiastuti
Jurnal Mantik Vol. 5 No. 4 (2022): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Data mining is the process of mining data from big data to get important information. The data mining process requires the use of artificial intelligence technology. The production of goat meat is very much needed in the fulfillment of protein ingredients for the people of Indonesia. It is necessary to make a grouping of goat meat production to see the condition of the map of the strength of meat production in Indonesia, so that the government can take appropriate steps to develop goat meat production in Indonesia. This study uses data mining techniques using the k-means clustering method to classify goat meat production in Indonesia. The results of this study are data on mutton product clustering, namely 2 nodes in the high group, the low group having 22 nodes, and the medium group having 10 nodes.
Comparison of Euclidean with Manhattan in K-Means Clustering for Grouping Palm Oil Production in the Province North Sumatra S Solikhun; Lise Pujiastuti
IJISTECH (International Journal of Information System and Technology) Vol 5, No 6 (2022): April
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (690.679 KB) | DOI: 10.30645/ijistech.v5i6.197

Abstract

North Sumatra is the largest palm oil-producing province in Indonesia. The region of North Sumatra has an extensive area of oil palm plantations compared to other provinces in Indonesia. To produce a good clustering of oil palm production using the K-Means Clustering method, it is necessary to compare several calculation methods to find the shortest distance in K-Means Clustering. This study focuses on comparing Euclidean Distance with Manhattan Distance on K-Means Clustering. To determine the best method of calculating the shortest distance, the researchers looked for the smallest Davies Bouldin Index (DBI). The smallest DBI value is at k=2 0.145. The result of grouping oil palm production in Sumatra province with k=2 is the high group being Asahan, Langkat and North Labuhanbatu regencies, while 30 other regencies/cities are in the low group
Determination of the Best Accuracy Model for Predicting Average Years of Schooling using the Fletcher Reeves Algorithm Ihsan Daulay; Mochamad Wahyudi; Solikhun; Lise Pujiastuti
International Journal of Basic and Applied Science Vol. 11 No. 1 (2022): June: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v11i1.78

Abstract

The average length of schooling is an important and significant factor in looking at the quality of an individual human being, with increasing the quality of human resources it can increase access to decent work which also promises a stable economic income, and to some extent affects the economy in a country. Therefore, a prediction was made. This prediction method uses the Fletcher Reeves algorithm which is an artificial neural network algorithm method for data prediction. However, this paper does not discuss the results of the prediction, but discusses the ability of the Fletcher Reeves neural network algorithm to predict data. The research dataset used in this study is data on the average length of schooling in North Sumatra Province from 2015-2020, this dataset was taken from BPS North Sumatra. The data is then formed into 5 models, namely 2-10-1, 2-15-1, 2-20-1, 2-25-1, 2-30-1. -30-1 with an MSE value of 0.000430727. With these results the 2-30-1 architectural model gets the lowest score, so it can be concluded that the model can be used to predict the average length of schooling in North Sumatra Province.
IMPLEMENTATION OF K-MEDOIDS METHOD FOR HEART DISEASE PREDICTION USING QUANTUM COMPUTING AND MANHATTAN DISTANCE Mochamad Wahyudi; Dimas Trianda; Lise Pujiastuti; Solikhun Solikhun
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5637

Abstract

Heart disease is a severe health condition characterized by dysfunctions in the heart and blood vessels, which can be fatal if not properly managed. Early detection and prediction of heart disease are crucial for understanding the prevalence and determining patients' quality of life. In this study, quantum computing is applied to enhance the performance of the K-Medoids method. A comparative analysis of these methods is conducted, focusing on their performance. The investigation utilizes a dataset of heart disease patient medical records. This dataset includes various attributes used to predict heart disease patterns. The dataset is tested using both the classical and K-Medoids methods with a quantum computing approach, employing Manhattan distance calculations. This study's findings reveal that applying quantum computing to the K-Medoids method results in clustering accuracy stability of 85%, equivalent to the classical method. Although there is no increase in accuracy, the quantum computing approach demonstrates potential improvements in data processing efficiency. These results highlight that the K-Medoids method with a quantum computing approach can contribute significantly to faster and more efficient medical data analysis. However, further research is needed for optimization and testing on more extensive and more diverse datasets.
Co-Authors Dimas Trianda Freshtiya Beby Larasati Ihsan Daulay Mochamad Wahyudi S Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun