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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INFOKUM Cendikia : Media Jurnal Ilmiah Pendidikan Jurnal Info Sains : Informatika dan Sains Jurnal Komputer Indonesia (JU-KOMI) Jurnal Manajemen, Akuntansi dan Rumpun Ilmu Ekonomi (MAR-Ekonomi) "Journal of Data Science
Simamora, Siska
SEAN Instiute, Sumatera Utara, Indonesia
Automotive Engineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Other

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The Difficulties Facing by Student in Using Participle in Sentences Zahari Amir Bin Ismani; Siska Simamora
Cendikia : Media Jurnal Ilmiah Pendidikan Vol 11 No 2 (2021): March: Education Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (527.151 KB) | DOI: 10.35335/cendikia.v11i2.1670

Abstract

This study deals with the student’s difficulties in using participle in sentences. The purposes of the study were to find out whether or not the students found difficulties in using participle in sentence and to find out the type of difficulties they faced. The population of the study was students. In this sampling, all the population has equal chance to be selected for the sample. The total numbers of samples was 32 students. The instrument used to collect the data was multiple choice test. This research was conducted by applying the descriptive quantitative design. The reliability of the test is counted by using KR21 formula. The formula testing result showed that the reliability of the test was 0,84, it means that the test was very good. The percentage of the student’s errors is dominantly occured in descriptive adjectives from 800 item occurrences, there were 436 errors which categorized into 5 sub categories on the use of adjectives and adverbs with the suffix–ly. The finding showed that the students found some difficulties in using participle, they were: Present Participle (22,52 %), Past Participle (63,73 %) and Perfect Participle (33,52 %). Past Participle was regarded as the most difficult type for them, especially in using it after certain verbs and in replacing relative pronoun, and then followed by Perfect Participle. And the last was Present Participle especially in using in it replacing relative pronoun and after certain verb. The percentage of each difficulty was taken by dividing the wrong answer to the total correct answer of the test.
Analysis of Dempster Shafer Method, Certainty Factor and Bayes Theorem in Expert Systems Diagnosing Tuberculosis Disease Khairul Khairul; Rian Farta Wijaya; Rizky Rinaldi Rizky; Rahmat Rezki Rahmat Rezki; Siska Simamora; Glorynta S B Nadeak Glorynta S B Nadeak; Reza Fahromi Reza Fahromi
INFOKUM Vol. 10 No. 5 (2022): December, Computer and Communication
Publisher : Sean Institute

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Abstract

This research discusses the design of an expert system that specifically deals with the problem of pulmonary tuberculosis. Tuberculosis or abbreviated TB / TB is an infectious disease infectious disease caused by bacteria from the Mycobacterium group, viz Mycobacterium tuberculosis. According to Infodatin Center for Data and Information Ministry of Health of the Republic of Indonesia (2016) tuberculosis or TB can attacks various organs, especially the lungs, which if not treatment or treatment is not complete then it can cause complications dangerous to death. Seeing the phenomena that occur is very much needed precise and easy information on pulmonary tuberculosis by developing an Artificial Intelligence technology, namely the Expert System. In the application of Expert Systems used to diagnose Pulmonary tuberculosis needs to be compared several methods including: Certantiy Factor, Dempster Shafer, and Bayes theorem so that later it can be known which method most appropriate and best in making a diagnosis. With this Expert System, it can later be used as a consulting service to assist in diagnosing the type of pulmonary tuberculosis based on clinical symptoms that occur in patients, so that it can be used in making initial diagnostic conclusions before carrying out intensive laboratory examinations.
Comparison of K-Means and Self Organizing Map Algorithms for Ground Acceleration Clustering Simamora, Siska; Muhammad Iqbal; Andysah Putera Utama Siahaan; Khairul, Khairul; Zulham Sitorus
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14120

Abstract

This study evaluates earthquake-induced ground acceleration in Indonesia, which is located in the Pacific Ring of Fire zone, using Donovan's empirical method and comparing two clustering algorithms, Self Organizing Map (SOM) and K-Means. The main problem faced is the high risk of earthquakes in Indonesia and the need for effective methods to predict potential damage to buildings and infrastructure. The research objective is to evaluate earthquake-induced ground acceleration and identify acceleration distribution patterns using clustering techniques. The solution methods used include the application of the Donovan method to calculate ground acceleration based on BMKG data, as well as the use of SOM and K-Means algorithms to cluster the ground acceleration data. GIS and Python applications are used to visualize the clustering results. The results show that the Donovan method integrated with SOM and K-Means provides significant insights into the distribution of ground acceleration, thus assisting in risk evaluation, disaster mitigation planning, and the development of more effective earthquake-resistant infrastructure development strategies in Indonesia
Literature Analysis on the Role of Technology in Economic Growth Siska Simamora
MAR-Ekonomi: Jurnal Manajemen, Akuntansi Dan Rumpun Ilmu Ekonomi Vol. 3 No. 01 (2024): Jurnal Manajemen, Akuntansi dan Rumpun Ilmu Ekonomi (MAR-Ekonomi), oktober 202
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/mar-ekonomi.v3i01.649

Abstract

Innovation-based economy has become a major driver of economic growth in various countries. Innovation, especially in the form of technological advancement, contributes to increased productivity, efficiency, and global competitiveness. This study is a literature analysis that explores the relationship between technology and economic growth, by reviewing previous studies that discuss the impact of innovation on the industrial sector, workforce, and economic policy. The results of the review show that the application of digital technology, automation, and artificial intelligence play a significant role in creating a more dynamic and adaptive economic model. In addition, investment in research and development (R&D) and policies that support the innovation ecosystem are key factors in driving sustainable economic growth. However, challenges such as the technology gap and human resource readiness are still obstacles in the implementation of an innovation-based economy. This study is expected to provide insight for policy makers and academics in developing economic strategies based on technological innovation.
Comparative Evaluation of Data Clustering Accuracy through Integration of Dimensionality Reduction and Distance Metric Hasugian, Paska Marto; Mathelinea, Devy; Simamora, Siska; Simangunsong, Pandi Barita Nauli
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.5057

Abstract

The primary issue in clustering analysis of multivariate data is the low accuracy resulting from a mismatch between the Distance Metric used and the characteristics of the data. This study aims to comprehensively evaluate the effect of eight Distance Metric in the KMeans algorithm integrated with the Principal Component Analysis (PCA)dimension reduction technique. The analysis process was conducted by transforming the data into two principal components using PCA, then applying K-Means to each Distance Metric. Performance evaluation was conducted based on five internal metrics: Silhouette Score, Davies-Bouldin Index, Sum of Squared Errors, Calinski-Harabasz Index, and Dunn Index. The results show that the Bray-Curtis formula provides the best performance, with a Silhouette Score of 0.4291 and SSE of 30.3673. This is followed by Euclidean and Minkowski, which yield the highest Calinski-Harabasz Index value of 2239.85 and Dunn Index of 0.0108, respectively. In contrast, Hamming’s formula yielded the lowest performance across all metrics, with a Silhouette Score of 0.0000 and an SSE of 1996.00. The ANOVA test revealed significant differences between the Distance Metric, with a p-value of ¡0.000 for all metrics, which was further supported by the Tukey HSD follow-up test results. The implications of these findings confirm the importance of selecting an appropriate Distance Metric in the clustering process to ensure the validity, efficiency, and interpretability of multivariate data analysis results.
Analysis Of Shortest Path Determination By Utilizing Breadth First Search Algorithm Sihotang, Jonhariono; Simamora, Siska
Jurnal Info Sains : Informatika dan Sains Vol. 10 No. 2 (2020): September, Informatics and Science
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (592.99 KB) | DOI: 10.54209/infosains.v10i2.30

Abstract

The rapid development of science requires the public to keep up with the development of such technology. The use of computers as one of the tools used to facilitate work and improve the efficiency and effectiveness of work is also high, this we can see from the development of such technology. Artificial Intelligence (AI) is one part of computer science that learns about how to make computers can do the job as humans do. At the beginning of its creation, the computer was only functioned as a counting tool. But along with the development of the times, the role of computers increasingly dominates the life of mankind. Computers are no longer only used as a calculation tool, more than that, computers are expected to be empowered to do everything that can be done by humans. People can be good at solving all problems in this world because people have knowledge andexperience. Knowledge is gained from learning. The more knowledge possessed by a person is certainly expected to be more able to solve problems. But the provision of knowledge alone is not enough, people are also given the sense to reason, draw conclusions based on their knowledge andexperience.
Ground Acceleration Clustering Using Self-Organizing Map Method Siska Simamora; Amran Manalu; Paska Marto Hasugian
Journal Of Data Science Vol. 3 No. 02 (2025): Journal Of Data Science, September 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i2.7281

Abstract

Peak Ground Acceleration (PGA) is an important parameter in seismic studies because it is directly related to the level of shaking felt on the earth's surface. Analysis of ground acceleration data is needed to identify patterns, group regions based on their seismic characteristics, and support earthquake disaster mitigation efforts. This study uses the Self-Organizing Map (SOM) method, which is an unsupervised learning approach based on artificial neural networks that can map high-dimensional data into a two-dimensional map representation without losing its topological structure. The ground acceleration dataset used in this study consists of key seismic parameters such as depth, magnitude, source distance, and PGA values. The SOM learning process is carried out iteratively to produce a cluster map that groups earthquake data into several groups with different ground acceleration characteristics. The results show that the SOM method is able to identify ground acceleration distribution patterns more clearly than conventional methods, by producing clusters that represent variations in PGA from low to high. These findings can provide important contributions to earthquake risk mapping, regional spatial planning, and the formulation of more accurate disaster mitigation strategies.
Implementation of C4.5 Algorithm for Diarrhea Prediction Sipra Barutu; Siska Simamora
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 02 (2025): Jurnal Komputer Indonesia (JU-KOMI), April 2025
Publisher : SEAN Institute

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Abstract

Diarrheal disease remains one of the major health problems among toddlers in Indonesia. Environmental factors such as drinking water quality, sanitation, mothers’ hand hygiene, and immunization status play an important role in influencing the occurrence of diarrhea. This study aims to analyze the application of the C4.5 algorithm in developing a predictive model for diarrhea among toddlers using secondary data from a Public Health Center (Puskesmas), consisting of 200 records divided into 150 training data and 50 testing data. The analysis process was carried out through entropy calculation, information gain assessment, and decision tree construction to obtain classification patterns. The results showed that the C4.5 model achieved an accuracy of 92%, precision of 87.5%, recall of 87.5%, F1-score of 87.5%, and specificity of 94.12%. These values indicate that the C4.5 algorithm is capable of making predictions with a good level of accuracy and balance in detecting both positive and negative cases. This study contributes to the utilization of data mining, particularly the C4.5 algorithm, as a decision-support tool in the health sector for the prevention of diarrheal disease among toddlers.
Implementation of Random Forest Algorithm for Diarrhea Prediction Sipra Barutu; Siska Simamora
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 02 (2025): Jurnal Komputer Indonesia (JU-KOMI), April 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Diarrhea is one of the leading causes of morbidity among toddlers in Indonesia. Environmental factors such as drinking water quality, sanitation, maternal hand hygiene, and immunization status contribute significantly to the incidence of diarrhea. This study aims to analyze the application of the Random Forest algorithm in developing a predictive model for diarrhea in toddlers using secondary data from a community health center (Puskesmas), consisting of 200 records divided into 150 training data and 50 testing data. The model was constructed by generating multiple decision trees and combining them using a majority voting technique. The results show that the Random Forest algorithm achieved an accuracy of 88%, precision of 77.78%, recall of 87.5%, F1-score of 82.35%, and specificity of 88.24%. These values indicate that Random Forest is quite reliable in detecting positive diarrhea cases, although some limitations remain in reducing misclassification of negative data. This study contributes to the utilization of machine learning algorithms, particularly Random Forest, as a decision-support tool in the health sector for diarrhea prevention among toddlers.
Co-Authors Amran Manalu Andysah Putera Utama Siahaan Glorynta S B Nadeak Glorynta S B Nadeak Hasugian , Paska Marto Khairul Khairul Khairul, Khairul Mathelinea, Devy Muhammad Iqbal Rahmat Rezki Rahmat Rezki Reza Fahromi Reza Fahromi Rian Farta Wijaya Rizky Rinaldi Rizky Sihotang, Jonhariono Simangunsong, Pandi Barita Nauli Sipra Barutu Sitorus, Zulham Zahari Amir Bin Ismani