Article Per Year (5 Year)
p-Index From 2021 - 2026
1.116
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB SAINTEK
Handala Simetris Harahap
Unknown Affiliation
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : SAINTEK

 1 
Penerapan Algoritma K-Means Clustering Untuk Memetakan 4G BTS (Base Tranceiver Station) yang Mengalami Congestion di Kabupaten Bekasi Handala Simetris Harahap; Eriska Febrianto
Prosiding Sains dan Teknologi Vol. 2 No. 1 (2023): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 2 - Februari 2023
Publisher : DPPM Universitas Pelita Bangsa

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

Abstract

The rapid development of wireless communication technology up to the fourth generation (4G) requires structured and systematic network data management. Datasets consisting of the number of Base Transceiver Stations (BTS), Physical Resource Block Utilization (PRB Utilization), and Downlink User Throughput (DLUT) need to be analyzed to identify areas with potential congestion and to support the planning of new BTS locations. This study aims to apply the Elbow method and K-Means Clustering to determine 4G BTS sites indicated to experience congestion in Bekasi Regency. The Elbow method was employed to identify the optimal number of clusters, resulting in K = 3. Subsequently, the K-Means algorithm was used to classify BTS based on network load levels. The results show that cluster 1 (C1), categorized as high load, consists of 153 BTS or approximately 40%, cluster 2 (C2), categorized as medium load, includes 155 BTS or about 40%, and cluster 3 (C3), categorized as low load, comprises 77 BTS or around 20%. These findings are expected to support decision-making in network optimization and 4G BTS development planning in the studied area.
Penerapan Algoritma SVM (Support Vector Machine) Untuk Prediksi Resiko Penyakit Jantung Dengan Kernel Sigmoid Handala Simetris Harahap; Safira Novianti
Prosiding Sains dan Teknologi Vol. 3 No. 1 (2024): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 3 - Januari 2024
Publisher : DPPM Universitas Pelita Bangsa

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

Abstract

Heart disease, also known as coronary heart disease, occurs when blood flow to the heart muscle is reduced or blocked, causing significant damage. The objective of this study is to develop a predictive model that can estimate the risk of heart disease using the Support Vector Machine (SVM) algorithm with a sigmoid kernel, so that patients can be classified into high-risk and low-risk categories. The modeling stage is carried out to select and implement the appropriate modeling technique, determine the data mining tools to be used, and set optimal parameter values. At this stage, the training data are learned by the selected algorithm model, and the testing data are then evaluated using the developed classifier to obtain performance metrics. The results of this study indicate that the SVM method with a sigmoid kernel provides a good level of accuracy in predicting heart disease risk based on measured risk factors such as age, gender, blood pressure, cholesterol levels, and others. From the experiments conducted, the classification performed well. Using 303 data instances that were randomly sampled into 1,220 data points, the model achieved an accuracy of 0.788, a precision of 0.787, and a recall of 0.788.
Penerapan Data Mining Untuk Analisis Pola Pembelian Pelanggan Menggunakan Algoritma Apriori (Studi Kasus: Toko Jihan) Handala Simetris Harahap; Ratna Arista
Prosiding Sains dan Teknologi Vol. 3 No. 1 (2024): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 3 - Januari 2024
Publisher : DPPM Universitas Pelita Bangsa

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

Abstract

Determining item combinations and item layout based on consumer purchasing tendencies is one of the solutions for Jihan Stores in developing marketing strategies so as to increase sales at the store. The algorithm that can be used to find any combination of items that are often purchased together at a time is the Apriori Algorithm, this Apriori Algorithm includes the type of rules in data mining, namely to determine associative rules between a combination of items, the results of associative rules from consumer purchasing analysis Thus, the shop owner can adjust the placement of his goods or design a marketing campaign by giving a discount on the combination of these items. Based on sales transaction data within 3 months and processed using WEKA at Jihan Stores, an analysis is carried out using an a priori algorithm with a minimum support parameter of 50% and a minimum confidence of 80%. The results of data processing with WEKA that meet the support value and the highest confidence value are that if you buy noodles, then you are likely to buy eggs.
Co-Authors Ahmad Fauzi Ahmad Sarif Alhadi Saputra Alya Shafira Eriska Febrianto Isarianto Ratna Arista Safira Novianti SUPRAPTO