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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Informatika Jurnal Tekinkom (Teknik Informasi dan Komputer) MEANS (Media Informasi Analisa dan Sistem) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) International Journal Of Science, Technology & Management (IJSTM) Jurnal Media Informatika Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Journal of Computer Science and Information Systems (JCoInS) Internet of Things and Artificial Intelligence Journal Dike
Sihombing, Volvo
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PENGGUNAAN METODE TOPSIS DALAM SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN JENIS USAHA WISATA DI LABUHAN BATU Simamora, Rikardo Lasroha; Munthe, Ibnu Rasyid; Sihombing, Volvo
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.962

Abstract

The development of the tourism sector is an important focus in driving local economic growth. In this context, choosing the right type of tourism business is a strategic step in maximizing regional potential. This study aims to investigate the use of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) Method in the development of a Decision Support System (DSS) for selecting types of tourism businesses in Labuhan Batu. The initial stage of the research involved analyzing the criteria for selecting the type of tourism business which included aspects of market potential, sustainability, social impact, and infrastructure. The ranking results provide a guide in selecting the type of tourism business that has the highest potential to develop in Labuhan Batu. Sensitivity analysis of changes in criteria weight provides further insight into the effect of weight on alternative rankings. The use of computer technology in this study allows the development of an interactive Decision Support System capable of providing recommendations based on the results of TOPSIS calculations. The conclusion from this study is that the TOPSIS method can be an effective tool in assisting decision makers in choosing the type of tourism business that is in accordance with the characteristics of the Labuhan Batu area.
Comparative Analysis of K-Nearest Neighbors and Decision Tree Methods in Determining Students’ Purchase Interest in MacBook Laptops Fadilla Hasibuan, Intan; Irmayani, Deci; Sihombing, Volvo
International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v5i5.1169

Abstract

In the context of increasingly competitive technology markets, companies need to know consumer preferences accurately to optimize product offerings and increase sales. Two classification methods that are often used in data mining, namely K-Nearest Neighbors and Decision Tree, have their own advantages and disadvantages. This study proposes a solution that involves processing student data using both classification methods to identify the most accurate and effective method for identifying purchase intentions. This study aims to compare the performance of the two methods in determining student purchase intentions for MacBook laptops. The research methodology includes collecting data from 100 students covering various factors such as user experience, design and portability, technical specifications, price, and security. This data is then classified using the K-Nearest Neighbors and Decision Tree methods. Furthermore, a confusion matrix is used to provide a more detailed picture of the performance of the two methods. The results of the study show that the Decision Tree method has a higher accuracy (91%) compared to K-Nearest Neighbors (88%). In addition, Decision Tree excels in other metrics such as precision (87.18% vs. 85.71%), recall (89.47% vs. 85.71%), specificity (91.94% vs. 89.66%), and F1-Score (88.31% vs. 85.71%). The decision tree also has a higher NPV value and lower FPR and FNR rates than K-Nearest Neighbors, indicating that it is superior in avoiding misclassification. The study's conclusion is that the Decision Tree method is more effective and accurate than K-Nearest Neighbors in determining students' purchase intentions for MacBook laptops. The decision tree shows better performance in almost all evaluation metrics, making it a more reliable method to use in consumer data analysis. The results of this study are expected to help companies choose a more appropriate and effective analysis method for their marketing strategies, as well as provide a basis for further research in the field of consumer purchase intention classification.
Student Graduation Predictions Using Comparison of C5.0 Algorithm With Linear Regression Ariska, Fevi; Sihombing, Volvo; Irmayani, Irmayani
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 1 (2021): Article Research Volume 6 Issue 1: January 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i1.11261

Abstract

Technological advances supported by human knowledge have a very good influence on data and information storage technology, including in predicting student graduation (Graduation Prediction) on time, by applying several existing algorithms. In this study, researchers used the C5.0 Algorithm and Linear Regression. The concept of the research is to compare two algorithms, namely C5.0 and Linear Regression to the case of graduating students on time. Based on the length of study, students who graduated correctly amounted to 651 (91%) with a male gender of 427 students and a female gender of 224 students while those who did not pass (late) correctly amounted to 64 (9%) with a male gender totaling 53 students and female gender totaling 11 students from 2017-2020. Comparison results The R2 score from the C5.0 algorithm reached 96.85% (training) and 93.
Implementation of the RAD Method to Build Catering Application Android-based Tasyabila, Tasyabila; Sihombing, Volvo; Nasution, Fitri Aini
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11421

Abstract

With the rise of culinary businesses that sell food and beverages at this time, business actors must be ready to compete to win and retain their customers so that their business can survive. Ara Catering is one of the culinary business actors in Rantauprapat City which focuses on the food and beverage ordering business. The ordering process applied by Ara Catering is still conventional, where customers come directly to the business location or place an order by telephone. The conventional model turned out to cause problems, including errors in ordering and ordering notes that had piled up. Therefore, a solution must be found to overcome this problem. Based on these problems, this study aims to build an android-based catering ordering application. The method applied in this research is the Rapid Application Development (RAD) method in which the process stages include: requirements planning, user design, and implementation. After the design and implementation have been carried out, the results show that the RAD method can be applied in building an Android-based catering application quickly and effectively. The conclusion drawn from this research is that to build an android-based catering application using the RAD method, the phases of the method must be carried out thoroughly. Hopefully the results of this research can make a positive contribution to Ara Catering in developing its business.
Analysis of the Decision Tree Method for Determining Interest in Prospective Student College Maizura, Safrina; Sihombing, Volvo; Dar, Muhammad Halmi
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12258

Abstract

Education is learning science, skills that are carried out by a person or a group of people. The education level starts from Elementary School Education, Junior High School and High School. Apart from that, the highest level of education is college. Lectures are further education carried out by people to gain knowledge and degrees. In college education everyone can choose their respective majors, according to their wishes and desires. With college education, there will be many prospective students who will go to college. But the interest of prospective students to study varies, there are some prospective students who want to study in public and there are some who want to study privately. Therefore the author will make research about prospective students' interest in college. This study aims to see the college interest of prospective students. For this research a data classification of prospective students will be carried out using the Decision Tree method. For this research stage using the Decision Tree method, the first is data analysis, then data preprocessing, then the Decision Tree method design and finally data mining testing. The classification was carried out using the Decision Tree method using 65 prospective student data. From the results of the classification using the Decision Tree method, the results of the Classification of prospective students who are interested in studying are 46 prospective students. The classification results above show that many prospective students are interested in studying.
Analysis of Community Satisfaction Levels using the Neural Network Method in Data Mining Hasibuan, Sabdi Albi; Sihombing, Volvo; Nasution, Fitri Aini
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12634

Abstract

Data mining is a process that is carried out to extract data into information. There are several models that can be done in data mining, such as classification, association, clustering, regression. But in this study will be carried out using a classification model. Research conducted on the level of public satisfaction for shopping on the Lazada application. This study aims to determine the level of public satisfaction on the Lazada application. This research was also conducted because the goods sold on the Lazada application are quite cheap and when compared to the original price there is a considerable difference. Therefore, research was conducted on the level of community satisfaction on the Lazada application. This research will be conducted on data mining with a classification model and using the neural network method. The results obtained from the data mining process using 100 community data, the results obtained are 81 community data (representation obtained by 81%) of people who are satisfied shopping on the lazada application and by 19 (representation obtained by 19%) people who are not satisfied shop on the Lazada app. From these results, many people are satisfied with shopping on the Lazada app. So from the results of this classification it can be concluded that the goods sold on the Lazada application are good goods.
Implementation of Exploratory Data Analysis and Artificial Neural Networks to Predict Student Graduation on-Time Muliani, Sonia Sri; Sihombing, Volvo; Munthe, Ibnu Rasyid
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13658

Abstract

Almost all universities in Indonesia face the problem of a low number of students graduating on time. This will affect higher education accreditation. For this reason, universities must pay attention to the timely graduation of their students. The way that can be taken is to predict students' graduation on time. This research aims to predict students' timely graduations using a combination of exploratory data analysis and artificial neural networks. Exploratory data analysis is used to study the relationship between features that influence students' on-time graduation, while artificial neural networks are used to predict on-time graduation. This research goes through method stages, starting with determining the dataset, exploratory data analysis, data preprocessing, dividing training and test data, and applying artificial neural networks. From the research, it was found that Work features and GPS features greatly influence graduation on time. Students who study while working are less likely to graduate on time compared to students who do not work. Students who have an average GPS above 3.00 for eight consecutive semesters will find it easier to graduate on time. Meanwhile, Age and Gender features have no effect on graduating on time. With a percentage of 50% training data and 50% test data, epoch 100, and learning rate 0.001, the best network model was obtained to predict graduation on time with an accuracy rate of 69.84%. The research results also show that the amount of test data and the learning rate can influence the level of accuracy. Meanwhile, the number of epochs does not affect the level of accuracy.
A Comparative Analysis of Machine Learning Algorithms for Predicting Paddy Production Aditya, Nanda; Munthe, Ibnu Rasyid; Sihombing, Volvo
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13666

Abstract

For countries with large populations, such as Indonesia, food security is a very important issue. The majority of Indonesia's population depends on rice as their main food, and paddy is one of the most widely cultivated food commodities. The very good and accurate national paddy production prediction results really support decisions regarding national paddy production targets for the coming period. Therefore, to ensure supply and price stability, paddy availability must be predicted. Many studies have used machine learning to predict crop yields. By learning important patterns and relationships from input data, machine learning can combine the advantages of other methods to make better predictions of paddy yields. The aim of this research is to conduct a comparative analysis between three machine learning algorithms, namely, random forest, decision tree, and k-nearest neighbors, in predicting paddy production. To determine which algorithm is the best, a model evaluation is carried out using the coefficient of determination (R2-score), mean absolute error (MAE), and mean squared error (MSE). This research goes through methodological stages, starting from collecting datasets, data preprocessing, training and testing split datasets, applying algorithms, and evaluating the model. From this research, results were obtained for the random forest algorithm with an R2-score of 82.38%, MAE of 261726.20, and MSE of 2.19495E+11. For the decision tree, the R2-score was 79.62%, MAE was 323257.99, and MSE was 2.49304E+11. Meanwhile, k-nearest neighbors obtained an R2-score of 76.25%, MAE of 318433.42, and MSE of 2.90577E+11. The results of this research show that the random forest algorithm is the best for predicting paddy production because it obtains a larger R2-score as well as smaller MAE and MSE results.
Implementasi Deep Learning Untuk Menentukan Harga Buah Sawit Manurung, Romtika; Sihombing, Volvo; Hasibuan, Mila Nirmala Sari
Jurnal Informatika Vol 12, No 3: INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6029

Abstract

This study aims to analyze the price of palm oil using Convolutional Neural Network (CNN) method in deep learning. CNN was chosen for its ability to process complex data and recognize patterns from diverse data. The stages of research include data analysis, data pre-processing, predictive model design for CNN method, CNN classification model prediction results, CNN method evaluation, and CNN method evaluation results. This study aims to produce a model that can predict the price of oil palm with high accuracy, based on data covering a variety of characteristics of farmers and the quality of oil palm crops. Prediction results were conducted using data from 50 oil palm farmers. From the prediction, as many as 23 data farmers get a price of IDR 2,300, 13 other farmers get a price of IDR 2,000, and the remaining 14 data farmers get a price of IDR 1,800. The results of this prediction are based on data from farmers and the quality of oil palm crops they grow and produce. By utilizing the CNN method, the model can capture various factors that affect the price of palm oil, including the quality of palm fruit and agricultural conditions. Evaluation of the CNN method showed very good results, with almost perfect accuracy. This method managed to predict palm oil prices very precisely, showing that CNN can be an effective tool in the analysis of palm oil prices. The results of this evaluation confirmed that the CNN method can be relied upon to provide accurate predictions, helping farmers and palm oil industry players in determining prices that are in accordance with the quality and condition of the crop.
Optimal Biaya Pengiriman Beras Menggunakan Model Transportasi Motode North Westh Corner (NWC) Dwiyanti, Didan; Irmayani, Deci; Sihombing, Volvo
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 7 No. 2 (2024): Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI)
Publisher : Utility Project Solution

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

Abstract

Transportasi berkaitan dengan distribusi barang dari sentra produksi ke lokasi penjualan. Penelitian ini menggunakan metode North West Corner (NWC) untuk mengoptimalkan biaya pengiriman beras dari Distributor Beras X di Kabupaten Karawang Data pengiriman beras bulan Juli 2019 diolah dengan metode NWC untuk optimasi biaya pengiriman. Distributor memasok beras ke 3 agen dengan biaya pengiriman Rp. 1.000.000 per kali pengiriman, tergantung pada jarak Distributor beras X di Kab. Karawang memasok beras pada setiap agen dan agen tersebut mendistribusikan beras kepada pelanggannya dengan jumlah beras sesuai dengan permintaan dari masing- masing pelanggan di pasar. Pengiriman beras dari agen ke 4 titik pasar tersebut memiliki biaya transportasi yang berbeda-beda disesuaikan dengan jarak pengiriman dalam setiap kali pengiriman beras. Biaya transportasi merupakan masalah yang sering dijumpai di berbagai bidang terutama yang bergerak di bidang produksi dan pemasaran
Co-Authors Abdillah, Ihsan Abdillah, Syakira Adi, Puput Dani Prasetyo Aditya, Nanda Alam, Dewi Pathimah Alpiansyah, Fredy Andriyani, Wahyu Fitri Angreani Hulu, Linca Elma Popi Ariska, Fevi Aurianda, Rieke Bangun, Budianto Barasa, Kristina Dewi Utami, Yarma Agustya Dwiyanti, Dida Dwiyanti, Didan Efendi, Davina Rizky Fadilla Hasibuan, Intan Gultom, Gregorius Apri K Halawa, Prianus Harefa, Sinema Hasibuan, Dilla Puspita Hasibuan, Mila Nirmala Sari Hasibuan, Sabdi Albi Hendriyanti, Yurika Cici Hia, Faomaha Hutagalung, Charles Efendy Irmayani , Deci Irmayani Irmayani Irmayani, Deci Jannah , Ely Jati, Dewi Sekar Juledi, Angga Putra Juni Yanris, Gomal Lumban Gaol, Tid Verawati Maizura, Safrina Manurung, Romtika mawarni, Putri Sigit Mega, Mega Muhammad Halmi Dar Muliani, Sonia Sri Munthe , Ibnu Rasyid Munthe, Ibnu Rasyid Mustamu, Novilda Elizabeth Nasution, Fitri Aini Pakto, Dedi Pertiwi, Nur Fajar Kurnia Purba, Mila Hanim Purwati Purwati Ramadhani, Siska Rangkuti, M. Andri Gautama Rani Rahayu, Rani Rusmiana, Rusmiana Safitri, Nina Simamora, Rikardo Lasroha siregar, Victor M.M. Siti Kholijah, Siti Kholijah Sitio, Arjon Samuel Sitompul, Joni Awendri Sitorus, Anggiat Selamat Syafitri, Risma Syahputra, Rapian Tasyabila, Tasyabila Tawanta Natalia Sembiring, Sri Tiara, Dewi Tria Wulandari Yanris, Gomal Juni