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Syaiful Zuhri Harahap
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Jl. S.M Raja No. 126 A Km 3.5 Aek Tapa, Rantauprapat, Kabupaten Labuhanbatu, Sumatera Utara, Indonesia
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Informatika
Published by Universitas Labuhanbatu
ISSN : 23032863     EISSN : 26151855     DOI : 10.36987
Core Subject : Science, Education, Engineering,
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Library & Information Science Other
INFORMATIKA : Jurnal Ilmiah Fakultas Sains & Teknologi Universitas Labuhanbatu diterbitkan oleh Universitas Labuhanbatu melalui Lembaga Penelitian dan Pengabdian Masyarakat, dimaksudkan sebagai media pertukaran informasi dan karya ilmiah antara staf pengajar, alumni, mahasiswa dan masyarakat pada umumnya yang terbit tiga kali dalam setahun (Januari-Mei-September), yang mulai awal terbit sejak tahun 2013.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 48 Documents
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Search results for , issue "Vol 12, No 3: INFORMATIKA" : 48 Documents clear
Analisis Perbandingan Algoritma C4.5 Dan Naive Bayes Dalam Menilai Kelayakan Bantuan Program Keluarga Harapan Hasibuan, Taufik Molid Hidayat; Harahap, Syaiful Zuhri; Ah, Rahma Muti
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.v12i3.6154

Abstract

Social assistance is a form of government intervention that aims to help people who are in less fortunate economic conditions. This form of assistance can be in the form of cash assistance, food assistance, or health service assistance. Social assistance programs are often aimed at reducing poverty, addressing hunger, and improving the overall well-being of society. Program Keluarga Harapan (PKH) is a form of conditional social assistance launched by the government of Indonesia to help poor and vulnerable families. The Program aims to improve the quality of life of poor families through the provision of cash assistance accompanied by obligations for recipients to meet certain requirements, such as ensuring their children attend school and regular health checks at health facilities. With the PKH, it is expected to improve the access of poor families to education and health services, which in turn will improve the quality of Indonesian human resources. Thus, the author can evaluate the advantages and disadvantages of each method in the context of the data used. In addition, this comparative analysis also aims to provide more informative recommendations for policy makers. If one of the methods proves to be superior, then it can be adopted to improve the selection process for CCT recipients in the future. However, if both methods have balanced performance, a combination or integration of the two can be the optimal solution. By comparing the performance of Naive Bayes and the C4.5 algorithm, the study not only focused on identifying the right recipients, but also provided valuable insights in choosing the most effective analytical tool for the purpose.
Analisis Pengaruh Ekspor dan Impor Terhadap Pertumbuhan Ekonomi di Indonesia Periode 2019-2023 Mandai, Nalla Azzahra; Amelia, Marsya; Kamila, Vita Elma; Bramantya, Naurah Hasanah; Desmawan, Deris
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.v12i2.5737

Abstract

In a country, economic sector growth is an increase in per capita output. When a country's economic growth increases, its capacity to meet the needs of its citizens also increases, thereby improving their overall welfare. Economic growth in Indonesia occurs from many factors, one of which is exports and imports. This study aims to analyze how the value of Indonesia's exports and imports in the 2019-2023 period affects its economic growth. This research applies secondary data in the form of time series covering the time span. The data is obtained through data statistics and previous journals. The application of the method utilized by researchers in this study is the OLS (Ordinary Least Square) method. The tests applied are partial data testing (T-test) and simultaneous data testing (F-test). The regression equation in this study is Y = 4.264 - 5.579EX1 + 9.323EX2. Exports have such an impact on Indonesia's economic growth that is fully supported by the results of the t-test. Similarly, imports also show a significant influence on economic growth at the country level. The reason why economic growth is not affected is because of the influence of other variables.
Sistem Pengambilan Keputusan Untuk Pengolahan Data Siswa Penerima Bantuan Belajar Komputer Gratis Sofiani, Anggi; Nasution, Marnis; Irmayanti, Irmayanti
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.v12i3.5992

Abstract

Students play an important role in learning computers, especially in the process of education and learning. According to Law No. 14 of 2005 article 51 paragraph 1 item b states that students are entitled to receive awards for their performance during computer Learning . On the other hand, information technology continues to develop rapidly, one of the developments in information technology is the emergence of Decision Support Systems. Decision support system is a system that helps decision makers in making a decision.There are many methods or algorithms that can be used in decision support systems, including Weighted Product (WP), TOPSIS, Simple Addictive Weighting (SAW), Analytical Hierarchy Process (AHP) methods and others. Analytical Hierarchy Process (AHP) method is one method that has been widely used in Decision Support Systems. This method has the ability to measure the degree of consistency of the decisions to be made. One of the consistency calculations performed in the AHP method is the calculation of the consistency ratio, which in this calculation is done using the value of the random index (IR). Over time, many researchers have conducted research on the value of the Analytical Hierarchy Process random index, so there are many new random index values in addition to the AHP random index found by students.
Analisis Faktor Yang Mempengaruhi Kepuasan Pegawai Dinas Pangan: Pendekatan Menggunakan Algoritma C4.5 Harahap, Tongku Hamonangan; Munthe, Ibnu Rasyid; Juledi, Angga Putra
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.v12i3.6118

Abstract

The level of satisfaction is an important measure in evaluating the extent to which the needs and expectations of a person or group are met by a product, service, or experience. The concept is often used in a business context to measure how well a product or service meets customer expectations. The level of satisfaction can be measured through various methods such as surveys, interviews or analysis of consumer behavior data. The results of this satisfaction level evaluation provide valuable insights for companies in improving the quality of their products or services, as well as maintaining customer loyalty. Therefore, the author will conduct a study on the level of employee satisfaction Department of food using machine learning approach with C4.5 method. This study aims to explore the patterns and factors that significantly affect the level of employee satisfaction in the context of the Department of food. The C4.5 method was chosen because of its ability to handle complex and diverse data, as well as being able to provide insight into the relationship of complex and non-linear variables.
Analisis Data Penjualan Menggunakan Algoritma Apriori pada Analisis Kopi Hidayat, Tomi; Munthe, Ibnu Rasyid; Juledi, Angga Putra
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.v12i3.6064

Abstract

Data Mining is a technique for finding, searching, or extracting new information or knowledge from a very large set of data, by integration or merging with other disciplines such as statistics, artificial intelligence, and machine learning, making Data Mining as one of the tools to analyze data and then produce useful information. Association Rule is a process in Data Mining to determine all associative rules that meet the minimum requirements for support (minsup) and confidence (minconf) in a database. In Association Rule, there are 2 methods that can be used, namely a priori method and FP-Growth method, where FP-Growth method is the development of a priori method where a priori method there are still some shortcomings such as there are many patterns of data combinations that often appear (many frequent patterns), many types of items but low minimum support fulfillment, it takes quite a long time because database scanning is done repeatedly to get the ideal frequent pattern. In this study the method used is a priori algorithm method, a priori algorithm method is one of the alternative ways to find the most frequently appearing data sets (frequent itemset) without using candidate generation that is suitable for analyzing a transaction data. Coffee analysis is a Cafe Shop engaged in the sale of food and beverages that many food and beverage sales transactions. Open on November 7, 2021 coffee analysis penetrates 245 sales transactions and this transaction data continues to grow every day.
Simulasi Kinerja Karyawan di Kantor Pertanahan Labuhanbatu Menggunakan Algoritma C4.5 Nasution, Khodijah; Masrizal, Masrizal; Harahap, Syaiful Zuhri
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.v12i3.6160

Abstract

Employee performance analysis using the C4.5 algorithm in data mining aims to identify and classify employees based on their performance. The analysis process includes several stages, namely data analysis, preprocessing, model design in data mining, and method evaluation. From 47 sample data analyzed, the results show that 40 employees have good characters, while 7 employees have bad characters. Good employee characters are characterized by punctuality and high discipline in carrying out their duties. Conversely, bad employee characters are characterized by unpunctuality and low discipline, which have a negative impact on productivity and efficiency in the workplace. The results of this classification help identify areas that require more attention and intervention to improve overall employee performance. Model evaluation is carried out using two widgets, namely Test and Score and Confusion Matrix. The evaluation results of these two widgets show perfect accuracy of 100%. Meanwhile, the Confusion Matrix widget shows that all predictions are in accordance with the actual data without any errors in classification. These results confirm that the C4.5 algorithm is very effective and accurate in classifying employee performance. The perfection of the evaluation results shows that the C4.5 algorithm is very suitable for use as a classification model in employee performance analysis. The 100% accuracy of both widgets indicates that this algorithm is not only able to predict correctly but also consistently in various evaluation tools.
Application of Apriori and Fp-Growth Methods in Analyzing Book Lending Patterns Penerapan Metode Apriori dan Fp-Growth dalam Analisis Pola Peminjaman Buku Faradilah, Rahma; Harahap, Syaiful Zuhri; Irmayanti, Irmayanti
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.v12i3.6155

Abstract

Clustering of book borrowing patterns in the University of Labuhanbatu library aims to identify and understand student preferences and habits in borrowing books. With this analysis, the library can be more effective in managing book collections, ensuring the availability of frequently borrowed books, and improving the quality of service according to student needs. Using clustering techniques also helps in designing a more targeted book procurement strategy, so that existing resources can be optimally utilized to support the teaching and learning process. In this study, the methods used are Kf-Growth and Apriori to identify book borrowing patterns. Kf-Growth is used to find frequent itemsets or collections of books that are often borrowed together, while Apriori is used to generate association rules that reveal the relationships between borrowed books. Both of these methods allow for a more in-depth and comprehensive analysis of book borrowing patterns in the library, with the ability to handle large amounts of data and identify significant relationships between items. This process involves several stages, including data preprocessing, algorithm application, and evaluation of the results to ensure the validity and accuracy of the resulting clustering. The results of the clustering analysis show a very good confidence value, with many male and female students borrowing the book "Pengantar Akuntansi" consistently. This borrowing pattern shows that books related to economics and accounting have a high level of demand. The Kf-Growth and Apriori methods have proven to be very effective in clustering, providing accurate and reliable results. With these results, the Labuhanbatu University library can take more informative and strategic steps in managing book collections, ensuring that frequently borrowed books are always available, and improving the borrowing experience for students.
Implementasi Metode Backpropagation Neural Network Untuk Memprediksi Saham Bank Terbesar di Indonesia Mubaraq, Muh. Falah; Sahera, Nelti Juliana; Indri, Indri; Saputra, Rizal Adi
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.v12i3.5966

Abstract

Prediksi harga saham yang akurat sangat penting bagi investor untuk membuat keputusan investasi yang bijak. Penelitian ini bertujuan untuk memprediksi harga saham harga close tiga bank besar di Indonesia menggunakan algoritma backpropagation neural network. Metode penelitian meliputi pengumpulan data historis harian dari Yahoo Finance, preprocessing data, dan pembuatan model neural network dengan satu hidden layer. Evaluasi model menggunakan metrik RMSE, MAE, dan MAPE. Hasil penelitian menunjukkan bahwa model prediksi memiliki tingkat akurasi yang tinggi untuk ketiga bank. Bank Mandiri mencapai performa terbaik dengan 3 neuron hidden layer, learning rate 0.01, dan toleransi error 0.000001, menghasilkan RMSE 49.565, MAE 38.3087, dan MAPE 0.63%. Bank BRI optimal dengan 12 neuron hidden layer (RMSE 50.003, MAE 29.5462, MAPE 0.74%), sementara Bank BCA dengan 6 neuron hidden layer (RMSE 62.434, MAE 47.7587, MAPE 0.51%). Kesimpulannya, algoritma backpropagation neural network terbukti efektif dalam memprediksi harga saham penutup dengan tingkat akurasi tinggi, ditunjukkan oleh nilai MAPE di bawah 1% untuk ketiga bank.
Analisis Kinerja Sistem Informasi SMK Swasta Pemda Saputri, Yuni; Nasution, Marnis; Masrizal, Masrizal
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.v12i3.6120

Abstract

This research aims to analyze the performance of the information system used in local government private vocational schools, which plays an important role in supporting the smooth operation of schools, including the teaching and learning process, administration and financial management. The research method used is benchmarking, which involves collecting data through interviews, observations and questionnaires. The research results show that the existing information system still has several weaknesses, such as lack of integration between systems, inaccurate data reporting, and low efficiency in data processing. Based on these findings, this research provides recommendations for improving the information system in local government private vocational schools to make it more effective and efficient in supporting school activities.
Determinasi Pertanian, Industri dan Perdagangan Terhadap Pengurangan Tingkat Kemiskinan di Nusa Tengara Barat Tahun 2018-2022 Faqih, Azfar; Hidayat, Nanda Nur; Ardiansyah, Julian; Cahya, Refin Dwi; Situmorang, Ricardo; Maulana, Yoga Tri
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.v12i3.5916

Abstract

The most serious problem faced by a country in realizing economic development is poverty. This study was conducted to see the effect of agriculture, industry, and trade on poverty reduction. The method used is the static panel regression method to analyze the relationship and influence of the independent variable on the dependent variable.  The results showed that there was a negative effect of the agricultural sector on the poverty rate and there was a positive effect of the industrial sector on poverty, but the trade sector had no effect on poverty. Then simultaneously the agricultural, industrial, and trade sectors affect the poverty rate.

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