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All Journal Informatika Journal of Computer Science and Information Systems (JCoInS) Journal of Student Development Information System (JoSDIS)
Ritonga, Irmayanti
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Library & Information Science Other

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Memprediksi Data Saham Bank Mandiri Menggunakan Metode Algoritma Regresi Linear Dengan Bantuan Rapid Miner Sari, Laila; Harahap, Syaiful Zuhri; Ritonga, Irmayanti
Jurnal Informatika Vol 12, No 2 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i2.5645

Abstract

Indonesia has been growing rapidly, one of which can be seen from the economy and technology in Indonesia, at this time the community is almost entirely using machine power technology as a helper of daily life, and the community has also processed a lot of its finances by way of stock investment, with stock investment, the community believes that stocks are invested safer and more profitable. A stock can be defined as a mark of participation or ownership of an individual investor or institutional investor or trader on their investment or a certain amount of funds invested in a company. Linear regression algorithm is one of the methods used to predict stock data in Bank Mandiri. Linear regression algorithm tries to model the relationship between two variables by matching the linear equation of the stock data to be studied. One variable is considered the explanatory variable and the other variable is called the dependent variable. Prediction a process for systematically estimating Bank Mandiri stock data that will appear in the future using data obtained from the past. Thus the company can easily find out the stock data in the future.
Sistem Informasi Pendataan Alat Bantu Bagi Penyandang Disabilitas Pada Dinas Sosial Kabupaten Labuhanbatu Hasibuan, Ardiansyah; Nasution, Marnis; Ritonga, Irmayanti
Jurnal Informatika Vol 12, No 1 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i1.5490

Abstract

Introduction the development of technology in recent years is very rapid. The development of technology today is used by many parties in carrying out daily life. The data collection that received a lot of attention was related to the data collection of supplies for people with disabilities at the Labuhanbatu District Social Office. Currently, people with disabilities are getting special attention from various parties, both government and non-governmental organizations. The fulfillment of the rights of persons with disabilities continues to be championed by a number of parties which will then be submitted to the government, in this case the Labuhanbatu District Social Office. Therefore, the Social Service of Labuhanbatu Regency really needs a computerized information system to facilitate the work. Processing large amounts of data is certainly very difficult without a support system in it. This encourages many parties to take advantage of the development of information systems to the fullest, both information that comes from within and information that comes from outside. Based on the observations of the researchers found that employees of the Labuhanbatu District Social Service to collect data on disability aids is still manually by recording and then input into microsoft office either microsoft word or microsoft excel. This makes long work and data accuracy ineffective. Therefore, the researchers felt that a computerized information system is needed that can facilitate and accelerate the work and can make the work of collecting disability aids by employees at the Labuhanbatu District Social Service more effective. Waterfall to implement interface design, user interface design design using a programming language .
Analisis K-Means dan Naive Bayes Untuk Pengelompokan Rawan Bencana di Daerah Kabupaten Labuhanbatu Lubis, Nadira Jannah Adeni; Harahap, Syaiful Zuhri; Ritonga, Irmayanti
Jurnal Informatika Vol 12, No 1 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i1.5492

Abstract

A natural disaster is an event that arises from the state of nature and has a significant impact on humans. Natural disasters can include events such as floods, volcanic eruptions, earthquakes, tsunamis, landslides, blizzards, droughts, hail, heat waves, hurricanes, tropical storms, typhoons, tornadoes, wildfires, and the spread of disease.Natural disasters that hit Labuhanbatu Regency include various types, such as floods, fires, tornadoes, and landslides. Each region within the district has specific characteristics associated with a particular type of natural disaster. In order to understand the level of vulnerability to disasters in Labuhanbatu District, K-Means and Naive Bayes methods are implemented to classify the level of vulnerability to frequent disasters.The results of this analysis will improve understanding of the level of vulnerability to disasters in Labuhan Batu Regency, enabling interested parties to identify areas that require increased attention in disaster mitigation and management efforts. In addition, the use of a combination of K-Means and Naive Bayes methods can serve as a solid basis for the development of more effective early warning systems in the future.
Application Of Data Mining In Selecting Superior Products Using The K-Means And K-Medoids Algorithm Methods Hermika, Eva; Harahap, Syaiful Zuhri; Ritonga, Irmayanti
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.5968

Abstract

As a supermarket, we are committed to always improving everything, including selecting the greatest goods. To evaluate which items are more superior or popular and which are less popular, you will want a sizable amount of information sources. To select products and identify those that belong in the superior product cluster, researchers employed the clustering method. The clustering strategy uses two forms of cluster analysis, k-means and k-medoids, which have related techniques. The research results show that the k-means algorithm's Davies Bouldin value is -0.430, whereas the k-medoids algorithm's Davies Bouldin value is -1.392. This suggests that the Davies Bouldin value of the k-medoids approach is the lowest, showing that the grouping findings of the k-means method are  a better method to apply to the issue of choosing better products.
Co-Authors Ananda, Muhammad Rizki Ananta, Yuli Dhea Anistasah, Cindy Christoval, Peter Dasopang, Khoirunisa Domo, Alfun Khoir Erviyanti, Anggi Harahap, Fadilla Arba Harahap, Muhammad Irham Harahap, Rini Rizkyana Hasibuan, Ardiansyah Hasibuan, Cahaya Nabila Hermika, Eva Kurnia, Ega Laila Sari Lubis, Nadira Jannah Adeni Marianti, Nike Mariyuandra, Alfanny Nasution, Marnis Nst, Mayang Alvi Perdana Panjaitan, Hadid Akhyarsyah Pramudita, Dewi Pratiwi, Cindi Rahma, Aulia Ramadhani Ramadhani Ritonga, Akbar Pramuja Rubaini, Mingle Oktaria Silaen, Siti Humairoh Simanjuntak, Hoirun Nisyah Sinta, Evi Yulia Sirait, Roby Gusmawan Siregar, Salsa Dea Oktavianda Siti, Siti Sukani, Sri Rejeki Syaiful Zuhri Harahap Tanjung, Eka Agustin Wijaya, Alief Achmad