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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science INFOKUM TEPIAN Journal of Artificial Intelligence and Digital Business
Harpad, Bartolomius
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Journal : Building of Informatics, Technology and Science

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Prediksi Pengajuan Kredit Usaha Pada Koperasi Menggunakan Algoritma K-Nearest Neighbor Harpad, Bartolomius; Bustomi, Tommy
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i1.1626

Abstract

Cooperative activities have become the activities most needed by many people because they are related to money, cooperatives are places that provide loans to housewives and also workers in a certain area or environment, the lack of interest offered by this cooperative is considered very easy. and very helpful for many parties in facilitating financial affairs, especially in financial matters, because the convenience offered by the cooperative makes many interested people ask for the same thing resulting in vulnerability to fraud, the importance of making predictions on prospective new business loan applications can help reduce the worst risks from various risks that occur in the future, in this study the k-nearest neighbor algorithm will be used as a prediction algorithm for prospective business credit applications at cooperatives, the value obtained is the value of training data or data records of several previous customers so as to easier to know new data as test data in a study. The results found in this study for prospective business credit applications are "Not Eligible" seen to the closest value based on the smallest value with (closest distance) between one another as many as 3 distances, namely numbers 1, 2 and 3 where number 1 states "Not feasible ”, at the second closest distance stating “Eligible” and number 3 stating “Not Eligible”, the most results stated Not Eligible so that the decision value on new customers had to be rejected “Not eligible” to be accepted
Penerapan Data Mining Untuk Prediksi Perkiraan Hujan dengan Menggunakan Algoritma K-Nearest Neighbor Nursobah, Nursobah; Lailiyah, Siti; Harpad, Bartolomius; Fahmi, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2564

Abstract

Rain is a condition where water droplets fall from clouds to the earth. In life, the presence of rain is highly anticipated, rain can help people who have a profession as farmers. Rain that occurs on a large scale will really provide obstacles for the community, in addition to hampering activities or activities especially those carried out on outdoor rain can also cause disaster for the community in the form of flooding. Estimating rain for the community is very important, knowing whether it will rain or not can make it easier for the community to anticipate the possibilities that may occur due to rain. However, in the process of delivering forecasts, there is often an uneven distribution of information and delays in conveying information to the public regarding whether or not rain will occur. The community should be able to independently predict whether or not rain will occur. Data processing should be done properly and correctly. Data mining is a way that can be done to assist in data processing. In this study, the settlement process will be carried out using the K-Nearest Neighbor (K-NN) algorithm. The results obtained show that the data testing decision is NO. In other words, data mining and the K-Nearest Neighbor algorithm can help the problem solving process
Pengolahan Data Penjualan Pakaian dengan Menerapkan Algoritma Apriori Data Mining Harpad, Bartolomius; Lailiyah, Siti; Yusika, Andi
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6478

Abstract

In its input, so far the use of sales transaction data has only been stored as an archive. In fact, the data can be utilized and processed into useful information to increase product sales or product innovation. In this case, sales data analysis needs to be carried out. With information about sales patterns, it can be seen what consumers buy most often. So from consumer purchasing patterns, decision making can also be done by the store related to the products to be sold. The data mining process in analyzing sales data, the Apriori algorithm can be utilized in the sales data process, namely by providing a relationship between sales transaction data. The data in question is sales data on clothes or pants that are ordered so that consumer purchasing patterns are obtained. Thus, the store can use the data to take suitable business actions. In this case, data can be used as a consideration to ensure the next sales strategy. The existence of information about sales patterns can find out what consumers buy most often. The products that are most often purchased by consumers are Hightwais Jeans Snow, Neda Tunik Full Buttons with 100% support for each product. by knowing the products that are most often purchased by consumers, the company can develop a strategy in determining the purchase of clothes and pants to maintain the availability of clothes and pants needed by consumers.
Determining the Country with the Best Economic Conditions 2025 using the MCDM Method Harpad, Bartolomius; Azahari, Azahari; Salmon, Salmon
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7346

Abstract

In the midst of increasingly complex global challenges in 2025, evaluating a country's economic condition is an important element in supporting strategic decision-making, whether at the government, corporate or individual level. The diversity of economic indicators such as Gross Domestic Product (GDP), inflation, unemployment, and human development index often makes it difficult to make an objective and comprehensive assessment. Reliance on a single indicator tends to produce a biased and unrepresentative picture. To address these issues, this research adopts a Multi-Criteria Decision Making (MCDM) approach that is able to consider various economic aspects simultaneously and systematically. The three MCDM methods used in this study are TOPSIS, VIKOR, and COCOSO. The analysis was conducted on 19 countries using four main indicators, namely GDP in billion USD, inflation rate, unemployment rate, and economic growth rate. Based on the results of data processing, the USA occupies the top position as the country with the best economic performance, followed by China. The three methods show consistency in ranking some countries, but there are also striking differences for some alternatives due to different approaches in normalisation and weighting. These findings emphasise the importance of choosing the right method in multicriteria evaluation. Therefore, a combined approach such as ensemble decision-making is recommended to strengthen the validity of the results. For further development, the use of additional indicators and the integration of artificial intelligence-based technology are suggested to improve accuracy and flexibility in analysing economic conditions between countries.
Co-Authors Azahari, Azahari Bustomi, Tommy Fachry Abda El Rahman Fahriawan, Muhammad Harianto, Kusno Husmul Beze, Husmul Khoirunnita, Aulia Muhammad Fahmi Nursobah, Nursobah Nurul Ikhsan Pahrudin, Pajar Pratama, Dion Pratiwi, Heny Reza Andrea Rudito Rudito Salmon Salmon, Salmon Yusika, Andi