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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming JURNAL IT: Media Infromasi STMIK Handayani Makassar Building of Informatics, Technology and Science INFOKUM Journal of Artificial Intelligence and Digital Business
Azahari, Azahari
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Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering Public Health

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Journal : Building of Informatics, Technology and Science

 1 
Penerapan Metode Topsis Pada Sistem Pendukung Keputusan Kelayakan Penerima Dana Bantuan Operasional Sekolah Azahari, Azahari; Pahrudin, Pajar; Yunita, Yunita
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.v4i3.2290

Abstract

One of the ways used to fulfill education. The Indonesian government implements a 12-year compulsory education program. Although there is a 12-year compulsory education program by the government, there are still some students who cannot continue their education due to factors from the family economy who are unable to meet the needs or costs of the education they take. The School Operational Assistance Fund (BOS) is a financial aid given to underprivileged students/I to be able to meet learning needs such as tuition fees, book fees or personal needs that support the implementation of education for students/I. For private schools, the School Operational Assistance Fund (BOS) has its own quota to be given to students. The organizing committee for the recipients of the School Operational Assistance Fund (BOS) is required to be fair and honest in the selection process. The error is because there is still no special provision used for the selection process or the assessment process carried out by the school. Decision Support System (DSS) is a system that has been integrated with a computer, where the decision support system is used to provide certain provisions that can be used to assist in providing recommendations in the decision-making process. TOPSIS uses the principle that the chosen alternative must have the closest distance from the positive ideal solution and the farthest from the negative ideal solution from a geometric point of view by using Euclidean distance to determine the relative proximity of an alternative to the optimal solution. By applying the TOPSIS method, Alternative 4 (A4) was selected as the beneficiary with a final score of 0.7251
Prediksi Persediaan Bahan Baku Makanan Menerapkan Algoritma Apriori Data Mining Salmon, Salmon; Azahari, Azahari; Yusnita, Amelia
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.v4i3.2563

Abstract

The company's operational activities are inseparable from the supply of raw materials that must be met every day to meet consumer demand. The restaurant uses raw materials, namely vegetables, raw meat which includes beef and chicken, yellow noodles and soun noodles, and the main seasoning. Sales of food at this restaurant quite a lot in a day. This will produce sales data that will continue to grow every day, but this data is useless if it is not processed again to get the knowledge contained in the data. The Apriori algorithm is a method for finding patterns of relationships between one or more items from a dataset. Thus the pile of data that has been collected can produce a sales pattern, from which the customer's buying interest in food can be identified. From the results of research using a data sample of 18 items with a minimum of 20% Support and 50% Confidence, it produces 5 interesting rules with the highest Support reaching 33.33% and the highest Confidence reaching 100%.
Perbandingan Kinerja Algoritma K-Nearest Neighbor dan Algoritma Random Forest Untuk Klasifikasi Data Mining Pada Penyakit Gagal Ginjal Salmon, Salmon; Azahari, Azahari; Ekawati, Hanifah
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.v6i3.6476

Abstract

Kidney failure is one of the most common chronic diseases worldwide. This condition occurs when the kidneys lose their ability to filter waste and excess fluid from the blood. Kidney failure is a serious condition that occurs when kidney function decreases significantly or stops altogether. Kidney failure has a wide impact on the physical, mental, and social health of patients. Therefore, early treatment and a holistic approach are needed to minimize its impact. In the health sector, technological advances have enabled more effective processing of medical data through the application of data mining. Data Mining is the process of exploring and analyzing large amounts of data to find patterns, relationships, or valuable information that was previously unknown. Classification in Data Mining is the process of grouping or categorizing data into certain classes or labels based on the attributes or features it has. In the classification itself, there are various algorithms in it such as the K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms. The K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms are two algorithms that are widely used in classification tasks. Therefore, this study will carry out a comparison process on the performance of the K-Nearest Neighbor algorithm and the Random Forest algorithm. Comparison of data mining algorithm performance to evaluate and determine which algorithm is the most effective and efficient in solving a particular problem based on various evaluation metrics. Overall, the accuracy value obtained is above 90%, but the Random Forest algorithm has better performance. Where the accuracy level results obtained from the Random Forest algorithm are 99.75%. Therefore, the model or pattern produced by the Random Forest algorithm will later be used to assist in the process of diagnosing kidney failure and the Random Forest algorithm is an algorithm that has better performance.
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 Amelia Yusnita Ar Rahman, Nurbalingga Suhendra Ekawati, Hanifah Halim, Ryan Artanto Harianto, Kusno Harpad, Bartolomius Pahrudin, Pajar Pratiwi, Heny Rosita, Dewi Salmon Salmon Salmon Salmon, Salmon Sari, Aulia Rahmita Syamsuddin Mallala Ukkas, Irwan Yulindawati, Yulindawati Yunita Yunita