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All Journal Semantik Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik Sebatik JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Nasional Komputasi dan Teknologi Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) JURNAL IT: Media Infromasi STMIK Handayani Makassar Journal of Technopreneurship and Information System (JTIS) Building of Informatics, Technology and Science TEPIAN JPM: JURNAL PENGABDIAN MASYARAKAT Buletin Poltanesa Journal of Social Responsibility Projects by Higher Education Forum J-SAKTI (Jurnal Sains Komputer dan Informatika) J-Intech (Journal of Information and Technology) Informatika Wicida Jurnal Teknik Informatika Journal of Artificial Intelligence and Digital Business
Ita Arfyanti
Jurusan Sistem Informasi STMIK Widya Cipta Dharma
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Journal : Building of Informatics, Technology and Science

 1 
Penerapan Metode Simple Additive Weighting (SAW) dan SWARA dalam Pendukung Keputusan Pemilihan Penerimaan Karyawan Apoteker Salmon, Salmon; Arfyanti, Ita
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 | Full PDF (454.746 KB) | DOI: 10.47065/bits.v4i1.1488

Abstract

Drugs are the most important needs in alleviating and overcoming complaints of illness suffered by patients, a patient will certainly go to the nearest drug store or pharmacy to ask for advice in drug selection to a pharmacist, as a pharmacy or drug store Of course, it is very dangerous to choose a pharmacist without proper knowledge and insight into medicines. Failure to choose a pharmacist will result in losses to the company or pharmacy as well as patient safety, so an expert understanding is needed to determine the standards of a pharmacist to be trusted, selecting employees using a decision support system is the right step to reduce the risk that will occur. in the future. The decision support system in this study uses the SWARA method in determining the weight of the criteria based on expert opinion and the Simple Additive weigh (SAW) method as a ranking method based on the highest value. The results of this study selected alternative A4 on behalf of Tika which has a value of 95% as the alternative that most meets the standards
Penerapan Algoritma Decision Tree Untuk Penentuan Pola Penerima Beasiswa KIP Kuliah Arfyanti, Ita; Fahmi, Muhammad; Adytia, Pitrasacha
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.v4i3.2275

Abstract

The Indonesian Smart College Card (KIP Lecture) is a government program that has been implemented from 2020 until now. KIP Lectures are distributed by the Ministry of Education, Culture, Research and Technology through universities in each region. Where each university gets a different quota - based on the level of progress of the college. The provision of quotas for each university based on the accreditation at each university raises its own problems for these universities. The problem faced is that the number of new prospective students who register to take the KIP Lecture program exceeds the quota set for each university. The provision of KIP Lecture assistance to the wrong person will lead to misuse of assistance and also inappropriate targets. The acceptance of the selection process for new prospective students can be seen from the previous process that has been carried out. Data mining is a technique used to solve problems in large data processing. Decision Tree is an algorithm that is included in the classification technique in data mining. The process in the decision tree aims to group or classify data against their respective classes. The results of the Decision Tree algorithm are in the form of decision trees and rules, the results obtained are in the form of rules that can be used for future decision-making processes
Penerapan Metode MOORA pada Sistem Pendukung Keputusan Pemilihan Kepala Laboran Harianto, Kusno; Arfyanti, Ita; Yusika, Andi
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.v4i3.2288

Abstract

In the process of carrying out academic activities in every university, it is inseparable from the existence of tendik. In college, the head of the laboratory is in charge of ensuring the implementation of the use of the laboratory in supporting the ongoing learning process. The head of the laboratory is in charge of regulating work mechanisms and procedures in the laboratory unit. The importance of the role of the head of laboratory for tertiary institutions requires universities to have a head of laboratory in accordance with the implementation of the tasks and responsibilities given. The selection of the head of the laboratory is not only done based on the length of work at the tertiary institution, but also must be seen from the knowledge, abilities, expertise, decision making and competency certificates possessed. Therefore, we need a way to help solve problems, especially by using a computerized system. Decision support system is a computerized information system. Decision support systems are widely used for corporate organizations to solve problems in the process of making or supporting decisions. The results obtained from the application of the MOORA Method are that alternative A1 was chosen to be the head of the laboratory with a final score of 0.48
Perbandingan Kinerja Algoritma Klasifikasi Data Mining Untuk Prediksi Penyakit Darah Tinggi Arfyanti, Ita; Bustomi, Tommy; Haristyawan, Ivan
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.v6i3.6477

Abstract

High blood pressure or hypertension is one of the major health problems in the world. Although this disease can be treated, many individuals are unaware that they have hypertension, because the symptoms are often not visible or felt. Therefore, early detection of high blood pressure is very important to prevent serious complications that can endanger health. In the digital era and advances in information technology, a lot of health data can be used for analysis. One of the rapidly developing approaches to help diagnose disease is by utilizing data mining. Data mining is the process of exploring and analyzing big data to find hidden patterns, information, and knowledge that can be used to support decision making and predictions. One technique in data mining that is often used to predict conditions or diseases is the classification algorithm. However, the comparison of performance between these classification algorithms in the context of hypertension prediction is still limited. This study aims to explore and compare the performance of classification algorithms in predicting hypertension, using a dataset containing medical information about factors that affect a person's blood pressure. The Naive Bayes algorithm is a classification method based on Bayes' theorem and the assumption of independence between features. The C4.5 algorithm is a machine learning algorithm for building decision trees used in data classification. The results of this study are expected to contribute to the development of a data mining-based decision support system that can be used to detect and predict the risk of hypertension. the accuracy value of the Naive Bayes algorithm is 87.01% and the accuracy value of the C4.5 algorithm is 94.72%. From the process that has been carried out, it can be said that the C4.5 algorithm is an algorithm with better performance than the Naive Bayes algorithm. Thus, the model used in the process of diagnosing hypertension is the model of the C4.5 algorithm.
Market Potential Analysis Based on Population and Land Area using K-Means Clustering and MCDM Approaches Arfyanti, Ita; Bustomi, Tommy; Haristyawan, Ivan
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.v7i1.7392

Abstract

In an increasingly competitive global market, accurately identifying untapped market potential in small to medium-sized regions, often overlooked by traditional single-indicator analyses, presents a significant challenge for strategic decision-making. This study addresses this by proposing a hybrid analytical framework integrating K-Means Clustering with Multi-Criteria Decision-Making (MCDM) methods, utilizing population size and land area as core indicators. The primary objective is to develop a robust market potential analysis model capable of systematically classifying regions and providing actionable insights for resource optimization and market expansion. The methodology involves determining the optimal number of clusters using the elbow method (k=3, with a silhouette score of 0.8862), followed by K-Means clustering to segment Asian countries into distinct groups. Subsequently, three MCDM methods SAW, WP, and WASPAS are applied to rank countries within the most relevant cluster (low population and area) under various weighting scenarios. The results consistently demonstrate Turkey's top ranking across all MCDM methods, highlighting its robust market potential regardless of weight variations. Crucially, a very strong agreement in rankings between the MCDM methods was observed, evidenced by Spearman's correlation coefficients consistently above 0.98, with the highest correlation between SAW and WASPAS (0.998379 for [0.3, 0.7] weights). This high correlation confirms the reliability and consistency of the model, concluding that SAW and WASPAS are highly suitable for this analysis, and identifying Turkey as the leading country in market potential among 50 Asian nations based on the criteria studied.
Co-Authors Ahmadhani Khoirri Arriyanti, Eka Aryanda, Muhammad Astika Erwin Putranto Azahari Bagus Perwira Utama Yoga Suwatno Bayu Purwanto Bonifasius Candra Bustomi, Tommy Cherly Silia Maretana Dana Sudana Hakim Dendy Kurniawan Dhanny Rindya Pradifta Dicky Rifaldi Didin Rosita Arini Dwi Andrian Nurbawono Edy Purwanto Ekawati Yulsilviana Hanifah Ekawati Harianto, Kusno Harianto, Kusno Haristyawan, Ivan Harpad, Bartolomius Hendri Setiawan Henny Afrida Heny Pratiwi Hidayatul Muttaqin Kresensia, Trisda Kusnandar Kusnandar Lailiyah , Siti Lamsi, Rahmadiansyah Zain Leonardo Leonardo Muhammad Fahmi Muhammad Ibnu Saad Saad Muhammad Iqbal Muhammad Rega Praduana Nurhuda, Asep Nursobah, Nursobah Pitrasacha Adytia Pratiwi, Heny Rajiansyah Rajiansyah Rajiansyah, Rajiansyah Ryan Agusta Salmon Salmon Salmon Siti Lailiyah Situmorang, Ira Sofia Sony Darmawan Sugeng Suryani Sururi, M Za’iem Suryani, Sugeng Tabrani Rija Tommy Bustomi Tommy Bustomi Ujang Sumarwan Vilianty Rafida Wahyuni - Wahyuni Wiwik Widiyatni Yulindawati, Yulindawati Yulindawati, Yulindawati YUNITA Yunita Yunita Yusika, Andi