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All Journal Madrasah: Jurnal Pendidikan dan Pembelajaran Dasar Building of Informatics, Technology and Science
Utami, Ulfa
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Computer Science & IT Education

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The Effectiveness of Guided Inquiry Learning Models for Students' Scientific Performances and Critical Skills Supriyatno, Triyo; Lestari, Dirga Ayu; Utami, Ulfa
Madrasah: Jurnal Pendidikan dan Pembelajaran Dasar Vol 13, No 1 (2020): Madrasah: Jurnal Pendidikan dan Pembelajaran Dasar
Publisher : Fakultas Ilmu Tarbiyah dan Keguruan Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mad.v13i1.9342

Abstract

This study aims to analyze learning activities using guided inquiry learning models in improving scientific attitudes and students 'critical thinking skills and the effectiveness of guided inquiry learning models in improving scientific attitudes and students' critical thinking skills in science learning. This research was a quasi-experimental research with purposive sampling technique. Subjects in this study were 51 students of class V MIN 1 Serang. Data collection used documentation, questionnaires, observations and tests. Data were collected from the pretest and posttest of the students' scientific attitude and critical thinking as well as documentation and observation of guided inquiry learning activities in the control class and the experimental class. The type of data analysis used t-Test and effect size cohen’s test with the help of the statistical package for the social sciences (SPSS) program version 23.0. The results showed that the guided inquiry model learning activities were better than conventional models namely. So, there is the effectiveness of guided inquiry learning models in improving scientific attitudes and critical thinking skills of students on science learning in class.
Penentuan Pola Pada Dataset Penjualan Dalam Data Mining Menggunakan Metode Apriori Utami, Ulfa; Irmayani, Deci; Bangun, Budianto
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.v7i1.7498

Abstract

In everyday life and the business world, buying and selling activities play a central role. For companies, daily transaction data is not just a record, but an important asset that holds the potential to increase sales through analysis. The volume of sales data generated daily is enormous, making manual processing inefficient and prone to errors. The complexity of the number of products sold also makes it difficult to gain a comprehensive understanding of purchasing patterns. Dynamic changes in consumer preferences further complicate demand forecasting and may lead to inventory issues. This study aims to address these issues by analysing sales data to identify products that are frequently purchased together. This information will be utilised in designing more effective marketing strategies, such as cross-promotions or product bundling. Additionally, this data is useful for demand forecasting and optimising inventory management. The ultimate goal is to provide relevant product recommendations to customers and enhance their satisfaction. To achieve this objective, this study applies data mining techniques, specifically the Apriori Association method. Data from 15 types of items in 28 weekly transactions at TOKO BANGUNAN MAJU BERSAMA will be analysed as an initial sample to identify the most frequently purchased combinations of construction tools. The Apriori method will associate each item based on a minimum support value of 0.25 and a minimum confidence value of 0.80. The application of this method resulted in 4 rules from 3-item patterns with confidence values ranging from 0.88 to 0.89.
Co-Authors Bangun, Budianto Dirga Ayu Lestari Irmayani, Deci Triyo Supriyatno