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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika UNIKA Santo Thomas Building of Informatics, Technology and Science Jurnal Tekinkom (Teknik Informasi dan Komputer) MEANS (Media Informasi Analisa dan Sistem) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) International Journal Of Science, Technology & Management (IJSTM) Jurnal Media Informatika Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT Jurnal Pengabdian Masyarakat Gemilang (JPMG) Internet of Things and Artificial Intelligence Journal Jurnal Pengabdian Harapan Bangsa
Irmayani, Deci
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Public Health Social Sciences Transportation

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Sistem Pendukung Keputusan untuk Kelayakan Kredit Berdasarkan Profil Keuangan Menggunakan Metode TOPSIS Fitriani, Sinta; Masrizal, Masrizal; Irmayani, Deci
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.v7i1.7394

Abstract

Decision Support System (DSS) is one of the essential tools in assisting complex decision-making processes, including creditworthiness assessment based on customers’ financial profiles. This study aims to design a DSS capable of evaluating credit eligibility more accurately, objectively, and efficiently by applying the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. This method is used to rank customer alternatives based on their proximity to an ideal solution by considering several criteria such as income, expenses, collateral, and credit history. The data used in this research were obtained from customer financial datasets containing information related to their financial profiles. The system was tested using simulation data, and the results showed that the TOPSIS method can provide creditworthiness evaluations with a high level of accuracy while reducing time and errors compared to manual assessment methods. The final research results identified the best alternative as A3 with a score of 0.8859, indicating the most optimal credit eligibility level. These findings are expected to serve as a valuable reference for financial institutions in making credit approval decisions, improving transparency, and minimizing risks in the credit process. The implementation of the TOPSIS method has proven to be an effective approach in supporting data-driven decision-making.
Klasifikasi Jenis Bunga Iris Berdasarkan Fitur Morfologi Menggunakan Algoritma Naive Bayes Sari, Ely Novita; 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.v7i1.7401

Abstract

This study aims to classify the types of Iris flowers based on morphological features using the Naive Bayes algorithm. Iris flowers consist of three types, namely Iris-Setosa, Iris-Versicolor, and Iris-Virginica, which can be distinguished based on the length and width of the petals as well as the length and width of the sepals. The dataset used in this research is the Iris dataset, which contains information on four morphological features from these three types of flowers. The Naive Bayes algorithm was chosen because of its advantages in performing probability-based classification in a simple, fast, and effective manner, especially for data with independent features. The research stages include data collection, feature extraction, splitting the data into training and testing sets, training the model using the Naive Bayes algorithm, and testing the model to evaluate classification accuracy. The results of the study show that the Naive Bayes model is able to classify the test data accurately, with the highest probability value obtained in the Iris-Versicolor class, with a value of P(Versicolor│X)=1. This indicates that the test data has the highest similarity to that species compared to the other two species. Thus, the Naive Bayes algorithm proves effective for classifying types of Iris flowers based on their morphological features.
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.
Implementasi Data Mining dengan Menggunakan Algoritma Apriori untuk Mengoptimalkan Pola Penjualan Produk Elektronik Dewi, Rahayu Kusnita; Juledi, Angga Putra; Irmayani, Deci
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.v7i1.7515

Abstract

This study discusses the application of the Apriori algorithm in analyzing electronic product sales data. The results show that the Apriori algorithm is effective in finding consumer purchasing patterns through association analysis, which allows the identification of product combinations that are often purchased together. Combinations of products with strong purchasing relationships, such as AAA Batteries (4-pack) and USB-C Charging Cable (confidence 0.9), and Wired Headphones and USB-C Charging Cable (confidence 0.7), can be utilized for bundling strategies and increasing sales. Of the 18 types of electronic products analyzed, seven products met the minimum support requirements, indicating high potential for further analysis. The Apriori algorithm also proved suitable for medium-scale datasets due to its simplicity, although it is less efficient than FP-Growth on big data. This study concludes that the application of the Apriori algorithm supports data-based business decision making, especially in understanding consumer behavior, stock management efficiency, and marketing strategy development.
Sistem Pendukung Keputusan Pemilihan Calon Ketua Komite Sekolah Menggunakan Metode CoCoSo Bilatasya, Yolanda; Juledi, Angga Putra; Irmayani, Deci; Harahap, Syaiful Zuhri
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.v7i1.7530

Abstract

The election of the school committee chairperson is one of the important decision-making processes that directly impacts the sustainability and effectiveness of collaboration between the school and parents/guardians. At SMP Negeri 1 Rantau Selatan, the selection process for the committee chairperson has been conventional, subjective, and lacks a structured and standardised assessment system. This results in the selection process being based on popularity and personal connections rather than objective competence and qualifications. To address this issue, this study aims to design and implement a Decision Support System (DSS) based on the Combined Compromise Solution (CoCoSo) method, which can accommodate various quantitative evaluation criteria and generate more objective, transparent, and accountable decision recommendations. The CoCoSo method was chosen for its ability to integrate a compromise approach to conflicting criteria and produce consistent alternative rankings through three aggregation techniques: arithmetic mean, relative sum, and compromise programming. This study uses five main criteria to assess the suitability of committee chair candidates: experience in the field of education, communication skills, leadership, understanding of education policy, and integrity. Data was obtained from 15 committee chair candidates based on observation and questionnaire results, which were then processed through the CoCoSo method stages, including decision matrix formation, value normalisation, positive and negative ideal solution calculations, and final score aggregation. The data processing results show that the candidate named Eko Prasetyo obtained the highest compromise value in all CoCoSo calculation approaches with a final Ki value of 2.505, consistently placing him as the top-ranked candidate in the system's recommendations. This demonstrates that the CoCoSo method is effective in evaluating and determining the best candidate based on a data-driven and scientifically rational approach. Additionally, the system built can also serve as a strategic tool to enhance the quality of participatory educational governance at the school unit level.
Co-Authors Adi, Puput Dani Prasetyo Afnita Afnita Agusti, Nelly Aini, Putri Andriani, Reza Silvia Anjar, Agus Aris Munandar Bangun, Budianto Bilatasya, Yolanda Dalimunthe, Ersandi Ahmad Delima Harahap, Risma Dewi, Rahayu Kusnita Dwiyanti, Dida Dwiyanti, Didan Efendi, Davina Rizky Fadilla Hasibuan, Intan Fitriani, Sinta Gultom, Gregorius Apri K Harahap, Raja Pahlevi Harahap, Vivi Nadenia Hasibuan, Mila Nirmala Sari Hsb, Elysa Rohayani imam maulana, imam Iwan Purnama JP, Gafar Ilyaz Juledi, Angga Putra Lesmana, Yandri Lukman Hakim Munthe, Ibnu Rasyid Muti’ah, Rahma Muwanti, Apri Nasution , Fitri Aini Nasution, Fitri Aini Nasution, Marnis Pane, Rahmadani Purba, Mila Hanim Rambe, Aida Zahrah Hasanati Br Rasyid Munthe, Ibnu Ritonga, Aldi Rapandi Ritonga, Ali Akbar Rohanita Hasibuan, Lily Sari Hasibuan, Mila Nirmala Sari, Ely Novita Sari, Mila Nirmala Sentosa Zega, Agus Mentro Sianipar, Vitasari Sihombing, Volvo Simanjuntak, Ebrika Nadia Sinaga, Fransisco Alexander Sinaga, Novica Handayani Siregar, Adinda Pratiwi Siregar, Siti Wahdina Siregar, Zakia El Husna Sitompul, Muhammad Sofyan Sudarman, Dita Auliya Suryadi, Sudi Suryadi, Sudi Syahputra, Rahmat Syaiful Zuhri Harahap Tawanta Natalia Sembiring, Sri Utami, Ulfa Yanris, Gomal Juni Yullyana, Yullyana