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All Journal Building of Informatics, Technology and Science Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal Teknik Informatika (JUTIF) Journal Social Science And Technology For Community Service Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Telematics and Information Technology (TELEFORTECH) Paradigma Jurnal Ilmiah Computer Science CHAIN: Journal of Computer Technology, Computer Engineering and Informatics
Priandika, Adhie Thyo
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media

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Journal : Building of Informatics, Technology and Science

 1 
Kombinasi Metode Pembobotan Entropy dan MARCOS Dalam Seleksi Penerimaan Karyawan Divisi Keuangan Wahyuni, Dita Septia; Priandika, Adhie Thyo
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.v6i3.5835

Abstract

The selection of employees for the Finance Division is a crucial process to ensure that the selected individuals have the appropriate skills and qualifications to handle complex financial responsibilities. The main problems in the selection of Finance Division employees often revolve around the difficulty in accurately assessing the candidate's technical skills and analytical abilities. The experience and qualifications listed on a resume do not necessarily reflect the candidate's apparent ability to handle complex financial situations or in the face of stringent regulatory challenges. This study aims to apply a combination of entropy and MARCOS weighting methods in the selection of employees of the Finance Division, in order to improve the objectivity and accuracy of the decision-making process. Through this approach, to identify candidates who best suit the company's needs and requirements based on a comprehensive multi-criteria analysis. The combination of Entropy and MARCOS weighting methods in the selection of financial division employees provides a comprehensive and objective approach in decision-making. The Entropy method is used to objectively determine the weight of the criteria based on the degree of uncertainty of the information provided by each criterion, the MARCOS method is used to evaluate and rank candidates based on their proximity to the ideal solution and the distance from the anti-ideal solution. The results of the financial division employee acceptance selection ranking show that Budi Santoso occupies the top position with the highest score of 4.8848. These results provide a clear picture of each candidate's relative position in terms of final assessment, and can serve as a basis for more targeted and objective hiring decisions.
Penerapan Kombinasi Metode Pembobotan Entropy dan Technique for Order of Preference by Similarity to Ideal Solution Dalam Pemilihan Karyawan Terbaik Ningsih, Ristia; Priandika, Adhie Thyo
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.v6i3.5896

Abstract

The process of selecting the best employees often faces various challenges that can affect the objectivity and fairness of the results. One of the main issues is the objectivity of selecting the best employees, where appraisers may have personal preferences or prejudices that influence their decisions in making the best employee selection. This study aims to apply a more objective and systematic approach in assessing employee criteria and integrate these factors into a more structured decision-making process. By using the entropy weighting method to objectively determine the weight of the criteria and TOPSIS to rank employees based on their proximity to the ideal solution, this study is expected to provide a solid foundation for more accurate and reliable decision-making in human resource management. The application of a combination of entropy weighting and TOPSIS methods in the selection of the best employees offers a comprehensive and structured approach in overcoming the complexity of human resource evaluation. The entropy weighting method is used to objectively determine the weight of the criteria based on data variation, thereby reducing subjectivity in assessment. Meanwhile, TOPSIS is used to rank employees based on their proximity to the positive ideal solution and their distance from the negative ideal solution. The combination of these two methods allows decision-makers to integrate different aspects of employee criteria. The results of the ranking of the best employees gave the results of the first best employee with a final preference score of 0.97858 obtained by Aisyah, the best second employee with a final preference score of 0.79125 obtained by Misri, and the third best employee with a final preference score of 0.69712 obtained by Rudi Setiawan.
Sistem Pendukung Keputusan Pemilihan Pelanggan Terbaik Menggunakan Kombinasi Pembobotan Logarithmic Least Square dan MOORA Rifaldo, Setiawan; Priandika, Adhie Thyo
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.v6i3.5897

Abstract

The best customers are individuals or groups who not only transact frequently but also provide more value to the business through loyalty, positive feedback, and referrals to others. They typically show a high level of satisfaction with the product or service offered, potentially bringing in new customers and improving the company's reputation. Selecting the best customers is often faced with a variety of issues that can affect the accuracy and effectiveness of the process. One of the main problems that occurs is the lack of a model used in determining the best customers. The purpose of this research is to implement a system that is able to effectively and accurately identify the best customers by integrating the LLS weighting technique and the MOORA method. In addition, this study also aims to overcome the shortcomings of existing weighting and evaluation methods by integrating the two techniques, providing a more robust and adaptive solution in the context of data-based decision-making. The ranking results in determining the best customers obtained the result, namely Sabtoni occupies the first position with the highest score of 0.47344. Furthermore, Zahra is in second place with a score of 0.39815, followed by Tuty with a value of 0.39498 in third place.
Perbandingan Random Forest dan XGBoost Untuk Prediksi Penjualan Produk E-Commerce Rumah Madu Hayatunnisa, Destaria; Permata, Permata; Priandika, Adhie Thyo; Gunawan, Rakhmat Dedi
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8491

Abstract

Inventory management is one of the main challenges for small and medium enterprises (SMEs), including Rumah Madu in Bandar Lampung, where honey stock levels are often determined based on estimation rather than precise calculation. This study aims to analyze and compare the performance of the Random Forest and XGBoost algorithms in predicting honey sales to achieve more measurable stock management. The dataset consists of 1,699 honey sales transactions that have undergone cleaning, feature transformation, and standardization processes. The variables used include honey type, unit price, day, month, holiday status, and promotion indicators. Modeling was conducted using a time-series split approach, where historical data served as the training set and recent data as the testing set. The evaluation results show that Random Forest achieved an MAE of 24.35, RMSE of 29.04, and R² of -0.9685, while XGBoost achieved an MAE of 25.50, RMSE of 30.58, and R² of -1.1825. The negative R² values indicate that both models were unable to explain data variation optimally, with performance falling below a simple baseline. Nevertheless, the feature importance analysis revealed that unit price and honey type were the dominant factors influencing sales. This study highlights the need for further model development through parameter optimization and improved data quality to enhance prediction accuracy.
Co-Authors Adi Sucipto, Adi Aditya Saputra Agus Wantoro Ahdan, Syaiful An’ars, M. Ghufroni Ari Sulistiyawati Aryani, Venty A’yun, M Qurrota Dedi Darwis Edison, Arif Rahman Fitratullah, M. Gunawan, Rakhmat Dedi Hayatunnisa, Destaria Koeswara, Wawan Lutfy, Azza’zunda Choibar Ningsih, Ristia Octaviansyah, A. Ferico Permata, Permata Prabowo, Fransiskus Wahyu Sandy Putra, Farhan Nopransyah Putri, Jeni Sagita Rifaldo, Setiawan Rohmat Indra Borman Rosella, Rosella Safira, Wilga Salsabila Indriyani Sari, Kevinda Setyani, Tria Simamora, Parningotan Tien Yulianti Very Hendra Saputra Wahyuni, Dita Septia Yuri Rahmanto Yusran, Muhamad