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All Journal Jurnal Telematics and Information Technology (TELEFORTECH) Paradigma CHAIN: Journal of Computer Technology, Computer Engineering and Informatics
Fadila Shely Amalia
Universitas Teknokrat Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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ANALISIS DATA PENJUALAN HANDPHONE DAN ELEKTRONIK MENGGUNAKAN ALGORITMA APRIORI (STUDI KASUS : CV REY GASENDRA) Fadila Shely Amalia; Setiawansyah Setiawansyah; Dedi Darwis
TELEFORTECH : Journal of Telematics and Information Technology Vol 2, No 1 (2021): TELEFORTECH VOL 2, NO 1 (JULI 2021)
Publisher : Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1317.513 KB) | DOI: 10.33365/tft.v2i1.1810

Abstract

Metode Market Basket Analysis dapat digunakan untuk menganalisa pola belanja konsumen. Dengan memanfaatkan data transaksi penjualan yang kemudian diolah untuk mendapatkan informasi dari kumpulan data transaksi tersebut. CV Rey Gasendra merupakan salah satu perusahaan yang bergerak dalam bidang penjualan perangkat mobile atau smartphone dan elektronik yang berada di Kota Bandar Lampung. Toko ini belum mengetahui pola belanja pada keranjang belanja konsumen. Algoritma yang digunakan yaitu algoritma apriori, penerapan algoritma apriori dapat membantu menemukan jenis perangkat handphone dan berang elektronik yang paling sering terjual secara bersamaan yang nantinya akan digunakan sebagai acuan persediaan barang. Dari hasil pengujian yang dilakukan yaitu dengan membandingkan antara perhitungan secara manual dengan perhitungan yang dilakukan menggunakan tools rapidminer dengan 100 data sampel menghasilkan hasil yang sama yaitu terdapat lima belas rules. Setelah mengetahui pola belanja konsumen yang terjadi pada CV Rey Gasendra maka penulis menyarankan kepada pemilik toko agar menyediakan stok lebih banyak untuk item Tempered Glass dan Bracket TV Standar karena selama periode Desember 2020 sampai dengan Februari 2021 item tersebut paling laku terjual dengan kombinasi barang lainnya yaitu handphone dan televisi. Strategi yang dapat dilakukkan setelah mengetahui pola belanja konsumen tersebut yaitu dengan menawarkan barang yang kemungkinan yang akan dibeli secara bersamaan.Kata Kunci: Apriori, Association Rules, Confidance, Data Mining, Rapidminer
Integration of LODECI Weighting Method and SPOTIS in Employee Performance Evaluation Based on Multi-Criteria Decision Making  Fadila Shely Amalia; Dedi Darwis; Abhishek R. Mehta
Paradigma - Jurnal Komputer dan Informatika Vol. 28 No. 1 (2026): March 2026 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v28i1.12508

Abstract

Employee performance evaluation in many organizations often faces challenges due to numerous assessment criteria and potential subjectivity in the decision-making process, making the evaluation results less consistent and objective. Multi-Criteria Decision Making (MCDM) methods have been widely used to address this problem; however, previous approaches generally still rely on subjective weight determination and do not fully consider the stability of results against data variation. Therefore, this study aims to develop a more objective and stable decision-making model by integrating the LODECI method to determine criteria weights based on data and the SPOTIS method to rank alternatives based on their distance from the ideal solution. Five evaluation criteria are used, namely productivity, work quality, discipline, teamwork, and responsibility, with data collected from eight employees as alternatives. The analysis process was carried out through the stages of constructing a decision matrix, calculating criterion weights using LODECI, and ranking using SPOTIS which produced a total distance value as a quantitative evaluation metric. The research results show that GS Employee achieved the smallest distance value of 0.058, thus ranking first, followed by CR Employee with a value of 0.086 and AN Employee with a value of 0.321. These findings indicate that the proposed model is capable of providing more measurable and consistent evaluation results. The main contribution of this study lies in the integration of objective weighting and ideal-solution-based ranking methods supported by sensitivity analysis, thereby producing a performance evaluation system that is more reliable, transparent, and robust compared to previous approaches.
Integration of Data Assessment Method Weighting and Proximity Indexed Value for Best Employee Selection in Decision Support Systems Setiawansyah Setiawansyah; Dyah Ayu Megawaty; Faruk Ulum; Agung Deni Wahyudi; Fadila Shely Amalia
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 4 (2026): Volume 4 Number 4 October 2026 (Issue in Progress)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i4.378

Abstract

The selection of the best employee is a multi-criteria decision-making problem because employee performance is evaluated using several criteria with different levels of importance. Subjective criterion weighting may not fully represent the characteristics of the assessment data and can influence the resulting decision. This study proposes the integration of Data Assessment Method (DAM) Weighting and Proximity Indexed Value (PIV) within a Decision Support System for best employee selection. DAM Weighting is employed to determine objective criterion weights based on the information contained in the assessment data, while PIV is used to evaluate the relative proximity of each employee alternative to the reference condition. The results show that the criterion weights are relatively balanced, with R1 obtaining the highest weight of 0.1494 and R4 the lowest weight of 0.1381. The PIV calculation produces different proximity values among the nine employee alternatives, with Alt-08 obtaining the lowest value of -2.2864, followed by Alt-09 at -1.8580 and Alt-06 at -1.7148. Based on the resulting ranking, Alt-08 is identified as the best employee. These findings indicate that the integration of DAM Weighting and PIV can provide an objective, quantitative, and data-oriented mechanism for supporting best employee selection in a Decision Support System.
Co-Authors Abhishek R. Mehta Dedi Darwis Dyah Ayu Megawaty Setiawansyah Setiawansyah Ulum, Faruk Wahyudi, Agung Deni