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All Journal JUTIS : Jurnal Teknik Informatika Journal of Computer System and Informatics (JoSYC)
Ernawati, Nindi
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Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Implementation Fuzzy Tsukamoto's method in decision support system for flight schedule Ernawati, Nindi; Nurrahman, Nurrahman
JURNAL TEKNIK INFORMATIKA UNIS Vol. 10 No. 1 (2022): Jutis (Jurnal Teknik Informatika)
Publisher : Universitas Islam Syekh Yusuf

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33592/jutis.v10i1.2327

Abstract

Pesawat adalah transportasi yang banyak digunakan oleh kalangan masyarakat dan telah menjadi transportasi pilihan yang cepat dan nyaman. Meskipun dengan harga yang dibayarkan jauh lebih mahal daripada transportasi lainnya, pesawat masih menjadi pilihan yang terbaik. Seringkali karena banyaknya orang menggunakan transportasi ini membuat manajemen tidak mudah untuk menentukan jumlah keberangkatan maupun kedatangan. Penelitian ini berfungsi untuk menyelesaikan permasalahan tersebut dengan suatu metode. Metode yang dipakai oleh penulis pada penelitian ini yaitu metode Fuzzy Tsukamoto. Dari keseluruhan hasil penelitian yang dilakukan oleh penulis dapat diperoleh hasil yaitu 1.825 penerbangan pesawat. Dapat disimpulkan dari hasil perhitungan tersebut bahwa jumlah keberangkatan lebih banyak dibandingkan dengan jumlah kedatangan. Sehingga penelitian ini dapat menjadi bahan pertimbangan untuk menghitung banyaknya jumlah penerbangan kedepannya.
Analisis Algoritma C45 dan Regresi Linear untuk Memprediksi Hasil Panen Kelapa Sawit Nurahman, Nurahman; Ernawati, Nindi
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5828

Abstract

Indonesia, as one of the main producers of palm oil in the world, has an agricultural sector that is very influential on the national economy, especially through palm oil exports. Prediction of oil palm yields is crucial to improve efficiency in planning and resource management. This study was conducted to compare the performance of two prediction methods, namely the C45 Algorithm and Linear Regression, in predicting oil palm yields. The formulation of the problems raised in this study includes: (1) How does the performance of the C45 Algorithm and Linear Regression compare in predicting oil palm yields? (2) How accurate are the predictions generated by the two algorithms based on historical data on crop yields? (3) What are the factors that influence the choice between C45 Algorithm and Linear Regression for oil palm yield prediction? The data used in this study is historical data from PT. Surya Inti Sawit Kahuripan, which includes 106 data blocks. The variables analyzed included land area, number of trees, number of trees per hectare, planting year, soil type, fertilizer use plan, and yield in tons. Data analysis was carried out using the C45 Algorithm, which forms a decision tree based on historical data, and the Linear Regression method, which analyzes the linear relationship between independent variables and dependent variables. Prediction accuracy is measured using Root Mean Squared Error (RMSE). The results show that the C45 Algorithm has a lower RMSE value than Linear Regression, indicating that the C45 Algorithm provides more accurate predictions.
Co-Authors Nurahman, Nurahman Nurrahman Nurrahman