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All Journal Jurnal Transformatika Jurnal Informatika Polinema (JIP)
Agung Pramudhita
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Library & Information Science

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PERBANDINGAN METODE FTS DAN MA PADA PERAMALAN PERSEDIAAN BERAS Deddy Kusbianto; Agung Pramudhita; Nurhalimah
Jurnal Informatika Polinema Vol. 6 No. 3 (2020): Vol 6 No 3 (2020)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v6i3.314

Abstract

Dalam memenuhi kebutuhan masyarakat Kabupaten Malang dan menjaga stabilitas ketersediaan beras pemerintah setempat perlu melakukan proses peramalan. Dimana dalam melakukan proses peramalan menggunakan metode peramalan, salah satunya dengan menggunakan metode Fuzzy Time Series dan Moving Average yaitu dengan menangkap pola dari data yang telah lalu kemudian digunakan untuk memproyeksikan data yang akan da¬¬tang. Dari hasil implementasi dua metode tersebut menghasilkan perbandingan jumlah persediaan beras. hasil perbandingan tersebut akan dipakai untuk mengukur tingkat error dari masing – masing metode dengan menggunakan MAD (Mean Absolute Deviation), MSE (Mean Square Error), RMSE ( Root Square Error ) dan MAPE (Mean Absolute Percentage Error). Kesimpulannya adalah metode fuzzy time series cocok digunakan untuk studi kasus peramalan persediaan beras dibandingkan menggunakan metode moving average. Sehingga untuk proses peramalan selanjutnya dan untuk mendapatkan hasil dengan tingkat error sedikit dapat menggunakan metode fuzzy time series.
IDENTIFIKASI PENYAKIT JANTUNG MENGGUNAKAN MACHINE LEARNING: STUDI KOMPARATIF Endah Septa Sintiya; Rizdania Rizdania; Ashri Shabrina Afrah; Agung Pramudhita
Jurnal Transformatika Vol. 21 No. 1 (2023): July 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.6274

Abstract

Heart disease is the number one cause of death globally. This condition is followed by an unhealthy lifestyle. Heart disease prediction needs to be done considering the importance of health. The presence of machine learning has made it easier for humans to make early detection of patterns that are close to heart disease. Prediction of heart disease is important given the behavior of people who are still prone to risk factors. Conditions where predictions using machine learning for heart disease have not been compared with many using machine learning methods. Predictions of heart disease are needed along with the interrelationships of the variables. This research compares 6 machine learning methods for disease classification with KNN, Naïve Bayes, Decision tree, Random forest, logistic regression, and SVM. The final classification obtained ranking accuracy with the highest value of 82% in the KNN method with the confusion matrix test, precision, accuracy, re-call, and fi-score. These results can be applied to real case studies of heart disease
Co-Authors Ashri Shabrina Afrah Deddy Kusbianto Endah Septa Sintiya NURHALIMAH Rizdania, Rizdania