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All Journal METIK JURNAL
Ayu Adelina Suyono
Universitas AMIKOM Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering

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Analisis Sentimen Haramnya Musik Secara Umum Mengunakan Metode KNN Rahmat Saudi Al Fathir; Thami Rusdi Agus; Ayu Adelina Suyono; Fardiansyah Ibrahim
METIK JURNAL Vol 5 No 2 (2021): METIK Jurnal
Publisher : LP3M Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v5i2.284

Abstract

Penelitian ini membahas tentang penerapan algoritma K-Nearest Network dalam kasus analisis sentimen. Analisis sentimen dilakukan terhadap komentar dalam video yang membahas tentang haramnya musik. Sumber data diambil ceramah Ustadz Dr. Syafiq Riza Basalamah, M.A. dengan judul Hukum Musik dalam Islam Beserta Dalilnya, Hukum Musik dalam Islam LENGKAP: Musik HALAL atau HARAM, dan Hadits Haramnya Musik Dhaif. Data komentar yang digunakan sejumlah 2114 data berbahasa Indonesia. Komentar dibagi menjadi 3 kelas, diantaranya Menerima, Tidak Menerima, dan Bingung. Hasil pengujian menunjukkan nilai akurasi yang relatif rendah, yaitu pada tingkat 65%. Hal ini diakibatkan karena kurangnya jumlah data yang disertakan dalam pengujian.
Prediksi Indeks Harga Konsumen Komoditas Makanan di Kota Surabaya menggunakan Support Vector Regression Ayu Adelina Suyono; Kusrini Kusrini; Muhammad Rudyanto Arief
METIK JURNAL Vol 6 No 1 (2022): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v6i1.339

Abstract

In data mining, predictions are known to find knowledge about what will happen in the future. Predictions are usually made on time-series data. The Consumer Price Index (CPI) is an index value derived from daily consumer price data. The results of the CPI calculation are derived from observations of commodity prices at the household consumer level, which are carried out routinely on a daily, weekly, bi-weekly, and monthly basis. CPI prediction can be done using a data mining algorithm, namely Support Vector Regression (SVR). SVR is part of the Support Vector Machine algorithm that functions to solve regression cases. SVR is a reliable algorithm in the case of regression because it can handle data overfitting well. The data used as input in this paper comes from 34 food commodity prices, and the output data is obtained from the CPI value data. The food commodity price data used is from Surabaya City. The data period used is from 2014-2020. The results of the implementation of SVR with 4 kernels show that the Polynomial kernel has the best error rate with a MAPE value of 4.31%.
Co-Authors Fardiansyah Ibrahim Kusrini Kusrini Muhammad Rudyanto Arief Rahmat Saudi Al Fathir Thami Rusdi Agus