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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah EDUMATIC: Jurnal Pendidikan Informatika Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Data and Science Buletin Sistem Informasi dan Teknologi Islam
Hasanuddin, Tasrif
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Mathematics

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Journal : Indonesian Journal of Data and Science

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Analisis Perbandingan Performa Metode Simple Moving Average dan Exponential Moving Average untuk Peramalan Jumlah Penderita Covid-19 Litha Sari, Nurul; Hasanuddin, Tasrif
Indonesian Journal of Data and Science Vol. 1 No. 3 (2020): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ijodas.v1i3.19

Abstract

Pada penelitian bertujuan untuk memprediksi jumlah penderita covid-19 menggunakan metode Moving Average (SMA, dan EMA). Pengolahan data tersebut digunakan untuk memprediksi jumlah penderita covid-19. Adapun akurasi permalan yang digunakan dalam penelitian ini yaitu MAD, MSE, RMSE , dan MAPE. Model Moving Average Model yang akan digunakan pada penelitian ini merupakan metode untuk memperkirakan kondisi pada masa yang akan datang dengan menggunakan kumpulan data-data masa lalu. Periode waktu yang akan dikumpulkan data tersebut dapat berupa Tahunan, Bulanan, Mingguan, bahkan Harian. Hasil pengujian Simple Moving Average (SMA) pada line graph menunjukkan peramalan nilai lebih dekat dengan data real dibandingkan dengan Exponential Moving Average (EMA). Pengunaan SMA 2 hingga SMA5 menunjukkan hasil peramalan SMA 2 paling mendekati dari data real.
Classification of Lontara Script Using K-NN Algorithm, Decision Tree, and Random Forest Based on Hu Moments and Canny Segmentation Septiani, Berlian; Hasanuddin, Tasrif; Astuti, Wistiani
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.281

Abstract

Lontara script is a traditional writing system of the Bugis-Makassar people in South Sulawesi, used to write the Bugis, Makassar, and Mandar languages. This system is based on an abugida, in which each letter represents a consonant with an inherent vowel. It was once used to record history, customary law, and literature, but its use has declined due to the influence of the Latin alphabet. Today, the Lontara script is preserved through education and digitization as part of the cultural heritage of the Indonesian archipelago. In this article, the researchers attempt to use a dataset of handwritten Lontara Bugis-Makassar characters. The process begins with the collection of character datasets, which are then processed through Canny segmentation and Hu Moment feature extraction to obtain a representation of the shape that is invariant to rotation and scale. The processed data was divided into training and testing data, then classified using the K-NN, Decision Tree, and Random Forest algorithms. The results showed that the KNN algorithm with 6 neighbors achieved the highest accuracy, precision, and recall of 98%. The Decision Tree algorithm achieved an accuracy of 96.67%, precision of 96.22%, recall of 95.33%, and an F1-score of 95.98%. Meanwhile, Random Forest showed an accuracy of 96.67%, precision of 96.34%, recall of 96%, and an F1-score of 95.98%.
Co-Authors Abdullah, Syahrul Mubarak Aji Prasetya Wibawa Arsyad, Muh Arya Astuti, Wistiani Baharuddin, Mus Mulyadi Citra Suardi Darwis, Herdianti Harlinda, Harlinda Huzain Azis Litha Sari, Nurul Mardiyyah Hasnawi Mokh Sholihul Hadi Moleo, Alif Safa Mude, Muh Aliyazid Muh. Aliyazid Mude Papua, Ahmad Ruslandia Qurrahman, Nur Taufiq Rahmat Riyadi Rosnani Rosnani Septiani, Berlian Sugiarto Cokrowibowo Sujito Sujito