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All Journal Jurnal CoreIT JISICOM (Journal of Information System, Infomatics and Computing)
Al Qadri, Muhammad Vannes
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Computer Science & IT

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PENERAPAN HADOOP DALAM ANALISIS SENTIMEN ULASAN PENGGUNA DI PLATFROM ECCOMERCE Kasim, Nurdian; Ardini, Ni Luh Ica; Muharramah, Alfi Zahrah; Hikma, Hikma; Al Qadri, Muhammad Vannes; Rosalina, Rosalina; Asriyani, Wa Ode; Eviriawan, Eviriawan; Sajiah, Adha Mashur
Journal of Information System, Informatics and Computing Vol 9 No 1 (2025): JISICOM (June 2025)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v9i1.1784

Abstract

Studi ini menyelidiki penggunaan teknologi Hadoop dan algoritma Naive Bayes untuk menganalisis sentimen ulasan pengguna di platform e-commerce. Data yang digunakan berasal dari 391.500 ulasan dari aplikasi Shopee yang dikumpulkan melalui scraping Google Play Store. Implementasi model klasifikasi sentimen, pengumpulan data melalui web scraping, dan pra-pemrosesan data menggunakan PySpark adalah metodologi penelitian. Hasil penelitian menunjukkan bahwa model Naive Bayes dapat mengklasifikasikan perasaan pengguna dengan akurasi 87%. Menurut analisis word cloud, elemen seperti gratis ongkir dan kemudahan penggunaan menjadi pendorong utama sentimen positif. Sementara itu, sentimen negatif didominasi oleh masalah teknis aplikasi dan layanan pelanggan. Penelitian ini menunjukkan bahwa penggunaan Hadoop dan Naive Bayes dalam analisis data ulasan berskala besar saat mengembangkan platform e-commerce adalah efektif.
Red Curly Chili Forecast in Southeast Sulawesi Using Auto Regressive Integrated Moving Average (ARIMA) Al Qadri, Muhammad Vannes; Saputra, Rizal Adi; Pramono, Bambang
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.33986

Abstract

Abstract. Price is a crucial aspect in the world of trade. Red curly chili peppers have become one of the plants favored by many consumers. This research aims to develop a forecasting model that can provide a more accurate insight into the future prices of red chili peppers, particularly in Southeast Sulawesi. Because price forecasting plays a crucial role in predicting future price trends, the Auto Regressive Integrated Moving Average (ARIMA) method becomes one of the models that can be used for time series analysis. The data for this research is sourced from the National Food Body Price Panel Website. The data period starts from August 8, 2022, to December 15, 2023, with the last 500 days' prices used as both test and training data. In this study, the ARIMA (1,1,1) model emerged as the best among the three ARIMA models analyzed. The ARIMA (1,1,1) model yielded a MAPE percentage of 17.97%, indicating that this model is suitable or reliable for time series forecasting. Furthermore, the results of this experiment show that the forecasted prices for the next 10 days do not experience significant decreases or increases, referring to several recent data points used as training data samples.
Co-Authors Ardini, Ni Luh Ica Asriyani, Wa Ode Bambang Pramono Eviriawan, Eviriawan Hikma Hikma, Hikma Kasim, Nurdian Muharramah, Alfi Zahrah Rizal Adi Saputra Rosalina Rosalina Sajiah, Adha Mashur