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All Journal Pendidikan Bahasa dan Sastra Indonesia Acta Aquatica: Aquatic Sciences Journal Jurnal Lemuru : Jurnal Ilmu Perikanan dan Kelautan Indonesia JISA (Jurnal Informatika dan Sains) Journal of Coastal and Ocean Sciences Ikhtisar: Jurnal Pengetahuan Islam Jurnal Ilmiah AgriSains Innovative: Journal Of Social Science Research Bangun Desa : Jurnal Pengabdian Masyarakat Journal of Learning and Technology in Physics Journal of Marine Studies Jurnal Perikanan Terpadu Journal of Aceh Aquatic Sciences Innotech
Rika Astuti
Jaringan Kualisi Untuk Laut Aceh (Kuala), Banda Aceh
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

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Journal : Innotech

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PERANCANGAN SISTEM INFORMASI PENJUALAN BERBASIS WEB PADA UMKM CHEESE STICK ALFAN Astuti, Rika
Innotech: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi Vol 1 No 1 (2024): Innotech Issue Januari 2024
Publisher : Universitas Siber Indonesia

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Abstract

Cheese Stick Alfan is one of the micro, small and medium businesses, which until now the production and sales process is still carried out by buyers who come directly. There are still many weaknesses found in the sales process at Cheese Stick Alfan, for example, they have not used technology. a tool to market a product they will sell. so that business actors can sell their products using the existing WEBSITE to cities or villages that are connected to the internet. Therefore, Cheese Stick Alfan requires an information system such as e-commerce to better identify the products being sold and provide convenience. The purpose of this writing is to give even better results, at the Alfan Cheese Stick store, by implementing the concept in the form of a web-based application. The design of this sales system will use the waterfall method and the end result is a web-based application which is also expected to help the sales process at the Cheese Stick Alfan store.
KLASIFIKASI BERITA HOAX POLITIK PADA MEDIA SOSIAL X MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN RANDOM FOREST Astuti, Rika; Tiarma Sipahutar, Angel
Innotech: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi Vol 2 No 1 (2025): Innotech Issue Januari 2025
Publisher : Universitas Siber Indonesia

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Social media has become a major source of information as well as a fertile ground for the spread of hoax news, particularly political news. This study examines two classification algorithms, Naive Bayes and Random Forest, to detect hoax news. The data used consists of political news labelled as hoax or non-hoax. The results show that the Random Forest algorithm has better accuracy and performance compared to Naive Bayes in terms of precision, recall, and F1-score. In conclusion, Random Forest is more effective for classifying political hoax news on social media. This research provides important insights into the application of machine learning algorithms in text classification and emphasizes the importance of selecting the right algorithm for optimal results.
Identifikasi Perbandingan Prediksi Harga Saham pada PT XYZ Menggunakan Teknik Algoritma FB Prophet dan Random Forest pada Metode CRISP-DM Astuti, Rika; Chandra Bagaskoro Setyoko, Dwi
Innotech: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi Vol 3 No 1 (2026): Innotech Issue Januari 2026
Publisher : Universitas Siber Indonesia

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Abstract

This research aims to compare the effectiveness of two stock price prediction algorithms, namely FB Prophet and Random Forest, using the CRISP-DM method. The main focus of the study is on the stocks of PT XYZ, with data taken from the period of March 1, 2019, to March 1, 2024. Stock price prediction is a significant topic in the field of finance as it can help investors make better decisions. Both algorithms are evaluated based on error rates measured using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). This study demonstrates that both FB Prophet and Random Forest algorithms have their respective advantages in predicting stock prices. This research is also expected to contribute to the scientific literature in the fields of data mining and stock price prediction analysis.
Co-Authors . Ermanto Akip, Muhamad Alhikam, Muhammad Iqbal Aziz As'ari, As'ari As'ari Auliyana, Nur Chandra Bagaskoro Setyoko, Dwi Edwarsyah Edwarsyah, Edwarsyah ekamaida, ekamaida Endriansyah, Muhammad Try Erlangga, Haqqy Rerian Fahreza, Muhammad Reza Feta, Neneng Rachmalia Fitria Fitria Friyuanita Lubis Giovanni Oktavinanda Gunandar, Jerry Hadinata, Fitra Wira Hayatun Nufus Imam Suyuti Inayah, Safa Aulafi Isma, Muhammad Fauzan Malik, Rizky Afdansyakur Martoyo, Anang Muhamad Faizul Amirudin Muhammad Haikal Muhammad Kadri, Muhammad MUHAMMAD RIZAL Muliyana, Agusriati Munandar, Roni Arif Natasya, Rizky Gita Nella Tri Agustini, Nella Tri Neneng Marlian, Neneng Nugraheni, Fika Irlandia Nur Hikmah Nurmaini Nurmaini Putri, Indira Amalia Putri, Levi Rianti Rahul, Rahul Ramadhan, Galuh Bakti Riri Ezraneti Saputra, Dedi Dwi Savira, Aulya Putri Seilawah, Ananda Putri Simatupang, Desima Natasya Sulasti, Mila Sulastri, Mila Sustiani, Purilia Ratna SYAHRIAL SYAHRIAL Syahrul R. Tiarma Sipahutar, Angel Umbekna, Sara Wardhani, Diky Yasin, Ali Zurba, Nabil