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All Journal Jurnal Simetris Jurnal S2 Pendidikan Matematika Prosiding SNATIF JURNAL MANAJEMEN (EDISI ELEKTRONIK) Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA Infotech: Journal of Technology Information International Journal of Community Service Simpatik: Jurnal sistem Informasi dan Informatika Buletin Sistem Informasi dan Teknologi Islam Journal of Management and Digital Business Duta Abdimas: Jurnal Pengabdian Masyarakat Innovative: Journal Of Social Science Research SmartComp CSRID Jurnal Sosialita: Jurnal Kajian Sosial dan Pendidikan
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Journal : Infotech: Journal of Technology Information

 1 
PENERAPAN SISTEM REKOMENDASI CONTENT BASED FILTERING PADA PEMILIHAN SKINCARE DI CAMELIA Alliya, Sania Qorrii; Widyaningsih, Pipin; nurmalitasari, Nurmalitasari
Infotech: Journal of Technology Information Vol 10, No 2 (2024): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v10i2.280

Abstract

Skincare is a vital activity to maintain or improve the condition of facial skin. In Indonesia, skincare trends continue to grow due to various factors such as skin problems, technological advances, research on certain substances, and increasing awareness of the importance of maintaining healthy skin. Camelia skincare, a skincare store that has a website that displays many products on the camelia website without any search or recommendation system, making it difficult for visitors to choose the right product. Therefore, researchers want to build a skincare product recommendation system based on product data on the website using the content-based filtering method and the naïve bayes algorithm for calculating the similarity value. This research uses primary and secondary data from existing data on the website. The development method used is the Agile Software Development method. The agile software development method has several stages, namely planning, implementation, testing, documentation, deployment and maintenance. In conducting the research, researchers used the observation method by looking at existing data on the camelia website. As for the resulting features themselves are skincare search, skincare list, skincare details, and also skincare recommendations. For testing the system itself using the recall and precision table method. And from these tests, the average percentage of recall value is 89% and the percentage of precision value is 96%. ABSTRAKSkincare merupakan kegiatan vital untuk menjaga atau memperbaiki kondisi kulit wajah. Di Indonesia, tren perawatan kulit terus berkembang karena berbagai faktor seperti permasalahan kulit, kemajuan teknologi, penelitian terhadap zat tertentu, dan meningkatnya kesadaran akan pentingnya menjaga kesehatan kulit. Camelia skincare, toko perawatan kulit yang memiliki website yang menampilkan banyak produk di website camelia tanpa ada sistem pencarian ataupun rekomendasi sehingga menyulitkan pengunjung dalam memilih produk yang sesuai. Oleh karena itu, peneliti ingin membangun sistem rekomendasi produk skincare berdasarkan data produk pada website tersebut dengan menggunakan metode content based filtering dan algoritma naïve bayes untuk perhitungan nilai similarity. Penelitian ini menggunakan data primer dan sekunder dari data yang ada pada website. Metode pengembangan yang digunakan adalah metode Agile Software Development. Metode pengembangan sistem agile software development memiliki beberapa tahapan, yaitu planning, implementation, testing, documentation, deployment dan maintenance. Pada pelaksanaan penelitian, peneliti menggunakan metode observasi dengan melihat data yang sudah ada pada website camelia. Sedangkan untuk fitur yang dihasilkan sendiri adalah pencarian skincare, list skincare, detail skincare, dan juga rekomendasi skincare. Untuk pengujian sistemnya sendiri menggunakan metode tabel recall dan  precision. Dan dari pengujian tersebut didapatkan rata-rata prosentase nilai  recall yaitu 89% dan prosentase nilai precision yaitu 96%.
PREDIKSI HARGA SAHAM PT BANK CENTRAL ASIA TBK DENGAN MENGGUNAKAN ALGORITMA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) Tri Wulandari, Rahmawati Desi; Nurmalitasari, Nurmalitasari; Permatasari, Hanifah
Infotech: Journal of Technology Information Vol 10, No 2 (2024): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v10i2.278

Abstract

Stock prediction is one of the important things in stock trading. It calculates the potential worth of stocks or other financial assets that are traded on stock exchanges. The precision of stock forecast outcomes serves several purposes, principally providing investors with protection from possible market hazards and opening doors for index traders, market speculators, and arbitrageurs to make money. This study used the Autoregressive Integrated Moving Average (ARIMA) model and the Google Colab website to examine and predict the closing price of PT Bank Central Asia Tbk shares. This study uses daily historical stock data from 01 January 2020 to 01 January 2024. The ARIMA (1.0.1) model is the most accurate model for predicting the closing price of PT Bank Central Asia Tbk shares, according to the study’s findings.ABSTRAKDalam trading saham, Prediksi saham adalah hal yang sangat penting. Mengetahui nilai masa depan saham perusahaan atau instrument keuangan lainnya yang diperdagangkan di bursa keuangan adalah sesuatu yang menarik. Banyak hal dipengaruhi oleh akurasi hasil prediksi saham, terutama kebutuhan bagi investor untuk melindungi nilai mereka dari resiko pasar yang mungkin terjadi, peluang bagi speculator pasar dan arbitrase mengambil keuntungan dari indeks perdagangan. Tujuan penelitian ini untuk memprediksi harga penutupan saham PT Bank Central Asia Tbk dengan menggunakan model Autoregressive Integrated Moving Average (ARIMA) dan menganalisisnya dengan menggunakan website Google Colab. Penelitian ini menggunakan data historis harian saham dari tanggal 1 Januari 2020 hingga tanggal 1 Januari 2024. Hasil studi menunjukkan bahwa model ARIMA (1.0.1) adalah yang terbaik untuk meramalkan harga penutupan saham pada perusahaan ini.
Co-Authors Adenta Sari, Nur Avia Al Mustofa, Muhammad Hafizh Alliya, Sania Qorrii Atina, Vihi Awang Long, Zalizah Dwi Hartanti Eko Purwanto Hasanah, Herliyani Imam Sujadi JOKO SUSANTO Miftakhul Jannah, Ulfarida Nugroho Arif Sudibyo Nugroho, Mohammad Yusuf Nurchim Nurchim Permatasari, Hanifah Pipin Widyaningsih Rifki Hidayat Sajad, Abdullah Sari, Nur Avia Adenta Setiawati, Neha Poetri Singgih Purnomo Syawaludin, M Ainur Taufik Hidayat Tri Atmojo Kusmayadi Tri Wulandari, Rahmawati Desi Utami, Maulinda Devi