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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Prosiding Semnastek Jurnal Teknologi dan Sistem Komputer Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Informatika Universitas Pamulang JURIKOM (Jurnal Riset Komputer) Informatika : Jurnal Informatika, Manajemen dan Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika KLIK: Kajian Ilmiah Informatika dan Komputer
Lestari Handayani
Fakultas Sains Dan Teknologi Universitas Islam Negeri (UIN) Sultan Syarif Kasim Riau
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Other

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Journal : JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)

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Implementasi Triple Exponential Smoothing dan Double Moving Average Untuk Peramalan Produksi Kernel Kelapa Sawit Risfi Ayu Sandika; Siska Kurnia Gusti; Lestari Handayani; Siti Ramadhani
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.3359

Abstract

The production of palm kernel is a significant product for the company and plays a crucial role. Nevertheless, the stability of kernel production is not always consistent, and the quality of the kernel can be detrimental to the company. As consumer demands change over time, companies must anticipate every fluctuation in palm kernel production. Hence it is vital to figure the long run with a settlement prepare utilizing information mining utilizing information within the past. The Triple Exponential Smoothing and Double Moving Average methods, which are data mining methods for future forecasting, were used in this study. The aim of this research is to predict the yield of future oil palm kernel production using the Triple Exponential Smoothing and Double Moving Average methods and to determine the level of forecasting errors using the Mean Absolute Percentage Error (MAPE) method. The data for the last ten years, from January 2013 to December 2022, were used in this study. After testing the Triple Exponential Smoothing method with parameters α=0.2,β=0.γ=0.2, the error rate using MAPE was 9.48%, and the Double Moving Average method had an error rate of 11.2%. The MAPE results of the Triple Exponential Smoothing method are considered very good, while the MAPE results of the Double Moving Average method are categorized as good based on the range of MAPE values. This research is expected to provide information to related companies as a supporting reference in anticipating palm oil kernel production. The conclusion of the research is that the Triple Exponential Smoothing method with the test parameters is the best method for forecasting.
Klasifikasi Citra Daging Sapi dan Babi Menggunakan CNN Alexnet dan Augmentasi Data Ikhwanul Akhmad DLY; Jasril Jasril; Suwanto Sanjaya; Lestari Handayani; Febi Yanto
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3702

Abstract

Konsumsi daging di Indonesia didominasi oleh sapi, kerbau, dan ayam. Namun, beberapa pedagang nakal mencampur daging sapi dengan daging babi sehingga sulit dibedakan oleh masyarakat awam. Beberapa penelitian telah menggunakan metode Convolutional Neural Network (CNN) untuk mengklasifikasikan citra, namun kekurangan data menjadi tantangan. Oleh karena itu, penelitian ini menerapkan teknik augmentasi data pada model CNN Alexnet untuk mengklasifikasikan daging sapi, babi, dan daging oplosan. Penelitian ini menggunakan dua rasio pembagian data yang berbeda, yaitu 90:10 dan 80:20, dengan total 600 data non-augmentasi dan 3000 data augmentasi yang dibagi menjadi tiga kelas. Beberapa hyperparameter diuji untuk mengoptimalkan kinerja model seperti optimizer Adaptive Moment Estimation (Adam), Stochastic Gradient Descent (SGD) dan Propagasi Root Mean Square (RMSprop) serta learning rate 0.1, 0.01, 0.001 dan 0.0001. Hasil menunjukkan bahwa penggunaan data citra augmentasi dengan optimizer Adam dan learning rate 0,001 memberikan accuracy tertinggi sebesar 85,00%. Sementara itu, penggunaan data citra non-augmentasi dengan skenario optimizer RMSprop dan learning rate 0, 0001 menghasilkan performa yang sedikit lebih rendah, yaitu mendapatkan accuracy 80.00%. Keduanya menggunakan perbandingan data 80:20. Teknik augmentasi data berhasil meningkatkan kinerja model deep learning dengan menciptakan data baru dari data yang ada.
Co-Authors Agung Surya Maulana Ahmad Paisal Alfin Hernandes Alwis Nazir Alwis Nazir Alwis Nazir Anggi Vasella Angraini Angraini Beni Basuki Cut Lira Kabaatun Nisa Deny Ardianto Eka Lona Maulida Elvia Budianita Fachrul Hakim Fadhilah Syafria Febi Yanto Fitriandini Fitriandini Gatot Suroto Gusti, Siska Kurnia Hafez Almirza Iis Afrianty Ikhwanul Akhmad DLY Indah Inzani Septa Iwan Iskandar Iwan Iskandar Jasril Jasril Liza Afriyanti M. Fadil Martias Meiky Surya Cahyana Mentari Aulia Putri Mohd. Ridho Zarkasih Rahim Muhammad Adri Muhammad Fikry Muhammad Irsyad Muhammad Rifaldo Al Magribi Mustakim Mustakim Nazruddin Safaat H Okfalisa Okfalisa Qurrata A'yuni Raja Indra Ramoza Rendra Arga Swaperi Risfi Ayu Sandika Rusnedy, Hidayati Shella Novi Siti Ramadhani Siti Ramadhani Suwanto Sanjaya Ulfah Adzkia