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All Journal Dinamik Journal of Natural Sciences and Mathematics Research Sinkron : Jurnal dan Penelitian Teknik Informatika Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Tekinkom (Teknik Informasi dan Komputer) INFOKUM Jurnal Teknologi Informatika dan Komputer Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Media Informatika TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Ilmiah Multidisiplin Indonesia INOVTEK Polbeng - Seri Informatika
Indra, Evta
Universitas Prima Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Physics

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Journal : Jurnal Teknologi Informatika dan Komputer

 1 
Prediksi Nilai Ekspor Pulp di Indonesia Mengunakan Metode Long Short Term Memory Silaen, Dinda Tamara; Simanjuntak, Aldowad Alles Sandro Hamonangan; Tarigan, Kurniawan; Indra, Evta
Jurnal Teknologi Informatika dan Komputer Vol. 9 No. 2 (2023): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v9i2.1599

Abstract

Ekspor pulp merupakan kegiatan ekonomi penting bagi perusahaan dan pemerintah, yang membutuhkan informasi akurat mengenai permintaan pasar dan strategi bisnis yang tepat. Metode Long Short Term Memory (LSTM) digunakan untuk memprediksi hasil produksi pulp di masa depan dengan memanfaatkan data historis dan faktor-faktor yang mempengaruhinya. Tahapan penelitian meliputi studi literatur untuk memahami metode LSTM, pengumpulan data, seleksi dan transformasi data untuk mempersiapkan dataset yang akan digunakan, serta visualisasi data ekspor untuk mendapatkan wawasan yang lebih baik. Selanjutnya, metode LSTM diterapkan dengan langkah-langkah pembentukan model, pelatihan model, prediksi nilai ekspor, dan evaluasi hasil prediksi. Hasil penelitian  ini untuk nilai Root Mean Squared Error (RMSE) terhadap produk Jumbo Roll Tissue, Napkin Tissue, Multi Purpose Tissue, dan Facial Tissue berturut-turut adalah 2.52, 1.88, 2.77, dan 2.67. Semakin kecil nilai RMSE, semakin baik performa model. Nilai RMSE yang kecil pada setiap produk menunjukkan bahwa model memiliki performa yang baik dalam memprediksi semua produk. Dengan prediksi yang lebih akurat, perencanaan produksi dan persediaan bahan baku dapat dilakukan dengan lebih efisien dan efektif, sehingga mengoptimalkan produktivitas dan mengurangi biaya produksi.Hasil penelitian ini diharapkan dapat membantu masyarakat dan pemerintah dalam pengambilan keputusan strategis terkait produksi pulp dan kebijakan ekspor. Selain itu, penelitian ini juga diharapkan dapat meningkatkan efisiensi pemasaran dan distribusi produk, meningkatkan pengetahuan tentang pasar dan produk, serta membuka peluang pasar baru untuk produk pulp.
Purchasing Prediction Using Machine Learning Algorithms for Optimizing Inventory Management Prayetno, Reza Hamdi; Purba, Rani Destika; Wirawan, Kyrene; Sweet, Kelvin; Indra, Evta
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 1 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i1.2522

Abstract

Effective inventory management is a crucial element in company operations, especially in maintaining a balance between demand and supply. Good inventory management can reduce storage costs, increase product availability, and maximize company profits. However, the challenges that companies often face are the uncertainty of market demand and changes in trends that are difficult to predict. Along with technological developments, traditional methods of inventory management are starting to be replaced by data-based approaches and machine learning algorithms. The use of machine learning is not only limited to predicting purchasing needs, but can also be applied in various other business aspects. This research aims to optimize HP spare parts inventory management at Store X using the Long Short-Term Memory (LSTM) method. By analyzing sales data for 2023 which consists of 96,630 lines, the research applies systematic stages: data acquisition, preprocessing, exploratory data analysis, model building, and evaluation. The LSTM method is used to predict spare parts stock with significant accuracy, demonstrated through evaluation metrics: Mean Absolute Error (MAE) 12%, Mean Squared Error (MSE) 2%, and Root Mean Square Error (RMSE) 15%. The model successfully captured seasonal patterns and trends in sales data, proving its ability to forecast stock requirements. The research results show that the LSTM-based machine learning approach is effective in supporting inventory management decision making, helps reduce the risk of losses due to stock uncertainty, and increases the efficiency of managing HP spare parts inventory.
Co-Authors ., Calvin ., Christnatalis Abellista, Tivanez Ballerina Ahmad Rifai Akbari, Deni Adha Alfi, Ahmad Haikal Alifah, Lutfi Aulia Alvarez, Stevin Amalia Amalia Aminatunnisa, Siti Amir Saleh Anglhoma, Filbert ANITA . Bangun, Dea Monica Bangun, Frans Aditya Banjarnahor, Jepri Barus, Daniel Haganta Brutu, Lolo Frans M. Butarbutar, Serly Yunarti Buulolo, Deniarwinus Candra, Kevin daniel christian Delima Sitanggang, Delima Dina Pratiwi, Dina Dwi Rizky, Atikah Edison, Rizki Edmi Fahmi, Mohammad Irfan Fando, Al Farrona, Rio Fidelis, Rio Giawa, Well Friend Ginting, Hardiansah Ginting, Nessa Sanjaya Ginting, Ricci Kincahar Bastoto Kevin Gulo, Agustinus Gultom, Yeni Gurusinga, Alta Harahap, Charles Bronson Hizkia, Hidayati Hutabarat, Fenna Kemala Hutabarat, Lerry Yos Santa Angelina Hutasoit, Leo Nardo Hutauruk, Jesika Avonia Juanta, Palma Juliandra, Vella Karim, Anggie Monica Keliat, Ribka Amelia Yunita Khu, Jerry Kumar, Sharen Loi, Mentari Hati Lowell, Alvis Lowell, Audric Lumbanraja, Lamtiur Rondang Wulan Lumbantobing, Josep Sutoyo Muda Maharja, Okta Jaya Manullang, Murni Esterlita Mariyanti, Eka Marpaung, Aldo Andy Yoseph Tama Matondang, Enjelika Mawaddah Harahap, Mawaddah MAYANTI, NUR Meizar, Abdul Muhammad Farhan Muhammad Yasir Muhardi Saputra Napitupuluh, Christian Deniro Nasution, Adli Abdillah Nasution, Syafrani Putri NK Nababan, Marlince Okta Jaya Harmaja Oloan Sihombing, Oloan Pakpahan, Ferdinand Linggo Panjaitan, Ezra Christina Septiana panjaitan, haris samuel pranada Piay, Clara Stephanie Bernadeth Pratama, Febryan Pratiwi Cristin Harnita, Pratiwi Cristin Prayetno, Reza Hamdi Purba, Rani Destika Purba, Salda Sari Putra Daniel Gohae, Hamdani Rahil, Rafif Rahmad, Julfikar Reinaldo, Erick Riady, Muhammad Alvin Rifaldo, Rifaldo Ruben Ruben, Ruben Saragih, Septua Fujima Sembiring, Diarnia Mega Selfia Sembiring, Joni Satrio Sembiring, Yudha Brema Agriva Sianturi, Santo Sanro Siburian, Astri Dahlia Sijabat, Ningot Putra Silaban, Herlan Silaen, Dinda Tamara Simajuntak, Yusuf Natanael Simamora, Wanda Pratama Putra Simangunsong, Sarah Simanjuntak, Aldowad Alles Sandro Hamonangan Simanjuntak, Mega Herlin Simarmarta, Brando Benedictus Simbolon, Ongki Sopie Sinaga, Putri tua Sinurat, Stiven Hamonangan Siregar, Frissy Siregar, Reinhrad Shodani Siringo Ringo, Jaka Tomi Ronaldo Sitanggang, Audina L Sitompul, Chris Samuel Sitompul, Daniel Ryan Hamonangan Sitorus, Sarah Tri Yosepha Situkkir, Miando Mangara Situmorang, Andreas Solly Aryza Stanley, Calvin Suhamdani, Dadan Suwanto, Jacky Suyanto, Jao Han Sweet, Kelvin Tampubolon, Irfan Saputra Tarigan, Kurniawan Tarigan, Nina Veronika Tarigan, Sri Wahyuni VERONICA VERONICA Vicraj, Vicraj Wijaya, Malvin Luckianto Wiranto, David Wiratama, Westlie Wirawan, Kyrene Wirhan Fahrozi, Wirhan Yonata Laia Ziegel, Dennis Jusuf