[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123979-id":3,"doc-seo-123979-113":31,"detail-sidebar-cat-0-id-113":84},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},123979,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",54,"Penelitian & Laporan","Analisis Perbandingan Algoritma Machine Learning untuk Forecasting Persediaan Produk Barang Pokok - Studi SARIMA dan MAPE","Penelitian ini menganalisis efektivitas algoritma machine learning untuk forecasting persediaan barang pokok pada UMKM dan ritel modern menggunakan data historis transaksi pembayaran serta variabel terkait. Data ditransformasikan dengan interval satu standar deviasi untuk menstasionerkan deret waktu agar konsisten secara statistik bagi algoritma prediksi. Evaluasi menunjukkan SARIMA menghasilkan kinerja terbaik untuk memprediksi persediaan pada kedua tipe toko. Pada data asli, MAPE UMKM sebesar 1,11% dan ritel modern 0,98%, sedangkan pada data termodifikasi MAPE masing-masing 0,74% dan 0,70%. Temuan ini mendukung perbaikan strategi manajemen persediaan agar stok lebih tepat terhadap dinamika pasar dan harapan konsumen.","Analisis Perbandingan Algoritma Machine Learning Untuk Forecasting Persediaan Produk  \nBarang Pokok  \n[http://dx.doi.org/10.28932/jutisi.v10i2.9357](http://dx.doi.org/10.28932/jutisi.v10i2.9357)  \nRiwayat Artikel  \nReceived: 8 Juli 2024 | Final Revision: 16 Juli 2024 | Accepted: 24 Juli 2024  \nCreative Commons License 4.0 (CC BY – NC)   \nAvinash 􀀍 \\#1 , Andreas Widjaja\\#2, Oscar Karnalim\\#3  \n\\# Program Studi Magister Ilmu Komputer, Universitas Kristen Maranatha, Bandung  \nJl. Prof. Drg. Suria Sumantri No. 65, Bandung 40164, Indonesia  \n[1](1 avinashawin48@gmail.com)[ avinashawin48@gmail.com](1 avinashawin48@gmail.com)  \n[2](2 andreas.widjaja@maranatha.ac.id)[ andreas.widjaja@maranatha.ac.id](2 andreas.widjaja@maranatha.ac.id)  \n[3](3 oscar.karnalim@maranatha.ac.id)[ oscar.karnalim@maranatha.ac.id](3 oscar.karnalim@maranatha.ac.id)  \n􁳖 Corresponding author: [avinashawin48@gmail.com](avinashawin48@gmail.com)  \nAbstrak—Dalam era teknologi yang terus berkembang, permintaan konsumen terhadap kebutuhan sehari-hari semakin kompleks. Perusahaan ritel harus mengadopsi pendekatan canggih untuk memahami dan memenuhi preferensi konsumen. Penelitian ini mengeksplorasi efektivitas algoritma machine learning dalam forecasting persediaan barang pada berbagai jenis toko ritel menggunakan data historis transaksi pembayaran dan variabel terkait. Salah satu pendekatan yang digunakan adalah transformasi data menggunakan interval satu standar deviasi untuk menstasionerkan data, memastikan konsistensi statistik yang penting bagi algoritma prediksi. Hasil penelitian menunjukkan bahwa Seasonal Autoregressive Integrated Moving Average (SARIMA) memiliki kinerja terbaik dalam memprediksi persediaan barang baik untuk Usaha Mikro, Kecil, dan Menengah (UMKM) maupun ritel modern. Untuk data asli, Mean Absolute Percentage Error (MAPE) untuk UMKM adalah 1.11% dan untuk ritel modern adalah 0.98%. Untuk data yang dimodifikasi dengan interval satu standar deviasi, MAPE untuk UMKM adalah 0.74% dan untuk ritel modern adalah 0.70%. Hasil ini menunjukkan akurasi prediksi yang unggul, membantu perusahaan dalam menyesuaikan stok barang dengan lebih tepat sesuai dinamika pasar dan harapan konsumen.Penelitian ini diharapkan mampu memberikan pedoman solid untuk perbaikan strategi manajemen persediaan, sehingga perusahaan dapat menyiapkan stok barang dengan lebih tepat sesuai dinamika pasar dan harapan konsumen.  \nKata kunci—Optimalisasi Forecasting; Perbandingan Algoritma; Ritel Modern; UMKM.  \nComparative Analysis Of Machine Learning Algorithms For Forecasting Of Basic Goods  \nProducts  \nAbstract—In the era of continuously evolving technology, consumers' demands for everyday needs are becoming more complex. Retail companies must adopt sophisticated approaches to understand and meet consumer preferences. This research explores the effectiveness of Machine Learning algorithms in forecasting inventory levels in various types of retail stores using historical transaction payment data and related variables. One approach used is data transformation using one standard deviation interval to stationarize data,  \nensuring statistical consistency that is important for prediction algorithms. The research results show that the Seasonal Autoregressive Integrated Moving Average (SARIMA) algorithm performs best in predicting inventory levels for both SMEs and modern retailers. For the original data, Mean Absolute Percentage Error (MAPE)for Small and Medium-sized Enterprises (SMEs )is 1.11% and for modern retailers is 0.98%. For data modified with one standard deviation interval, the MAPE for SMEs is 0.74% and for modern retailers is 0.70%. These results indicate superior prediction accuracy, helping companies adjust their inventory levels more accurately according to market dynamics and consumer expectations. This research is expected to provide a solid guideline for improving inventory management strategies, enabling companies to prepare inventory levels more accurately according to m","cbCaiiwGPFVbnkKR","https://ap.wps.com/l/cbCaiiwGPFVbnkKR","pdf",917818,3,1,18,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang UMKM dan ritel modern\n## Peran machine learning untuk forecasting persediaan\n## Studi terdahulu\n# Abstrak dan tujuan penelitian\n## Transformasi data dengan interval satu standar deviasi\n## Evaluasi kinerja algoritma (SARIMA dan metrik MAPE)","[{\"question\":\"Apa manfaat temuan penelitian bagi strategi manajemen persediaan?\",\"answer\":\"Temuan digunakan sebagai pedoman untuk meningkatkan strategi manajemen persediaan agar perusahaan dapat menyiapkan stok lebih akurat sesuai dinamika pasar dan harapan konsumen.\"}]","Analisis Perbandingan Algoritma Machine Learning untuk Forecasting Persediaan Produk Barang Pokok - Studi SARIMA dan MAPE | PDF",1785819562,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":29},"comparative-analysis-of-machine-learning-algorithms-for-forecasting-inventory-of-basic-goods-products-sarima-and-mape-study","",{"@graph":37,"@context":78},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/id/document/penelitian-laporan/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/comparative-analysis-of-machine-learning-algorithms-for-forecasting-inventory-of-basic-goods-products-sarima-and-mape-study/123979/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"Apa manfaat temuan penelitian bagi strategi manajemen persediaan?","Question",{"text":76,"@type":77},"Temuan digunakan sebagai pedoman untuk meningkatkan strategi manajemen persediaan agar perusahaan dapat menyiapkan stok lebih akurat sesuai dinamika pasar dan harapan konsumen.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":85},[86,91,95,99,103,107,109,113,117,121,125],{"id":87,"doc_module":4,"doc_module_name":47,"category_name":88,"show_sort_weight":89,"slug":90},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":92,"doc_module":4,"doc_module_name":47,"category_name":93,"show_sort_weight":89,"slug":94},48,"Cerita & Novel","story-novel",{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":89,"slug":98},56,"Gaya Hidup","lifestyle",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":89,"slug":102},51,"Komik","comic",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":89,"slug":106},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":89,"slug":108},"research-report",{"id":110,"doc_module":4,"doc_module_name":47,"category_name":111,"show_sort_weight":89,"slug":112},49,"Sastra","literature",{"id":114,"doc_module":4,"doc_module_name":47,"category_name":115,"show_sort_weight":89,"slug":116},52,"Teknologi","technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":89,"slug":120},50,"Ujian","exam",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":89,"slug":124},57,"Umum","general",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":4,"slug":128},181,"Formulir","formulir"]