Inventory control is an important thing that must be considered by every business actor, especially in the retail sector, too much inventory results in increased and inefficient sales time and can even result inlosses. the need to estimate demand and inventory Stock is very necessary to minimize over stock and also under stock to reduce the risk of loss, the ability of retail business actors to predict demand is certainly very helpful in carrying out good inventory management, utilization of transaction data in a certain amount using machine learning methods can be one approach to see consumer behavior trends. The purpose of this study is to analyze and performance testing the forecasting accuracy, using machine learning approach with the Naive method on sales data transaction in automotive companies and then compare the accuracy between the Stock Order Quantity approach methods used so far. The results of this study indicate forecasting accuracy with a forecasting error of 2% (MAPE), This research tries to analyze the time series data of the spare parts sales transaction, predict the future demand, The results of this study indicate forecasting accuracy with error of 2% (MAPE), This is expected to be an added value in inventory management.
Every humanitarian organization needs to raise funds and channel these funds. Distribution of funds from donors is often not in accordance with the programs that have been planned by social service agencies. This study aims to prioritize logistical assistance for flood disasters using the Analytical Hierarchy Process (AHP) Method as a supporting material that can be taken into consideration in making decisions about providing assistance on the crowdfunding platform which is used as an online fundraising medium, the AHP method is used as a supporting material for analyze the reference variables that are considered in making a decision to provide assistance. AHP analysis results recommend the order of potential beneficiaries from the highest priority to the lowest. In determining assisting decisions by sorting prospective recipients based on priority, the AHP method produces a consistency ratio value that is less than 0.1 so it can be concluded that the results of these decisions have consistent characteristics as supporting materials for decision making
Semenjak penetapan status sebagai Pandemi global oleh badan kesehatan dunia (WHO), wabah Corona Virus Disease (Covid-19) sudah menjadi momok di seluruh penjuru dunia. Berbagai standar prosedur penaggulangan penularan telah ditetapkan oleh WHO untuk memutus mata rantai penularan. Pemerintah kabupaten Bone melalui surat edaran Sekretaris Daerah No. 800/1919/VI/BKPSDM/2020 tanggal 4 Juni 2020 perihal sistem kerja Pegawai Aparatur Sipil Negara (ASN) dalam tatanan normal baru, mengatur kehadiran pegawai menggunakan absensi secara manual dan tidak menggunakan mesin absensi sidik jari. Hal ini tentunya akan berpengaruh pada pencatatan kinerja tiap ASN, dimana data absensi sudah terhubung dengan aplikasi e-kinerja yang sudah diterapkan pada lingkup kabupaten Bone. Tujuan dari penelitian ini adalah pembuatan aplikasi absensi online berbasis mobile Android untuk menjadi alternatif cara absen dengan memanfaatkan web service menggunakan metode komunikasi data Representational State Transfer (Rest) serta memanfaatkan protocol HTTP menggunakan format JavaScript Object Notation (JSon) dan bahasa pemrograman Java sebagai bahasa pemrograman aplikasi berbasis mobile. Hasil dari penelitian ini berupa aplikasi absensi berbasis mobile yang telah dilakukan pengujian performa web service menggunakan Aplikasi Apache JMETER untuk memastikan aplikasi ini sudah siap digunakan secara bersamaan oleh banyak ASN.
The current generation of smartphones is increasingly sophisticated, equipped with several sensors such as accelerometer, gravity sensor, and gyroscope that can be used to recognize human activities such as going up stairs, going down stairs, running and walking. To get information, the data will be grouped using statistical methods. The performance of statistical methods has shortcomings in classifying data because of the procedures that must be met. To cover this shortcoming, the ensemble technique is used. In this paper, we propose to apply the Multi-Class Ensemble Gradientboost algorithm to improve the performance of the logistic regression method in classifying such as climbing stairs, descending stairs, running and walking. The process of taking data using a smartphone by designing an Android-based .apk system. Then, the entire dataset was separated into training data and test data with a comparison percentage of 70:30. The results obtained show that the Multi-Class Ensemble Gradientboost algorithm succeeded in increasing the logistic regression performance by 27.93%
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