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All Journal JAIS (Journal of Applied Intelligent System) Jurnal Sistem Informasi dan Sains Teknologi Madaniya Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Innovative: Journal Of Social Science Research
Amali Amali
Universitas Pelita Bangsa
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Other

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FORECASTING WITH WEIGHTED MOVING AVERAGE METHOD FOR PRODUCT PROCUREMENT STOCK Amali Amali; Gatot Tri Pranoto; Muhammad Darwis
Jurnal Sistem Informasi dan Sains Teknologi Vol 4, No 2 (2022): Jurnal Sistem Informasi dan Sains Teknologi
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/sistek.v4i2.1268

Abstract

ABSTRACTDhanty Store is a family start-up located in East Jakarta. It was initiated in 2018, engaged in retail with the main product in the form of women's clothing and accessories. One of the important processes in Dhanty Store operations is the product procurement process. Currently, Dhanty Store request products according to their wishes without looking at their sales data. This causes their product stock is not well controlled. When there is a lot of demand, sometimes Dhanty Shops run out of stock so their customers will move to other stores. In addition, the process of requesting and procuring products to suppliers also takes a long time so that it can further disrupt the operations of Dhanty Store. This study develops a forecasting application prototype with the Weighted Moving Average method to assist Dhanty Store in the process of requesting and procuring their products. Forecasting results in the period (t) of the 1st week of January were 275 products. In addition, this study predicts product stock with a 4-week moving average and the MAD tracking signal value is ranged from -1.51 to 3.86 and the MAPE value is 35.4%. As for the reliability and level of user acceptance of the prototype model in this study, tested using the System Usability Scale (SUS) method and it is known that the average value given by respondents was 82 with details 0% considered inappropriate, 40% considered neutral and 60% rated it according to need.                                                                                                                                                        Keywords: data mining, forecasting, weighted moving average, MAD, MAPE, SUS
Manhattan, Euclidean And Chebyshev Methods In K-Means Algorithm For Village Status Grouping In Aceh Province Amali Amali; Gatot Tri Pranoto
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.7037

Abstract

The Ministry of Villages, Development of Disadvantaged Regions and Transmigration (Ministry of Villages PDTT) is a ministry within the Government of Indonesia in charge of developing villages and rural areas, empowering rural communities, accelerating the development of disadvantaged areas, and transmigration. The 2014 Village Potential Data (Podes 2014) is data released by the Central Statistics Agency in collaboration with the Ministry of Villages PDTT in unsupervised form and consists of 6474 villages in the province of Aceh. Podes 2014 data is based on the level of village development (village specific) in Indonesia by using the village as the unit of analysis. Data mining is a method that can be used to group objects in a data into classes that have the same criteria (clustering). One of the algorithms that can be used for the clustering process is the k-means algorithm. Grouping data using k-means is done by calculating the shortest distance from a data point to a centroid point. In this study, a comparison of the distance calculation method on k-means between Manhattan, Euclidean and Chebyshev will be carried out. Tests will be performed using the execution time and the davies boulder index. From the tests that have been carried out, it is found that the number of villages in each cluster is 2,639 developing villages, 1,188 independent villages, 1,182 very underdeveloped villages, 1,266 developed villages and 199 disadvantaged clusters. The Chebyshev distance calculation method has the most efficient accumulation of time compared to Manhattan and Euclidean, while the Euclidean method has the most optimal Davies Index.
Perbandingan Algoritma Sentimen Analisis media data Twitter Pilgub Jabar 2018 Amali Amali
Jurnal Pelita Teknologi Vol 15 No 1 (2020): Maret 2020
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (572.205 KB) | DOI: 10.37366/pelitatekno.v15i1.298

Abstract

This paper is published to review and compare the performance of the SVM algorithms and CNN algorithms as an update composition in analyzing sentiment with tweeter attributes, the comparison of these algorithms using the Python application as a tool to support machine learning. The classification of negative, neutral and positive sentiments in the tweet dataset is tested and to determine and measure the accuracy, precision, recall, f_Measure and configuration matrix weights of both the SVM algorithms and CNN algorithms. The tools used with the Python Jupyter application, Tensorflow, Noted ++ are applied to the Indonesian language Twitter classification, the measurement results are precise and accurate according to human measurement parameters related to tweet data sentiment on Twitter social media commenting on the election of the Governor of West Java with the candidate for governor and deputy governor for the period 2018-2023. The results of this study, Testing Experiments before stemming with the SVM Algorithm was carried out seven times with an average accuracy rate of around 67%, and the CNN Algorithm before stemming also with seven trials with an average accuracy of around 67%, then the Testing Experiment after stemming. with SVM conducted seven trials the average accuracy rate was around 67%, while CNN algorithms before stemming was also carried out with seven trials with an average accuracy of about 52% lower than SVM algorithms
Perancangan Sistem Aplikasi Inventory Matrial Gudang Berbasis Web Dan Scan Barcode PT. Cabinindo Putra Angga Angga; Amali Amali; Agus Suwarno
Innovative: Journal Of Social Science Research Vol. 3 No. 3 (2023): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v3i3.3452

Abstract

Penelitian ini merumuskan masalah tentang bagaimana merancang dan mengimplementasikan sistem inventory material gudang di PT. Cabinindo Putra yang efisien dan efektif dibandingkan saat menggunakan pengelolaan manual dengan Ms. Excel. Sedangkan penelitian tujuan ini untuk pembuatan aplikasi sistem inventori berbasis website agar dapat mengoptimalkan proses pengadaan, penyimpanan, dan distribusi material. Penelitian ini menggunakan metode waterfall yaitu : analisis kebutuhan, perancangan sistem, implementasi, pengujian dan pemeliharaan. Pada tahap analisis kebutuhan, dilakukan survei dan wawancara dengan pihak terkait untuk memahami kebutuhan perusahaan dalam hal manajemen inventory material gudang. Selanjutnya dilakukan perancangan sistem yang mencakup desain database, antarmuka, dan logika bisnis yang mendukung pengelolaan inventori material gudang pengguna secara efisien. Implementasi sistem inventory material gudang dilakukan dengan membangun aplikasi berbasis website. Aplikasi ini memungkinkan pengguna untuk melacak dan mengelola inventory material gudang secara real-time, selanjutnya melakukan pengujian menggunakan metode black box dan white box dan melakukan pemeliharaan secara berkala. Hasil dari penelitian ini yaitu untuk mengoptimalkan pengelolaan inventaris gudang material, mengurangi operasional, dan meningkatkan kepuasan pelanggan melalui pengadaan material yang tepat waktu dan efisien.
Pemberdayaan Pelajar Digital Melalui Pengenalan Dasar Rekayasa Perangkat Lunak Amali Amali; Antika Zahrotul Kamalia; Wiyarno Wiyarno; Yefta Ondi Spalanzani Simatupang; Ahmad Amrullahalhafizh
Madaniya Vol. 6 No. 1 (2025)
Publisher : Pusat Studi Bahasa dan Publikasi Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53696/27214834.1143

Abstract

Program Pemberdayaan Pelajar Digital melalui Pengenalan Dasar Rekayasa Perangkat Lunak (RPL) pada siswa SMPN 5 Cikarang Selatan bertujuan untuk meningkatkan keterampilan digital dan pemahaman dasar mengenai pengembangan perangkat lunak. Program ini mencakup pengenalan konsep dasar RPL, penggunaan alat pengembangan perangkat lunak, serta keterampilan pemrograman dan pengalaman berkoloborasi perancangan aplikasi RPL. Hasil evaluasi menunjukkan bahwa siswa berhasil mencapai hasil yang melebihi target pada hampir seluruh indikator yang ditetapkan, seperti pemahaman dasar RPL (90%), kemampuan menggunakan alat pengembangan perangkat lunak (85%), keterampilan pemrograman (80%), pemahaman etika digital (75%), dan kemampuan koloborasi (85%). Peningkatan ini mencerminkan efektivitas pendekatan yang menggabungkan teori dengan praktik langsung, serta kolaborasi. Program ini tidak hanya memberikan keterampilan teknis, tetapi juga membangun kesadaran siswa tentang etika dalam teknologi dan pentingnya kerjasama koloborasi. Secara keseluruhan, program ini berhasil mempersiapkan siswa untuk menghadapi tantangan di dunia digital dengan keterampilan yang relevan dan bertanggung jawab.
Co-Authors Agus Suwarno Ahmad Amrullahalhafizh Angga Angga Antika Zahrotul Kamalia Gatot Tri Pranoto Muhammad Darwis Wiyarno Yefta Ondi Spalanzani Simatupang