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All Journal INFORMATIKA Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Febriyanti Darnis
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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APLIKASI PENJUALAN DAN PEMBELIAN BARANG DENGAN MENGUNAKAN JAVA 2 STANDARD EDITION (J2SE) SEBAGAI COLLABORATIVE FULFILLMENT PADA SUPPLYCHAIN MANAGEMENT Febriyanti Darnis; Edy Siswanto; Sendy Suprianto
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 2 No. 1 (2022): Maret : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (988.396 KB) | DOI: 10.55606/teknik.v2i1.429

Abstract

Technological growth is increasing from year to year, this is indicated by the use of computers in all aspects, be it service companies, trade, or industry, ranging from small, large, to multi-national companies. KAMDATU is the procurement of office furniture. The supply chain process at CV. Kamdatu involves several external parties, namely suppliers, customers, shipping, and management. The problems faced by CV. Kamdatu is located in a sales and purchase processing system that is still manual (not yet computerized) so that it is prone to calculation errors, searching for item data, and delays in stock information, in addition to recapitulating reports takes a long time because employees have to recap data first this is because the absence of management in the purchase of goods and production The E-Supply Chain Management system that will be applied to CV Kamdatu focuses on the automation of information between companies, suppliers, consumers and delivery of goods. Inventory and fulfillment of customer orders are more controlled. Based on these problems the researcher uses a methodological method, namely the Unified Approach (UA) UA, which is an object-oriented development methodology that combines existing processes and methodologies with uses the Unified Modeling Language (UML) as the modeling standard. The result of this research is to produce an application that has features that support the processing of buying and selling transactions
SMART MAPPING BERBASIS QGIS: PEMETAAN DIGITAL DAERAH RAWAN BENCANA MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS Teguh Setiadi; Febriyanti Darnis
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v5i1.954

Abstract

Batang Regency features diverse geographical characteristics, including coastal areas, lowlands, and mountains, which make it vulnerable to various natural disasters such as floods, landslides, and volcanic eruptions. The increasing activity of sand and stone mining along rivers has further exacerbated environmental degradation, heightening the risk of such disasters. To address this issue, a system is needed to help the local government accurately classify disaster-prone areas. A Geographic Information System (GIS) for disaster risk mapping serves as an effective solution to identify and visualise vulnerable locations across Batang Regency. This system supports disaster prevention, response, and targeted aid distribution. With public access to the system, it also promotes data transparency, strengthens community trust in the local government, and raises public awareness of potential disaster threats in their surroundings.
PERAMALAN SUHU UDARA MENGGUNAKAN ALGORITMA LONG SHORT TERM MEMORY Yulian Ansori; Arief Rahman; Febriyanti Darnis; Miftahus Sholihin
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v5i1.1033

Abstract

This study presents an air temperature forecasting model employing the Long Short-Term Memory (LSTM) algorithm to address the challenges posed by climate variability and extreme weather conditions. Historical daily temperature data from NASA POWER—collected between January 1, 2014, and December 31, 2024, in Serang City (totaling 4,018 records)—were used. The data were normalized using a min–max scaling technique and divided into training (70%) and testing (30%) sets. Multiple experimental scenarios were run by varying the number of training epochs and the hidden layer unit counts. The optimal configuration was achieved in Scenario 7, which incorporated two hidden layers, each with 50 units, and employed 30 epochs; this setup yielded a prediction accuracy of 98.4% with a Root Mean Squared Error (RMSE) of 27.11. The results indicate that the LSTM model effectively captures the seasonal variations and long-term trends in air temperature, making it a reliable tool for forecasting and supporting decision-making in climate adaptation strategies. Keywords: Air Temperature Forecasting, Long Short-Term Memory, Deep Learning, Climate Change, Data Normalization.
Co-Authors Arief Rahman Miftahus Sholihin Sendy Suprianto Siswanto, Edy Teguh Setiadi Yulian Ansori