Article Per Year (5 Year)
p-Index From 2021 - 2026
1.959
P-Index
This Author published in this journals
All Journal Teika METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Digit : Digital of Information Technology Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI
Lena Magdalena
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Energy Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
SISTEM INFORMASI FORECASTING DATA PENJUALAN KENDARAAN MENGGUNAKAN METODE SINGLE EXPONENTIAL SMOOTHING (STUDI KASUS: PT. SENDANG SUMBER ARUM VIAR MOTOR CIREBON) Lestari, Lina; Lena Magdalena; Mesi Febima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3283

Abstract

Limited Liability Companies (PT) are found in almost all regions in Indonesia, one of which is PT.  Sendang Sumber Arum 'VIAR Motor which provides sales of Karya 3-wheeled motorcycle units, e-motorcycles, razors, Cross Adventure, vintech, and e-bikes. E-bikes are the best-selling vehicles for each period, especially the UNO3 type. The problem faced by this company is the imbalance in sales figures which causes damage to the e-bike batteries that are sold over a long period of time and requires forecasting. The Single Exponential Smoothing method is the right forecasting method used to predict demand for goods that change very quickly, which aims to determine the estimated availability of vehicle units that must be held in the future, based on previous sales data. In determining the error value in forecasting, the author uses the Mean Square Error (MSE) which is based on the alpha value. This forecasting is implemented into an information system that produces a forecast for the UNO3 type e-bike with the smallest Mean Square Error (MSE) value obtained with an alpha of 0.3, namely with a value of 167.294. This proves the best forecast for predicting the quantity of UNO3-type e-bike stock units at PT. Sendang Sumber Arum ‘VIAR Motor’ Cirebon for the period of June 2024 using alpha 0.3. So the forecast value of UNO3 type e-bike unit sales for June 2024 in the 11-month forecast period with alpha 0.3 is 24.89 or around 25 units with actual data.
IMPLEMENTASI K-MEANS RFM DAN HOLT-WINTERS EXPONENTIAL SMOOTHING ADDITIVE DALAM SISTEM BUSINESS INTELLIGENCE UNTUK STRATEGI PENGELOLAAN PELANGGAN PADA PERUSAHAAN TRANSPORTASI Belfania Priandini; Marsani Asfi; Lena Magdalena
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The growth of customer data in the transportation industry drives the need for analytical systems capable of segmenting customers objectively and strategically. This study aims to apply the K-Means Clustering method based on the Recency, Frequency, and Monetary (RFM) model for customer segmentation and utilize the Holt-Winters Exponential Smoothing Additive method to forecast passenger numbers. The dataset comprises 10,440 customer transactions from PT XYZ during the 2022–2024 period. RFM values were calculated, normalized, and processed using the K-Means algorithm to produce three customer clusters: Loyal, Regular, and Passive. Subsequently, the Holt-Winters method was employed to forecast passenger numbers, achieving the smallest Mean Absolute Percentage Error (MAPE) of 6.88%, indicating a high level of accuracy. The results were visualized through an interactive dashboard using Tableau, enabling management to make data-driven decisions. This research demonstrates that integrating segmentation and forecasting methods into a Business Intelligence system can enhance the effectiveness of marketing strategies and the operational efficiency of the company.
MODEL PENGUKURAN KINERJA RANTAI PASOK BERBASIS GREEN SCOR DAN FUZZY AHP: STUDI KASUS PT. ARTERIA DAYA MULIA Suci Nurpatimah; Lena Magdalena; Mesi Febima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Supply chain performance measurement plays a crucial role in supporting operational continuity and corporate competitiveness, especially in meeting the demands for efficiency, effectiveness, and environmental sustainability. Imbalances in supply chain management can lead to resource waste, environmental pollution, and decreased customer satisfaction. PT. Arteria Daya Mulia, a rope manufacturing company, currently lacks a supply chain performance measurement system that fully incorporates sustainability aspects. This study aims to design a performance measurement model based on the Green SCOR framework and the Fuzzy AHP method as a strategic decision-making tool that considers sustainability dimensions. Performance indicators were determined according to the five main Green SCOR processes (Plan, Source, Make, Deliver, and Return), comprising 14 KPIs developed through literature review and field validation. Data were collected through observations, interviews, and questionnaires, then processed using the Fuzzy AHP method to obtain the priority weight of each indicator. The results show that the total supply chain performance score is 88, calculated by combining the weights with the Snorm de Boer values. Several indicators demonstrated excellent performance with a maximum Snorm value (100). However, one critical indicator was identified with the lowest Snorm value—% Error-free Return Shipped in the Return process—scoring 0.02 with a final SCM score of 0.0008, indicating the need for immediate improvement. The developed information system also generates automatic improvement recommendations based on the measurement results. This model is expected to assist the company in monitoring, evaluating, and continuously improving supply chain performance.
PENERAPAN METODE FUZZY TIME SERIES CHEN DALAM SISTEM PERAMALAN PRODUKSI UNTUK OPTIMALISASI PENGADAAN BAHAN BAKU PADA PERUSAHAAN MANUFAKTUR Sasha Alicia; Lena Magdalena; Ridho Taufiq Subagio
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Fluctuating and dynamic production demands require manufacturing companies to adopt adaptive, data-driven planning systems. However, in manufacturing companies producing plastic ropes such as twine, nets, and yarn, production planning is still conducted manually without a systematic quantitative approach. This study aims to design a production forecasting system using the Fuzzy Time Series Chen method, which can address uncertainty in time series data. Monthly production data from January 2022 to December 2024 were used for testing. The results show that this method provides good forecasting accuracy, with MAPE values of 30.15% for twine, 17.89% for nets, and 16.91% for yarn. The production estimates for January 2025 are 70,162 units (twine), 50,599 units (nets), and 81,315 units (yarn). These findings indicate that the FTS Chen method can improve efficiency and accuracy in production planning.
Co-Authors ., Nico Firmansyah Abdul Rachman adminDewi Yenny Sitanggang Ayuni Alfiani Nurkholifah Belfania Priandini Chaerul Anwar Deny Martha Edwin Sulistio Limas Efriza Yunardi Eunike Surya Mosory Fahrudin, Rifqi Hamdani . Ida Wati Kusnadi Kusnad Kusnadi Kusnadi Lia dahlia Lina Lestari, Lina Lisa Natalia Marsani Asfi Mesi Febima Muhammad Hatta Muhammad Hatta Muhammad Ricky Umar Natalia Gunawan, Trivena Petrus Sokibi Priandini, Belfania Ridho Taufiq Subagio Sasha Alicia Suci Nurpatimah Triana Ramadani