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All Journal Unram Journal of Community Service (UJCS) Codeverse: Journal of Emerging Digital Realities
Aulia Padhila Ersu
University of Mataram
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Pelatihan Pembuatan Pori Busa (Pupuk Organik desa Bug-Bug Kecamatan Lingsar) Taufik Rahman; M. Audi Adyan; Fathurrahman Fathurrahman; Baiq Annisa Sulistia Ayuni; Srikandi Ayu Kartini; Nur Hafizatun Aulya; Aulia Padhila Ersu; Yuni Nur’Azizah; Maswinda Maswinda; Baiq Himayatussifa Salmiah; Ida Ayu Oka Suwati Sideman
Unram Journal of Community Service Vol. 4 No. 4 (2023): December
Publisher : Pascasarjana Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ujcs.v4i4.398

Abstract

Bug-Bug Village, which is located in Lingsar District, West Lombok Regency, has the potential for regional development as an agricultural area, because apart from the agricultural area being 42% of the total village area, it is also recorded that 27.45% of the population works as farmers. As a farmer, the need for subsidized fertilizer is certainly very large. Unfortunately, the government's ability to provide subsidized fertilizer is only 37-42%. By studying these conditions, the ability of farmers to make their own organic fertilizer is really needed. The benefits of organic fertilizer on the health of production results, the health of the surrounding environment and also the health and welfare of farmers, is also the reason that supports the "socialization and practice of making organic fertilizer" program by participants of the Mataram University Real Work Lecture (KKN) in Bug-Bug Village. The result of this activity is that farmers have high enthusiasm for the work program, local Agricultural Field Extension Officers (PPL) give high appreciation for the program and hope for support in the coming period. The Head of Bug-Bug Village also expressed high appreciation and hoped that in the next period of KKN, they would prepare a packaging and marketing program for fertilizer products
Comparison of Apple Inc Stock Forecasting Accuracy Using Hybrid TSR Linear-ARIMA Model and ARIMA Model Aulia Padhila Ersu; Muhammad Rijal Alfian; Nur Asmita Purnamasari
Codeverse: Journal of Emerging Digital Realities Vol. 1 No. 2 (2025): CODEVERSE: Journal of Emerging Digital Realities
Publisher : Codeverse: Journal of Emerging Digital Realities

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

Abstract

This study aims to compare the accuracy of Apple Inc. stock price forcasting using two time series models, namely the hybrid TSR Linear-ARIMA model and the ARIMA model. The background of this research is the need for more accurate forcasting methods in a dynamic stock market, especially for technology stocks such as Apple which have high volatility. The research methodology uses the quantitative approach with daily Apple stock price time series data for the period 2023. The hybrid TSR Linear-ARIMA model incorporates trend and residual components, while the ARIMA model uses the Box-Jenkins approach. Both models were implemented using statistical software R Studio and Minitab. The results that the ARIMA model provided better forcasting accuracy compared to the hybrid TSR Linear-ARIMA model. Comparative analysis using the MAPE shows the ARIMA model has a lowwer error rate. Specifically, the ARIMA model produces a MAPE of 2.909%, while the hybrid TSR Linear-ARIMA model produces a MAPE of 3.780%. in conclusion, the ARIMA model proved to be more effective in forecasting the stock price of Apple Inc. compared to the hybrid TSR Linear-ARIMA model. This research contributes to the development of forecasting techniques in finance and investment, especially for technology stock.
Co-Authors Baiq Annisa Sulistia Ayuni Baiq Himayatussifa Salmiah Fathurrahman Fathurrahman Ida Ayu Oka Suwati Sideman M. Audi Adyan Maswinda Maswinda Muhammad Rijal Alfian Nur Asmita Purnamasari Nur Hafizatun Aulya Srikandi Ayu Kartini Taufik Rahman Yuni Nur’Azizah