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All Journal Media Statistika Paedagoria : Jurnal Kajian, Penelitian dan Pengembangan Kependidikan EKOBIS SYARIAH Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka Jurnal Multidisipliner Kapalamada
Tri Wijayanti Septiarini
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Contribution To Empowerment Of Tourism Industry To Community Welfare Azidni Rofiqo; Abdul Hamid; Tri Wijayanti Septiarini
EKOBIS SYARIAH Vol 5, No 1 (2021)
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (621.685 KB) | DOI: 10.22373/ekobis.v5i1.10328

Abstract

This study aims to examine the effect of empowerment on welfare. Empowerment here is proxied by empowering, fostering, supporting, and protecting. Welfare is meant as a maqoshid sharia as measured by hifdz addin, an nafs, al nasl, al aql, and al mal. This study proposes a hypothesis of empowering, fostering, supporting, and protecting a positive effect on welfare. This research was conducted in 2019-2020 which took place at Ngebel Lake with 50 respondents in the tourism sector. This type of research is quantitative with questionnaire data collection that can be measured by a Likert scale. Data analysis through classical assumption test and multiple linear regression. The results of the analysis show that empowering, supporting, and protection have a significant positive effect on welfare. Fostering has no significant effect on welfare. Adjusted R-square value, 0.798119 which states that the four independent variables contributed 79% to the dependent variable. This research is limited to the place, time and samples. Future research is expected to be more comprehensive.
FAMILY FINANCIAL LITERACY OUTREACH PROGRAM AT SDI KHAZANAH KEBAJIKAN Romantica, Krishna Prafidya; Septiarini, Tri Wijayanti; Husni Johan, Arsyelina; Putri Martinasari, Made Diyah; Andriani, Rosa; Iriani Tarigan, Asmara; Kurniawan, Heri
Jurnal Pengabdian Masyarakat Sabangka Vol 4 No 01 (2025): Jurnal Pengabdian Masyarakat Sabangka
Publisher : Pusat Studi Ekonomi, Publikasi Ilmiah dan Pengembangan SDM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62668/sabangka.v4i01.1348

Abstract

Financial literacy is a crucial element in raising public awareness regarding financial management. It is closely linked to financial planning, with family financial planning being a key aspect of financial management, specifically in creating a monthly budget. According to the 2024 OJK survey, the financial literacy index for women (66.75%) is higher compared to men (64.14%). Based on these findings, the outreach team from the Mathematics Study Program at Universitas Terbuka aims to support OJK's efforts to improve family financial literacy among female parents at SDI Khazanah Kebajikan. The program implementation is divided into three phases: preparation (where the outreach team conducts a direct visit to the school), execution, and evaluation (via a partner satisfaction survey). The survey results reveal that participants were highly satisfied with the program, with a satisfaction rate of 95%. This satisfaction was attributed to various factors, including the speaker's knowledge, the perceived benefits of the program, the relevance of the topics presented by the outreach team in line with their expertise, the program's ability to address current issues, and the clarity with which the material was delivered.
IMPROVING FORECAST ACCURACY OF INDONESIAN AGRICULTURAL EXPORTS USING ANFIS SPLITTING RATIOS Septiarini, Tri Wijayanti; Martinasari, Made Diyah Putri
Jurnal Multidisipliner Kapalamada Vol. 4 No. 03 (2025): JURNAL MULTIDISIPLINER KAPALAMADA
Publisher : Pusat Studi Ekonomi, Publikasi Ilmiah dan Pengembangan SDM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62668/kapalamada.v4i03.1764

Abstract

Agricultural exports are highly vulnerable to global price volatility and seasonal fluctuations, creating demand for more accurate forecasting methods. This study evaluates the Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting Indonesia’s monthly agricultural exports, addressing a gap in the literature where soft computing approaches have rarely been systematically applied. Using official trade data from 2012 to 2025, two alternative training–testing schemes (75%:25% and 80%:20%) were implemented with standard preprocessing, and forecasting accuracy was measured using RMSE, MAE, and MAPE. The results show that ANFIS delivered accuracy within widely accepted thresholds under the 75%:25% split, while accuracy declined under the 80%:20% split. Theoretically, the study contributes by clarifying conditions for reliable neuro-fuzzy forecasting and emphasizing standardized evaluation protocols. Practically, the findings provide decision-relevant insights for policymakers and exporters, supporting export target setting, forward-contract planning during volatile price swings, and logistics coordination during peak harvest seasons.
Unravelling the Drivers of Digital Literacy in Indonesia’s Distance Learning Era Kharis, Selly Anastassia Amellia; Septiarini, Tri Wijayanti; Zili, Arman Haqqi Anna; Arisanty, Melisa; Permatasari, Sri Maulidia
Paedagoria : Jurnal Kajian, Penelitian dan Pengembangan Kependidikan Vol 16, No 4 (2025): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/paedagoria.v16i4.34482

Abstract

In Indonesia’s rapidly evolving distance learning landscape, digital literacy has become a critical competency for ensuring academic quality and ethical online engagement. However, existing assessments often focus narrowly on technical skills, overlooking behavioral and ethical dimensions that are equally essential in digital environments. This study aims to identify the key factors influencing digital literacy among students in Indonesia's distance learning environment, focusing on Universitas Terbuka. Utilizing a Random Forest algorithm, the research involves data preprocessing, model development and evaluation, and feature importance analysis to determine the most influential predictors. The findings reveal that digital ethics and behavioral aspects-such as academic integrity, proper citation practices, and responsible social media conduct- are the strongest indicators of digital literacy, whereas technical proficiency in e-learning platforms plays a less dominant role. This study's novel use of Machine Learning offers a methodological contribution to the assessment of digital literacy in educational settings. Based on these insights, the paper recommends a holistic approach to digital literacy education at Universitas Terbuka, advocating for programs that integrate technical training with robust digital ethics instruction, including awareness of citation, plagiarism, communication etiquette, privacy, and responsible information dissemination.
FORECAST EVALUATION OF ARIMA AND ANFIS FOR INDONESIA'S MONTHLY EXPORT (2009-2024) Septiarini, Tri Wijayanti; Rofiqo, Azidni; Pariyanti, Eka; Abdulmana, Sahidan
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.93-104

Abstract

Indonesia’s export sector is a key driver of economic growth, contributing significantly to foreign exchange, employment, and industrial development. Accurate forecasting of export trends is crucial for policymakers, economists, and businesses in shaping strategies and reducing risks. This study applies the Autoregressive Integrated Moving Average (ARIMA) model to forecast Indonesia’s monthly export values from January 2014 to August 2024. Dataset has been divided into training (75%) and testing (25%) subsets, and the Box-Jenkins methodology was employed, including stationarity testing, identification via ACF and PACF plots, parameter estimation, and residual diagnostics. The optimal ARIMA(1,1,1) model achieved strong predictive performance in RMSE, MSE, and MAPE. To benchmark classical methods against modern approaches, ARIMA was compared with the Adaptive Neuro-Fuzzy Inference System (ANFIS). Results indicated that ARIMA delivered higher accuracy for this dataset, reaffirming the robustness of traditional models when data characteristics align with their assumptions. It has conducted prior research evaluation via 75%:25% holdout and rolling-actual back test. This research demonstrates that classical time-series models remain highly relevant in the era of artificial intelligence, emphasizing the importance of appropriate model selection in economic forecasting.
Co-Authors Abdul Hamid Abdulmana, Sahidan Andriani, Rosa Arman Haqqi Anna Zili Eka Pariyanti Heri Kurniawan, Heri Husni Johan, Arsyelina Iriani Tarigan, Asmara Martinasari, Made Diyah Putri Melisa Arisanty Putri Martinasari, Made Diyah Rofiqo, Azidni Romantica, Krishna Prafidya Selly Anastassia Amellia Kharis Sri Maulidia Permatasari