Article Per Year (5 Year)
p-Index From 2021 - 2026
2.552
P-Index
This Author published in this journals
All Journal Jurnal Sains dan Seni ITS JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Onoma: Pendidikan, Bahasa, dan Sastra bit-Tech Indonesian Journal of Data and Science Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Djtechno: Jurnal Teknologi Informasi Journal of Renewable Energy, Electrical, and Computer Engineering Journal of Artificial Intelligence and Digital Business SAINTEK Jurnal PETISI (Pendidikan Teknologi Informasi)
Aviolla Terza Damaliana
Data Science Department, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search
Journal : bit-tech

 1 
Application Of Hybrid ARIMAX-ANN In Forecasting The Price Of Chili Bird's Eye Dina Magdalena Manurung; Aviolla Terza Damaliana; Dwi Arman Prasetya
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3091

Abstract

Chili peppers are a vital horticultural commodity in Indonesia, especially within the culinary industry, due to their high economic value and demand. In Medan, the demand for chili peppers is notably high, yet production limitations often lead to significant price fluctuations. These price variations are influenced by multiple factors, including weather conditions, such as rainfall, and increased demand during national holidays. This study focuses on predicting the prices of both green and red bird's eye chili, which are widely consumed for their distinct spicy flavor. The data used in this study consists of daily chili prices spanning from January 1, 2019, to February 28, 2025, along with external variables such as precipitation and national holiday weeks. To predict the price fluctuations, a Hybrid ARIMAX-ANN model was employed, combining the linear ARIMAX model and the non-linear ANN model to better capture the complex price patterns. The findings revealed that the optimal model for green bird's eye chili was Hybrid ARIMAX(4,0,0)-ANN(6,64,1) with a MAPE of 3.98%, while for red bird's eye chili, the Hybrid ARIMAX(4,0,0)-ANN(6,64,1) model achieved a MAPE of 4.15%. This model was then applied to forecast the chili prices for the next 5 days, and the predictions demonstrated similar price trends for both green and red bird's eye chili. The results highlight the effectiveness of the Hybrid ARIMAX-ANN model in providing accurate chili price forecasts, which could be useful for better price management and planning in the agricultural sector.
Space-Time Modeling for Forecasting Large Red Chili Prices Based on Significant Parameter Selection Sandria Amelia Putri; Mohammad Idhom; Aviolla Terza Damaliana
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3212

Abstract

FVolatile fluctuations in large red chili prices pose a persistent challenge to Indonesia’s food security and regional economic stability, as price shocks directly affect household purchasing power, inflation, and agricultural income. Addressing this issue requires a forecasting framework that captures both spatial interdependence among producing and consuming regions and temporal price dynamics. This study develops an advanced forecasting model for large red chili prices in East Java covering Malang Regency, Banyuwangi Regency, and Surabaya City using the Generalized Space-Time Autoregressive–Seemingly Unrelated Regression (GSTAR-SUR) method. The model integrates the Generalized Least Squares (GLS) approach to enhance parameter estimation efficiency under correlated residuals and applies a partial t-test–based parameter elimination procedure to retain only statistically significant predictors. Compared to traditional univariate time-series approaches such as ARIMA, GSTAR-SUR more effectively captures cross-regional price linkages and residual dependencies, yielding higher forecasting accuracy. The best-performing specification, GSTAR-SUR(3,1)-I(1) with a uniform spatial weighting matrix, achieved RMSE = 1426.73, MAPE = 3.29%, and R² = 0.8482, representing a substantial improvement in precision over conventional GSTAR and ARIMA models. Fourteen-day forecasts reveal region-specific dynamics: a mild downward trend in Malang, an initial rise followed by decline in Banyuwangi, and relative stability in Surabaya. These results demonstrate that the GSTAR-SUR framework can effectively model complex spatio-temporal dependencies in commodity markets and serves as a practical decision-support tool for policymakers in stabilizing food prices, improving distribution strategies, and strengthening agricultural market resilience across East Java.
Comparison of the Effectiveness IndoBERT and mBERT for Sentiment Analysis of SME Customer Reviews Selena Nurmanina Afandy; Kartika Maulida Hindrayani; Aviolla Terza Damaliana
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3501

Abstract

This study presents a structured comparative evaluation of IndoBERT and Multilingual BERT (mBERT) for three-class sentiment classification of customer reviews from Pawonkoe Banyuwangi, an Indonesian small and medium-sized enterprise (SME). Motivated by the limited transferability of IndoNLU-style benchmarks to real SME feedback, the central question is whether monolingual versus multilingual transformers remain reliable when fine-tuned on small, domain-specific, and operationally noisy datasets. A total of 365 survey-based reviews (January–December 2024), which is substantially smaller than typical transformer fine-tuning corpora, served as the empirical basis. Models were fine-tuned under matched hyperparameters and evaluated using a single stratified hold-out train–test split (not cross-validation), reporting accuracy, precision, recall, and F1-score. To reflect the deployed pipeline, mBERT additionally incorporates the original 1–5 rating as an auxiliary numeric signal alongside the review text, whereas IndoBERT is trained on text only. The results reveal a substantial performance gap: mBERT achieved 81% test accuracy, whereas IndoBERT reached 48% under the same evaluation setting. Because the label distribution is strongly imbalanced (with very few negative instances), these aggregate scores should be interpreted as overall effectiveness rather than minority-class robustness. Overall, the findings indicate that multilingual representations combined with auxiliary rating information can generalize more effectively in low-resource SME scenarios, while IndoBERT appears more sensitive to data scarcity in this context. The study offers practical guidance for model selection in resource-constrained Indonesian sentiment analytics and contributes evidence on transformer behavior beyond curated benchmarks.
Modeling the Open Unemployment Rate in West Java: A Comparison of Panel Data Regression Models Mohamad Ibnu Fajar Maulana; Aviolla Terza Damaliana; Wahyu Syaifullah J. S.
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3705

Abstract

The Open Unemployment Rate (OUR) across regencies and municipalities in West Java Province reflects substantial structural heterogeneity associated with divergent local socio-economic dynamics. This study addresses the central question of which socio-economic factors systematically explain within-region variations in unemployment over time when unobserved, time-invariant regional heterogeneity is explicitly controlled. Using annual panel data for 27 regencies/cities over the period 2019–2024 (162 observations), a panel regression framework is implemented through a One-Way Fixed Effects (OWFE) model estimated via the Least Squares Dummy Variable (LSDV) approach. The explanatory variables include Regency/City Minimum Wage (UMK), Labor Force Participation Rate (TPAK), and Human Development Index (IPM). Beyond conventional fixed-effects applications, the analysis integrates a backward elimination procedure within the OWFE framework to derive a parsimonious specification; this refinement is treated as an exploratory model-selection strategy and interpreted cautiously with respect to potential sample sensitivity. Model comparison based on the Chow and Hausman tests confirms the superiority of OWFE over pooled and random specifications. The final model demonstrates substantial explanatory power (R² = 0.813) and acceptable predictive accuracy (MAPE = 10.73%), indicating that a large proportion of within-region unemployment variation is captured. Diagnostic tests show no evidence of autocorrelation (Durbin–Watson = 1.777) or heteroskedasticity under the implemented procedures. Empirically, TPAK and IPM exhibit significant negative associations with unemployment, while UMK shows a positive relationship, highlighting human capital, participation dynamics, and wage–employment trade-offs in regional labor markets.
Generalized Autoregressive Conditional Heteroskedasticity Approach for Television Program Viewership Trend Analysis Alyssa Amorita Azzah; Aviolla Terza Damaliana; Wahyu Syaifullah Jauharis Saputra
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3710

Abstract

This study aims to determine whether daily television audience dynamics exhibit statistically significant conditional variance dependence that is systematically overlooked in conventional ARIMA-based broadcasting forecasts and to assess the incremental empirical value of integrating ARIMA with GARCH modeling. Using 1,096 consecutive daily observations (2022–2024) of viewers for a nationally broadcast program, we implement a diagnostic-first framework that jointly evaluates conditional mean and variance processes. Stationarity is confirmed through the Augmented Dickey–Fuller test (ADF = −3.4693, p = 0.0088), and an MA(1) specification is selected for the conditional mean (AIC = 1302.76). Residual diagnostics reveal pronounced ARCH effects (ARCH-LM = 78.4602, p < 0.001), justifying second-moment modeling. Among competing variance specifications, GARCH(2,2) yields the lowest information criterion (AIC = 1060.321) and indicates near-unit volatility persistence (Σα + Σβ = 0.9856), evidencing durable intertemporal uncertainty transmission. Out-of-sample forecast evaluation demonstrates low relative error (MAPE ≈ 1.0%), supporting empirical robustness. Unlike prior ARIMA-centered broadcasting studies that prioritize point accuracy under homoscedastic assumptions, this integration explicitly models volatility clustering as an object of inference, aligning media analytics with established volatility frameworks without overstating cross-domain novelty. The findings show that incorporating conditional variance dynamics provides measurable gains in risk-sensitive forecasting, offering a replicable approach for advertising allocation and scheduling decisions in competitive media environments.
Co-Authors Abdul Rezha Efrat Najaf Adinda Rusdianti Maulani Putri Alfan Rizaldy Pratama Alyssa Amorita Azzah Anas Ahmadi Annabel Gracia Puryani Brescia Ayundina Yuniarossy Citra Amelia Intan Permadani Dea Putri Pascha Febriyanti Dina Magdalena Manurung Dwi Arman Prasetya Fajria Ulumin Nafiah Ilmatus Sa’diyah Irma Amanda Putri Kartika Maulida Hindrayani M Naswan Izzudin Akmal Mohamad Ibnu Fajar Maulana Mohammad Idhom Mohammad Idhom Muhammad Idhom Muhammad Muharrom Al Haromainy Nabila Rizky Amalia Putri Nasywa Azzah Nabila Permatasari, Reisa Reza Sadiya Purwadwika Rizki Amanda Sandria Amelia Putri Selena Nurmanina Afandy Setiawan Setiawan Shindi Shella May Wara Trimono Trimono Trimono, Trimono Wahyu Syaifullah J. S. Wahyu Syaifullah Jauharis Saputra