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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Jiko (Jurnal Informatika dan komputer) Jurnal Onoma: Pendidikan, Bahasa, dan Sastra bit-Tech International Journal of Advances in Data and Information Systems ESTIMASI: Journal of Statistics and Its Application Indonesian Journal of Data and Science Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Djtechno: Jurnal Teknologi Informasi International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Impresi Indonesia Journal of Renewable Energy, Electrical, and Computer Engineering Buletin Sistem Informasi dan Teknologi Islam Journal of Data Mining and Information Systems Kohesi: Jurnal Sains dan Teknologi Parameter: Jurnal Matematika, Statistika dan Terapannya Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal PETISI (Pendidikan Teknologi Informasi) Jurnal Komtika (Komputasi dan Informatika)
Damaliana, Aviolla Terza
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Journal : bit-Tech

 1 
Comparison of the Effectiveness IndoBERT and mBERT for Sentiment Analysis of SME Customer Reviews Afandy, Selena Nurmanina; Hindrayani, Kartika Maulida; Damaliana, Aviolla Terza
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.
Bus Passenger Demand Forecasting Using A Hybrid ARIMA–MLP Model Moerrin, Naufal Baihaqi; Damaliana, Aviolla Terza; Diyasa, I Gede Susrama Mas
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.v8i3.3549

Abstract

Accurate passenger demand forecasting is crucial for operational planning and service reliability in public transportation systems. Despite the effectiveness of traditional models, existing approaches often struggle with nonlinear fluctuations in demand, which limits their ability to adapt to real-world variability. This study proposes a hybrid forecasting framework that combines the Autoregressive Integrated Moving Average (ARIMA) model with a Multi-Layer Perceptron (MLP) neural network for short-term passenger demand prediction. By using ARIMA to capture linear components like trend, seasonality, and autocorrelation, and MLP to model the residuals that contain nonlinear patterns, the proposed approach integrates the strengths of both models. This hybrid method addresses gaps in current forecasting techniques by improving adaptability and precision. Empirical analysis was conducted using daily passenger count data from Bus Trans Jatim during 2023–2024. Data preprocessing included exploratory time series analysis, variance stabilization, and outlier assessment to ensure compatibility with the modeling assumptions. Forecast performance was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the hybrid ARIMA–MLP model achieved a MAPE of 4.95%, outperforming the standalone ARIMA model in providing more adaptive and accurate short-term forecasts. These findings have practical implications for public transportation planning, enabling more responsive and efficient operations, particularly for forecasting demand fluctuations.
Modeling the Open Unemployment Rate in West Java: A Comparison of Panel Data Regression Models Maulana, Mohamad Ibnu Fajar; Damaliana, Aviolla Terza; J. S., Wahyu Syaifullah
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 Azzah, Alyssa Amorita; Damaliana, Aviolla Terza; Saputra, Wahyu Syaifullah Jauharis
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 Abda Abda Afandy, Selena Nurmanina Amanda, Rizki Amri Muhaimin Ananta, Aditya Putra Anas Ahmadi Anugrah, Muhammad Cahya Raka Azzah, Alyssa Amorita Bey Lirna, Cagiva Chaedar Devi, Arsinta Safira Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Fahrudin, Tresna Maulana Febriyanti, Dea Putri Pascha Febyanti, Iin Hindrayani , Kartika Maulida idhom, Mohammad Idhom, Muhammad Illah, Ibnu Zahy' Atha Ilmatus Sa'diyah J. S., Wahyu Syaifullah Jauharis Sapu, Wahyu Syaifullah Kaffi, Laisal Kartika Maulida Hindrayani Kurniawan, Muhammad Erlangga Lestari, Amanda Ayu Dewi Maulana, Mohamad Ibnu Fajar Maulana, Mohammad Hikmal Maulidiyyah, Nova Auliyatul Moerrin, Naufal Baihaqi Mohammad Idhom Muhaimin, Amri Muizzadin, Muizzadin Nabilah Selayanti Nugroho, Muhammad Rafli Feandika Oktaviani, Sheny Eka Permadani, Citra Amelia Intan Pradipta, Alvino Hadiyan Purwadwika, Reza Sadiya Putri, Adinda Rusdianti Maulani Putri, Irma Amanda Putri, Maretta Fairuz Luthfia Winoto Putri, Nabila Rizky Amalia Putri, Shafira Amanda Ramadhani, Dafinah Riyantoko, Prismahardi Aji Rizky Mahendra, Rakha Saputra, Wahyu Syaifullah Jauharis Shindi Shella May Wara Trimono, Trimono Wara, Shindi Sheila May Wara, Shindy Shella May Wardah, Salsabila Wiyono, Farhan Syah Putra Zahwa, Aniq Farichatus