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All Journal Jurnal Sains dan Seni ITS Jurnal Onoma: Pendidikan, Bahasa, dan Sastra bit-Tech Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Journal of Artificial Intelligence and Digital Business SAINTEK
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 Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology

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Pemodelan Penyerapan Tenaga Kerja Sektor Industri di Indonesia dengan Pendekatan Regresi Data Panel Dinamis Aviolla Terza Damaliana; Setiawan Setiawan
Jurnal Sains dan Seni ITS Vol 5, No 2 (2016)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (553.367 KB) | DOI: 10.12962/j23373520.v5i2.16550

Abstract

Penyerapan tenaga kerja sektor industri adalah lowongan pekerjaan di sektor Industri yang sudah diisi oleh pencari kerja dan pekerja. Penyerapan tenaga kerja tersebut diperlukan dalam distribusi pendapatan yang nantinya akan berdampak pada pembangunan ekonomi. Tujuan penelitian ini adalah memodelkan penyerapan tenaga kerja sektor industri serta melihat pengaruh elastisitas jangka pendek maupun jangka panjangnya. Variabel yang diduga mempengaruhi penyerapan tenaga kerja sektor industri di Indonesia antara lain, PDRB, UMP, dan produktivitas tenaga kerja. Model yang digunakan adalah regrei data panel dinamis dengan menggunakan GMM Arellano-Bond. Hasil analisis menunjukkan bahwa secara jangka pendek elastisitas untuk PDRB, UMP, dan produktivitas tenaga kerja sebesar 0,350, -0,163, dan -0,005. Adapun elastisitas jangka panjang untuk PDRB, UMP, dan produktivitas tenaga kerja sebesar 1,210, -0,564, dan -0,017.
Hyper Smart Cart As Hypermart Business Process Improvement In Minimizing In-Efficiency At The Cashier Muhammad Muharrom Al Haromainy; Aviolla Terza Damaliana; Abdul Rezha Efrat Najaf; Reisa Permatasari
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 1 No. 1 (2022)
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2903.844 KB) | DOI: 10.31004/riggs.v1i1.3

Abstract

The world today has a rapidly growing human population whose daily needs are certainly increasing. Supermarket is a place to fulfill daily needs. When going to the supermarket, we have to spend a lot of time both shopping and also queuing at the cashier. This turns out to be a problem for customers because it can take up customers' time which is also experienced by one of the largest supermarkets, namely Hypermart. Not only a problem for customers, but it can also create a threat to Hypermart companies. However, with advances in technology and information systems, the world is growing to adapt to current conditions, namely by improvising. The improvisation carried out in this research is the payment transaction business process at Hypermart, namely by implementing the Self-CheckOut system by replacing the old trolley with a "Hyper Smart Cart" as an improvisation which will certainly answer all existing problems.
ANALISIS SENTIMEN TERHADAP ISU FEMINISME DI TWITTER MENGGUNAKAN MODEL CONVOLUTIONAL NEURAL NETWORK (CNN) Brescia Ayundina Yuniarossy; Kartika Maulida Hindrayani; Aviolla Terza Damaliana
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i1.585

Abstract

The development of technology is very significant in various fields, especially in the field of digital technology. Sentiment analysis of feminism issues on Twitter tends to be significant in understanding public opinion, especially Twitter users. Being a place for people to vent, Twitter spreads the message of those who tweet to a wide audience and it often happens that a tweet becomes an influence on public opinion. Twitter can be a tool to find out public sentiment towards a figure, group, and organization. Feminism is a movement to voice the rights of a human being to be equal regardless of gender. In this study, a Convolutional Neural Network (CNN) approach is used to analyze sentiment towards the issue of feminism on Twitter. The data collected from Twitter contains a variety of conversations, opinions, and views on feminism. By building and training a CNN model that is able to process text data and classify sentiment based on each tweet. By applying the CNN model, it aims to identify sentiment patterns towards Twitter users on the issue of feminism, especially the topics of domestic violence and sexual harassment. Where these two topics will be discussed in this research. Another goal is to provide valuable insights for researchers, activists, and policy makers in understanding the dynamics of public opinion on the issue of domestic violence and sexual harassment. The results of this sentiment analysis are expected to make a significant contribution to supporting discussions on social issues on social media
Modelling of Return of S&P 500 Using the Non Linear Generalized Autoregressive Conditional Heteroscedasticity (NGARCH) Model Trimono Trimono; Aviolla Terza Damaliana; Irma Amanda Putri
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4110

Abstract

ARIMA Box-Jenkins is one of the most popular forecasting methods. ARIMA modeling requires a non-heteroskedastic care that shows constant residual variants. Awake, meaning residual residue from heteroscedastic ARIMA modeling (not constant). To overcome the problem of residual heteroskedasticity ARIMA used modeling volatility that is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). GARCH is used to model the ARIMA residual variant which means symmetric. Some financial data has an asymmetric nature caused by the influence of good news and bad news. To accommodate these asymmetric properties, we use the Non-Linear Generalized Autoregressive Conditional Heteroscedasticity (NGARCH) volatility model which is the development of the GARCH model. This research applies NGARCH model using S & P 500 share price index data from January 1, 2019, until July 31, 2023 during active day (Monday-Friday). The purpose of this study, to find the best model NGARCH. The best model generated for S & P 500 stock price index data is ARIMA (1,0,1) NGARCH (1,1) because it has small AIC.
Integrasi Metode Pembelajaran Project Based Learning, Outcame Based Education, dan Bermain Peran dengan Model Webinar Mini untuk Meningkatkan Keterampilan Berbicara Mahasiswa Ilmatus Sa’idah; Anas Ahmadi; Aviolla Terza Damaliana; Adinda Rusdianti Maulani Putri; Dea Putri Pascha Febriyanti
Jurnal Onoma: Pendidikan, Bahasa, dan Sastra Vol. 11 No. 1 (2025)
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/onoma.v11i1.5144

Abstract

Penelitian ini dilakukan untuk melakukan uji efektivitas pada penggunaan metode pembelajaran project based learning, outcame based education, dan bermain peran terhadap peningkatan keterampilan berbicara mahasiswa. Metode yang digunakan adalah metode penelitian tindakan kelas dengan menguji coba integrasi tiga metode secara bersamaan di dalam pembelajaran. Data kemudian diolah secara kualitatif dan kuantitatif. Dalam pelaksanaannya, metode ini mengajak mahasiswa untuk secara langsung berbicara di depan publik. Melalui project based learning, mahasiswa dipandu untuk mengadakan kegiatan webinar mini. Secara langsung, mahasiswa bisa mendapatkan pengalaman nyata berbicara di depan publik menghasilkan video materi webinar dan modul materi secara singkat sebagai luaran pembelajaran di kelas. Sementara itu, melalui bermain peran, mahasiswa menjadi narasumber, moderator, MC, pembaca doa, dan pengarah kuis dalam kegiatan webinar. Mahasiswa juga mencari peserta di luar kelas untuk hadir dalam acara webinar. Setelah uji coba, nilai rata-rata sebelum pelaksanaan metode adalah 65, sementara setelah metode diterapkan, nilai rata-rata meningkat menjadi 85. Kemudian, integrasi metode ini efektif dilaksanakan karena mahasiswa menyatakan bahwa kegiatan pembelajaran dapat meningkatkan keterampilan berbicara mereka.
Faktor Pemengaruh Tingkat Keparahan Kebakaran Hutan di Kalimantan Selatan pada Tahun 2023 Menggunakan Geographically Weighted Regression M Naswan Izzudin Akmal; Dwi Arman Prasetya; Aviolla Terza Damaliana
Prosiding Sains dan Teknologi Vol. 5 No. 1 (2026): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 5 - Februari 2026
Publisher : DPPM Universitas Pelita Bangsa

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

Abstract

Kebakaran hutan di Kalimantan Selatan menunjukkan peningkatan tajam dalam frekuensi kejadian pada tahun 2023, bertepatan dengan kondisi kering yang terkait dengan fenomena El Niño. Meskipun jumlah kejadian kebakaran tinggi, tingkat keparahan kebakaran bervariasi di berbagai lokasi. Penelitian ini menganalisis faktor lingkungan yang mempengaruhi tingkat keparahan kebakaran hutan menggunakan Geographically Weighted Regression (GWR) untuk memodelkan variasi spasial. Keparahan kebakaran diukur menggunakan Differenced Normalized Burn Ratio yang diperoleh dari citra satelit. Prediktor lingkungan meliputi suhu udara, curah hujan total, ketinggian, dan kelembaban vegetasi, yang diperoleh dari data reanalisis ERA5 Land, SRTM, dan citra Landsat. Hasil menunjukkan bahwa keparahan kebakaran menunjukkan korelasi spasial positif, menunjukkan hubungan yang bervariasi secara spasial antara faktor lingkungan dan keparahan kebakaran. Dibandingkan dengan model Ordinary Least Squares global, model GWR memberikan daya penjelas yang lebih baik dan mengurangi autokorelasi spasial pada residual. Curah hujan total dan kelembaban vegetasi menunjukkan pengaruh spasial yang paling konsisten terhadap tingkat keparahan kebakaran, sementara suhu dan ketinggian memiliki efek yang lebih lemah dan lebih lokal. Temuan ini menunjukkan bahwa pemodelan spasial berguna untuk memahami variasi tingkat keparahan kebakaran hutan dan untuk mendukung strategi pengelolaan kebakaran yang spesifik lokasi.
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.
Co-Authors Abdul Rezha Efrat Najaf Adinda Rusdianti Maulani Putri Anas Ahmadi Brescia Ayundina Yuniarossy Dea Putri Pascha Febriyanti Dina Magdalena Manurung Dwi Arman Prasetya Ilmatus Sa’diyah Irma Amanda Putri Kartika Maulida Hindrayani M Naswan Izzudin Akmal Mohammad Idhom Muhammad Muharrom Al Haromainy Permatasari, Reisa Sandria Amelia Putri Setiawan Setiawan Trimono, Trimono