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All Journal Jurnal Pendidikan Teknologi dan Kejuruan Jurnal Reka Lingkungan Journal of Engineering and Technological Sciences Journal of Science and Applicative Technology Sumatra Journal of Disaster, Geography and Geography Education Jurnal Geografi Jurnal Teknik Lingkungan Pharmacy Reports Newton-Maxwell Journal of Physics AMMA : Jurnal Pengabdian Masyarakat Jurnal Teknologi Lingkungan Journal Serambi Engineering (JSE) Prosiding Seminar Nasional Pendidikan FKIP Universitas Lampung
Alvin Pratama
Institut Teknologi Sumatera
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Nursing Physics Social Sciences Other

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Analysis distribution sulfur dioxide and nitrogen dioxide concentration from PLTU Pangkalan susu with callpuff method Zulkarnain, Randy; Suryati, Isra’; Pratama, Alvin
Jurnal Pendidikan Teknologi Kejuruan Vol 3 No 4 (2020): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jptk.v3i4.15923

Abstract

Coal-fired power plants will emit several types of pollutants into the ambient air such as particulates and gases. One way to estimate the extent of the impact distribution of these pollutants is by using air quality modeling. The model used in this study is Calpuff, where this model is a non-steady state model and is influenced by variations in meteorological factors. The research location is PLTU Pangkalan Susu (2x200 MW) with SO2 and NO2 parameters. The purpose of this study was to calculate the concentrations of SO2 and NO2 with Calpuff, to validate modeling with field observations and to simulate the distribution of impacts. The results showed that the concentration of SO2 model obtained was 0.32 - 3.57 µg/m3 and NO2 was 0.51 - 5.15 µg/m3. Meanwhile, the observation results showed that the SO2 concentration was 27 - 39.88 µg/m3 and NO2 was 19.77 - 29.73 µg/m3. The simulation results of the distribution of SO2 and NO2 concentrations with the Calpuff model show that the impact distribution area is in the direction of the wind in the windrose and the affected area is in the southwest of the PLTU Pangkalan Susu. The results of model validation for the values ​​of d = 0.97, r = 0.616 - 0.665 and FB = -1.719 - -1.849, which means that the Calpuff model is quite valid and can be applied to predict the impact distribution area at PLTU Pangkalan Susu.
Prediksi Curah Hujan Jangka Menengah di Provinsi Lampung Menggunakan Artificial Neural Network Berbasis Data CHIRPS dan Indeks Iklim Global Pratama, Alvin
Newton-Maxwell Journal of Physics Vol. 7 No. 1: April 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/nmj.v7i1.48129

Abstract

Informasi curah hujan yang akurat dan andal sangat penting untuk mendukung perencanaan pertanian, pengelolaan sumber daya air, serta mitigasi bencana hidrometeorologi. Provinsi Lampung merupakan wilayah dengan tingkat kerentanan tinggi terhadap variabilitas iklim yang dipengaruhi oleh fenomena iklim global seperti El Niño–Southern Oscillation (ENSO) dan Indian Ocean Dipole (IOD). Penelitian ini bertujuan mengembangkan model prediksi curah hujan bulanan jangka menengah di Provinsi Lampung menggunakan Artificial Neural Network (ANN) berbasis data satelit CHIRPS dan indeks iklim global. Data yang digunakan meliputi curah hujan observasi dari 19 pos hujan, CHIRPS, Oceanic Niño Index (ONI), dan Dipole Mode Index (DMI) selama periode 1991–2020. Tiga konfigurasi model ANN diuji, yaitu kombinasi CHIRPS–DMI, CHIRPS–ONI, serta CHIRPS–DMI–ONI. Hasil evaluasi menunjukkan bahwa data CHIRPS memiliki korelasi kuat terhadap curah hujan observasi dengan nilai rata-rata 0,659 (p < 0,001). Analisis korelasi menunjukkan bahwa IOD memiliki pengaruh lebih dominan terhadap curah hujan Lampung dibandingkan ENSO. Model ANN terbaik diperoleh pada konfigurasi CHIRPS–DMI dengan nilai RMSE terendah sebesar 55,49 mm dan koefisien korelasi tertinggi sebesar 0,671. Model ini menunjukkan performa prediksi yang stabil hingga 12 bulan ke depan. Hasil penelitian ini menunjukkan potensi integrasi data satelit dan indeks iklim global dalam mendukung sistem prediksi curah hujan dan mitigasi risiko iklim di wilayah tropis.
Transformasi Folklor Indonesia dalam Game Roblox sebagai Media Pendidikan Nilai, Kearifan Lokal, dan Kreativitas Budaya Digital Roma Kyo Kae Saniro; M. Yusuf; Alvin Pratama; Andina Meutia Hawa; Ahmad Hamidi
Prosiding Seminar Nasional Pendidikan FKIP Universitas Lampung 2026: Prosiding Seminar Nasional Pendidikan FKIP Universitas Lampung
Publisher : FKIP Universitas Lampung

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Abstract

Penelitian ini bertujuan untuk menganalisis mengkaji transformasi folklor Indonesia dalam gim Roblox sebagai sastra digital, media pendidikan nilai, kearifan lokal, dan kreativitas budaya digital, dengan menempatkan gim sebagai praktik budaya, bukan sekadar hiburan. Hal ini dilatarbelakangi oleh perkembangan media interaktif telah mendorong transformasi folklor Indonesia dari tradisi lisan dan teks ke dalam medium gim digital. Roblox, sebagai platform dunia virtual partisipatoris, membuka ruang baru bagi folklor untuk dihadirkan sebagai pengalaman naratif interaktif. Metode yang digunakan berupa metode deskriptif-analitis dengan pendekatan kualitatif berbasis humaniora digital. Data diperoleh melalui studi pustaka dan analisis terhadap transkrip siaran langsung permainan gim horor Roblox bertema folklor Indonesia yang dipublikasikan di platform digital. Data dianalisis sebagai teks budaya digital multimodal yang mencakup aspek naratif, visual, audio, bahasa lisan, dan interaksi sosial, untuk memahami bagaimana folklor direpresentasikan, dinegosiasikan, dan dimaknai dalam praktik bermain dan streaming. Hasil penelitian menunjukkan bahwa Roblox berfungsi sebagai teks sastra digital interaktif yang bersifat ergodik, di mana narasi folklor tidak disajikan secara linear, melainkan dibangun melalui eksplorasi ruang, mekanika permainan, dan pilihan tindakan pemain. Folklor hadir sebagai pengalaman imersif dan performatif, yang melibatkan emosi, tubuh, dan respons afektif pemain. Kearifan lokal direpresentasikan melalui sistem tanda budaya yang terintegrasi dalam lingkungan virtual dan struktur permainan, seperti norma sosial, larangan adat, dan konsekuensi naratif. Pendidikan nilai berlangsung secara eksperiensial melalui logika sebab–akibat, bukan melalui pesan moral eksplisit, sehingga pemain menginternalisasi nilai budaya melalui pengalaman bermain. Selain itu, transformasi folklor ke dalam estetika horor dan mekanika gim modern menunjukkan praktik kreativitas budaya digital, di mana tradisi lokal direinterpretasi dan dihidupkan kembali sesuai dengan logika media interaktif.
Penyuluhan Tentang Pengiklanan Dan Pengemasan Produk Kewirausahaan Dalam Rangka Pencegahan Stunting Annisa Maulidia Rahayyu; Alvin Pratama; Robiatul Muztaba; Tefur Nur Rohman
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 6 : Juli (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Stunting is a serious health problem that has a long-term impact on the quality of human resources, especially in rural areas. This community service aims to increase the understanding of PKK and Posyandu cadres about stunting prevention through advertising and packaging education about simple nutritious food processing made from local ingredients. The method used is interactive socialization. This activity was carried out on December 18, 2024 in Bumisari Village, Natar District, South Lampung Regency. The results of the activity showed an increase in participants' understanding of advertising and packaging. The cadres are committed to disseminating this information to the wider community. Continuous assistance is expected to make Bumisari Village a pilot for simple nutritious food processing products made from local ingredients with attractive packaging.
Urban Flood Susceptibility Modeling Using GIS and Machine Learning in Bandar Lampung Alvin Pratama; Andreas Boni Baik Simamora; Farras Ghaly
JURNAL GEOGRAFI Vol. 18 No. 1 (2026): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v18i1.72252

Abstract

Urban flooding increasingly affects rapidly urbanizing tropical cities, where terrain, rainfall, and anthropogenic surface modification interact to shape spatial flood patterns. This study develops a GIS–machine learning framework to model urban flood susceptibility in Bandar Lampung, Indonesia, using a multi-year flood inventory (2015–2024). A balanced dataset (n = 308; 1:1 flood to pseudo-absence ratio) was constructed using buffered pseudo-absence sampling with spatial separation constraints to reduce bias. Nine environmental and infrastructure-related predictors were evaluated using Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM). Model performance was assessed through five-fold stratified cross-validation, generalization gap analysis (Train AUC − CV AUC), learning curves, and a 20% hold-out test set. GB achieved the highest cross-validation performance (CV AUC = 0.8953), followed by RF (0.8782), SVM (0.8007), and LR (0.6925). However, ensemble models exhibited larger generalization gaps (RF = 0.1218; GB = 0.1047) compared to LR (0.0333), indicating stronger overfitting tendencies. Learning curves confirmed that LR maintained the most stable convergence between training and validation scores. On the independent test set (n = 61), GB achieved the highest predictive accuracy (ROC AUC = 0.9462), whereas LR showed lower discriminative performance (AUC = 0.7065) but greater validation stability. Flood susceptibility was concentrated in low-elevation areas, near major roads, and adjacent to river networks. By integrating learning curve diagnostics with cross-validation and hold-out testing, this study provides a rigorous framework for model selection in data-limited urban environments.
Improving Rainfall Prediction in Lampung, Southern Sumatra Using Wrf: The Role if Bias Correction and Ensemble Techniques For Disaster Mitigation Andreas Boni Baik Simamora; Pratama, Alvin; Ridlo Wahyudi Wibowo; Farras Ghaly
Sumatra Journal of Disaster, Geography and Geography Education Vol. 10 No. 1 (2026): Sumatra Journal of Disaster, Geography and Geography Education (June Edition)
Publisher : Sumatra Journal of Disaster, Geography and Geography Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/sjdgge.v10i1.752

Abstract

Rainfall prediction in the tropics is highly challenging due to strong convective variability and limited observations. This study evaluates the Weather Research and Forecasting (WRF) model over southern Sumatra, using ERA5 and GFS-FNL as boundary datasets. Ten combinations of microphysics and cumulus parameterizations were tested, followed by bias correction, ensemble construction, and verification against AWS observations. Two bias correction methods were compared: Linear Scaling (LS) and Quantile Mapping (QM). LS consistently improved correlation and reduced RMSE, while QM often degraded performance. For rainfall intensity, ERA5-driven simulations outperformed GFS, with ERA5 Member 9 (WSM6 + BMJ) showing the highest skill (r = 0.91; RMSE ≈ 20 mm). Rainfall occurrence verification showed that ERA5 ensembles, particularly Ensemble 3 with POD–FAR weighting, achieved the most balanced skill (CSI ≈ 0.60), while GFS Member 5 occasionally surpassed the ensemble mean. ROC analysis revealed complementary strengths: ERA5 was more effective for moderate to heavy rainfall, whereas GFS showed greater sensitivity to light events. These results underscore ERA5’s advantage for tropical downscaling and highlight the potential of multisource ensembles to improve rainfall prediction in data-scarce regions.
TRACING THE SOURCES AND TRANSPORT PATHWAYS OF PARTICULATE IN JAKARTA USING THE WRF-HYSPLIT MODEL DURING THE JULY 2023 POLLUTION EPISODE Ririn Anggina Batubara; Alvin Pratama; Amalia Nurlatifah
Jurnal Reka Lingkungan Vol 14, No 1 (2026)
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekalingkungan.v14i1.13-26

Abstract

PM2.5 pollution remains a critical environmental and public health issue in Jakarta, particularly during the dry season when unfavorable meteorological conditions enhance pollutant accumulation. This study examines the sources and transport pathways of PM2.5 during a severe pollution episode in July 2023, utilizing a combined Weather Research and Forecasting (WRF) and Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) modeling approach. Meteorological simulations were evaluated against surface observations, while backward trajectory analyses were conducted using multiple meteorological datasets to assess the consistency of transport. The results indicate that PM2.5 transport into Jakarta was predominantly influenced by air masses originating from the east and southeast, associated with industrial activities, power plants, and local fire events in surrounding regions. The presence of the southeast monsoon contributed to reduced atmospheric dispersion, resulting in prolonged pollutant residence times over the urban area. Despite some limitations in wind speed simulation, the WRF model adequately represented key meteorological parameters relevant to trajectory analysis. These findings highlight that Jakarta’s air pollution is driven by the combined effects of local emissions and regional transport processes, emphasizing the need for integrated air quality management strategies that extend beyond administrative boundaries.
Co-Authors Ade Ayu Oktaviana Ade Ayu Oktaviana Ahmad Hamidi Amalia Nurlatifah Andreas Boni Baik Simamora Andreas Boni Baik Simamora Angga F. Pratama Annisa Maulidia Rahayyu Aristyo R. Wijaya Armi Susandi Destika Agustina Widiawan Dyah Purwaningrum Faisal, Irvan Farras Ghaly Farras Ghaly Hawa, Andina Meutia Hayim Muhammad Agiel Ikhsan Buyung Arifin Irvan Faisal Joni Hermana Kombara, Prawira Yudha M. Yusuf Mamad Tamamadin Mareta, Lesi Oktaviana, Ade Ayu Olgha P. Pandini Pandini, Olgha P. Pratama, Angga F. Ridlo Wahyudi Wibowo Ririn Anggina Batubara Robiatul Muztaba Roma Kyo Kae Saniro Sofyan, Asep Suryati, Isra’ Susandi, Armi Tefur Nur Rohman Widiawan, Destika Agustina Wijaya, Aristyo R. Zulkarnain, Randy