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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JUITA : Jurnal Informatika Jurnal E-Komtek Techno Jurnal Pengabdian Teknik dan Sains (JPTS)
Dimara Kusuma Hakim
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Forecasting Air Temperature Using the Triple Exponential Smoothing Method Heri Susanto; Dimara Kusuma Hakim
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 8 No 1 (2024)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v8i1.1723

Abstract

Changes in air temperature are a major challenge in Indonesian agriculture. Erratic air temperatures hurt plant growth, so farmers must adjust planting schedules and plant selection according to the air temperature at a certain period. The author aims to predict minimum and maximum air temperatures in Temanggung district in 2024 using the Triple Exponential Smoothing (TES) method. Monthly air temperature data in Temanggung district for the period 2020 to 2023 is used for air temperature forecasting using the TES method. The analysis results show that the TES model can predict air temperature with fairly good accuracy. The minimum temperature is expected to be 23°C, maximum 26-27°C. The research results provide benefits for the agricultural sector in Temanggung. Farmers can use the results of air temperature predictions to adjust planting schedules based on crops that suit the air temperature to minimize the negative impact of air temperature on plant growth and agricultural yields.
Forecasting Rainfall in Planting Onion Crops in Brebes District, Brebes District Using Holt-Winters Exponential Smoothing Nufus Mar'Atu Sholikhah Wasirudin; Dimara Kusuma Hakim
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 8 No 1 (2024)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v8i1.1746

Abstract

Planting shallots is very dependent on rainfall. High rainfall when planting shallots can result in shallot plants not growing well, resulting in reduced selling value at harvest time and low rainfall. Therefore, this study aims to determine the rainfall forecasting process for planting shallot plants in Brebes District, Brebes Regency. The data used in rainfall forecasting is monthly data in Brebes District from January 2019 to December 2023 using the Holt-Winters Exponential Smoothing method. Data sourced from NASA's Power Data Access Viewer. In the surface data, get the MAPE value0.05241. Earth Skin Temperature data gets a MAPE value of 2.34346. Wind Speed data gets a MAPE value of 14.5396. DataPrecipitationgot a value of 138.829583. These findings contribute to further understanding regarding rainfall forecasting in shallot planting, which can support the planting process so that the harvest is good and produces high selling value.
Comparison of Double and Triple Exponential Smoothing Methods for Rainfall Prediction Wisnu Adji, Muhammad; Dimara Kusuma Hakim
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 8 No 1 (2024)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v8i1.1755

Abstract

Rainfall is water that falls to the ground surface over a certain period and is measured in millimeters (mm). Rainfall is essential for the life of living things. Forecasting plays a significant role in decision-making in modern times, with two main methods: causal models and time series. Time series models have five types of data patterns: random, constant, seasonal, cyclical, and trend. For rainfall forecasting, the Double Exponential Smoothing and Triple Exponential Smoothing methods are used for trend pattern data. This research compares the two approaches based on error values using average rainfall data in Bojonegoro. The results show that Double Exponential Smoothing has a Mean Absolute Percentage Error (MAPE) of 0.6996%, while Triple Exponential Smoothing has a MAPE of 119.1497%. So, Double Exponential Smoothing is more accurate.
Prakiraan Kecepatan Angin Menggunakan Metode Triple Exponential Smoothing di Pantai Pangandaran Dimara Kusuma Hakim; Yuana Wangsa Putri Setiawan; Supriyono; Maulida Ayu Fitriani4
Jurnal E-Komtek Vol 8 No 2 (2024)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v8i2.2243

Abstract

Indonesia as a maritime country with a long coastline, holds significant potential in the marine and tourism sectors. However, these sectors are often disrupted by adverse weather conditions, particularly irregular wind speeds. Accurate wind speed forecasting is therefore essential for disaster mitigation. This study aims to forecast wind speed at Pantai Pangandaran using the Triple Exponential Smoothing (TES) method, which is more effective in handling data fluctuations with trend and seasonal patterns. The data used includes daily data from January 2014 to September 2024. The results show that the TES method provides highly accurate forecasts, with a low error rate evaluated through an RMSE of 0.51 and a MAPE of 17.85% for wind speed. These forecasts are expected to support disaster mitigation, enhance safety, and improve the efficiency of activities in coastal areas, particularly at Pantai Pangandaran, in facing adverse weather conditions.
Pelatihan Pembuatan Buku Cerita Anak Berbasis Generative AI untuk Menumbuhkan Semangat Bela Negara pada Ibu-Ibu PKK Desa Wilayut Rizky Parlika; Dimara Kusuma Hakim
Jurnal Pengabdian Teknik dan Sains (JPTS) Vol. 6 No. 2 (2026): Juli 2026
Publisher : Lembaga Publikasi Ilmiah dan Penerbitan (LPIP)

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

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memberikan pelatihan pembuatan buku cerita anak berbasis Generative Artificial Intelligence (AI) kepada Ibu-Ibu PKK Desa Wilayut, Kecamatan Sukodono, Kabupaten Sidoarjo. Permasalahan utama yang diidentifikasi adalah tingginya intensitas penggunaan gawai pada anak-anak usia Sekolah Dasar yang berpotensi menyebabkan fenomena brain rot, yaitu menurunnya kemampuan kognitif akibat konsumsi konten digital yang pasif dan berlebihan. Pendekatan yang ditawarkan adalah memberdayakan ibu sebagai agen perubahan melalui pelatihan pembuatan cerita bergambar interaktif menggunakan Generative AI, yang selanjutnya dapat diajarkan kepada putra-putri mereka. Metode pelaksanaan terdiri dari empat tahap: (1) sosialisasi dan identifikasi kebutuhan; (2) pelatihan teknis penggunaan Generative AI untuk membuat cerita dan ilustrasi; (3) pendampingan pembuatan buku cerita; dan (4) evaluasi dan presentasi hasil. Hasil kegiatan menunjukkan bahwa 92% peserta mampu mengoperasikan alat Generative AI secara mandiri, 85% peserta berhasil menghasilkan buku cerita berilustrasi, dan 88% peserta menyatakan bahwa keterampilan ini dapat mengalihkan perhatian anak dari gawai. Kegiatan ini berkontribusi pada pengembangan literasi digital keluarga serta penanaman nilai-nilai bela negara melalui media cerita anak yang kreatif dan edukatif.
Analytical Hierarchy Process (AHP) untuk Zona Kerentanan Tanah Longsor di Daerah Gumelar Reza Fahmi Pahlevi; Dimara Kusuma Hakim; Tito Pinandita; Muhammad Hamka
Jurnal E-Komtek Vol 9 No 2 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i2.2556

Abstract

Landslides are geological disasters that frequently occur in areas with steep topography and minimal vegetation, including in Gumelar Subdistrict, Banyumas Regency. This study aims to map landslide vulnerability zones using a multi-criteria approach with the Analytical Hierarchy Process (AHP) method integrated into a Geographic Information System (GIS). Four main parameters analyzed include slope gradient, rainfall, lithology, and land cover, with weights determined through a pairwise comparison matrix by experts. The results indicate that slope gradient (49.2%) and rainfall (30.9%) are the dominant factors in determining vulnerability levels. The resulting vulnerability map shows the distribution of areas with low, moderate, and high risks, validated using field landslide event data. This study provides an accurate spatial basis for landslide disaster mitigation planning in the study area
Co-Authors Achmad Fauzan Fathul Wahid Fitria Ekowati HERI SUSANTO Hindayati Mustafidah Irfan Ardiansyah Maulida Ayu Fitriani4 Muhammad Hamka Nufus Mar'Atu Sholikhah Wasirudin Parlika, Rizky Reza Fahmi Pahlevi Sigit Sugiyanto Supriyono Tito Pinandita Tresna Yudha Prawira Windy Agus Setiasih Wisnu Adji, Muhammad Yuana Wangsa Putri Setiawan