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All Journal Indonesian Journal of Geography Jurnal Mahasiswa Jurusan Teknik Pengairan Jurnal Teknik Pengairan: Journal of Water Resources Engineering Rekayasa Sipil Jurnal Teknik Sipil U Karst Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Irigasi Civil and Environmental Science Journal (CIVENSE) Jurnal DISPROTEK International Journal of Engineering, Science and Information Technology Jurnal Teknologi dan Rekayasa Sumber Daya Air
Lily Montarcih Limantara
Civil Engineering Department, Faculty Of Engineering, Brawijaya University, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Studi Optimasi Pemanfaatan Air Irigasi Menggunakan Program Linier Pada Daerah Irigasi Manikin Kabupaten Kupang Kharistanto, Robertus Tegar Kurnia; Limantara, Lily Montarcih; Soetopo, Widandi
Jurnal Teknologi dan Rekayasa Sumber Daya Air Vol. 3 No. 1 (2023): Jurnal Teknologi dan Rekayasa Sumber Daya Air (JTRESDA)
Publisher : Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtresda.2023.003.01.42

Abstract

Manikin Area of Irrigation irrigates 201.87 hectares of rice fields, located in NTT which is known for its long dry season, this directly affects the availability ofwater for irrigation. So that the availability of water during the dry season is sufficient and can irrigate the fields, optimization is carried out on the area of plants to be irrigated using the available water availability with the linear programming method in order to get the optimal planting area and the benefits obtained are also more optimal. This study was conducted by optimizing the existing cropping pattern and adding three alternative cropping patterns as an option at 80% (dry), 50% (normal), and 30% (wet) discharge. After optimizing all mainstay discharges, the average water balance which can only be met by 80% of the fulfilled period can be maximized to 100% of the fulfilled period, with details for the existing planting intensity of 200%, and a profit of Rp. 4,643,010,000, alternative 1 has a planting intensity 269.78%, anda profit of Rp. 6,234,664,440, alternative 2 has a planting intensity of 300%, and a profit of Rp. 6,915,540,000, alternative 3 has a planting intensity of 269.78%, and a profit of Rp. 5,854,139,490. Alternative 2 cropping pattern with rice-paddy/peanutrice was chosen as the best one to implement because it has an optimal planting intensity of 300% and a profit of Rp.6,915,540,000 per year, which is greater than Rp. 2,272,530,000 from the profit of the existing cropping pattern which is only Rp. 4,643,010,000 per year. Daerah Irigasi Manikin mengairi sawah seluas 201,87 hektar, terletak di NTT yang dikenal dengan musim kemarau panjang, hal tersebut secara langsung berpengaruh pada ketersediaan air untuk irigasinya. Agar ketersediaan air saat musim kemarau tercukupi dan dapat mengairi sawah, dilakukan optimasi pada luas areal tanaman yang akan diairi menggunakan ketersediaan air yang ada dengan metode program linier supaya mendapatkan luasan tanam yang optimal dan keuntungan yang didapat juga lebih optimal. Studi ini dilakukan dengan pengoptimalan pola tanaman di lapangan dan penambahan tiga pola tanam alternatif sebagai pilihan pada debit andalan 80% (kering), 50% (normal), dan 30% (basah). Setelah dioptimasi pada semua debit andalan, rerata neraca air yang hanya dapat tepenuhi 80% periode tercukupi dapat di maksimalkan ke 100% periode tercukupi, dengan rincian untuk eksisting memiliki intensitas tanam 200%, dan keuntungan Rp.4.643.010.000, alternatif 1 memiliki intensitas tanam 269,78%, dan keuntungan Rp.6.234.664.440, alternatif 2 memiliki intensitas tanam 300%, dan keuntungan Rp.6.915.540.000, alternatif 3 memiliki intensitas tanam 269,78%, dan keuntungan Rp.5.854.139.490. Pola tanam alternatif 2 dengan tanaman padi- padi/kacang tanah-padi dipilih sebagai yang terbaik untuk dilaksanakan karena memiliki intensitas tanam yang optimal 300% dan keuntungan sebesar Rp.6.915.540.000 per tahun lebih besar Rp. 2.272.530.000 dari keuntungan pola tanam eksisting yang hanya Rp.4.643.010.000 per tahun.
Rasionalisasi Kerapatan Pos Stasiun Hujan dan Pos Duga Air Sub DAS Pacal dengan Metode Stepwise Yanuar Wicaksono, R. Fajar; Limantara, Lily Montarcih; Wahyuni, Sri
Jurnal Teknologi dan Rekayasa Sumber Daya Air Vol. 3 No. 1 (2023): Jurnal Teknologi dan Rekayasa Sumber Daya Air (JTRESDA)
Publisher : Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtresda.2023.003.01.33

Abstract

Dalam kegiatan perencanaan serta pengembangan sumber daya air, dibutuhkan data hidrologi dengan kuantitas dan kualitas data yang akurat. Kualitas data yang dimaksud adalah bahwa data dapat menggambarkan kondisi hidrologi sesuai dengan yang terjadi di lapangan, seperti apakah data hujan berkaitan dengan data debit yang ada. Penyebaran pos stasiun hujan di suatu wilayah serta nilai kerapatannya dapat mempengaruhi tingkat kesalahan rerata suatu data hidrologi. Rasionalisasi kerapatan pos stasiun hujan dan pos duga air dilakukan dengan metode Stepwise dan standar WMO pada Sub DAS Pacal. Data yang digunakan adalah data hujanusebagai variabelibebas dan dataodebit sebagai variabeloterikat dengan panjang data selama 10 tahun. Metode Stepwise ini merupakan metode stastistika yang dapat mengetahui pos hujan mana yang berkorelasi secara signifikan terhadap data debit. Standar WMO dapat mengetahui kebutuhan minimal jumlah pos hujan berdasarkan karateristik geografi suatu daerah. Hasil analisis pada studi ini didapatkan rekomendasi dengan kombinasi 2 dan 3 pos stasiun hujan. Kombinasi antara pos stasiun hujan Gondang dan pos stasiun hujan Tretes/Pacal merupakan kombinasi yang paling rasional apabila menggunakan 2 pos stasiun hujan dengan koefisien determinasi sebesar 50,41% dan dengan koefisisen thiessen masing-masing sebesar 31,46% dan 68,54%. Kombinasi antara pos stasiun hujan Gondang, Klepek, dan Pajeng merupakan kombinasi yang paling rasional apabila menggunakan 3 pos stasiun hujan dengan koefisien determinasi sebesar 47,70% dan dengan koefisisen thiessen masing-masing sebesar 37,60%, 28,39 % ,dan 34,00 %. Kedua kombinasi ini telah memenuhi standar minimum WMO dan masing-masing pos hujan juga memiliki peran yang sama-sama efektif dan efisien.In planning and developing water resources, hydrological data with accurate quantity and quality is required. The quality of the data in question is that the data can describe the hydrological conditions following what is happening in the field, such as whether the rain data is related to the existing discharge data. The distribution of rainfall station in an area and their density values can affect the average error rate of hydrological data. The rationalization of the density of the rain station post and water level station was carried out using the Stepwise method and the WMO standard in the Pacal sub-watershed. The data used is rain data as the independent variable and debit data as the dependent variable with a data length of 10 years. This Stepwise method is a statistical method that can find out which rainfall station are significantly correlated with discharge data. The WMO standard can determine the minimum requirement for the number of rainfall station based on the geographical characteristics of an area. The results of the analysis in this study obtained recommendations with a combination of 2 and 3 rainfall station. The combination of Gondang rainfall station and Tretes/Pacal rainfall station is the most rational combination when using 2 rain stations with a coefficient of determination of 50.41% and a Thiessen coefficient of 31.46% and 68.54%, respectively. The combination of Gondang, Klepek, and Pajeng rainfall stations is the most rational combination when using 3 rain stations with a coefficient of determination of 47.70% and a Thiessen coefficient of 37.60%, 28.39%, and 34,00%. Both of these combinations have met the minimum WMO standards and each rain post also has an equally effective and efficient role.
Estimasi Tinggi Curah Hujan dari Data Klimatologi Menggunakan Model Artificial Neural Network (ANN) di Jakarta Pusat, Provinsi DKI Jakarta Diando, Azamulail; Limantara, Lily Montarcih; Wahyuni, Sri
Jurnal Teknologi dan Rekayasa Sumber Daya Air Vol. 4 No. 1 (2024): Jurnal Teknologi dan Rekayasa Sumber Daya Air (JTRESDA)
Publisher : Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtresda.2024.004.01.002

Abstract

Banyaknya data yang hilang maupun kurang akibat kerusakan alat pencatat, proses perbaikan, maupun kesalahan manusia, berpengaruh besar terhadap proses Analisa sumber daya air. Kebutuhan akan data curah hujan yang lengkap dan akurat, pada proses perencanaan suatu wilayah sangatlah penting. Studi ini diharapkan dapat mengatasi kemungkinan adanya kekurangan informasi mengenai data yang dibutuhkan dalam sebuah analisis sumber daya air. Dengan menggunakan data curah hujan dan klimatologi berupa suhu udara, kelembapan, lama penyinaran matahari, dan kecepatan angin, dapat diperkirakan estimasi tinggi curah hujan yang turun di daerah dalam kurun waktu tertentu dengan menggunakan metode permodelan jaringan syaraf tiruan. Pada studi ini ditemukan nilai validasi terbaik pada proses kalibrasi dengan rentang 29 tahun, dengan epoch 1500, didapatkan nilai Nash-Sutcliffe Efficiency (NSE) = 0,81, Root Mean Square Error (RMSE) = 66,12, dan Koefisien Korelasi (R) = 0,9, sedangkan nilai validasi terbaik pada proses verifikasi dengan rentang 1 tahun, epoch 1000, didapatkan nilai Nash-Sutcliffe Efficiency (NSE) = 0,83, Root Mean Square Error (RMSE) = 57,81, dan Koefisien Korelasi (R) = 0.98.
Performance Evaluation of Machine Learning and Deep Learning for Rainfall Forecasting Soebroto, Arief Andy; Limantara, Lily Montarcih; Mahmudy, Wayan Firdaus; Sholichin, Moh.; Hidayat, Nurul; Kharisma, Agi Putra
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1179

Abstract

Climate change is a significant challenge for both humans and the environment, with its impacts increasingly felt across various regions of the world. The most evident consequence is the alteration of extreme weather patterns, which often lead to destructive and life-threatening natural disasters. Among these, extreme rainfall was the most damaging factor, frequently triggering floods. However, the increasing occurrence of related events outlined the urgent need for developing more accurate rainfall forecasting systems as a strategic measure for disaster risk reduction. This research adopted daily rainfall data from Samarinda City, collected between 2004 and 2012, to conduct prediction using both machine and deep learning methods. The implementation of machine learning methods, such as Support Vector Regression (SVR), enabled the model to learn from historical data and uncover complex patterns, resulting in accurate forecasts and improved adaptability to climate variability. Meanwhile, deep learning models, including Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), enhanced prediction performance by capturing more intricate and abstract data relationships. Performance evaluations conducted using Mean Absolute Error (MAE) and Mean Squared Error (MSE) showed that deep learning outperformed machine learning in accuracy. The LSTM model achieved the best performance, with loss values of 0.0482 and 0.0527 for MSE and MAE, respectively. The advantage of deep learning lies in its ability to build more complex models for handling non-linear problems and to learn data representations at various levels of abstraction, which has led to more accurate results. Furthermore, LSTM surpassed RNN by effectively overcoming the vanishing gradient issue, allowing for more stable and efficient training that led to superior predictive performance.
Co-Authors A.A. Ketut Agung Cahyawan W Achmad Hariyadi Adiputra, Dhimas Satibi Agi Putra Kharisma, Agi Putra Agung Rahmadi Agus Priombodo Agustina Pagatiku Alamsyah, Muhammad Bayu Ambarwati, Arum Nurwidya Anggara Wiyono Wit Saputra, Anggara Wiyono Wit Aniek Masrevaniah Arfiyanti, Anandini Fatma Arief Andy Soebroto Arif Rahmad Darmawan Ariston Samosir Azwar Annas Kunaifi Chandrasasi, Dian Dian Chandrasasi Dian Sisinggih Diando, Azamulail Djunaedi Djunaedi Donny Harisuseno Dwi Priyantoro Edison Hukom Eka Agus Subiyantoro Endang Purwati RN Ery Suhartanto Ery Suhartanto Ery Suhartanto Fathia, Ayasha Fauziyah, Fauziyah Februanto, Aaron Jeremy Ferina, Marisa Ayu Hana Arum Rossy Tamaya Haris Djafar, Haris Harisuseno, Donny Harri Pranowo Ikrar Hanggara, Ikrar Ilham, Rendy Khoirul Indra Kusuma Sari Islamiyanto, Yudho Putra Iwan Nursyriwan Jadfan Sidqi Fidari Jamhari Jamhari Juni, Riska Wulan Kharistanto, Robertus Tegar Kurnia Lalu Sigar Canggih Ranesa, Lalu Sigar Canggih Lenny Febriana Ideawati, Lenny Febriana Linda Prasetyorini Lucky Dyah Ekorini M. Bisri Mahendra, Hardiman Maulana, Mokhamad Rusdha Maulida Hayati Megantara, Anggit Gilang Mochammad Ibrahim Moh. Sholichin Moh. Sholichin Mohammad Bisri Muhamad Rodhita Muhammad Bisri Muhammad Ilham Muhammad Walidi Juma'a nalurita, sari Nuf'a, Hilma Nurdiyanto Nurdiyanto, Nurdiyanto nurfitriani, Alvina nurfitriani, Alvina Nurul Hidayat Pitojo Tri Juwono Pramasela, Pramasela Putra, Whima Regianto Qomarul Huda, Qomarul Rachma, Siti Talitha Rahmah Dara Lufira Ramadian, Bagas ramdhani, fitroh Respatiningrum, Amalia Wara Rini Wahyu Sayekti Rini, Firda Agustiya Rispiningtati Rispiningtati Riwin Andono Rony Rudson Rossy Tamaya, Hana Arum Runi Asmaranto Safira Anisah Haromain Safira Anisah Haromain Salimah, Ghaida Nurul Salsabila, Nadia Semuel J. Ch. Ahab, Semuel J. Ch. Shihab, Muhammad Qurais Sri Wahyuni Sri Wahyuni Sri Wahyuni Suhardjono Suhardjono Sulianto Sulianto Suwanto Marsudi Tae Lake, Geovani Valerian Maria Tri Budi Prayogo Tri Budi Prayogo, Tri Budi Triwidianto, Heru Tyas Daru, Tyas Ussy Andawayanti Very Dermawan Wahyuni, Sri Wahyuni, Sri Wayan Firdaus Mahmudy Whima Regianto Putra Widandi Soetopo Yanuar Wicaksono, R. Fajar Yudha Mediawan Yumna Atika