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All Journal Jurnal Teknik Sipil Unika Soegijapranata : G-SMART (Geoteknik, Struktur, Manajemen Konstruksi, Sumber Daya Air, Transfortasi)

Budi Santosa

Department Of Civil Engineering, Soegijapranata Catholic University

Author-ID : 3397039

Civil Engineering, Building, Construction & Architecture Engineering Transportation

Published : 17 Documents Claim Missing Document

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Kajian Kapasitas Sungai Sengkarang Kabupaten Pekalongan Dengan Menggunakan HEC-RAS Johanes Among Timur; Abraham Daksa; Budi Santosa
G-SMART Vol 3, No 1: Juni 2019
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gs.v3i1.1570

Flooding was natural disasters the minimize the impact or can be avoided when you know a source of the problem. The increase in discharge flood most affected by the existence of the to watershed ( DAS ), and the form of profile river that could not be accommodate flooding luminance stretch out. The purpose of this research is to evaluate capacity river Sengkarang in order to accommodate flooding luminance stretch out, and give the alternative solution flood. The necessary data in the form of rainfall daily from year 2001 - 2016 in five station rain, namely station Karang Gondang, PS.Kletak station, Karangsari station, Pekalongan station, and Kutosari station. Daily rainfall data is then processed into hourly rain data using Mononobe method. Then from the parameters that have been specified in the insert into HEC-HMS software. The result of HEC-HMS this is discharge flood simulations with the period repeated 2 annual of 322.8 m3 / s, the period repeated 5 annual of 582.6 m3 / s, the period repeated of 10 years of 765.1 m3 / s, the period repeated 25 annual of 1034.3 m3 / s, the period repeated 50 annual of 1148.9 m3 / s. after obtained discharge simulations with the period repeated certain then afterward did the simulation profile river sengkarang by using HEC-RAS software. This permodelan it can be found in which a part is happened flood. Therefore done solution altermatif of normalization river.

Durabilitas Mortar Polimer Termodifikasi Alami dengan Amylum dan Bahan Tambah Madu Nanda Isdian Prasetyo; Gerald Arsa Adhiyaksa Abiyoga; Rr. M.I. Retno Susilorini; Budi Santosa
G-SMART Vol 2, No 1: Juni 2018
Publisher : Universitas Katolik Soegijapranata Semarang

The research aimed to investigate the durability of natural modified polymer mortar with amylum and honey admixture of aggressive environment that was modeled by 3 curing media, sea water, brakish water, and tidal flooding water. There were 855 specimens of mortar cubes with addition of amylum of 0,1%, 0,2%, 1%, 2%, and 5% and also honey of 0%, 0,03%, and 0,03%. All specimenswere cured and distributed into 3 curing medias for 7, 14, and 28 days. The result of this research met conclusion that optimum compressive strength was achieved by specimen with composition 0,1% amylum and 0,03% honey that was cured by sea water.

Kajian Potensi Sedimentasi Pada Waduk Jatibarang Dengan Pemodelan SWAT (Soil and Water Assesment Tool) Han Lois Herlambang; Montana Raisya Putri; Budi Santosa; Djoko Suwarno
G-SMART Vol 6, No 1: Juni 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i1.3130

Sedimentasi menjadi salah satu permasalahan dalam pengelolaan waduk. Sedimen yang mengendap pada waduk akan mempengaruhi kapasitas tampungan mati dan umur waduk tersebut. Tujuan dari penelitian yaitu menghitung besaran potensi sedimentasi dan hubungan volume sedimentasi dengan umur perkiraan Waduk Jatibarang. Hasil pemodelan Soil and Water Assesment Tool (SWAT) menunjukan potensi volume sedimentasi dari Mei 2014 sampai Desember 2019 mencapai 1.361.811,915 m3 dan Maret 2034 diprediksi tampungan mati pada Waduk Jatibarang akan penuh. Hubungan volume sedimen (x) dengan umur perkiraan Waduk Jatibarang (y) berdasarkan grafik regresi yaitu y = -0,1215x3 + 81,822x2 + 71490.img 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

Analisis Tingkat Bahaya Erosi Lahan Di Daerah Aliran Sungai (Das) Kupang Menggunakan Metode Modified Universal Soil Loss Equation (Musle) Aldian Seputra Sudianto; Aditya Manung Pratama; Daniel Hartanto; Budi Santosa
G-SMART Vol 6, No 2: Desember 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i2.4388

Penelitian ini dilakukan dengan tujuan untuk mengetahui laju erosi yang terjadi pada DAS Kupang, Jawa Tengah. Proses erosi yang terjadi perlu dilakukan analisis menggunakan rumus MUSLE (Modified Universal Soil Loss Equation) dengan aplikasi ArcGIS. Metode penelitian ini digunakan untuk mengetahui tingkat bahaya erosi pada area DAS Kupang yang dapat ditampilkan dalam bentuk peta. Hasil penelitian yang diperoleh berupa total laju erosi DAS Kupang sebesar 63.720,681 ton/ha/th kriteria sangat berat dengan luasan DAS sebesar 181,109 km2. Erosi terbesar terjadi pada Kecamatan Talun dengan 28.784,349 ton/ha/th dengan luas kecamatan sebesar 44,821 km2Adapun skala prioritas untuk penanganan bahaya erosi yakni pada Kecamatan Pekalongan Barat, Kota Pekalongan dengan total skor 36,339, Kecamatan Talun, Kabupaten Pekalongan dengan total skor 32,578, dan Kecamatan Petungkriyono, Kabupaten Pekalongan dengan total skor 25,857

Analisis Sempadan Sungai Pepe Kota Surakarta Satria Dinda Pradana; Budi Santosa; Djoko Suwarno
G-SMART Vol 7, No 1: Juni 2023
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v7i1.5992

Sungai Pepe yang berada di Kota Surakarta, adalah salah satu sungai yang sebagian daerah sempadan sungainya saat ini telah mengalami alih fungsi. Pencemaran akibat aktifitas masyarakat yang berada disekitar wilayah sempadan sungai Pepe telah mengakibatkan terjadinya pencemaran air di sungai tersebut. Tujuan penelitian ini adalah untuk mendapatkan gambaran tentang sempadan yang sesuai dan tidak sesuai dengan Peraturan Pemerintah no 38 tahun 2011. Penelitian ini memanfaatkan program ArcGIS dan peta citra Kota Surakarta untuk menentukan garis sempadan sungai sesuai dengan aturan Peraturan Pemerintah no 38 Tahun 2011. Hasil dari penelitian menunjukkan semua sungai bertanggul sesuai dengan aturan yang berlaku dan untuk sungai tidak bertanggul ada yang tidak sesuai dengan peraturan yaitu Kelurahan Kepatihan Wetan, Kelurahan Kampung Baru, Kelurahan Sudiroprajan, Kelurahan Kedung Lumbu, dan Kelurahan Gandekan. Maka Perlu dilakukan peninjauan kembali oleh pemerintah terhadap pemukiman yang berada di sempadan sungai agar pemanfaatan daerah sempadan sungai sesuai dengan peraturan yang ada dan segera dilakukan perencanaan dan pelaksanaan penataan sempadan Sungai Pepe agar dapat berfungsi kembali sesuai dengan peruntukannya.Sungai Pepe yang berada di Kota Surakarta, adalah salah satu sungai yang sebagian daerah sempadan sungainya saat ini telah mengalami alih fungsi. Pencemaran akibat aktifitas masyarakat yang berada disekitar wilayah sempadan sungai Pepe telah mengakibatkan terjadinya pencemaran air di sungai tersebut. Tujuan penelitian ini adalah untuk mendapatkan gambaran tentang sempadan yang sesuai dan tidak sesuai dengan Peraturan Pemerintah no 38 tahun 2011. Penelitian ini memanfaatkan program ArcGIS dan peta citra Kota Surakarta untuk menentukan garis sempadan sungai sesuai dengan aturan Peraturan Pemerintah no 38 Tahun 2011. Hasil dari penelitian menunjukkan semua sungai bertanggul sesuai dengan aturan yang berlaku dan untuk sungai tidak bertanggul ada yang tidak sesuai dengan peraturan yaitu Kelurahan Kepatihan Wetan, Kelurahan Kampung Baru, Kelurahan Sudiroprajan, Kelurahan Kedung Lumbu, dan Kelurahan Gandekan. Maka Perlu dilakukan peninjauan kembali oleh pemerintah terhadap pemukiman yang berada di sempadan sungai agar pemanfaatan daerah sempadan sungai sesuai dengan peraturan yang ada dan segera dilakukan perencanaan dan pelaksanaan penataan sempadan Sungai Pepe agar dapat berfungsi kembali sesuai dengan peruntukannya

Potensi Penurunan Debit Banjir Di Sungai Jragung Akibat Pembangunan Bendungan Jragung Guntoro Riki Wibisono; Avin Ananta Paranindya; Budi Santosa; Djoko Suwarno
G-SMART Vol 7, No 1: Juni 2023
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v7i1.10079

Daerah Aliran Sungai Jragung pada saat debit banjir yang mengalir cukup besar menyebabkan daerah tengah dan hilir dilanda banjir setiap tahun, bahkan banjir besar di beberapa daerah salah satunya di Bendung Guntur dapat terjadi dua kali dalam setahun, kondisi Daerah Aliran Sungai Jragung sudah sebagian besar terbuka karena hampir 70% berupa kebun dan ladang, tetapi dengan kondisi tersebut masih terdapat oknum yang melakukan kegiatan pembukaan lahan dan illegal logging sehingga pemerintah membuat Bendungan Jragung untuk mereduksi banjir pada aliran Sungai Jragung. Penelitian ini berguna untuk mengetahui debit banjir sebelum ada Bendungan Jragung dan setelah ada Bendungan Jragung sehingga dapat diketahui angka reduksi dari debit banjir pada Sungai Jragung untuk kala ulang 2, 5,1 0, 20, 50, 100, 200, 500, dan 1000 Tahun. Untuk menganalisis pengaruh pembangunan Bendungan Jragung terhadap potensi penurunan debit banjir Sungai Jragung pada penelitian ini digunakan analisis Hidrograf Satuan Sintetik dan debit melalui waduk. Penelitian ini menggunakan Hidrograf Satuan Sintetik Snyder dan debit melalui waduk menggunakan metode level pool routing. Data yang digunakan pada penelitian ini adalah curah hujan peta Daerah Aliran Sungai Jragung, data parameter bendungan, data parameter Daerah Aliran Sungai, dan debit aliran Sungai Jragung. Dari hasil penelitian dapat diketahui debit sebelum ada bendungan yaitu sebagai aliran inflow untuk kala ulang 2, 5, 10, 20, 50, 100, 200, 500, dan 1000 tahun sebesar sebesar 26,79 m3/det, 36,80 m3/det, 43,46 m3/det, 49,79 m3/det, 58,14 m3/det, 64,53 m3/det, 71,03 m3/det, 79,56 m3/det dan 86,13 m3/det. Untuk hasil penelitian debit setelah ada bendungan sebagai outflow untuk kala ulang 2, 5, 10, 20, 50, 100, 200, 500, dan 1000 tahun sebesar 24,41 m3/det, 33,46 m3/det, 39,46 m3/det, 45,19 m3/det, 52,90 m3/det, 58,84 m3/det, 64,61 m3/det, 72,30 m3/det, dan 78,22 m3/det. Berdasarkan hasil debit banjir sebelum ada bendungan dan setelah ada bendungan dapat diketahui potensi penurunan debit banjir pada Sungai Jragung untuk kala ulang 2, 5, 10, 20, 50, 100, 200, 500, dan 1000 tahun sebesar 8,88%, 9,06%, 9,18%, 9,25%, 9,01%, 8,95%, 9,04%, 9,12% dan 9,18%.Kata kunci: debit banjir, hidrograf satuan sintetik snyder, debit melalui waduk.

Optimalisasi Penyediaan Air Baku Di Desa Wiru Kecamatan Bringin Kabupaten Semarang Menggunakan Program Epanet 2.2 Rizky Arnata; Dava Fahrezi Nurtanto; Budi Santosa; Yohanes Yuli Mulyanto
G-SMART Vol 7, No 1: Juni 2023
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v7i1.10185

Kebutuhan air pada penduduk terus meningkat seiring terjadinya peningkatan jumlah penduduk. Aktivitas makhluk hidup terkait dengan kebutuhan air yang tersedia. Kejadian tersebut merupakan suatu yang harus sudah diprediksi dan sudah direncanakan untuk melaksanakan pengoptimalan supaya kebutuhan air tercukupi. Masalah yang dapat menyebabkan terganggunya pemenuhan kebutuhan air pada penduduk yaitu tersedia atau tidaknya sumber mata air yang mencukupi, untuk memenuhi kebutuhan air pada penduduk pada masa mendatang. Untuk mencapai pemenuhan kebutuhan air pada penduduk perlu dilakukan upaya optimalisasi dalam penyediaan air baku.Proyeksi pertumbuhan penduduk pada Desa Wiru diperhitungkan dari tahun 2021 hingga 2040. Optimalisasi yang dilaksanakan yaitu menggunakan proyeksi pertumbuhan penduduk pada tahun 2025, 2030, dan 2040. Pertumbuhan penduduk sebesar 3240 orang pada tahun 2025. Untuk 5 tahun yang akan datang pada tahun 2030 diproyeksikan pertumuhan penduduk sebesar 3405 orang. Kemudian pada tahun 2035 sebesar 3578 orang. Pada tahun 2040 sebesar 3760 orang.Jumlah kebutuhan air yang digunakan menggunakan peraturan berdasarkan Peraturan Mentri Dalam Negeri No. 23 Tahun 2006. Peraturan tersebut dikembangkan dari peraturan Unesco Tahun 2002. Peraturan yang digunakan memberikan kebutuhan air baku sebesar 60 lt/org/hari. Kebutuhan air baku pada tahun 2025 sebesar 3,51 lt/dtk, untuk 5 tahun yang akan datang yaitu pada tahun 2030 sebesar 3,69 lt/dtk, kemudian tahun 2035 sebesar 3,88 lt/dtk, dan pada tahun 2040 sebesar 4,07 lt/dtk.Optimalisasi dalam pemenuhan kebutuhan air baku dilaksanakan pada daerah Desa Wiru, Kecamatan Bringin, Kabupaten Semarang. Daerah tersebut terdiri dari Dusun Krajan Wiru, Mojo, Ngelo, Pelem, Jrebeng, dan Kedunglaran. Pemenuhan kebutuhan air tidak mengalami kekurangan dalam pemenuhan air baku terhadap dusun yang terletak pada Desa Wiru. Jumlah air maksimum yang mengalir pada Tahun 2025 sebesar 0,54 lt/dtk, kemudian pada 5 tahun selanjutnya yaitu tahun 2030 sebesar 0,45 lt/dtk, pada tahun 2035 sebesar 0,63 lt/dtk, dan pada tahun 2040 sebesar 1,52 lt/dtk. Ketersediaan air yang tersedia pada Sungai Tuntang memiliki minimal debit air pada 3,22 lt/dtk.Proyeksi pertumbuhan penduduk pada Desa Wiru diperhitungkan dari tahun 2021 hingga 2040. Optimalisasi yang dilaksanakan yaitu menggunakan proyeksi pertumbuhan penduduk pada tahun 2025, 2030, dan 2040. Pertumbuhan penduduk sebesar 3240 orang pada tahun 2025. Untuk 5 tahun yang akan datang pada tahun 2030 diproyeksikan pertumuhan penduduk sebesar 3405 orang. Kemudian pada tahun 2035 sebesar 3578 orang. Pada tahun 2040 sebesar 3760 orang.Jumlah kebutuhan air yang digunakan menggunakan peraturan berdasarkan Peraturan Mentri Dalam Negeri No. 23 Tahun 2006. Peraturan tersebut dikembangkan dari peraturan Unesco Tahun 2002. Peraturan yang digunakan memberikan kebutuhan air baku sebesar 60 lt/org/hari. Kebutuhan air baku pada tahun 2025 sebesar 3,51 lt/dtk, untuk 5 tahun yang akan datang yaitu pada tahun 2030 sebesar 3,69 lt/dtk, kemudian tahun 2035 sebesar 3,88 lt/dtk, dan pada tahun 2040 sebesar 4,07 lt/dtk.Optimalisasi dalam pemenuhan kebutuhan air baku dilaksanakan pada daerah Desa Wiru, Kecamatan Bringin, Kabupaten Semarang. Daerah tersebut terdiri dari Dusun Krajan Wiru, Mojo, Ngelo, Pelem, Jrebeng, dan Kedunglaran. Pemenuhan kebutuhan air tidak mengalami kekurangan dalam pemenuhan air baku terhadap dusun yang terletak pada Desa Wiru. Jumlah air maksimum yang mengalir pada Tahun 2025 sebesar 0,54 lt/dtk, kemudian pada 5 tahun selanjutnya yaitu tahun 2030 sebesar 0,45 lt/dtk, pada tahun 2035 sebesar 0,63 lt/dtk, dan pada tahun 2040 sebesar 1,52 lt/dtk. Ketersediaan air yang tersedia pada Sungai Tuntang memiliki minimal debit air pada 3,22 lt/dtk.

Co-Authors Abraham Daksa Aditya Manung Pratama Aldian Seputra Sudianto Angsa Dilaguna Biru Avin Ananta Paranindya Cindy Deniswara Daniel Hartanto Dava Fahrezi Nurtanto Dimas Jalu Setyawan Djoko Suwarno Erik Kurniawan Febri Satrio Nugroho Gerald Arsa Adhiyaksa Abiyoga Guntoro Riki Wibisono Han Lois Herlambang Ivan Kurniawan Johanes Among Timur Junario Vito Ardana Ludfie Hardian Putra M.F. Devita Riangsari Maria Marcellina Harnita Permatasari Montana Raisya Putri Muhammad Genta Nanda Isdian Prasetyo Prambudi Terrano Putra Bintang Rizalditya Rahmat Harta Revangga Dandha Pratama Richardo Pelipus Boru Ratomesang Rigel Putra Samudera Rizky Arnata Rr. M. I. Retno Susilorini Rr. M.I. Retno Susilorini Rr.M.I Rretno Susilorini Rr.M.I. Retno Susilorini Satria Dinda Pradana Satrio Sinung Nugroho Tamara Budi A VG Sri Rejeki Yan’s Dianaga Hananta Yohanes Yuli Mulyanto

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