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Prakoso, Puguh Budi
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Transportation

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KAJIAN JEMBATAN A-HALF THROUGH ARCH DENGAN SNI 1725:2016 DAN SNI 2833:2016 (STUDI KASUS JEMBATAN RUMPIANG KABUPATEN BARITO KUALA) Chairunnisa, Nursiah; Pratiwi, Ade Yuniati; Cahyadi, Ahmad; Karim, Abdul; Nurwidayati, Ratni; Prakoso, Puguh Budi
JURNAL TEKNIK SIPIL Vol 13, No 1 (2024): Volume 13 Nomor 1 Mei 2024
Publisher : Jurusan Teknik Sipil, Fakultas Teknik, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jts.v13i1.37056

Abstract

The Rumpiang Bridge is a Half Through Arch type bridge in Marabahan City, Barito Kuala District. This bridge was constructed in 2003 using loading regulations based on the BMS 1992 regulations. In 2016, the government issued the bridge loading regulations SNI 1725:2016, which introduced differences in loading compared to BMS 1992. Therefore, a structural modeling of the bridge was carried out to determine its capacity using the updated loadings from SNI 1725:2016 and SNI 2833:2016. The research utilized the Midas Civil software for assistance. The assessment of the bridge's capacity was based on the deflection values experienced by the bridge. The analysis results indicated that the maximum deflection values occurred under the combination of Static Load Case 1 and Dynamic Load Case 1. For Static Load Case 1, the maximum deflection values at the center span according to BMS 1992 and SNI 1725:2016 were 47.37 cm and 47.25 cm respectively. For Dynamic Load Case 1, the maximum deflection values at the center span according to BMS 1992 and SNI 1725:2016 were 30.108 cm and 28.41 cm respectively. On the other hand, under seismic loading based on SNI 2833:2016, the displacement experienced by the bridge due to the extreme combination 1 was 13.933 mm. Based on the conducted analysis, it is evident that the loading requirements from SNI 1725:2016 yield deflection results that still meet the criteria compared to the loading requirements from BMS 1992.
Perbandingan Metode Pembobotan Tf-Rf Dan Tf-Idf Dikombinasikan Dengan Weighted Tree Similarity Untuk Sistem Rekomendasi Buku Sari, Yuslena; Baskara, Andreyan RIzky; Prakoso, Puguh Budi; Royani, Noorhanida
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 6: Desember 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022935709

Abstract

Unit Pusat Terpadu Perpustakaan merupakan perpustakaan pusat yang ada di Universitas Lambung Mangkurat. Perpustakaan ini mempunyai sistem pencarian buku namun sistem tersebut belum adanya fitur rekomendasi buku sehingga anggota menjadi kesulitan dalam melakukan pencarian buku yang sesuai dengan keinginan anggota. Oleh karena itu, dengan adanya rekomendasi buku atau saran buku yang lain dapat menjadi alternatif untuk membantu anggota dalam melakukan pencarian buku yang sesuai. Dalam penelitian ini menggunakan perbandingan pembobotan kata TF-IDF dan TF-RF dengan weighted tree similarity sebagai pengukur kemiripan diantara beberapa data dengan parameter tree yang sudah ditentukan dan dilakukan perbandingan perhitungan dengan menghitung tf-idf dengan tf-rf menggunakan perhitungan excel mendapatkan nilai yang berbeda antara tf-idf dengan tf-rf, pembobotan tf-idf dapat mengukur kemiripan antara dokumen dan kata kunci buku yang paling mirip dengan buku yang dianggap paling relevan. Sehingga anggota memasukan kata kunci kemudian akan menemukan kemiripan buku dari kata kunci yang dimasukan sebelumnya namun untuk pembobotan tf-rf memberikan kata kunci dari setiap kategori. Hasil perbandingan yang di dapat yaitu 96% untuk tf-idf dan 98% untuk tf-rf. Sistem ini menggunakan bahasa pemrograman python dengan web framework django. AbstractThe Central Integrated Library Unit is the central library at Lambung Mangkurat University. This library has a book search system but the system does not have a book recommendation feature so that members find it difficult to search for books that match the wishes of members. Therefore, the existence of book recommendations or other book suggestions can be an alternative to assist members in searching for suiTabel books. In this study using a comparison of the weighting of the words TF-IDF and TF-RF with weighted tree similarity as a measure of the similarity between several data and a comparison of calculations is carried out by calculating tf-idf with tf-rf using excel calculations to get different values between tf-idf and tf -rf, tf-idf weighting can measure the similarity between documents and keywords of the book that is most similar to the book that is considered the most relevant. So that members enter keywords and then find the similarity of books from the keywords entered previously but for weighting tf-rf provides keywords from each category. The comparison results obtained are 76% for tf-idf and 80% for tf-rf. This system uses the python programming language with the django web framework.
Penerapan Metode K-Means Berbasis Jarak untuk Deteksi Kendaraan Bergerak Sari, Yuslena; Baskara, Andreyan Rizky; Prakoso, Puguh Budi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 4: Agustus 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022945768

Abstract

Deteksi kendaraan bergerak adalah salah satu elemen penting dalam aplikasi Intelligent Transport System (ITS). Deteksi kendaraan bergerak juga merupakan bagian dari pendeteksian benda bergerak. Metode K-Means berhasil diterapkan pada piksel cluster yang tidak diawasi untuk mendeteksi objek bergerak. Secara umum, K-Means adalah algoritma heuristik yang mempartisi kumpulan data menjadi K cluster dengan meminimalkan jumlah kuadrat jarak di setiap cluster. Dalam makalah ini, algoritma K-Means menerapkan jarak Euclidean, jarak Manhattan, jarak Canberra, jarak Chebyshev dan jarak Braycurtis. Penelitian ini bertujuan untuk membandingkan dan mengevaluasi implementasi jarak tersebut pada algoritma clustering K-Means. Perbandingan dilakukan dengan basis K-Means yang dinilai dengan berbagai parameter evaluasi yaitu MSE, PSNR, SSIM dan PCQI. Hasilnya menunjukkan bahwa jarak Manhattan memberikan nilai MSE = 1.328 , PSNR = 21.14, SSIM = 0.83 dan PCQI = 0.79 terbaik dibandingkan dengan jarak lainnya. Sedangkan untuk waktu pemrosesan data memperlihatkan bahwa jarak Braycurtis memiliki keunggulan lebih yaitu 0.3 detik. AbstractDetection moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detection moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE = 1.328 , PSNR = 21.14, SSIM = 0.83 and PCQI = 0.79 values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages 
Co-Authors Abdul Karim Ade Yuniati Pratiwi Ahmad Cahyadi **) Andreyan Rizky Baskara Nursiah Chairunnisa Nurwidayati, Ratni Rahman, Muhammad Arif Royani, Noorhanida Yuslena Sari, Yuslena