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FACTORS AFFECTING THE SPEED OF CERTIFICATE ACCEPTANCE OF FUNCTIONALITY Andhyni Kusumahastiti; Suprapto Siswosukarto; Henricus Priyosulistyo
Journal of Development and Integrated Engineering Vol 2, No 1 (2022): Journal of Development and Integrated Engineering, v2i1 June 2022
Publisher : Program Studi Arsitektur S-2 Universitas Pendidikan Indonesia

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Topic of this research was carried out in relation to the Regulation of the Minister of Public Works 25/PRT/M/2007 concerning the Certificate of Feasibility of Function (SLF). The factors affecting the speed of getting the certificate will be investigated. Each region is required to make further regional regulations that refer to the Regulation of the Minister of Public Works 25/PRT/M/2007. Such a regional regulation can affect the speed and quality of results. The case studies selected in this study were SLF at the Microbiology Building, FKKMK UGM and Hotel de Braga, Bandung. This research based on the results of interviews and data obtained in the microbology building UGM Yogyakarta and Hotel de Braga Bandung. The factors that influence the SLF application process are socialization related to SLF, human resources in the application and issuance process, and understanding of the main tasks and functions of each related human resource. The difference requirements and procedures of the regional regulations does not affect each process but does affect the duration of the SLF issuance process. Substantially, the regional regulations in Sleman Regency has no differences compared to the regional regulations in the city of Bandung regarding SLF but sequence.

Estimasi Biaya Struktur Gedung Rumah Sakit dengan Bentuk Persegi Panjang Menggunakan Metode Artificial Neural Network Ratih Dewanti; Akhmad Aminullah; Henricus Priyosulistyo
Journal of Civil Engineering and Planning (JCEP) Vol. 2 No. 2 (2021)
Publisher : Program Studi Sarjana Teknik Sipil Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/jcep.v2i2.5351

Estimasi biaya struktur memiliki peran penting pada tahap perencanaan. Penelitian ini memanfaatkan metode Artificial Neural Network (ANN) sebagai metode pendekatan untuk menghasilkan estimasi biaya struktur gedung yang keakuratannya dapat diuji. Permodelan gedung merupakan struktur beton bertulang dengan model tipikal bentuk persegi panjang yang berfungsi sebagai gedung rumah sakit. Estimasi biaya struktur terdiri dari biaya pekerjaan balok, kolom dan pelat. Tahap pelatihan permodelan ANN menggunakan 60 variasi data dari pengembangan variabel penelitian berupa luas lantai, jumlah lantai, tinggi kolom, kelas situs, dimensi balok dan dimensi kolom. Hasil permodelan ANN menunjukkan estimasi biaya struktur lebih sensitif terhadap variabel dimensi balok dan kolom. Permodelan ANN estimasi biaya struktur menghasilkan nilai persentase error/MMRE sebesar Â±1,55% pada data learning dan Â±9,75% pada data uji random.

Analisis Life Cycle Cost pada Bangunan Sekolah SMP Islam Al Azhar 17 Pontianak Rabbani, Admiral Hazel; Priyosulistyo, Henricus
RekaRacana: Jurnal Teknik Sipil Vol 10, No 1: Maret 2024
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaracana.v10i1.38

ABSTRAKDalam rangka mendukung program wajib belajar 12 tahun yang telah diterapkan Pemerintah Indonesia, bangunan sekolah membutuhkan metode yang baik dalam pemeliharaannya agar selalu dalam kondisi yang layak guna. Salah satu metode pemeliharaan yang dapat digunakan adalah metode Life Cycle Cost yang mengacu pada ISO 15686:5. Komponen biaya pada Metode LCC yang digunakan pada penelitian ini adalah analisis biaya konstruksi awal, biaya operasional dan biaya pemeliharaan dan penggantian. Untuk mendukung penggunaan LCC, dilakukan perhitungan indeks kondisi bangunan agar dapat menentukan kondisi bangungan saat ini. Sedangkan untuk kelayakan dari segi investasi, dilakukan perhitungan Net Present Value. Dari hasil penelitian yang direncanakan untuk 25 tahun ke depan, didapatkan nilai biaya konstruksi sebesar Rp7.344.650.553 (39,91%), biaya operasional sebesar Rp7.520.911.599 (40,86%), dan biaya pemeliharaan sebesar Rp3.538.849.941 (19,23%). Total Life Cycle Cost bangunan sekolah adalah sebesar Rp18.404.412.094 (100%). Untuk nilai indeks kondisi bangunan saat ini, didapatkan nilai sebesar 91,054. Yang berarti bangunan masih dalam kondisi baik sekali dan belum membutuhkan penanganan segera. Sedangkan untuk perhitungan NPV bangunan untuk 25 tahun ke depan, didapatkan nilai positif sebesar Rp29.336.060.666. Hasil tersebut dapat diartikan bahwa investasi bangunan dinyatakan layak atau menguntungkan.Kata kunci: biaya siklus hidup, indeks kondisi bangunan, pemeliharaan bangunan, NPV ABSTRACTIn order to support the 12-year compulsory education program implemented by the Indonesian Government, school buildings require good maintenance so that they are always in a usable condition. One maintenance method that can be used is the Life Cycle Cost method which refers to ISO 15686:5. The cost components of the LCC method used in this research are analysis of initial construction costs, operational costs, and maintenance and replacement costs. To support the use of LCC, a building condition index is calculated to determine the current condition of the building. Meanwhile, for feasibility in terms of investment, Net Present Value is calculated. From the results of research planned for the next 25 years, construction costs amounted to IDR 7,344,650,553 (39.91%), operational costs amounted to IDR 7,520,911,599 (40.86%), and maintenance costs amounted to IDR 3,538,849,941. (19.23%). ). Total Life Cycle Cost of school buildings is IDR 18,404,412,094 (100%). For the current building condition index value, a value of 91.054 was obtained. This means that the building is still in very good condition and does not require immediate maintenance. Meanwhile, for calculating the building's NPV for the next 25 years, a positive value of IDR 29,336,060,666 was obtained. These results can be interpreted as meaning that building investment is declared feasible or profitable.Keywords: life cycle cost, building condition index, building maintenance, NPV

Infrastructure Management of the Smart and Green Learning Center Building Using the Analytical Hierarchy Process (AHP) Balqis, Ratu Irmi; Priyosulistyo, Henricus; Irawati, Inggar Septhia
INERSIA lnformasi dan Ekspose Hasil Riset Teknik Sipil dan Arsitektur Vol. 20 No. 1 (2024): May
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/inersia.v20i1.67639

Building infrastructure management is a key element in maintaining and improving the efficiency, sustainability, and quality of a building. The Smart and Green Learning Center (SGLC) building at Gadjah Mada University is a modern building that functions as both an administrative office and a lecture hall. The building boasts a modern design featuring extensive use of glass and stands 11 stories tall. It embodies the concept of a green and smart building. Hence, it requires serious attention in terms of effective maintenance and proper maintenance to ensure the building can function properly. This research uses the Analytical Hierarchy Process (AHP) method to obtain weighting values for room components/elements. This weighting value is then used to calculate the SGLC building component condition index, which can provide information of whether the component is damaged or not. The weighting values are obtained by distributing questionnaires, while the condition index is determined through observation. The components observed include structural, architectural, and utility components. The results show that weighting values for structure, architecture and utilities were 46.3%, 33.9% and 19.8%, respectively. In the structural components, it is found that the column, beam, and plate weighting values are 48.3%, 42.7% and 8.9%, respectively. In the architectural components, the weighting values for ceilings, walls, floors & ceramics, doors & windows are 11.7%, 36.6%, 8.8%, 42.9% respectively. In the utility components, the weighting values for electricity, electronics, elevators, building sensor systems, and plumbing are 30%, 22.3%, 4.9%, 19.1%, and 23.7%, respectively. After searching floors 1 to 11, it was seen that the structural components were still in exceptionally good condition. There is only minor damage to utility and architectural components that require maintenance such as repair or replacement of components that are no longer suitable for use.

PARAMETRIC STUDY OF WARREN STEEL TRUSS BRIDGE USING ARTIFICIAL NEURAL NETWORKS Astuti, Yana; Priyosulistyo, Henricus; Triwiyono, Andreas; Aminullah , Akhmad
Jurnal HPJI (Himpunan Pengembangan Jalan Indonesia) Vol. 11 No. 1 (2025)
Publisher : Himpunan Pengembangan Jalan Indonesia (HPJI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/jhpji.v11i1.9060.1-16

Abstract Steel truss bridges are a popular type of amongst several other standard bridges in Indonesia due to their lightweight yet robust and strong structure. In this study Artificial Neural Networks is used to optimize the dimensions of the steel truss bridge. The Artificial Neural Network method was chosen as it can handle complex and nonlinear problems, as well as its potential to generate accurate prediction models. Data of 319 existing constructed steel truss bridges in Indonesia were used to train the Artificial Neural Network model. The results show that the Artificial Neural Network model can predict the stress ratio of the structural elements of steel truss bridges with high accuracy (R2 > 0.99). The trained ANN model was then used to optimize the dimensions of the steel truss bridges with spans range from 40 meters to 60 meters with interval of 5 meters. The optimization results showed a 5.60% weight reduction compared to previous research results and a 20% less compared to the average weight of the existing bridge. This study contributes to improving the efficiency of development steel truss bridge in Indonesia. Keywords: truss bridge; optimization; Artificial Neural Network; design efficiency Abstrak Jembatan rangka baja merupakan jenis jembatan standar yang populer di Indonesia karena struktur yang ringan namun kuat dan kokoh. Pada penelitian ini, Artificial Neural Network digunakan untuk mengoptimalkan dimensi jembatan rangka baja. Metode ini dipilih karena kemampuannya menangani masalah kompleks dan nonlinier, serta potensinya untuk menghasilkan model prediksi yang akurat. Data 319 jembatan rangka baja yang ada di Indonesia digunakan untuk melatih model Artificial Neural Network. Hasilnya menunjukkan bahwa model Artificial Neural Network dapat memprediksi rasio tegangan pada elemen struktural jembatan rangka baja dengan akurasi yang tinggi. Model Artificial Neural Network yang terlatih kemudian digunakan untuk mengoptimalkan dimensi jembatan rangka baja dengan rentang bentang 40 meter hingga 60 meter dengan interval 5 meter. Hasil optimasi menunjukkan efisiensi berat sebesar 5,60% dibandingkan dengan penelitian sebelumnya dan 20% lebih efisien dibandingkan dengan jembatan yang sudah ada. Penelitian ini berkontribusi dalam meningkatkan efisiensi pengembangan jembatan rangka baja di Indonesia. Kata-kata kunci: jembatan rangka baja; optimasi; Artificial Neural Network; efisiensi desain

Seismic Performance Comparison of Simply Supported Hollow Slab on Pile Group Structure with Different Operational Category and Shear Panel Damper Application Yusuf Haroki; Ali Awaludin; Henricus Priyosulistyo; Angga Fajar Setiawan; Iman Satyarno
Civil Engineering Dimension Vol. 25 No. 1 (2023): MARCH 2023
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/ced.25.1.10-19

This study is aimed to compare the seismic performance of simply supported hollow slab on pile group (SHSPG) structures designed as “critical” and “essential” viaducts with shear panel damper (SPD) devices. There were three numerical models to be compared, namely SHSPG-A, SHSPG-B, and SHSPG-C. SHSPG-A is a “critical” viaduct with 35 piles per one pile head. SHSPG-B is an “essential” viaduct with 18 piles per one pile head. SHSPG-C is an “essential” viaduct with 18 piles per one pile head plus sixteen SPDs. Numerical models considered the prestressing effect of the spun pile. Nonlinear time history analyses were executed using seven pairs of recorded ground motions that had been scaled and adjusted to the seismic characteristics of Yogyakarta, Indonesia. As the result, the performance level of SHSPG-A was much better than SHSPG-B. The SPDs application could maintain SHSPG-C’s performance at the same level as SHSPG-A and dissipate 34.28%-53.03% of the seismic energy.

Pendekatan Artificial Neural Network untuk Mengestimasi Dimensi Optimum dan Rasio Tulangan Gedung Harahap, Kinanti Faradiba; Aminullah, Akhmad; Priyosulistyo, Henricus
INERSIA lnformasi dan Ekspose Hasil Riset Teknik Sipil dan Arsitektur Vol. 18 No. 1 (2022): Mei
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/inersia.v18i1.45481

The conceptual design stage is necessary because it is considered as a fundamental input in decision making for maximizing the performance of a building. On the other hand, to maximize the performance of the building, there are many things that need to be considered. Therefore, an estimation of the optimum dimensions and the reinforcement ratios of beam and column was carried out at the conceptual design stage using the artificial neural network (ANN). ANN is a network based method that allows to get an accurate approach even with the limited information provided. This study aims to help engineers shorten the time for trial at the conceptual design stage. A total of 36 building variations modelling were prepared as the training data for the set up ANN model. Eight parameters used which consist of earthquake accelarations, soil sites class, joint types, beam spans, number of storey, high of storey, concrete strengths and diameters of the reinforcement. There are 16 empirical formulas for estimating the optimum dimensions and the reinforcement ratios of beam and column. The results showed that the dimensional regression values and the reinforcement ratio were 98.53% and 96.06% respectively. This value indicates that ANN can estimate well.

Rancang Bangun Sistem Pendeteksi Tsunami (Studi Kasus Selat Sunda) Saputro, Agung; Triarmadja, Radianta; Priyosulistyo, Henricus
INERSIA lnformasi dan Ekspose Hasil Riset Teknik Sipil dan Arsitektur Vol. 18 No. 1 (2022): Mei
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/inersia.v18i1.47103

Tsunami is not only caused by the strength of tectonic earthquakes with the dislocation of the seafloor, but also by landslides /rocks in shallow waters, or the relatively large fall of meteors/ celestial bodies. Unlike other tsunamis caused by slab collisions, tsunamis in the Sunda Strait tsunami caused by high tides and underwater landslides due to the eruption of krakatoa. Tsunami due to the eruption of Mount Anak Krakatau threatens at any time so that technology is needed to be able to help the community and BMKG monitor activities in the Sunda Strait so as to provide security and comfort of the surrounding residents. Therefore, research was made to measure the height of waves to detect high waves causing tsunamis in the Sunda Strait. In this research, Tsunami detection system using electrical circuits in its testing with submersible sensors as a wave height reader. The result of the sensor height readings read by the sensor in the process uses a Resberry-Pi microcontroller. The height limit set on the sensor produces siren sounds and sends short messages sent to residents of coastal communities of the Sunda Strait. The results showed that the tsunami detection system managed to read the water level in real time accurately according to the calibration equation. The water level reading tool set by the limit is 2 meters with the length of the wave using a maximum time of 90 seconds. The device will send a short message and produce a siren sound when the wave reaches a height of 2 meters for 90 seconds in a row. Sending messages takes 2-5 seconds, if there is a tsunami due to the eruption of Mount Anak Krakatau the evacuation time to the safe zone for coastal communities of the Sunda Strait is still affordable and safe.

PARAMETRIC STUDY OF WARREN STEEL TRUSS BRIDGE USING ARTIFICIAL NEURAL NETWORKS Astuti, Yana; Priyosulistyo, Henricus; Triwiyono, Andreas; Aminullah , Akhmad
Jurnal HPJI (Himpunan Pengembangan Jalan Indonesia) Vol. 11 No. 1 (2025)
Publisher : Himpunan Pengembangan Jalan Indonesia (HPJI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/jhpji.v11i1.9060.1-16

Abstract Steel truss bridges are a popular type of amongst several other standard bridges in Indonesia due to their lightweight yet robust and strong structure. In this study Artificial Neural Networks is used to optimize the dimensions of the steel truss bridge. The Artificial Neural Network method was chosen as it can handle complex and nonlinear problems, as well as its potential to generate accurate prediction models. Data of 319 existing constructed steel truss bridges in Indonesia were used to train the Artificial Neural Network model. The results show that the Artificial Neural Network model can predict the stress ratio of the structural elements of steel truss bridges with high accuracy (R2 > 0.99). The trained ANN model was then used to optimize the dimensions of the steel truss bridges with spans range from 40 meters to 60 meters with interval of 5 meters. The optimization results showed a 5.60% weight reduction compared to previous research results and a 20% less compared to the average weight of the existing bridge. This study contributes to improving the efficiency of development steel truss bridge in Indonesia. Keywords: truss bridge; optimization; Artificial Neural Network; design efficiency Abstrak Jembatan rangka baja merupakan jenis jembatan standar yang populer di Indonesia karena struktur yang ringan namun kuat dan kokoh. Pada penelitian ini, Artificial Neural Network digunakan untuk mengoptimalkan dimensi jembatan rangka baja. Metode ini dipilih karena kemampuannya menangani masalah kompleks dan nonlinier, serta potensinya untuk menghasilkan model prediksi yang akurat. Data 319 jembatan rangka baja yang ada di Indonesia digunakan untuk melatih model Artificial Neural Network. Hasilnya menunjukkan bahwa model Artificial Neural Network dapat memprediksi rasio tegangan pada elemen struktural jembatan rangka baja dengan akurasi yang tinggi. Model Artificial Neural Network yang terlatih kemudian digunakan untuk mengoptimalkan dimensi jembatan rangka baja dengan rentang bentang 40 meter hingga 60 meter dengan interval 5 meter. Hasil optimasi menunjukkan efisiensi berat sebesar 5,60% dibandingkan dengan penelitian sebelumnya dan 20% lebih efisien dibandingkan dengan jembatan yang sudah ada. Penelitian ini berkontribusi dalam meningkatkan efisiensi pengembangan jembatan rangka baja di Indonesia. Kata-kata kunci: jembatan rangka baja; optimasi; Artificial Neural Network; efisiensi desain

Seismic Performance Evaluation of Simple Reinforced Masonry Houses with Brick Walls: Experimental and Numerical Approaches Pasya, Namira Risza; Saputra, Ashar; Setiawan, Angga Fajar; Priyosulistyo, Henricus
ASTONJADRO Vol. 14 No. 4 (2025): ASTONJADRO
Publisher : Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/astonjadro.v14i4.19253

This study aims to evaluate the performance of brick houses against earthquakes through experimental and numerical approaches. The research objects include two single-story houses located at University of Gadjah Mada (UGM) and Turi, Sleman, Yogyakarta. Microtremor measurement was carried out using accelerometers to record building vibrations, which were then analyzed using Fast Fourier Transform (FFT) to obtain the natural frequency on site of the structure. Numerical modeling was carried out using finite element analysis to validate and asses the building’s frequency response to earthquake loads based on Service Level Earthquake (SLE), Design Basis Earthquake (DBE), and Maximum Considered Earthquake (MCE). The results of the study indicate that the modeling can be validated based on the natural frequency approach from field and numerical evaluate. The maximum displacement that occured at the SLE, DBE, and MCE levels exceeded the allowable limits, indicating that both houses are in an unsafe condition. The structural performance evaluation based on Federal Emergency Management Agency (FEMA 356) shows that the houses in UGM and Turi fall into the Collapse Prevention (CP) category, which mean that the building can no longer be used as houses on the verge of collapse. Although the maximum acceleration analysis of the houses indicates that the values are lower than the design peak ground acceleration (PGAM), structural failure may still occur. The results of this study are expected to provide insights into earthquake-resistant house design as well as recommendations for improving structural resistance to seismic load.

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