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Aminullah, Akhmad
Civil And Environment Engineering Of Gadjah Mada University
Aerospace Engineering Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Neuroscience Public Health Social Sciences Transportation

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Bridge Displacement Estimation using Tiltmeter Data Raka Bagus Panuntun; Akhmad Aminullah; Bambang Suhendro; Panji Krisna Wardana
Journal of the Civil Engineering Forum Vol. 5 No. 2 (May 2019)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1325.971 KB) | DOI: 10.22146/jcef.43670

Abstract

Structural Health Monitoring System (SHMS) works as an efficient platform for monitoring health condition and and deterioration of civil structures during long-term service periods. One of the sensors is currently applied to Soekarno Bridge is tiltmeter. Vertical displacement data are often required to reflect the overall response of bridge span, however the Soekarno Bridge does not have any displacement sensor. On the other hand, the displacement sensor is very costly compared to tiltmeter. A method is proposed to estimate bridge displacement using data that is collected through tiltmeter. The method is using interpolation and numerical integration to evaluate the displacement. The result shows that the result obtained from the proposed method is reliable with accuracy of displacement around 5 mm. Some recommendations for further implementation of the sensors are provided.
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

Abstract

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 ±1,55% pada data learning dan ±9,75% pada data uji random.
STUDI EKSPLORASI FAKTOR-FAKTOR KESUKSESAN PROYEK KONSTRUKSI DI INDONESIA Annisa Azhar Firdausi; Toriq arif ghuzdewan; Ashar Saputra; Akhmad Aminullah
Journal of Civil Engineering and Planning (JCEP) Vol 1 No 2 (2020)
Publisher : Program Studi Sarjana Teknik Sipil Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/jcep.v1i2.1493

Abstract

The high rate of growth of the construction sector in Indonesia has not been matched by its level of success. Problems are still found in many construction projects that result in project unsuccessfulness, including time loss and bad quality work. In order to support the success of construction projects in Indonesia, the aim of this study is to determine the critical factors that influence the success of construction projects in Indonesia. Data collection through questionnaires on 417 respondents who were directly involved in the project. The questionnaire was based on 4 previous studies, measured by a 5-point Likert scale of agreement and consisting of 64 project success variables. The analysis was performed using the Exploratory Factor Analysis (EFA). The results of this study indicate that there are 6 factors that influence the success of construction projects in Indonesia, namely: (1) Factors related to the Project Manager; (2) Factors related to the Contractor; (3) Factors related to Supervisors; (4) factors related to the project; (5) Factors related to Owner; and (6) Factors related to Owner2. Keywords : construction project, project success, critical success factor, EFA
Study of community-based waste management strategy determination in Magelang City Ni Nyoman Nepi Marleni; Nurul Alvia Istiqomah; Bambang Agus Kironoto; Bambang Suhendro; Akhmad Aminullah; Danang Parikesit; Ahmad Rifa`i
Community Empowerment Vol 7 No 5 (2022)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (265.259 KB) | DOI: 10.31603/ce.6601

Abstract

The city of Magelang is having trouble providing Final Disposal Sites (TPA), as the Banyuurip TPA's storage capacity has reached its limit and expansion is no longer possible. The only approach to reduce garbage production and residue transferred to landfills is to engage the community and all other stakeholders in waste management. This program intends to develop a waste management strategy in Magelang City starting with the smallest units, namely RT and RW. According to the findings, waste management solutions for the community of RW 02, Magelang Village, Central Magelang District may be classified as socialization strategies, work plans or regulations, resource procurement, cooperation, and empowerment. Furthermore, the study's findings reveal that an effective strategy must include multiple activities that operate concurrently, progressively, and sustainably, as well as the participation of various parties (community, local government, and universities) in assuring effective management implementation.
Estimasi Dimensi Optimum dan Rasio Tulangan Gedung Hotel Yogyakarta dengan Artificial Neural Network Akhmad Aminullah; Kinanti Faradiba Harahap; Henricus Priyosulistyo
Rekayasa Sipil Vol. 16 No. 3 (2022): Rekayasa Sipil Vol.16. No.3
Publisher : Department of Civil Engineering, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.rekayasasipil.2022.016.03.5

Abstract

Cost estimation at the conceptual design stage of a building is necessary because it is considered as a fundamental input in further decision making. This paper presented a neural network method in approximating the optimum dimension and the minimum reinforcement ratio of beams and columns at the conceptual design stage of hotel buildings in Yogyakarta. A group of 27 building variations were prepared as the training data for the set up neural network model. Fourteen empirical formulas were obtained which could be used to estimate the optimum dimension and minimum reinforcement ratio of beams and columns with 5 parameters which consist of soil sites class, beam spans, number of floors, concrete strengths and diameters of the reinforcing bar.
STUDI EKSPLORASI FAKTOR-FAKTOR KESUKSESAN PROYEK KONSTRUKSI DI INDONESIA Annisa Azhar Firdausi; Toriq arif ghuzdewan; Ashar Saputra; Akhmad Aminullah
Journal of Civil Engineering and Planning (JCEP) Vol. 1 No. 2 (2020)
Publisher : Program Studi Sarjana Teknik Sipil Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/jcep.v1i2.1493

Abstract

The high rate of growth of the construction sector in Indonesia has not been matched by its level of success. Problems are still found in many construction projects that result in project unsuccessfulness, including time loss and bad quality work. In order to support the success of construction projects in Indonesia, the aim of this study is to determine the critical factors that influence the success of construction projects in Indonesia. Data collection through questionnaires on 417 respondents who were directly involved in the project. The questionnaire was based on 4 previous studies, measured by a 5-point Likert scale of agreement and consisting of 64 project success variables. The analysis was performed using the Exploratory Factor Analysis (EFA). The results of this study indicate that there are 6 factors that influence the success of construction projects in Indonesia, namely: (1) Factors related to the Project Manager; (2) Factors related to the Contractor; (3) Factors related to Supervisors; (4) factors related to the project; (5) Factors related to Owner; and (6) Factors related to Owner2. Keywords : construction project, project success, critical success factor, EFA
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

Abstract

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 ±1,55% pada data learning dan ±9,75% pada data uji random.
Structural Health Assessment of Kretek II Bridge using Enhanced Frequency Domain Decomposition Astalis Ulul Absor, Didib; Aminullah, Akhmad; Supriyadi, Bambang
Journal of the Civil Engineering Forum Vol. 10 No. 2 (May 2024)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jcef.9151

Abstract

The development of infrastructure is growing rapidly in Indonesia. Kretek II bridge is one of the infrastructures built in the country. Dynamics aspects are one of the very important aspects used to validate structural analysis model or being linked to Structural Health Monitoring (SHM) of the bridge. Dynamics properties such as natural frequency, damping ratio, and mode shape also referred as modal parameters. The objective of this research is to determine the modal parameters of Kretek II bridge using Enhanced Frequency Domain Decomposition (EFDD) which is commonly used to extract modal parameters from the acceleration data recorded. Compared to the conventional method, EFDD is very practical and robust in structural health assessment because of its user-friendly uses in the ARTeMIS Modal software. To make sure that the results from EFDD are accurate, numerical modeling is necessary to validate it. This research was conducted using dynamics load test results as data for the modal extraction with EFDD method assisted by ARTeMIS Modal software. Dynamics load test was performed by placing 8 accelerometers in the span and exciting it with impact load from dropped truck on ramp. Modal parameters from the EFDD results are then compared to the numerical modeling results. The first two modes of the EFDD and numerical modeling results consecutively are 3.09 Hz, 3.745 Hz and 3.06 Hz, 3.49 Hz. The EFDD and numerical modeling results are both in agreement with both results having similar mode shape on their first two mode and low error percentage with only 0.89% and 7.17% respectively.
Bridge Assessment Analysis using Sufficiency Rating Method (Case Study of The Mungkung Overpass) Lau, Debora; Aminullah, Akhmad; Triwiyono, Andreas
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.v20i1.71348

Abstract

Bridges play a crucial role in connecting various routes and water bodies, necessitating regular monitoring to prevent potential hazards and losses due to deterioration. In Indonesia employs the Bridge Management System (BMS) for decision-making regarding maintenance, repair, and replacement activities, its reliance on a limited set of criteria may lead to uncertainties in bridge condition assessmsents. This study explores the Sufficiency Rating (SR) method as an alternative for assessing bridge feasibility. By considering multiple factors, including structural adequacy, serviceability, functionality, and public interest, the SR method offers a comprehensive approach to bridge evaluation. An analysis of the Mungkung Overpass on the Ngawi Kertasono toll road was conducted to evaluate the effectiveness of the SR method. The findings classified the bridge as excellent, indicating no immediate need for treatment. However, the SR method has limitations, particularly in visually assessing damage to superstructure and substructure elements, which may introduce subjectivity. Overall, this research contributes to the ongoing development of bridge assessment methodologies in Indonesia, aiming to enhance decision-making processes and ensure the safety and longevity of bridge infrastructure. After analyzing the data obtained through the implementation of the SR method, it was revealed that S1 achieved a value of 45%, S2 achieved 23%, S3 achieved 12.7%, and S4 achieved 6%. The total actual value of the four parameters calculated based on the SR method was 74.7%, with a difference of 25.3% from the maximum value. These findings clearly illustrated that the bridge satisfied the criteria for deficiency, thereby necessitating comprehensive rehabilitation measures to be undertaken.
Pengaruh Variasi Hidden Layer Terhadap Nilai MAPE Pada Pengembangan Model Estimasi Biaya Menggunakan Artificial Neural Network: (Studi Kasus: Biaya Peningkatan Jalan Aspal di D.I. Yogyakarta) Kesuma, I Made Sutrisna Ari; Nugroho, Arief Setiawan Budi; Aminullah, Akhmad
Siklus : Jurnal Teknik Sipil Vol. 9 No. 2 (2023): Siklus: Jurnal Teknik Sipil
Publisher : Program Studi Teknik Sipil Fakultas Teknik Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/siklus.v9i2.14221

Abstract

Pekerjaan peningkatan jalan menjadi suatu kebutuhan yang tidak dapat dielakkan guna mendapatkan infrastruktur transportasi yang lebih handal. Dukungan perencanaan anggaran dan estimasi biaya yang baik oleh karenanya harus dilakukan. Model persamaan prediksi anggaran dan biaya dengan Artificial Neural Network (ANN) menjadi alternatif solusinya. ANN menuntut rancangan arsitektur jaringan yang tepat guna memperoleh model dengan tingkat akurasi yang tinggi. Penelitian ini bertujuan mengetahui jumlah efektif neuron dalam hidden layer yang memberikan hasil model persamaan ANN dengan tingkat akurasi tinggi dengan nilai Mean Absolute Percentage Error (MAPE) kecil. Pengembangan model didasarkan pada 33 data pekerjaan peningkatan jalan aspal di Provinsi Daerah Istimewa Yogyakarta dari tahun 2010 sampai dengan tahun 2021. Delapan belas variabel proyek yang berpengaruh signifikan terhadap total biaya pekerjaan digunakan sebagai data input model ANN dan dianalisis dengan berbagai variasi data model dan validator. Hasil penelitian menunjukkan bahwa variasi jumlah neuron dalam hidden layer menghasilkan nilai MAPE dengan pola tidak beraturan yang mana tingkat akurasi sangat dipengaruhi oleh data input dan validator. Namun demikian secara umum model dengan jumlah neuron dalam hidden layer 11/­3 kali lipat dari jumlah variabel input menjanjikan hasil akurasi paling tinggi.
Co-Authors Adhitama, Y. A. Agustono Setiawan Ahmad Rifa`i Aisyah Aisyah Ali Awaludin Almulianur Almulianur Andreas Triwiyono Annisa Azhar Firdausi Arief Setiawan Budi Nugroho Arif Setiawan Budi Nugroho Ashar Saputra Astalis Ulul Absor, Didib Baehaki Baehaki Baehaki _ Bambang Agus Kironoto Bambang Suhendro Bambang Suhendro Bambang Supriyadi Bara, Alvin A. Brigitta Petra Kartika Narindri Danang Parikesit Darmawan, M. Fauzi Djoko Sulistyo Djoko Sulistyo Gherry, K. T. N. Ghuzdewa, Toriq Arif Handayani , Tantri Nastiti Harahap, Kinanti Faradiba Henricus Priyosulistyo, Henricus Iman Satyarno Irawati, Inggar Septhia Kencana, Yoga Kesuma, I Made sutrisna Ari Kinanti Faradiba Harahap KMS. M. Rama Perwira Lau, Debora Lautan Wijaya Nusantara, Johan Maulana Agung Sedayu Mufid Kusuma, Muhammad Farhan Muhammad Akbar Muslikh Muslikh Ni Nyoman Nepi Marleni Nugroho, Arief Setiawan Budi Nurul Alvia Istiqomah Panji Ginaya Taufik Panji Krisna Wardana Prasinda, Yulita Pratama, Herdian Rachmad Jayadi Raka Bagus Panuntun Ratih Dewanti Remigildus Cornelis restu faizah Risang Aji Dananjoyo Sanjaya, Rudi Sari, Halima Irianti Puspita Setiawan, Angga Fajar Suprapto Siswosukarto Suprapto Siswosukarto Suprapto Siswosukarto Tambunan, Reinhard Taufik, Panji Ginaya Toriq arif ghuzdewan Triyoso, Christopher Vallentino, Rizky WARKA, I GEDE PUTU Wijaya, Miguel Felix Wulandari, Irvebry Ayu Yusuf, Effendi Zulkifli, Rifdah Rofifah