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All Journal Kompak : Jurnal Ilmiah Komputerisasi Akuntansi Jurnal Teknik Sipil Cendekia (JTSC)
Endah Murtiana Sari
Industrial Engineering, Universitas Sains Indonesia, Bekasi, Indonesia
Civil Engineering, Building, Construction & Architecture Education

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Analisis Faktor Risiko Investasi Proyek Jalan Tol KPBU Berbasis Sistem Informasi: Literature Review Dwi Wahyu Prasetyo; Lina Lina; Endah Murtiana Sari
Kompak :Jurnal Ilmiah Komputerisasi Akuntansi Vol. 19 No. 1 (2026): Kompak : Jurnal Ilmiah Komputerisasi Akuntansi
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/kompak.v19i1.4038

Abstract

Investasi proyek jalan tol melalui skema Kerja Sama Pemerintah dan Badan Usaha (KPBU) menghadapi tingkat ketidakpastian yang tinggi akibat kompleksitas karakteristik proyek, besarnya nilai investasi, panjangnya periode konsesi, serta keterlibatan berbagai pemangku kepentingan. Risiko investasi tidak hanya berasal dari aspek konstruksi, tetapi juga dipengaruhi oleh faktor finansial, permintaan lalu lintas (traffic risk), regulasi, dan dinamika kebijakan yang dapat memengaruhi kelayakan investasi. Pendekatan analisis risiko yang masih bersifat konvensional belum mampu menyediakan informasi yang terintegrasi, objektif, dan adaptif sebagai dasar pengambilan keputusan investasi. Penelitian ini bertujuan mengembangkan model konseptual analisis risiko investasi proyek jalan tol KPBU berbasis sistem informasi sebagai landasan pengembangan Decision Support System (DSS). Metode penelitian menggunakan Systematic Literature Review (SLR) dengan pedoman PRISMA 2020 untuk mengidentifikasi dan mensintesis variabel serta indikator risiko investasi dari publikasi bereputasi yang terindeks Scopus dan Web of Science pada periode 2015–2025. Hasil sintesis literatur mengidentifikasi empat kelompok risiko utama, yaitu risiko konstruksi, risiko finansial, risiko permintaan (traffic risk), dan risiko regulasi. Keempat variabel tersebut dirumuskan sebagai variabel independen yang memengaruhi keputusan investasi, dengan sistem informasi analisis risiko diposisikan sebagai variabel mediasi yang mengintegrasikan proses identifikasi, penilaian, dan visualisasi risiko. 
Electrical Project Model Using the Local Component Level (TKDN) in Government Building Construction Mas’ud Dohim; Dyah Erny Herwindiati; Endah Murtiana Sari
JURNAL TEKNIK SIPIL CENDEKIA (JTSC) Vol 7 No 3 (2026): Juli
Publisher : Departement of Civil Engineering, Universitas Winaya Mukti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51988/jtsc.v7i3.642

Abstract

The implementation of TKDN level in electrical work is one of the government's strategic policies to enhance national industrial competitiveness, reduce dependence on imported products, and support sustainable infrastructure development. However, the implementation of TKDN in government building construction projects still faces various obstacles, such as the limited availability of TKDN-certified products, low integration between planning and procurement, and the absence of a model capable of explaining the factors that influence the level of TKDN implementation. This study aims to develop an electrical work model using the TKDN level in government building construction by identifying the factors that influence its implementation and analyzing the relationships among these factors with respect to the success of TKDN implementation. The study employed a quantitative approach through the distribution of questionnaires to 210 respondents; the data were then analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) to test the relationships among variables and to construct an empirical model. The results produced a model explaining the influence of the unit price of electrical work, electrical material, labor, electrical equipment, and TKDN policy on the level of TKDN implementation, as well as its impact on project performance. The novelty of this study lies in the development of a TKDN implementation model for electrical work that integrates regulatory compliance and supervision, technical, and managerial aspects within a single empirical framework. The resulting model is expected to serve as a basis for decision-making  in increasing the use of domestic products in government building projects.
Development of a Machine Learning-Based DSS for Predicting Unit Price Escalation of Water Resources Projects: Literature Review Ince Suil Febryan Maula; Lina Lina; Endah Murtiana Sari
JURNAL TEKNIK SIPIL CENDEKIA (JTSC) Vol 7 No 3 (2026): Juli
Publisher : Departement of Civil Engineering, Universitas Winaya Mukti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51988/jtsc.v7i3.646

Abstract

Unit price escalation in Water Resources (SDA) projects is a challenge for project implementation success. The uncertainty of material prices, labor wages, equipment costs, energy prices, inflation, exchange rates, changes in government policies, spatial conditions, project characteristics, and various external risks make price escalation predictions increasingly complex and difficult to perform using conventional approaches. The development of ML combined with DSS offers a more adaptive, accurate, and data-driven predictive approach to support decision-making  of projects. This study aims to examine the application of ML in predicting unit price escalation in SDA projects, identify factors that influence prediction accuracy, and formulate directions for integrated DSS development. The study uses the Systematic Literature Review (SLR) method based on the PRISMA 2020 guidelines by reviewing articles published in the period 2015–2025 from the Scopus, Web of Science, ScienceDirect, IEEE Xplore, and SpringerLink databases. The results of the study indicate that the Random Forest, Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Gradient Boosting algorithms provide better predictive performance than conventional statistical methods. Most studies are still oriented towards general project cost estimation and have not developed a DSS capable of dynamically integrating economic factors, labor, project characteristics, spatial conditions, risks, and historical data to predict unit price escalation of natural resource projects. 
Co-Authors Dwi Wahyu Prasetyo Dyah Erny Herwindiati Ince Suil Febryan Maula Lina Lina Lina Lina Mas’ud Dohim