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All Journal Jurnal Titra Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Management Studies and Entrepreneurship Journal (MSEJ)
Indriati Njoto Bisono, Indriati Njoto
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Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Efisiensi Mesin Diesel Pada Tambak Udang Dengan Eksperimen Desain Handoko, Nicholas; Bisono, Indriati Njoto
Jurnal Titra Vol 3, No 2 (2015)
Publisher : Jurnal Titra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (78.005 KB)

Abstract

The operational cost to operate paddle wheel with generator at PT X is very high; for daily shrimp cultivation each farm need 14 paddle wheels that use 24 hours electricity from the generators. Thus an experiment was conducted to minimize the number of paddle wheels operated. Naturally, pond water contains phytoplankton that carries photosyntesis to produce oxygen at noon. Photosyntesis processes need carbon dioxide to turn to oxygen at noon, on the other hand paddle wheel also need the carbon dioxide in the pond and turn it into oxygen. Thus the two are conflicted. Turning off the paddle wheels at noon then increase the photosynthesis process, make it more effective. The experiment results show that turning off paddle wheel at noon did not affect oxygen supply and the average weight of the shrimps.
Quantifying Risk in Waterfall Methodology: Case Study at Aplikasi Super Bisono, Indriati Njoto; Arvin, Vincent; Soewandi, Hanijanto
Management Studies and Entrepreneurship Journal (MSEJ) Vol. 5 No. 2 (2024): Management Studies and Entrepreneurship Journal (MSEJ)
Publisher : Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/msej.v5i2.5664

Abstract

In this paper, we presented a straightforward mathematical model based on Dynamic Programming to answer one of the biggest concern in Waterfall methodology, namely: quantifying risk. Our approach essentially resembles Elmaghraby (2005), but we have more stages and use uniform distributions. With this approach, we can show and quantify the risk in Waterfall methodology so that decision maker can understand the implication of his decision. Dynamic Programming solution also provides a blue print for adjusting decision if the early (previous) stage does not go as planned. A case study at Aplikasi Super with some sensitivity analysis is provided as a numerical illustration.
Finding Random Integer Ideal Flow Network Signature Algorithms Teknomo, Kardi; Nababan, Erna Budhiarti; Bisono, Indriati Njoto; Lim, Resmana
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 1 (2025): June 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.1.105-120

Abstract

We propose a Random Integer Ideal Flow Network (IFN) Signature Algorithm that generates integral flow assignments in strongly connected directed graphs under uncertainty. Existing models often fail to incorporate the inherent randomness and integer constraints present in systems like social networks. Unlike traditional approaches that enforce integrality through large scaling factors, our method distributes integer coefficients across multiple canonical cycles, ensuring precise balance where the sum of inflows exactly equals the sum of outflows at each node. We introduce two pseudocode algorithms that uphold flow conservation while maintaining network irreducibility, ensuring autonomy through strong connectivity. Theoretical contributions include the decomposition of IFNs into canonical cycles and the construction of network signatures, string-based representations that allow efficient performance evaluation through direct string manipulation. These signatures enable quick validation of key network properties such as total flow, balanced link flows, and structural irreducibility. To demonstrate practical applications, we apply our algorithm to modeling family power dynamics, illustrating how IFN can create minimal yet resilient networks that balance autonomy with accountability. This framework lays the foundation for future advancements in predictive modeling and network optimization. To ensure reproducibility, we provide an open-source Python implementation on GitHub.
Co-Authors Arvin, Vincent Erna Budhiarti Nababan Kardi Teknomo Nicholas Handoko, Nicholas Resmana Lim Soewandi, Hanijanto