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Wiharto
Universitas Sebelas Maret
Computer Science & IT Engineering

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Implementasi Algoritma Load Balancing PLBA Komputasi Grid pada Lab Environment Menggunakan PVM3 Taufiq Odhi Dwi Putra; Wisnu Widiarto; Wiharto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 6 (2020): Desember 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (310.782 KB) | DOI: 10.29207/resti.v4i6.2606

Abstract

Load balancing is one of the main parts of scheduling Grid resources. One of the load balancing models on Grid resources is the hierarchical model. This model has the advantage that it requires minimal communication costs between one resource and another. The PLBA load balancing algorithm uses a hierarchical model with dynamically obtained threshold values, so that it can adjust conditions at a time, both the state of the resource, the state of the computer network, and the state of the recipient or client. PVM3 is a software system capable of optimizing heterogeneous resources, so that resources can work in parallel. Resources can also complete tasks well, even though they are very large and complex tasks. This research has implemented the PLBA load balancing algorithm, with the aim of optimizing Grid resources. This research has also developed the PLBA load balancing algorithm by changing the arguments for NPEList, so that resources can be grouped more optimally. The PLBA load balancing algorithm has been successfully developed by modifying the arguments for NPEList, so that the running time required to complete the given tasks is shorter, because resources can be grouped more optimally. This has been shown by the shorter average running time when using the modified NPEList argument (0.75 * threshold1 <= ALCi <= 1.25 * threshold1) is shorter, than using the NPEList argument in previous research (ALCi = threshold1). Comparison of the average running time has been obtained as follows : (82513.63740 : 67837.71720); (63869.92450 : 50722.17210); (858,96710 : 207,33680); (321.88000 : 126.89100); (768.54560 : 468.27190); (780.22770 : 279.43730).
Fuzzy-IOWA-Based Group Decision Support System (GDSS) for Interpreting DASS Scores with Dynamic Expert Weighting Wiharto; Eka P. Meravigliosi; Umi Salamah; Esti Suryani; Vihi Atina; Pradityo Utomo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7340

Abstract

This research develops a Group Decision Support System (GDSS) to address subjectivity and ambiguity in the interpretation of Depression Anxiety Stress Scales (DASS-21) scores, particularly in cases of symptom overlap across dimensions. The system introduces a structured weighting mechanism in which the influence of each expert is determined based on objective criteria, including clinical experience, education level, and academic contributions. The methodology applies Simple Additive Weighting (SAW) to quantify the relative importance of five clinical experts, resulting in Expert 2 (27.66%) and Expert 1 (27.36%) having the highest influence within the group. These weights are then incorporated into a Fuzzy-Induced Ordered Weighted Averaging (Fuzzy-IOWA) framework to aggregate expert judgments into a unified consensus model. The results indicate that the proposed approach is able to produce a consistent interpretation structure, with a tendency toward the Anxiety dimension in cases of overlapping symptoms. By integrating expert consensus with patient self-report scores, the system generates a structured interpretation profile of DASS-21 responses. The proposed GDSS provides a systematic and transparent aggregation framework that reflects expert reasoning. However, it is intended as a methodological support tool rather than a substitute for clinical diagnosis. The novelty of this work lies in the integration of SAW-based objective expert capability quantification with Fuzzy-IOWA aggregation and QGDD-based consensus characterization, forming a transparent and mathematically accountable GDSS pipeline that has not been previously applied in the context of DASS-21 interpretation.
Co-Authors Atina, Vihi Eka P. Meravigliosi Esti Suryani Pradityo Utomo Taufiq Odhi Dwi Putra Umi Salamah Wisnu Widiarto