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All Journal Jurnal Teknik Informatika (JUTIF) PSEJ (Pancasakti Science Education Journal) Teknikom: Teknologi Informasi, Ilmu Komputer dan Manajemen
Lestari, Ika Indah
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Chemistry Computer Science & IT Economics, Econometrics & Finance Education Mathematics Physics

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Journal : Jurnal Teknik Informatika (JUTIF)

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THE EVALUATIONS FOR THE BACKEND OF ONTI MEASURES WITH BLACK BOX METHOD Ekowati, Nur Alfi; Sulistiyasni, Sulistiyasni; Lestari, Ika Indah
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2089

Abstract

Inconsistency in an ontology can be a serious problem since it can mess up the information in the ontology. Ontology-based inconsistency measure gives inconsistency value of the whole base of the OWL ontology. It means the produced inconsistency value is used to evaluate its whole base. Based on this characteristic, there were 10 inconsistency measures created in the previous research and collected into one package of measures in an application program, namely Onti Measures. The application will not be useful if the measures do not work well. This problem leads to conduct evaluations. In this research, evaluations for the backend part of Onti Measures with the use of three kinds of OWL reasoners are done to know the performance of the application system with the comparison of each reasoner usage. The evaluations for the whole part of the application are not the scope of this research since they are only done for the backend part. Particularly, they are done with the black box method since the structure of the codes are not necessary to be known. They are evaluated with several OWL files as test cases and as the inputs of the backend program. The evaluation shows that the same inconsistent OWL file that is computed with a different type of inconsistency measure with any chosen reasoner may result in different inconsistency value. Other evaluations are provided. Overall, they show that Pellet is better than the two other reasoners and I_(D_f ) is more efficient than the other measures.
Optimization of Software Effort Estimation Using Hybrid Consistent Fuzzy Preference Relation and Least Squares Support Vector Machine Lestari, Ika Indah; Purwanto, Adnan; Sulistiyasni, Sulistiyasni; Sambath, Khoem
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5465

Abstract

The success of software project management hinges on the ability to reliably forecast development effort. However, achieving precise estimates is notoriously difficult, primarily due to inherent project complexities and numerous uncertain variables. While various techniques exist, no single method has proven consistently reliable, leading to inaccurate scheduling and cost overruns. This study aims to develop a more accurate and robust estimation model by hybridizing a multi-criteria decision-making (MCDM) method for handling uncertainty with a machine learning algorithm for predictive modeling. The proposed approach integrates the Consistent Fuzzy Preference Relation (CFPR) method to derive consistent weights for cost drivers from expert judgments. These weights are then used as Effort Adjustment Factors (EAF) to preprocess the COCOMO and NASA datasets, which are subsequently modeled using the Least Squares Support Vector Machine (LSSVM). Evaluation of the hybrid CFPR-LSSVM model confirmed its enhanced predictive accuracy. For the COCOMO dataset, the model yielded an MMRE of 28.463% and an RMSE of 0.4705. Its performance on the NASA dataset was particularly remarkable, with results indicating an MMRE of 1.104% and an RMSE of 0.4593, demonstrating a level of precision that underscores the model's effectiveness. This research contributes a novel hybrid framework that effectively combines consistent fuzzy preference handling with powerful non-linear regression. By providing a more structured and robust methodology for managing uncertainty, this approach offers a substantial advancement in software effort estimation, delivering more reliable predictions for improved project planning. 
Co-Authors Adnan Purwanto Beni Habibi Muhammad Akbar Setiawan Nur Alfi Ekowati Pratama, Bayu Rizkya Rianti Yunita Kisworini, Rianti Yunita Riyandari, Lutvi Sambath, Khoem Sulistiyasni . Taufiqulloh Taufiqulloh