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Catur Iswahyudi
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RINTISAN GREEN ECONOMY MELALUI PLTMH DI KRAJAN, CANGKRINGAN, SLEMAN Muhammad Andang Novianta; Catur Iswahyudi; Muhammad Andang Novianta
DHARMA BAKTI Dharma Bakti-Vol 6 No 1-April 2023
Publisher : LPPM IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Krajan is located about 20 km from the summit of Mount Merapi. The main problem in this village is the Community-based Drinking Water and Sanitation Program (PAMSIMAS) which has played a role in meeting the community's clean water needs, but has not been able to be used economically by the residents despite having abundant mountain springs. In addition, water problems arise especially during the rainy season, where water does not meet quality standards so it is not suitable for consumption. To optimize the use of springs that have an impact on green economy activities as well as improve people's welfare, the PKM Team of the IST AKPRIND Yogyakarta applies a (PLTMH) in Padukuhan Salam-Krajan, Wukirsari, Cangkringan. PLTMH was developed by utilizing abundant springs. By utilizing the water discharge from the water input (inlet) flow to the PAMSIMAS reservoir, the electricity generated can be used for street lighting as well as building ready-to-drink water installations using the electricity network from PLTMH. Based on the evaluation of the activity, the implementation of community service activities that harmonize the needs of partners with the competence of the Team is a very good model for implementing community service activities, so this kind of model needs to be continued. This PKM activity is able to provide opportunities to support the implementation of development that is oriented towards environmental and ecosystem aspects, able to improve the welfare of rural communities, plan sustainable development, a green economy, overcome poverty and produce ready-to-drink water products.
Optimization of software defects prediction in imbalanced class using a combination of resampling methods with support vector machine and logistic regression Windyaning Ustyannie; Emy Setyaningsih; Catur Iswahyudi
JURNAL INFOTEL Vol 13 No 4 (2021): November 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i4.726

Abstract

The main problem in producing high accuracy software defect prediction is if the data set has an imbalance class and dichotomous characteristics. The imbalanced class problem can be solved using a data level approach, such as resampling methods. While the problem of software defects predicting if the data set has dichotomous characteristics can be approached using the classification method. This study aimed to analyze the performance of the proposed software defect prediction method to identify the best combination of resampling methods with the appropriate classification method to provide the highest accuracy. The combination of the proposed methods first is the resampling process using oversampling, under-sampling, or hybrid methods. The second process uses the classification method, namely the Support Vector Machine (SVM) algorithm and the Logistic Regression (LR) algorithm. The proposed, tested model uses five NASA MDP data sets with the same number attributes of 37. Based on the t-test, the < = 0.0344 < 0.05 and the > = 3.1524 > 2.7765 which indicates that the combination of the proposed methods is suitable for classifying imbalanced class. The performance of the classification algorithm has also improved with the use of the resampling process. The average increase in AUC values using the resampling in the SVM algorithm is 17.19%, and the LR algorithm is at 7.26% compared to without the resampling process. Combining the three resampling methods with the SVM algorithm and the LR algorithm shows that the best combining method is the oversampling method with the SVM algorithm to software defects prediction in imbalanced class with an average accuracy value of 84.02% and AUC 91.65%.
Co-Authors Emy Setyaningsih Muhammad Andang Novianta Muhammad Andang Novianta Windyaning Ustyannie