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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks
Setiya Nugroho
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Computer Science & IT Education Engineering

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Peningkatan Pemahaman Kurikulum 2013 bagi Guru Sekolah Dasar Noto Widodo; Setiya Nugroho
Berdikari: Jurnal Inovasi dan Penerapan Ipteks Vol 7, No 2 (2019): August
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/bdr.7270

Abstract

This community service activity aims at developing skills for elementary school (SD) teachers of Muhammadiyah Sirojuddin in using information technology media, namely Microsoft Excel to process the data of the assessment result to the report in accordance with curriculum 2013. The method used was through information technology training divided into three stages, namely: (1) description making of each grade on the fields of affective, cognitive, and psychomotor; (2) submitting grade of affective, cognitive, and psychomotor fields; (3) converting grade from number to descriptive form. The community service was carried out in six times of meetings with 17 teachers of SD Muhammadiyah Sirojuddin. The result of the service community are 15 teachers are helped in understanding curriculum 2013 and grade conversion process based on Microsoft Excel program. The result of the descriptive grade is the one that will be benefitted by the teachers as data input in the report in accordance with curriculum 2013.
Comparative Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste Classification Aulia Afifah; Arumi, Endah Ratna; Maimunah, Maimunah; Setiya Nugroho
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6211

Abstract

Waste management remains a critical challenge for many countries, including Indonesia, which ranks as the world's second-largest contributor of waste. As tens of millions of tons are produced each year and the management system remains ineffective, environmental conditions and public health continue to deteriorate. To address this issue, it is imperative to develop more accurate and efficient solutions to enhance waste classification and management. This study investigates the influence of various image preprocessing techniques on the performance of MobileNetV1 and MobileNetV2 models in the classification of waste images. Preprocessing is crucial for enhancing data quality, particularly when dealing with real-world images that are affected by inconsistent lighting, texture, and clarity. Five preprocessing scenarios were evaluated: Baseline, CLAHE with Bilateral Filtering, CLAHE with Sharpening, Grayscale with CLAHE, and Gaussian Blur with Bilateral Filtering. Among these, the combination of CLAHE and Bilateral Filtering applied to MobileNetV1 achieved the best results, with 85% training accuracy, 96% validation accuracy, a training loss of 0.3178, and the lowest validation loss of 0.1630. Overall, MobileNetV1 benefited more significantly from preprocessing variations than MobileNetV2, particularly in terms of accuracy improvement and reduction in prediction error. These findings underscore the importance of effective preprocessing in enhancing model performance for waste image classification
Co-Authors Ardhin Primadewi Aulia Afifah Endah Ratna Arumi, Endah Ratna Maimunah, Maimunah Noto Widodo Uky Yudatama