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All Journal JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI RJOCS (Riau Journal of Computer Science)
Marsellinus Bachtiar
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Public Health

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Redesain Balanced Scorecard Strategy Map Berdasar Konsep KPI dan KRI Vivi Triyanti; Marsellinus Bachtiar; Carlos Yohan Rafavy
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 2, No 2 (2013)
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v2i2.126

Abstract

Abstrak - Tulisan ini terkait dengan penelitian mengenai pengembangan manajemen dashboard di Fakultas Teknik Unika Atma Jaya. Pada penelitian sebelumnya telah dihasilkan rancangan peta strategi yang memuat tujuan-tujuan strategis (strategic objectives) dan indikator (lead dan lag) dari FT Unika Atma Jaya (UAJ) yang terbagi dalam empat perspektif balanced scorecard (BSC), yaitu:  Perspektif Pelayanan, Perspektif Pelanggan, Perspektif Proses Bisnis Internal, serta Perspektif Pembelajaran dan Organisasi. Peta tersebut kemudian direklasifikasi lagi sehingga lebih sesuai dengan konsep indikator kinerja (Performance indicator – PI) dan indikator hasil (Result Indicators – RI).  Terdapat 8 indikator proses yang diubah letaknya menjadi indikator hasil. Lebih lanjut, dari 120 indikator, terpilih 35 indikator yang dinyatakan sebagai KPI dan KRI, dan akan digunakan sebagai input di pembuatan aplikasi manajemen dashboard. Abstract - The paper is related to research about dashboard management development in Faculty of Engineering, Atma Jaya Catholic University of Indonesia. Previous research has resulted a strategy map with subsequent strategic objectives and indicators (lead and lag) for the Faculty of Engineering, which comprises of four Balanced Scorecards (BSC) perspectives as follows: Service, Customer, Internal Business Process, and Learning and Growth. The map then reclassified so that it fits to Performance indicators (PI) and Result Indicators (RI) concepts. which at the end of the day will be the basis for choosing key indicators (KPI and KRI). Finally, there are 8 process indicators that relocate to result indicators. Further, from 120 indicators, 35 indicators are chosen as KPI and KRI, which will be used in the next step of dashboard management application development.  
IMPLEMENTATION OF HUE SATURATION INTENSITY (HSI) COLOR SPACE TRANSFORMATION ALGORITHM WITH RED, GREEN, BLUE (RGB) COLOR BRIGHTNESS IN ASSESSING TOMATO FRUIT MATURITY Yanto`, Budi; Maria Angela Kartawidjaja; Ronald Sukwadi; Marsellinus Bachtiar
RJOCS (Riau Journal of Computer Science) Vol. 9 No. 2 (2023): RJOCS (Riau Journal of Computer Science)
Publisher : Fakultas Ilmu Komputer, Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjocs.v9i2.2428

Abstract

Tomatoes, as a type of vegetable or fruit, are often susceptible to damage, making handling them a complex challenge. Distinguishing between fresh and damaged tomatoes is very important, considering the significant impact on nutritional value and economic aspects. Traditional approaches via visual inspection have proven to be less efficient and inconsistent in their detection accuracy. To overcome this challenge, the use of images is a vital solution for distinguishing ripe, half-ripe and unripe tomatoes. In this context, HSI (Hue, Saturation, Intensity) calculations are applied to measure RGB color and room transformations. Images are extracted in jpg format, saved as training data, and this method is implemented using the Python programming language and GUI interface design in MATLAB. The research results show the HSI value for each class, with the ripe tomato class having an average hue of 0.0051 – 0.026, saturation 0.1862 – 0.3291, and intensity 0.0975 – 0.7522. Half-ripe tomatoes have hue 0.0208 – 0.0848, saturation 0.1346 – 0.5746, and intensity 0.1056 – 0.4714, while immature tomatoes have hue 0.0174 – 0.0689, saturation 0.0474 – 0.2072, and intensity 0.0595 – 0.3203. The integration of the HSI algorithm steps with the RGB color space provides an additional dimension to color analysis, which has the potential to increase the accuracy of tomato ripeness detection.
Co-Authors Carlos Yohan Rafavy Maria Angela Kartawidjaja Ronald Sukwadi Vivi Triyanti Yanto`, Budi