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All Journal International Journal Software Engineering and Computer Science (IJSECS)
Munawir
Institut Teknologi dan Bisnis Muhammadiyah Bali
Computer Science & IT

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College Performance Monitoring Application using Appclay Shephertz Munawir; Imfrianti Augtiah; Afrizal; Sunardi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 2 No. 2 (2022): OCTOBER 2022
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v2i2.808

Abstract

This college monitoring application was developed using Appclay Shephertz by applying 8 Key Performance Indicators for all Colleges. The results of the application have succeeded in displaying data on eight college performance KPIs, namely; 1) graduates get decent jobs, 2) students get experience off campus, 3) lecturers have activities outside of campus, 4) teaching practitioners on campus, 5) lecturers' work is used by the community, 6) study programs work together with class partners world, 7) Collaborative and participatory classes, and 8) international standard study programs. Users can access by pressing the button. Then a black-box test is carried out on this application, the results are that all the buttons tested run well and are ready to be used. This research uses the R&D (Research and Development) development model. To test the success of the Higher Education Performance Monitoring Application, the Higher Education Performance Monitoring Application test was used with 17 test items having a 100% success rate, the Feasibility Test and the Interpretation Percentage were used by experts in measuring aspects of design, communication, and software assessment so that the percentage was 81, 42%, and in the validation test, the provisions on item 25 in the Aiken V table are with a lower limit of 0.64 to an upper limit of 0.93 or a V value of 0.83. The results of the average value of V = 0.794 are declared valid.
College Performance Monitoring Application using Appclay Shephertz Munawir; Imfrianti Augtiah; Afrizal; Sunardi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 2 No. 2 (2022): OCTOBER 2022
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v2i2.808

Abstract

This college monitoring application was developed using Appclay Shephertz by applying 8 Key Performance Indicators for all Colleges. The results of the application have succeeded in displaying data on eight college performance KPIs, namely; 1) graduates get decent jobs, 2) students get experience off campus, 3) lecturers have activities outside of campus, 4) teaching practitioners on campus, 5) lecturers' work is used by the community, 6) study programs work together with class partners world, 7) Collaborative and participatory classes, and 8) international standard study programs. Users can access by pressing the button. Then a black-box test is carried out on this application, the results are that all the buttons tested run well and are ready to be used. This research uses the R&D (Research and Development) development model. To test the success of the Higher Education Performance Monitoring Application, the Higher Education Performance Monitoring Application test was used with 17 test items having a 100% success rate, the Feasibility Test and the Interpretation Percentage were used by experts in measuring aspects of design, communication, and software assessment so that the percentage was 81, 42%, and in the validation test, the provisions on item 25 in the Aiken V table are with a lower limit of 0.64 to an upper limit of 0.93 or a V value of 0.83. The results of the average value of V = 0.794 are declared valid.
Stock Portfolio Analysis with Machine Learning Algorithmic Approach for Smart Investment Decisions Munawir; Upik Sri Sulistyawati
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.2606

Abstract

This study investigates the application of machine learning algorithms in stock portfolio analysis within the Indonesia Stock Exchange (IDX) and their impact on investment decision-making. By engaging 500 respondents from diverse market segments, including retail investors, institutional investors, and stock traders, the research provides a comprehensive overview of adopting and utilising machine learning technologies in the Indonesian stock market. The findings reveal that over 80% of respondents have integrated machine learning algorithms into their investment strategies. The algorithms are applied in various capacities: 45% of respondents use them for portfolio risk analysis, 30% for stock price prediction, and 25% for identifying new investment opportunities. Preferences for specific algorithms vary, with regression, Support Vector Machines (SVM), and Random Forest emerging as the most used tools. The integration of machine learning was strongly associated with improved investment decisions, as more than 60% of respondents reported enhanced portfolio performance and greater accuracy in their decision-making. These results highlight the transformative potential of machine learning algorithms in enabling more innovative and more adaptive investment strategies.
Co-Authors Afrizal Imfrianti Augtiah Sulistyawati, Upik Sri Sunardi