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All Journal Media Abdimas Jurnal Testing dan Implementasi Sistem Informasi
Essy Malays Sari Sakti
Universitas Persada Indonesia YAI
Humanities Computer Science & IT Health Professions Industrial & Manufacturing Engineering Library & Information Science Public Health Social Sciences Other

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Penerapan Metode Jigsaw Dalam Meningkatkan Minat Belajar Siswa di Era Digital Essy Malays Sari Sakti; Erdina Indrawati; Tatiyani Tatiyani; Usman Effendi; RR. Dini Diah Nurhadianti
Media Abdimas Vol 3 No 2 (2023): Jurnal Media Abdimas Vol 3 No 2 Juli 2023
Publisher : Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/mediaabdimas.v3i2.2771

Abstract

Di era digital penerapan metode Jigsaw memungkinkan adanya kolaborasi dan pembelajaran yang efektif melalui platform online, seperti Google Docs, Slack, Trello, Padlet, Zoom , dll. Metode Jigsaw adalah suatu pendekatan pembelajaran kooperatif yang dapat digunakan untuk meningkatkan minat belajar siswa. Metode ini mengharuskan siswa bekerja sama dalam kelompok kecil untuk memecahkan tugas atau masalah yang diberikan, sehingga mereka saling bergantung satu sama lain dalam mencapai tujuan pembelajaran. SMA Muhammadiyah 1 Jakarta merupakan salah satu sekolah swasta dibawah naungan Yayasan Muhammdiyah. Dengan visi dan misi yang ada di harapkan lulusannya akan menjadi siswa yang berpresatasi. Untuk mendukung harapan SMA Muhammadiyah 1 Jakarta maka kegiatan pengabdian kepada masyarakat dilakukan dengan metode penyuluhan dan pelatihan. Metode pelatihan merupakan penerapan metode jigsaw yang dilakukan dengan mengelompok siswa menjadi 5 kelompok untuk berdiskusi.. Kegiatan PKM ini bersifat insidensial, Keberhasilan kegiatan ini terlihat dengan banyaknya pertanyaan pada saat penyuluhan dan pada saat pelatihan siswa tampak bersungh-sungguh melakukan diskusi sebagai penerapan metode jigsaw
Design and Evaluation of an AI-Assisted Digital KPI Information System for Employee Performance Monitoring and Recommendation Asnefi Asnefi; Essy Malays Sari Sakti; Dayanti Dayanti; Irwan Syarif
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2342

Abstract

Manual and fragmented KPI-based evaluation often weakens employee performance monitoring by causing delays, input errors, and inconsistent interpretation. This study addresses that problem by designing and evaluating an AI-assisted digital KPI information system that integrates competency assessment, digital KPI records, performance scoring, dashboard-based monitoring, and recommendation support in a single environment. The study contributes by transforming the conventional competency–KPI–performance framework into an operational decision-support artifact with embedded AI classification. Using a prototype-based approach, the system was evaluated with a structured dataset of 340 employee-year records from 2021–2025. Three machine learning models were compared for classifying employee performance into High, Moderate, and Low categories. The results show that the system is functionally feasible and usable for KPI-based performance monitoring, while Random Forest achieved the best classification performance with 0.9853 accuracy and 0.9852 F1-score. The findings indicate that the proposed system can improve the structure of digital KPI monitoring and provide AI-assisted support for managerial review and follow-up actions. The study contributes theoretically by extending KPI-based performance management into an intelligent information system context and practically by offering a feasible model for organizations operating under limited implementation conditions.
Design and Evaluation of an AI-Assisted Digital KPI Information System for Employee Performance Monitoring and Recommendation Asnefi Asnefi; Essy Malays Sari Sakti; Dayanti Dayanti; Irwan Syarif
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2342

Abstract

Manual and fragmented KPI-based evaluation often weakens employee performance monitoring by causing delays, input errors, and inconsistent interpretation. This study addresses that problem by designing and evaluating an AI-assisted digital KPI information system that integrates competency assessment, digital KPI records, performance scoring, dashboard-based monitoring, and recommendation support in a single environment. The study contributes by transforming the conventional competency–KPI–performance framework into an operational decision-support artifact with embedded AI classification. Using a prototype-based approach, the system was evaluated with a structured dataset of 340 employee-year records from 2021–2025. Three machine learning models were compared for classifying employee performance into High, Moderate, and Low categories. The results show that the system is functionally feasible and usable for KPI-based performance monitoring, while Random Forest achieved the best classification performance with 0.9853 accuracy and 0.9852 F1-score. The findings indicate that the proposed system can improve the structure of digital KPI monitoring and provide AI-assisted support for managerial review and follow-up actions. The study contributes theoretically by extending KPI-based performance management into an intelligent information system context and practically by offering a feasible model for organizations operating under limited implementation conditions.
Co-Authors Asnefi Asnefi Dayanti Dayanti Indrawati, Erdina Karno Diantoro Nur Aziz Nurhadianti, Rr. Dini Diah Syarif, Irwan Tatiyani Tatiyani Usman Effendi