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All Journal Lingua Cultura Sistemasi: Jurnal Sistem Informasi JIKO (Jurnal Informatika dan Komputer) JURNAL REKAYASA TEKNOLOGI INFORMASI PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Justek : Jurnal Sains Dan Teknologi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi INTEK: Informatika dan Teknologi Informasi Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Bulletin of Computer Science Research Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat Akademika ABDIMASTEK Jurnal Pengabdian kepada Masyarakat (DIASYA)
Wahju Tjahjo Saputro
Universitas Muhammadiyah Purworejo
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Public Health Social Sciences Other

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Optimizing Business Process To Government Using Business Process Improvement (BPI) and Bizagi Modeler (Case Study: PT. XYZ) Dwiyono, Pangga Cahyo; Murhadi, Murhadi; Saputro, Wahju Tjahjo; Huda, Miftahul; Nepomuceno, Matthew
Justek : Jurnal Sains dan Teknologi Vol 9, No 1 (2026): March
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v9i1.37752

Abstract

Business to Government (B2G) processes frequently suffer from inefficiencies caused by manual workflows and limited system integration. This study aims to enhance B2G business process performance at PT XYZ by applying the Business Process Improvement (BPI) approach using Business Process Model and Notation (BPMN) implemented through Bizagi Modeler. The research methodology involves analysing the existing process, designing an optimized process model, and conducting performance simulations based on processing time, operational costs, data accuracy, delay risk, and institutional satisfaction. The results indicate significant improvements, including a 41.7% reduction in processing time, a 26.7% decrease in operational costs, a 90% increase in data accuracy, a 40.9% reduction in delay risk, and an 85% improvement in institutional satisfaction. These findings demonstrate that the integration of BPI and BPMN effectively improves the efficiency and quality of B2G business processes in government procurement.
Sentiment Analysis of the Skyscanner Application on Google Play Store with a Comparison of Naive Bayes and Support Vector Machines Triana, Laely; Saputro, Wahju Tjahjo; Chirzah, Dewi
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29212

Abstract

The digital world is growing rapidly and has a significant impact on the tourism sector. Therefore, technology must adapt to developments to meet human needs. Travel booking services such as Skyscanner allow users to book flights, accommodation, and transportation online through the app. With the large number of Skyscanner user reviews on the Google Play Store. The majority of data reviews use Indonesian languanges; sentiment analysis is needed to determine user sentiment towards the app. This study aims to analyze user sentiment towards the Skyscanner app using collected user comment review data. The data is then classified into two sentiment classes: positive and negative. The classification results using a comparison of two algorithms, Naive Bayes and Support Vector Machine, SVM produced a higher accuracy of 89.74%. Naive Bayes achieves lower accuracy 82.08% than SVM. This concludes that the SVM algorithm is more effective in producing optimal classification accuracy than its comparison algorithm, Naive Bayes.
Co-Authors Abdul Fadlil Achmad, Lukman Ade Davy Wiranata Ambotang, Abdul Said Bin Arum Ratnaningsih Arum Ratnaningsih Athallah, Mustafa Iffat Shafi Aviani, Anggi Bambang Priyo Darminto Chirzah, Dewi Dimas Yunianto Herbowo Dwiyono, Pangga Cahyo Erni Seniwati Haidarrafiyan Perdanakusuma Hanifatus Sa’diyah Widihasaniputri Hermansah, Lutfi Hesti Wulan Sari Hidayatullah, Ridwan Huwae, Hans Andriano Pradana Ike Yunia Pasa Indra Ardiansyah Jayanti Putri Purwaningrum Jumasa, Hamid Muhammad Krisna Widatama Lestari, Widya Ayu Miftahul Huda Muchlas Muhammad Faisal Natsir Asrofi Murhadi Murhadi Murhadi Murhadi Murhadi Murinto Murinto Murinto Nepomuceno, Matthew Nia Ekawati, Nia Puspa Dewi Rayadika, Bintang Fauzan Riawan Yudi Purwoko Risma Hardini Ruzaini, Hafidz Sain, Zohaib Hassan Shofwan Hanief Singgih Arif Widodo Teguh Wibowo Tresnawati, Shandy Triana, Laely Tusino Tusino Wahyu Miftakhul Huda Widiati, Wahyu Eka Widihasaniputri, Hanifatus Sa’diyah Wuri Setyowardani