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All Journal JURNAL SISTEM INFORMASI Syntax Literate: Jurnal Ilmiah Indonesia Applied Information System and Management Infoman's: Jurnal Ilmu-Ilmu Manajeman dan Informatika Journal of Software Engineering Ampera Jurnal Pengabdian kepada Masyarakat Riwayat: Educational Journal of History and Humanities
Elsy Rahajeng, Elsy
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Applied Information System and Management

 1 
Usulan Evaluasi Sistem Keamanan Informasi Berdasarkan Standar ISO/IEC 27002:2013 pada Pondok Pesantren Kafila International Islamic School Jakarta Aditya Teguh Septoaji; Fitroh Fitroh; Elsy Rahajeng
Applied Information System and Management (AISM) Vol 1, No 2 (2018): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v1i2.20107

Abstract

Kafila International Islamic School (KIIS) adalah sebuah pesantren (boarding school) yang mempunyai banyak prestasi di beberapa perlombaan dan nilai Ujian Nasional. Sejak 2007, KIIS sudah menerapkan standar ISO 9001:2008 sebagai standar mutu pendidikan. Namun, dengan penerapan ISO 9001:2008 belum menutup semua celah baik kelemahan (vulnerable) atau ancaman (threat) yang timbul ketika proses pembelajaran sekolah berlangsung. Dalam wawancara penulis, KIIS memerlukan solusi untuk improvisasi keamanan dan dokumentasi yang lebih baik terutama dalam fasilitas informasi, yaiu dengan melakukan penelitian evaluasi menggunakan ISO 27002:2013, penelitian Keamanan Sistem Informasi ini dilakukan pada 8 klausul, pada kebijakan keamanan informasi, keamanan informasi organisasi, keamanan sumber daya (pekerjaan), manajemen aset, kontrol akses, keamanan fisik dan lingkungan, keamanan operasi, akusisi sistem informasi, pembangunan dan pemeliharaan). Dalam penelitian ini, penulis menggunakan metode pengukuran kapabilitas tingkat kedewasaan (CMM). Dapat dihasilkan nilai kedewasaan 4 (managed) pada klausul kebijakan keamanan informasi, keamanan informasi organisasi, keamanan sumber daya dan manajemen aset. Selanjutnya Nilai kedewasaan 3 (defined) pada klausul Kontrol Akses, Keamanan fisik dan lingkungan, Keamanan operasi, Akusisi sistem informasi dan pembangunan dan pemeliharaan.
Predictive Modeling of Student Dropout Using Academic Data and Machine Learning Techniques Aini, Qurrotul; Rahajeng, Elsy; Tiohandra, Mufadha; Pratama, Hamzah Aji; Hammad, Jehad
Applied Information System and Management (AISM) Vol. 8 No. 2 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i2.46659

Abstract

This study's objective is to investigate the performance of a predictive model for students at risk of dropout (DO) by considering several internal criteria of an academic program. This research uses academic information from UIN Syarif Hidayatullah Jakarta and applies the C4.5, Naive Bayes Classification (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) to forecast which students might drop out. The data used consists of 714 student records from Department of Information Systems for the academic year 2010–2015 as training and 2018 as testing data. The research method refers to the SEMMA framework (Sample, Explore, Modify, Model, and Assess) to ensure systematic and accurate data processing. Meanwhile, the internal criteria used are the completed courses, the status of the internship report, and the final project proposal. According to the study's findings, the C4.5 and SVM algorithms get the best accuracy rates of 94.44%, while KNN and NBC come in second and third, respectively, with 93%. The results show that the C4.5 and SVM algorithms work well with academic data. This study provides a substantial contribution to the development of a prediction system for students at risk of dropping out, which can be integrated into data-based applications or dashboards. This solution is expected to help higher education institutions identify students who need further academic support. In addition, this research also opens up opportunities for the progress of more accurate forecasting models through the integration of additional variables such as behavioral or psychological data. With this data-driven approach, higher education institutions can enhance their efficiency in monitoring and preventing student dropouts, thereby supporting a vision of quality and sustainable education.
Co-Authors A'ang Subiyakto Aditya Teguh Septoaji Agus Herwanto Annisa Uswatun Hasanah Aprisya Krispriana Aulia Riski Molanda Bayu Waspodo Diana Novita Fitroh Fitroh Hammad, Jehad Meinarini C. Utami Melinda Melinda Nia Kumaladewi Nida'ul Hasanati Nuryasin Nuryasin Pratama, Hamzah Aji Qurrotul Aini Rinda Hesti Kusumaningtyas, Rinda Hesti Roy Budiharjo Sitti Suhada Tiohandra, Mufadha Yuliati -