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All Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
Diana Diana
Universitas Pradita
Computer Science & IT

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Komparasi Algoritma Naïve Bayes, Logistic Regression Dan Support Vector Machine pada Klasifikasi File Application Package Kit Android Malware Diana Diana; Richardus Eko Indrajit; Erick Dazki
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 11, No 1: April 2022
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1112.786 KB) | DOI: 10.35889/jutisi.v11i1.815

Abstract

Abstrak. Fenomena malware yang terus bertumbuh pada sistem Android menjadikan peneliti berfokus untuk menganalisa malware dengan memanfaatkan teknologi kecerdasan buatan. Tujuan dari penelitian ini adalah menganalisa file-file APK (Application Package Kit) Android dengan mengklasifikasi keluarga malware. File malware akan dijadikan dataset untuk dilakukan training menggunakan algoritma pembelajaran mesin. Pembelajaran mesin yang digunakan adalah Naïve Bayes, Logistic Regression dan Support Vector Machine. Pengukuran performansi dan akurasi juga disajikan dalam perbandingan antara algoritma Naïve bayes, Logistic Regression dan Support Vector Machine yang merupakan algoritma Machine Learning dan bagian dari kecerdasan buatan. Hasil uji akurasi menunjukkan algoritma Naive Bayes mampu mengklasifikasi keluarga malware dengan tingkat akurasi 97.75%, sedangkan algoritma Logistic Regression akurasinya 88.75% dan akurasi Support Vector Machine mencapai 96,75%. Meskipun akurasi tidak setinggi penelitian sebelumnya, teknik analisa statis dengan fitur Permission dan fitur Intent cukup sederhana untuk mendeteksi file APK Android adalah malware atau bukan malware.Kata kunci: Malware Android; Naïve Bayes; Logistic Regression; Support Vector Machine Abstract. The phenomenon of malware that continues to grow on the Android system makes researchers focus on analyzing malware by utilizing artificial intelligence technology. The purpose of this research is to analyze Android APK (Application Package Kit) files by classifying malware families. The malware files will be used as a dataset for training using machine learning algorithms. The machine learning used is Naïve Bayes, Logistic Regression and Support Vector Machine. Performance and accuracy measurements are also presented in a comparison between the Naïve Bayes algorithm, Logistic Regression and Support Vector Machine which is a Machine Learning algorithm and part of artificial intelligence. The accuracy test results show that the Naive Bayes algorithm is able to classify malware families with an accuracy rate of 97.75%, while the Logistic Regression algorithm has an accuracy of 88.75% and an accuracy of Support Vector Machine reaches 96.75%. Although the accuracy is not as high as previous studies, the static analysis technique with the Permission feature and the Intent feature is quite simple to detect Android APK files are malware or not malware.Keyword: Malware Android; Naïve Bayes; Logistic Regression; Support Vector Machine
Penerapan Enterprise Architecture Pada Industri Kosmetik dengan TOGAF ADM Diana Diana; Richardus Eko Indrajit; Erick Dazki
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 11, No 1: April 2022
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (212.503 KB) | DOI: 10.35889/jutisi.v11i1.820

Abstract

Abstrak. Industri kosmetik di Indonesia menempati urutan ketiga pasar terbesar Asia, menjadikan peluang besar para pelaku industri kosmetik untuk pasar internasional. Untuk bersaing secara global, perencanaan yang matang terhadap proses bisnis diperlukan. Tulisan ini mengusulkan rancangan dengan memaksimalkan CRM (Customer Relation Management) sistem yang ditujukan untuk menentukan strategi pemasaran efektif. Pengaruh pada penyediaan produk, nilai kualitas, pengalaman yang memuaskan dapat menjangkau segmen pelanggan yang lebih besar. Pengembangan CRM sistem menggunakan kerangka kerja Business Model Canvas yang tepat di industri kosmetik. Adaptasi diperlukan dengan kerangka kerja TOGAF ADM dan bahasa pemodelan Archimate Core Framework, menghasilkan model bisnis proses yang lebih rapi dan mengggambarkan struktur organisasi yang terintegrasi. Dimulai dari penargetan pasar, pengumpulan data, promosi, pemesanan, pembagian informasi, dan layanan konsumen yang saling terhubung. Hasil penelitian ini menambahkan tahapan Migration Planning dan Change Management dimana kedua tahapan ini sangat diperlukan dalam menjalankan kegiatan operasional industri komestik secara baik dan tertata rapi sesuai dengan kebutuhan bisnis.Kata kunci: Customer Relation Management; Business Model Canvas; Archimate Core Framework; Migration Planning; Change Management Abstract. The cosmetic industry in Indonesia is the third largest market in Asia, creating a great opportunity for cosmetic industry players for the international market. To compete globally, careful planning of business processes is required. This paper proposes a design by maximizing the CRM (Customer Relation Management) system aimed at determining an effective marketing strategy. Influence on product provision, quality value, satisfying experience can reach larger customer segments. CRM system development using the Business Model Canvas framework is right in the cosmetics industry. Adaptation is required with the TOGAF ADM framework and the Archimate Core Framework modeling language, resulting in a more streamlined business process model and depicting an integrated organizational structure. Starting from market targeting, data collection, promotion, ordering, information sharing, and connected consumer service. The results of this study add the stages of Migration Planning and Change Management where these two stages are very necessary in carrying out the operational activities of the cosmetic industry well and neatly in accordance with business needs.Keywords: Customer Relations Management;Business Model Canvas; Archimate Core Framework; Migration Planning; Change Management
Co-Authors Dazki, Erick Richardus Eko Indrajit