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All Journal JUKI : Jurnal Komputer dan Informatika PERSEPTIF: Jurnal Ilmu Sosial dan Humaniora
Majid, Nurcholis
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Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Analisis Klasifikasi Keamanan dalam Shorting Malware Android dengan Algoritma K-Nearest Neighbors Majid, Nurcholis; Sunge, Aswan S.; Suherman, Suherman
JUKI : Jurnal Komputer dan Informatika Vol. 5 No. 2 (2023): JUKI : Jurnal Komputer dan Informatika, Edisi Nopember 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v5i2.378

Abstract

Abstrak. Perkembangan teknologi dan popularitas perangkat berbasis Android telah membuka pintu lebar bagi pengembangan aplikasi yang beragam dan inovatif. Namun, kesuksesan Android juga telah menarik perhatian para penjahat cyber untuk mengembangkan Malware. Berbagai jenis perangkat dapat diinfeksi oleh malware, salah satunya adalah smartphone, dimana kasus malware terbanyak didominasi pada sistem operasi Android. Rentannya serangan malware dan dapat merugikan para pengguna Android sehingga diperlukan analisis lebih lanjut, pada kasus ini digunakan pendekatan Machine Learning untuk melakukan klasifikasi data serangan malware android. Algoritma yang digunakan adalah K-Nearest Neighbor. Mendapatkan hasil accuracy dengan nilai sebesar 98%, precision sebesar 98%, dan recall sebesar 98%. Hasil ini membuktikan bahwa algoritma K-Nearest Neighbor memberikan hasil yang cukup baik dalam mengklasifikasi malware android. Abstract. Technological developments and the popularity of Android-based devices have opened wide doors for the development of diverse and innovative applications. However, Android's success has also attracted the attention of cybercriminals to develop Malware. Various types of devices can be infected by malware, one of which is smartphones, where the majority of malware cases are dominated by the Android operating system. Malware attacks are vulnerable and can harm Android users so further analysis is needed, in this case a Machine Learning approach is used to classify Android malware attack data. The algorithm used is K-Nearest Neighbor. Get accuracy results with a value of 98%, precision of 98%, and recall of 98%. These results prove that the K-Nearest Neighbor algorithm provides quite good results in classifying Android malware.
Strategi Komunikasi Politik Dalam Pemilihan Umum Di Era Digital Majid, Nurcholis; Nur Hidayah, Aisyah
PERSEPTIF: Jurnal Ilmu Sosial dan Humaniora Vol. 1 No. 2 (2023): PERSEPTIF: Jurnal Ilmu Sosial dan Humaniora
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/perseptif.v1i2.357

Abstract

Political communication has undergone fundamental changes due to the rapid expansion of digital media in general elections. Social media platforms, data-driven targeting, and interactive content have reshaped how political actors engage with voters. This study aims to systematically examine political communication strategies in digital-era elections by synthesizing empirical findings from international and Indonesian contexts. The research employs a qualitative literature-based approach, using a systematic review of peer-reviewed journal articles, conference proceedings, and scholarly book chapters published between 2014 and 2024. Data were analyzed through thematic content analysis to identify dominant strategic patterns, communication models, and their implications for democratic engagement. The findings show that digital political communication strategies increasingly emphasize disintermediation, microtargeting, platform diversification, and personalization of political messages. However, these strategies also generate challenges related to misinformation, uneven participation, and regulatory gaps. The study concludes that digital political communication enhances voter engagement and campaign efficiency but requires stronger ethical standards and institutional oversight to sustain democratic quality. This research contributes theoretically by integrating classical political communication theory with contemporary digital practices and practically by offering insights for electoral stakeholders in developing effective and accountable communication strategies.
Co-Authors Nur Hidayah, Aisyah Suherman, Suherman Sunge, Aswan S.