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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) SAINTEK Jurnal Dirosah Islamiyah
Sella Monica
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Social Sciences Other

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Defending Your Mobile Fortress: An In-Depth Look at on-Device Trojan Detection in Machine Learning: Systematic Literature Review Lila Setiyani; Koo Tito Novelianto; Rusdianto Roestam; Sella Monica; Ayu Nur Indahsari; Amadeuz Ezrafel; Alinda Endang Poerwati; Yuliarman Saragih
Jurnal Penelitian Pendidikan IPA Vol 9 No 7 (2023): July
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i7.4209

Abstract

Mobile app trojans are becoming an increasingly serious threat to personal information security. They can cause severe damage by exposing sensitive and personally-identifying information to malicious actors. This paper’s contribution is a comprehensive review of the attack vectors for trojan attacks, and ways to eliminate the risks posed by attack vectors and generate settlement automatically. As such, such attacks must be prevented. In this study, we explore to find how to detect the trojan attack in detail, and the way that we know in machine learning. A review is conducted on the state-of-the-art methods using the preferred reporting items for reviews and meta-analyses (PRISMA) guidelines. We review literature from several publications and analyze the use of machine learning for on-device trojan detection. This review provides evidence for the effectiveness of machine learning in detecting such threats. The current trend shows that signature-based analysis using various metadata, such as permission, intent, API and system calls, and network analysis, are capable of detecting trojan attacks before and after the initial infection
Prediksi Pembatalan Pemesanan Hotel menggunakan Algoritma Machine Learning Selma Ohoira; Sella Monica
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Industri perhotelan sering menghadapi tantangan terkait pembatalan pemesanan yang dapat mempengaruhi pendapatan dan operasional hotel. Pembatalan yang tidak terduga tidak hanya menyebabkan kerugian pendapatan, tetapi juga menganggu perencanaan kapasitas. Teknologi machine learning dapat membantu memprediksi pembatalan berdasarkan data historis dan variabel terkait. Penelitian ini bertujuan untuk menganalisis penerapan algoritma machine learning khususnya regresi logistik dan klasifikasi hutan acak, dalam memprediksi pembatalan pemesanan hotel. Evaluasi model dilakukan dengan menggunakan metrik akurasi, presisi, dan recall. Hasil penelitian menunjukkan bahwa algoritma klasifikasi hutan acak lebih unggul dibandingkan regresi logistik dengan nilai akurasi sebesar 87,85%, presisi 84,28% dan recall 76,26%. Penelitian ini diharapkan dapat membantu pengelola hotel dalam mengoptimalkan manajemen reservasi, mengurangi dampak negatif pembatalan serta meningkatkan pendapatan melalui prediksi yang lebih akurat.
Prediksi Harga Emas Menggunakan Metode Long Short-Term Memory Prayudha Ragil Musthofa; Sella Monica; Ukinda Feriando Setiawan; Siti Utami; Bayu Aji Laksono
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Fluktuasi harga emas yang signifikan memerlukan prediksi yang akurat untuk mendukung pengambilan keputusan investasi dan mitigasi risiko keuangan. Penelitian ini bertujuan untuk mengembangkan model prediksi harga emas menggunakan metode Long Short-Term Memory (LSTM), yang merupakan salah satu pendekatan deep learning yang unggul dalam analisis data deret waktu. Metode penelitian menggunakan model LSTM. Model diuji dengan data uji yang terpisah untuk mengevaluasi performa berdasarkan metrik akurasi seperti Mean Squared Error (MSE) dan Mean Absolute Error (MAE). Hasil penelitian menunjukkan bahwa model LSTM mampu memprediksi harga emas dengan tingkat akurasi yang tinggi. Temuan ini mendukung penerapan LSTM sebagai alat analitik yang efektif dalam pasar keuangan dan dapat dikembangkan lebih lanjut untuk memprediksi harga komoditas lain.
Studi Literatur tentang Faktor-Faktor yang Mempengaruhi Kemunduran Pendidikan Islam Mardinal Tarigan; Sella Monica; Dhea Alfira
Jurnal Dirosah Islamiyah Vol. 6 No. 3 (2024): Jurnal Dirosah Islamiyah
Publisher : Pascasarjana IAI Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/jdi.v6i3.2394

Abstract

Uu's research investigates changes and setbacks in Islamic education from its heyday to contemporary times. The research methodology used is a Literature Study/Library Study approach, by collecting data from various sources such as digital libraries and the internet. Analysis was carried out on the factors that influenced the decline of Islamic education, including the tendency of excessive philosophy, lack of appreciation for scientists, internal and external conflicts, as well as the impact of Mongol attacks and the Crusades. The results of the research showed that the decline of Islamic education had significant impacts such as stagnation in motivation, a decline in the quality of education and intellectual thinking and a narrowing of the curriculum in madrasas. However, efforts to purify Islamic teachings in the eighteenth century gave hope for a more progressive renewal of Islamic education
Co-Authors Alinda Endang Poerwati Amadeuz Ezrafel Ayu Nur Indahsari Bayu Aji Laksono Dhea Alfira Koo Tito Novelianto Lila Setiyani Mardinal Tarigan Prayudha Ragil Musthofa Rusdianto Roestam Selma Ohoira Siti Utami Ukinda Feriando Setiawan Yuliarman Saragih