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All Journal Indonesia Chimica Acta JURNAL MANAJEMEN MOTIVASI Jurnal Informatika Global Jurnal Ecoment Global: Kajian Bisnis dan Manajemen Jurnal Alwatzikhoebillah : Kajian Islam, Pendidikan, Ekonomi, Humaniora Jurnal Ekonomi Bisnis, Manajemen dan Akuntansi (JEBMA) JABKO: Jurnal Akuntansi dan Bisnis Kontemporer Konsentrasi: Jurnal Manajemen dan Bisnis Masip: Jurnal Manajemen Administrasi Bisnis dan Publik Terapan Journal of Computer Science Application and Engineering Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Jurnal Pengabdian Masyarakat ISEI Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Bumi Anoa Jurnal Praba: Jurnal Rumpun Kesehatan Umum Masyarakat: Jurnal Pengabdian Jurnal MediLab Mandala Waluya Ekopedia: Jurnal Ilmiah Ekonomi Indonesia Economic Journal Journal of Golden Generation Economic
Amirah
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Preliminary Study for Cyber Intrusion Detection Using Machine Learning Approach Amirah; Karimah, Fitrah
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 1 No. 1 (2023): JAFOTIK - February
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v1i1.4

Abstract

This article discusses the importance of information system security in the current technological era and how the increasingly complex threat of cyber attacks demands a more sophisticated approach to detection and prevention. This initial study explores the potential of applying Machine Learning in cyber intrusion detection as a first step to developing detection systems that are adaptive and responsive to evolving threats. Through a methodology involving the collection of representative data on cyber attacks, data preparation, and Machine Learning model selection, this article describes the initial stages for understanding and testing the potential of this technology in the context of cyber security. Although it includes an example dataset, data preparation steps, and the selection of several Machine Learning algorithms, this study only gets to the model selection stage, while the model training process and performance evaluation are the focus of future work. The conclusions of this initial study emphasize the importance of selecting appropriate algorithms with specific features for effective intrusion detection against growing cyber threats.
Leveraging Open Data with Machine Learning Algorithms Amirah; Karimah, Fitrah
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 1 No. 2 (2023): JAFOTIK - August
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v1i2.19

Abstract

In the evolving landscape of technology, the amalgamation of open data and machine learning stands as a powerful catalyst for innovation. This study explores the dynamic synergy between these domains, where open data's accessibility and transparency converge with machine learning's pattern recognition and predictive capabilities. The fusion holds immense promise across diverse sectors, from healthcare to finance, urban planning, and environmental science. By leveraging advanced algorithms on openly available information, organizations can gain unprecedented insights into trends, correlations, and anomalies, fostering a culture of innovation. The methodology involves a comprehensive literature review, knowledge enrichment, case studies, and conclusion, providing a systematic approach to understanding the intersection of open data and machine learning. The results showcase practical applications in predictive policing, healthcare resource allocation, smart traffic management, and more. Each application is supported by relevant machine learning algorithms, emphasizing their role in addressing complex challenges. The study culminates with a simplified example of predictive policing using a Support Vector Machine (SVM) algorithm, showcasing its pseudocode and decision function equation. This example illustrates how machine learning can predict crime occurrences based on patrol data and historical crime rates. Overall, this fusion marks a pivotal chapter in technological progress and societal advancement.
Ethical and Legal Implications of AI in Decision-Making Zahra, Y; Amirah
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 2 No. 2 (2024): JAFOTIK - August
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v2i2.42

Abstract

This study explores the ethical and legal implications of integrating artificial intelligence (AI) into decision-making processes across various industries. As AI systems become increasingly prevalent, concerns arise regarding their transparency, fairness, and accountability. The study reviews examples from healthcare, finance, criminal justice, human resources, and retail to highlight issues such as bias, lack of transparency, and privacy concerns. Current regulations often inadequately address the unique challenges posed by AI, particularly regarding accountability and the ethical use of personal data. By developing a comprehensive framework that integrates ethical principles—such as fairness, justice, and autonomy—with legal concepts like liability and data protection, the study proposes practical solutions to mitigate these risks. The findings underscore the need for enhanced oversight, rigorous validation, and transparent practices to ensure AI systems are used responsibly, thereby aligning technological advancements with ethical and legal standards.
Pengaruh Ukuran Perusahaan, Kebijakan Dividen, Struktur Modal, Leverage, dan Profitabilitas Terhadap Nilai Perusahaan pada Sektor Perindustrian yang Terdaftar di BEI Tahun 2017-2021 Amin, Mohammad Arridho Nur; Amirah; Faizal, Achmad Fajar
Jurnal Ekonomi Bisnis, Manajemen dan Akuntansi (Jebma) Vol. 2 No. 3 (2022): Article Research Volume 2 Issue 3, November 2022
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jebma.v2i3.1828

Abstract

Sektor perindustri menjadi peran penting dalam kehidupan masyarakat, karena sektor ini memproduksi barang dan jasa. Pandemi Covid-19 menyebabkan penurunan pada faktor yang mempengaruhi nilai perusahaan seperti ukuran perusahaan, kebijakan dividen, struktur modal, leverage, profitabilitas tahun 2020. Dalam hal ini apakah ketika masyarakat berin­ves­tasi di sektor perindustrian akan mendapatkan keuntungan yang diharapkan. Penelitian ini bertujuan untuk menganalisis pengaruh ukuran perusahaan, kebijakan dividen, struktur modal, leverage, profitabilitas terhadap nilai perusahaan pada sektor yang terdaftar di BEI tahun 2017-2021. Penelitian ini adalah penelitian kuantitatif dengan menggunakan sam­pel penelitian berjumlah 14 perusahaan yang dipilih berdasarkan teknik purposive sampling. Teknik analisis data yang digu­na­kan adalah analisis regresi linier berganda. Uji hipotesis menggunakan t-statistik dan f-sta­tis­tik dengan tingkat signifikan 5%. Pengolahan data menggunakan aplikasi SPSS versi 22. Hasil penelitian ini menunjukkan variabel kebijakan dividen, struktur modal, dan pro­fi­ta­bi­litas berpengaruh positif dan signifikan terhadap nilai perusahaan, sedangkan ukuran peru­sa­haan, dan leverage berpengaruh positif dan tidak signifikan terhadap nilai perusahaan. Secara simultan variabel ukuran perusahaan, kebijakan dividen, struktur modal, leverage,dan profitabilitas berpengaruh signifikan terhadap nilai perusahaan.
Deteksi Intrusi Siber pada Sistem Pembelajaran Elektronik berbasis Machine Learning Amirah; Sanmorino, Ahmad
Jurnal Ilmiah Informatika Global Vol. 14 No. 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v14i2.3227

Abstract

This study aims to develop a mechanism for detecting machine learning-based cyber intrusions in electronic learning systems. In today's digital era, e-learning systems have become an integral part of education and training, providing global accessibility and more interactive learning efficiency. However, security and privacy challenges are becoming critical issues due to the increasingly real threat of cyber intrusion. Attackers try to take advantage of vulnerabilities and weaknesses in e-learning systems to steal sensitive data or disrupt operations. To overcome this problem, this study focuses on the use of artificial intelligence technologies, especially machine learning, to proactively detect and respond to intrusive threats. Through e-learning security analysis, identification of weaknesses, and potential loopholes for cyber-attacks, the most suitable machine learning algorithms are selected to detect patterns and signs of intrusion attacks on network data. The evaluation results show that several machine learning algorithms, such as SVM and Decision Tree, have good performance in recognizing cyber intrusions with high accuracy, precision, recall, F1-score, and ROC-AUC. By implementing machine learning-based intrusion detection technology, it is expected that electronic learning systems can be more proactive in identifying and responding to intrusion threats before significant damage occurs. This research has significant benefits in increasing security and privacy in the use of electronic learning systems. In addition, this study is expected to be a reference for further research in the world of cyber security and the application of artificial intelligence technology in supporting digital security.
Manajemen Risiko sebagai Strategi Penguatan UMKM Ritel Pakaian di Dukuh Kebranten Salsabila, Amelia Azhar; Amirah
Journal of Golden Generation Economic Vol. 1 No. 1 (2025): AGUSTUS 2025 : Journal of Golden Generation Economic
Publisher : PT. Lembaga Penerbit Penelitian Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65244/jggeconomic.v1i2.119

Abstract

Penelitian ini bertujuan menilai sejauh mana manajemen risiko diterapkan pada sebuah UMKM ritel pakaian dengan menelaah tiga aspek utama: identifikasi, pengukuran, dan pengelolaan risiko. Metode penelitian yang digunakan berbasis deskriptif kuantitatif melalui kuesioner berskala lima poin yang diisi oleh lima pengelola toko. Hasil penelitian memberi gambaran bahwa tingkat penerapan manajemen risiko berada pada kategori tinggi. Identifikasi risiko menunjukkan nilai rata-rata 4,03, terutama pada risiko persediaan, persaingan, dan ketepatan pasokan, meskipun beberapa risiko seperti gangguan sistem pembayaran dan ketidaksesuaian harga belum teridentifikasi secara optimal. Pengukuran risiko memperoleh rata-rata 4,01, didominasi pemanfaatan data penjualan dan laporan keuangan, namun belum mengakomodasi pengukuran frekuensi kejadian serta analisis risiko operasional dan pemasok secara terpadu. Pengelolaan risiko memiliki nilai tertinggi (4,45) melalui praktik seperti monitoring stok, pelatihan karyawan, dana cadangan, rencana darurat, dan digitalisasi pencatatan. Meskipun demikian, evaluasi rutin terhadap strategi dan keterlibatan pihak eksternal masih perlu ditingkatkan. Hasil ini menegaskan bahwa UMKM telah memiliki fondasi manajemen risiko yang kuat, tetapi membutuhkan penguatan dalam dokumentasi dan sistematisasi proses agar pengelolaan risiko lebih komprehensif dan berkelanjutan.
Analisis  Manajemen Risiko Konter Voucher dan Pulsa di Kecamatan Adiwerna, Tegal Akbar Salafudin; Amirah
Ekopedia: Jurnal Ilmiah Ekonomi Vol. 1 No. 4 (2025): OKTOBER-DESEMBER
Publisher : Indo Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/3cdym903

Abstract

This study analyzes risk management in voucher and mobile phone credit counter businesses in Adiwerna District, Tegal Regency, focusing on risk identification, evaluation, and management strategies. The research method employed a descriptive approach, using primary data obtained through a Google Form questionnaire from 30 respondents, consisting of counter owners, employees, and distributors. The data collected included risk assessments based on frequency, probability, and impact. The results indicate that the most frequent risks are price competition and digital services (73.3%), technical risks such as power and internet outages (66.7%), supply disruptions from providers (60%), internal fraud (50%), and seasonal demand fluctuations (46.7%). Using a probability-impact matrix, technical risks are ranked "very high," followed by supply disruptions and internal fraud at "high-medium." Recommended mitigation strategies include diversifying supply sources, implementing a point-of-sale (POS) system, daily reconciliation, enhancing physical security, and utilizing digital technology. These findings are expected to help businesses improve operational resilience and provide practical recommendations for the development of micro-enterprises in the mobile phone credit and voucher service sector.
Kadar Hemoglobin Pada Pasien Diabetes Melitus Tipe 2 Berdasarkan Stadium Nefropati Diabetik Dalam Skrining Kejadian Anemia Desyani Ariza; Amirah; Andi Maya Kesrianti
Jurnal Praba : Jurnal Rumpun Kesehatan Umum Vol. 3 No. 4 (2025): Desember : Jurnal Praba : Jurnal Rumpun Kesehatan Umum
Publisher : STIKES Columbia Asia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62027/praba.v3i4.609

Abstract

Diabetes Mellitus often causes disorders in other organs, one of which is kidney disorders, commonly known as Diabetic Nephropathy. Diabetic Nephropathy (DN) is a chronic microvascular complication of Diabetes Mellitus, which is the main cause of end-stage kidney disease. Decreased hemoglobin (Hb) levels, or anemia, are very common clinical manifestations found in patients with this impaired kidney function. Pathophysiologically, the decrease in hemoglobin in DN patients is multifactorial, involving absolute or relative erythropoietin (EPO) deficiency due to damage to renal peritubular interstitial cells. As a result, there is a significant disruption of erythropoiesis, which can disrupt the process of red blood cell maturation in the hematopoiesis process. Monitoring hemoglobin levels is a crucial parameter because anemia in this DN population often appears earlier and is more severe than in non-diabetic chronic kidney disease patients. Therefore, early identification of fluctuations in hemoglobin levels is crucial in clinical management to prevent worsening of the systemic condition of patients with Diabetic Nephropathy. This study aims to review Hemoglobin levels in Type 2 Diabetes Mellitus Patients Based on Diabetic Nephropathy Stage in Anemia Screening. The method in this study used Descriptive Analytical with a sample of 47 respondents who met the inclusion criteria. The sampling and research location were conducted at Dr. Wahidin Sudirohusodo Hospital, Makassar. Hemoglobin measurements were measured using Sysmex Series XN-1000 with the Flowcytometry tool method. The results of the study found normal Hb levels (not anemic) in 25 respondents (53.20%), Mild Anemia in 6 respondents (12.80%), Moderate Anemia in 13 respondents (27.60%) and Severe Anemia in 3 respondents (6.40%). From these data, it is known that not all Diabetic Nephropathy patients in this study experienced Anemia.
Risk Management of Grocery Businesses in the Tegal Region Using an ISO 31000-Based Approach: Management of Grocery Businesses in the Tegal Region Using an ISO 31000-Based Approach Retno Wulandari; Amirah
Jurnal Ecoment Global Vol. 10 No. 3 (2025): Volume 10 No. 3 (Edisi Desember 2025)
Publisher : Universitas Indo Global Mandiri Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jeg.v10i3.6454

Abstract

AbstractObjective: This study aims to analyze various risks faced by grocery businesses in the Tegal area using anISO 31000-based risk management approach.Design/Methods/Approach: The research method used was descriptive quantitative, involving 50 MSMEgrocery business actors in the Tegal area who were selected using purposive sampling techniques. Data wascollected through questionnaires using a Likert scale and analyzed through the stages of identification,analysis, evaluation, and risk management in accordance with the ISO 31000 standard.Findings: The results of the study show that six major risks are in the high category, namely the emergenceof competitors, customer decline, theft, changes in government regulations, and price fluctuations. Fourother risks are in the moderate category, namely recording errors, damaged or expired goods, stockshortages, and disaster risks. Risk management is largely at a high level of efficiency, although some risksare still at a moderate level and still require mitigation reinforcement.Originality/Value: This study provides added value through the application of ISO 31000 to small andmedium-sized grocery businesses, a context that has not been widely studied. This study presents a morestructured risk mapping, thereby enriching the literature on risk management in small-scale retailbusinesses.Practical/Policy implication: The application of ISO 31000 helps grocery businesses determine riskpriorities, improve risk management effectiveness, and increase business operational resilience tobusiness environment uncertainties. Keywords: ISO 31000, Risk Management, MSMEs, Grocery Businesses
Analisis Manajemen Risiko pada Pekerja Pabrik yang ada di Kabupaten Brebes Dila Lailatul Munawaroh; Amirah
Masip: Jurnal Manajemen Administrasi Bisnis dan Publik Terapan Vol. 3 No. 4 (2025): Desember: MASIP: Jurnal Manajemen Administrasi Bisnis dan Publik Terapan
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/masip.v3i4.1286

Abstract

The manufacturing industry in Brebes Regency employs a large workforce and has high occupational safety and health risks, requiring the implementation of systematic risk management. This study aims to analyze the implementation of risk management among factory workers in Brebes Regency based on the stages of risk identification, measurement, and management. The study used a quantitative descriptive approach with 100 respondents from three companies. Data were collected through a five-point Likert questionnaire consisting of 39 statements and analyzed using average scores with categories of Poor (1.00–<2.50), Fair (2.50–<3.50), and Very Good (3.50–5.00). The results show that the average indicator value is in the range of 3.96–4.71, so that all indicators are in the Very Good category, with an average risk identification aspect of 4.48, risk measurement of 4.43, and risk management of 4.50. The highest scores were for recording personal property loss (4.71) and awareness of noise hazards (4.56), while the lowest scores were for assessing muscle complaints/minor injuries due to work posture (3.96) and work position and short break arrangements (4.33). Overall, the implementation of risk management has been very good, but strengthening of the ergonomics aspect is still needed through more formal recording of complaints, periodic observation of posture and workload, and ergonomic interventions so that the implementation of risk management is more balanced and sustainable.
Co-Authors Ahmad Sanmorino Akbar Salafudin Andi Maya Kesrianti Anson, Adriel Moses Arlitha Deka Yana Catur Wahyudi, Catur Desyani Ariza Dewi, Risma Arlia Sinta Dila Lailatul Munawaroh Efrianto Elnan Maheasa Arya Sahdewa Fadilah, Dina Jihan Faizal, Achmad Fajar Feni Marlia Fitriyani, Anisa Gilbreth Pamean Jaka Waskito Janah, Nur Lutfiatul Juli Riyanto Tri Wijaya Karimah, Fitrah Kasmuddin Darmo Ka’ba Paharu Mei Rani Amalia Mohammad Arridho Nur Amin Muh Sainal Abidin Najmalia, Risa Nurun Putri Salsabila, Nabila Retno Wulandari Romadhon, Istiqomah Salsabila, Amelia Azhar Santi Santi Sarinah Basri K Sri Murdiati Sri Murdiati Sri, Diyana Sukamto, Ika Sumiyarsi Susilawati, Agnes Dwita Syaputra, Eko Maulana Tabrani Titi Purnama Tri Sulistyani Tubety, Ari Yanasari, Dita Yudistira Yuni Utami Yuniarti Herwinarni Zahra, Y