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All Journal Format : Jurnal Imiah Teknik Informatika jeti Software Development Digital Business Intelligence and Computer Engineering Jurnal Rekayasa Sistem Informasi dan Teknologi Journal Innovations Computer Science
Nur Amalia Hasma
Universitas Islam Kebangsaan Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Other

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Implementasi Machine Learning Dalam Menganalisis Dan Mendeteksi Berita Palsu Pada Portal Berita Bahasa Inggris Nur Amalia Hasma
Jurnal Rekayasa Sistem Informasi dan Teknologi Vol. 1 No. 3 (2024): Februari
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jrsit.v1i3.455

Abstract

Kemajuan teknologi informasi di era digital saat ini telah memberikan dampak yang signifikan terhadap akses informasi melalui media online. Hal ini disebabkan semakin populernya penggunaan media online karena biayanya yang murah dan akses yang mudah. Berita palsu dapat disebarkan melalui berbagai platform media, termasuk penggunaan website, media sosial, email, dan  platform digital lainnya. Berita palsu disebarkan untuk berbagai tujuan, termasuk menghasilkan pendapatan iklan melalui clickbait, mempengaruhi opini publik mengenai peristiwa terkini, menyebarkan perselisihan, dan mempromosikan agenda tertentu.Dalam penelitian ini diusulkan metodologi untuk mendeteksi berita asli dan berita palsu dengan menggunakan metode seleksi fitur TF-IDF untuk melakukan klasifikasi dan menerapkan algoritma pembelajaran mesin yaitu, Support Vector Machine, Logistic Regression, Random Forest, dan Decision Tree.  Hasil dari klasifikasi tersebut ditampilkan dalam bentuk confusion matrix. Berdasarkan hasil penelitian yang telah dilakukan algoritma Support Vector Machine menghasilkan akurasi sebesar 95,65% dengan nilai presisi 94,91%, disusul dengan algoritma Logistic Regression, algoritma Random Forest, dan algoritma Decision Tree yang memperoleh nilai akurasi terkecil sebesar 91,25%.
Pengembangan Model Prediksi Diabetes Melitus Menggunakan Metode Stochastic Gradient Boosting Sah, Andrian; Niesa, Chaeroen; Damuri, Amat; Hasma, Nur Amalia
FORMAT Vol 14, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2025.v14.i1.002

Abstract

Diabetes mellitus is one of the global health issues with a continuously increasing prevalence. Its high prevalence significantly impacts economic burdens and healthcare systems, as it often leads to severe complications such as cardiovascular diseases and kidney failure. Therefore, early prediction and detection of diabetes mellitus are crucial in mitigating its adverse effects. Data mining and machine learning technologies offer innovative solutions for processing complex medical data, providing deeper insights, and supporting data-driven decision-making. This study aims to develop a diabetes mellitus prediction model using the Stochastic Gradient Boosting (SGB) algorithm. The model utilizes a dataset comprising clinical variables such as glucose levels, blood pressure, body mass index (BMI), and genetic history to identify diabetes risk. The results indicate that the developed prediction model demonstrates high performance across various dataset splitting ratios: 70:30, 80:20, and 90:10. The model achieved the highest accuracy of 95.50% at the 70:30 ratio, with an AUC (Area Under the Curve) value of 0.9862, showcasing its ability to effectively differentiate between positive (diabetes) and negative (non-diabetes) classes. At the 80:20 and 90:10 ratios, the model achieved accuracies of 92.75% and 92.31%, with AUC values of 0.9767 and 0.9777, respectively, indicating consistent performance. The model’s high accuracy is attributed to the iterative boosting approach in the SGB algorithm, which adaptively corrects prediction errors at each iteration. Additionally, regulatory mechanisms such as learning rate and subsampling help prevent overfitting, making the algorithm effective for datasets with complex patterns.
Implementation of Edge Computing for Optimizing Sensor Data Collection in Smart Buildings Fajri, T. Irfan; Ningsih, Liasulistia; Octiva, Cut Susan; Hakim, Muhammad Lukman; Hasma, Nur Amalia
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.369

Abstract

The development of the Internet of Things (IoT) has driven the implementation of smart buildings that rely on real-time sensor data collection and analysis. However, cloud computing-based systems often face problems of high latency and large network loads. This research implements an edge computing architecture to optimize sensor data collection in smart buildings. A prototype was built using edge nodes (Raspberry Pi) that process data from temperature, humidity, light, and motion sensors locally before sending it to the cloud. Test results show that edge computing can reduce latency by up to 45% and reduce data traffic to the cloud by 60%, while also improving the energy efficiency of sensor devices. Thus, edge computing has been proven to effectively improve the performance and efficiency of data collection systems in smart buildings
Rancang Bangun Sistem Informasi Alumni Fakultas Komputer Dan Multimedia Universitas Islam Kebangsaan Indonesia Kabupaten Bireuen Hera Novianti; Nur Amalia Hasma; T. Irfan Fajri
Jurnal Elektronika dan Teknologi Informasi Vol 5 No 2 (2024): September 2024
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5201/jet.v5i2.493

Abstract

Pengolahan data alumni pada Fakultas Komputer Universitas Islam Kebangsaan Indonesia masih menggunakan cara manual yaitu pendataan masih disimpan di microsoft excel. Dengan cara manual tentu saja banyak menimbulkan kesulitan bagi kasi fasilkom dalam hal pengelolaan data alumni dan penyampaian informasi ke alumni. Oleh karena itu dibangun sistem informasi berbasis Mobile yang lebih efektif dan efisien sehingga mengurangi proses-proses yang masih dilakukan secara manual. Tujuan dari penelitian yang penulis lakukan adalah Mengembangkan sistem informasi Pengolahan data alumni di Fakultas Ilmu Komputer Universitas Islam Kebangsaan Indonesia yang berbasis Mobile, sehingga bisa mempermudah dalam proses pengolahan data alumni dan penyampaian informasi baik berita agenda kegiatan alumni secara tepat dan cepat. Pengumpulan data dilakukan dengan metode wawancara dan metode observasi. Tahapan perancangan yang digunakan meliputi perancangan desain sistem yaitu dengan menggunakan Context Diagram (CD) dan Data Flow Diagram (DFD), Perancangan database yaitu Entity Relationship Diagram (ERD), perancangan desain fisik. Sistem ini dibuat dengan menggunakan bahasa pemrograman JAVA, MySQL dan dengan bantuan aplikasi Android Studio. Hasil penelitian ini adalah Pengembangan Sistem Informasi Alumni Berbasis Android di Fakultas Ilmu Komputer Universitas Islam Kebangsaan Indonesia. Sistem ini mudah digunakan, kecepatan aksesnya cepat, laporannya sesuai dengan yang pengelola/akademk inginkan sehingga diharapkan sistem ini dapat membantu pihak akademik dalam pengelolaan data alumni dan menyampaikan informasi.
Sistem Cerdas Penunjang Keputusan Penyaluran Beras Menggunakan Metode Fuzzy dan Klasifikasi Naïve Bayes VIVI ASBAR; Nur Amalia Hasma
Software Development, Digital Business Intelligence, and Computer Engineering Vol. 4 No. 2 (2026): SESSION (MARET)
Publisher : Politeknik Negeri Banyuwangi Jl. Raya Jember km. 13 Labanasem, Kabat, Banyuwangi, Jawa Timur (68461) Telp. (0333) 636780

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57203/session.v4i2.2026.21-29

Abstract

The distribution of subsidized rice in Indonesia faces persistent challenges such as mistargeting, inefficiency, and delays, which hinder food access among vulnerable populations. This study aims to address these issues by developing an intelligent decision support system that leverages Fuzzy Logic and Naïve Bayes classification to enhance the accuracy and efficiency of distribution decisions. Fuzzy Logic is utilized to process uncertain and imprecise data in decision criteria, while Naïve Bayes is employed to analyze historical data and predict recipient eligibility based on socioeconomic indicators such as income level, number of dependents, and asset ownership. The system demonstrates high classification accuracy and generates recommendations that align well with real-world conditions. The integration of these two methods effectively simplifies complex decision-making processes in social aid distribution. In conclusion, the proposed system offers a robust and objective tool to support the fair and transparent allocation of rice subsidies. Clinically or practically, this work has the potential to be adopted by government agencies to improve policy implementation in food assistance programs, ensuring more equitable and data-driven outcomes.
Co-Authors Chaeroen Niesa Damuri, Amat Hera Novianti Muhammad Lukman Hakim, Muhammad Lukman Ningsih, Liasulistia Octiva, Cut Susan Sah, Andrian Siswahyudianto VIVI ASBAR