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Perancangan Video Pembelajaran Matematika Keuangan Menggunakan Metode Multimedia Development Life Cycle (MDLC) Tanjung, Rovidatul Hikmah; Damayanti, Fera
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 9, No 1 (2025): April 2025
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v9i1.24133

The advantages of learning videos are able to present independent learning with time flexibility and precisely leaving behind traditional teaching that relies on textbooks and lectures in the classroom. In addition, learning videos that are packaged with attractive visual elements such as the use of animation can increase students' motivation in learning. The purpose of this study was to design a financial mathematics learning video using the Multimedia Development Life Cycle (MDLC) method. This method consists of six stages, namely: concept, design, collection of materials, creation, testing, and distribution. Learning videos were tested using alpha testing and beta testing to assess the feasibility of the multimedia created. Alpha testing was carried out on two content experts with the results of the learning video feasibility of "Very High" so that it could be continued to the implementation stage. Beta testing was carried out by distributing questionnaires to an audience of 25 students and obtained a score above 85%, namely "Very Eligible" on all indicators. Overall, the test results showed that the financial mathematics learning video was considered feasible in terms of technical quality, visuals, content, support for independent learning, and increasing student learning motivation. Keywords: Learning Videos, Financial Mathematics, MDLC Method

Game Edukasi Pengenalan Warna Untuk Anak PAUD Menggunakan Unity 3D Dengan Metode Design Thinking Studi Kasus PAUD-KB-TK GBKP Salsalina Ginting, Agung Suka; Fachri, Barany; Badawi, Afif
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 9, No 1 (2025): April 2025
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v9i1.24006

Educational games are effective tools for combining entertainment and education for children, especially in the context of Early Childhood Education (ECE). The introduction of colors is an essential aspect of children's cognitive development; however, conventional teaching methods often lack engagement, hindering the learning process. This study demonstrates that the application of technology, such as educational games, can enhance children's motivation and involvement in learning. By utilizing the Unity 3D platform, developers can create interactive and engaging learning environments. The Design Thinking method is employed in this research to design solutions that focus on user needs, based on a deep understanding of children. Usability aspects are tested using a Likert scale, encompassing learnability, efficiency, memorability, error prevention, and user satisfaction. The results indicate that the designed educational game provides an enjoyable and effective learning experience in introducing color concepts to children.

Prototipe Smart Lighting System Berbasis Sensor Lux dan Algoritma Genetika untuk Efisiensi Energi Hasibuan, Ade Zulkarnain; Asih, Munjiat Setiani
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 9, No 1 (2025): April 2025
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v9i1.23965

Sistem pencahayaan cerdas (smart lighting) merupakan teknologi yang dapat mengatur nyala dan mati lampu secara otomatis sesuai kondisi lingkungan. Penelitian ini bertujuan untuk mengembangkan sistem smart lighting berbasis sensor lux BH1750 dan algoritma genetika guna meningkatkan efisiensi energi. Data intensitas cahaya dikumpulkan dari sensor lux yang dihubungkan ke Arduino dan dikirim ke komputer melalui komunikasi serial untuk dianalisis menggunakan bahasa pemrograman Python. Algoritma genetika digunakan untuk mengoptimalkan jadwal nyala lampu dengan mempertimbangkan kebutuhan pencahayaan berdasarkan intensitas lux. Proses optimasi dilakukan melalui pembentukan populasi awal, evaluasi fitness berdasarkan kondisi terang dan gelap, seleksi menggunakan tournament selection, serta crossover dan mutasi untuk membentuk generasi baru. Hasil implementasi menunjukkan bahwa sistem berhasil mengurangi waktu nyala lampu dari 12 jam menjadi 7 jam per hari, menghasilkan penghematan energi sebesar 41,67%. Penelitian ini menunjukkan bahwa pendekatan kombinasi sensor lux dan algoritma genetika efektif dalam menciptakan sistem pencahayaan hemat energi yang adaptif terhadap kondisi pencahayaan harian.

Analisis Komparatif Algoritma Klasifikasi Machine Learning untuk Memprediksi Diabetes Saleh, Alfa; Eka Sari, Ria; Ramadani, Ramadani; Fujiati, Fujiati; Lestari, Ratna
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 9, No 1 (2025): April 2025
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v9i1.23794

Diabetes Mellitus is one of the most common chronic diseases, this disease is also a major concern in global public health issues. in this study, a Machine Learning approach was carried out to help predict diabetes in the community. Machine learning is very useful in analyzing health data because of its good ability to process large amounts of data. A comparative study with several machine learning classification algorithms such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes and Decision Tree (C4.5) has been conducted to determine which algorithm gives the best results in terms of predicting diabetes. Where, the features used in predicting diabetes include gender, age, history of hypertension, history of heart disease, history of smoking, BMI, level of HbA1c and blood glucose levels. From the results of this study, the accuracy rate of diabetes prediction for the K-Nearest Neighbors (KNN) algorithm is 92.5%, the Support Vector Machine (SVM) algorithm is 94.5%, the Naive Bayes algorithm is 90% and the last for the Decision Tree (C4.5) algorithm is 93.5%. So, from the test results of several machine learning classification algorithms it can be concluded that the Support Vector Machine (SVM) algorithm is the most optimal algorithm in terms of predicting diabetes. Keywords: Machine Learning, KNN, SVM, Naive Bayes, Decision Tree, Diabetes.

Analisis Tren Luas Wilayah dan Produksi Kelapa Sawit di Provinsi Aceh: Studi Kuantitatif dan Prediktif Fadhilla, Cut Alna; Gunawan, Chichi Rizka; Sofia Amriza, Rona Nisa
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 9, No 1 (2025): April 2025
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v9i1.23928

Palm oil is a strategic commodity that plays an important role in the economy of Aceh Province. This study aims to analyze the trend of changes in the area of oil palm plantations and their production results using quantitative data from recent years, as well as to predict palm oil production for the next five years. The methods used include descriptive statistical analysis to identify development patterns and predictive models based on time series forecasting to accurately estimate future trends. The results of the study show a significant increase in the area of land and oil palm production in several main districts, with Nagan Raya as the largest contributor. The prediction of harvest results for the next five years indicates a positive trend that can be used as a basis for planning the development of the plantation sector. These findings provide important information for policy makers and industry players in making strategic decisions to increase the productivity and sustainability of the oil palm business in Aceh Province. Keywords: Palm Oil Production, Area Analysis, Prediction Model

Penerapan Algoritma Naive Bayes untuk Klasifikasi Penduduk Miskin dan Mampu di Desa Tanoh Anou Kecamatan Idi Rayeuk Gunawan, Chichi Rizka; Gunawan, Chicha Rizka; Akram, Rizalul; Novianda, Novianda; Khairiana, Khairiana
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 9, No 1 (2025): April 2025
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v9i1.23843

Palm oil is a strategic commodity that plays an important role in the economy of Aceh Province. This study aims to analyze the trend of changes in the area of oil palm plantations and their production results using quantitative data from recent years, as well as to predict palm oil production for the next five years. The methods used include descriptive statistical analysis to identify development patterns and predictive models based on time series forecasting to accurately estimate future trends. The results of the study show a significant increase in the area of land and oil palm production in several main districts, with Nagan Raya as the largest contributor. The prediction of harvest results for the next five years indicates a positive trend that can be used as a basis for planning the development of the plantation sector. These findings provide important information for policy makers and industry players in making strategic decisions to increase the productivity and sustainability of the oil palm business in Aceh Province. Keywords: Palm Oil Production, Area Analysis, Prediction Model

Komparasi Fungsionalitas EVE-NG dan GNS3: Simulasi Protokol Jaringan Sholeha, Eka Wahyu; Jaya, Dery Yuswanto; Supriyanto, Arif; Utomo, Hendrik Setyo
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 9, No 1 (2025): April 2025
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v9i1.23864

The development of network technology drives the need for efficient, flexible, and economical simulation tools for learning and testing network architectures. This research compares two popular platforms, GNS3 (Graphical Network Simulator-3) and EVE-NG (Emulated Virtual Environment - Next Generation), in terms of functionality, network performance, and resource consumption. Testing was carried out through network protocol simulations with identical topologies, and parameters such as throughput, delay, jitter, packet loss, convergence time, and CPU, RAM, and disk I/O usage were measured. The results show that EVE-NG is more stable with high throughput, low jitter, and optimal transmission efficiency, making it suitable for complex simulations. Meanwhile, GNS3 is more resource-efficient and has faster convergence, ideal for basic learning. The conclusion of this research emphasizes that the choice of platform depends on the needs: EVE-NG for large scale, GNS3 for education. These findings provide guidance for educators, professionals, and researchers in choosing simulation tools according to the context. Keywords: Network Simulation, GNS3, EVE-NG, Protocol Emulation, Performance Evaluation

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Abstract

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Contact Name
-

Contact Email
-

Phone
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Journal Mail Official
-

Editorial Address
-

Location
Kota medan,

Sumatera utara

INDONESIA