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Journal of Computer Science and Research
Published by Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer Provinsi Sumatera Utara
ISSN : -     EISSN : 29862337     DOI : -
Core Subject : Economy, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science
Journal of Computer Science and Research (JoCoSiR) is aimed to publish research articles on theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. Journal of Computer Science and Research (JoCoSiR) published quarterly and is a peer reviewed journal covers the latest and most compelling research of the time. Journal of Computer Science and Research (JoCoSiR) is managed and published by APTIKOM Wilayah 1 Sumatera Utara.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
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Search results for , issue "Vol. 2 No. 4 (2024): October: Artificial Intelligence" : 5 Documents clear
I-V Characterization and Electrical Performance Analysis of Undoped, N-Type, and P-Type Silicon for Semiconductor Applications Anyaora , Sunday Chimezie; Nwokeocha, Tochukwu Obialor; Nwaokafor, Innocent Chisom Chukwuma-; Takim , Stephen A.
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 4 (2024): October: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v3i1.47

Abstract

Silicon remains the backbone of modern semiconductor technology; however, its intrinsic electrical limitations, such as low conductivity and restricted charge carrier concentration, constrain device performance. To enhance its functionality in electronic and photovoltaic applications, doping with suitable impurities is essential. This study focused on the I–V characterization and electrical performance analysis of undoped, N-type, and P-type silicon to assess the effect of doping on charge transport behavior. The experiment involved I–V characterization of intrinsic, N-type, and P-type silicon samples using precise materials, contact metals, and cleaning agents to ensure accuracy. A DC power supply, Source Measure Unit (Keithley 2400), and four-point probe station were employed for voltage application and current measurement. Samples were cleaned, coated with silver or aluminum contacts, annealed, and stored under nitrogen to prevent oxidation. I–V measurements were conducted under controlled environmental conditions, using calibrated equipment and multiple readings for accuracy. Data analysis in MATLAB included filtering, curve fitting, and extraction of key parameters like resistance and ideality factor to compare doped and undoped samples. The I–V characterization revealed clear differences between undoped and doped silicon samples. The undoped silicon exhibited Ohmic behavior with low conductivity ((5.3±0.2)×10⁻⁵ Ω⁻¹), while the N-doped ((1.4±0.1)×10⁻³ Ω⁻¹) and P-doped ((9.7±0.8)×10⁻⁴ Ω⁻¹) samples showed rectifying characteristics. N-type silicon displayed a lower turn-on voltage (0.65±0.02 V) than P-type (0.72±0.03 V), reflecting higher electron mobility. Ideality factors near unity (1.12 and 1.18) indicated diffusion-controlled transport. Conductivity improved 26-fold for N-type and 18-fold for P-type compared to intrinsic silicon, confirming doping’s strong influence on charge carrier concentration and validating measurement accuracy (standard deviation <3%). The study concludes that controlled doping significantly improves silicon’s electrical properties, making it more suitable for high-efficiency semiconductor and photovoltaic device applications.
The use of Artificial Intelligence (AI) tool by lecturers in the teaching and learning of English language in Federal College of Education (Technical) Omoku and Federal College of Education (Technical) Umunze Assimonye, Augusta Chiedu; Chinasa Florence Okoh
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 4 (2024): October: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In recent years, the integration of Artificial Intelligence (AI) in education has transformed how teachers and students interact with learning materials. However, in many teacher education institutions in Nigeria, the use of AI tools in language instruction remains limited and uneven. Lecturers often rely on traditional methods, which restrict students’ exposure to innovative learning technologies that could enhance writing, grammar, comprehension, and communication skills. This gap raises concern about how effectively lecturers are adopting AI to support English Language teaching and learning in Colleges of Education. The study used a descriptive survey design to explore how lecturers and students perceived the use of artificial intelligence in teaching English Language. It was conducted in the Federal Colleges of Education (Technical) at Omoku in Rivers State and Umunze in Anambra State, involving forty-four participants—seventeen lecturers and twenty-seven final-year students. Data were collected through a validated and reliable structured questionnaire analyzed using mean and standard deviation to determine agreement levels on issues relating to AI use in English Language education. The results revealed that lecturers moderately used artificial intelligence (AI) tools in teaching English Language in the Federal Colleges of Education (Technical) at Omoku and Umunze. Findings showed strong agreement that AI supported grammar and punctuation correction (Mean = 3.57, SD = 0.78) and provided instant feedback on assignments (Mean = 2.96, SD = 0.52). The grand mean (3.02, SD = 0.74) indicated moderate adoption. Similarly, AI usage extent was modest (Grand Mean = 2.64, SD = 0.85), mainly in grammar (Mean = 2.50) and listening comprehension (Mean = 2.60), suggesting gradual but growing integration. The study concluded that while AI has begun to improve teaching effectiveness, its application remains limited. It recommended increased institutional support, lecturer training, and infrastructure development to promote full AI integration in English Language education.
Text Summarization of Online News Articles Using the Text Rank Algorithm Indra Marto Silaban
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 4 (2024): October: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Text Summarization in online news media is useful for helping readers to get the essence of a news story. Summarization will be less effective if it is done manually by humans, so we need an application that can do summaries quickly and precisely. By utilizing preprocessing technics with sastrawi python library and the implementation of TextRank algorithm which is part of the extraction method, news that was previously long can be presented in a very concise form. This application is developed using the Python programming language with sastrawi libraries, nltk and StemmerFactory. While the framework used is Django as the backend and bootstrap as the frontend framework.
Optimization of Nutritional Meal Allocation Using the Greedy Algorithm : A Data – Driven Approach for Food Security in Indonesia Irwansyah Sitorus; Aprilia, Katharina Tyas; Muhammad Rasyid Ridha; Ricky Martin Ginting
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 4 (2024): October: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Food security and nutrition programs play a crucial role in improving public welfare, particularly in developing countries such as Indonesia. Efficient allocation of limited government resources to regions most in need remains a key challenge in reducing poverty and malnutrition. This study applies the Greedy Algorithm as a computational optimization method to determine the most effective and equitable distribution of nutritional meal program budgets cross Indonesian provinces. The algorithm prioritizes provinces with higher poverty rates and greater nutritional needs while ensuring that the total expenditure does not exceed the national budget constraint. By employing a data-driven approach and calculating the value-to-cost ratio for each province, the algorithm selects allocations that yield the maximum nutritional impact per unit of cost. The results indicate that the Greedy-based allocation model improves efficiency by approximately 18–25% compared to traditional allocation methods. This approach offers a transparent, adaptable, and computationally efficient framework that can support policymakers in enhancing food security, promoting social equity, and advancing sustainable development goals.
Ai-Based Road Performance Prediction for Supporting Smart Infrastructure Maintenance Pane, Muhammad Syahrul
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 4 (2024): October: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research aims to develop an artificial intelligence-based road performance prediction system to support smart infrastructure maintenance. Current road maintenance systems are still traditional and reactive, leading to infrastructure degradation and high repair costs. This study uses AI methods combining Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) to analyze road condition data, traffic volume, and weather conditions. ANN is effective in detecting nonlinear patterns from statistical data, while LSTM excels in processing time-series data of historical road conditions. The system is designed using UML modeling and implements a relational database for storing road, traffic, weather, and prediction data. Based on the analysis, the proposed system successfully provides a predictive maintenance solution that is proactive rather than reactive. The system's performance demonstrates that AI-based predictions can extend road service life, optimize maintenance budget allocation, and minimize public service disruptions. However, prediction accuracy is still influenced by factors such as data quality and model parameter selection.

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