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All Journal JISTech (Journal of Islamic Science and Technology) Building of Informatics, Technology and Science Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer
M Fakhriza
Universitas Islam Negeri Sumatera Utara, Medan
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Materials Science & Nanotechnology Other

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MEDIA PEMBELAJARAN SISTEM PERIODIK UNSUR DENGAN KONSEP JEMBATAN KELEDAI MENGGUNAKAN TEKNOLOGI AUGMENTED REALITY BERBASIS ANDROID Anggi Wulandari; M Fakhriza
JISTech (Journal of Islamic Science and Technology) Vol 6, No 1 (2021)
Publisher : UIN Sumatera Utara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/jistech.v6i1.11904

Abstract

Augmented Reality is a technology that unites the real world view with the virtual world in real time. Augmented Reality has been widely used in various fields, one of which is education. Augmented Reality can be used by students to make it easier to learn the periodic system. The mnemonic concept is a concept that is used to help students remember vocabulary information more quickly and easily. In this study, a learning media application for the periodic system of elements was developed using the mnemonic concept that utilizes Augmented Reality technology that can help students understand and remember material related to the periodic system of chemical elements. This application is made with Blender 3D software to create 3D atomic structure objects and their uses, Vuforia SDK and Unity 3D for building applications and marker detection. The final result of making this learning media application is Augmented Reality which can display 3D objects from the atomic structure and uses of these elements as well as mnemonic concepts from groups IA to VIIIA. This application uses a Markerless Based Tracking technique. From the tests that have been carried out on users, it is concluded that the applications built are suitable for use
Sistem Pendukung Keputusan Pemilihan SSD Laptop Menggunakan Kombinasi Metode AHP dan SAW Wahyu Rahmansyah; Ilka Zufria; M Fakhriza
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1257

Abstract

The development of computer technology has increased the demand for the right choice in buying an SSD (Solid State Drive) for a laptop. This research is intended to develop a decision support system (DSS) that uses a combination of the AHP (Analytical Hierarchy Process) and SAW (Simple Additive Weighting) methods in selecting laptop SSD at a Medan computer shop. This research consists of several stages. The first stage involves identifying relevant criteria in selecting a laptop SSD, such as price, capacity, speed, durability, and warranty. The AHP method is used to determine the relative weight of each criterion. The second phase involves using the SAW method to assign a value to each criterion and the SSD alternatives available in the store. The suitability score for each alternative is calculated and the system generates recommendations based on the highest score. The decision support system developed in this research is expected to help computer shop owners and customers choose the most suitable laptop SSD according to their needs and preferences. By using a combination of AHP and SAW methods, more objective and accurate results can be obtained in selecting a laptop SSD at a computer shop in Medan. In the context of laptop hardware selection, this research is expected to increase efficiency and customer satisfaction.
Prediction of Palm Oil Fresh Fruit Bunch Yield using Support Vector Machine (SVM) Try Widyawanti; M Fakhriza
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1002

Abstract

Palm oil fresh fruit bunch (FFB) production plays a crucial role in plantation management and decision making. However, fluctuations in environmental conditions and plantation characteristics often make yield estimation difficult to perform accurately. This study aims to predict palm oil fresh fruit bunch yield using the Support Vector Machine (SVM) algorithm as a machine learning–based approach. The dataset used in this research consists of monthly production data from 2020 to 2024, including several influential variables such as plant age, land area, rainfall, and soil characteristics. The data were preprocessed through cleaning, transformation, and normalization using the min–max scaling method to ensure consistency and stability during model training. The SVM model was implemented using the Radial Basis Function (RBF) kernel, which is suitable for handling nonlinear data patterns. Model evaluation was conducted by dividing the dataset into training and testing data with a ratio of 80% and 20%, respectively. The performance of the proposed model was measured using Root Mean Square Error (RMSE) and accuracy metrics. Experimental results show that the SVM model achieved an RMSE value of 1.316561 and an accuracy rate of 56.6%, indicating that the model is able to capture the general pattern of palm oil FFB yield data with a relatively small prediction error. Although the accuracy obtained is moderate, the results demonstrate that SVM can be applied as an initial predictive tool for estimating palm oil yield. The findings of this study are expected to support plantation managers in planning harvest activities and optimizing resource allocation.
Analysis of Air Pollution Standard Index Using Support Vector Machine Algorithm Fitra Hidayat Lubis; Raissa Amanda Putri; M Fakhriza
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9506

Abstract

Air pollution is one of the major environmental problems in urban areas, including Medan City, Indonesia. The Air Pollution Standard Index (Indeks Standar Pencemar Udara / ISPU) data provided by the Environmental Agency is often difficult for the public to interpret due to its numerical format. This study aims to analyze and classify air quality using the Support Vector Machine (SVM) algorithm and present the results through data visualization. The dataset used in this research is secondary data obtained from the Environmental Agency of Medan City, including pollutant parameters such as PM10, PM2.5, SO₂, NO₂, CO, O₃, and HC. The research method follows a quantitative descriptive approach, including data preprocessing, ISPU calculation based on government regulations, classification using SVM, and visualization using graphical methods such as line charts, bar charts, and heatmaps. The results indicate that SVM is effective in classifying air quality categories into Good, Moderate, Unhealthy, Very Unhealthy, and Hazardous. Additionally, visualization techniques improve the interpretability of air quality data, making it easier for stakeholders and the public to understand environmental conditions. This study contributes to decision support systems for environmental monitoring and public awareness.
Co-Authors Anggi Wulandari Fitra Hidayat Lubis Ilka Zufria Raissa Amanda Putri Try Widyawanti Wahyu Rahmansyah