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Makara Journal of Technology
Published by Universitas Indonesia
ISSN : 23552786     EISSN : 23564539     DOI : https://doi.org/10.7454/mjt
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering
MAKARA Journal of Technology is a peer-reviewed multidisciplinary journal committed to the advancement of scholarly knowledge and research findings of the several branches of Engineering and Technology. The Journal publishes new results, original articles, reviews, and research notes whose content and approach are of interest to a wide range of scholars. It also offers rapid dissemination. MAKARA Journal of Technology covers the recent research in several branches of engineering and technology include Electrical & Electronics Engineering, Computer Engineering, Mechanical Engineering, Chemical & Bioprocess Engineering, Material & Metallurgical Engineering, Industrial Engineering, Civil & Architecture Engineering, and Marine Engineering. Criteria used in determining acceptability of contributions include newsworthiness to a substantial part of the engineering & technology and the effect of rapid publication on the research of others. This journal, published three times each year, is where readers look for the advancement of discoveries in engineering and technology.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 29, No. 1" : 5 Documents clear
Mobile Robots and Autonomous Vehicle Control: A Comprehensive Review of the Advancements and Challenges Tamakloe, Elvis; Kommey, Benjamin; Addo, Ernest Ofosu; Opoku, Daniel
Makara Journal of Technology Vol. 29, No. 1
Publisher : UI Scholars Hub

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Abstract

Since their inception, mobile robots have enormously changed the landscape of robotics engineering in recent years. Imperatively, the impact of mobile robots has positively transformed many sectors of human endeavors, i.e., complemented and substituted humans in areas where human interactions were difficult, hazardous, and impossible to thrive and operate. In this regard, the contributions of mobile robots to scientific, social, and economic growth, development, and advancement cannot be overlooked, especially through its decades of transition from Industry 3.0 to 4.0 over the years. To achieve maximum benefits from the use of mobile robots across all important facets, their advancements and technologies need to be continuously improved to address all relevant issues with regard to associated challenges in navigation, control, remote sensing, and tele-operability. This paper presents a comprehensive review of selected key areas of mobile robot technology where major advancements have been made and are currently ongoing to solve numerous problems effectively with less human effort. In addition, highlights of the challenges faced by mobile robots and autonomous vehicle control have been extensively discussed and recommendations have been given to enhance the efficient and safe use of mobile robots in the event of a change in task complexity in all essentials of human life.
Effect of the CaO Catalyst Concentration Based on Barnacle Shells (Tetraclita squamosa) on Biodiesel Production from Coconut Oil Rakasiwi, Rinjani; Rezeki, Sri; Hafiza, Nurul; Ivontianti, Wivina Diah
Makara Journal of Technology Vol. 29, No. 1
Publisher : UI Scholars Hub

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Abstract

Barnacle shells are a source of the calcium oxide (CaO) catalyst, which is nontoxic, highly alkaline, and biodegradable. This study aims to determine the characteristics and the effect of the CaO catalyst concentration obtained from the calcination of barnacle shell particles at a temperature of 900 ℃ for 2 and 4 h on the transesterification reaction of biodiesel from coconut oil. Transesterification was conducted at 65℃ for 2 h with a methanol/coconut oil molar ratio of 6:1. The concentration of the CaO catalyst was varied (i.e., 1wt%, 3wt%, 5wt%, 7wt%, and 9wt%) for coconut oil. The results of the characterization of the catalyst through the X-ray diffraction test showed that the best calcination time was 4 h. The characteristic peaks of CaO appear at diffraction angles (2θ) of 31.4°, 36.0°, 54.3°, and 64.7°. The optimum catalyst concentration was 5wt%, where the highest yield (92.17%) was obtained with the following biodiesel characteristics: density at 40 ℃ of 863 kg/m3 , kinematic viscosity at 40 ℃ of 3.03 cSt, water content of 0.01%, and acid number of 0.26 mg KOH/g. The results of the gas chromatography–mass spectrometry analysis based on the optimum catalyst concentration showed that biodiesel was composed of methyl ester compounds, which were dominated by methyl laurate (54.52%) and methyl myristate (19.37%).
Effect of Audio–Sonic Waves on Heat Transfer Enhancement in a Distilled Water-Based Heat Exchanger Tetuko, Agggito Pringgo; Sari, Ayu Yuswati; Simbolon, Silviana; Sebayang, Achmad M.S.; Effendi, Nur H; Ernando, Riko; Fachredzy, Amdy; Asri, Nining S; Setiadi, Eko A; Sebayang, Perdamean
Makara Journal of Technology Vol. 29, No. 1
Publisher : UI Scholars Hub

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Abstract

In this research, the influence of audio–sonic waves on distilled water used as the working fluid in a shell-and-tube heat exchanger was examined. The audio–sonic wave frequencies and flow rates of the cold and hot liquids were varied as follows: 4.85, 6.78, and 13.43 kHz for the audio–sonic waves; 0.3, 0.4, and 0.5 l/min for the cold liquid; and 0.76, 1.0, and 1.5 l/min for the hot liquid. An increase in the audio–sonic wave frequency enhanced both the overall heat transfer coefficient (U) and the enhancement factor (EF). The optimum values of U and EF, measured at 300 W/m3 ·°C and 1.05, respectively, were achieved at the highest frequency of 13.43 kHz. Additionally, the flow rates of the hot and cold liquids in the heat exchanger significantly influenced the optimal values of U and EF.
Designing and Implementing a Classification Model for Mangoes Based on Size and Ripeness using Image Processing Vo, An van; Mai, Hau van; Ha, Hung Duy; bui, tam thanh
Makara Journal of Technology Vol. 29, No. 1
Publisher : UI Scholars Hub

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Abstract

Image processing is an advanced technology that significantly supports production, identification, and quality control for fruits. This paper uses image processing techniques to develop a mango classification system based on size and ripeness. The system integrates hardware, including an Arduino microcontroller, camera, sensors, actuators, and a user-friendly computer interface for monitoring and control. The classification algorithm extracts key features of the mangoes, such as their color and shape, to categorize them into predefined quality classes. Experimental results demonstrate that the system achieves an accuracy exceeding 90% for both ripeness and size classification, with a productivity level of 300 kg/hour, surpassing the initial target of 250 kg/hour. Furthermore, the system operates reliably under varying lighting conditions, ensuring flexibility and continuous productivity. These advancements highlight the system’s potential to enhance efficiency and quality in fruit processing industries.
A Comprehensive Analysis of Recognition of Hand Gestures using Machine Learning Shivani, Shivani; Gupta, Satinder Bal
Makara Journal of Technology Vol. 29, No. 1
Publisher : UI Scholars Hub

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Abstract

Hand gestures are a natural means of conveying information and thus, there is an increasing interest in utilizing gestures for communication with computers. This study focuses on systematically reviewing different machine learning algorithms while assessing their working mechanisms and accuracy. Articles were analyzed for comparing the performance of K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). In accordance with input data, intricacy of gestures, processing resources, and real-time demands, the study shows that each technique has distinct advantages and disadvantages. RNN showed the best accuracy of 99.28% in recognizing dynamic gestures, indicating that it can be employed in applications that need high accuracy. CNN also performed well in recognizing static gestures and provide an accuracy of 93.61% accuracy. In order to improve human-machine interaction through efficient hand gesture detection, this systematic and comprehensive analysis offers some insight into the trade-offs between choice of algorithm and performance.

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