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Journal of Computing Innovations and Emerging Technologies
Published by Nova Mind Press
ISSN : -     EISSN : 31097111     DOI : 10.64472
Core Subject : Science,
Computer Science & IT
JCIET welcomes contributions that explore theoretical foundations, practical implementations, and innovative applications across a broad range of topics, including but not limited to: Artificial Intelligence and Machine Learning Data Science and Big Data Analytics Internet of Things (IoT) and Embedded Systems Cloud Computing and Edge Computing Cybersecurity and Cryptography Computer Vision and Image Processing Human-Computer Interaction Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality Software Engineering and System Development Web and Mobile Application Development Blockchain Technology and Decentralized Systems Natural Language Processing Robotics and Automation Educational Technology and E-Learning Platforms Smart Systems and Intelligent Environments JCIET is committed to supporting innovation, ethical research practice, and open science by ensuring a transparent and fair peer-review process. Articles published in JCIET are freely accessible to researchers worldwide.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 12 Documents
 1   2 
Decision Support System for Village Head Election Using the Weighted Product Method: Case Study in Lumar Village Noviyanti; Angelia Deli; Laura Gloria
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 1 (2025): Volume 1 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i1.1

Abstract

The election of the village head is an important process in determining leaders who can manage the village government effectively and meet the needs of the community. Election leader of village is very important to determine the direction of the region and the importance of the capabilities of the chosen leader based on real data. This study discusses the application of the Weighted Product (WP) method in the decision-making support system for the election of village heads in Lumar Village. The WP method is used because it is able to handle various criteria by giving weight to each criterion according to its level of importance. The criteria used include work experience, education, integrity, and community support. This system is designed to process data in a structured and transparent manner, generating a preference value for each prospective village head. The candidate with the highest score is considered the most qualified. The results of the study show that the WP method improves the accuracy, objectivity, and efficiency of the village head election process, resulting in accountable decisions.
Performance Evaluation of CLAHE-Enhanced Edge Detection on Low-Light Faces Duddy Arisandi; Ahshonat Khoerunnisa; Ruminto Subekti; Aan Eko Setiawan; Cepi Ramdani
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 1 (2025): Volume 1 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i1.2

Abstract

Edge detection is an important early stage in an image processing-based face detection system. However, the quality of edge detection is highly dependent on the lighting and contrast of the input image. A common problem is the low contrast quality of facial images, which causes edge detection results to be suboptimal, especially in low-light images. This study evaluates the effect of the use of the Contrast Limited Adaptive Histogram Equalization (CLAHE) method on edge detection performance using Canny operators. Two scenarios were tested: edge detection without preprocessing and edge detection after image processing with CLAHE. Evaluation was carried out using two metrics: the number of contours and the total area of the contours of the detected results. The test results showed that the use of CLAHE consistently increased the number of contours and stabilized the contour area distribution, indicating an increased sensitivity to facial edge details. Although an increase in the number of contours can increase the risk of noise detection, the results suggest that CLAHE is able to clarify facial structures that were previously uncaptured. CLAHE has proven to be effective as an image enhancement method in edge detection-based facial detection systems
Forecasting Monthly Sales Using Single Exponential Smoothing: An Evaluation and Performance Analysis Dyah Listianing Tyas; Layth Charifi
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 1 (2025): Volume 1 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i1.3

Abstract

Micro, Small, and Medium Enterprises (MSMEs) have a strategic role in Indonesia's economy, contributing more than 60% to the Gross Domestic Product (GDP) and absorbing the majority of the national workforce. One of the MSME sectors that is growing rapidly is the food and beverage industry, including home cake shops. However, many MSME actors have not utilized scientific methods in business decision-making, especially in sales forecasting. In fact, accurate sales predictions are very important in managing production, raw material procurement, and operational efficiency. This study examines the performance of the Single Exponential Smoothing (SES) method compared to Holt's Linear Trend in predicting cake shop sales over the past two years. Based on the evaluation using MAE, RMSE, and MAPE, the SES model showed higher accuracy, with a MAPE value of 2.82%, lower than Holt's which reached 3.73%. These results indicate that a simple model like SES is better suited for sales data that does not have strong trends. These findings confirm that the selection of prediction models should consider the characteristics of the data, not just the complexity of the algorithms used.
Analyzing the Effectiveness of Apriori and ECLAT Algorithms in Frequent Itemset Mining Dhina Puspasari Wijaya; Pipit Febriana Dewi; Nurul Mega Saraswati
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 1 (2025): Volume 1 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i1.4

Abstract

Consumer transaction data recorded through the point of sale (POS) system contains purchase patterns that can be used to support marketing strategies. In the context of convenience stores that have high transaction volumes and large product variations, product association analysis is important to uncover customers' shopping habits. This study compares the effectiveness of a priori and ECLAT algorithms in conducting frequent itemset mining on transaction data of daily necessities. Both algorithms were evaluated based on the number of rules generated, support value, confidence, lift, and execution time efficiency. The dataset used is Groceries, which represents actual transactions in the retail environment. The results showed that although a priori and ECLAT produced 25 rules. ECLAT was superior in execution times four times faster than Apriori without compromising the quality of the rules. Most rules have a confidence between 30–50% and a lift above 1.5, signifying a meaningful association. This study concludes that ECLAT is more suitable for use in complex and dynamic minimarket transaction data scenarios, and is recommended as the basis for the development of product recommendation systems and association-based bundling strategies.
Anomaly Detection of Road Ranking Shifts Due to Traffic Accidents Using Deep Learning on Time Series Data Adita Utami; Novi Trisman Hadi
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 1 (2025): Volume 1 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i1.5

Abstract

This study developed an anomaly detection model based on Long Short-Term Memory (LSTM) autoencoders to identify abnormal shifts in road ranking scores caused by traffic accidents in Magelang, Indonesia. Road rankings were derived from time-series data of traffic indicators collected between 2015 and 2020, including volume-to-capacity ratios, heavy vehicle proportions, and average speed. The model was trained on non-accident data to learn normal traffic behavior and subsequently detect deviations. Anomalies were identified when reconstruction errors exceeded statistically defined thresholds and were evaluated against verified accident records. The model achieved a precision of 82%, recall of 75%, and an AUC-ROC of 0.87, demonstrating strong performance in detecting significant disruptions, particularly severe accidents involving fatalities or serious injuries. Analysis showed that detected anomalies were concentrated on high-risk roads and during peak traffic hours. These findings highlight the potential of LSTM-based models for integration into intelligent transportation systems to support real-time accident detection and proactive traffic management in developing urban environments.
Implementation of IoT-Based Automatic Irrigation System Using Decision Tree Algorithm on Hydroponic Garden at Institut Shanti Bhuana Bengkayang Kristian Novando; Noviyanti P
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 2 (2025): Volume 1 No 2
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i2.6

Abstract

This study presents the development and implementation of an automatic irrigation system based on the Internet of Things (IoT) utilizing the Decision Tree algorithm. The system was applied in a hydroponic garden at Institut Shanti Bhuana Bengkayang. It employs a water level sensor to detect the volume of water, which is then processed using the Decision Tree classification to determine whether the irrigation valve should be opened or closed. Data collected from the sensor were analyzed both manually and programmatically to find the optimal threshold for decision-making. The system was integrated with the Blynk platform, allowing real-time monitoring and control. Testing was conducted over 7 days with 210 data points, and the classification model achieved an accuracy of 100%. The results indicate that the proposed system effectively automates irrigation, minimizes manual intervention, and provides a reliable solution for small-scale smart farming applications.
How Code Smell and Refactoring Affect the Software Product Line Maintainability Maryam Mehmood; Asad Ijaz
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 2 (2025): Volume 1 No 2
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i2.10

Abstract

Code cloning remains a significant challenge in modern software development, particularly within the Object-Oriented paradigm and advanced methodologies such as the Software Product Line (SPL) approach. In this context, code smells and refactoring can be seen as two sides of the same coin—one representing the symptoms of poor design, and the other offering systematic strategies for improvement. Among the various software quality attributes, maintainability stands out as a critical factor in determining the long-term success of SPL-based systems. However, the presence of cloned code directly impacts this maintainability, making the detection and mitigation of such clones an essential concern. Although multiple quality models exist to assess the relationship between code cloning, refactoring, and maintainability, most lack the granularity to accurately capture the specific effects of code cloning within SPL environments. This research undertakes a systematic literature review to consolidate and analyze findings from existing surveys, with a particular focus on identifying software metrics capable of evaluating the impact of refactoring on SPL maintainability. Refactoring serves as a deliberate means to eliminate code smells, and numerous tools and techniques have been developed to support this process. By synthesizing the current body of knowledge, this study provides a foundation for researchers and practitioners to better understand, select, and apply effective practices and tools to reduce code smells, improve maintainability, and ultimately enhance the overall quality of SPL-based software systems
Web-Based Decision Support System for Major Selection Using the SAW Method at Efata Ombarade Vocational School Ina Tena Bolo; Friden Elefri Neno; Emerensiana Dappa Ege
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 2 (2025): Volume 1 No 2
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i2.12

Abstract

Efata Ombarade Vocational High School is located in West Sumba Regency, East Wewewa District, East Nusa Tenggara Province. It is a vocational high school offering two majors: tourism and hospitality services business. Every new school year, this school routinely accepts new students, where each applicant chooses a major according to their preferences, which may not be in line with their abilities. To improve the quality of the school and its students, each new student admission involves a selection process based on criteria set by the school, such as National Examination Scores, Report Card Scores, written tests, interviews, and health checks. The current student registration and selection process has several weaknesses, including the time-consuming process of entering data into Microsoft Excel and the delay in obtaining results due to the lack of a specific application to support the calculations. In view of these issues, a system is needed to assist in the process of making faster, more accurate, and more objective decisions regarding student majors. One solution offered is the implementation of a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method. This method works by assigning weights to each criterion used in the assessment, then calculating preference scores to determine the best alternative. The data used includes students' academic scores, particularly their National Examination results, as well as data on their interests. The use of the SAW method in the major selection decision support system is expected to reduce unfairness in assessment, as small differences in scores can be processed proportionally. With this system in place, schools can more easily determine the majors that suit students' abilities and interests. Additionally, this system can also speed up the major selection process, reduce the potential for manual errors, and provide more accurate and fair recommendations for each student.
Geographic Information System for Mapping Solar Power Plants in Lolo Wano Village Haryance Umbu Dasa; Friden Elefri Neno; Alexander Adis
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 2 (2025): Volume 1 No 2
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i2.13

Abstract

The use of renewable energy, particularly solar power plants (SPPs), is one of the strategic solutions to overcome limited access to electricity in rural areas. Lolo Wano Village, as one of the villages with high solar radiation intensity, has great potential for SPP development. However, the planning of SPP construction is often hampered by a lack of integrated spatial data related to residential locations, public facilities, and land availability. This study aims to design a Geographic Information System (GIS) capable of mapping the potential and determining strategic coordinates for SPP construction in Lolo Wano Village. The research method was conducted by collecting primary data in the form of GPS coordinates of residents' houses, schools, village offices, and vacant land with potential for use. Secondary data included administrative maps, topographic maps, and solar radiation data from BMKG and global sources. The data was processed using QGIS/ArcGIS software through the stages of map digitization, spatial overlay, and land suitability analysis based on criteria of solar radiation, accessibility, land area, and proximity to residential areas. The results of the study show that the use of GIS can produce digital maps of the distribution of existing solar power plant locations as well as recommendations for new locations suitable for development. This system not only assists village governments in making decisions on renewable energy development, but also supports equitable access to electricity for the community. Thus, the application of GIS in mapping solar power plants in Lolo Wano Village plays an important role in supporting sustainable development and improving the quality of life of the local community.
Analysis of A Decision Support System for the Selection of the Best Coffee Supplier at Arion Coffee Using AHP Diva Anjeliansyah Putri; Wahit Desta Prastowo
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 2 (2025): Volume 1 No 2
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i2.18

Abstract

This study aims to determine the best coffee supplier for Arion Coffee using the Analytical Hierarchy Process (AHP) as a structured multi-criteria decision-making approach. The evaluation involves four key criteria price, quality, delivery timeliness, and service with three supplier alternatives: Kopi Nusantara (SUP A), Java Beans Supply (SUP B), and Tropical Roast Indonesia (SUP C). Data were collected through interviews and expert evaluations from Arion Coffee’s procurement team. The AHP method was used to assign weights to each criterion, conduct pairwise comparisons, and calculate consistency ratios. Results indicate that quality holds the highest importance (0.623), followed by price (0.216), delivery timeliness (0.106), and service (0.055). Based on the overall synthesis, Tropical Roast Indonesia (SUP C) achieved the highest score (0.441), followed by Java Beans Supply (0.337) and Kopi Nusantara (0.214). The findings highlight that coffee bean quality is the dominant factor influencing supplier selection, reflecting Arion Coffee’s priority on maintaining product excellence over cost efficiency. The application of AHP provides a rational and objective framework for supplier evaluation, improving decision accuracy and reducing subjectivity.

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