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INDONESIA
Jurnal Komtekinfo
Published by Universitas Putra Indonesia YPTK Padang
ISSN : 23560010     EISSN : 25028758     DOI : DOI: 10.35134/komtekinfo.v9i2.1
Core Subject : Science,
Computer Science & IT
Software Engineering, Multimedia, Artificial intelligence, Data Mining, Knowledge Database System, Computer network, Information Systems, Robotic, Cloud Computing, Computer Technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 253 Documents
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Decision Support System in Determining TPQ/TQA Teacher Certification Categories Using the SAW Method Zikri, Afdal; Nurcahyo, Gunadi Widi; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.666

Abstract

TPQ/TQA teacher certification is an effort to improve the quality of educators in deepening their knowledge of the Qur'an. The certification assessment process often faces challenges related to subjectivity and inconsistencies in criteria, thus requiring a decision support system capable of producing more objective and measurable assessment results. Based on the problems described above, this study aims to analyze the TPQ/TQA teacher certification assessment in Padang City. The SAW method is very suitable for this study because of its ability to perform calculations based on predetermined criteria. The research data consists of 60 assessment documents. The analysis process includes determining criteria, normalizing weights, calculations, and rankings. Based on the 60 datasets, 9 individuals obtained a certification score of A, 11 obtained a B, and 40 obtained a C. The results of this study indicate that the decision support system is capable of providing highly accurate, transparent, and efficient results in determining TPQ/TQA teacher certification scores. These findings are expected to be useful for TPQ/TQA management institutions in determining certification scores.
Comparison of Random Forest and Support Vector Machine Learning Algorithms in Sentiment Analysis of Gojek User Reviews Sandiva, Tesa Vausia; Kristiyanto, Arip
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.669

Abstract

The development of digital technology has brought significant changes across various sectors of life, including transportation. One of the most popular modes of transportation among the public today is online motorcycle taxis, such as Gojek. Gojek continues to innovate to meet customer needs more effectively and to expand its range of services. This study aims to identify the number of positive, neutral, and negative sentiments in a user review dataset, as well as to evaluate the performance of the algorithms used—namely, SVM and Random Forest. The analysis was conducted on 10,000 customer reviews from the Play Store application, resulting in 2,057 positive sentiments, 1,135 neutral sentiments, and 6,295 negative sentiments. The classification model compared the SVM algorithm with the Random Forest algorithm, and the results show that Random Forest achieved better performance, with 91% accuracy compared to SVM’s 89%. These findings demonstrate that Random Forest performs better in handling word distribution within review texts than the SVM method.
Combination of Active Contour and CNN-based Segmentation Methods to Improve Accuracy in Detecting Rice Diseases Saptha Negoro, Wahyu; Adinda Destari, Ratih; Hendra Azhar, Asbon; Syahrian, Achmad
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.671

Abstract

Rice diseases are one of the main factors causing decreased productivity and threatening national food security. The main problem in controlling rice diseases is the delay and inaccuracy of symptom identification in the field. This study aims to develop an artificial intelligence-based rice disease detection system through a combination of Active Contour and Convolutional Neural Network (CNN) methods. The research object is rice leaf images taken from rice fields in Pulau Sejuk Village, Batubara Medan, with a dataset of 600 images consisting of healthy leaves and 3 types of rice diseases. The Active Contour method is used in the segmentation stage to extract leaf areas precisely, while CNN is applied for the disease classification process. The results show that this combination of methods can significantly improve the accuracy of rice disease detection. The developed system is expected to assist farmers and stakeholders in the early detection of rice diseases, thereby supporting food innovation and increasing sustainable agricultural productivity.

Page 26 of 26 | Total Record : 253

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