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Program Studi Teknik Informatika, Fakultas Komunikasi dan Informatika, Universitas Muhammadiyah Surakarta Gedung J Lantai 1 Sayap Barat Jl. A. Yani No 1, Pabelan 57162, Surakarta Indonesia
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Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Published by Universitas Muhammadiyah Surakarta
ISSN : 2621038X     EISSN : 2477698X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika, an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 23 Documents
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Using SVM and KNN for Predicting Customer Response Sentiment of M-PAJAK Application Muhammad Titan Rama Adi Wijaya; Ida Widaningrum; Angga Prasetyo; Dyah Mustikasari
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.4528

Abstract

M-Pajak, an application initiated by the Directorate General of Taxes, signifies the modernization of taxation and serves a crucial function. This application facilitates taxpayers in meeting their tax obligations. User satisfaction with this application may be assessed via reviews on the Google Play Store. While this application fulfills client satisfaction, its sustained success is significantly contingent upon user contentment and experience. Sentiment analysis is essential for elucidating user evaluations and interactions with the program. This research analyses the sentiment of M-Pajak application reviews on Google Play using Support Vector Machine (SVM) and K-Nearest Neighbour (KNN), supported by the Term Frequency-inverse Document Frequency (TF-IDF) feature extraction method. A total of 1000 reviews between December 11, 2022 and December 2, 2023 were processed using KNN and SVM. The KNN algorithm yielded 153 positive predictions and 847 negative predictions and achieved 94% of accuracy. Meanwhile, SVM achieved an accuracy of 88.10%, with 325 positive predictions and 675 negative predictions. The results demonstrate the superiority of KNN in sentiment classification of M-Pajak reviews. This study also indicates that negative comments outnumber positive ones in this application. This serves as a signal for the Directorate General of Taxation to enhance user satisfaction with the M-Pajak application through continuous updates.
Region Enhanced Edge-Based Multi-Class Object Proposal for Self-Driving Vehicles Haq, Muhamad Amirul; Huy, Le Nam Quoc; Ridlwan, Muhammad
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.4662

Abstract

On-road object detection is a fundamental element for the safety and reliability of autonomous driving systems. A primary challenge is developing object detection algorithms that are both fast and robust. This paper introduces a novel object proposal algorithm, named Region Enhanced Edge-Based (REEB) proposal, designed to accelerate object detection by significantly reducing the number of candidate regions requiring evaluation by a subsequent classification network. REEB leverages edge-map cues to score and rank initial proposals. To further enhance both detection quality and processing speed, the algorithm integrates efficient complementary techniques: image entropy is used to guide proposal generation density in relevant image regions, and road segmentation aids in refining proposal scores by differentiating road from non-road areas. Experimental evaluations on the KITTI dataset demonstrate that REEB achieves an average recall rate of 72.1% across four classes (pedestrian, cyclist, car, and truck) with an average processing time of 15 milliseconds per image. These results indicate strong performance when compared to other traditional, non-deep learning object proposal algorithms.
Performance Comparison of Random Forest, Bagging, and CART Methods in Classifying Recipients of the Family Program in North Aceh Hari Yanni, Meri; Anwar Notodiputro, Khairil; Sartono, Bagus
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.5098

Abstract

Machine learning is a method in data mining, it is used to study large data patterns through classification methods including Random Forest, Bagging, and CART. The Random Forest method develops the Bagging technique and Decision Tree components (CART) in decision-making. The difference between RF and Bagging is the selection of random features in forming a decision tree. It is only found in RF. Bagging can improve performance, model stability, and reduce variance by forming many different models. The research aims to see the performance of the Random Forest, Bagging, and CART methods in classifying family recipient programs in North Aceh. The results show that the performance of the RF, Bagging, and CART classification methods using the SMOTE technique for handling unbalanced classes is better than before handling unbalanced data. The classification method is evaluated through each model's accuracy, sensitivity, specificity, precision, F1 score, and AUC values. The results show good performance with accuracy values of 90% Smote-RF and 86% Smote Bagging. The best performance was seen in the Smote-RF model which was obtained by tuning the Grid Search CV model parameters with k = 5 and repeat = 1 for a data set proportion of 90:10. This shows that the model can correctly predict all observations with an accuracy percentage of 90% with an average AUC value of 93.52%. On the other hand, the CART method has a very low accuracy value, so the model is less able to accurately predict all observations. Measurement of the level of importance of predictor variables that have the greatest influence in predicting recipient households is the floor area of the house, the number of household members aged 10 years and over, and the type of work of the head of the household.

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