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Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika
Published by Universitas Muhammadiyah Surakarta
ISSN : 2621038X     EISSN : 2477698X     DOI : -
Core Subject : Science,
Computer Science & IT
Khazanah Informatika: Jurnal Ilmiah 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 : -
Articles 10 Documents
 1 
Search results for , issue "Vol. 9 No. 2 October 2023" : 10 Documents clear
Medical External Wound Image Classification Using Support Vector Machine Technique Syifa'ah Setya Mawarni; Murinto Murinto; Sunardi Sunardi
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.22541

Abstract

Diagnosis is an activity that refers to the examination of something. Diagnosis is often associated with medical activities as a determinant of a person's condition, in the health sector diagnosis means a procedure performed by a doctor to determine a patient's condition. Unfortunately, it is rare to diagnose disease using an object wound, whereas if the wound is not treated immediately it can lead to more serious illnesses such as ulcers and tetanus or in some cases it can cause infection which then becomes a complication, in the worst case amputation occurs. The skin protects the body from various threats, the skin is also the first fortress for the body. Before implementing a prototype external wound diagnosis, it is necessary to test the accuracy of the algorithm to be used. The algorithm that can be used for diagnosis or classification is the Support Vector Machine or SVM which in the process goes through 3 stages, namely data collection, preprocessing, and classification. This research obtained the results of feature extraction on the wound image test data using GLCM with a contrast value of 0.0082, a correlation value of 0.9769, an energy value of 0.6391, and a homogeneity value of 0.9959 as well as the accuracy of using the SVM algorithm which was measured using a confusion matrix to get an accuracy value of 96.39%, 93.06% precision, recall 92.85%, and F1-score 92.58%. The results of the accuracy of the classification of external wound images using the SVM algorithm are 92.85%.
Contact Lens Detection Using Domain Specific BSIF and Discrete Wavelet Transform Muhamad Ilham Aji Vachroni; Raden Sumiharto; Dyah Aruming Tyas
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.20084

Abstract

Iris is one of the reliable biometrics because it has a texture that rich properties and the texture is not changeable lifetime. Iris recognition has drawbacks in the matching process when using contact lenses. Contact lens can changes in the texture of the iris, which can reduce the accuracy of recognition. Therefore, a system is needed to detect contact lenses while someone is detected using contact lens, the system can reject the registration or authentication process. Methods used to detect contact lenses are Domain Specific Binarized Statistical Image Feature (BSIF) and Discrete Wavelet Transform (DWT) for feature extraction. Both methods are fused and modeled using the Support Vector Machine (SVM). Based on the test results, the most optimal kernel is 5x5 12bit. Using the kernel, the accuracy and f1 score obtained 99.1%. In the experiments conducted, this research applies Principal Component Analysis (PCA) to reduce features. However, the role of PCA does not affect the performance of the model. The best model tested with real life data, the Pocophone f1 smartphone and CCTV were used to take pictures of the eyes. The Result 6 experiments wich are 4 without contact lenses and 2 wearing contact lenses, there are only 2 detected correctly. This is because the ability of the images taken from the Poco F1 and CCTV have low resolution.
Automatic Language Identification for Indonesian-Malaysian Language Using Machine Learning Abdiansah Abdiansah; Muhammad Qurhanul Rizqie
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.21669

Abstract

Language Identification (LID) aims to guess or identify which language the text or sound is coming from. Language identification tends to be easier in languages with different characteristics (e.g., Indonesian and English), but not for languages with similar characteristics (e.g., Indonesian and Malaysian). Similar languages can cause ambiguity that will be a bias for machine learning. Using Support Vector Machine (SVM) technique, this research tried to identify the Indonesian or Malaysian language. The training and testing data are taken from Leipzig Corpora Collection and Twitter dataset. The feature representation technique uses TF-IDF, and the baseline testing uses Naive Bayes Multinomial. We used two training techniques: split (20:80) and 10-cross validation. The experimental results show that the accuracy between the baseline and SVM is not too far. Both provide accuracy of around 90% and above. The results indicate that Indonesian and Malaysian language identification accuracy is relatively high even though using simple techniques.
The Advantage of Transfer Learning with Pre-Trained Model in CNN Towards Ct-Scan Classification Jasman Pardede; Adwityo S. Purohita
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.19872

Abstract

Medical image classification plays significant role in the process of medical decisions making, especially during the difficult period of the pandemic. One method being considered good at such classification is Convolutional Neural Network, in which we use pre-trained model approach with transfer learning since the limitation of medical images may require optimal effort. Through this pre-trained model with transfer learning, the objective is to maximize the accuracy of classification and to push forward the training session throughout the comparison of both transfer learning and non-transfer learning based pre-train models. The first type provides average accuracy of 0.84 with approximate training time 0.54 hour while the latter shows the average result of accuracy as 0.74 with average training time 0.58 hour. As the result, the optimizations are 1.13x for accuracy and 1.1x for training time. EfficientNetV2 is one pre-trained model selected for this project, being exposed to both transfer learning and non-transfer learning approach systems. The transfer learning version provides the superior accuracy as 0.88 and training time as 31 minutes - 50 seconds, showing the accuracy of 0.94 on validation and 0.88 on testing
Rice Seedling Image Classification Using Light Convolutional Neural Network Indra Hermawan; Maria Agustin; Defiana Arnaldy
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.20401

Abstract

The need for food, especially rice, continues to increase. Therefore, a production increase of around 70% is required to meet the demand. In this case, supervision and care of rice from planting must be carried out efficiently, which can be done by deep learning, namely Convolutional Neural Networks (CNN). The classification was carriedout on the image of rice seedlings in the form of rice seedlings and bare land patch images. The main purpose of this research is to conduct a comparison test of the performance of each CNN model with a lightweight architecture and validate the architecture. A lightweight CNN architecture is used due to its lower architecture size but still has decent performance compared to the regular CNN model for the rice seedlings dataset. Training and testing were carried out on the Rice Seedling Dataset to determine the performance of the proposed method. The research was built using the PyTorch library and the Python programming language and resulted in 99% of accuracy, precision, recall, kappa, and F-1 Score. In addition, validation was carried out using K-Fold Cross Validation which also had the best accuracy of 99%. Therefore, we conclude that the developed model can properly classify images of rice seedlings and arable land.
Classification of Colon Cancer Based on Hispathological Images using Adaptive Neuro Fuzzy Inference System (ANFIS) Nur Hidayah; Alvin Nuralif Ramadanti; Dian Candra Rini Novitasari
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.17611

Abstract

Cancer is a disease that is widely known and suffered by people in various countries. One type of cancer classified as the third contributor to death is colon cancer, with a mortality rate of 9.4%. Colon cancer is cancer that attacks the large intestine or rectum. Classification of colon cancer promptly is necessary to carry out appropriate treatment to reduce the death rate from colon cancer. This study uses the ANFIS method to classify colon cancer with its texture analysis using GLRLM. In addition, the evaluation model used in this study is the K-fold cross-validation method. This research uses colon cancer histopathological image data, which is 10000 image data divided into 2 classes, namely 5000 benign class and 5000 adenocarcinoma class. The best result in this study is when using k = 5 at an orientation angle of 135°, the accuracy value is 85.57%, sensitivity is 91.72%, and specificity is 80.55%.
Role of Finite State Automata in Transliterating Latin Script into Javanese Script Suprihatin Suprihatin; Imam Riadi; Furizal Furizal; Izzan Julda D.E Purwadi Putra
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.22303

Abstract

Writing Javanese script is considered complicated and difficult for people who learn it. The process of transliterating Latin into Javanese script cannot be done directly, because each alphabet is not always represented by one Javanese script. Javanese script is not represented by one or more Latin letters, so if transliteration of Latin letters to Javanese letters is required, a parsing process is required. The rows of Javanese letters form a ligature with certain rules, so parsing is also needed to arrange the rows of Javanese letters correctly. This study aims to design a program to facilitate the transliteration of Latin script to Javanese script.  Finite State Automata (FSA) is used to describe writing rules. This study is limited to lowercase letters only, capital letters will be subtracted first, number symbols are not discussed in this study. The results of the study are in the form of a program design that can transliterate Latin writing into Javanese. Experiments were carried out as many as 4 structures of vowel consonant variations. All syllabic structures that include KV, KKV, KVK, KKVK have been tried. The transliteration results show conformity with a 100% accuracy rate in accordance with the rules of writing Javanese script. This research shows that the application of FSA can handle the transliteration of Latin letters into Javanese.
Analysis of Community Satisfaction with the Service Systems in Civil Registry Service Office, South Buru Regency using the TAM (Technology Acceptance Model) Method Juneth Manuputty; Irwan Sembiring; Kristoko D Hartomo
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.22595

Abstract

A good service system will satisfy the community, making that community’s contentment the deciding factor or the key factor in determining how successful an organization is in providing the service. This research aims to analyze the service system that has been available in the Department of Population and Civil Registration of Buru South district through the public satisfaction survey as well as to understand the services system that should be improved to minimize public dissatisfaction with the procedures provided by using the machine learning model, namely Random Forest Classifier technique to obtain a prediction of the satisfaction of the public with the services provided and perform validity testing on the prediction results obtained from the Random forest classifier technique using the Technology Acceptance Model. (TAM). The results of the trials carried out there are 3 determining factors to be able to increase public dissatisfaction namely the complaint service, the service process and the behavior of the officer supported by the validity test results using TAM with the results showing that the 3 services are valid means to be a factor that can be used to increase the public satisfaction with the result obtained from the T-computed value greater than T-table with the value for the Complaint Service 4.4794, service process 2.1345 and the Officer Behavior 1.9675 of the value of the table 1.6517.
Combination of Graph-based Approach and Sequential Pattern Mining for Extractive Text Summarization with Indonesian Language Dian Sa'adillah Maylawati; Yogan Jaya Kumar; Fauziah Binti Kasmin
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.21495

Abstract

The great challenge in Indonesian automatic text summarization research is producing readable summaries. The quality of text summary can be reached if the meaning of the text can be maintained properly. As a result, the purpose of this study is to improve the quality of extractive Indonesian automatic text summarization by taking into account the quality of structured text representation. This study employs Sequential Pattern Mining (SPM) to generate a sequence of words as a structured representation of text and a graph-based approach to generate automatic text summarization. The SPM algorithm used is PrefixSpan, and the graph-based approach uses the Bellman-Ford algorithm. The results of an experiment using the IndoSum dataset show that combining SPM and Bellman-Ford can improve the precision, recall, and f-measure of ROUGE-1, ROUGE-2, and ROUGE-L. When Bellman-Ford is combined with SPM, the F-measure of ROUGE-1 increases from 0.2299 to 0.3342. The ROUGE-2 f-measure increases from 0.1342 to 0.2191, and the ROUGE-L f-measure increases from 0.1904 to 0.2878. This result demonstrates that SPM can improve the performance of the Bellman-Ford algorithm in producing Indonesian text summaries.
Identifying Hate Speech in Tweets with Sentiment Analysis on Indonesian Twitter Utilizing Support Vector Machine Algorithm Imam Riadi; Abdul Fadlil; Murni Murni
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.22470

Abstract

Twitter had 24 million users in Indonesia at the beginning of 2023. Despite having fewer users than other platforms, its fast and instant nature makes Twitter a significant source of information dissemination. Tweets shared on Twitter offer various advantages. However, it also has negative consequences, including the dissemination of fake news, instances of cyberbullying, and the expression of hate speech. Specifically, hate speech employs offensive language to discriminate against an individual or group based on race, ethnicity, nationality, religion, gender, sexual orientation, or other personal attributes, leading to discord. Such behavior comes under the jurisdiction of various legal statutes, including the Constitution, the Criminal Code, and the ITE Law. The primary objective of this research is to categorize tweets shared on Twitter into hate speech and non-hate speech sentiments, utilizing a Support Vector Machine (SVM) algorithm based on a dataset of 5,000 tweets. This research involved data preprocessing, labeling, feature extraction using TF-IDF, model training (80%), and testing (20%). The final stage includes enhancing SVM parameters through GridSearch and cross-validation methods (GridSearchCV), followed by analysis using a Confusion Matrix with the Matplotlib Library. Radial Basis Function (RBF) kernels, defined by parameters C=10 and gamma=0.1, exhibited the highest performance among SVM models, boasting an 84% accuracy. The RBF kernel also attained 85% precision, 97% recall, and a 91% F1-score for hate speech identification. In conclusion, the evaluation of SVM kernel performance highlights the superiority of RBF kernels in achieving the highest accuracy, complemented by nuanced insights into hate speech precision, recall, and F1-score values across various kernel types.

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