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INDONESIA
Jurnal Riset Informatika
Published by KresnaMedia Publisher
ISSN : 26561743     EISSN : 26561735     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik Informatika.
Arjuna Subject : -
Articles 432 Documents
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IMAGE SEGMENTATION OF YOGYAKARTA BATIK PATTERN USING SEGNET Nur Fahrudin, Irfan; Akbar, Mutaqin
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.491

Abstract

Batik is an Indonesian intangible cultural heritage with high artistic value. However, the complexity of classical Yogyakarta patterns, particularly Parang and Kawung, characterized by intricate structures, color variations, and indistinct boundaries, poses significant challenges for automated image processing. Therefore, image segmentation becomes a crucial step in batik identification and digitalization. This study aims to develop an efficient segmentation model for Yogyakarta batik patterns using a modified SegNet architecture. The dataset comprises 720 RGB images, consisting of 360 Parang pattern images and 360 Kawung pattern images. All images were processed into binary ground truth masks through a combination of K-Means Clustering and morphological operations. The SegNet architecture was modified into three encoder and decoder blocks, employing Conv2DTranspose for upsampling and a sigmoid activation function in the output layer. The model was trained for 50 epochs using the Adam optimizer and binary cross entropy loss function. Based on evaluation on the test dataset, the modified SegNet model achieved strong performance with an accuracy of 91.72%, a mean Intersection over Union of 77.23%, and a mean Dice Coefficient of 87.07%. Visual inspection of the prediction results further confirms the model’s ability to accurately separate pattern regions from the background. These findings demonstrate that the modified SegNet architecture performs well in segmenting Parang and Kawung batik patterns and shows strong potential for supporting future batik recognition and digitalization systems.
A Real-Time Web Information System Based on A Global Positioning System for Monitoring Environmental Pollution Eko Gustriyadi; Volvo Sihombing; Masrizal Masrizal; Puput Dani Prasetyo Adi
Jurnal Riset Informatika Vol 5 No 1 (2022): Priode of December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.463

Abstract

This research will discuss monitoring pollution in waterways in real time based on GPS. A website-based information system is an essential factor for information media, not only database-based but can be communicated with GPS. GPS is a satellite system that can determine the point of an area with Longitude and Latitude parameters. The Global Positioning System is one of the parameters used in this study. Longitude and Latitude are the primary keys to getting the point in a particular area or point. In research, this is the location used in sensor or environmental pollution monitoring. In this paper, we try to review the projects carried out and perform analysis, management, and governance on the server and local host. The program is made by developing the FrontEnd and BackEnd sides. Development can be done on Desktop-based programming and then extended to Mobile by manipulating and modifying programs using Javascript, JSON, and other building scripts for better performance and suitable for deployment on various platforms such as Mobile-based. This system is very efficient in determining various parameters, for example, the environmental pollution factor. From testing, the GPS data is not perfect, all data can be sent, but the accuracy of GPS data can reach 96%. This is due to data errors during uplinking and downlinking data.
Comparison of Breast Cancer Classification Using the Decision Tree ID3 Algorithm and K-Nearest Neighbors Algorithm Zyhan Faradilla Daldiri; Desti Fitriati
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.406

Abstract

One of the leading causes of death is cancer. The most common cancer in women is breast cancer. Breast cancer (Carcinoma mammae) is a malignant neoplasm originating from the parenchyma. Breast cancer ranks first in terms of the highest number of cancers in Indonesia and is among the first contributors to cancer deaths. Globocan data in 2020 shows that the number of new breast cancer cases reached 68,858 (16.6%) of the total 396,914 new cancer cases in Indonesia. Meanwhile, deaths reached more than 22 thousand cases (Romkom, 2022). This death rate is increasing due to insufficient information about breast cancer’s early symptoms and dangers. Of this lack of information, a system is needed that can provide information about breast cancer, such as early diagnosis. Several parameters and classification data mining techniques can predict which patients will develop breast cancer and which do not. In this study, a comparison of the classification of breast cancer using the Decision Tree ID3 algorithm and the K-Nearest Neighbors algorithm will be carried out. Attribute data consists of Menopause, Tumor-Size, Node-Caps, Deg-Malig, Breast-Squad,, and Irradiant. The main objective of this study is to improve classification performance in breast cancer diagnosis by applying feature selection to several classification algorithms. The Decision Tree ID3 algorithm has an accuracy rate of 93.333%, and the K-Nearest Neighbors algorithm has an accuracy rate of 76.6667%.
Evaluation of Machine Learning Using The K-NN Algorithm to determine The Quality of Meat before consumption Feronika Feronika; Masrizal Masrizal; Ibnu Rasyid Munthe
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.467

Abstract

Meat is one of the sources of animal protein for humans, and one of the requirements that must be met so that the human body does not lack protein, especially animal; this protein can be obtained from beef, chicken, and other meats, but the most important thing here is the content contained in meat, whether it has been contaminated with chemicals, e.g., chicken that has been injected with chemicals that cause the chicken to look fat, or beef whose flexibility has decreased and the pH is getting more acidic. This research tries to predict meat quality by looking at two parameters: flexibility and acidity. The programming language used is R Language, using the k-NN method or Algorithm to determine the meat's condition suitable for consumption. In detail, it will be processed in Machine Learning using the k-NN Algorithm; there are two criteria for consumption of meat, namely good or not good for consumption; in detail, the output will be explained using a specific graph using a plot function, and array data will be specifically classified to represent values. The value of 2 variables, namely feasible or not suitable for consumption.
Sentiment Analysis of Pedulilindungi Application Reviews Using Machine Learning and Deep Learning Ahmad Rais Dwijaya; Arif Dwi Laksito
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.505

Abstract

The COVID-19 pandemic that hit the world at the end of early 2020 caused many losses. The Indonesian government has established various ways to reduce the path of the COVID-19 pandemic by launching the PeduliLindungi application to reduce the spread of COVID-19. Various layers of society responded to the launch of the application with various opinions. This research mainly analyzes public opinion sentiment toward the PeduliLindungi application, as determined by 10,000 reviews on the Google Play Store. This study aims to compare the performance of deep learning and machine learning models in sentiment analysis. The stages of the research method begin with data collection methods, data pre-processing, and sentiment analysis using a machine learning model with the embedding of the word TF-IDF, which includes the Nave Bayes algorithm, Decision Tree, Random Forest, K-Nearest Neighbour, and SVM. As for the deep learning model with the fastText word embedding word representation technique using the LSTM algorithm, an evaluation is carried out using the confusion matrix. The results of this study state that deep learning models perform better than machine learning models.
Comparative Analysis of Using Word Embedding in Deep Learning for Text Classification Mukhamad Rizal Ilham; Arif Dwi Laksito
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.507

Abstract

A group of theory-driven computing techniques known as natural language processing (NLP) are used to interpret and represent human discourse automatically. From part-of-speech (POS) parsing and tagging to machine translation and dialogue systems, NLP enables computers to carry out various natural language-related activities at all levels. In this research, we compared word embedding techniques FastText and GloVe, which are used for text representation. This study aims to evaluate and compare the effectiveness of word embedding in text classification using LSTM (Long Short-Term Memory). The research stages start with dataset collection, pre-processing, word embedding, split data, and the last is deep learning techniques. According to the results of the experiments, when compared to the glove technique it seems that FastText is superior, the accuracy obtained reaches 90%. The number of epochs did not significantly improve the accuracy of the LSTM model with GloVe and FastText. It can be concluded that the FastText word embedding technique is superior to the GloVe technique.
SENTIMENT CLASSIFICATION OF PUBLIC PERCEPTIONS ON RP200 TRILLION HIMBARA STIMULUS USING NAÏVE BAYES Wan Sobri Amin; Fikry, Muhammad; Abdillah, Rahmad; Agustian, Surya
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.500

Abstract

The government's policy in the form of a fund stimulus of Rp200 trillion to the Himpunan Bank Milik Negara (HIMBARA) is a strategic step to maintain national economic stability and encourage real sector recovery. However, the implementation of public policy is inseparable from the response and public perception that develops on social media. This study aims to classify public sentiment towards the Rp200 trillion fund stimulus policy to Bank HIMBARA based on Instagram user comments and test the performance of the Naïve Bayes Classifier method in analyzing public policy sentiment. This study uses a quantitative approach with text mining and machine learning methods. Data in the form of 1.309 Instagram comments was collected through web scraping techniques from several online media accounts, then processed through text preprocessing and manual labeling stages into positive, neutral, and negative sentiments. Feature weighting was carried out using TF-IDF, then the data were classified using Multinomial Naïve Bayes and Complement Naïve Bayes. The results show that the Complement Naïve Bayes model achieved the best performance with an accuracy of 84%, an F1-score of 81%, and a high ROC-AUC value. These findings indicate that the majority of public sentiment toward the stimulus policy tends to be positive, and that the Naïve Bayes method is effective for social media–based sentiment analysis.
The Implementation of C4.5 Algorithm for Determining the Department of Vocational High School Mirza Sutrisno; Jefri Kusuma Rambe; Asruddin Asruddin; Ade Davy Wiranata
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.516

Abstract

The selection of departments in vocational high schools (SMK) is a must for students to determine the concentration of student learning interest for three years in a school. The lack of student knowledge and outreach about this department caused many students to choose their majors by the most choices and following other students. This problem can cause some difficulties for the students to participate in learning, and most fail. Students must select their major based on their interests, abilities, and talents because every student has different abilities and talents. The C4.5 algorithm can provide convenience in grouping students based on majors. Using the decision tree method with attributes such as grades in mathematics, English, interests, and talents, the system can recommend majors based on students' interest levels. The results of this study are the determination of the departments with the accuracy of the calculation using the confusion matrix method with a 98,55% accuracy rate and 100% recall rate value.
Mobile Based Student Presence System Using Haar Cascade and Eigenface Facial Recognition Methods Suherman Achmad; Nazori AZ; Achmad Solichin
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.340

Abstract

Using biometric technology for recording attendance in the school environment is still not widely done by researchers. In this study, a solution was proposed to the problems that occurred in the school environment where parents/guardians could not monitor the presence of their children in school. The solution offered is a student attendance recording system based on facial recognition algorithms (face recognition). The built system can record the presence of students when entering the classroom and when returning home or out of class. Proposed methods for identifying student attendance are the Haar Cascade and Eigenface algorithms. The system can also provide notice of attendance or absence of students in real time to parents/guardians via email that has been registered. The method can provide accurate and fast facial recognition results based on the test results. The presence system developed based on mobile can recognize faces up to a distance of 200-300 cm with low and moderate light intensity.
An Interactive Medium to Introduce Sasando Traditional Music Using Multimedia Development Life Cycle Method Salam Irianto Nadeak; Yusmar Ali; Djaka Suryadi
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.518

Abstract

ActionScript-based programming is one of the software which includes applications that teachers widely use to create interactive learning media in the world of education. ActionScript-based programming technology is a type of graphic animation software that can create a graphic object that can be animated without using other supporting software. At present, there are many educational circles to expedite the process of learning activities, especially for students. Interactive learning media is one means of delivering subject matter that is very important to apply today. In implementing student learning at school, it is necessary to present a practical and theoretical learning system which is the main point in helping to develop student competence. One form of culture in Indonesia is the traditional musical instrument Sasando. Sasando belongs to the chordophone instrument because it is played by picking it. The form of Sasando itself is in the form of a guitar, violin or harp. The central part of the Sasando is in the form of a long bamboo tube. In the middle, rounded from top to bottom, there is a wedge to stretch the strings. Developing interactive learning media requires a software development method; one of the development methods used is the MDLC (Multimedia Development Life Cycle) method. Making the MDLC has five stages: Concept, Design, Material Collecting, Assembly, and Testing

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