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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 10 Documents
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Search results for , issue "Vol. 5 No. 2 (2023): March 2023" : 10 Documents clear
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): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1375.967 KB) | DOI: 10.34288/jri.v5i2.205

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.
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): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1298.196 KB)

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 experiments' results, it seems that FastText is superior compared to the glove technique. 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. Keywords: Word Embedding; ; ;
Comparison of Breast Cancer Classification Using Decision Tree ID3 and K-Nearest Neighbors Algorithm to Predict the Best Performance of Algorithm Zyhan Faradilla Daldiri; Desti Fitriati
Jurnal Riset Informatika Vol. 5 No. 2 (2023): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.206

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%.
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): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.207

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.
Implementation of PDDIKTI Neo Feeder Web Service in Recording of Independent Campus Activities Herlambang Brawijaya; Slamet Widodo; Samudi Samudi
Jurnal Riset Informatika Vol. 5 No. 2 (2023): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.210

Abstract

Independent Learning-Independent Campus Program (MBKM) is a policy of granting the right for students to be able to take study activities outside the study program as many as three semesters with the division of two semesters of study outside the college and one semester in different study programs in one college. As well as teaching and learning activities, universities must report Independent Learning-Independent Campus activities to DIKTI every semester through the Neo Feeder PDDIKTI application. The Neo Feeder PDDIKTI application has a feature to enter the activities the operator will carry out. The operator enters this data individually on the Neo Feeder PDDIKTI application. This is a significant problem because the data entry process will take quite a lot of time, even though PDDIKTI has provided web service access to universities to optimize the data reporting process using the Neo Feeder PDDIKTI application. Building an application that can be used for recording MBKM activities by utilizing web services provided by PDDIKTI is the primary purpose of this study. The development of an application certainly requires a method as a framework or guide in facilitating the manufacturing process. The extreme Programming (XP) method becomes essential for applications with variable or non-fixed needs. This method has four working elements: planning, designing, coding, testing and software increment. The output generated by this study is an application for recording MBKM activities that use web service restful API technology so that data entry can be done en masse and not one by one.
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): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.211

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): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.213

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. Based on the test results, the method can provide accurate and fast facial recognition 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): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.214

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.
Clustering the Impacts of The Russia-Ukraine War on Personnel and Equipment Wargijono Utomo
Jurnal Riset Informatika Vol. 5 No. 2 (2023): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.215

Abstract

In post-pandemic recovery efforts, uncertainty arose due to the unresolved conflict between the Russia-Ukraine war. This conflict impacts world security stability and affects the economic, energy, and food sectors. This conflict also impacts humanity by causing death to civilians and military personnel, including children in Ukraine. The clustering analysis results of the impact of the Russian-Ukrainian war show losses and losses in personnel and war equipment, with three cluster optimization methods used through k-means. Of the two methods that can be recommended, namely elbow and Silhouette, both produce K=3. The profiling results show that losses or losses in Ukrainian personnel and war equipment are categorized into three clusters, with cluster one being the lowest category, cluster two being the very high category, and cluster three being the moderate category. This research is helpful for state agencies, international organizations (NGOs), and other stakeholders.
Decision Support System for Selection of The Best Fuel for Households Using the Weighted Product (WP) Method Yasmin Khairunnisa; Dewi Primasari; Nurul Kamilah
Jurnal Riset Informatika Vol. 5 No. 2 (2023): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.216

Abstract

Fuel is one of the most critical needs for the community in carrying out various household activities dominated by fuel oil. Meanwhile, the availability of fuel is increasingly running low. Most people in the Botumoito area in Gorontalo province work in the agricultural sector with low local income. Availability of fuels such as kerosene and gas there is quite challenging because there is no source or supply of gas either directly from the gas field or terminal. The government needs to make the right policy on fuel selection according to the problems in the region. This research aims to design and build a decision support system for selecting the best fuel and implementing the Weighted Product (WP) method into the system. The method used in designing and manufacturing this system is the waterfall method. This research results in a decision support system that can help fuel ranking. The ranking results are based on the magnitude of the Vector (V) value obtained from testing ten alternative fuels. Bioethanol occupies the top priority with a vector value of 0.152. This research can help consultants, fuel experts, and local governments speed up their work in determining the best household fuel according to their respective regions.

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