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All Journal Edutech VISIKES JSI: Jurnal Sistem Informasi (E-Journal) Jurnas Nasional Teknologi dan Sistem Informasi Journal of Information System JURNAL MEDIA INFORMATIKA BUDIDARMA Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Layanan Masyarakat (Journal of Public Service) Jounal of Education and Teaching (JET) Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Masyarakat Information Technology (JPM ITech)
Sri Winarno
Universitas Dian Nuswantoro
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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DECISION TREE SIMPLIFICATION THROUGH FEATURE SELECTION APPROACH IN SELECTING FISH FEED SELLERS Esmi Nur Fitri; Sri Winarno; Fikri Budiman; Asih Rohmani; Junta Zeniarja; Edi Sugiarto
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 2 (2023): JUTIF Volume 4, Number 2, April 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.2.747

Abstract

Feed is a crucial variable because it can determine the success of fish farming. Breeders can use two types of artificial feed, namely alternative feed and pellets. Many cultivators need pellets as the main consumption for the fish they are cultivating because the pellets contain a composition that has been adjusted to their needs based on the type and age of the fish. However, currently, cultivators are facing a problem, namely the high price of fish pellets on the market. Therefore, an analysis of the classification of the selection of fish feed sellers is needed that is adjusted to several criteria like the number of types of feed, price, order, delivery, and availability of discounts. This study conducted a classification analysis of simplification of characteristics in selecting fish feed sellers in Kendal Regency that would then be compared with a model without feature selection by utilizing the Decision Tree C4.5 method. The results of this study are the decision tree with the best performance where C4.5 with the application of the selected feature has an accuracy value of 92%, while C4.5 without the selection feature has an accuracy of 86.8%. The results of this study indicate that the C4.5 method with the application of selection features is better than C4.5 without selection features so that it can be applied to the selection of freshwater fish feed sellers in Kendal Regency.
Continous Formative Assessment pada Pendekatan Hybrid Learning: Sebuah Study Evaluasi Persepsi Asih Rohmani; Sri Winarno; M. Hafidz Ariansyah
Journal of Education and Teaching (JET) Vol 4 No 3 (2023): September 2023
Publisher : Fakultas Keguruan dan Ilmu Pendidikan, Universitas Muhammadiyah kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/jet.v4i3.256

Abstract

Assessment is a crucial part of the learning process. In addition to assisting students in determining their level of comprehension and subject-matter mastery, assessment is an essential component of the learning process. Formative evaluation is now regarded as being at the forefront of the learning process. Formative assessment is a learning activity that tracks how well learners are acquiring competencies with the goal of obtaining maximum mastery. Consequently, Continuous Formative Assessment (CFA) is created as an alternative evaluation strategy that can be used to the educational process in the post-pandemic period, particularly in the setting of hybrid learning. The courses are split into two groups for this study's quasi-experimental methodology: the Control Group and the Experimental Group. While the Experimental Group uses CFA for evaluation, the Control Group uses formative-summative assessment. In terms of how students view CFA, data processing results show an average score of 3.35 and a standard deviation of 0.697. The highest average score, 3.78, shows that students can use CFA to achieve the desired learning results.
PERFORMANCE OF K-MEANS CLUSTERING AND KNN CLASSIFIER IN FISH FEED SELLER DETERMINATION MODELS Esmi Nur Fitri; M. Hafidz Ariansyah; Sri Winarno; Fikri Budiman; Asih Rohmani; Junta Zeniarja; Edi Sugiarto
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.3.725

Abstract

Feed is a crucial variable because it can determine the success of fish farming. Farmers can use two types of artificial feed, namely alternative feed and pellets. Many cultivators need pellets as the main food for the fish they are cultivating because the pellets contain a composition that has been adjusted to their needs based on the type and age of the fish. However, currently, cultivators are facing problem, namely the high price of fish pellets on the market. Therefore, an analysis of the classification of the selection of fish feed sellers is needed according to several criteria like the number of types of feed, price, order, delivery, payment, availability of discounts, and the number of assessments. This study conducted a predictive analysis to determine the criteria for selecting fish feed sellers in Kendal Regency by utilizing the K-Means Clustering and KNN Classifier methods in the classification method. This research aims to compare the fish feed seller classification method where the pattern of fish feed seller is identified by K-Means Clustering and KNN Classifier, and then the researcher conducts performance appraisal and evaluation. The results of this study are decision-making patterns to help formulate strategies for cultivators and other interested parties. For verifying the method used, measurements were made to obtain an accuracy value where K-Means was 98.6% and KNN was 86.7%.The results of this study indicate that the K-Means Clustering and KNN Classifier methods can classify the selection of freshwater fish feed sellers in Kendal Regency.
IMPROVING PERFORMANCE OF STUDENTS’ GRADE CLASSIFICATION MODEL USES NAÏVE BAYES GAUSSIAN TUNING MODEL AND FEATURE SELECTION M Hafidz Ariansyah; Esmi Nur Fitri; Sri Winarno
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.3.737

Abstract

Student grades are a relevant variable for predicting student academic performance. In achieving good and quality student performance, it is necessary to analyze or evaluate the factors that influence student performance. When a educator can predict students' academic performance from the start, the educator can adjust the way of learning so that learning can run effectively. The purpose of this research is to study how it is applied to determine the interrelationships between variables and find out which variables have an effect, then use it as a feature selection technique. Then, researchers review the most popular classifier, Gaussian Naïve Bayes (GNB). Next, we survey the feature selection models and discuss the feature selection approach. In this study, researchers will classify student grades based on existing features to evaluate student performance, so it can guide educators in selecting learning methods and assist students in planning the learning process. The result is that applying Gaussian Naïve Bayes (GNB) without feature selection has a lower accuracy of 10.12% while using feature selection the accuracy increases to 10.12%.
Analisis Optimasi Algoritma Decision Tree, Logistic Regression dan SVM Menggunakan Soft Voting Yosiko Aditya Pratama; Fikri Budiman; Sri Winarno; Defri Kurniawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6856

Abstract

Agriculture constitutes a fundamental pillar of a nation's economy. One key to success in agriculture is the selection of suitable land. The prediction of whether land is fertile or not can be efficiently accomplished through a data mining approach. This is because data mining offers several algorithms for extracting crucial information from vast datasets through classification. However, classification algorithms in data mining often encounter the challenge of data imbalance, which can lead to low accuracy rates. Processing data with calculation models that have low accuracy rates can result in numerous erroneous predictions (fail predictions). To address this issue, this research conducts testing and comparative analysis of the confusion matrix results from four calculation models: the Decision Tree algorithm, Logistic Regression, SVM, and the combination of these three algorithms using the Soft Voting ensemble technique. The test results indicate that processing data using the Decision Tree, Logistic Regression, and SVM algorithms, along with the optimization of the Soft Voting ensemble model, achieves the highest accuracy rate of 91.53%. This accuracy rate is higher compared to the other three calculation models: the Decision Tree algorithm with a difference of 3.83%, Logistic Regression with a difference of 2.66%, and SVM with a difference of 1.36%. This research makes a significant contribution by identifying an efficient solution to improve the accuracy of identifying fertile agricultural land, which is a crucial step in supporting the success of the agricultural sector in the country's economy.
Pengembangan Chatbot Kesehatan Mental Menggunakan Algoritma Long Short-Term Memory Fajarudin Zakariya; Junta Zeniarja; Sri Winarno
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7177

Abstract

Mental health has now become a crucial aspect of contemporary society, especially in Indonesia. This reflects the emotional, psychological, and social well-being of individuals, encompassing the ability to cope with stress in daily life. A comprehensive understanding of mental health has become highly important for the community to prevent the occurrence of mental health problems or disorders. The objective of this research is to design a chatbot as an information and solution hub for maintaining mental health, with the hope that the development of this chatbot can help reduce the risk of mental health-related issues. In the development process of this chatbot, the author applies the AI Project Cycle and utilizes a deep learning approach for the chatbot model. The development involves the Flask platform, and to achieve high accuracy, the model employs the Long Short-Term Memory (LSTM) architecturea type of recurrent neural network (RNN) specifically designed to handle long-term dependency issues common in complex mental health contexts. LSTM enables the model to store and access long-term contextual information, which can be highly beneficial in providing accurate solutions and understanding emotional condition changes. The trained LSTM model demonstrates an accuracy of 93%, validation accuracy of 82%, a loss of 0.3%, and validation loss of 1.6% after 200 epochs. Therefore, it can be concluded that using the LSTM algorithm for the chatbot model in this development is quite effective.
Improving Multi-label Classification Performance on Imbalanced Datasets Through SMOTE Technique and Data Augmentation Using IndoBERT Model Leno Dwi Cahya; Ardytha Luthfiarta; Julius Immanuel Theo Krisna; Sri Winarno; Adhitya Nugraha
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9, No 3 (2023): Desember 2023
Publisher : Jurusan Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i3.2023.290-298

Abstract

Sentiment and emotion analysis is a common classification task aimed at enhancing the benefit and comfort of consumers of a product. However, the data obtained often lacks balance between each class or aspect to be analyzed, commonly known as an imbalanced dataset. Imbalanced datasets are frequently challenging in machine learning tasks, particularly text datasets. Our research tackles imbalanced datasets using two techniques, namely SMOTE and Augmentation. In the SMOTE technique, text datasets need to undergo numerical representation using TF-IDF. The classification model employed is the IndoBERT model. Both oversampling techniques can address data imbalance by generating synthetic and new data. The newly created dataset enhances the classification model's performance. With the Augmentation technique, the classification model's performance improves by up to 20%, with accuracy reaching 78%, precision at 85%, recall at 82%, and an F1-score of 83%. On the other hand, using the SMOTE technique, the evaluation results achieve the best values between the two techniques, enhancing the model's accuracy to a high 82% with precision at 87%, recall at 85%, and an F1-score of 86%.
Co-Authors Adhitya Nugraha Alzami, Farrikh Ardytha Luthfiarta Asih Rohmani Asih Rohmani Asih Rohmani Asih Rohmani Asih Rohmani Asih Rohmani Defri Kurniawan Denny Senata Edi Sugiarto Edi Sugiarto Edi Sugiarto Erika Devi Udayanti Esmi Nur Fitri Esmi Nur Fitri Esmi Nur Fitri Esmi Nur Fitri Fajarudin Zakariya Farah Syadza Mufidah Farrikh Al Zami Farrikh Al Zami Fikri Budiman Fikri Budiman Indra Gamayanto Julius Immanuel Theo Krisna Junta Zeniarja Leno Dwi Cahya M Hafidz Ariansyah M Hafidz Ariansyah M. Hafidz Ariansyah M. Hafidz Ariansyah M. Hafidz Ariansyah Pulung Nurtantio Andono Ramadhan Rakhmat Sani Rosana Podesta Sasono Wibowo Sendi Novianto Sendi Novianto Setyo Budi Setyo Budi Sumardi Sumardi Titien Suhartini Sukamto Titien Suhartini Sukamto Yosiko Aditya Pratama Yupie Kusumawati