Articles
<![CDATA[Sosialisasi Fenomena Cyber Crime dan Penanggulangannya Bagi Pengelola Informasi Publik Kapanewon Mlati Sleman Yogyakarta ]]>
<![CDATA[Uning Lestari]]>;
<![CDATA[Amir Hamzah]]>;
<![CDATA[Muhammad Sholeh]]>
<![CDATA[ NEAR: Jurnal Pengabdian kepada Masyarakat Vol. 1 No. 2 (2022): NEAR ]]>
Publisher : <![CDATA[Komunitas Dosen Indonesia ]]>
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DOI: 10.32877/nr.v1i2.432
<![CDATA[Pada era digital sekarang ini penggunaan Teknologi Informasi dan Komunikasi (TIK) telah dimanfaatkan juga di bidang pemerintahan untuk berbagai keperluan, termasuk juga di Kapanewon atau Kecamatan Mlati. Saat ini Kecamatan Mlati telah memanfaatkan teknologi digital mendukung kegiatan administratif maupun penyampaian informasi publik ke masyarakat. Dalam penyampaian informasi publik, yang sebelumnya disampaian secara manual, sekarang telah berbasis digital. Dalam rangka menghadapi era digital tersebut, Kapanewon Mlati bekerjasama dengan IST AKPRIND Yogyakarta menyelenggarakan sosialisasi pengelolaan informasi publik. Target peserta adalah Pejabat Pengelola Informasi dan Dokumentasi (PPID) kapanewon dan kalurahan, Kelompok Informasi Masyarakat (KIM) dan pengelola website kapanewon dan kalurahan. Tujuan sosialisasi agar peserta memahami pentingnya penyampaian informasi publik kepada masyarakat secara baik dan benar serta untuk mengindari perilaku yang mengarah ke Cyber Crime. Hasil evaluasi kegiatan sosialisasi kegiatan ini dan kriteria Kinerja/Kepuasan menunjukkan 86,2% peserta merasa puas terhadap kinerja program sosialisasi]]>
<![CDATA[Penerapan Data Mining dengan Metode Regresi Linear untuk Memprediksi Data Nilai Hasil Ujian Menggunakan RapidMiner ]]>
<![CDATA[Muhammad Sholeh]]>;
<![CDATA[Erna Kumalasari Nurnawati]]>;
<![CDATA[Uning Lestari]]>
<![CDATA[ JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 1 (2023): Januari 2023 ]]>
Publisher : <![CDATA[UIN Sunan Kalijaga Yogyakarta ]]>
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DOI: 10.14421/jiska.2023.8.1.10-21
<![CDATA[Prediction is one of the methods in data mining. One of the models that can be used in prediction is using linear regression. Linear regression is used to make predictions on the data that has been provided. In this study, a linear regression model was made with a datasheet containing data that affected student achievement in achieving final exam scores. The linear regression model developed can be used to predict student test scores. The linear regression model developed can be used to predict student test scores. The datasheet used in the test uses a public datasheet, namely student_performance.csv. The datasheet consists of 395 records and 33 attributes. The attributes used are selected that influence the label. The selection of attributes is based on the results of the weighting in the process of checking the correlation matrix. Based on the weighting, the attributes used are seven attributes and one attribute becomes a label. The research method uses CRISP DM which consists of business understanding, data understanding, data preparation, model making, evaluation, and deploying. The data mining process uses the Rapid Miner application. The results of the study resulted in a linear regression model y=0.729-(0.024×Medu)-(0.020×Fedu)+(0.053×failures)-(0.077×goout)-(0.012×absences)+(0.126×G1)+(0.862×G2). The result of evaluating the performance of the RMSE value was 0.675. Based on these results, it can be concluded that the resulting model can be recommended for use in predicting student test scores.]]>
<![CDATA[Sosialisasi Penggunaan Media Sosial Untuk Meningkatkan Kinerja E-Government di Kecamatan Mlati Kabupaten Sleman Yogyakarta ]]>
<![CDATA[Amir Hamzah]]>;
<![CDATA[Muhammad Sholeh]]>;
<![CDATA[Uning]]>
<![CDATA[ Jurnal Pengabdian kepada Masyarakat Radisi Vol 1 No 3 (2021): Desember ]]>
Publisher : <![CDATA[Yayasan Kajian Riset dan Pengembangan RADISI ]]>
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DOI: 10.55266/pkmradisi.v1i3.67
<![CDATA[The use of information technology to support E-government has long been proclaimed by the Government. However, implementation at the sub-district and village levels still faces many obstacles. Based on a field study in Mlati District, it was found that the obstacles were a lack of awareness and technical ability of media managers. The purpose of this service is to increase awareness and ability of official media managers at the sub-district and village levels so that the communication of government programs is better. The targets of this activity are media managers from representatives of the Community Information Group (KIM), village representatives and Information Management and Documentation Officers (PPID) in Mlati District. The method used is lectures and assistance in the use of social media for disseminating government program information. Evaluation of activities is carried out with post-activity questionnaires to participants. The evaluation results show that 86% of participants are satisfied and 90% get additional knowledge in managing official social media of sub-district and village governments]]>
<![CDATA[Evaluation of Data Clustering Accuracy using K-Means Algorithm ]]>
<![CDATA[Suraya, Suraya]]>;
<![CDATA[Sholeh, Muhammad]]>;
<![CDATA[Lestari, Uning]]>
<![CDATA[ International Journal of Multidisciplinary Approach Research and Science Том 2 № 01 (2024): International Journal of Multidisciplinary Approach Research and Science ]]>
Publisher : <![CDATA[PT. Riset Press International ]]>
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DOI: 10.59653/ijmars.v2i01.504
<![CDATA[Data clustering is one of the methods in data science that is often used in data analysis. This method is used in making groupings from a collection of datasheets. Data clustering is done to find patterns or relationships between data. This research aims to evaluate the accuracy of data clustering using K-Means algorithm on wine datasheet. Wine datasheet has 13 features that describe the chemical characteristics of three types of wine. The clustering process must produce the best clustering evaluation metrics. The evaluation metric is done through comparison between the clustering results of K-Means algorithm with Davies Bouldin and Silhouette. The research steps involved data standardization, selection of the optimal number of clusters, and assessment of clustering accuracy. The research method uses KDD which consists of pre-processing, transformation, model building and model evaluation. Experimental results show that appropriate parameters and cluster initialization can improve clustering evaluation metrics. The clustering results show that the normalized datasheet produces evaluation metrics for Davies Bouldin 2 groups and Silhouette produces 3 groups. Before normalization, Davies Boulidin results in 7 groups and Silhouette results in 2 groups. In conclusion, this study produced different evaluation metrics between normalized and non-normalized datasheets. The selection of the number of groups chosen depends on the context of the data analysis performed and is selected into 3 groups which can be labelled "Superior Variety", the second group "Intermediate Variety" and the third group "Standard Variety".]]>
<![CDATA[Automatic prediction of learning styles: a comprehensive analysis of classification models ]]>
<![CDATA[Lestari, Uning]]>;
<![CDATA[Salam, Sazilah]]>;
<![CDATA[Choo, Yun-Huoy]]>;
<![CDATA[Alomoush, Ashraf]]>;
<![CDATA[Al Qallab, Kholoud]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v13i5.7456
<![CDATA[Learning styles are a topic of interest in educational research about how individuals acquire and process information in offline or online learning. Identification of learning styles in the online learning environment is challenging. The existing approaches for the identification of learning styles are limited. This study aims to review the many learning styles characterized by various classification approaches toward the automatic prediction of learning styles from learning management system (LMS) datasets. A systematic literature review (SLR) was conducted to select and analyze the most pertinent and significant papers for automatically predicting learning styles. Fifty-two research papers were published between 2015-2023. This research divides analysis into five categories: the classification of learning style models, the collection of the collected dataset, learning styles based on the curriculum, research objectives related to learning styles, and the comprehensive analysis of learning styles. This study found that learning style research encompasses diverse theories, models, and algorithms to understand individual learning preferences. Statistical analysis, explicit data collection, and the Felder-Silverman model are prevalent in research, highlighting the significance of algorithm improvement for optimizing learning processes, particularly in computer science. The categorization and understanding of various methods offer valuable insights for enhancing learning experiences in the future.]]>
<![CDATA[Empirical analysis of language learning strategies for optimizing online language courses ]]>
<![CDATA[Lip, Rashidah]]>;
<![CDATA[Salam, Sazilah]]>;
<![CDATA[Mohamad, Siti Nurul Mahfuzah]]>;
<![CDATA[Kar Mee, Cheong]]>;
<![CDATA[Poh Ee, Tan]]>;
<![CDATA[Ismail, Nurmaisarah]]>;
<![CDATA[Mohd Yusoff, Azizul]]>;
<![CDATA[Lestari, Uning]]>;
<![CDATA[Ahmad Fesol, Siti Feirusz]]>
<![CDATA[ International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 6: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijere.v13i6.29418
<![CDATA[In today’s changing education world, online language classes are becoming more important. Recognizing the important role of the relationship between language learning strategies and students’ preferences, our empirical study examines the patterns or factors that explain the observed correlations among variables to provide insights in optimizing online language courses. Addressing a critical gap in the existing literature that has traditionally treated language learning strategies and online language education as distinct entities, our survey-based research collected comprehensive data from students enrolled in online language courses. Focused on six key language learning strategies: memory, cognitive, compensation, metacognitive, affective, and social. The research shows a delicate connection between these strategies and students’ preferences in online teaching mode. The empirical findings provide insights into certain strategies that work better for specific online learning methods. This helps us grasp the varied preferences of groups of students. This research enriches online language education by revealing an unexplored connection between strategies and preferences and provides a valuable resource for educators and course designers. The information given helps make online language classes better. It ensures that students learn languages more effectively online, considering their functional and practical needs in online learning.]]>
<![CDATA[Comparison of Feature Selection with Information Gain Method in Decision Tree, Regression Logistic and Random Forest Algorithms ]]>
<![CDATA[Sholeh, Muhammad]]>;
<![CDATA[Lestari, Uning]]>;
<![CDATA[Andayati, Dina]]>
<![CDATA[ Journal of Applied Business and Technology Vol. 5 No. 3 (2024): Journal of Applied Business and Technology ]]>
Publisher : <![CDATA[Institut Bisnis dan Teknologi Pelita Indonesia ]]>
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DOI: 10.35145/jabt.v5i3.155
<![CDATA[One of the approaches that can be done is to perform feature selection. Feature selection is done by identifying the most informative features and not using features that do not directly contribute to the target feature. The purpose of feature selection is to increase the accuracy of the model. The research was conducted by comparing the performance of the model by comparing the accuracy results of the model without any feature selection with the model that has done feature selection. The process is done by comparing the accuracy results with decision tree, random forest and SVM algorithms. In the research method of feature selection on science data, the steps include understanding the domain and dataset, exploratory analysis, data cleaning, measuring feature relevance with criteria such as Information Gain, and feature ranking. The results are evaluated and validated using model performance metrics before and after feature selection. This process ensures selection of relevant features, improving accuracy. The research process used the Lung Cancer Prediction datasheet which consists of 306 rows and 16 attributes. The results show that feature selection can improve the performance of the classification model by reducing features that do not contribute to the target. Comparison results using decision tree, Regression Logistic and random forest classification model algorithms and feature selection resulted in a high accuracy value of 0.968 in the Regression Logistic algorithm with a feature selection of 5.]]>
<![CDATA[SENTIMENT ANALYSIS FOR EXTRACTING STUDENT OPINION DATA ON HIGHER EDUCATION SERVICES USING THE NAIVE BAYES CLASSIFIER AND SUPPORT VECTOR MACHINE METHODS (CASE STUDY AKPRIND INSTITUTE OF SCIENCE AND TECHNOLOGY YOGYAKARTA) ]]>
<![CDATA[Uning Lestari]]>;
<![CDATA[Tri Romadhani]]>;
<![CDATA[Suraya Suraya]]>;
<![CDATA[Erfanti Fatkhiyah]]>
<![CDATA[ Jurnal TAM (Technology Acceptance Model) Vol 13, No 1 (2022): Jurnal TAM (Technology Acceptance Model) ]]>
Publisher : <![CDATA[LPPM STMIK Pringsewu ]]>
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DOI: 10.56327/jurnaltam.v13i1.1220
<![CDATA[Opinions are ideas, opinions, or the results of someone's subjective thoughts in explaining or addressing something. IST AKPRIND Yogyakarta provides comment and suggestion box facilities in the learning evaluation questionnaire. Opinions that have been collected can be used to determine the sentiment of the campus community. This sentiment information can be used in future campus development. The development of a system that can analyze sentiment automatically is designed by comparing the Naive Bayes Classifier (NBC) method and the support vector machine (SVM) optimized by selecting the Information Gain (IG) feature. Prior opinion data needs to be prepared before being analyzed. Preprocessing (text preprocessing) used includes: cleanning, text folding, normalization, stemming, stopword removal, convert negation, and tokenization. The results of this study show that the SVM method produces higher accuracy than NBC. The accuracy test shows the highest accuracy of SVM reaches 99.09% while NBC is 96.56%. The application of IG did not significantly affect the accuracy of the analysis. GI greatly influenced the analysis duration of the SVM method, which could shorten the time by 195.71%.]]>
<![CDATA[SISTEM PENDUKUNG KEPUTUSAN KLASIFIKASI KELUARGA MISKIN MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) SEBAGAI ACUAN PENERIMA BANTUAN DANA PEMERINTAH (STUDI KASUS: PEMERINTAH DESA TAMANMARTANI, SLEMAN) ]]>
<![CDATA[Uning Lestari]]>;
<![CDATA[Muhammad Targiono]]>
<![CDATA[ Jurnal TAM (Technology Acceptance Model) Vol 8, No 1 (2017): Jurnal TAM (Technology Acceptance Model) ]]>
Publisher : <![CDATA[LPPM STMIK Pringsewu ]]>
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DOI: 10.56327/jurnaltam.v8i1.97
<![CDATA[Various types of government programs in poverty alleviation efforts have been widely implemented, but the assistance that reached the hands of the people has not been in accordance with what is expected. One reason is that the determination of the status of poor families as beneficiaries has not been optimal, so that in providing poverty assistance has not been well targeted. Application Development Decision Support System Determination of Poor Family is made by the method used in determining the decision is the method of Simple Additive Weighting (SAW). The results of the assessment conducted by the system are given poverty status such as Very Poor, Poor, Vulnerable Poor and Not Poor. SAW method is chosen because it can determine the weight value for each attribute, then proceed with the ranking process that will select the best alternative from a number of alternatives, in this case the alternatives referred to are families categorized as poor families based on the criteria specified. With the ranking process, the assessment will be more precise because it is based on predetermined criteria and weights, so it will get more accurate results for anyone who is categorized as poor. These results can then form the basis for the TPK (Poverty Reduction Team) team of Tamanmartani villages to determine which families are entitled to receive government funding so that the distribution of aid is targeted.]]>
<![CDATA[APPLICATION OF HOME LIGHT CONTROL SYSTEM USING ARDUINO WITH MOBILE BASED WIFI MEDIA ]]>
<![CDATA[Uning Lestari]]>;
<![CDATA[Erfanti Fatkhiyah]]>;
<![CDATA[Andung Febi Prakoso]]>
<![CDATA[ IJISCS (International Journal of Information System and Computer Science) Vol 2, No 2 (2018): IJISCS (International Journal of Information System and Computer Science) ]]>
Publisher : <![CDATA[Bakti Nusantara Institute ]]>
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DOI: 10.56327/ijiscs.v2i2.606
<![CDATA[Nowadays, technology development encourage people to think creatively, not not only to explore new discoveries, but also to maximize existing technological performance to ease human work in everyday life. The need for an automatic control system is needed with the increasing activity of each individual community with various erratic activities and times. As a result, many activities in the household are delayed, such as turning on or turning off the lights in every room at night and in the morning. Smart home system is one solution that suits the needs of the current automatic controllers. Smart home system is a home or building is equipped with an integrated technology with the help of the tool/tools which can be a computer or other device, for example a smartphone to provide all the comfort, safety, security and energy saving is automatic and programmed. The smart home system can be used to control almost all equipment and equipment at home, from lighting settings to various household appliances, which can be done only by using sound, infrared light or remote control. In this study, a smart home system was created for home light control system applications using Arduino Uno microcontroller via mobile-based wifi media. With this application the user can control the home lights by turning off or turning on the home lights remotely through the mobile media. Thus the efficiency of electricity use becomes more maintained.]]>
