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All Journal Informasi Interaktif EDUMATIC: Jurnal Pendidikan Informatika Jurnal Pengabdian kepada Masyarakat Nusantara SmartComp
Sri Rahayu
Program Studi Informatika Fakultas Teknik Universitas Janabadra
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences

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PERBANDINGAN ANALISIS DATA FITUR NOMINAL MULTI-KATEGORI MENGGUNAKAN METODE ADAPTIVE SYNTHETIC NOMINAL (ADASYN-N) SERTA ADAPTIVE SYNTHETIC-KNN (ADASYN-KNN) Putra, Jeffry Andhika; Rahayu, Sri
Informasi Interaktif Vol 6, No 2 (2021): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Growing need for efficient algorithms for data manipulation, analysis, and intelligent use has been a very active research area in machine learning field. However, some research areas still not fully developed, especially when unbalanced data classification is needed. Datasets with this class imbalance occur because of an unbalanced ratio between one case and another. This class imbalance will be detrimental to data mining because machine learning in data mining has difficulty in classifying minority classes (small instances) correctly. There are several approaches to handling imbalances, one of which is by using the original data sampling method. The first sampling method approach to overcome class imbalance is undersampling which is a method to balance classes by randomly reducing the majority class instances. Over-sampling is a method of balancing class distribution by randomly replicating instances in minority classes.This study presents comparison of over-sampling techniques to overcome problem of class imbalances in datasets with nominal-multi categories features between Adaptive Synthetic-Nominal (ADASYN-N) and Adaptive Synthetic-kNN (ADASYN-KNN) methods. There are seven datasets with nominal-multi categories features which have an unbalanced class distribution. Then the dataset that has been over-sampled with both methods is classified using the Random Forest method. Furthermore, a comparison of the accuracy of the original dataset and the dataset of the ADASYN-N and ADASYN-KNN over-sampling techniques was carried out.  Keywords: ADASYN-KNN, ADASYN-N, class imbalance, nominal, multi-category, over-sampling. 
Pra-rancangan Sistem Pengelolaan Arsip Surat Berbasis Website (Kasus: Kapanewon Mlati, Sleman, Yogyakarta) Jeffry Andhika Putra; Sri Rahayu
Informasi Interaktif Vol 7, No 2 (2022): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

The purpose of this study was to design a mail archive management system at Kapanewon Mlati to archive government letter data (data of incoming letters, outgoing letters, incoming invitations, and outgoing invitations). With the design of this system, it is hoped that it can be implemented into a storage and archive management system in accordance with their respective fields into the system. The facts of this study indicate that the management of the Kapanewon Mlati archives has not been carried out optimally. This can be seen from the use of agenda books that are not accompanied by disposition sheets, archive storage systems that are still combined, controls that do not use form cards and the existence of damaged and dirty archives. The development of technology is currently becoming information as an important pillar with the operational activities of an institution to achieve goals that are beneficial to the institution. One technology that is currently very useful and is often used is the world wide web or known as the website. Even now the web has penetrated every agency, one of which is government agencies.
Aplikasi Dashboard Visualisasi Data Calon Mahasiswa Baru mengunakan Metabase Yumarlin MZ; Jemmy Edwin Bororing; Sri Rahayu; Tan Anugrah Ramadhani
Jurnal Pendidikan Informatika (EDUMATIC) Vol 6, No 1 (2022): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v6i1.5483

Abstract

New student data Information systems can be used as a supporting tool to support decisions. Janabadra University is one of the universities in Yogyakarta, the information system for recording transactional data for students is still done simply in the form of text and numbers. The purpose of this research is to design and build a dashboard application for data visualization of prospective students at Janabadra University for new student admissions (PMB). The method used is a business intelligence roadmap with six stages, the stages are (1) justification, (2) planning, (3) business case, (4) design, (5) construction, and (6) deployment which is a reference in the design and construction of a data warehouse using Metabase. The results of this study can build 7 (seven) visualization dashboards are  (1) Dashboard of information students grouped based on the total number of PMB registrants, (2) Dashboard of the number of registrants based on the academic year, (3) Dashboard of income from PMB registration based on the payment date, (4) Dashboard of the number of students based on the academic year of each study program, (5) Dashboard of the number of registrants by class, (6) Dashboard of the number of PMB registrants by semester and (7) Dashboard of the number of PMB registrants by study program and class.
Implementasi Metode K-Nearest Neighbor (K-NN) untuk Analisis Sentimen Kepuasan Pengguna Aplikasi Teknologi Finansial FLIP Sri Rahayu; Yumarlin MZ; Jemmy Edwin Bororing; Rahmat Hadiyat
Jurnal Pendidikan Informatika (EDUMATIC) Vol 6, No 1 (2022): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v6i1.5433

Abstract

The phenomenon of technological development can transform systems in various sectors to provide efficiency and convenience at a lower cost, including the financial sector. Flip is a financial service application that makes it easy to transfer money between banks without administrative fees. By the end of 2021, the Flip will have a 4.9 rating on the Google Play Store. The purpose of this study was to analyze user sentiment towards the Flip app to see if flip user ratings were as positive as the ratings received. This study uses a set of text mining processes on the user rating data of the Flip app on the Google Play Store, using the classification algorithm K-Nearest Neighbor with TF-IDF weighting. The results show that 77.67% of the test data are correctly classified as positive evaluation classes, with high accuracy and recall rates of 82.67% and 86.92%, respectively. In addition, from the results of applying the Flip user rating data classification method, the comparison between training data and test data is 80%:20%, and the classification accuracy using the K-Nearest Neighbor algorithm is 76.68%. User reviews of the Flip app have shown positive results, as well as the ratings obtained in the Google Play Store and the K-Nearest Neighbor algorithm, TF-IDF weighting process used to analyze user sentiment towards the Flip app.
PERBANDINGAN ANALISIS DATA FITUR NOMINAL MULTI-KATEGORI MENGGUNAKAN METODE ADAPTIVE SYNTHETIC NOMINAL (ADASYN-N) SERTA ADAPTIVE SYNTHETIC-KNN (ADASYN-KNN) Jeffry Andhika Putra; Sri Rahayu
Informasi Interaktif Vol 6, No 2 (2021): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

ABSTRACTGrowing need for efficient algorithms for data manipulation, analysis, and intelligent use has been a very active research area in machine learning field. However, some research areas still not fully developed, especially when unbalanced data classification is needed. Datasets with this class imbalance occur because of an unbalanced ratio between one case and another. This class imbalance will be detrimental to data mining because machine learning in data mining has difficulty in classifying minority classes (small instances) correctly. There are several approaches to handling imbalances, one of which is by using the original data sampling method. The first sampling method approach to overcome class imbalance is undersampling which is a method to balance classes by randomly reducing the majority class instances. Over-sampling is a method of balancing class distribution by randomly replicating instances in minority classes.This study presents comparison of over-sampling techniques to overcome problem of class imbalances in datasets with nominal-multi categories features between Adaptive Synthetic-Nominal (ADASYN-N) and Adaptive Synthetic-kNN (ADASYN-KNN) methods. There are seven datasets with nominal-multi categories features which have an unbalanced class distribution. Then the dataset that has been over-sampled with both methods is classified using the Random Forest method. Furthermore, a comparison of the accuracy of the original dataset and the dataset of the ADASYN-N and ADASYN-KNN over-sampling techniques was carried out.Keywords: ADASYN-KNN, ADASYN-N, class imbalance, nominal, multi-category, over-sampling
PRA-RANCANGAN SISTEM PENGELOLAAN ARSIP SURAT BERBASIS WEBSITE (KASUS: KAPANEWON MLATI, SLEMAN, YOGYAKARTA) Jeffry Andhika Putra; Sri Rahayu
Informasi Interaktif Vol 7, No 2 (2022): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

ABSTRACTThe purpose of this study was to design a mail archive management system at Kapanewon Mlati to archive government letter data (data of incoming letters, outgoing letters, incoming invitations, and outgoing invitations). With the design of this system, it is hoped that it can be implemented into a storage and archive management system in accordance with their respective fields into the system.The facts of this study indicate that the management of the Kapanewon Mlati archives has not been carried out optimally. This can be seen from the use of agenda books that are not accompanied by disposition sheets, archive storage systems that are still combined, controls that do not use form cards and the existence of damaged and dirty archives.The development of technology is currently becoming information as an important pillar with the operational activities of an institution to achieve goals that are beneficial to the institution. One technology that is currently very useful and is often used is the world wide web or known as the website. Even now the web has penetrated every agency, one of which is government agencies. Keywords: Archive, Website, Kapanewon, Data Flow Diagram, MySQL
Perancangan Sistem Informasi Sekolah Dasar Negeri 1 Gaden Berbasis Website Sri Rahayu; Yumarlin MZ; Jemmy Edwin Bororing; Larasati
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 2 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Cv. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v4i2.1004

Abstract

Seiring dengan perkembangan teknologi digital, peran komputer dan jaringan internet sangat penting untuk menunjang produktivitas serta akurasi pengolahan informasi. Salah satu penerapan teknologi digital masa kini yaitu sebagai penunjang dalam bidang pendidikan. Sistem informasi akademik pada instansi sekolah sangat diperlukan, untuk menunjang produktivitas dan efisiensi waktu. SD Negeri 1 Gaden merupakan sebuah Sekolah dasar yang terletak di Dukuh Bodrorejo RT 19 RW 07, Kelurahan Gaden, Kecamatan Trucuk, Kabupaten Klaten, yang sudah memanfaatkan perangkat keras komputer sebagai media pembelajaran, namun untuk media informasi SD Negeri 1 Gaden ini belum memanfaatkan perangkat keras komputer sebagai media informasi yang baik. Oleh karena itu kegiatan pengabdian ini bertujuan untuk membantu membuat Sistem Informasi Sekolah Dasar Negeri 1 Gaden Berbasis Website yang dapat dijadikan sebagai sarana informasi hingga promosi bagi masyarakat luas dan juga lingkup sekolah. Berdasarkan evaluasi yang telah dilakukan dapat dikatakan bahwa pembuatan Sistem Informasi Website SD Negeri 1 Gaden telah bekerja sesuai dengan apa yang diharapkan dari pihak SD Negeri 1 Gaden.
Sistem Pakar Pasal-Pasal Pidana Penghapusan Kekerasan dalam Rumah Tangga dengan Metode Forward Chaining Yumarlin MZ; Sri Rahayu
Jurnal Pendidikan Informatika (EDUMATIC) Vol 7 No 1 (2023): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v7i1.13688

Abstract

Domestic and Family Violence (DFV) is a social issue that often occurs in Indonesia, especially for women. Domestic violence cases often cannot be handled thoroughly because legal issues are very complex, making it difficult for ordinary people to understand and sort out the articles that regulate a legal case in domestic violence. This research aims to produce an expert system that can make it easier for the public to find solutions in the form of articles suspected of being involved in a crime of domestic violence. An expert system built with the stages of analysis, design, implementation, and testing. The analysis phase was carried out by observation and interviews or discussions with staff from the District Attorney's Office for the Sleman Regency, Special Region of Yogyakarta. The implementation phase uses the Forward Chaining inference technique to produce the basic rules for determining the articles suspected of acts of violence committed. System testing using SUPR-Q involved 30 respondents to fill out questionnaires from 3 aspects of user experience namely usability, user interface, and satisfaction. The test results obtained a percentage of 80.66% indicating a very good level of ease and satisfaction of the expert system for eliminating domestic violence.
Analisis Sentimen Pengguna Apliaksi Shopee Menggunakan Metode Naive Bayes Classifier dan K-NN Yumarlin MZ; Jemmy Edwin Bororing; Sri Rahayu; Jeffry Andhika Putra
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 3 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i3.5494

Abstract

Perkembangan aplikasi e-commerce mengalami kemajuan pesat dalam beberapa tahun terakhir. Aplikasi e-commerce memberikan pengalaman belanja yang lebih mudah, nyaman, dan personal bagi pengguna. Fitur-fitur seperti pencarian produk yang efisien, ulasan pelanggan, rekomendasi produk dan keamanan pembayaran. Shopee adalah salah satu platform ecommerce yang populer di Indonesia dan memberikan pengguna akses yang mudah untuk berbelanja secara online dengan berbagai pilihan produk dan penawaran menarik. Tujuan penelitian ini untuk mengetahui analisis sentimen pengguna aplikasi Shopee berdasarkan data ulasan yang di dapat dari situs website google play menggunakan metode Naive Bayes Classifier dan K-Nearest Neighbour (K-NN) untuk mengklasifikasikan ulasan berdasarkan komentar sentimen positif, sentimen negatif dan sentiment netral. Hasil penelitian dengan menerapkan metode Naive Bayes Classifier di dapat nilai akurasi sebesar 75.97%, dengan prediksi komentar positif sebesar 742, komentar negative 519 dan komentar netral sebesar 86. Dan metode K-Nearest Neighbor nilai akurasi sebesar 16.69%, dengan prediksi komentar positif sebesar 154, komentar negative 80 dan komentar netral sebesar 62. Analisis Sentimen aplikasi shopee berdasarkan komentar pengguna google play store menunjukkan tingkat kepuasan konsumen baik di lihat dari besarnya nilai respon komentar positif berdasarkan hasil perhitungan machine learning yang sudah dilakukan. 
Analisis Sentimen Masyarakat terhadap Tindakan Vaksinasi Covid 19 Menggunakan Algortima Naïve Bayes Classifier Yumarlin MZ; Jemmy Edwin Bororing; Sri Rahayu; Fenthy Faharani
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 11, No 3 (2022): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v11i3.3893

Abstract

Coronavirus Disease-19 (COVID-19) merupakan ancaman kesehatan masyarakat yang kemudian ditetapkan oleh Organisasi Kesehatan Dunia sebagai pandemi karena telah menyebar di 199 negara di seluruh dunia. Jumlah kasus positif COVID-19 di seluruh dunia pada tahun 2021 mencapai 237.655.302 juta kasus dan jumlah kasus positif COVID-19 di Indonesia saja mencapai 4.229.813 juta kasus. Salah satu kebijakan pemerintah Indonesia dalam menangani COVID-19 adalah dengan melakukan vaksinasi. Namun, kebijakan ini mengundang banyak pihak dari masyarakat untuk memberikan pendapatnya. Penelitian ini bertujuan untuk mengetahui opini masyarakat terhadap tindakan vaksinasi yang dilakukan pemerintah untuk mengatasi virus covid-19. Dengan tindakan vaksinasi yang dilakukan, apakah ada komentar yang lebih positif, negatif, atau netral? Data dalam penelitian ini adalah data komentar publik di media sosial Twitter. Pengumpulan data dilakukan dengan bantuan software RStudio dan juga website kaggle. Data yang digunakan adalah dalam bahasa Indonesia dengan total 102.933 data. Data tersebut akan diklasifikasikan menggunakan metode Naïve bayes classifier. Data tersebut akan melalui tahap preprocessing sebelum proses klasifikasi data. Tahapan preprocessing meliputi proses Cleansing, Case Folding, Remove Character, Remove Duplicate, Translate, Normalization Word, Stemming, Stopword. Hasil penelitian ini menunjukkan sentimen positif sebanyak 66,45% dari 21.356 komentar, sentimen negatif sebanyak 25,21%, sedangkan sentimen netral sebanyak 8,34%.
Co-Authors Bororing, Jemmy Edwin Fenthy Faharani Jeffry Andhika F Jeffry Andhika Putra Larasati Rahmat Hadiyat Tan Anugrah Ramadhani Yumarlin MZ