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All Journal Journal of Intelligent Systems Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Information Management For Educators And Professionals (IMBI) Jurnal Teknik Informatika STMIK Antar Bangsa JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Riset Informatika Budimas : Jurnal Pengabdian Masyarakat Jurnal Infortech International Journal Of Science, Technology & Management (IJSTM) Jurnal Sistem Informasi Jurnal AbdiMas Nusa Mandiri Algoritme Jurnal Mahasiswa Teknik Informatika INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Pengabdian Kreatif Cemerlang Indonesia AMMA : Jurnal Pengabdian Masyarakat Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Prawara Jurnal Pengabdian Masyarakat
Tyas Setiyorini
Universitas Nusa Mandiri
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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VILLAGE GROUPING BASED ON THE NUMBER OF HEALTH FACILITIES IN WEST JAVA USING K-MEANS CLUSTERING ALGORITHM Frieyadie Frieyadie; Anggie Andriansyah; Tyas Setiyorini
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i1.140

Abstract

Health is very important for the welfare and development of the Indonesian nation because as a capital for the implementation of national development, it is essentially the development of all Indonesian people and the development of all Indonesian people. Due to the outbreak of the Covid-19 virus, many health facilities must be provided for patients. Of course, the government must pay attention to the health facilities that can be used in every district/city in West Java in the future. Therefore, to determine the level of availability of sanitation facilities in each district/city in West Java, we need a technology that can classify data correctly. One method of data processing in data mining is clustering. The application of clustering to this problem can use the K-Means algorithm method to group the most frequently used data. The purpose of this study is to classify sanitation data on the highest sanitation facilities, medium sanitation facilities, and low sanitation facilities, so that areas/cities that are included in the low cluster will receive more attention from the government to improve/provide sanitation facilities.
COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING COVID-19 RECOVERED CASES Tyas Setiyorini; Frieyadie Frieyadie
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i3.184

Abstract

The emergence of the Covid-19 outbreak for the first time in China killed thousands to millions of people. Since the beginning of its emergence, the number of cases of Covid-19 has continued to increase until now. The increase in Covid-19 cases has a very bad impact on health and social and economic life. The need for future forecasting to predict the number of deaths and recoveries from cases that occur so that the government and the public can understand the spread, prevent and plan actions as early as possible. Several previous studies have forecast the future impact of Covid-19 using the Machine Learning method. Time series forecasting uses traditional methods with Linear Regression or Artificial Intelligence methods with neural networks. The research proves a linear relationship in the time series data of Covid-19 recovered cases in China, so it is proven that Linear Regression performance is better than the Neural Network.
Covid-19 Social Aid Admission Selection Using Simple Additive Weighting Method as Decision Support Tyas Setiyorini; Frieyadie Frieyadie; Aditiya Yoga Pratama
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (826.739 KB) | DOI: 10.34288/jri.v5i3.231

Abstract

The process of receiving Covid-19 social assistance to residents who are recorded as social aid recipients in the RT.07 RW.10 Kp. Sukapura Jaya area is still uneven. The second problem is that there is no particular mathematical calculation to determine the value of the weight of the criteria, especially for residents who are recorded as receiving Covid-19 social aid in the RT.007 RW.10 Kp. Sukapura Jaya area. The gradual decline in social aid programs so that the number that falls does not match the data of social aid recipients. This caused a polemic for RT administrators in distributing social aid programs. The decline in social aid programs does not match the number of citizens recorded. It overcomes citizens who cause social jealousy—analyzing the problems experienced by the RT management in the distribution of Covid-19 social assistance, especially the RT.07 RW.10 Kp. Sukapura Jaya area to residents who are recorded as recipients. Selecting Covid-19 social assistance recipients, especially in the RT.07 RW.10 Kp. Sukapura Jaya area. So the application of methods as decision support is needed, and it is needed to help determine the weight of particular criteria for citizens who are recorded as more in need. This study proposes a decision support method using the Simple Additive Weighting (SAW) method, which is expected to help decision-making in solving problems for selecting Covid-19 social aid recipients in the RT.07 RW.10 Kp. Sukapura Jaya community. The purpose of the study is to select residents who are recorded to receive social aid who are more in need first will get Covid-19 social aid.
Sosialisasi Keamanan Password Dalam Menggunakan Internet Bagi Para Santri Majelis Ta’lim Faizul Haq Ibnu Rusdi; Maryanah Safitri; Sita Anggraeni; Tyas Setiyorini
BUDIMAS : JURNAL PENGABDIAN MASYARAKAT Vol 5, No 1 (2023): BUDIMAS : VOL. 5, NO.1, 2023
Publisher : LPPM ITB AAS Indonesia Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/budimas.v5i1.7634

Abstract

Almost all activities today can be done online. The internet has provided convenience in many ways and provides access to information quickly anywhere. However, various dangers can arise, including data theft, intellectual property theft, sabotage, and many more. Therefore, internet users must be aware of the crimes that exist on the internet. One way is to maintain the security of user passwords on the internet. After the community service was held in the form of counseling regarding password security tips on the internet, now the students of the Faizul Haq Ta'lim assembly get broader and very useful knowledge about internet security. They can apply tips and tricks in maintaining password security on the internet life in their daily lives.
OPTIMASI MESIN PENCARI BAGI SANTRI MAJELIS TA’LIM FAIZUL HAQ CISAUK Maryanah Safitri; Sita Anggraeni; Tyas Setiyorini; Ibnu Rusdi
BUDIMAS : JURNAL PENGABDIAN MASYARAKAT Vol 4, No 2 (2022): BUDIMAS : VOL. 04 NO. 02, 2022
Publisher : LPPM ITB AAS Indonesia Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/budimas.v4i2.5322

Abstract

The Faizul Haq Ta'lim Council is a non-formal institution that can make a considerable contribution to the development and progress of the State both before and after independence, as a potential means to convey Islamic da'wah and foster society. The students who study there acquire religious knowledge from the ustadz and ustadzah who teach at the Faizul Haq Ta'lim assembly based on the qura'an and sunnah. To further enrich the treasures and reference sources of religious knowledge, students also get it through internet media, namely with the help of search engines, but there are still many students who have not been able to use it optimally so that the search takes a long time. Therefore, we hold community service activities in the form of webinars to provide information and knowledge related to optimizing the use of search engines to assist students in finding the references they need and provide tips for faster and more effective searches. After holding community service in the form of counseling on search engine optimization for the students of the Faizul Haq Cisauk Ta'lim Council, the students can now use search engines like Google properly and correctly. They feel the difference from the previous one, it takes a long time and it is difficult to find the right reference and according to what they need. Now they can search for the references they need more effectively and efficiently.
Usability Testing Analysis on Digital Wallet Applications to Measure User Satisfaction Nur Mutia Eka Pusparani; Tyas Setiyorini; Frieyadie Frieyadie
Jurnal Riset Informatika Vol. 5 No. 4 (2023): September 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1096.109 KB) | DOI: 10.34288/jri.v5i4.119

Abstract

One of the issues that users of digital wallet apps often face is slow loading, which can cause frustration and disrupt the user experience. In addition, lack of app responsiveness due to server errors is also a complaint of users, which can lower their trust in the app. Another problem is the difficulty in the login process, which can make it difficult for users to access the application. From these problems, it is necessary to conduct a "usability testing analysis on digital wallets to measure user satisfaction." a study evaluates user satisfaction using ShopeePay, Dana, and Ovo as digital wallets. In this study, TCR is used as an indicator to measure the level of user satisfaction, and the variables considered are Attractiveness, Understandability, Learnability, and Operability. The results show that ShopeePay has the highest TCR of 78.77%, followed by Ovo at 77.32% and Dana at 75.58%. Attractiveness factors affect user satisfaction in ShopeePay, while in Dana, Learnability and Attractiveness factors influence. In Ovo, Operability and Attractiveness factors affect user satisfaction, while Understandability and Learnability have no significant effect. The findings from this study provide valuable insights for digital wallet service providers to optimize the factors that influence user satisfaction. This can help increase the acceptance and utilization of digital wallets in the growing market.
Comparison of the Application of Neural Networks with K-Fold Cross Validation and Sliding Window Validation for Forecasting Covid-19 Recovered Cases Tyas Setiyorini
Jurnal Riset Informatika Vol. 6 No. 1 (2023): December 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i1.263

Abstract

The Covid-19 virus first appeared in China resulting in millions of confirmed cases, deaths and recovered cases to date. The spread and increase in the death rate due to Covid-19 is very worrying. Health workers and researchers continue to struggle to improve recovery from Covid-19 cases. There is a need for future forecasting to predict recovery from cases that occur, so that the public or government can understand the spread, take precautions and prepare for action as early as possible. Several previous studies have carried out forecasting the future impact of Covid-19 using Machine Learning methods. Neural Network and Sliding Window are appropriate methods for forecasting time series data. In this research, it has been proven that the application of a Neural Network with a Sliding Window can improve performance which is much better than without using a Sliding Window in forecasting Covid-19 recovery cases in China.
Comparison of the Application of Linear Regression with Sliding Window Validation and K-Fold Cross-Validation for Forecasting Covid-19 Recovered Cases Tyas Setiyorini; Frieyadie Frieyadie
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i3.288

Abstract

The increase in confirmed cases and deaths due to Covid-10 continues to spread and increase day by day throughout the world. This has resulted in a world health crisis that impacts all sectors of life. The government declared a movement to suppress the spread of Covid-19, so it is necessary to understand the pattern of Covid-19 problems. Researchers contribute scientifically to finding patterns of death or recovery due to COVID-19 by applying Machine Learning methods. The Linear Regression and Sliding Window preprocessing methods are appropriate for forecasting time series data. This research obtained RMSE results at 0.320 with linear regression with sliding window validation and RMSE at 0.320 with linear regression with K-Fold cross-validation. This proves that Linear Regression with Sliding Window Validation can improve performance much better than k-fold cross-validation in forecasting COVID-19 recovery cases in China. The sliding window validation method has been proven to increase accuracy for forecasting with time series data compared to other standard preprocessing methods, namely K-Fold cross-validation. In the future, further research is needed to test different types of time series data by comparing the application of sliding window validation and K-Fold cross-validation or developing other validation models.
Alat Pendeteksi Dini Kebocoran Gas LPG Dengan Sensor MQ2 Dan Sensor Api Berbasis IoT Menggununakan NodeMCU Istiyanto, Ilham; Solehudin, Rizki; Nofarenzi, Yosari; Setiyorini, Tyas
Jurnal Infortech Vol 4, No 1 (2022): Juni 2022
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v4i1.12279

Abstract

Keamanan merupakan salah satu aspek terpenting dari suatu sistem atau lingkungan, seperti rumah, kantor, kampus, pedesaan, perkotaan, pusat perbelanjaan, dan lokasi lainnya. Kebakaran seringkali disebabkan oleh kelalaian manusia yang disebabkan oleh berbagai faktor, seperti puntung rokok yang tidak sengaja dibuang, korsleting listrik yang menyebabkan kebakaran dan merembet ke bagian lain dan juga kebocoran tabung gas LPG (liquefied petroleum gas) baik yang besar maupun yang kecil. Tujuan dari penelitian ini untuk meningkatkan keamanan terutama dari kebocoran gas LPG yang disebabkan oleh tabung gas itu sendiri ataupun dari kelalaian manusia. Untuk mengatasi hal tersebut maka kami membuat alat ini, agar saat kebocoran gas terjadi dapat di deteksi sejak dini. Metode yang menggunakan NodeMCU sebagai mikrokontroller, Relay sebagai saklar on off, MQ2 sebagai sensor gas, Flame sensor sebagai pendeteksi api, LCD 16x2 sebagai output , Buzzer sebagai output , LED sebagai output, Kipas sebagai ouput serta aplikasi Telegram sebagai media informasi. Hasil akhir dari alat ini sudah mencapai harapan peneliti karena alat dapat mendeteksi dini gas ataupun api serta untuk notifikasi melalui Telegram dengan baik.
PENERAPAN GINI INDEX DAN K-NEAREST NEIGHBOR UNTUK KLASIFIKASI TINGKAT KOGNITIF SOAL PADA TAKSONOMI BLOOM Setiyorini, Tyas; Asmono, Rizky Tri
Jurnal Pilar Nusa Mandiri Vol 13 No 2 (2017): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (893.144 KB)

Abstract

Sebagai pedoman dalam merancang ujian yang layak, yang terdiri dari soal-soal yang memiliki berbagai tingkatan secara kognitif, Taksonomi Bloom telah diterapkan secara luas. Saat ini, kalangan pendidik mengidentifikasi tingkat kognitif soal pada Taksonomi Bloom masih menggunakan cara manual. Hanya sedikit pendidik yang dapat mengidentifikasi tingkat kognitif dengan benar, sebagian besar melakukan kesalahan dalam mengklasifikasikan soal-soal. K-Nearest Neighbor (KNN) adalah metode yang efektif untuk klasifikasi tingkat kognitif soal pada Taksonomi Bloom, tetapi KNN memiliki kelemahan yaitu kompleksitas komputasi kemiripan datanya besar apabila dimensi fitur datanya tinggi. Untuk menyelesaikan kelemahan tersebut diperlukan metode Gini Index untuk mengurangi dimensi fitur yang tinggi. Beberapa percobaan dilakukan untuk memperoleh arsitektur yang terbaik dan menghasilkan klasifikasi yang akurat. Hasil dari 10 percobaan pada dataset Question Bank dengan KNN diperoleh akurasi tertinggi yaitu 59,97% dan kappa tertinggi yaitu 0,496. Kemudian pada KNN+Gini Index diperoleh akurasi tertinggi yaitu 66,18% dan kappa tertinggi yaitu 0,574. Berdasarkan hasil tersebut maka dapat disimpulkan bahwa Gini Index mampu mengurangi dimensi fitur yang tinggi, sehingga meningkatkan kinerja KNN dan meningkatkan tingkat akurasi klasifikasi tingkat kognitif soal pada Taksonomi Bloom.
Co-Authors Achmad Rifai ACHMAD RIFAI Aditiya Yoga Pratama Albert Riyandi Alengka, Son Gohan Angga Subrata Anggie Andriansyah Dedi Saputra Fitra Septia Nugraha Frieyadie Frieyadie Frieyadie Harsih Rianto Ibnu Alfarobi Ibnu Rusdi Istiyanto, Ilham Jordy Lasmana Putra Kudiantoro Widianto Kudiantoro Widianto, Kudiantoro Maryanah Safitri Maryanah Safitri Muhamad Ryansyah Nofarenzi, Yosari Nur Mutia Eka Pusparani Nurajijah Rismaniah Rismaniah Rizky Tri Asmono Rizky Tri Asmono Rizky Tri Asmono Rizky Tri Asmono Rizky Tri Asmono Romi Satria Wahono Sita Anggraeni Sita Anggraeni Sita Anggraeni, Sita Siti Nurdiani Solehudin, Rizki Sri Syaifur Rahmatullah Syaifur Rahmatullah, Syaifur