Article Per Year (5 Year)
p-Index From 2019 - 2024
1.07
P-Index
This Author published in this journals
All Journal Jurnal Dedikasi Perfecting a Video Game with Game Metrics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika)
Lailis Syafa’ah
Universitas Muhammadiyah Malang
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Enabling seamless communication over several IoT messaging protocols in OpenFlow network Fauzi Dwi Setiawan Sumadi; Agus Eko Minarno; Lailis Syafa’ah; Muhammad Irfan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.20412

Abstract

The most prominent protocols for data transfer in internet of things (IoT) are message queuing telemetry transport (MQTT) and constrained application protocol (CoAP). The existing clients from both sides are unable to communicate directly because of the packet’s header structure difference in application and transport layer. In response, this paper aims to develop a bidirectional conversion server used to translate the specified messaging protocol interchangeably in the OpenFlow network and transmit the converted packet from both sides. The conversion server integrated the MQTT subscriber and CoAP POST object for converting the MQTT message into CoAP data. Similarly, the CoAP-MQTT translation was processed by CoAP GET and MQTT publisher object. The research was evaluated by analysing the round trip time (RTT) value, conversion delay, and power consumption. The RTT value for MQTT-CoAP required 0.5 s while the CoAP-MQTT was accumulated in 0.1 s for single-packet transmission. In addition, the SDN controller and the conversion server only consumed less than 1% central processing unit (CPU) usage during the experiment. The result indicated that the proposed conversion server could handle the translation even though there was an overwhelming request from the clients.
Bootcamp Seminar and Machine Learning Algorithm Workshop for the Data Science Club Agus Eko Minarno; Lailis Syafa’ah; Moch. Chamdani Mustaqim
Jurnal Dedikasi Vol. 18 No. 2 (2021): November
Publisher : Direktorat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/dedikasi.v18i2.16189

Abstract

The development of data and information needs in the era of Society 5.0 is very crucial because it determines many business decisions. Data in the past becomes valuable when it becomes a historical fact that can illustrate findings to assess future business directions. Based at the University of Muhammadiyah Malang, the Data Science Club has a total membership of more than 200 people spread across East Java. The problem that often occurs in the Data Science community is Machine Learning algorithms' low literacy, especially for new members. Coupled with the development of the Machine Learning algorithm that is so fast and massive. For that, we need activities that can directly impact the Data Science community by presenting the latest algorithms and programming techniques. This service activity proposes a Machine Learning workshop for Data Science by teaching various computational algorithms to the Indonesian Data Science community, which has spread in Indonesia and the East Java region. This activity presents 12 workshop materials for participants who will be delivered by speakers who have expertise in their fields, both from the University of Muhammadiyah Malang, and present national speakers in collaboration with the Data Science Club of the University of Muhammadiyah Malang.
Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes Merinda Lestandy; Abdurrahim Abdurrahim; Lailis Syafa’ah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (401.645 KB) | DOI: 10.29207/resti.v5i4.3308

Abstract

COVID-19 has become a global pandemic including Indonesia, so the government is taking vaccinations as a preventive measure. The public's response to this continues to appear on social media platforms, one of which is Twitter. Tweets about the COVID-19 vaccine have generated various kinds of positive and negative opinions in the community. Therefore, it is very important to detect and filter it to prevent the spread of incorrect information. Sentiment analysis is a method used to determine the content of a dataset in the form of negative, positive or neutral text. The dataset in this study was obtained from 5000 COVID-19 vaccine tweets with the distribution of 3800 positive sentiment tweets, 800 negative sentiment tweets and 400 neutral sentiment tweets. The dataset obtained is then pre-processed data to optimize data processing. There are 4 stages of pre-processing, including remove punctuation, case folding, stemming and tokenizing. This study examines the performance of RNN and Naïve Bayes by adding the TF-IDF (Term Frequency-Inverse Document Frequency) technique which aims to give weight to the word relationship (term) of a document. The test results show that RNN (TF-IDF) has a greater accuracy of 97.77% compared to Naïve Bayes (TF-IDF) of 80%.
Penerapan Deep Learning untuk Prediksi Kasus Aktif Covid-19 Lailis Syafa’ah; Merinda Lestandy
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (896.278 KB) | DOI: 10.30645/j-sakti.v5i1.337

Abstract

Coronavirus disease (Covid-19) is increasingly spreading in Indonesia, so it requires an approach to predict its spread. One approach method that is often used is the Deep Learning (DL) method. DL is a branch of Machine Learning (ML) which is modeled based on the human nervous system. In this study, the prediction of active Covid-19 cases was resolved using the DL method. The dataset used is 260 data with 10 parameters. DL is able to provide an accurate prediction of active cases of Covid-19 with an MSE of 0.032 and an accuracy of 81.333%.
Penerapan Deep Learning untuk Prediksi Kasus Aktif Covid-19 Lailis Syafa’ah; Merinda Lestandy
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.337

Abstract

Coronavirus disease (Covid-19) is increasingly spreading in Indonesia, so it requires an approach to predict its spread. One approach method that is often used is the Deep Learning (DL) method. DL is a branch of Machine Learning (ML) which is modeled based on the human nervous system. In this study, the prediction of active Covid-19 cases was resolved using the DL method. The dataset used is 260 data with 10 parameters. DL is able to provide an accurate prediction of active cases of Covid-19 with an MSE of 0.032 and an accuracy of 81.333%.
Co-Authors Abdurrahim Abdurrahim Agus Eko Minarno Irfan, Muhammad Merinda Lestandy Moch. Chamdani Mustaqim Sumadi, Fauzi Dwi Setiawan