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APLIKASI SISTEM SKRINING MANDIRI BERBASIS WEB DALAM UPAYA MEMBANTU PENANGANAN PANDEMI COVID-19 Sulis Setiowati; Rika Novita Wardhani; Riandini
Panrita Abdi - Jurnal Pengabdian pada Masyarakat Vol. 5 No. 4 (2021): Jurnal Panrita Abdi - Oktober 2021
Publisher : LP2M Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/pa.v5i4.11461

Corona virus is a group of viruses that can cause disease in animals or humans. Since the first case was discovered, the spread of COVID-19 in Indonesia has been very fast and massive to date. nowadays, technology and digitization have started to simplify all human jobs. One of them is the covid-19 screening system in various regions. This is very helpful in reducing the rate of spread of the virus, but not all regions can use the system. This community service aims to reduce the spread of COVID-19 in Indonesia, especially in the Beji area, Depok City with a web-based self-screening system application as an effort to detect COVID-19 early. The method used consists of several steps, namely collecting data on the residents who live in Beji, Depok; create a screening website related to travel history and health conditions of residents, especially symptoms of COVID-19. The data collected is used as access to the website to fill in the questions on the screening website. That way, it is hoped that the collection of citizen data can be carried out without physical contact because it can be done in their respective homes, thereby reducing the risk of spreading COVID-19. The result showed that the screening application was effective in assisting the COVID-19 task force in the Beji area in conducting the tracing and screening of its citizens. --- Virus Corona adalah suatu kelompok virus yang dapat menyebabkan penyakit pada hewan atau manusia. Sejak kasus pertama ditemukan, penyebaran COVID-19 di Indonesia sangat cepat dan masif sampai saat ini. Saat ini teknologi dan digitalisasi sudah mulai mempermudah segala pekerjaan manusia. Salah satunya mulai bermunculan sistem skrinning covid-19 diberbagai daerah. Hal ini tentu sangat membantu dalam menekan laju penyebaran virus, akan tetapi belum semua daerah dapat menggunakan sistem tersebut. Pengabdian masyarakat ini bertujuan untuk mengupayakan mengurangi penyebaran COVID-19 di Indonesia terutama di wilayah Beji, Kota Depok dengan aplikasi sistem skrining mandiri berbasis web sebagai upaya deteksi awal COVID-19. Metode yang dilakukan terdiri dari beberapa tahap yaitu mendata warga yang bertempat tinggal di Beji, Depok; membuat website skrinning terkait riwayat perjalanan dan kondisi kesehatan warga terutama gejala COVID-19. Data yang dikumpulkan tadi digunakan sebagai akses masuk kedalam website untuk setiap warga yang nantinya akan diminta untuk mengisi pertanyaan pada website skrinning tersebut. Dengan begitu diharapkan pengumpulan data warga dapat dilakukan tanpa adanya kontak fisik karena dapat dilakukan di rumah masing-masing sehingga mengurangi resiko penyebaran COVID-19. Dari hasil uji coba sistem, didapati bahwa aplikasi skrinning efektif dalam membantu satgas COVID kelurahan Beji dalam melakukan tracing dan screening terhadap warganya.

Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering Sulis Setiowati; Teguh Bharata Adji; Igi Ardiyanto
Computer Engineering and Applications Journal Vol 11 No 2 (2022)
Publisher : Universitas Sriwijaya

Recommendation system always involves huge volumes of data, therefore it causes the scalability issues that do not only increase the processing time but also reduce the accuracy. In addition, the type of data used also greatly affects the result of the recommendations. In the recommendation system, there are two common types of data namely implicit (binary) rating and explicit (scalar) rating. Binary rating produces lower accuracy when it is not handled with the properly. Thus, optimized K-Means+ clustering and user-based collaborative filtering are proposed in this research. The K-Means clustering is optimized by selecting the K value using the Davies-Bouldin Index (DBI) method. The experimental result shows that the optimization of the K values produces better clustering than Elbow Method. The K-Means+ and User-Based Collaborative Filtering (UBCF) produce precision of 8.6% and f-measure of 7.2%, respectively. The proposed method was compared to DBSCAN algorithm with UBCF, and had better accuracy of 1% increase in precision value. This result proves that K-Means+ with UBCF can handle implicit feedback datasets and improve precision.

Prediction of Digital Eye Strain Due to Online Learning Based on the Number of Blinks Riandini Riandini; Satria Arief Aditya; Rika Novita Wardhani; Sulis Setiowati
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 2: June 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i2.3500

Eye strain is a big concern, especially when it comes to continuous and prolonged online learning. If this is allowed to continue, it will result in Computer Vision Syndrome, also known as Digital Eye Strain (DES), which includes headaches, blurred vision, dry eyes, and even neck and shoulder pain. This condition can be observed either directly based on excessive eye blinking or indirectly based on observations of the electrical activity of eye movements or electrooculography (EOG). The observed blink signal from the EOG, as a representation of eye strain, is the focus of this study. Data acquisition was obtained using the EOG sensor and was carried out on the condition that the participants were conducting online learning activities. There are four different modes of observation taken in succession: when the eye is in a viewing state but without blinking, when the eye blinks intentionally, when the eye is closed, and finally when the eye sees naturally. Observation time is 10s, 20s and 30s, where each interval is performed three times for every mode. The obtained signal is processed by the proposed method. The resulting signal is then labeled as a Blinking signal. Determination of the number of blinks or CNT_PEAK is the result of training this signal by tuning its threshold and width. If the number of blinks is less than or more than 17 then the system will provide a prediction of eye status which is stated in two categories, the first is normal eye while the last is eye strain or fatigue.

LoRa Communication in the Service Level Monitoring Satu Duit Bogor Bridge Sulis Setiowati; Riandini Riandini; Via Arsita Sari; Indah Luthfiyyah Purwanti; Noval Andriansyah
JITCE (Journal of Information Technology and Computer Engineering) Vol 7 No 01 (2023): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.7.01.19-28.2023

Lora is the solution to the problem of the need for long-distance two-way communication between machines that are targeted by IoT (Internet of Thins. LoRa has long-distance transmission capabilities, has power, and a low bit rate. Based on the needs related to LoRa, further research is needed, to analyze the performance of LoRa communication. The LoRa communication protocol will be applied to the One Duit Bogor bridge monitoring system using the Website and LabVIEW. This study used LoRa SX1276 with a frequency of 915MHz with the LoRa point-to-point method and LoRa gateway. The parameters analyzed include RSSI (Received Signal Strength Indicator), SNR (Signal to Noise Ratio), Delay, Throughput, and Packet loss to determine the quality of LoRa performance with TIPHON standards. Based on the tests that have been carried out, it proves that LoRa communication has good performance. In urban areas or around the Satu Duit Bogor bridge, LoRa can transmit data from a distance of 0 to 500 m with an average delay of 217 ms, an average packet loss of 10.237%, an average throughput of 137.881 bps, an average SNR of 7.54 dB, and an average RSSI of -71,798 dBm. At a distance of 0-400 m there is an insignificant change in LoRa parameters, but at a distance of 500 m a high change occurs, this is due to the fact that the distance greatly affects the transmission of data. The longer the range, the more obstacles will be passed so that data transmission is disrupted.

DESAIN SISTEM MONITORING CERDAS KUALITAS AIR KERAMBA BUDIDAYA TERIPANG BERBASIS IOT Sulis Setiowati; Rika Novita Wardhani; Sri Danaryani; Riandini Riandini
Jurnal Ilmiah Matrik Vol 24 No 1 (2022): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/jurnalmatrik.v24i1.1648

Sistem monitoring kualitas air budidaya ikan laut akan dirancang menggunakan sensor cerdas dengan menyesuaikan kondisi lingkungan teripang, yaitu kualitas air pada salinitas 30-37%, dimana air laut umumnya mempunyai salinitas antara 33-37%, di perairan pantai berkisar antara 32-35% dan kondisi perairan dengan kisaran optimum pH 7,5-8,0 serta kondisi jumlah oksigen terlarut (Dissolved Oxygen) berkisar antara 5,0-5,5 mg/L dalam perairan. Salinitas, pH, dan DO merupakan faktor utama sebuah keramba menjadi lebih sensitif terhadap budidaya teripang, apabila tidak terpantau rutin. Maka dikembangkanlah inference engine dengan logika fuzzy untuk memantau DO, pH, dan salinitas serta model algoritma pembelajaran supervise. Hasil simulasi akan dianalisis dengan algoritma pembelajaran berbasis supervisi, menghitung bobot dan bias secara iteratif. Representasi data diakuisisi dan dikembangkan kecerdasan buatan model fuzzy untuk memantau DO, pH, dan salinitas. Kemudian menggunakan software LabVIEW yang mampu memonitor dan mengakuisisi data secara cepat dan akurat serta microcontroller sebagai pengolah data dari sensor DO, pH, dan salinitas. Luaran penelitian ini akan merealisasikan prototipe system monitoring jarak jauh dengan teknologi IoT yang ditujukan untuk memonitor nilai pH 7,77-8,27, DO pada 5,0-5,5 mg/L, dan salinitas pada 27,33-30 ppt secara kontinyu dan akurat

Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering Sulis Setiowati; Teguh Bharata Adji; Igi Ardiyanto
Computer Engineering and Applications Journal Vol 13 No 1 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i1.388

Recommendation system always involves huge volumes of data, therefore it causes the scalability issues that do not only increase the processing time but also reduce the accuracy. In addition, the type of data used also greatly affects the result of the recommendations. In the recommendation system, there are two common types of data namely implicit (binary) rating and explicit (scalar) rating. Binary rating produces lower accuracy when it is not handled with the properly. Thus, optimized K-Means+ clustering and user-based collaborative filtering are proposed in this research. The K-Means clustering is optimized by selecting the K value using the Davies-Bouldin Index (DBI) method. The experimental result shows that the optimization of the K values produces better clustering than Elbow Method. The K-Means+ and User-Based Collaborative Filtering (UBCF) produce precision of 8.6% and f-measure of 7.2%, respectively. The proposed method was compared to DBSCAN algorithm with UBCF, and had better accuracy of 1% increase in precision value. This result proves that K-Means+ with UBCF can handle implicit feedback datasets and improve precision.

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