From Fingerprint to Footprint: Using Point of Interest (POI) Recommendation System in Marketing Applications

Abstract. Companies should be willing to adopt new technologies and business models to be able to stay competitive in the changing world, both regionally and globally. However, the US forest sector industry, including wood furniture sector seems to be lagging when it comes to implementing digital technologies. This study proposes a design of Point of Interest (POI) recommendation system to enhance the marketing practices to promote wood furniture stores. We produced a personal recommendation design utilising K-Means+ clustering, a combination between K-Means algorithm for spatial data clustering and Davies-Bouldin Index (DBI) methods to determine the optimal K value. This design can assist mobile users who are potential customers to find wood furniture store locations based on other users’ preferences. Keywords:  Digitalisation; location-based social networks; user-based collaborative filtering; K-Means+ clustering; DBI metho

APLIKASI SISTEM SKRINING MANDIRI BERBASIS WEB DALAM UPAYA MEMBANTU PENANGANAN PANDEMI COVID-19

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

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

DESAIN SISTEM MONITORING CERDAS KUALITAS AIR KERAMBA BUDIDAYA TERIPANG BERBASIS IOT

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 akura

PENGARUH PENERAPAN STRATEGI PEMBELAJARAN THINK TALK WRITE TERHADAP KEMAMPUAN KOMUNIKASI MATEMATIS SISWA

This quasi experimental research aimed to know the influence of implementation of think talk write learning strategy towards student’s mathematical commu­nication ability. The population of this research was all grade eight students of Junior High School 1 Bangunrejo Lampung Tengah in academic year of 2013/2014 and students of VIII A and VIII B class as samples which were taken by purposive sampling technique. The research data were obtained by test of mathematical communication ability. The conclusion of this research was think talk write learning strategy affects towards the student’s mathematical communication ability.Penelitian eksperimen semu ini bertujuan untuk mengetahui pengaruh penerapan  strategi pembelajaran think talk write terhadap kemampuan komunikasi matematis siswa. Populasi penelitian ini adalah seluruh siswa kelas VIII SMP Negeri 1 Bangunrejo Lampung Tengah tahun pelajaran 2013/2014 dan siswa kelas VIII A dan VIII B sebagai sampel yang diambil menggunakan teknik purposive sampling. Data penelitian diperoleh dari tes kemampuan komunikasi matematis. Kesimpulan penelitian ini adalah strategi pembelajaran think talk write berpengaruh terhadap kemampuan komunikasi matematis siswa.Kata kunci: komunikasi matematis, konvensional, think talk writ

Prediction of Digital Eye Strain Due to Online Learning Based on the Number of Blinks

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

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

Pengembangan Sistem Informasi Inventarisasi Jurusan Teknik Elektro Fakultas Teknik Universitas Negeri Malang

ABSTRAK   Setiowati, Sulis. 2014. Pengembangan Sistem Informasi Inventaris  Jurusan TeknikElektro Fakultas Teknik Universitas Negeri Malang. Skripsi, JurusanTeknik Elektro, Fakultas Teknik, Universitas Negeri Malang,Pembimbing: (1) Heru Wahyu Herwanto, S.T., M.Kom., (2) TriyannaWidiyaningtyas, S.T., M.T.   Keywords : Sistem Informasi, Inventaris, Web database Peningkatan mutu dalam suatu universitas maupun sekolah tidak lepasdari kegiatan pengelolaan data administarasi dan inventarisasi dalam jumlahbanyak, sehingga diperlukan teknologi informasi yang dapat membantu kegiatantersebut supaya pelayanan dapat dilakukan dengan maksimal. Jurusan TeknikElektro Universitas Negeri Malang adalah salah satu instansi yang belumsepenuhnya menerapkan aplikasi sistem informasi dalam pengelolaan data daninventarisasi. Dari hasil observasi didapatkan bahwa pelayanan dilakukan secaramanual baik peminjaman alat maupun peminjaman bahan habis pakai, sehinggaseringkali terdapat kehilangan barang maupun kesulitan dalam rekap datapelayanan yang menyebabkan kerugian pada jurusan. Sebelumnya telah adapengembangan Sistem Informasi Inventarisasi Laboratorium Berbasis Web diJurusan Teknik Elektro oleh Hardini Ratna Puspitasari pada tahun 2012, akan tetapiterdapat beberapa kekurangan dalam aspek fungsionalitas sehingga diperlukanpengembangan lebih lanjut untuk meningkatkan kinerja sistem. Merujuk dari haltersebut maka dibutuhkan sebuah perangkat lunak aplikasi sistem informasiinventarisasi (SYSIN). Tujuan dari pengembangan SYSIN adalah mengembangkan sisteminformasi yang sudah ada sebelumnya dengan perbaikan dan penambahan beberapafitur yang belum tersedia. Sistem informasi yang dikembangkan dapat melayanipeminjaman alat dan bahan habis pakai oleh pengguna (mahasiswa, dosen danpegawai), inventarisasi oleh Laboran. Pengembangan sistem informasiinventarisasi menggunakan metode pengembangan sistem Waterfall melaluitahapan sebagai berikut: (1) Requirements Definition, (2) System And SoftwareDesign, (3) Implementation and Unit Testing, (4) Integration and System Testing,dan (5) Operation and Maintenance. Subyek uji coba sistem informasi inventarisadalah ahli rekayasa web, pengguna (mahasiswa, dosen, dan pegawai), laboran danadministrator dengan menggunakan uji Black Box. Dari hasil data kuantitatif didapatkan presentase skor total validasisebesar 100% yang menunjukkan bahwa fungsionalitas SYSIN TE UM sudahditerima/valid dan dapat digunakan dengan beberapa perbaikan untukmeningkatkan kemampuan sistem. Sistem yang telah dikembangkan mempunyaifitur peminjaman alat, penggunaan bahan habis pakai dan manajemen inventarisalat dan bahan habis pakai

Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering

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