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Analisis Data Sebaran Penyakit Menggunakan Algoritma Density Based Spatial Clustering Of Applications With Noise Teguh Iman Hermanto; Muhamad Agus Sunandar
Jurnal Sains Komputer dan Teknologi Informasi Vol 3 No 1 (2020): Jurnal Sains Komputer dan Teknologi Informasi
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/jsakti.v3i1.1775

Dalam membantu mengembangkan teknologi untuk meningkatkan pelayanan kesehatan masyarakat yang lebih baik, membutuhkan suatu informasi serta data yang cukup agar dapat dianalisis lebih lanjut. Namun dalam hal melakukan monitoring daerah rentan penyakit masih menggunakan perhitungan dengan rata-rata dari hasil data yang ada dan dalam hal ini menghasilkan output yang kurang maksimal untuk melakukan kebijakan-kebijakan terkait pelayanan kesehatan. Masalah ini dapat diatasi dengan menerapkan teknik data mining dengan metode clustering. Tujuan dari penelitian ini adalah melakukan analisis daerah rentan penyakit yang ada di puskesmas mulyamekar agar dapat membantu kebijakan dalam memberikan penyuluhan ke daerah yang sesuai. Metode analisis yang digunakan adalah Knowledge Discovery in Database (KDD). Metode pengelompokan daerah rentan penyakit menggunakan metode clustering dan algoritma Density Based Spatial Clustering of Applications with Noise (DBSCAN) sebagai perhitungan clusteringnya dimana Clustering ini bertujuan untuk membagi daerah ke dalam cluster berdasarkan lokasi titik sebaran penyakit. Hasil penelitian yang diperoleh adalah dashboard hasil analisis sebaran data penyakit menggunakan algoritma DBSCAN yang terdiri dari peta sebaran penyakit, hasil cluster penyakit, presentase sebaran penyakit per kecamatan dan perbandingan minimum point dan minimum epsilon. Dengan adanya analisis ini dapat menjadi acuan terhadap kebijakan-kebijakan yang akan diambil oleh UPTD Puskesmas Mulyamekar dalam pengambilan keputusan.

ANALISIS ASSOCIATION RULE UNTUK IDENTIFIKASI POLA GEJALA PENYAKIT HIPERTENSI MENGGUNAKAN ALGORITMA APRIORI (STUDI KASUS: KLINIK RAFINA MEDICAL CENTER) Salsabilla Choerunnisa Nurzanah; Syariful Alam; Teguh Iman Hermanto
Jurnal Informatika dan Komputer Vol 5, No 2 (2022)
Publisher : JIKO (Jurnal Informatika dan Komputer)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v5i2.4792

Hipertensi didefinisikan sebagai penyakit krnis yang dapat menyebabkan tekanan darah meningkat melebihi 140/90 mmHg. Tidak adanya gejala spesifik dari penyakit hipertensi dapat menyebabkan penyakit yang serius atau komplikasi pada penderita hipertensi, oleh karena itu hipertensi dapat dilakukan sebagai “silent killer”. Klinik Rafina Medical Center merupakan suatu fasilitas kesehatan tingkat pertama di Kabupaten Purwakarta, Jawa Barat. Di Klinik Rafina Medical Center sendiri penyakit hipertensi dari tahun 2020 sampai awal tahun 2022 selalu termasuk ke dalam 10 diagnosa terbanyak, artinya banyak pasien yang terdiagnosa hipertensi di klinik tersebut. Oleh karena itu, dilakukan identifikasi menggunakan metode data mining yang bertujuan untuk menghasilkan informasi mengenai pola gejala hipertensi. Metode analisis data pada penelitian ini yaitu metode Knowledge Discovery in Database (KDD). Metode data mining yang digunakan yaitu metode association rule, dimana metode ini dapat mencari dan mengidentifikasi pola asosiasi antar atribut dalam suatu dataset. Algoritma pada penelitian ini adalah algoritma apriori. Tools dalam penelitian ini menggunakan software Orange. Hasil dari penelitian ini berupa aturan asosiasi atau pola gejala hipertensi, sehingga dari hasil tersebut diharapkan dapat memberikan pengetahuan baru mengenai pola gejala hipertensi sehingga dapat digunakan dalam penyuluhan dan penyediaan obat bagi penderita hipertensi khususnya di Klinik Rafina Medical Center.

SENTIMENT ANALYSIS OF INTERNET SERVICE PROVIDERS USING NAÏVE BAYES BASED ON PARTICLE SWARM OPTIMIZATION Anugrah Anugrah; Teguh Iman Hermanto; Ismi Kaniawulan
Jurnal Riset Informatika Vol 4 No 4 (2022): Period of September 2022
Publisher : Kresnamedia Publisher

Twitter is a social media application that is widely used. Where as many as 18.45 million users in Indonesia, Twitter users can send and read messages with a maximum of 280 characters displayed. Many opinions and reviews uploaded by users via tweets on social media are experienced in everyday life. Lately, comments about internet service providers in the covid-19 pandemic have been widely reviewed by Twitter users. Problems about internet providers through words often uploaded include internet provider complaints related to network quality, package prices, user satisfaction, and others. This study aims to classify Twitter users' tweets against internet service providers in Indonesia by analyzing the sentiments of 3 internet service providers, namely with the keywords Biznet, first media, and Indihome, using the Naïve Bayes algorithm and optimization with Particle Swarm Optimization. This research is also helpful in helping to become a measure where prospective new users will see the quality of an internet service provider in Indonesia through tweets and then divide the opinion into positive and negative. The results of Biznet's research using Naïve Bayes produce an accuracy of 77.94%, and after optimization, it becomes 81.62%. First media using Naïve Bayes produces 91.39% accuracy, and after optimization, it becomes 92.88%. Indihome using Naïve Bayes produces an accuracy of 85.78%, and after optimization, it becomes 87.48%. It can be concluded that the Naïve Bayes algorithm is a good algorithm for classification, and optimization using Particle Swarm Optimization has an effect on increasing accuracy results

SENTIMENT ANALYSIS USING K-NEAREST NEIGHBOR BASED ON PARTICLE SWARM OPTIMIZATION ACCORDING TO SUNSCREEN’S REVIEWS Anita Nur Syifa Rahayu; Teguh Iman Hermanto; Imam Ma'ruf Nugroho
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.6.425

High UV exposure and tropical climate are afftected by the equator that passing through Indonesia. In this case, early aging will haunted not only by skincare lovers but also the rest of Indonesians including male and female. By so, we need to protect our skin using sun protector like sunscreen. Sunscreen application awareness through reviews by many different user probably are the most effective way to get to know the suitable sunscreen. By scrolling through the reviews surely will be time wasting. As of, sentiment analysis is the solution to classifying between negative and positive sentiments from the reviews. This research uses K-Nearest Neighbor (K-NN) algorithm as classification method because this method way more easy and efficient to use by its self learning, thus K-NN can learned its own data through its neighbor. Particle Swarm Optimization is used to increasing the accuration. Evaluation method using Confusion Matrix and the results are accuracy, precision and recall. Classification result using only k-NN and optimized with PSO are getting increase. Brand ‘Azarine’ rank the first accuration value with 91,89% using k-NN and 92,80% using k-NN PSO.

KLASIFIKASI KONDISI BAN KENDARAAN MENGGUNAKAN ARSITEKTUR VGG16 ahmad fudolizaenun nazhirin; Muhammad Rafi Muttaqin; Teguh Iman Hermanto
INTI Nusa Mandiri Vol 18 No 1 (2023): INTI Periode Agustus 2023
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i1.4270

Tyres are the main component that a vehicle needs to work with reducing vibration due to uneven road surfaces, protecting the wheels from wear to provide stability between the vehicle and the ground helping to improve acceleration to facilitate travel while driving. Wear ensures stability between the vehicle and the ground helps improve acceleration for easy movement and driving. Caused including components that are often used, tires can experience damage such as the appearance of cracks in the tires. Cracks in tires can be triggered by factors such as age or the cause of the road that has been exceeded. Detection of tire cracks at this time is still carried out conventionally, where users see directly the state of the tire whether the tire is in good condition or cracked. Conventional methods are important because they maintain tire quality and rider safety. The Conventional Method certainly has weaknesses because vehicle users must have good vision and the ability to distinguish normal tires or cracked tires, but this method is considered less effective because it still uses human labor, causing the risk of human error (human negligence) which can hinder the process of identifying tire cracks. Based on this problem, this study will develop a deep learning model that can classify cracked tires using the VGG16 architecture. In this study, the model was created using 8 scenarios by changing the value of epochs, to get the best parameters in making the model. The results of the 8 scenarios carried out in this study are the best scenario obtained in scenarios 1,3,4 which get 98% accuracy in model testing

SENTIMENT ANALYSIS OF INTERNET SERVICE PROVIDERS USING NAÏVE BAYES BASED ON PARTICLE SWARM OPTIMIZATION Anugrah Anugrah; Teguh Iman Hermanto; Ismi Kaniawulan
Jurnal Riset Informatika Vol. 4 No. 4 (2022): September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i4.117

Twitter is a social media application that is widely used. Where as many as 18.45 million users in Indonesia, Twitter users can send and read messages with a maximum of 280 characters displayed. Many opinions and reviews uploaded by users via tweets on social media are experienced in everyday life. Lately, comments about internet service providers in the covid-19 pandemic have been widely reviewed by Twitter users. Problems about internet providers through words often uploaded include internet provider complaints related to network quality, package prices, user satisfaction, and others. This study aims to classify Twitter users' tweets against internet service providers in Indonesia by analyzing the sentiments of 3 internet service providers, namely with the keywords Biznet, first media, and Indihome, using the Naïve Bayes algorithm and optimization with Particle Swarm Optimization. This research is also helpful in helping to become a measure where prospective new users will see the quality of an internet service provider in Indonesia through tweets and then divide the opinion into positive and negative. The results of Biznet's research using Naïve Bayes produce an accuracy of 77.94%, and after optimization, it becomes 81.62%. First media using Naïve Bayes produces 91.39% accuracy, and after optimization, it becomes 92.88%. Indihome using Naïve Bayes produces an accuracy of 85.78%, and after optimization, it becomes 87.48%. It can be concluded that the Naïve Bayes algorithm is a good algorithm for classification, and optimization using Particle Swarm Optimization has an effect on increasing accuracy results.

ANALISIS SEGMENTASI PELANGGAN BERBASIS MODEL RECENCY FREQUENCY DAN MONETARY (RFM) MENGGUNAKAN ALGORITMA K-MEANS Panji Indra Pangestu; Teguh Iman Hermanto; Dede Irmayanti
Jurnal Informatika dan Teknik Elektro Terapan Vol 11, No 3s1 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3s1.3396

Business development is currently growing very rapidly, with the development of internet technology that can facilitate all business activities. Increasing business development has an impact on presenting new business competitors, so companies need strategies that are able to maintain customer quality. This study aims to segment customers from the company's sales transaction data, with a large number of transactions, technology is needed to group a data so that the method used in this study is a data mining method and uses the K-Means algorithm. With the K-Means Algorithm, it can help in grouping customers to make it easier for companies to strategize each customer group. This customer grouping uses an initial model of Recency, Frequency and Monetary (RFM) to help calculate customer groups. Data mining evaluation was carried out using Silhouette Coefficient with test results using Visual Studio Code software python programming language, The results of this study selected 3 clusters consisting of Low Loyalty totaling 137 customers, Medium Loyalty totaling 1636 customers and Highest Loyalty totaling 2395 customers.

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