Garuda - Garba Rujukan Digital

IMPLEMENTASI ALGORITMA LINEAR REGRESSION UNTUK PREDIKSI HARGA SAHAM PT. ANEKA TAMBANG TBK Teguh Iman Hermanto; Imam Ma’ruf Nugroho; Muhamad Agus Sunandar; Mochamad Hafid Totohendarto
Jurnal Transformatika Vol 19, No 2 (2022): January 2022
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v19i2.4396

Stock investments that provide high returns, but the higher the benefits offered, the higher the risk that will be faced in investing, especially if it is not supported by knowledge of analyzing stocks. This study utilizes the Data Mining prediction technique with the Linear Regression algorithm on the shares of PT. Aneka Tambang Tbk or ANTM. The dataset that will be used is downloaded through the Yahoo Finance website in the period January 2016 - March 2021. In this study the analytical method used is SEMMA (Sample, Explore, Modify, Model, Assess). With RapidMiner Studio 9.9 tools. The result of testing the RMSE (Root Mean Squared Error) value is 17.135, MSE (Mean Squared Error) is 293.599 and the MAPE (Mean Absolute Percentage Error) value is 1.87%. Based on the MAPE, the accuracy of the Linear Regression algorithm in predicting the stock price of PT. Aneka Tambang Tbk provides high-accuracy predictions.

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

NAÏVE BAYES ALGORITHM OPTIMIZATION USING PARTICLE SWARM OPTIMIZATION (PSO) FOR COVID-19 VACCINE SENTIMENT ANALYSIS ON TWITTER Rivan Adi Nugraha; Teguh Iman Hermanto; Imam Ma’ruf Nugroho
Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA) Vol. 6 No. 1 (2022): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM Prima)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2776

The Covid-19 vaccine is a vaccine that is quite popular, because it is the most needed and most discussed vaccine. There are 5 types of vaccines that are very popular including AstraZeneca, Moderna, Pfizer, Sinopharm and Sinovac. Sentiment analysis is a branch of text classification with computational linguistics and natural language processing that refers to a broad field, and text mining has a function to analyze opinions, judgments, sentiments, attitudes, evaluations and emotions of a person regarding an individual, organization, certain topics, services and other activities. This study aims to classify public sentiment towards the type of Covid-19 vaccine on social media Twitter, whether the opinion is positive or negative by using the Naïve Bayes algorithm based on Particle Swarm Optimization (PSO). The conclusion of this study is that the results of testing the Naïve Bayes algorithm with PSO using RapidMiner software are 79.17% accuracy, 87.69% precision, 85.07% recall for AstraZeneca vaccine, 68.82% accuracy, 92.29% precision, 71.72% recall for Moderna vaccine, 67.54% accuracy, precision 77.83%, recall 62.95% for Pfizer vaccine, accuracy 93.33%, precision 91.67%, recall 100.00% for Sinopharm vaccine, and accuracy 74.93%, precision 82.61%, recall 70.90% for Sinovac vaccine. It can be concluded that with the help of optimization PSO, the resulting confusion matrix value is greater and is proven to be more accurate. Keywords : Vaccine; Covid-19; Sentiment Analysis; Naive Bayes; Particle Swarm Optimization.

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.

Sentiment Analysis Of Tourist Reviews Using K-Nearest Neighbors Algorithm And Support Vector Machine Anita Wulan Sari; Teguh Iman Hermanto; Meriska Defriani
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2023): Article Research Volume 8 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12447

After Indonesia was awarded as a country with extraordinary natural charm, many foreign tourists came to Indonesia. According to the records of the Central Bureau of Statistics for 2020, approximately 5.47 million foreign tourists entered Indonesia. With the large number of foreign tourist visits, the need for tourist attractions is increasing, but finding information is now not difficult. One source of information for finding reviews of tourist attractions is TripAdvisor. On this website, there is a lot of information or reviews about various tourist attractions. However, the number of reviews makes tourists confused about identifying the quality of tourist attractions to be visited, so sentiment analysis needs to be done. Sentiment analysis itself is a technique to extract, identify, and understand sentiments or opinions contained in a text. In this research, two classification methods will be used in sentiment analysis techniques, namely K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM). Besides that, the object of this research will be to focus on the most popular tourist attractions in Indonesia according to Trip Advisor, namely Waterbom Bali, Mandala Suci Wenara Wana, Teras Sawah Tegalalang, Pura Tanah Lot, and Pura Luhur Uluwatu. The purpose of the research is to find out the results of accurate sentiment analysis for the five tourist attractions and compare the two algorithms used. and after testing, it was found that the Support Vector Machine algorithm is superior to the K-Nearest Neighbors algorithm.

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.

Implementasi Algoritma Support Vector Machine dan Randoom Forest Terhadap Analisis Sentimen Masyarakat Dalam Penggunaan Aplikasi Tiket.com, Traveloka, dan Agoda Pada Google Playstore Calleb Bhaskoro Prabowo; Teguh Iman Hermanto; Imam Ma'ruf
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 13, No 1 (2024): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v13i1.5378

Internet hadir sebagai elemen penting dalam menyokong perkembangan teknologi dan informasi dalam segala sektor aktifitas manusia. Pada sektor perdagangan dan pariwisata contohnya, aplikasi Tiket.com, Traveloka, dan Agoda menjadi aplikasi yang paling diminati masyrakat Indonesia saat ini. Ulasan atau review yang disematkan oleh para pengguna merupakan hal penting bagi pihak perusahaan untuk mengetahui kepuasan pelanggan yang nantinya digunakan untuk meningkatkan kualitas dalam segi pelayanan. Proses menganalisis ulasan komentar memiliki beberapa tahapan karena memiliki data yang jumlahnya tidak sedikit. Penggunaan suatu metode membantu dalam melakukan proses klasifikasi komentar yang bersifat positif atau negatif. Ulasan pengguna aplikasi yang diproses diambil dari platform layanan penyedia aplikasi Google Playstore, lalu menarik data yang diinginkan dengan mamasukan library Google Scraper pada Python. Data yang sudah ditarik selanjutnya diberi label untuk memisahkan ulasan yang bersifat positif dan negatif hal ini bertujuan untuk mepermudah proses klasifikasi dengan menggunakan metode Support Vector Machine (SVM) dan Random Forest. Hasil yang didapatkan nantinya merupakan tingkat akurasi dari dua metode yang digunakan berdasarkan pengolahan data yang sudah dilakukan pada masing-masing ulasan yang menjadi data set pada setiap aplikasi. Support Vector Machine memiliki akurasi yang lebih baik dibandingkan dengan Random Forest dengan rincian 85,5%, 87%, dan 88,7% pada urutan aplikasi Tiket.com, Traveloka, dan Agoda. Sedangkan Random Forest memiliki akurasi 84.7%, 84.7%, dan 88.2% dengan urutan aplikasi yang sama.

Title

Found 10 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 10 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 10 Documents
Search