Article Per Year (5 Year)
p-Index From 2021 - 2026
1.066
P-Index
This Author published in this journals
All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) KLIK: Kajian Ilmiah Informatika dan Komputer sudo Jurnal Teknik Informatika
Teguh Iman Hermanto
Sekolah Tinggi Teknologi Wastukancana, Purwakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Analisis Sentimen Opini Pengguna Twitter Terhadap Perusahaan Jasa Ekspedisi Menggunakan Algoritma Naïve Bayes Berbasis PSO Nenden Legiawati; Teguh Iman Hermanto; Yudhi Raymond Ramadhan
JURIKOM (Jurnal Riset Komputer) Vol 9, No 4 (2022): Agustus 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i4.4629

Abstract

Expeditions are used in the process of delivering goods or selling them remotely. Twitter has become a social media for information sharing and opinions, including those on the expeditionary services both negative and positive. The solution for the problem which occurs is that of sentiment analysis, helpful in grouping the data and predicting the tweet. The aim of the research to predict sentient tweeted data using a file classification method, naive bayes's algorithm calculated the value of the tweets of the Anteraja expedition service that had the results accuracy 87,77%, precision 76,67%, recall 52,27%. JNE with accuracy 81,48%, precision 71,43%, dan recall 62,50%. JNT with accuracy 91,46%, precision 48,15%, recall 86,67%. Shopee Express with accuracy 92,68%, precision 9,09%, recall 16,67% and Sicepat with accuracy 91,50%, presision 100,00% dan recall 38,10%. Particle Swarm Optimization (PSO) serves to increase the value of the results of the nave Bayes classification with the results of Anteraja accuracy 91,70%, precision 82,05%, recall 72,73%. JNE accuracy 93,83%, precision 88,00%, recall 91,67%. JNT accuracy 92,18%, precision 70,97%, recall 81,48%. Shopee Express accuracy 94%, precision 20,00%, recall 33,33% and Sicepat accuracy 95,42%, precision 93,75%, recall 71,43%. From the results of naïve bayes research and Particle Swarm Optimization (PSO) it can be compared that Particle Swarm Optimization (PSO) is proven to be able to increase the value of nave Bayes.
Rancangan UI/UX Design Aplikasi Pembelajaran Bahasa Jepang Pada Sekolah Menengah Atas Menggunakan Metode Design Thinking Muhammad Adhitya Dhita Pratama; Yudhi Raymond Ramadhan; Teguh Iman Hermanto
JURIKOM (Jurnal Riset Komputer) Vol 9, No 4 (2022): Agustus 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i4.4442

Abstract

In the world of education that has developed, of course, we are indirectly required to be able to understand or master the use of foreign languages. SMA Negeri 1 Cikarang Pusat is one of the schools that learn Japanese. The students who study Japanese at the school have various kinds of great motivation, but also not a few of the students who can understand and even have difficulty in learning Japanese. Given that there are many innovations in the world of education in this era that involve technology to carry out the learning process. There are many ways to learn Japanese, for example by learning through learning media that utilize technology such as smartphones. Supported by increasingly advanced technological innovations and new interaction patterns, the role of the User Interface (UI) and User Experience (UX) is important to the needs of users in solving problems. This research was conducted to find out how we can meet and know user needs by using design thinking. With this Japanese language learning media, it is hoped that it can help students learn Japanese easily.
Analisis Prediksi Mood Genre Musik Pop Menggunakan Algoritma K-Means dan C4.5 Lia Nurhalimah; Teguh Iman Hermanto; Ismi Kaniawulan
JURIKOM (Jurnal Riset Komputer) Vol 9, No 4 (2022): Agustus 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i4.4597

Abstract

Spotify is a music streaming platform that has a variety of diverse features and is always updated in terms of the latest music. The features in spotify have an interesting thing for users to enjoy music more optimally both in listening to songs based on songs, most popular artists and genres. Research on classifying songs based on mood by using energy and valence in a song is often done, especially in western pop songs. In every thought music has emotional energy that radiates and is strongly related to human psychology. The problem with spotify is that there is no feature to listen to songs based on mood. If pop songs are categorized by mood, it will be easier for people to listen to pop songs and choose the appropriate one based on mood. In this study, pop music data will be grouped based on 4 categories of Thayer's mood models using the k-means and c4.5 algorithms. The purpose of this study is to analyze the mood prediction of the pop music genre using the k-means and c4.5 algorithms. The research methodology used is SEMMA, the stages in Semma are sample, explore, modify, model and assess. The attributes used are danceability, energy, tempo and valence. From these attributes, data clustering is made using the k-means algorithm using RapidMiner. Then visualized using Power BI. The results of the research from cluster data are grouped into moods consisting of angry, sad, cheerful and happy. The most abundant mood is in the cheerful mood. Then evaluate the assess using the calculation of the confusion matrix which produces an accuracy rate of 91.9%..
Rancang Bangun Aplikasi Pelayanan E-Trayek Berbasis Mobile Menggunakan Metode Extreme Programming: Studi Kasus: Dinas Perhubungan Kab. Purwakarta Marwian Aditya Sahputra; Meriska Defriani; Teguh Iman Hermanto
sudo Jurnal Teknik Informatika Vol. 2 No. 1 (2023): Edisi Maret
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/sudo.v2i1.229

Abstract

Dinas Perhubungan Kabupaten Purwakarta dalam pelayanan trayek di input secara manual, yakni konsumen datang dengan membawa berkas yang akan habis masa berlaku ke kantor Dinas Perhubungan Kabupaten Purwakarta yang dimana sering terjadi antrean yang cukup memakan waktu dan juga kurang efisien dengan perkembangan teknologi saat ini. menyebabkan antrean yang cukup panjang dan memakan waktu. Apabila konsumen yang datang ke kantor Dinas Perhubungan Kabupaten Purwakarta tidak memiliki atau hilang berkas trayek kartu pengawasaannya lalu ingin di proses maka itu akan memakan waktu cukup lama, karena pegawai harus memeriksa, mengecek dan mencari data kendaraan sebelumnya. Tujuan dari penelitian ini adalah untuk memudahkan masyarakat dalam melakukan perizinan trayek melalui aplikasi smartphone. Yang mana masyarakat dapat mengakses dengan mudah maka aplikasi ini dibangun menggunakan kerangka kerja Flutter. Untuk mendukung penggunaan sistem pelayanan e-trayek yang mobile dan efisien, dengan menggunakan Metode Extreme Programming maka aplikasi pelayanan e-trayek ini sangat cocok untuk dibuat dan digunakan dengan menggunakan smartphone. selain smartphone semakin canggih dan performanya semakin baik dan juga lebih praktis karena berukuran kecil sehingga dapat dibawa kemana saja. Dengan demikian hasil dari penelitian ini dapat disimpulkan bahwa aplikasi pelayanan trayek berbasis mobile ini dapat berjalan dengan baik dan dapat diterima.
Sentiment Analysis of User Reviews of Mutual Fund Investment Applications on Google Playstore using Long Short Term Memory (LSTM) Algorithm Nurlaela; Teguh Iman Hermanto; Dede Irmayanti
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1109

Abstract

Mutual fund investment is increasing, as evidenced by the increasing number of mutual fund application users on the Playstore platform in Indonesia. The Financial Services Authority (OJK) reported that the number of mutual funds in Indonesia until August 2022 reached 2,193 units. In this research, the data collection used is the data scrapping method on the Google Playstore website. The result of the scrapping data is an excel-formatted document of 3000 data which is then stored and processed using the Long Short Term Memory (LSTM) model. In order to facilitate the modeling stage later, the sentiment review data must go through a text preprocessing process. To improve the performance and performance of LSTM modeling more optimally, then in this study a choice of hyperparameters was made. The hyperparameters tested are Epoch, Batch Size and Layer LSTM. The highest accuracy value on the Ajaib dataset is 99.3% which is located at epoch 32 and batch size 50, the highest accuracy value on the Bareksa dataset is 95.1% which is located at epoch 32 and batch size 50, and the highest accuracy value on the Bibit dataset is 94.9% which is located at epoch and batch size 50. So that the highest accuracy value among the three datasets is obtained by the Ajaib dataset where the accuracy reaches 99.3%. From the test results of the three parameters, it proves that there is an increase in accuracy results that is good enough to reach the highest accuracy value of 0.9933.
Perbandingan Algoritma SVM, KNN dan NBC Terhadap Analisis Sentimen Aplikasi Loan Service Dewi Nurmalasari; Teguh Iman Hermanto; Iman Ma'ruf Nugroho
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6427

Abstract

According to data on the number of credit card users in Indonesia, it has decreased from late 2019 to 2021 oneThe reason is because of the Covid-19 pandemic that hit. Because of this condition, many people are starting to switch to digital credit because they are considered to minimize transmission of viruses and the process is felt to be more efficient than having to use a credit card. This study aims to compare the level of accuracy between the three algorithms, namely the naïve Bayes classifier, k-nearest neighbor and support vector machine for digital credit applications or often called loan services, namely Kredivo. Akulaku, and Indodana in Indonesia by classifying it into two classes namely positive and negative by using the help of the Python programming language to analyze a sentiment by going through text preprocessing and weighting processes said TF-IDF. The results for the accuracy of the Kredivo application using K-NN get a score of 84%, Naïve Bayes 88%, and SVM get 89%. For the application of the K-NN method, it gets 79%, Naïve Bayes 86%, and SVM 87%. As for the indodana application, the K-NN method gets 81%, Naïve Bayes 88%, and SVM 88%. From the results of this accuracy it can be concluded that the Support Vector Machine method has better accuracy results compared to the K-Nearest Neighbor and Naïve Bayes methods.
Co-Authors Dede Irmayanti Dewi Nurmalasari Iman Ma'ruf Nugroho Ismi Kaniawulan, Ismi Lia Nurhalimah Marwian Aditya Sahputra Meriska Defriani Muhammad Adhitya Dhita Pratama Nenden Legiawati Nurlaela Ramadhan, Yudhi Raymond