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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Antivirus : Jurnal Ilmiah Teknik Informatika Journal of Information Systems and Informatics Specta Journal of Technology Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) VCD (Journal of Visual Communication Design)
Syamsul Mujahidin
Institut Teknologi Kalimantan
Arts Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Social Sciences

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PENGEMBANGAN SMART SERVICE VILLAGE SYSTEM (SSVS) DALAM MENDUKUNG SMART GOVERNANCE MENGGUNAKAN METODE PERSONAL EXTREME PROGRAMMING M. Ihsan Alfani Putera Putera; Nur Fajri Azhar; Syamsul Mujahidin
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 15 No 2 (2021): November 2021
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/antivirus.v15i2.1824

Abstract

To realize a Smart City, the government applies the use of technology in various aspects, one of which is in the public service sector in supporting smart governance by creating a Smart Service Village System (SSVS). One of the systems developed is the Balikpapan City Housing and Settlement Service in charge of carrying out government affairs in the areas of housing, settlements, landscaping and cemeteries. One of the existing fields, namely the housing sector, has a program of providing sanitation assistance and uninhabitable houses to the people of the city of Balikpapan. In its implementation, several obstacles were found, such as the slowness of data on recommendations for aid recipients, ineffective data management, and unclear process transparency. Therefore, a system was created that assists in collecting data on prospective beneficiaries, providing actual and fast progress, as well as a more optimal process of assisting. This SSVS development uses the Personal Extreme Programming method with seven phases, namely Requirements, Planning, Iteration Initialization, Design, Implementation, System Testing, and Retrospective. It is hoped that the existence of SSVS can help the implementation of aid provision more effectively, efficiently, and transparently. This research produces a web-based system with a total of 44 use cases that are carried out in 5 iterations. Testing is carried out by user training to see acceptance from related users. user training is carried out with 3 parties to represent several types of users, from the three parties it shows very good satisfaction and acceptance with a percentage of 100%.
Implementasi Analisis Sentimen Opini Publik Mengenai Sirkuit Internasional Mandalika Pada Twitter Menggunakan Metode Multinomial Naïve Bayes Classifier Syamsul Mujahidin; Muhamad Nur Hasyim; Boby Mugi Pratama
Bianglala Informatika Vol 10, No 2 (2022): Bianglala Informatika 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bi.v10i2.13544

Abstract

Abstrak  - Analisis sentimen sangat berguna untuk mengetahui sentimen opini mengenai suatu topik. Hasil analisis dapat digunakan oleh para pemangku kepentingan dalam melakukan pengambilan keputusan ataupun melihat respon publik terhadap suatu kebijakan. Namun demikian, analisis sentimen yang dilakukan secara manual tentunya memerlukan waktu dan sumber daya yang tidak sedikit. Penelitian ini bertujuan untuk membangun sebuah sistem yang mampu melakukan analisis sentimen opini publik mengenai Sirkuit Internasional Mandalika menggunakan metode Multinomial Naïve Bayes Classifier secara otomatis melalui media sosial twitter. Metode ini cocok digunakan pada kasus analisis sentimen yang pada umumnya memegang asumsi independensi pada feature-nya dan cocok digunakan pada sistem real time karena waktu prediksi dan training-nya yang cepat. Dataset yang digunakan pada penelitian ini berjumlah 6184 data tweet mentah yang dibagi menjadi data training dan data testing. Berdasarkan hasil penelitian, model dengan kinerja terbaik didapatkan pada pembagian dataset 90%:10% dan kelas dataset yang diseimbangkan dengan nilai accuracy 78%, precision pada kelas positif 84% dan pada kelas negatif 73%, recall pada kelas positif 70% sedangkan pada kelas negatif 86%, dan nilai F1-Score pada kelas positif 76% sedangkan pada kelas negatif 79%. Adapun hasil analisis sentimen pada data tanggal 18 Juni – 28 Juni 2022 adalah 56% tweet memiliki sentimen negatif dan 46% tweet memiliki sentimen positif.Kata Kunci : Analisis Sentimen, Mandalika, Multinomial Naïve Bayes Classifier, MotoGP, Twitter Abstract  - Sentiment analysis is very useful to find out the sentiment of opinion about a topic. The results of the analysis can be used by stakeholders in making decisions or seeing the public's response to a policy. However, sentimen analysis that conducted manually can need more time and resources. This study aims to build a system capable of analyzing public opinion sentiment regarding the Mandalika International Circuit using the Multinomial Naïve Bayes Classifier method automatically through social media twitter. This method is suitable for use in the case of sentiment analysis which generally holds the assumption of independence in its features and is suitable for use in real time systems because of its fast prediction and training time. The dataset used in this study amounted to 6184 raw tweet data which was divided into training data and testing data. Based on the results of the study, the model with the best performance was obtained in the distribution of the dataset of 90%:10% and the balanced dataset class with an accuracy value of 78%, precision in the positive class 84% and in the negative class 73%, recall in the positive class 70% while the negative 86%, and the F1-Score value in the positive class is 76% while the negative class is 79%. The results of sentiment analysis on data from June 18 to June 28 2022 are 56% of tweets have negative sentiments and 46% of tweets have positive sentiments.Keywords: Sentiment Analysis, Mandalika, Multinomial Naïve Bayes Classifier, MotoGP, Twitter
Implementasi Analisis Sentimen Masyarakat Mengenai Kenaikan Harga BBM Pada Komentar Youtube Dengan Metode Gaussian naïve bayes Syamsul Mujahidin; Bagus Prasetio; Muchammad Chandra Cahyo Utomo
Voteteknika (Vocational Teknik Elektronika dan Informatika) Vol 10, No 3 (2022): Vol. 10, No 3, September 2022
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/voteteknika.v10i3.118299

Abstract

Youtube merupakan platform video terbesar di dunia dengan total pengguna sebanyak 1,5 miliar pada tahun 2018. Youtube menjadi salah satu platform penyedia informasi, salah satunya yakni kenaikan harga minyak mentah dunia hingga berada di atas US$100 per barel. Berdasarkan permasalahan tersebut, penulis melakukan penelitian terkait analisis sentimen dari komentar pengguna Youtube mengenai kenaikan harga BBM menggunakan metode Gaussian naïve bayes. Percobaan dilakukan menggunakan 3053 dataset dengan pelabelan menggunakan lexicon dan split data 8:2. Penerapan vektorisasi kata menggunakan word embedding Fasttext dan Bag of word sebagai pembanding terhadap akurasi. Percobaan dilakukan dengan kombinasi perbedaan dimensi size pada proses pembuatan language model fasttext. Berdasarkan hasil penelitian yang telah dilakukan, didapatkan nilai akurasi tertinggi pada percobaan dengan dataset tanpa filtering stopword dan model fasttext size 100 dengan akurasi sebesar 74%. Berdasarkan hasil evaluasi, sistem yang dibangun dapat mengklasifikasikan sentimen atau opini publik ke dalam sentimen positif dan sentiment negatif secara otomatis.Kata kunci : BBM, Fasttext, Lexicon, Gaussian naïve bayes, Word embedding Youtube is the largest video platform in the world with a total of 1.5 billion users in 2018. Youtube is one of the information provider platforms, one of which is the increase in world crude oil prices to above US$100/barrel. Based on these problems, the authors conducted research related to sentiment analysis from Youtube user comments regarding the increase in fuel prices using the Gaussian nave Bayes method. The experiment was carried out using 3053 datasets with labeling using lexicon and 8:2 data split. The vectorization uses Fasttext and BoW as a comparison of accuracy. The experiment was carried out with a combination of size dimensions fasttext. Based on the results of the research, the highest accuracy value was obtained in experiments with a dataset without stopword and fasttext size 100 with an accuracy of 74%. The system built can classify public sentiment into positive and negative sentiments automatically. Keywords: Fuel, Fasttext, Lexicon, Gaussian naïve bayes, Word embedding
Implementation of Automated Test Case Generation in REST API on Android-Based Koperasi Application Syamsul Mujahidin; M. Reinaldy Hermawan; Chandra Cahyo Utomo
Journal of Information System and Informatics Vol 5 No 1 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i1.431

Abstract

This study is focused on developing a data collection system to enhance the performance of Koperasi, an organization with complex data collection. An Android application was created to automate the processing of member and transaction data, significantly improving data processing efficiency. However, building a quality system takes time and requires error-free data processing. To achieve this, Automated Test Case Generation with EvoMaster was used to test the REST API and identify errors. The testing process went through several iterations until almost no errors were found. EvoMaster generated over 19.5 million scenarios and found 78 errors in the REST API in 58 hours, which were promptly fixed between iterations. The use of EvoMaster not only reduced development time but also helped maintain code quality.
Analisis Sentimen Isu Vaksinasi Covid-19 pada Twitter dengan Metode Naive Bayes dan Pembobotan TF-IDF Tokenisasi 1-2 Gram Yashmine Hapsari; Syamsul Mujahidin; Nisa Fadhliana
SPECTA Journal of Technology Vol. 7 No. 2 (2023): SPECTA Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35718/specta.v7i2.812

Abstract

The COVID-19 vaccination has been implemented to cut down the spread of the virus in society, but the status of the vaccine, which has been in the development stage, is one of the factors causing people to hesitate to vaccinate. Therefore, a sentiment analysis was carried out on the issue of COVID-19 vaccination with processes and parameters that could increase the model’s accuracy. In this study, sentiment classification was performed using the Naive Bayes method and a dataset of 5,000 tweets related to the vaccination of COVID-19. The weighting stage was applied using the TF-IDF method in which a comparison was made of the effect of using unigram, bigram and 1-2 gram tokenization on model accuracy. The results of one of the experiments with the Gaussian classifier and the ratio train: test is 7:3, the model accuracy is 67.4% for the unigram parameter, 65.5% for the bigram parameter, and 70% for the 1-2 gram parameter, where the model with the combined token is 1 -2 grams has a higher accuracy when compared to using only 1 type of token. Based on these results, it can be concluded that the combination of unigram and bigram tokenization types can provide added value to the model for classifying data, thereby increasing accuracy in analysis related to public sentiment.
Implementasi Support Vector Regression (SVR) dalam Memprediksi Nilai Tukar Mata Uang Rupiah Terhadap Euro Syamsul Mujahidin; Yauliana Dwianti; Tegar Palyus Fiqar
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 5 No 2 (2023): September 2023
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v5i2.272

Abstract

Integration of 3D Scanning and Augmented Reality (AR) Technology in East Kalimantan Furniture Products in E-Commerce Eko Agung Syaputra; Syamsul Mujahidin; Widya Sartika; Novianti Rossalina
VCD Vol. 8 No. 2 (2023): Journal of Visual Communication Design VCD
Publisher : Universitas Ciputra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37715/vcd.v8i2.4242

Abstract

The creative furniture industry in East Kalimantan faces the challenge of less effective online marketing in e-commerce. In 2018, there were more than 370 furniture entrepreneurs in the region, but only 37% of their total production managed to reach the market. According to projections from Markets and Markets, the market value of Augmented Reality (AR) in e-commerce is expected to grow by 34% between 2020 and 2025. To overcome this problem, the use of 3D object configuration visual technology and AR presents a better solution. promising solution. This technology can improve product presentation, create a more interactive and realistic shopping experience, and reduce the environmental impact of e-commerce due to the re-shipment of non-conforming products. The main objective of the research is to assess the effectiveness of product communication, the ease of the purchasing process, and the level of consumer trust in the product. This visual method of 3D object configuration combines 3D scanning to produce detailed and realistic 3D images. This research uses a sprint design method based on design thinking, which involves co-creation to refine ideas. Next, a prototype design was developed with a focus on aspects of the user interface and user experience, fostering intelligent interaction between users and their environment, thus contributing to the progress of the Smart City concept in IKN Nusantara.
Co-Authors Bagus Prasetio Boby Mugi Pratama Chandra Cahyo Utomo Eko Agung Syaputra M. Ihsan Alfani Putera Putera M. Reinaldy Hermawan Muchammad Chandra Cahyo Utomo Muhamad Nur Hasyim Nisa Fadhliana Novianti Rossalina Nur Fajri Azhar Tegar Palyus Fiqar Widya Sartika Yashmine Hapsari Yauliana Dwianti