Article Per Year (5 Year)
p-Index From 2021 - 2026
7.354
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Annual Research Seminar Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research Jurnal Informatika, Komputer dan Bisnis (JIKOBIS) Journal of Applied Business and Technology Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Bulletin of Informatics and Data Science Scientific Journal of Informatics Jurnal Pengabdian Masyarakat Unama Prosiding Seminar Nasional Ilmu Teknik
Errissya Rasywir
Universitas Dinamika Bangsa
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

Published : 69 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 69 Documents
Search

 3   4   5   6   7 
Comparison of Naive Bayes and SVM Algorithms for Sentiment Analysis of PUBG Mobile on Google Play Store Sari, Putri Ratna; Indah, Dwi Rosa; Rasywir, Errissya; firdaus, Mgs Afriyan; Athalina, Ghita
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4814

Abstract

PlayerUnknown's Battlegrounds (PUBG) Mobile is one of the most popular mobile games in Indonesia, according to data from the Google Play Store. According to the Google Play Store, the game has a rating of 3.8 with 49.5 million reviews. While a considerable number of users express satisfaction, a significant proportion of reviews also contain criticism regarding the gameplay and features. However, a cursory examination of reviews may not fully capture the nuances of user sentiment, necessitating a more comprehensive sentiment analysis. This research will employ a positive and negative sentiment analysis of Indonesian PUBG Mobile reviews on the Google Play Store, utilizing a comparative approach to evaluate the performance of two algorithms: Naïve Bayes and Support Vector Machine (SVM). The data set comprised 2,000 user reviews, which were collected using a scraping technique. Following this, a labeling process was conducted based on the rating, data were preprocessed, TF-IDF weighting was applied, and both algorithms were implemented. The findings indicated that users expressed satisfaction with the game's visuals and gameplay. However, there were also technical concerns that required attention, including bugs, server instability, lag, and performance issues. The SVM algorithm demonstrated superior performance, with an accuracy rate of 70.95%, compared to Naïve Bayes, which reached 69.83%. Despite Naïve Bayes's faster processing speed, SVM exhibited greater precision, recall, and F1-score
Analisis Kualitas Aplikasi SIMEKA Terhadap Kepuasan Pengguna Dengan Metode Delone and Mclean ophelia, chandy; Nur Azmi; Errissya Rasywir; Suyanti
Jurnal Informatika, Komputer dan Bisnis (JIKOBIS) Vol. 5 No. 1 (2025): Vol. 5 No 1 April 2025
Publisher : LPPM ITB AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/622pmn91

Abstract

SIMEKA adalah perangkat lunak yang digunakan untuk mentransformasi sistem manajemen kepegawaian secara digital, mempermudah pengelolaan dan pengendalian, serta memotivasi peningkatan produktivitas kinerja ASN di Pemerintah Kabupaten Tanjung Jabung Barat. Permasalahan pada aplikasi ini yaitu mengalami beberapa kendala seperti error pada absensi fingerprint, titik lokasi yang tidak akurat, sering mengalami bug, dan kesulitan login. Hal tersebut membuat penulis perlu melakukan analisis untuk mengetahui kepuasan pengguna pada aplikasi tersebut. Tujuan penelitian ini berfokus pada kepuasan pengguna aplikasi SIMEKA menggunakan metode DeLone and McLean yang terdiri dari tiga variabel bebas (independen) yaitu Kualitas Sistem (System Quality), Kualitas Informasi (Information Quality), dan Kualitas Layanan (Service Quality) dan satu variabel terikat (dependen) yaitu Kepuasan Pengguna (User Satisfaction). Data diambil dengan menyebarkan kusioner dan mendapatkan sebanyak 182 orang yang merupakan pengguna dari aplikasi SIMEKA. Pengelolaan data penelitian ini menggunakan SmartPLS (Smart Partial Least Square) 4. Hasil dari penelitian ini menunjukkan bahwa hasil uji yang dilakukan terhadap variabel memiliki nilai yang signifikan berpengaruh terhadap kepuasan pengguna pada aplikasi SIMEKA, dengan indikator yang mempengaruhi yaitu System Quality, Information Quality, Service Quality, User Satisfaction sehingga pengguna puas terhadap aplikasi SIMEKA
Analysis of the Application of Transaction Data with Association Techniques using the Apriori Algorithm in Pharmacy Nasutioni, Wahyudi; Abidin, Dodo Zaenal; Rasywir, Errissya
Journal of Applied Business and Technology Vol. 4 No. 3 (2023): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v4i3.141

Abstract

The development of information technology influences the rapid growth in the amount of data collected and stored in large t. Dimas Pharmacy, located at Jl. Segara Kec. Nipah Panjang is one of the public health services that sells various medicines, medical devices, and so on. This study is expected to provide positive benefits for owners of Dimas Nipah Panjang Pharmacy in Providing information about the pattern of medicine purchases made by consumers and Facilitating pharmacy owners to know the available medicine supplies in the warehouse so as not to experience emptiness when needed. Problem Formulation, Literature Study, Data Collection, Calculation and Analysis of Associations with Priori Algorithms, Results Evaluation and Analysis and Report Making. Based on the results of interviews and observations that have been made, the authors obtain data from the Dimas Pharmacy sales transaction. Data held ± 1000 sales transaction data for the period of May and June. But the author only entered 216 sales transactions in May and 141 sales transactions in June. After knowing the method of data selection, the authors conducted data selection by taking 6 items of significant data specifications in certain contexts, namely Anti Serotonin / Allergy, Antacid / Ulcer, Antibiotics, Antipyretics, Inflammation, Hypertension Each of these items had different brands. From these results it can be explained that the sales transaction of the Dimas Pharmacy in May and June generates or generates relationships between shopping product items. With the calculation of the Apriori Association Algorithm, a Market Basket Analysis relationship was found between Medicinal Pronicy and Dexa items. With the Rule "IF Buy Pronicy, THEN Buy Dexa". The rule is generated from the highest support and confident values of the overall support and confident items. The highest support value is 0.15 and the highest confident value is 0.5 ".
Comparison of word embedding features using deep learning in sentiment analysis Jasmir Jasmir; Errissya Rasywir; Herti Yani; Agus Nugroho
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.26223

Abstract

In this research, we use several deep learning methods with the word embedding feature to see their effect on increasing the evaluation value of classification performance from processing sentiment analysis data. The deep learning methods used are conditional random field (CRF), bidirectional long short term memory (BLSTM) and convolutional neural network (CNN). Our test uses social media data from Netflix application user comments. Through experimentation on different iterations of various deep learning techniques alongside multiple word embedding characteristics, the BLSTM algorithm achieved the most notable accuracy rate of 79.5% prior to integrating word embedding features. On the other hand, the highest accuracy value results when using the word embedding feature can be seen in the BLSTM algorithm which uses the word to vector (Word2Vec) feature with a value of 87.1%. Meanwhile, a very significant change in value increase was obtained from the FastText feature in the CNN algorithm. After all the evaluation processes were carried out, the best classification evaluation results were obtained, namely the BLSTM algorithm with stable values on all word embedding features.
Experimental of vectorizer and classifier for scrapped social media data Setiawan Assegaff; Errissya Rasywir; Yovi Pratama
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 4: August 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i4.24180

Abstract

In this study, we used several classifiers and vectorizers to see their effect on processing social media data. In this study, the classifiers used were random forest, logistic regression, Bernoulli Naive Bayes (NB), and support vector clustering (SVC). Random forests are used to reduce spatial complexity, and also to minimize errors. Logistic regression is a method with a statistical model whose basic form uses a logistic function to represent the binary dependent variable. Then, the Naive Bayes function uses binary elements and SVC which has so far given good results rivals other guided learning. Our tests use social media data. Based on the tests that have been carried out on classifier variations and vectorizer variations, it was found that the best classifier is a linear regression algorithm based on predictive adaptive compared to the random forest method based on decision trees, probability-based Bernoulli NB and SVC which work by clustering. Meanwhile, from the test results on the count vectorizer, term frequency-inverse document frequency (TFIDF), and hashing, the best accuracy is achieved on the TFIDF vectorizer. In this case, it means that the TFIDF vectorizer has a better value in presenting word feature dimensions.
Network and layer experiment using convolutional neural network for content based image retrieval work Fachruddin Fachruddin; Saparudin Saparudin; Errissya Rasywir; Yovi Pratama
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.19759

Abstract

In this study, a test will be conducted to find out how the results of experiments on the network and layer used on the convolutional neural network algorithm. The performance and accuracy of the retrieval process method that was tested using the algorithm approach to do an object image retrieval. The expected results of this study are the techniques offered can provide relatively better results compared to previous studies. The results of the classification of object images with different levels of confusion on the Caltech 101 database resulted an average accuracy value. From the experiments conducted in the study, content based image retrieval work (CBIR) work using convolutional neural network (CNN) algorithm in terms of execution time, loss testing and accuracy testing. From several experiments on layers and networks shows that, the more hidden layers used, then the result is better. The graph of validation loss decreases at fewer epochs, slightly fluctuating at more epochs. Likewise, validation accuracy increases insignificantly on epochs with small amounts, but tends to be stable on more epochs.
Klasifikasi Penggunaan Daya Listrik Rumah Tangga dengan Menggunakan Metode Naive Bayes An Nisa Ziah Putri; Dodo Zaenal Abidin; Errissya Rasywir; Athallah, Ibni Faiq Athallah
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.60

Abstract

Data mining is a technique of several fields of science to find previously unknown relationships in the data warehouse so that it becomes an information that can be used later. The unwise use of electricity will of course have an impact on the high use of electricity, therefore it is expected that every community understands the effort to use electricity wisely. Therefore, authors perform analysis of data mining on these electrical usage data in order to know which is a small, medium and large category. The authors use data on electrical use questionnaire as much as 200 data which is then presented into the ARFF format. In performing author analysis using WEKA Tools. The method used is Naive Bayes classification method with the greatest percentage of accuracy obtained using the Use Training Set Correctly of 80.5%, using a 5-Fold Cross Validation Correctly of 75%, and using 10-Fold Cross Validation amounted to 74%. While the result of the selection of the attributes using the algorithm classifier attribute evaluation (ClassifierAttributeEval) is stated that the most influential attribute against the electrical power usage classification is Electonic Goods.
Implementasi YOLOv8 untuk Deteksi Pekerja Tanpa Safety Helmet dan Penghitung Durasi Pelanggaran di Area Industri Berbasis Real-Time Rizky Syahrul Amar; Errissya Rasywir; Lies Aryani
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.180

Abstract

The use of protective equipment in the form of helmets is an important aspect of ensuring motorcycle rider safety. However, violations of helmet usage still frequently occur and are difficult to monitor continuously. This study proposes a real-time helmet detection system using the YOLOv8 object detection method. The YOLOv8n model was trained using a helmet and no-helmet image dataset that underwent data augmentation to improve the model’s robustness against variations in environmental conditions. The system was implemented using the Python programming language with the support of the Ultralytics and OpenCV libraries. The system input was obtained from a webcam with a resolution of 640×640 pixels, where each video frame was processed in real time to detect the Helmet and No Helmet classes. The system displays bounding boxes and class labels in real time and is equipped with a violation duration calculation mechanism. When a no-helmet condition is detected continuously, the system generates pop-up alerts and automatic notifications via the Telegram application. The experimental results show that the system is capable of detecting helmet usage and no-helmet violations in real time with stable performance. The integration of violation duration calculation helps reduce momentary detection errors and improves the reliability of identifying valid violations
Public complaint tweet data feature analysis for sentiment classification Errissya Rasywir; Yovi Pratama; Irawan Irawan; Marrylinteri Istoningtyas
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7172

Abstract

The perception of the public regarding a government's performance significantly impacts a city's advancement. This research involved analyzing complaint tweets from Jambi City residents directed at the government to gauge sentiment. In the testing phase, 500 Twitter accounts were examined to categorize sentiment as positive, negative, or neutral. Training data was prepared by extracting tokens through feature selection techniques such as information gain (IG) and mutual information (MI). For testing, all tokens are entered as data in the input layer in the recurrent neural network (RNN). From the tests carried out, the average use of feature selection can achieve a good value compared to no feature selection. But more specifically the use of IG produces better accuracy compared to the use of MI. From the research conducted, Twitter data is classified using a RNN and several tests by adding feature selection to produce differences. The results are proven to improve classification performance. With a recall value of 92.243%, it shows the system's success rate in sentiment classification and a precision of 92% indicates a level of accuracy that is sufficient to support the government's sentiment assessment.
Co-Authors Abdul Haris Abdul Harris Abdurrahman Abidin, Dodo Zaenal Ade Saputra Agus Nugroho Agus Siswanto Akwan Sunoto An Nisa Ziah Putri Anggraini, Dila Riski Anita Anita Nurjanah Annisa putri Anton Prayitno Arya Atmanegara Aryani, Lies asih asmarani Athalina, Ghita Athallah, Ibni Faiq Athallah Bayu saputra Beni Irawan Betantiyo Prayatna Borroek, Maria Rosario Briyan Chairullah Candra Adi Rahmat Carenina, Babel Tio Clara Zuliani Syahputri Defrin Azrian Desi Kisbianty, Desi Despita Meisak desy ayu ramadhanty Dimas Pratama Dodo Zaenal Abidin Dwi Rosa Indah Elsa Charolina L Siantar Evan Albert Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin, Fachruddin farchan akbar Feranika, Ayu Fernando Fernando fiqri ansyah Fradea Novi Ramadhayanti GILLIANI, WENNY Hani Prastiwi Hartiwi, Yessi Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Herti Yani Hilda Permatasari Hussaein, Ahmad Ilham Adriansyah Ilham Fahrozi ilham permana Imelda Yose Iqbal Pradibya Irawan Irawan Irawan, Beni Istoningtyas, Marrylinteri Jasmir Jasmir Jasmir Jasmir Jeny Pricilia Johari, Riyan Jopi Mariyanto khalil gibran ahmad Kholil Ikhsan Lazuardi Yudha Pradana Li Sensia Rahmawati Lies Aryani Lies Aryani Luthfi Rifky M.Rizky Wijaya Macharani Raschintasofi Maliyatul Khasanah Maria Rosario Borroek Marrylinteri Istoningtyas Marrylinteri Istoningtyas Marrylinteri Istoningtyas Mayang Ruza Mgs Afriyan Firdaus Migi Sulistiono Moh. Ismail Muhammad David Adrilyan Muhammad Diemas Mahendra Muhammad Ismail Muhammad Riza Pahlevi Muhammad Satria Mubin Muhammad Wahyu Prayogi Mulyadi Mulyadi Mumtaz Ilham S Mumtaz Ilham Syafatullah Muttaqin Nabila Khumairo Najmul Laila Nanda Ghina Nasrul Ahlunaza Nasutioni, Wahyudi Nilu Widyawati Nungky Septia Kurnicova Nur Aini Nur Azmi Nurhadi Nurhadi Nurul Aulia OPHELIA, CHANDY Pahlevi, M. Riza Pahlevi, M.Riza Pareza Alam Jusia Pareza Alam Jusia Pareza Alam Jusia, Pareza Alam Putri Ratna Sari, Putri Ratna Rani Oktavia, Feby Renita Syafitri Reza Pahlevi Rio Ferdinand Rizky Syahrul Amar ROBY SETIAWAN Rofi'i, Imam Rohaini, Eni Rosario B, Maria Rts CiptaNingsi Rudolf Sinaga Sandi Pramadi Saparudin, Saparudin Satria Oldie Versileno Setiawan Assegaff Sri Wahyuni Sulistia Ramadhani Suyanti Suyanti Suyanti Tasya Basalia Sihombing Tedy Hardiyanto Tondy Maulana Tambunan Verwin Juniansyah virginia casanova andiko andiko Wahid Hasyim Yaasin, Muhammad Yessi Hartiwi Yessi Hartiwi Yoga Rizki Yovi Pratama Yuga Pramudya Zahlan Nugraha