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All Journal Jurnal Informatika Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research Jurnal Informatika Kaputama (JIK) JURSIMA (Jurnal Sistem Informasi dan Manajemen) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal Responsif : Riset Sains dan Informatika Bulletin of Computer Science Research Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Computer Science (CO-SCIENCE) Reputasi: Jurnal Rekayasa Perangkat Lunak Jurnal Abdimas Komunikasi dan Bahasa Profitabilitas Indonesian Journal of Networking and Security - IJNS JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Interkom : Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi J-Intech (Journal of Information and Technology) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY JEECS (Journal of Electrical Engineering and Computer Sciences) JURSIMA Sinergi: Jurnal Pengabdian Kepada Masyarakat Journal of Accounting Information System TEKNOSIA Bulletin of Informatics and Data Science Jurnal Sistem Informasi dan Manajemen Journal of Artificial Intelligence and Technology Information Help: Journal of Community Service Media Teknologi dan Informatika Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Komtika (Komputasi dan Informatika) Journal of Information Technology Jurnal Teknoinfo
Riska Aryanti
Universitas Bina Sarana Informatika, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Analisis Sentimen Pemanfaatan Artificial Intelligence di Dunia Pendidikan Menggunakan SVM Berbasis Particle Swarm Optimization Saepudin, Atang; Aryanti, Riska; Fitriani, Eka; Royadi, Royadi; Ardiansyah, Dian
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2921

Abstract

The utilization of Artificial Intelligence (AI) in the field of education in Indonesia has witnessed significant developments in recent years. The advancements in AI technology have opened up new opportunities to enhance the quality of education, and address various challenges faced by the Indonesian education system. This has naturally sparked diverse opinions and comments from the public, particularly on the social media platform X/Twitter. This research focuses on sentiment analysis of reviews expressed on the X/Twitter social media platform. The primary goal of this study is to develop an effective sentiment analysis method by leveraging the Support Vector Machine (SVM) algorithm optimized with Particle Swarm Optimization (PSO) for feature selection. In this research, user reviews from X/Twitter were collected and analyzed to identify positive or negative sentiments within the context of each comment. The SVM algorithm was used to classify sentiments based on similarity to comments with known sentiments. Feature Selection PSO was employed to optimize the parameters within SVM to enhance sentiment analysis accuracy. The results of sentiment analysis on comments or tweets on the X/Twitter social media platform using both SVM and PSO-based SVM algorithms indicated that the PSO-based SVM algorithm achieved a higher accuracy. The SVM algorithm with feature selection PSO produced accuracy 89.50%, precision 86.98%, recall 93.00%, and AUC 0.964. Meanwhile, the SVM algorithm had accuracy 87.50%, precision 85.46%, recall 90.50%, and AUC 0.956. This demonstrates that the use of feature selection PSO in the SVM algorithm is capable of improving the accuracy of the results.
Sentiment Analysis of E-Grocery Application Reviews Using Lexicon-Based and Support Vector Machine Aryanti, Riska; Fitriani, Eka; Royadi, Royadi; Ardiansyah, Dian; Saepudin, Atang
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i3.301

Abstract

This research aims to conduct sentiment analysis of e-grocery application reviews using the Support Vector Machine (SVM) algorithm. Sentiment analysis is used to distinguish between positive and negative reviews by users who have provided reviews so that an evaluation of the services offered can be made. This research uses scraping techniques to obtain all the needed review data, focusing only on reviews of the Segari and Sayurbox applications. Datasets were collected from reviews using a library in Python, namely, google-play-scraper, obtained by the sayurbox application 4235 reviews and the segari application 5575. The dataset collected does not yet have a label, and the labeling process is impossible to perform manually by looking at the reviews one by one because it takes a long time and requires an expert in the field of language who can interpret the reviews and group them into positive and negative sentiments. Therefore, the sentiment-labeling process applies a lexicon-based method that works based on the inset lexicon dictionary by calculating each review's polarity value. The analysis process of this research uses the SVM algorithm because the SVM method has been proven to provide consistent and accurate results in various classification tasks, including sentiment analysis. The results show that the lexicon-based method and SVM produce good accuracy in determining the sentiment of e-grocery reviews, with a vegetable box application accuracy rate of 94%. In comparison, the segari application accuracy rate reached 97%.
Waste Classification using EfficientNetB3-Based Deep Learning for Supporting Sustainable Waste Management Agustiani, Sarifah; Junaidi, Agus; Aryanti, Riska; Kamil, Anton Abdul Basah
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.108

Abstract

Waste management is a critical issue in sustainable development, particularly in large urban areas that generate a high volume of waste daily. One of the main challenges is the absence of a fast, accurate, and efficient waste sorting system. This study aims to develop a waste classification model using deep learning based on the EfficientNetB3 architecture to support more sustainable waste management. The model was trained on a dataset obtained from a Kaggle repository, consisting of 4,650 images evenly distributed across six waste categories: batteries, glass, metal, organic, paper, and plastic (775 images per class). The training and evaluation were conducted using a supervised image classification approach. The model achieved an overall accuracy of 93%, with average precision, recall, and F1-score values of 93%. Among all categories, organic waste achieved the highest F1-score (0.99), followed by paper (0.97) and batteries (0.97), while plastic and metal categories obtained F1-scores of 0.89. These results demonstrate that the EfficientNetB3 architecture is effective in performing multi-class waste classification. This model has the potential to be implemented in camera-based waste sorting systems such as smart bins or automated recycling units, thereby contributing to the reduction of unprocessed waste and supporting the achievement of Sustainable Development Goal (SDG) 12: responsible consumption and production
Optimalisasi Random Forest dan Support Vector Machine dengan Hyperparameter GridSearchCV untuk Analisis Sentimen Ulasan PrimaKu Misriati, Titik; Aryanti, Riska
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5347

Abstract

PrimaKu App has been a pioneer in the field of digital health since 2017. Through this application, parents can regularly and continuously monitor their children’s health and development. PrimaKu also has a formal alliance with the Indonesian Pediatric Association (IDAI) to promote child health in Indonesia. This application can be downloaded through the Google Play Store. Google Play Store has a feature that allows users to review the app before downloading. Sentiment analysis is used to distinguish between positive and negative reviews by users who have provided reviews so that an evaluation of the services provided can be made. This research aims to conduct sentiment analysis of user reviews of the PrimaKu application using Random Forest (RF) and Support Vector Machine (SVM) algorithms with TF-IDF weighting. Optimization was performed using hyperparameters to improve the performance of the Random Forest and SVM algorithms. The data used consisted of the 2,293 most relevant reviews collected from the Google Play Store. The most effective models for the Random Forest and Support Vector Machine were selected by adjusting the hyperparameters using GridSearch CV. The results of this study show that Random Forest has a higher success rate in classifying PrimaKu user review data, with an accuracy of 89%, precision of 88%, recall of 81%, and F1-Score of 85%.
Optimalisasi Pengelolaan Keuangan melalui Digitalisasi Pencatatan pada Usaha Mikro dan Kecil Misriati, Titik; Setyaningsih, Eka Dyah; Aryanti, Riska
JPM: Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jpm.v6i2.2744

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in driving the local economy, including in the city of Bekasi. However, the main challenge faced by MSME players is their low capacity to manage and record finances effectively. Most business owners still use manual recording methods, which risk errors, data loss, and difficulties in analyzing financial conditions. In addition, the lack of separation between business and family finances causes uncertainty in profit and loss calculations and capital planning. The lack of knowledge about the use of digital financial applications is also a significant obstacle. This community service activity aims to empower MSMEs in Bekasi City to move up the ladder through digital innovation in financial recording. The partners in this activity are MSMEs in the Bekasi City area that have not yet implemented a digital recording system. The implementation method includes stages of needs analysis, financial literacy training, assistance in using digital recording applications, and evaluation of the level of understanding through pre-tests and post-tests. The results of the activity show an average increase in understanding and application of digital financial management of 80%. Business owners have become more disciplined in recording transactions, able to separate business and personal finances, and understand profit and loss calculations more accurately. In addition to its economic impact, this activity has also fostered social change in the form of the Bekasi Digital MSME community, which serves as a platform for continuous learning. Digital innovation has proven effective in increasing the professionalism and competitiveness of MSME players towards a technology-based economic transformation.
Perancangan Sistem Informasi Arus Kas Pada PKBM Ratu Kencana Misriati, Titik; Alfarizi, Salman; Aryanti, Riska; Martenia, Rina
JAIS - Journal of Accounting Information System Vol. 3 No. 1 (2023): Juni
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jais.v1i03.1634

Abstract

Sistem pengelolaan arus kas pada PKBM Ratu Kencana masih dilakukan secara manual. Kondisi tersebut dapat mempengaruhi kinerja serta tata kelola laporan keuangan pada PKBM Ratu Kencana. Pembuatan laporan arus kas masih belum terkontrol dengan maksimal, misalnya dalam menangani proses penerimaan kas penulisan data masih menggunakan buku besar yang harus ditulis tangan kemudian di input ulang di Microsoft Excel. Sedangkan untuk mencatat pengeluaran kas yang ada pada PKBM Ratu Kencana harus ditulis tangan di buku pengeluaran kas, kemudian di input ulang di aplikasi SIMDAK (Sistem Informasi Dana Alokasi Khusus Kemendikbud) sesuai RKAS (Surat Rencana Kegiatan Dan Anggaran Sistem Pendidikan). Metode pengembangan software yang digunakan dalam pembuatan website penerimaan dan pengeluaran arus kas pada PKBM Ratu Kencana Karawang menggunakan waterfall mulai dari analisis kebutuhan perangkat lunak, desain, pembuatan kode program, dan pengujian. Untuk mengatasi permasalahan tersebut, peneliti merancang sistem pemasukan kas dan pengeluaran kas serta pembuatan laporan menggunakan aplikasi berbasis website. Penggunaan aplikasi berbasis website dapat mempermudah dalam kegiatan administrasi berupa pencatatan penerimaan maupun pengeluaran kas, untuk melakukan pencatatan bisa langsung diinputkan melalui aplikasi berbasis web serta memudahkan bendahara dalam pembuatan laporan untuk meminimalisir terjadinya kesalahan, dan memudahkan dalam pencarian data.
Klasifikasi Sentimen Terhadap Kebijakan Tapera Menggunakan Komparasi Machine Learning dan SMOTE Leidiyana, Henny; Misriati, Titik; Aryanti, Riska
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 2 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i2.12595

Abstract

The Indonesian government's Public Housing Savings Program (Tapera) aims to help low- and middle-income persons get housing financing. Although the initiative strives to satisfy housing requirements, the public has responded in a variety of ways, as evidenced by social media posts. The goal of this study is to examine public sentiment towards the Tapera policy using YouTube comment data to provide an overview of popular perspective. This study combines sentiment analysis with machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multinomial Naïve Bayes (NB), and Decision Tree. The data is divided into three scenarios, namely 70% training data and 30% test data, 80% training data and 20% test data, and 90% training data and 10% test data. Data balancing is also performed with SMOTE. The performance evaluation is based on each algorithm's accuracy, precision, recall, and F1 Score values. The results showed that the SVM algorithm performed the best in all circumstances, with the greatest accuracy of 88% and an F1 score of 85%. The multinomial Naïve Bayes algorithm ranked second with steady accuracy, whereas KNN and Decision Tree had poorer performance. This suggests that SVM is the most effective method for processing sentiment data regarding Tapera policy.
Combination of Response to Criteria Weighting Method and Multi-Attribute Utility Theory in the Decision Support System for the Best Supplier Selection Faruk Ulum; Junhai Wang; Dyah Ayu Megawaty; Ari Sulistiyawati; Riska Aryanti; Sumanto Sumanto; Setiawansyah Setiawansyah
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1810

Abstract

Choosing the right supplier is a strategic factor in supporting operational efficiency and a company's competitive advantage. This process requires a decision support system that is able to assess various alternatives objectively and in a structured manner. This study aims to develop a decision support system in the selection of the best supplier by combining the Response to Criteria Weighting (RECA) and Multi-Attribute Utility Theory (MAUT) methods. The RECA method is used to objectively determine the weight of each criterion based on the variation of data between alternatives, so as to reduce subjectivity in the weighting process. Meanwhile, the MAUT method functions to calculate the total utility value of each supplier based on the normalization value and weight that has been obtained. The results of the RECA method show the objective weight of each criterion, which is then used in the MAUT calculation process. The results of the analysis, obtained in the best supplier selection based on the total score of each candidate, it can be seen that PT Global Niaga Mandiri ranks first with the highest score of 0.6512, this shows that this company is the best choice in the supplier selection process. In second place is UD Anugrah Bersama with a score of 0.399, followed by PT Indo Logistik Prima in third place with a score of 0.3451. The combination of the RECA and MAUT methods has been proven to be able to produce accurate, rational, and accountable decisions. This system provides a measurable approach in filtering supplier alternatives efficiently and is relevant to be applied to various other multi-criteria decision-making contexts.
OPTIMASI KLASIFIKASI GANGGUAN TIDUR PADA DATASET TIDAK SEIMBANG MENGGUNAKAN SMOTE DAN ALGORITMA MACHINE LEARNING Titik Misriati; Riska Aryanti
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v19i2.295

Abstract

Sleep disorders are increasingly prevalent health issues that significantly affect individual’s quality of life. Timely detection and accurate classification of these disorders are essential for proper diagnosis and effective clinical intervention. However, a major challenge in classifying sleep disorders lies in the imbalance of data distribution—where majority classes have substantially more data than minority ones. This imbalance often leads to predictive models that favor the dominant class, thereby reducing overall classification accuracy. This study focuses on enhancing sleep disorder classification performance on imbalanced datasets by applying the Synthetic Minority Over-sampling Technique (SMOTE) to balance the data. It also evaluates the effectiveness of various machine learning algorithms in identifying sleep disorders. The algorithms analyzed include Random Forest (RF), Neural Network (NN), Naive Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), tested both before and after applying SMOTE. Model performance was assessed using accuracy, precision, recall, and F1-score to ensure a comprehensive evaluation. The findings indicate that SMOTE consistently boosts the performance of all tested models. Among them, the Neural Network combined with SMOTE achieved the highest performance, with an accuracy of 92.00%, precision of 91.88%, recall of 92.00%, and an F1-score of 91.91%. Additionally, the Random Forest model with SMOTE produced the highest F1-score at 93.18%, demonstrating strong performance stability. These results highlight the effectiveness of integrating oversampling techniques like SMOTE with machine learning models to address class imbalance, leading to more accurate and reliable classification outcomes. The study offers valuable insights for developing AI-based medical decision support systems focused on sleep disorder diagnosis.
Decision Support System for Selecting the Best Restaurant Waiter Using a Combination of WENSLO Weighting and AROMAN Methods Aryanti, Riska; Wang, Junhai; Wahyudi, Agung Deni; Setiawansyah, Setiawansyah; Darwis, Dedi
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 2 (2025): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i2.4

Abstract

The quality of service staff is a key factor in determining business success because they are the front line that interacts directly with consumers. However, performance evaluations of service staff are often still carried out subjectively, based only on the supervisor's perception or brief experiences with customers. This research discusses the application of a decision support system to determine the best restaurant service by combining the Weights by Envelope and Slope (WENSLO) method in criteria weighting and the Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN) in the alternative ranking process. The dataset used in this study was collected in 2025 from one of the restaurants in the Lampung area, involving nine waiters as evaluation candidates using six criteria. The six criteria used consist of four benefit criteria: service speed, friendliness, accuracy, and customer satisfaction. The weighting results using the WENSLO method indicate that the order mistakes criterion received the highest weight of 0.7253, followed by completion time with a weight of 0.1700, while the other criteria have relatively small weights. The AROMAN method is used to calculate the final values of alternatives based on the specified weights, resulting in a ranking of restaurant servers. The analysis shows that alternative Waiters KS ranks first with the highest score of 1.6097, followed by Waiters QN and Waiters RB. This finding proves that the combination of the WENSLO and AROMAN methods can produce objective, systematic results, and supports restaurant management in making strategic decisions regarding the selection of the best employees.
Co-Authors A Ferico Octaviansyah Pasaribu Agus Junaidi Agustiani, Sarifah Aldian Mauluda Alif Rizqi Mulyawan Andi Saryoko Andika Bayu Hasta Yanto Andreas Roy Prasetya Ari Sulistiyawati Arifin, Yosep Tajul Arman Ramadhani Asriyani Sagiyanto ASRIYANI SAGIYANTO, ASRIYANI Atang Saepudin Atang Saepudin Atang Saepudin Azis, Munawar Abdul Bayu Kusuma Ilyasa Universitas Bina Sarana Informatika Dahlia Dahlia Darma Setiawan Putra Dede Firmansyah Dede Firmansyah Saefudin Dedi Darwis Deni Gunawan Diah Puspitasari Dian Ardiansyah Dian Ardiansyah Dyah Ayu Megawaty Eka Dyah Setyaningsih Eka Fitriani Eka Fitriani Eka Fitriani Eka Fitriyani Elah Nurlelah Fachri, Muhamad Farah Diba Azkia Faruk Ulum Faruk Ulum Fuad Nur Hasan Haliza Ramadhanti, Pristya Harefa, Kristine Haryani Hasan, Fuad Nur Henny Leidiyana Herdian Pratama I Gede Iwan Sudipa Irfan Ridwan Jananto Watori Junhai Wang Junhai Wang Junhai Wang Kamil, Anton Abdul Basah KOMALASARI, YULI Kristine Harefa Martenia, Rina Masngud Megawaty, Dyah Ayu Mesran, Mesran Mochamad Wahyudi Munawar Abdul Azis Oktaviyani Oktaviyani Perani Rosyani Pristya Haliza Ramadhanti Pristya Haliza Ramadhanti Rachilsyah Ramdhani Efendi Raditya Rimbawan Oprasto Rahmat Hidayat Ramadhani, Arya Richardus Eko Indrajit Rifky Permana Rifqi Rizaldi Rina Martenia Rizqi Nur Esmeralda Rosiun Universitas Bina Sarana Informatika Roy Prasetya, Andreas Royadi - Royadi Royadi Royadi, Royadi Salman Alfarizi SALMAN ALFARIZI Samudi Sari Dewi Universitas Bina Sarana Informatika PSDKU Pontianak Setiawansyah Setiawansyah Siti Khotimatul Wildah Siti Marlina Siti Marlina, Siti Sopiyan Dalis Sumanto Sumanto Titik Misriati Tri Wahyuni Tri Wahyuni tri wahyuni Ulum, Faruk Wahyudi, Agung Deni Walim Walim Wang, Junhai Yarimani Laia