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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 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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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.
Optimization of Crop Recommendation Model Using Ensemble Learning Techniques for Multiclass Classification Marlina, Siti; Misriati, Titik; Aryanti, Riska
Computer Science (CO-SCIENCE) Vol. 6 No. 1 (2026): January 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i1.10044

Abstract

Crop recommendation systems play a crucial role in modern agriculture by helping farmers make data-driven decisions to maximize yield, optimize resource use, and ensure sustainable farming practices. By analyzing environmental and soil parameters, these systems can suggest the most suitable crops for specific conditions, reducing the risks of crop failure and improving overall productivity. This study evaluates the performance of five ensemble learning algorithms—Random Forest, Extra Trees, CatBoost, XGBoost, and LightGBM—for multiclass classification in a crop recommendation system. All models achieved high accuracy above 98%, with Random Forest demonstrating the best and most stable performance. The feature importance analysis revealed that climatic factors, particularly rainfall and humidity, contributed the most to prediction outcomes, followed by macronutrients such as potassium, phosphorus, and nitrogen. In contrast, temperature and soil pH showed relatively lower influence. These findings highlight the dominance of climatic factors over soil chemical properties and demonstrate the capability of ensemble learning methods to capture complex data patterns. Random Forest is recommended as the primary model to support more effective land management and crop cultivation strategies.
Analisis Sentimen Pengguna GoPay pada Layanan Keuangan Digital dengan Perbandingan Naïve Bayes dan SVM Dian Ardiansyah; Riska Aryanti; Eka Fitriani; Royadi
PROFITABILITAS Vol 5 No 2 (2025): JURNAL PROFITABILITAS
Publisher : Sistem Informasi Akuntansi Kampu Kabupaten Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/profitabilitas.v5i2.11513

Abstract

The rapid development of digital financial services has led to increased use of digital wallets, one of which is the GoPay application, resulting in a large volume of user reviews. These reviews contain valuable information regarding user satisfaction and service-related issues, making automated methods necessary to accurately analyze user sentiment. This study aims to analyze sentiment in GoPay user reviews and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms for sentiment classification.This research uses a dataset of 132,393 GoPay user reviews obtained from the Kaggle platform. The data are labeled based on user ratings into three sentiment classes: positive, neutral, and negative. The research stages include text preprocessing, feature transformation using the Term Frequency–Inverse Document Frequency (TF-IDF) method, sentiment classification using the Naïve Bayes and SVM algorithms, and model performance evaluation using accuracy, precision, recall, and F1-score metrics.The results show that 79.2% of the reviews are classified as positive, 17.1% as negative, and 3.7% as neutral. Based on performance evaluation, the SVM algorithm demonstrates superior results with an accuracy of 90.65%, precision of 90.7%, recall of 90.65%, and F1-score of 89.05%, compared to Naïve Bayes, which achieves an accuracy of 87.89%, precision of 89.1%, recall of 87.89%, and F1-score of 88.42%. These findings indicate that SVM is a more optimal method for sentiment analysis of GoPay user reviews, while Naïve Bayes remains an efficient and competitive alternative for large-scale text classification.
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.
Optimasi Model Machine Learning Menggunakan Teknik SMOTE pada Analisis Sentimen Pengguna RedBus Arman Ramadhani; Riska Aryanti; Sarifah Agustiani
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 1 (2026): Volume 4 Number 1 March 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i1.182

Abstract

Perkembangan teknologi digital semakin memudahkan masyarakat dalam memenuhi kebutuhan transportasi, salah satunya melalui aplikasi pemesanan tiket bus seperti RedBus. Aplikasi ini menghadirkan layanan pemesanan secara praktis, namun ulasan pengguna yang semakin banyak di Google Play Store bersifat tidak terstruktur sehingga memerlukan analisis lebih lanjut untuk menilai kualitas layanan secara objektif. Penelitian ini bertujuan untuk mengklasifikasikan sentimen kepuasan pengguna aplikasi RedBus dengan memanfaatkan algoritma Naïve Bayes dan Random Forest. Untuk mengatasi masalah ketidakseimbangan data, digunakan teknik Synthetic Minority Over-sampling Technique (SMOTE). Data yang digunakan berjumlah 2.000 ulasan yang dikumpulkan melalui metode web scraping, kemudian diproses melalui tahapan preprocessing yang meliputi data cleaning, cleansing, case folding, tokenization, stopword, dan stemming. Selanjutnya, data diberi label kepuasan berdasarkan rating, lalu dikonversi menjadi fitur numerik dengan metode TF-IDF. Data dibagi menjadi 90% data latih dan 10% data uji agar dapat dievaluasi secara menyeluruh. Hasil pengujian menunjukkan bahwa algoritma Naïve Bayes menghasilkan akurasi 91%, precision 97%, recall 89%, dan F1-score 92%. Sementara itu, algoritma Random Forest memperoleh akurasi 90%, precision 94%, recall 90%, dan F1-score 92%. Keunggulan Naïve Bayes terlihat pada nilai precision yang tinggi, menunjukkan kemampuannya dalam meminimalkan kesalahan klasifikasi positif palsu. Kesimpulannya, penerapan Naïve Bayes dengan dukungan SMOTE dinilai lebih optimal dalam mengklasifikasikan sentimen ulasan, sehingga dapat menjadi masukan bagi pengembang RedBus dalam meningkatkan kualitas layanan dan kepuasan pengguna.
Enhancing Sentiment Classification Performance on Tentang Anak Application Reviews Using Optimized Support Vector Machine Riska Aryanti; Eka Fitriani; Royadi Royadi; Dian Ardiansyah
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 2 (2026): Volume 4 Number 2 June 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i2.271

Abstract

The increasing use of parenting and child development applications has generated a large volume of user reviews containing valuable insights regarding application quality, usability, and user satisfaction. One of the widely used applications in Indonesia is Tentang Anak: Kehamilan & Anak. However, manually analyzing these reviews is inefficient due to the large amount of unstructured textual data. Therefore, this study aims to enhance sentiment classification performance on user reviews of the Tentang Anak: Kehamilan & Anak application using an optimized Support Vector Machine (SVM) model. The dataset consisted of user reviews collected from application platforms, which were processed through several text preprocessing stages, including cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using polarity scores to classify reviews into positive and negative sentiments. The proposed model was evaluated using different test size scenarios (0.1, 0.2, 0.3, and 0.4) and random state configurations to identify the optimal parameter setting. Experimental results demonstrate that the best performance was achieved at a test size of 0.1 with random state 0, obtaining an accuracy of 89.8%, precision of 91.7%, recall of 55.0%, and F1-score of 68.8%. The findings indicate that the optimized SVM model is effective in classifying sentiment in reviews of the Tentang Anak: Kehamilan & Anak application, particularly in achieving high precision and classification stability across multiple testing scenarios. Furthermore, the study highlights the importance of parameter optimization in improving sentiment analysis performance for user-generated textual data.
Penerapan Metode Rapid Application Development Dalam Pengembangan Aplikasi Persediaan Material Panel Listrik Berbasis Web Munawar Abdul Azis; Mochamad Wahyudi; Riska Aryanti
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 4 No. 2 (2023): November 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v4i2.2496

Abstract

PT Indomitra Global is a company engaged in electrical contracting services and provides various types of electrical panels needed by clients. Electrical panels require many important materials, for example mcb, sockets, cables, and many other important components. The material inventory system carried out at PT Indomitra Global still uses a manual method in the material inventory system. This process has several obstacles, namely not having a centralized database that makes material inventory data vulnerable to loss and there are often differences in the suitability of the amount of material in the warehouse with the amount in Microsoft Excel, because data management is still not easy enough and due to human error or input errors. On the basis of this problem, a web-based material inventory application was made using the Rapid Application Development (RAD) method. The material inventory system produced in this study is able to handle material data management which previously was still not easy enough to do, such as searching for data, managing incoming and outgoing material transaction data and making it easier to generate incoming and outgoing material reports based on time periods
HYBRID METHOD USING ITARA AND MACONT FOR SELECTING THE BEST CUSTOMERS IN A DECISION SUPPORT SYSTEM Junhai Wang; Setiawansyah Setiawansyah; Riska Aryanti
Teknosia Vol. 20 No. 1 (2026): Vol. 20 No. 01 (2026): June 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research is motivated by the company's challenges in objectively identifying high-value customers due to the numerous assessment criteria, heterogeneous data, and the use of conventional methods that are prone to subjective bias and ranking instability. To address these challenges, this study develops a decision support system based on the hybrid ITARA–MACONT method, where ITARA is used to determine criteria weights rationally based on indifference threshold deviation, while MACONT is applied to perform compromise aggregation in the alternative ranking process. The results show that the system can produce clear and consistent customer rankings, with Customer TY achieving a score of 0.7141 and ranking first, followed by Customer RD with a score of 0.6561 in second place, and Customer AH with a score of 0.5859 in third place. These findings indicate that the integration of ITARA–MACONT is effective in enhancing the objectivity, transparency, and stability of top customer selection results, thereby supporting strategic decision-making aimed at improving customer loyalty and business profitability.
Co-Authors Agus Junaidi Agustiani, Sarifah Aldian Mauluda Alif Rizqi Mulyawan Andi Saryoko Andika Bayu Hasta Yanto Andreas Roy Prasetya Ari Sulistiyawati 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 Fachri, Muhamad Faruk Ulum 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 Kamil, Anton Abdul Basah KOMALASARI, YULI Martenia, Rina Masngud Megawaty, Dyah Ayu Mesran, Mesran Mochamad Wahyudi Munawar Abdul Azis Oktaviyani Oktaviyani Oprasto, Raditya Rimbawan Pasaribu, A. Ferico Octaviansyah Perani Rosyani Pristya Haliza Ramadhanti Rachilsyah Ramdhani Efendi 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, Royadi Salman Alfarizi SALMAN ALFARIZI Samudi Sari Dewi Universitas Bina Sarana Informatika PSDKU Pontianak Setiawansyah Setiawansyah Siti Khotimatul Wildah Siti Marlina, Siti Sopiyan Dalis Sumanto Sumanto Titik Misriati Tri Wahyuni tri wahyuni Ulum, Faruk Wahyudi, Agung Deni Walim Walim Wang, Junhai Yarimani Laia