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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) Community Development Journal: Jurnal Pengabdian Masyarakat 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 Bulletin of Informatics and Data Science Jurnal Ilmiah Manajemen Ekonomi Dan Akuntansi (JIMEA) Jurnal Sistem Informasi dan Manajemen Jurnal Ekonomi Manajemen Dan Bisnis (JEMB) Media Teknologi dan Informatika Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Komtika (Komputasi dan Informatika) Jurnal Teknoinfo
Riska Aryanti
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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.
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.
Peran Bahasa Indonesia dalam Akuntansi: Penataan Istilah Akuntansi untuk Kemudahan Pemahaman dan Transparansi Informasi Hesniati; Ramadhani Adinda Salsabilla; Riska Aryanti; Meilan Sri Despitra; Hafidatul Husna; Nheza Aulia Putri; Rahma Gustina Putri; Putri Ernisa; Alya Sari; Hariyani Ningsih; Cindy Aulia Putri; Ghinanda Nasywa; Lia Trinanda; Suci Syafitry; Airin Triyana; Miftahul Jannah; Perawati
Jurnal Ekonomi Manajemen Dan Bisnis (JEMB) Vol. 3 No. 3 (2026): Januari
Publisher : Publikasi Inspirasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62017/jemb.v3i3.6890

Abstract

Bahasa Indonesia memiliki peranan penting dalam penyajian informasi akuntansi, terutama dalam pengaturan istilah akuntansi agar dapat dipahami secara jelas dan transparan oleh pengguna laporan keuangan. Banyak istilah akuntansi yang bersumber dari bahasa asing dan bersifat teknis, sehingga berpotensi menimbulkan perbedaan penafsiran apabila tidak disesuaikan dengan kaidah bahasa Indonesia yang tepat. Penelitian ini bertujuan untuk menelaah peran bahasa Indonesia dalam penataan istilah akuntansi serta implikasinya terhadap pemahaman dan transparansi informasi keuangan. Metode yang digunakan adalah tinjauan literatur dengan pendekatan kualitatif melalui analisis buku, standar akuntansi, dan artikel jurnal yang relevan. Hasil kajian menunjukkan bahwa penggunaan bahasa Indonesia yang baku, konsisten, dan kontekstual mampu meningkatkan keterbacaan laporan keuangan, mempermudah pemahaman pengguna non-akuntan, serta memperkuat transparansi dan akuntabilitas informasi keuangan. Namun demikian, penyesuaian istilah akuntansi tetap harus menjaga ketepatan makna teknis agar selaras dengan standar yang berlaku.
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.
Co-Authors Agus Junaidi Agustiani, Sarifah Airin Triyana Aldian Mauluda Alif Rizqi Mulyawan Alya Sari Andi Saryoko Andreas Roy Prasetya Ari Sulistiyawati Arifin, Yosep Tajul Asriyani Sagiyanto ASRIYANI SAGIYANTO, ASRIYANI Atang Saepudin Atang Saepudin Atang Saepudin Azis, Munawar Abdul Bayu Kusuma Ilyasa Universitas Bina Sarana Informatika Cindy Aulia Putri Cindy Sri Wahyuni Dahlia Dahlia Darma Setiawan Putra Dede Firmansyah Dede Firmansyah Saefudin Dedi Darwis Deni Gunawan Diah Puspitasari Dian Ardiansyah Dian Ardiansyah Eka Dyah Setyaningsih Eka Fitriani Eka Fitriani Eka Fitriani Eka Fitriyani Fachri, Muhamad Faradiva, Aulia Ghinanda Nasywa Hafidatul Husna Haliza Ramadhanti, Pristya Harefa, Kristine Hariyani Ningsih Hariyanto, Gebby Amara Putri Sugeng Haryani Hasan, Fuad Nur Henny Leidiyana Herdian Pratama Hesniati, Hesniati I Gede Iwan Sudipa Ilham Hudi Aim Abdulkarim Kokom Komalasari, Ilham Hudi Aim Abdulkarim Irfan Ridwan Jananto Watori Kamil, Anton Abdul Basah Khairani, Yashinta KOMALASARI, YULI Lia Trinanda Lubis, Anisah Azzahra Martenia, Rina Masjuwita Aulia Munthe Masngud Megawaty, Dyah Ayu Meilan Sri Despitra Mesran, Mesran MIFTAHUL JANNAH Mochamad Wahyudi Nheza Aulia Putri Nova Damai Yanti Bancin Nurazila, Riska Oktaviyani Oktaviyani Oprasto, Raditya Rimbawan Pasaribu, A. Ferico Octaviansyah Perani Rosyani Perawati Permana, Rifky Pristya Haliza Ramadhanti Putri Ernisa Rachilsyah Ramdhani Efendi Rahma Gustina Putri Rahmat Hidayat Rahmat Hidayat Ramadhani Adinda Salsabilla Ramadhani, Arya Ramadhani, Nadia Thalia 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 siti rodiah Sopiyan Dalis Suci Syafitry Sumanto, Sumanto Titik Misriati tri wahyuni Tri Wahyuni Ulum, Faruk Utami, Ajeng Ayun Dining Vitantri, Vitantri Wahyudi, Agung Deni Wahyuni, Cindy Sri Walim Walim Wang, Junhai Yanto, Andika Bayu Hasta Yarimani Laia