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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 (HJCS) 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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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 | DOI: 10.33369/teknosia.v20i1.48331

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.
Explainable Machine Learning for Multi-Class Classification of Internet Firewall Traffic Titik Misriati; Riska Aryanti
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.163

Abstract

The increasing diversity and scale of network traffic introduce significant challenges in performing accurate and interpretable firewall analysis. This research aims to bridge the gap between predictive performance and model transparency by developing an explainable machine learning framework for multi-class firewall traffic classification. The study utilizes the Internet Firewall Data dataset consisting of 65,532 network traffic instances distributed across four firewall action classes and evaluates seven classification algorithms, including Decision Tree, Random Forest, XGBoost, Support Vector Machine, k-Nearest Neighbors, Naïve Bayes, and Logistic Regression. The dataset was partitioned using a stratified 80:20 hold-out approach to preserve the original class distribution and the experimental process involves data preprocessing, normalization, and validation on an independent test set using accuracy, precision, recall, and F1-score metrics. The findings reveal that XGBoost achieves the highest performance, reaching an accuracy of 99.81%, followed by Decision Tree and Random Forest. This indicates that ensemble and tree-based approaches are highly effective in modeling complex and non-linear traffic patterns. To improve interpretability, this study incorporates explainable artificial intelligence techniques, including feature importance and SHAP analysis. The results show that traffic-related attributes significantly influence classification outcomes, providing meaningful insights into firewall decision behavior
Selection of the Best E-Commerce Platform Based on User Ratings using a Combination Entropy and SAW Methods Faruk Ulum; Junhai Wang; Setiawansyah Setiawansyah; Riska Aryanti
Bulletin of Informatics and Data Science Vol 3, No 2 (2024): November 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i2.92

Abstract

Choosing the right e-commerce platform has a crucial role for consumers and business actors. For consumers, a reliable and user-friendly platform provides a safe, convenient, and efficient shopping experience. Considering various aspects of choosing the right e-commerce platform is a strategic investment that can provide long-term added value for all parties involved in the digital ecosystem. The purpose of this study is to identify and determine the best e-commerce platforms based on user experience and assessment with an objective and structured decision-making approach using a combination of Entropy and SAW methods. The results of the ranking of the best e-commerce platform selection determined through the combination of the Entropy and SAW methods, obtained that Shopee ranked first with the highest preference value of 0.9819, followed by Tokopedia in second place with a value of 0.973. Furthermore, Blibli is in third place with a score of 0.9401, followed by Lazada with a score of 0.9305, and the last is Bukalapak with a score of 0.9021. This research makes a significant contribution to multi-criteria decision-making by applying a combination of Entropy and SAW methods to evaluate and determine the best e-commerce platform based on user assessments. The results of this research can be used as a practical reference as a basis for strategic decision-making in choosing the e-commerce platform that best suits market needs
Empowering Community Digital Literacy through Participatory Artificial Intelligence Training Using Participatory Action Research in South Jakarta Sarifah Agustiani; Riska Aryanti; Tri Wahyuni; Elah Nurlelah; Pristya Haliza Ramadhanti; Farah Diba Azkia
Help: Journal of Community Service Vol. 3 No. 1 (2026): June 2026
Publisher : PT Agung Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62569/hjcs.v3i1.279

Abstract

Digital literacy has become a fundamental competency for communities in responding to the rapid advancement of Artificial Intelligence (AI) technologies. However, many community members still have limited knowledge and practical skills in utilizing AI productively, ethically, and responsibly. This community service program aimed to empower community digital literacy through participatory Artificial Intelligence training using a Participatory Action Research (PAR) approach in RT 05, Cikoko Urban Village, South Jakarta. The program was implemented through four stages of PAR, including problem identification, collaborative planning, participatory action, and reflection. Training activities consisted of interactive lectures, live demonstrations, guided hands-on practice, group discussions, and mentoring on AI applications, digital ethics, information verification, and online security. Program evaluation was conducted using observations, reflective discussions, and post-training questionnaires involving sixteen participants. The findings revealed three major outcomes. First, the participatory learning approach successfully increased community engagement, with 75% female participants and 69% of participants aged 12–20 years actively involved throughout the learning process. Second, the training achieved high participant satisfaction, with information delivery, training materials, presenter performance, and event organization each receiving an 81% satisfaction score. Third, the program significantly improved community digital literacy and readiness for AI adoption. Participants reported that the program provided substantial benefits (88%), increased their knowledge (81%), improved practical AI utilization skills (81%), and enhanced their overall satisfaction (81%), while sustainable technology utilization, practical relevance, systematic implementation, and willingness to participate in future activities each achieved 75% positive responses. 
Pemanfaatan Internet Dalam Menunjang Kegiatan Belajar Mengajar Di Masa Pandemi Covid-19 Riska Aryanti; Atang Saepudin; Tri Wahyuni; Fuad Nur Hasan; Kristine Harefa
Jurnal Abdimas Komunikasi dan Bahasa Vol. 1 No. 1 (2021): Juni
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/abdikom.v1i1.331

Abstract

Pandemi Covid-19 berdampak besar pada berbagai sektor, salah satunya pendidikan. Dunia pendidikan juga ikut merasakan dampaknya. Pendidik harus memastikan kegiatan belajar mengajar tetap berjalan, meskipun peserta didik berada di rumah. Solusinya, pendidik dituntut mendesain media pembelajaran sebagai inovasi dengan memanfaatkan media daring (online). Ini sesuai dengan Menteri Pendidikan dan Kebudayaan Republik Indonesia terkait Surat Edaran Nomor 4 Tahun 2020 tentang Pelaksanaan Kebijakan Pendidikan dalam Masa Darurat Penyebaran Corona Virus Disease (Covid-19). Sistem pembelajaran dilaksanakan melalui perangkat Mobile Phone, Personal Computer (PC) atau laptop yang terhubung dengan koneksi jaringan internet. Proses belajar mengajar akhirnya berubah dari bertatap muka secara langsung dikelas menjadi pembelajaran berbasis jaringan/internet atau yang biasa disebut dengan istilah daring. Tidak sedikit peserta didik bahkan pendidik yang masih belum terbiasa dengan sistem pembelajaran daring ini. Oleh karena itu, dosen Program Studi Ilmu Komputer (S1) Universitas Bina Sarana Informatika akan menyelenggarakan sosialisasi/pelatihan terhadap peserta didik khususnya pada warga RT. 002/RW.002 Tegal Parang–Jakarta Selatan. Pelatihan tersebut memiliki tema Pemanfaatan Internet Dalam Menunjang Kegiatan Belajar Mengajar Di Masa Pandemi Covid-19. Pelatihan ini diharapkan mampu menambah pemahaman dan keterampilan para peserta agar lebih mampu memanfaatkan layanan internet dengan lebih bijak untuk menunjang proses belajar mengajar di masa pandemi Covid-19 saat ini. Adapun target luaran dari pelaksanaan Pengabdian Masyrakat ini yaitu berupa publikasi artikel di media online, video dokumentasi kegiatan dan meningkatkan pengetahuan dan keterampilan peserta dalam memanfaatkan layanan internet.
Decision Support System for Selecting the Best Restaurant Waiter Using a Combination of WENSLO Weighting and AROMAN Methods Riska Aryanti; Junhai Wang; Agung Deni Wahyudi; Setiawansyah Setiawansyah; Dedi Darwis
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 2 (2025): December
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.
PERANCANGAN SISTEM INFORMASI AKADEMIK MADRASAH ALIYAH NEGERI (MAN) 4 KARAWANG BERBASIS WEB Dian Ardiansyah; Atang Saepudin; Riska Aryanti; Eka Fitriani
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 3 No. 2 (2020): Jurnal Teknologi dan Open Source, December 2020
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v3i2.783

Abstract

The development of web-based information technology is currently increasing. One of them is the website. The website has the advantage that all the information you want can be easily and cheaply obtained. Madrasah Aliyah Negeri (MAN) Rengasdengklok or now known as MAN 4 Karawang currently processing academic data, especially in processing grades is still very simple, namely only with Microsoft Excel. The presence of this academic information system is expected to help teachers and students get academic information more quickly, easily and cheaply. In building this system the writer uses the waterfall methodology. The design and implementation were carried out with the Dreamweaver programming language and PHP and the database used by PhpMyAdmin. With this academic information system, hopefully it can help to support school performance, especially for processing academic data.
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