Article Per Year (5 Year)
p-Index From 2021 - 2026
3.225
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi Prosiding University Research Colloquium Jurnal Indonesia Sehat (JURINSE) Journal of Artificial Intelligence and Digital Business Innovative: Journal Of Social Science Research Jurnal Pengabdian West Science Parapemikir Jurnal Ilmiah Farmasi Advances in Healthcare Research IJF (Indonesia Jurnal Farmasi) JOURNAL OF PHILANTHROPY: The Journal of Community Service
Rahmawati, Riana Putri
Unknown Affiliation
Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
Uji Efektivitas Spray Repellent Minyak Atsiri Serai Wangi (Cymbopogon nardus L.) pada Nyamuk Aedes aegypti Handayani, Ratna Fitriana; Rahmawati, Riana Putri; Besan, Emma Jayanti
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.5520

Abstract

There are two commonly known types of lemongrass, namely kitchen lemongrass (Cymbopogon citratus) and fragrant lemongrass (Cymbopogon nardus L.), both of which are widely used by the community as traditional medicine. This study focuses on fresh fragrant lemongrass, in which the stems are utilized to produce a spray preparation that functions as a mosquito repellent. The study aims to evaluate the effectiveness of fragrant lemongrass essential oil formulated as a spray with different concentrations of 5%, 10%, and 15%. The essential oil of fragrant lemongrass is obtained using the steam distillation method. Prior to formulation into a spray, phytochemical screening was conducted to identify the presence of flavonoids, tannins, saponins, and terpenoids. After completing the phytochemical screening, spray formulations were prepared and divided into three formulation groups. Each formulated spray was evaluated for physical properties, including organoleptic characteristics, pH value, clarity, and homogeneity. In addition, an activity test of the repellent spray was carried out using mosquito test animals. The results showed that the citronella essential oil spray (Cymbopogon nardus L.) exhibited an average repellent power of 61.6% in formulation I, 81.40% in formulation II, and 81.41% in formulation III. The protection power test indicated that the highest repellent activity was observed in formulation III. Based on the One Way ANOVA analysis followed by a Post Hoc test, there was no significant difference between formulation II and formulation III. These findings support the potential use of citronella spray as repellent.
Detecting Muslim Students Mental Health with an Islamic Educational Approach using Machine Learning Pratama, Taftazani Ghazi; Rafsanjani, Toni Ardi; Rahmawati, Riana Putri; Imaduddin, Helmi
SISTEMASI Vol 15, No 1 (2026): 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.v15i1.5732

Abstract

Mental health among university students has become a major concern in higher education, particularly in the post-pandemic era, which has left students facing various academic, social, and psychological pressures. Unfortunately, efforts for early detection of mental health issues on campus remain limited, especially in the context of Muslim students who live within an Islamic cultural framework. This study offers an innovative approach by integrating advanced machine learning technology with the depth of Islamic educational values to develop an early detection system that is not only accurate but also humanistic and contextually relevant. The dataset for this study was obtained through a survey of 127 students at Universitas Muhammadiyah Kudus, including variables related to psychological conditions and the intensity of religious practices, used to detect whether students experience mental health problems or maintain good mental health. The research methodology includes data collection, preprocessing, feature analysis, model development using classification algorithms such as Random Forest, SVM, KNN, and Decision Tree, model performance optimization using GridSearchCV, and evaluation. Evaluation of the four models indicated that prior to optimization, SVM and KNN achieved the best performance, both with an accuracy of 88.46%. After optimization with GridSearchCV, SVM became the top-performing model, achieving an accuracy improvement of more than 5%, reaching 94.05%. Feature analysis revealed that levels of anxiety, fatigue, and religious practices such as prayer and dhikr were the primary determinants in mapping students’ mental health conditions. These findings suggest that Islamic values such as tawakkul (trust in God), sabr (patience), and syukur (gratitude) are not merely theological concepts but can also serve as scientific instruments, converted into predictive features in data-driven technologies. This study demonstrates that an SVM model optimized with GridSearchCV is effective in detecting university students’ mental health and has the potential to serve as an early warning system in Islamic campus settings.
Co-Authors Aguswina, Melany Eka Ahmad Suriyadi Muslim Alviona, Juana Della Andjarwati, Fitri Nur Anggraeni, Ira Ari Simbara, Ari Besan, Emma Jayanti Chabibah, Chabibah Etikasari, Ria Fahrudin Arif Faizin, Fadli Akbar Faradila, Fatarani Febrianti, Noor Alfina Firdausi, Zainatul Fitri Apriliyani GALIH KURNIAWAN Handayani, Ratna Fitriana Hardiani, Elia Hayyu, Ghea Permata Helmi Imaduddin Hidayatunnadiya, Rida Isnaini, Ratna Dewi Kurniawati, Pratiwi Linda Amalia Mafitri, Hanifah Miftah Malihatin, Putri Dewi Mardhiyah, Sofiana Nur Muhamad Khudzaifi Murharyanti, Rika Nazilla, Khunut Putri Susanti, Dina Eka Rafsanjani, Toni Ardi Rahayu, Ika Marta Santi Dwi Rahayu Saputri, Ardiani Maulina Sari, Fatika Septiani, Hanim Ayun Setianingsih, Indah Wahyu Setyaningrum, Intansari Silva, Elfanesya Sugiarti, Retno Sukoharjanti, Bintari Tri Taftazani Ghazi Pratama Wibowo, Teguh Setiawan Wilisa, Oni Yulianta Wulandari, Ninuk Zaenal Fanani