Article Per Year (5 Year)
p-Index From 2021 - 2026
3.416
P-Index
This Author published in this journals
All Journal JTERA (Jurnal Teknologi Rekayasa) JMM (Jurnal Masyarakat Mandiri) Zonasi: Jurnal Sistem Informasi SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Jurnal Inovatif : Inovasi Teknologi Informasi dan Informatika Journal Collabits International Journal of Applied Information Systems and Informatics Technologia Journal
Natalia, Nila
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Other

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal Collabits

 1 
Designing User Interface and User Experience for Habit Tracker Application for Android Mobile Devices Natalia, Nila
Journal Collabits Vol 2, No 2 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i2.35091

Abstract

Positive habit formation is one of the main challenges in everyday life that requires consistency and continuous motivation. This study aims to design and develop an Android-based habit tracker application with a focus on optimizing the User Interface (UI) and User Experience (UX) to increase user engagement and effectiveness in building positive habits. The research methodology uses the User-Centered Design (UCD) approach which includes the stages of user needs analysis, wireframe and mockup design, prototyping, and usability testing evaluation. Data collection was carried out through a survey of 150 respondents to identify user needs and preferences for the habit tracker application. The UI/UX design process integrates the principles of Material Design Guidelines and the psychology of habit formation theory to create an intuitive and motivating interface. The main features of the application include habit tracking with progress visualization, reward and gamification systems, reminder notifications, and analytics dashboards. The results of the usability testing evaluation showed a user satisfaction level of 87% with a System Usability Scale (SUS) score of 82.5, which is included in the "excellent" category. A/B testing on various UI elements showed an increase in user retention rate of 34% and completion rate of 28% compared to conventional design. The application successfully implemented a consistent responsive design across different screen sizes with an average loading time of 2.3 seconds. This research contributes to the development of user-friendly mobile applications for habit tracking, provides insights into the importance of a psychological approach in UI/UX design, and serves as a reference for the development of similar applications in the future.
Implementation of DBSCAN Clustering and Random Forest Algorithm for Mapping and Predicting Shooting Incidents in New York Rangkuti, Azka Niaji; Arifin, Samoedra Cakra; Putra, Muhammad Ramadansyah Kurnia; Natalia, Nila
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37587

Abstract

Shooting incidents in crowded, heavily populated areas of cities cause serious threats to public safety and social security. New York State, which includes large metropolitan areas and suburban regions, experiences complex spatial and temporal crime patterns that are difficult to identify using traditional crime analysis methods that rely only on descriptive statistics and manual hot spot identification. This study proposes a data-driven quantitative approach to mapping and predicting shooting incidents by integrating spatial clustering and machine learning techniques. Density-based clustering methods are applied to the geographic coordinates of shooting incidents to identify areas with high incident concentrations while filtering out isolated events as noise. The resulting spatial clusters are then interpreted as hotspot locations and used as reference labels for a supervised classification model. A Random Forest algorithm is then used to predict hotspot and non-hotspot locations using spatial and temporal features, including geographic position and time of occurrence. The model is evaluated using standard classification performance measures, including accuracy, precision, recall, F1 score, and confusion matrix analysis.
Co-Authors Abdul Jalil Adi Khairul, Muhammad Aldiana Novian Pratama Alvi Maulana ANDIKA, ERICK Arifin, Samoedra Cakra Astikarani, Ester Krisdianti Aziz Abdullah Aziz Ma’sum Eka Hidayat Eris Dwi Purnama Errysa Subekthi Febri Dolis Herdiani Foezi Arisandi Foezi Arisandi Foezi Foezi Arisandi SJ Isa, Indra Griha Tofik Ismail Goro Lani Nurlani Lia Liliawati Ma?sum, Aziz Maulana, Alvi Muwahhid, Syayidul Nafis Sjamsuddin, Irfan Nur Rohmah Arraudoh Mahmud Nurul Anisa Purnama, Eris Dwi Putra, Muhammad Ramadansyah Kurnia Rahayu, Samirah Rangkuti, Azka Niaji Rohmatullah, Mochammad Irhas Salya Lidya Handi Siti Elsa Khoirunnisa Sukarsa Sukarsa Sukarsa Sukarsa Syamsiar, Syamsiar Syayidul Muwahhid Tirta Putra, Kentlie Trisiani Dewi Hendrawati Waruwu, Nover Lindra Yusup Andriyanto Zanbiya, Muhammad