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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
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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

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Sentiment Analysis Of The Shopee Marketplace On Twitter Using The Naive Bayes Classifier Method Natalia, Nila; Astikarani, Ester Krisdianti; Adi Khairul, Muhammad; Nafis Sjamsuddin, Irfan
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 2 (2025): November 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i2.17077

Abstract

Shopee is the number one most downloaded marketplace application on the App Store and Play Store. In its promotion, Shopee provides discounts on shipping costs, price discounts, and cashback for each transaction; however, not all of its users are satisfied with the service. There are criticisms and suggestions, one of which is conveyed via social media, Twitter. Sentiment analysis was conducted to extract information related to Shopee user reviews on Twitter. The stages of the research carried out followed the Cross Industry Standard Process for Data mining (CRISP-DM) method, namely Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Data collection was carried out by scraping, and all classification processes were carried out using the RapidMiner tool. The data obtained tends to contain negative sentiment rather than positive; most reviews are made by buyers and discuss promos. Sentiment classification is carried out by applying the Naive Bayes Classifier and TF-IDF as feature extraction. Testing using 10-fold cross-validation and a Confusion Matrix resulted in an accuracy value of 84.20%, a precision value of 87.21%, and a recall value of 84.20%.
SISTEM INFORMASI PENGADUAN KAMPUS MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS (AHP) BERBASIS WEB (STUDI KASUS POLITEKNIK SUKABUMI) Waruwu, Nover Lindra; Natalia, Nila
SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Vol 7 (2025)
Publisher : SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan)

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Abstract

Sistem informasi pengaduan kampus adalah sistem yang memberikan infromasi kepada pihak kampus dalam menangani pengaduan dari mahasiswa namun, di Politeknik Sukabumi proses pengaduan masih dilakukan secara manual, yang mengakibatkan mahasiswa sering mengalami kendala dalam mengungkapkan keluhan dan aspirasi mereka. keadaan ini menimbulkan keterlambatan dalam penanganan. dalam menyelesaikan permasalahan maka tugas akhir ini bertujuan untuk merancang dan menciptakan sistem informasi pengaduan kampus yang berbasis web dengan menggunakan metode Analytical Hierarchy Process (AHP) sebagai pendukung keputusan. aplikasi ini juga dirancang menggunakan framework laravel, dengan MySQL sebagai basis data. metode AHP diterapkan untuk mengidentifikasi prioritas dalam menentukan pengaduan dengan mempertimbangkan beberapa kriteria, termasuk urgensi , dampak , waktu penyelesaian. hasil pengujian black box testing menunjukkan semua skenario fungsional pada sistem telah berjalan valid dan sesuai harapan. Sementara itu, implementasi metode AHP menunjukkan tingkat akurasi perhitungan yang sangat tinggi, berkisar antara 95,93% hingga 99,94% jika dibandingkan dengan perhitungan manual. sistem yang dihasilkan berhasil menyediakan fitur penting seperti notifikasi real-time dan pelacakan status.
Cloud Computing Integration in the Digital Transformation of MSMEs: A Case Study on the Retail Sector Nila Natalia; Errysa Subekthi; Febri Dolis Herdiani; Foezi Arisandi SJ
Technologia Journal Vol. 2 No. 4 (2025): Technologia Journal-November
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/jkbsjt34

Abstract

This study aims to analyze the role of cloud computing integration in supporting the digital transformation process of Micro, Small, and Medium Enterprises (MSMEs) in the retail sector in Indonesia. Digital transformation has become a strategic necessity in facing the dynamics of global competition and changes in technology-based consumer behavior. This study uses a qualitative method with a case study approach on several retail MSMEs that have adopted cloud computing services. Data were obtained through in-depth interviews, field observations, and documentation, then analyzed descriptively through the stages of data reduction, data presentation, and conclusion drawing. The results show that the implementation of cloud computing can improve operational efficiency, strengthen data management, and accelerate the decision-making process based on real-time information. MSMEs that adopt this technology experience a reduction in IT infrastructure costs of up to 40% and an increase in employee productivity of up to 30%. However, challenges remain such as limited digital literacy, uneven internet infrastructure, and concerns about data security. This study confirms that cloud computing functions not only as a technological solution, but also as a catalyst for changing work culture and business management systems towards sustainable digitalization.
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