Garuda - Garba Rujukan Digital

Analisis Sentimen Tiktok: Wajib Militer dengan Metode Lexicon Based dan Naive Bayes Classifier Saprizal, Arpan Mualief; Nor Anisa
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2 (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2.pp242-246

The issue of conscription in Indonesia has sparked a heated debate among the public, especially on the social media platform TikTok. This study aims to analyze public sentiment on the issue through analysis of TikTok user comments. The method used is lexicon-based sentiment analysis. Data of 5,212 comments were collected using web scraping techniques with the keyword "conscription in Indonesia". The results of the analysis showed that the majority of comments (53.28%) were positive, followed by neutral comments (35.79%), and negative comments (10.92%). This finding indicates that there is considerable support for the issue of military service among TikTok users. The research process includes data collection, data processing, sentiment analysis using a lexicon-based approach, and visualization of results. The results of this study are expected to provide a clearer picture of public perception of the issue of military conscription in Indonesia.

Analisis Data Judi Online di 5 Provinsi Indonesia Dengan Metode K-Means dan Decision Tree Saputra, Muhammad Bayu; Anisa, Nor
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2 (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2.pp254-258

This study aims to analyze online gambling data detected in five provinces in Indonesia using the K-Means and Decision Tree methods. The data includes player counts, transaction values, and geographical distribution in West Java, Jakarta, Central Java, Banten, and East Java. The K-Means method was applied to cluster provinces based on player counts and transaction values, while the Decision Tree was used to identify classification rules. The results reveal three main clusters with distinct characteristics: provinces with high player counts and high transactions, provinces with low player counts and moderate transactions, and provinces with moderate player counts but low transactions. These findings provide critical insights into the patterns of online gambling activities in Indonesia and serve as a foundation for more effective policies in managing its impacts.

Analisis Pengelolaan Digital Community dalam Mendukung Penjualan di Showroom Ralif Motor Rahmi, Halimatur; Rina, Rina; Adha, Fajar; Saputra, Muhammad Fitri; Anisa, Nor
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2 (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2.pp293-298

This journal examines the management of a digital community at Ralif Motor Showroom to support marketing efforts and business performance in addressing competitive challenges. The digital community is utilized to strengthen customer interactions, promotion, and loyalty, contributing to market expansion and building consumer trust. The study employs a qualitative approach through in-depth interviews with showroom representatives and several customers. The analysis reveals that while the digital community has positive impacts, such as increasing showroom visibility and fostering closer relationships with customers, its implementation faces challenges. Key issues include limited resources, inefficient manual recording processes, and suboptimal social media strategies. As a solution, this study recommends adopting an integrated management system, such as technology for stock management and transaction recording. Additionally, implementing a more structured and innovative social media strategy is necessary to support promotions and enhance customer interactions effectively. By taking these steps, the showroom is expected to improve operational efficiency, expand market share, and drive sustainable business growth.

Evaluasi dan Pengujian Internal Compatibility pada Aplikasi SIAKAD Universitas Sari Mulia Halimatur Rahmi; Nor Anisa
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 1 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No1.pp1-6

The Academic Information System (SIAKAD) is a vital component in managing academic data at higher education institutions, including Universitas Sari Mulia. This study aims to evaluate the internal compatibility of the SIAKAD application, specifically the alignment and consistency of data across modules such as student profiles, course registration (KRS), academic grades, attendance, and payment. The primary issue addressed is the potential for data mismatches between modules, which could affect academic processes and user trust. This research employs a qualitative approach using the black-box testing method to assess the application's technical performance based on predefined test scenarios, alongside questionnaires to evaluate user experiences. The testing results demonstrate that all features, including login, profile management, attendance, grading, and payment, functioned as expected according to the test scenarios. Based on responses from 18 participants, 72.22% of users found the application easy to use and consistent in displaying data, though some features were deemed less optimal (5.56%). This study concludes that the SIAKAD application at Universitas Sari Mulia effectively supports academic data management, but further development is needed to improve feature quality and user satisfaction.

Analisis Big Data APS SLTA dan Strategi Pendidikan Menggunakan K-Means Berbasis Rapidminer Menuju Indonesia Emas Khoirun Nisa; Nor Anisa
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 1 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No1.pp11-16

The Golden Indonesia Vision 2045 places education as the main pillar in creating superior human resources. School Participation Rate (APS) data is an important indicator to evaluate student access and participation in education. This study utilizes the K-Means Clustering method to analyze APS big data to identify patterns of education participation in Indonesia. The results of the analysis show significant participation clusters based on demographic, socio-economic, and geographical factors, and reveal gaps and potential for education improvement in various regions. In this study, RapidMiner is used as an analysis tool to process and visualize APS data. The results of clustering show a striking difference between areas with good access to education and areas with poor access to education. Factors such as income levels, educational infrastructure, and geographical location were found to have a major impact on student participation rates. Strategic recommendations include increasing access to education in disadvantaged areas through equitable distribution of education facilities, infrastructure development, and flexible data-based policies. In addition, scholarship programs in vulnerable areas are also proposed as a solution. This research supports strategic efforts towards the vision of Golden Indonesia 2045 by providing a strong foundation for policies that focus on the sustainability of national education.

Penerapan Lexicon Based Untuk Analisis Sentimen pada Game PUBG dengan Ekstraksi Fitur TF-IDF Anisa, Nor
Journal of Practical Computer Science Vol. 5 No. 1 (2025): Mei 2025
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v5i1.5826

This study conducts sentiment analysis on user reviews of PUBG (PlayerUnknown's Battlegrounds) to understand players' perceptions of their gaming experience. The main issue addressed is the need for a deeper understanding of player satisfaction and dissatisfaction through available review data. The objective of this research is to identify sentiment tendencies—positive, neutral, or negative—and the specific aspects of the game that receive the most user feedback. The methodology combines a lexicon-based sentiment analysis approach with Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. The lexicon-based technique is used to classify words with positive or negative connotations, while TF-IDF highlights the most influential terms in the user review corpus. Results from various testing scenarios show that this combined method improves sentiment classification accuracy. The analysis reveals that the majority of reviews express positive sentiment, with a smaller portion being neutral or negative. Furthermore, the approach successfully identifies the most praised and criticized features of the game. These findings offer valuable insights for game developers to evaluate and improve game elements that directly impact player satisfaction and retention

Optimizing Big Data Driven Strategic Management to Enhance the Quality of Adaptive Contemporary Islamic Education Prasasti Karunia Farista Ananto; Cecep Hilman; Eka Muzalfitri Ridwan; Nor Anisa
Khazanah: Journal of Islamic Education and Science Vol. 2 No. 1 (2026): Khazanah: Journal of Islamic Education and Science
Publisher : Institut Bahri Asyiq Galis Bangkalan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61815/khazanah.v2i1.888

Digital transformation in education has driven the utilization of large-scale data as a basis for strategic decision-making. However, Islamic education has continued to face limitations in integrating data-based approaches into its management systems, resulting in limited adaptability to change. This study aimed to analyze the optimization of big data -based strategic management in improving the quality of adaptive contemporary Islamic education. The study employed a descriptive qualitative approach with a meta-analysis method of scientific literature published between 2018 and 2025 from reputable digital databases. Data were collected through digital documentation using Boolean keyword techniques and were selected through a systematic protocol. Data analysis was conducted using content and thematic analysis to identify patterns, concepts, and relationships among variables. The findings indicated that the utilization of big data significantly enhanced the effectiveness of strategic management through the strengthening of data-driven planning, implementation, and evaluation. In addition, such integration improved the quality of Islamic education in terms of curriculum, governance, and institutional adaptability. This study contributed to the development of an integrative model of data-based Islamic education management relevant to the demands of the digital era.

Analisis Tingkat Kepuasan Mahasiswa Terhadap Kualitas Layanan dan Konten Musik pada Aplikasi Spotify Muhammad Maigy Pratama; Nor Anisa
Majalah Ilmiah METHODA Vol. 15 No. 3 (2025): Majalah Ilmiah METHODA
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methoda.Vol15No3.pp276-282

This study aims to evaluate student satisfaction with the Spotify music streaming application by examining two primary factors: service quality and music content. Using a quantitative survey approach, data were collected from university students who actively use Spotify for digital music consumption. The results show that most people who answered the survey (66.7%) think Spotify is easy to use and (70.4%) think it looks good. Also, 72.2% of users said that Spotify's features make their listening experience better, and 64.8% said that the audio quality is good. However, a notable proportion of respondents still experience errors or disturbances while using the application (57.4%). 57.4% of respondents think that music recommendations are usually good for their moods, and 55.6% think that being able to find new songs through recommendations is helpful. Overall satisfaction levels reached only 50%, indicating that Spotify has not fully met user expectations. These results indicate that there must be improvements in service quality, system stability, and recommendation accuracy to enhance overall user satisfaction among students.

Evaluasi dan Pengujian Internal Compatibility pada Aplikasi SIAKAD Universitas Sari Mulia Halimatur Rahmi; Nor Anisa
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 1 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No1.pp1-6

The Academic Information System (SIAKAD) is a vital component in managing academic data at higher education institutions, including Universitas Sari Mulia. This study aims to evaluate the internal compatibility of the SIAKAD application, specifically the alignment and consistency of data across modules such as student profiles, course registration (KRS), academic grades, attendance, and payment. The primary issue addressed is the potential for data mismatches between modules, which could affect academic processes and user trust. This research employs a qualitative approach using the black-box testing method to assess the application's technical performance based on predefined test scenarios, alongside questionnaires to evaluate user experiences. The testing results demonstrate that all features, including login, profile management, attendance, grading, and payment, functioned as expected according to the test scenarios. Based on responses from 18 participants, 72.22% of users found the application easy to use and consistent in displaying data, though some features were deemed less optimal (5.56%). This study concludes that the SIAKAD application at Universitas Sari Mulia effectively supports academic data management, but further development is needed to improve feature quality and user satisfaction.

Penerapan Algoritma Random Forest untuk Klasifikasi Depresi Berdasarkan Faktor Tekanan Kerja dan Kebiasaan Hidup Muhammad Abdul Hamid; Nor Anisa
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 1 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No1.pp46-52

Depression is a serious mental condition that affects both individuals and society. The Random Forest algorithm will be used in this project to create a depression categorization model based on work pressure and lifestyle characteristics. The Depression Professional Dataset was analyzed using the Knowledge Discovery in Databases (KDD) approach, which included 2,054 data points with 11 factors such as age, work pressure, working hours, sleep habits, and family mental health history. The results showed that the Random Forest algorithm classified depressive states with 91% accuracy. The investigation found that age was the most important predictor, followed by work pressure, working hours, and job happiness. In contrast, gender and family mental health history had a smaller impact. This study demonstrates that the risk factors for depression are multifaceted, including demography and work pressure. These findings can be used to develop mental health preventive and intervention methods in the workplace. Future model development can include new factors, such as socioeconomic status, to produce a more comprehensive study

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