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All Journal MEDIA ELEKTRIKA Jurnal Teknologi Informasi dan Ilmu Komputer JUSIFO : Jurnal Sistem Informasi Seminar Nasional Informatika (SEMNASIF) Sistemasi: Jurnal Sistem Informasi Justek : Jurnal Sains Dan Teknologi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI International Journal of New Media Technology Jurnal Sisfokom (Sistem Informasi dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Sistem informasi dan informatika (SIMIKA) Santika : Jurnal Ilmiah Sains dan Teknologi Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Cakrawala Repositori Imwi Community Empowerment Jurnal Computer Science and Information Technology (CoSciTech) IICS J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat Abdi Nusa Jurnal Pengabdian Kepada Masyarakat Abdi Putra Jurnal AKAL: Abdimas dan Kearifan Lokal
Adhitia Erfina
Nusa Putra University
Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Public Health Social Sciences Transportation Other

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Analisis Sentimen Objek Wisata Bali Di Google Maps Menggunakan Algoritma Naive Bayes Utami, Dian Siti; Erfina, Adhitia
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.456

Abstract

Bali is one of the most popular tourist destinations in Indonesia because it has a variety of tourist attractions. So this study aims to analyze a tourist review on Google Maps of the most recommended tourist attractions in Bali. The author considers that the review can be used as a data by scrapping data from the Data Miner Website. Then the data that has been extracted is analyzed by predicting a rapid miner using the Naïve Bayes Algorithm, which is considered to have a high enough level of accuracy so that it can determine 5 recommended Bali tourist attractions based on tourist reviews on Google Maps. The results of this study conclude that Nusa Pedina with an accuracy value of 94.64% is the most visited Bali tourist attraction because the accuracy value is superior to Garuda Wisnu Kencana with an accuracy value of 82.86%, edge with an accuracy value of 80%, Pandawa with an accuracy value by 82.86%. The accuracy value is 90.71%, Uluwutu Temple with an accuracy value of 85.54%.
IMPLEMENTASI MODEL FEAF PADA PERANCANGAN SISTEM INFORMASI SEKOLAH BERBASIS WEB DI SMP PGRI KARAWANG SUKABUMI Andini Wangsa Putri; Sudin Saepudin; Carti Irawan; Erfina, Adhitia; Mupaat
Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Vol 6 No 3 (2024): JURSISTEKNI (Jurnal Sistem Informasi dan Teknologi Informasi)
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/jursistekni.v6i3.337

Abstract

Penelitian ini bertujuan untuk merancang sistem informasi akademik bagi SMP PGRI Karawang Sukabumi guna mengoptimalkan pengelolaan informasi sekolah yang saat ini masih dilakukan secara manual. Dengan menggunakan kerangka kerja Federal Enterprise Architecture Framework (FEAF), penelitian ini menawarkan solusi komprehensif untuk meningkatkan layanan penerimaan siswa baru dan pengelolaan data sekolah. Metode pengumpulan data meliputi observasi, wawancara, dan studi literatur. Model FEAF diterapkan melalui empat tingkat: Analisis PEST dan SWOT, Value Chain, Business System Planning (BSP), serta Perspektif Planner, Owner, Designer, Builder, dan Subcontractor. Hasil penelitian menunjukkan bahwa model FEAF mampu memberikan panduan terperinci dalam merancang sistem informasi sekolah yang efektif dan efisien. Pengujian usability dalam penelitian ini menghasilkan skor rata-rata 79,27, yang mengindikasikan bahwa sistem ini mudah digunakan oleh guru, staf, dan siswa. Implementasi FEAF dengan empat level memberikan kerangka kerja yang terstruktur untuk membangun sistem informasi yang sesuai dengan kebutuhan SMP PGRI Karawang Sukabumi, sehingga diharapkan dapat meningkatkan kualitas dan aksesibilitas pendidikan di sekolah tersebut.
Analisis Sentimen Ulasan Terkait UNESCO Global Geopark Di Google Maps dengan Algoritma Naive Bayes Utami, Dian Siti; Erfina, Adhitia; Mupaat, M
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.524

Abstract

Ciletuh Geopark is part of the UNESCO Global Geopark Network. This study will analyze a tourist review of the Ciletuh Pelabuhan Ratu Geopark based on reviews on Google Maps. The author believes that customer reviews should be taken into consideration because they allow travelers to share their experiences. Reviews from tourists who have visited geoparks are the most important thing because these reviews can be used as information to be used as data. Because the Naïve Bayesian Algorithm is thought to have a high enough level of accuracy to identify the Unesco Global Geopark (UGG) Ciletuh Pelabuhan Ratu tourist destination that is often frequented based on visitor ratings on Google Maps, then this study utilizes it. Successively the highest accuracy values from this study were Palangpang with an accuracy value of 98.61%, Cisolok Geyser tourist attraction 94.44%, Ujung Genteng tourist attraction 98.36%, Cikaso tourist attraction 98.36%, Citepus tourist attraction 97 ,22%, Puncak Manic attractions 96.92%, Sodong attractions 95.83%, Cipanarikan attractions 95.01%, Teletubis Hill attractions 94.48%, and finally Cimarinjung attractions 94.44%.
Analisis Sentimen Objek Wisata Bali Di Google Maps Menggunakan Algoritma Naive Bayes Utami, Dian Siti; Erfina, Adhitia
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.456

Abstract

Bali is one of the most popular tourist destinations in Indonesia because it has a variety of tourist attractions. So this study aims to analyze a tourist review on Google Maps of the most recommended tourist attractions in Bali. The author considers that the review can be used as a data by scrapping data from the Data Miner Website. Then the data that has been extracted is analyzed by predicting a rapid miner using the Naïve Bayes Algorithm, which is considered to have a high enough level of accuracy so that it can determine 5 recommended Bali tourist attractions based on tourist reviews on Google Maps. The results of this study conclude that Nusa Pedina with an accuracy value of 94.64% is the most visited Bali tourist attraction because the accuracy value is superior to Garuda Wisnu Kencana with an accuracy value of 82.86%, edge with an accuracy value of 80%, Pandawa with an accuracy value by 82.86%. The accuracy value is 90.71%, Uluwutu Temple with an accuracy value of 85.54%.
Sentiment Analysis of the Issue of Eliminating the Independent Curriculum using the Naïve Bayes Classifier Algorithm Hidayat, Ainul Haq Nurridha Warahmat; Erfina, Adhitia
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (2025): 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.v14i2.5039

Abstract

Sentiment analysis regarding the issue of eliminating the Independent Curriculum is a crucial tool for understanding public opinion, particularly among students and teachers, on changes in the education system. This study applies the Naïve Bayes Classifier method to classify positive and negative sentiments from data collected through social media platforms such as YouTube. The collected data undergoes text preprocessing techniques, including cleaning, case folding, tokenization, stopword removal, and stemming, to enhance model accuracy. The analysis is conducted using Python 3.12.3 in Google Colab with the Naïve Bayes Classifier algorithm. The results demonstrate strong performance, with a positive precision of 5% and a recall of 68%, using an 80% training and 20% testing data ratio. Findings indicate that the overall sentiment leans more negative than positive, with the majority of respondents supporting the elimination of the Independent Curriculum. This study validates the effectiveness of the Naïve Bayes method in sentiment analysis and highlights the importance of text preprocessing in improving model accuracy. Furthermore, there is potential for exploring other methods, such as word embedding and deep learning, to enhance model performance. The findings of this study can serve as a valuable reference for policymakers in understanding public opinion before making further decisions in the education sector.
Prediction of SDG 6.2 Achievement in Indonesia Using Double Exponential Smoothing Nabila, Nazwa; Erfina, Adhitia; Warman, Cecep
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2411

Abstract

This research aims to forecast Indonesia’s progress in achieving Sustainable Development Goal (SDG) 6.2, which targets 100% access to adequate sanitation and elimination of open defecation (OD) by 2030. The Double Exponential Smoothing (DES) method was used on provincial time series data from 2013–2024 (sanitation) and 2020–2024 (OD), with performance evaluated using Mean Absolute Percentage Error (MAPE). Results showed consistently high forecasting accuracy, with DKI Jakarta (0.99%), South Sulawesi (1.87%), and DI Yogyakarta (2.21%) among the most accurate for sanitation, and Maluku (2.79%), Papua (3.03%), and Gorontalo (4.49%) for OD. Spearman correlation analysis revealed a strong national negative correlation (r = –0.991, p < 0.001) between sanitation access and OD. However, provinces like DKI Jakarta (+0.36) and DI Yogyakarta (+0.86) showed positive anomalies, indicating behavioral gaps despite infrastructure growth. These findings clearly highlight the importance of integrating behavioral interventions and localized strategies to effectively accelerate progress toward SDG 6.2.
Perancangan dan Pembangunan Sistem Pelayanan Data Penduduk dengan Metode BPR (Business Process Reengineering) Erfina, Adhitia; Neng Mira Indri Anggraeni; Dudih Gustian
Cakrawala Repositori IMWI Vol. 3 No. 1 (2020): Cakrawala Repositori IMWI
Publisher : Institut Manajemen Wiyata Indonesia & Asosiasi Peneliti Manajemen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52851/cakrawala.v3i1.34

Abstract

Population data is data used for various purposes such as taxpayer services, insurance, health insurance and much more. The process of collecting population data, especially in the Takokak sub-district is still very slow and the data are often not in accordance with the conditions in the community. One of the concepts that can be applied in facilitating population and regional processing is to reengineer the Business Process Reengineering (BPR) business process. In designing the system needed an enterprise architecture model of management information systems in processing population data in order to minimize failure when implementing the system can run as needed. For modeling, UML is used and analysis using the BPR method helps in the process of designing the basic structure of data processing so that it can help the design and development of information systems. The system design carried out in the BPR work plan will be web-based population data processing.
Fine-Tuned IndoBERT for Aspect-Based Sentiment Analysis of Indonesian Five-Star Hotel Reviews Apriliani, Sinta; Erfina, Adhitia; Warman, Cecep
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2491

Abstract

Online reviews significantly shape public perception and play a crucial role in customer decision-making within the hospitality sector. This research aims to conduct aspect-based sentiment analysis on Indonesian five-star hotel reviews using a fine-tuned IndoBERT model. Unlike prior studies that mainly applied IndoBERT to single hotels or small-scale datasets, this study fills that gap by examining 2,499 reviews collected from five luxury hotels in Jakarta. The analysis focuses on five essential service aspects: cleanliness, service quality, room comfort, food & beverages, and core facilities. The IndoBERT-base model was fine-tuned with annotated aspect-sentiment data and assessed using accuracy, precision, recall, F1-score, and confusion matrices. Experimental results show that the model reached 95.28% accuracy with a macro F1-score of 82.44%. Positive sentiment dominated the reviews (81.4%), while neutral and negative sentiments represented 16.9% and 1.7%, respectively. Service, along with food & beverages, received the highest praise, whereas cleanliness and core facilities were more often evaluated neutrally. Aspect and sentiment annotations were carried out semi-automatically using large language models (LLMs) and later validated by human annotators to ensure reliability. These findings highlight IndoBERT’s strong capability in aspect-based sentiment classification for Indonesian hotel reviews and provide actionable insights for hotel managers to enhance service quality. Moreover, this study demonstrates both the academic and practical significance of applying fine-tuned Transformer models to real-world customer experience analysis.
Optimalisasi Teknologi Informasi Dalam Affiliate Marketing Untuk Strategi Promosi Digital wiguna, sindy indira; Erfina, Adhitia
Jurnal Pengabdian Kepada Masyarakat Abdi Putra Vol. 5 No. 3 (2025): September 2025
Publisher : Universitas Nusa Putra & Persatuan Insinyur Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/kpcpsx39

Abstract

Intership merupakan kegiatan belajar langsung yang dilakukan oleh mahasiswa, dengan tujuan untuk mengembangkan kemampuan. Dalam program Intership, mahasiswa mendapatkan pengalaman langsung di bawah bimbingan dan pengawasan dosen pembimbing yang memiliki kompetensi dan pengalaman profesional. Internship merupakan salah satu jalur ‘Study Completion’ bagi mahasiswa Universitas Nusa Putra, program internship ini dapat dilaksanakan di dalam negri maupun luar negeri, penulis berkesempatan Intership di PT Sogeh Bareng. Pada kegiatan ini penulis ditugaskan sebagai Affiliate Marketing, Program affiliate marketing di PT Sogeh Bareng dirancang untuk mendukung peningkatan penjualan, margin keuntungan, serta efisiensi transaksi, baik untuk perusahaan maupun mitra yang berpartisipasi. Pada kegiatan ini penulis berperan dalam bertugas mempromosikan layanan dan fitur aplikasi kepada calon pengguna di PT Sogeh Bareng, baik melalui media sosial, blog, atau kampanye digital lainnya ke dalam media sosial untuk menarik minat pengguna.
Penerapan Algoritma Support Vector Machine pada Analisis Sentimen Terhadap Identitas Kependudukan Digital Lestari, Rita Ajeng; Erfina, Adhitia; Jatmiko, Wisuda
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107264

Abstract

Identitas Kependudukan Digital merupakan inovasi yang dikeluarkan oleh pemerintah yang diklaim dapat menjadi solusi permasalahan pencetakan dan pendistribusian e-KTP. Pemerintah telah berkomitmen untuk mendukung upaya digitalisasi E-KTP menjadi Identitas Kependudukan Digital yang mana masyarakat harus ikut mendukung upaya digitalisasi ini. Kehadiran Identitas Kependudukan Digital telah menjadi sorotan publik yang menimbulkan pro dan kontra. Himpunan data penelitian berasal dari crawling komentar pengguna Facebook dari 16 Februari hingga 10 Maret 2023, dengan proses pengolahan menggunakan pembobotan kata TF-IDF dan algoritma Support Vector Machine. Python merupakan bahasa pemrograman yang dipilih untuk melakukan pengumpulan hingga pengolahan data penelitian. Dari 902 yang diproses, dihasilkan 78,27% negatif, 12,97 netral, dan 8,76% positif. Dengan menggunakan perbandingan data latih dan data uji sebesar 80:20, didapati nilai akurasi pada data uji yang dihasilkan oleh Support Vector Machine adalah 77%. Tingginya angka persentase negatif yang diperoleh menunjukkan ketidakpuasan masyarakat terhadap Identitas Kependudukan Digital, dan diharapkan adanya penelitian ini dapat menjadi informasi bagi pihak-pihak terkait guna perbaikan di masa mendatang.   Abstract   Identitas Kependudukan Digital is an innovation issued by the government which is claimed to be a solution to the problem of printing and distributing e-KTP. The government has committed to supporting efforts to digitize E-KTP into Identitas Kependudukan Digital which the public must participate in supporting this digitization effort. The presence of Identitas Kependudukan Digital has been in the public spotlight which raises pros and cons. The research dataset comes from crawling Facebook user comments from February 16 to March 10, 2023, with processing using TF-IDF word weighting and Support Vector Machine algorithms. Python is the programming language chosen to collect and process research data. Of the 902 processed, 78.27% were negative, 12.97 were neutral, and 8.76% were positive. Using a comparison of training data and test data of 80:20, it was found that the accuracy value of the test data produced by the Support Vector Machine was 77%. The high number of negative percentages obtained shows public dissatisfaction with Digital Population Identity, and it is hoped that this research can be an information for related parties for future improvements.
Co-Authors Acep Saepulrohman adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Andini Wangsa Putri Apriliani, Sinta Carti Irawan Cecep Warman Dede Sukmawan Dhea Lestari Didit Supriyadi Dinda Tasya Mahardika Dudih Gustian Egi Salehudin Basryah Elok Setianingtyas falentino sembiring Gina Syabani Gustian, Dudih Hidayat, Ainul Haq Nurridha Warahmat IVANA LUCIA KHARISMA Jatmiko, Wisuda Juandi Hadi Juhdi Rosadi Kamdan Karim, Khobir Abdul Lestari, Rita Ajeng M. Havidz Alkautsar Melawati Melawati Melawati Moh. Abd. Aziz Hidayat Mohammad Daniel Gumilar Muchtar Ali Setyo Yudono Muhamad Anton Permana Muhamad Fajar Muhamad Fani Al-shufi Muhammad Ikhsan Thohir Muhammad Iqbal Jauharuzzaman Muhammad Ramdani Mupaat Mupaat, M Murweni, Ira Muslih, Muhamad Nabila, Nazwa Neng Mira Indri Anggraeni Neng Resti Wardani Neng Sri Intan Septiani Nunik Destria Arianti Pratama Haditua Reyner Siregar Retna Ayu Puspatarini Retna Ayu Puspatarini Risma Meidawati Rusdiana Rita Ajeng Lestari Saepudin, Sudin Sastradipraja, C K sihabudin sihab Solihin, Hanhan Hanfiah Sri Lestari Tarikh Agusti Ijudin Tarikh Agustia Ijudin Tarikh Agustia Ijudin Theresia Septiani Nainggolan Tofik Hidayat Utami, Dian Siti Wafa Nur’aeni wiguna, sindy indira Wisuda Jatmiko Yoga Vikriansyah Yoga Vikriansyah Wijaya