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All Journal Syntax Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi INTECOMS: Journal of Information Technology and Computer Science J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Riset Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) Journal of Information System, Applied, Management, Accounting and Research METIK JURNAL Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jusikom: Jurnal Sistem Informasi Ilmu Komputer Systematics Zonasi: Jurnal Sistem Informasi Jurnal Informasi dan Teknologi Buana Information Technology and Computer Sciences (BIT and CS) JURSIMA (Jurnal Sistem Informasi dan Manajemen) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Infotek : Jurnal Informatika dan Teknologi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Engineering, Science and Information Technology Djtechno: Jurnal Teknologi Informasi Jurnal Tika Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mandiri IT Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Jurnal Minfo Polgan (JMP) Society: Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Komtekinfo Jurnal Buana Pengabdian Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science JUSIFOR : Jurnal Sistem Informasi dan Informatika Golden Ratio of Data in Summary Jurnal Ilmiah Teknik Informatika dan Komunikasi Innovative: Journal Of Social Science Research Bulletin of Network Engineer and Informatics (BUFNETS) JURSIMA Journal Of Artificial Intelligence And Software Engineering VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Sistem Informasi dan Manajemen Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial Jurnal Abdimas Mahakam
Novalia, Elfina
Universitas Buana Perjuangan Karawang
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ANALISIS OPINI PENGGUNA APLIKASI SHOPEE DENGAN NAÏVE BAYES CLASSIFIER atikah, dwi; hananto, agustia; paryono, tukino; novalia, elfina
Jurnal Informatika Vol 9, No 3 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i3.14462

Abstract

Pertumbuhan pesantnya e-commerce di Indonesia berdampak pada meningkatnya ulasan pengguna terhadap aplikasi belanja berani seperti Shopee. Ulasan ini mewakili persepsi pengguna dan dapat dimanfaatkan untuk memancarkan kepuasan serta meningkatkan kualitas layanan. Menggunakan algoritma Naive Bayes, studi ini menerapkan strategi klasifikasi untuk memahami sikap dalam ulasan pengguna aplikasi shopee di Google Play Store. Data diperoleh menggunakan teknik web scraping dan kemudian menjalani beberapa proses, termasuk pembersihan data teks, tokenisasi, penghapusan kata-kata yang tidak relevan, dan normalisasi. Sentimen evaluasi dirinci secara manual ke dalam tiga kelompok berbeda: sangat_puas, puas, dan tidak_puas. Untuk mengatasi distribusi kelas, digunakan teknik RandomOverSampler Sebelum data dibagi menjadi set pelatihan dan pengujian, teks kemudian dianalisis menggunakan teknik TF-IDF dan dibor dengan algoritma Multinomial Naive Bayes. Akurasi, presisi, recall, skor F1, dan matriks kebingungan dimasukkan ke dalam proses evaluasi untuk menyalakan kinerja model. Hasil penelitian menunjukkan bahwa model memperoleh tingkat ketepatan mencapai 75,33% dengan kinerja yang cukup konsisten di semua label. Teknik oversampling terbukti efektif dalam menyeimbangkan kelas, meskipun masih terdapat prediksi silang antar kategori yang mirip. Penelitian ini menjadi pijakan awal bagi pengembangan sistem analisis sentimen otomatis berbasis bahasa Indonesia.
KLASIFIKASI SENTIMEN ULASAN PRODUK SUNSCREEN PADA FEMALE DAILY MENGGUNAKAN METODE NAÏVE BAYES baktria, leonyka; huda, baenil; novalia, elfina; paryono, tukino
Jurnal Informatika Vol 9, No 3 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i3.14461

Abstract

Perkembangan teknologi internet mendorong konsumen untuk lebih aktif membagikan pengalamannya melalui ulasan, salah satunya pada platform Female Daily. Ulasan produk tabir surya dari pengguna memberikan wawasan sentimen yang berharga. Namun, menganalisis data dalam skala besar secara manual tidaklah efektif. Studi ini bertujuan untuk menganalisis sentimen ulasan produk tabir surya menggunakan algoritma Naïve Bayes Classifier. Data dikumpulkan melalui web scraping, diikuti oleh pra-pemrosesan teks dan pelabelan sentimen menurut skor peringkat menjadi tiga kategori: sangat cocok, cocok, dan tidak cocok. Distribusi dalam distribusi kelas diatasi menggunakan teknik oversampling, dan data kemudian diubah menjadi format numerik dengan TF-IDF. Model dibor dengan algoritma Multinomial Naïve Bayes dan dievaluasi menggunakan matriks konfusi dengan metrik akurasi, presisi, recall, dan F1-score. Hasil evaluasi menunjukkan bahwa model mencapai akurasi 83,33%, dengan presisi 0,84, recall 0,83, dan skor F1-score 0,83. Visualisasi WordCloud digunakan untuk mengidentifikasi kata-kata dominan di setiap kategori sentimen. Temuan ini menunjukkan efektivitas algoritma Naïve Bayes dalam mengklasifikasikan opini konsumen dengan baik dan menyoroti potensinya untuk mengembangkan sistem rekomendasi produk berbasis ulasan, serta untuk memahami persepsi konsumen dalam industri kecantikan.
Klasifikasi Tingkat Penjualan Produk pada Toko Jati Karebet Menggunakan Algoritma Naïve Bayes Setiawan, Revi; Priyatna, Bayu; Novalia, Elfina; Huda, Baenil
JURNAL FASILKOM Vol. 15 No. 2 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i2.9614

Abstract

Penelitian ini bertujuan menerapkan algoritma Naive Bayes untuk mengklasifikasikan tingkat penjualan produk di Toko Jati Karebet selama tahun 2024. Latar belakang penelitian ini adalah belum optimalnya pemanfaatan strategi penjualan berbasis data dalam menentukan prioritas stok dan promosi, yang sering menyebabkan inefisiensi persediaan pada Usaha Mikro, Kecil, dan Menengah (UMKM). Data penjualan historis dianalisis menggunakan pendekatan data mining untuk mengenali pola penjualan dan membangun model prediksi. Tahap awal meliputi preprocessing data, seleksi pesanan yang berstatus selesai, agregasi penjualan per produk, dan pelabelan kategori kelarisan menjadi tiga kelas: laris (>100 unit), kurang laris (20–100 unit), dan tidak laris (<20 unit). Model Gaussian Naive Bayes dilatih dan diuji dengan metode supervised learning menggunakan pembagian data 70% untuk pelatihan dan 30% untuk pengujian. Evaluasi model dilakukan dengan confusion matrix dan metrik klasifikasi. Hasil pengujian menunjukkan akurasi sebesar 76%, dengan precision 0,79, recall 0,98, dan F1-score 0,87 pada kategori laris. Temuan ini membuktikan bahwa Naive Bayes mampu memberikan hasil prediksi yang cukup andal untuk kategori mayoritas, namun kinerjanya menurun pada kategori minoritas akibat ketidakseimbangan distribusi data. Penelitian ini menyimpulkan bahwa algoritma Naive Bayes dapat digunakan sebagai alat bantu pengambilan keputusan dalam manajemen stok dan strategi penjualan UMKM, serta merekomendasikan penerapan teknik penyeimbangan data atau eksplorasi algoritma lain pada penelitian berikutnya untuk meningkatkan performa di semua kategori
Classification of Reject Patterns Based on Production Stages Using the K-Means Clustering Method Lestari, Renita; Novalia, Elfina; Tukino; Nurapriani, Fitria
Golden Ratio of Data in Summary Vol. 5 No. 4 (2025): August - October
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grdis.v5i4.1301

Abstract

This study aims to classify reject patterns in the production process using the K-Means Clustering method. The dataset consists of 870 records collected from the production line, containing information such as product name, reject type, process stage, and production quantity. Through a data mining approach, data preprocessing steps such as cleaning, encoding, and normalization were performed prior to the clustering process. The Elbow Method indicated that the optimal number of clusters is three. Each cluster exhibits distinct characteristics: light rejects with small quantities in early stages, heavy rejects with large quantities, and moderate rejects with random distribution. These findings are expected to assist management in formulating more targeted strategies for process improvement and quality control. By identifying common reject patterns within each cluster, companies can adopt a more proactive approach to minimizing production defects and enhancing overall operational efficiency.
Implementasi Enkripsi Hybrid AES-RSA pada Layanan Cloud Storage AWS S3 Firdaus, Mohamad Ricky; Hilabi, Shofa Shofiah; Novalia, Elfina; Hananto, April Lia
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 2: Agustus 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i2.2724

Abstract

Data security has become a critical aspect in the digital era, especially with the increasing use of cloud storage services for data storage and management. This study examines the implementation of hybrid encryption combining the Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) algorithms on the Amazon Web Services Simple Storage Service (AWS S3) cloud platform. The hybrid method leverages the speed of AES for encrypting large data volumes and the security of RSA for key management. The encryption and decryption processes are performed entirely on the client side before uploading data to AWS S3, ensuring that the stored data remains securely encrypted. The developed application provides features for encryption, decryption, and uploading encrypted data with a user-friendly interface. Testing results demonstrate that this method achieves an optimal balance between performance and data security. This research contributes to the development of efficient and reliable cloud-based data security solutions.Keywords: Advanced Encryption Standard; Rivest–Shamir–Adleman; Amazon Web Services Simple Storage Service; Data Security; Cloud Storage AbstrakKeamanan data menjadi aspek penting dalam era digital, terutama dengan meningkatnya penggunaan layanan cloud storage untuk penyimpanan dan pengelolaan data. Penelitian ini mengkaji implementasi enkripsi hybrid yang menggabungkan algoritma Advanced Encryption Standard (AES) dan Rivest-Shamir-Adleman (RSA) pada layanan cloud storage Amazon Web Services Simple Storage Service (AWS S3). Metode hybrid ini memanfaatkan kecepatan AES dalam mengenkripsi data berukuran besar dan keamanan RSA dalam pengelolaan kunci enkripsi. Proses enkripsi dan dekripsi dilakukan sepenuhnya di sisi klien sebelum data diunggah ke AWS S3, sehingga memastikan data tersimpan dalam bentuk terenkripsi yang aman. Aplikasi yang dikembangkan menyediakan fitur enkripsi, dekripsi, dan upload data terenkripsi dengan antarmuka yang mudah digunakan. Hasil pengujian menunjukkan bahwa metode ini memberikan keseimbangan optimal antara performa dan keamanan data. Penelitian ini memberikan kontribusi dalam pengembangan solusi keamanan data berbasis cloud yang efisien dan dapat diandalkan. 
SATPOL PP Performance Assessment Using the WASPAS Method in Decision Making Effectiveness Lutfiah, Siti; Priyatna, Bayu; Hananto, April Lia; Novalia, Elfina
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1739

Abstract

Civil Service Police Unit (SATPOLPP) as one of the government's instruments enforces regional regulations and maintains security and protects the community. Leaders have difficulties when evaluating the performance of their members. Manual performance measurement is very ineffective if carried out randomly or by self-assessment. Performance assessments in local government must follow the procedures or rules applicable in local government regulations. Apart from that, the standard for evaluating honorary staff must be based on assessment criteria. In carrying out the analysis, an effective system is needed that can assess the results of member performance. So a performance assessment decision support system is needed using the Weighted Aggregated Sum Product Assessment (WASPAS) algorithm. The WASPAS method has the ability to solve multi-criteria decision problems which are able to reduce errors and optimize in providing assessments and determining alternative highest and lowest values, speed in data management and provide information output results in the form of reports containing performance assessment ranking results. The weights for each criterion are Absence (20%), Work (40%), Collaboration (10%), Discipline (10%), and Knowledge (20%). The results of manual calculations and the application of the WASPAS method show that the highest alternative value obtained a value of 50.5 to the lowest alternative which obtained a value of 26.5 with the same accuracy. so that the evaluation and sanctions obtained can decide who gets ownership and which members can be recommended to extend the work contract using the criteria for consideration. With this calculation system, it becomes faster and more effective in obtaining performance scores for SATPOLPP members and speeding up the leadership decision-making process.
Membangun Identitas Digital Workshop Pembuatan Website Dengan Wordpress (Pesantren At-Taubah Karawang) Sukmawati, Cici Emilia; Juwita, Ayu Ratna; Novalia, Elfina; Nurmayanti, Trisya; Tejayanda, Rigger Damaiarta; Faisal, Muhamad Agus
Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Vol. 4 No. 1 (2024): Mei 2024 - Abdiformatika: Jurnal Pengabdian Masyarakat Informatika
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/abdiformatika.v4i1.209

Abstract

Workshop "Membangun Identitas Digital: Workshop Pembuatan Website dengan WordPress" di Pesantren At Taubah Karawang merupakan kegiatan yang bertujuan untuk memberikan pemahaman dan keterampilan kepada peserta dalam membangun identitas digital lembaga pendidikan melalui pembuatan dan pengelolaan website. Workshop ini berhasil mencapai beberapa hasil yang signifikan, antara lain pemahaman konsep identitas digital yang lebih baik, keterampilan penggunaan platform WordPress yang lebih mahir, dan peningkatan kualitas konten digital lembaga. Selain memberikan manfaat bagi Pesantren At Taubah Karawang dalam memperkuat identitas digitalnya, workshop ini juga memberikan dampak positif jangka panjang dalam pengembangan sumber daya manusia dan pemberdayaan masyarakat di era digital saat ini. Saran yang diberikan meliputi pengembangan konten berkelanjutan, pemanfaatan media sosial, peningkatan interaksi dengan pengguna, pelatihan dan pembinaan lanjutan, serta evaluasi dan pembaruan reguler terhadap website. Diharapkan workshop ini dapat menjadi langkah awal yang bermanfaat bagi Pesantren At Taubah Karawang dalam memanfaatkan teknologi digital untuk meningkatkan kualitas pendidikan dan pelayanan kepada masyarakat.
Klasifikasi Hasil Penjualan Minuman Ringan Pada Koperasi Berdasarkan Jenis Barang Menggunakan Algoritma K-Means Clustering Situmorang, Awaljan; Tukino, Tukino; Novalia, Elfina; Ahmad, Sandi
Jurnal Tika Vol 7 No 3 (2022): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v7i3.1565

Abstract

The joint cooperative store is one of the efforts given by the joint cooperative management to increase cooperative income by calculating profits every year and distributing them to cooperative members in the form of money, commonly known as SHU or the remaining results of operations. However, there are still shortcomings in the implementation of cooperative sales management, one of which is the sale of soft drinks. There are still errors in determining the high and low volume of beverage sales. This research will help cooperative managers to categorize beverage sales data so that customer demand for soft drinks can be fulfilled properly. The data collected from January 2020 to September 2022 is the sale of 11,945 drinks from 15 soft drinks at the Koperasi Bersama store. This research aims to group the sales recapitulation results into a cluster using a data mining approach using the K-Means clustering algorithm. Grouping sales data according to its characteristics. The results of this study indicate that 1 soft drink is included in cluster 0 which is classified as high sales volume, while 14 soft drinks are included in cluster 1 which is classified as low sales volume.
Klasterisasi Kesiapan Digital Daerah: Studi Kasus Indeks SPBE di Jawa Barat, Indonesia Agustia Hananto; Tukino Tukino; Elfina Novalia
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 1 (2025): Maret-Juni : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i1.979

Abstract

Public sector digital transformation requires a deep understanding of the digital readiness of each administrative region. The Electronic Government System (EGIS) Index is used by the Government of Indonesia as a measuring tool to assess the digital maturity of government agencies. This study aims to cluster districts/cities in West Java Province based on their 2023 EGIS scores to identify hidden patterns of digital readiness. Three unsupervised learning algorithms—K-Means, DBSCAN, and Agglomerative Clustering—are used to explore data-driven regional segmentation. The analyzed dataset includes 27 administrative regions and a number of numerical features related to the EGIS dimensions. The results show that each method is able to form clusters that reflect variations in digital readiness, with DBSCAN producing the most detailed segmentation and being able to detect outliers. Agglomerative Clustering shows good hierarchical separation, while K-Means provides a fairly representative general division. This study provides an analytical basis for contextual and targeted cluster-based policy making in developing regional digital transformation.
Socialization of Digital Literacy and Artificial Intelligence to Improve Knowledge and Skills in Rangdumulya Village Voutama, Apriade; Maulana, Iqbal; Yusup, Dadang; Garno, Garno; Novalia, Elfina
Society : Jurnal Pengabdian Masyarakat Vol. 4 No. 3 (2025): Mei
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i3.524

Abstract

The socialization of Digital Literacy and Artificial Intelligence was carried out in Randumulya Village, Karawang Regency, which focused on increasing the community's insight into skills so that they can train and create creative ideas and opportunities. The socialization was carried out by two targets, namely to village officials and the community and students at elementary schools in the village. This activity was carried out in the Village Hall with resource persons who were expert lecturers in their fields and activities with students were carried out at the school and accompanied by students to be more interactive. The results of the satisfaction of 55 respondents who were carried out by distributing questionnaires reached 85% who stated that they were satisfied. With this training, it can help the village community to know and get benefits from digital literacy and technological intelligence so that it has a positive impact and standard of living in the village.
Co-Authors Abdul Hafiz Agustina, Alvi Ahmad Fauzi Ahmad, Sandi Al Khadzik, Fahmi Alfiansyah, Muhammad Rindra Ani Ani Anita Saptiani Apriade Voutama April Hananto April Lia Hananto Arif Budimansyah Purba atikah, dwi Aurel Adhitya Anwar Aviv Yuniar Rahman Awal, Elsa Elvira Awaljan Situmorang Awaludin, Sidqi Baenil Huda Baenil Huda baktria, leonyka Bayu Yoga Astario Cahya Diningrat Desfianthy, Fatiya Hanifah Emilia Sukmawati, Cici Fadli, Muhammad Abil Faisal, Muhamad Agus Firdaus, Mohamad Ricky Fitri Nur Masruriyah, Anis Fitria Nurapriani Garno garno, Garno Gefira Rahmaifha Goenawan Brotosaputro Gugy Guztaman Munzi Hananto , Agustia Hananto, Agustia Henry Adam Hilabi, Shofa Shofiah Hilabi, Shofa Shofiah Huban Kabir Huda, Baenil Indra, Jamaludin Iqbal Maulana Irawan, Agung Susilo Yuda Juwita, Ayu Ratna kastiawan, Nurhayadi Lestari, Renita Lutfiah, Siti Muhamad Djaka Permana Muhamad Helmi Fauzi Nijunnihayah, Uktupi Nur ‘Azah Nurapriani, Fitria Nuriza, Adjeng Putri Nurmayanti, Trisya Paryono, Tukino Prasetya, Rafli Pratama, Daffa Agung Prayono, Tukino Priyatna, Bayu Purba, Arif Budimansyah Rian Pratama Sandi Ahmad Saptiani, Anita Seia Piantara Setiawan, Pratama Wahyu Setiawan, Pratama Wahyu Setiawan, Revi Shofa Shofia Hilabi Shofiah Hilabi, Shofa Situmorang, Awaljan Sopian, Jajang Sukmawati, Cici Emilia Surala, Lyvia Syafana, Vinka Syahri Susanto Tamala, Evi TARMUJI TARMUJI, TARMUJI Tejayanda, Rigger Damaiarta Tita Puspita Sari Tukino Tukino, Tukino Tukino, Tukino tukino, tukino Wahyu Aziz Ramadhani Wahyu, Pratama Widyanti, Tyas Wirlandika, Devri Yoga Astario, Bayu Yusup, Dadang Zhalifunas, Satria Dawas