Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Agustin, Dari Dianata
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
ANALISIS FUNGSI BASIS DATA BERBENTUK FILE MSGSTORE.DB.CRYPT DALAM APLIKASI WHATSAPP Mulyani, Sri; Rizkita, Syeila Ayu; Almawati, Novia Ayu; Agustin, Dari Dianata; Izzati, Puteri Marchanda; Puspitasari, Mila; Manalu, Rini Junita; Firmansyah, Ricky
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 2 No. 2 (2022): Juli : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (742.606 KB) | DOI: 10.55606/teknik.v2i2.318

Abstract

Abstrak Kini teknologi sangat berkembang pesat, aplikasi chatting sangatlah digemari oleh banyak pihak salah satunya WhatsApp Messenger. WhatsApp ini dapat diakses oleh pengguna android dan ios, aplikasi ini menyediakan fasilitas tidak hanya untuk mengirimkan teks namun bisa juga berupa video, foto, file, dan panggilan suara maupun video. WhatsApp memiliki database yang dinamai msgstore.db.crypt untuk membackup data yang digunakan oleh penggunanya namun kebanyakan masyarakat tidak memahami tujuan adanya file tersebut. Dengan mengunakan metodologi pendekatan kualitatif yaitu dengan cara mengumpulkan data strudi pustaka dengan menggunakan sumber data berupa buku referensi dan jurnal ilmiah maka pada penelitian ini menghasilkan beberapa fungsi dari msgstore.db.crypt yang sebelumnya tidak diketahui oleh khalayak umum, yaitu sebagai penyimpan data diwhatsapp, mencadangkan pesan whatsapp menjadi lebih mudah, dapat mengembalikan data pesan yang telah terhapus, dapat melakukan sinkronisasi antar aplikasi dan beberapa hal lainnya. File database ini berguna untuk menjalankan aplikasi, semua aktivitas dan pesan akan langsung tersimpan dan diproses melalui database. Jenis file dapat berbeda-beda tergantung dengan versi aplikasi WhatsApp para pengguna jika sudah lama tidak diperbarui maka kemungkinan crypt yang digunakan adalah crypt7, crypt8, crypt10 atau crypt12 ini merupakan jenis file yang sama namun dengan tingkat enkripsi yang berbeda. File msgstore sendiri terletak di dalam folder WhatsApp / Databases dan membuka database file ini dapat menggunakan aplikasi SQLite Browser Database.
Breast cancer identification using machine learning and hyperparameter optimization Arifin, Toni; Prasetyo Agung, Ignatius Wiseto; Junianto, Erfian; Rachman, Rizal; Wibowo, Ilham Rachmat; Agustin, Dari Dianata
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1620-1630

Abstract

Breast cancer identification can be analyzed through genomic analysis using gene expression data, one type of which is mRNA. This involves analyzing gene expression patterns of breast tissue samples to distinguish breast cancer from healthy tissue or to differentiate subtypes of different breast cancers. This research developed the right computational model for breast cancer classification using machine learning and hyperparameter optimization algorithms. The primary objective of this research is to utilize various machine learning algorithms to classify breast cancer based on gene expression and enhance the models developed in previous studies. This paper provides an extensive literature review of prior breast cancer classification research and offers new theoretical perspectives. This research used a problem-solving approach with conventional machine learning techniques, most notably the decision tree. It also evaluates other machine learning algorithms for comparison, including k-nearest neighbor, naïve bayes, random forest, extra tree classifier, and support vector machine. The evaluation process used classification reports that provide insight into the precision, recall, F1-score, and accuracy of each machine learning model. The evaluation results show that the performance of the decision tree algorithm model is superior and impressive, achieving 99.73% accuracy and a score of 1 for precision, recall, and F1-score.
Breast cancer identification using a hybrid machine learning system Arifin, Toni; Agung, Ignatius Wiseto Prasetyo; Junianto, Erfian; Agustin, Dari Dianata; Wibowo, Ilham Rachmat; Rachman, Rizal
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3928-3937

Abstract

Breast cancer remains one of the most prevalent malignancies among women and is frequently diagnosed at an advanced stage. Early detection is critical to improving patient prognosis and survival rates. Messenger ribonucleic acid (mRNA) gene expression data, which captures the molecular alterations in cancer cells, offers a promising avenue for enhancing diagnostic accuracy. The objective of this study is to develop a machine learning-based model for breast cancer detection using mRNA gene expression profiles. To achieve this, we implemented a hybrid machine learning system (HMLS) that integrates classification algorithms with feature selection and extraction techniques. This approach enables the effective handling of heterogeneous and high-dimensional genomic data, such as mRNA expression datasets, while simultaneously reducing dimensionality without sacrificing critical information. The classification algorithms applied in this study include support vector machine (SVM), random forest (RF), naïve Bayes (NB), k-nearest neighbors (KNN), extra trees classifier (ETC), and logistic regression (LR). Feature selection was conducted using analysis of variance (ANOVA), mutual information (MI), ETC, LR, whereas principal component analysis (PCA) was employed for feature extraction. The performance of the proposed model was evaluated using standard metrics, including recall, F1-score, and accuracy. Experimental results demonstrate that the combination of the SVM classifier with MI feature selection outperformed other configurations and conventional machine learning approaches, achieving a classification accuracy of 99.4%.
Co-Authors Almawati, Novia Ayu Erfian Junianto Ignatius Wiseto Prasetyo Agung Izzati, Puteri Marchanda Manalu, Rini Junita Prasetyo Agung, Ignatius Wiseto Puspitasari, Mila Ricky Firmansyah Rizal Rachman, Rizal Rizkita, Syeila Ayu Sri Mulyani Toni Arifin Wibowo, Ilham Rachmat